{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs/master_log.txt
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}19 Aug 2025, 22:09:45
{txt}
{com}. 
. * ---------------------------------------------------------------------------
. * 1: Data construction and cleaning for ENAPROCE database
. * This step imports raw data, performs initial cleaning, constructs key variables and merges/appends datasets as needed.
. do "$programs/Data prepare 2015 mock data.do"
{txt}
{com}. /***********************************************************************************************************************************************************
> * Inputs: ENAPROCE 2015 Small Business (SMEs and large firms) Database
> *
> * Program Description: This program generates variables and indicators that will be used in the descriptive and estimations of the paper.
> *
> * Note: This program was developed within the premises of INEGI's Microdata Data Laboratory.
> * Last Update: 09/26/2021 */
. 
. ************************************************************************************************************************************************************
. 
. use "$data\ejem_base_pyme_ciega_2015.dta", clear
{txt}
{com}. 
. *If the state and NAICS variables are included in the dataset (per request)
. *gen ent=E03
. *gen mun=E04
. *gen clase=E17
. *gen id_mun=ent+mun
. 
. gen id_mun=substr(clee,1,5)
{txt}
{com}. gen clase=substr(clee,6,6)
{txt}
{com}. *gen E03=substr(clee,1,2)
. gen scian2=substr(clase,1,2)
{txt}
{com}. gen scian4= substr(clase,1,4)
{txt}
{com}. gen scian1= substr(clase,1,1)
{txt}
{com}. 
. gen str sector="MAN" if inlist(scian2,"31","32","33")
{txt}(9,040 missing values generated)

{com}. replace sector="COM" if inlist(scian2,"43","46")
{txt}(0 real changes made)

{com}. replace sector="SER" if sector==""
{txt}(9,040 real changes made)

{com}. 
. 
. 
. 
. *Add price deflators
. cap drop _merge
{txt}
{com}. merge m:1 clase using "$data/Deflators.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           6,158
{txt}{col 9}from master{col 30}{res}           5,500{txt}  (_merge==1)
{col 9}from using{col 30}{res}             658{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           4,540{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge==2
{txt}(658 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. 
. *Complete missing PRICE INDEX
. 
. 
. 
. foreach var of varlist   impatp_base20132013 INPP_prodB20132013 PRICE_IND_INVEST_MX_B20132013 PRICE_IND_STRUCT_MX_B20132013 PRICE_IND_SOFTW_MX_B20132013 PRICE_IND_EQUIP_MX_B20132013 impatp_base20132014 INPP_prodB20132014 PRICE_IND_INVEST_MX_B20132014 PRICE_IND_STRUCT_MX_B20132014 PRICE_IND_SOFTW_MX_B20132014 PRICE_IND_EQUIP_MX_B20132014 impatp_base20132015 INPP_prodB20132015 PRICE_IND_INVEST_MX_B20132015 PRICE_IND_STRUCT_MX_B20132015 PRICE_IND_SOFTW_MX_B20132015 PRICE_IND_EQUIP_MX_B20132015 impatp_base20132016 INPP_prodB20132016 PRICE_IND_INVEST_MX_B20132016 PRICE_IND_STRUCT_MX_B20132016 PRICE_IND_SOFTW_MX_B20132016 PRICE_IND_EQUIP_MX_B20132016 impatp_base20132017 INPP_prodB20132017 PRICE_IND_INVEST_MX_B20132017 PRICE_IND_STRUCT_MX_B20132017 PRICE_IND_SOFTW_MX_B20132017 PRICE_IND_EQUIP_MX_B20132017 {c -(}
{txt}  2{com}. 
. bysort scian4: egen s4`var'=mean(`var')
{txt}  3{com}. bysort scian2: egen s2`var'=mean(`var')
{txt}  4{com}. bysort scian1: egen s1`var'=mean(`var')
{txt}  5{com}. egen sA`var'=mean(`var')
{txt}  6{com}. 
. replace `var'=s4`var' if `var'==. 
{txt}  7{com}. replace `var'=s2`var' if `var'==. 
{txt}  8{com}. replace `var'=s1`var' if `var'==. 
{txt}  9{com}. replace `var'=sA`var' if `var'==. 
{txt} 10{com}. 
. {c )-}
{txt}(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)
(5,500 missing values generated)
(5,000 missing values generated)
(3,500 missing values generated)
(0 real changes made)
(500 real changes made)
(1,500 real changes made)
(3,500 real changes made)

{com}. 
. gen sector_2014=sector
{txt}
{com}. 
. 
. 
. 
. gen str sector2_2014="MAN" if sector_2014=="MAN"
{txt}(9,040 missing values generated)

{com}. replace sector2_2014="SERCOM" if sector_2014=="COM"
{txt}(0 real changes made)

{com}. replace sector2_2014="SERCOM" if sector_2014=="SER"
{txt}variable {bf}{res}sector2_2014{sf}{txt} was {bf}{res}str3{sf}{txt} now {bf}{res}str6{sf}
{txt}(9,040 real changes made)

{com}. gen dum_man2=sector2=="MAN"
{txt}
{com}. 
. *Grupo sector entidad
. egen sec_ent_2014=group(sector E03)
{txt}
{com}. 
. ************************************************************************************************************************************************************
. *Performance measures and general characteristics
. ************************************************************************************************************************************************************
. 
. *Months the firms opperates
. gen months_2014=P9
{txt}
{com}. 
. *Monthly salaries
. gen rem_dir_month_2014=P14_1
{txt}
{com}. gen rem_op_supp_2014=P14_2
{txt}
{com}. 
. *Annual remunerations (Calculated according to the months the firm operates)
. gen rem_tot_month_2014=rem_dir_month_2014+rem_op_supp_2014
{txt}
{com}. gen rem_tot_anual_2014=rem_tot_month_2014*months
{txt}
{com}. gen rem_tot_anual_real_2014=rem_tot_anual_2014*(100/INPP_prodB20132014)
{txt}
{com}. 
. *Total expenses in training
. gen g_train_2014=P18_2B
{txt}(4,518 missing values generated)

{com}. replace g_train_2014=0 if g_train_2014==.
{txt}(4,518 real changes made)

{com}. 
. gen g_train_2013=P18_2A
{txt}(4,518 missing values generated)

{com}. replace g_train_2013=0 if g_train_2013==.
{txt}(4,518 real changes made)

{com}. 
. *Energy expenses
. gen g_energy_2014=P19_3
{txt}
{com}. 
. *Materials
. gen g_materials_2014=P19_1   
{txt}
{com}. 
. *Total inputs
. gen g_insum_2014=P19_0
{txt}
{com}. gen g_insum_cut_2014=P19_0-P19_5-P19_9
{txt}
{com}. gen g_insum_tot_2014=g_insum_2014+g_train_2014
{txt}
{com}. 
. *Total revenues
. gen ingre_tot_2014=P20_0
{txt}
{com}. gen ingre_tot_real_2014=ingre_tot_2014*(100/INPP_prodB20132014)
{txt}
{com}. 
. *Fixed assets
. gen activ_fijos_2014=P28_0
{txt}
{com}. gen activ_fijos_real_2014=activ_fijos_2014*(100/PRICE_IND_INVEST_MX_B20132014)
{txt}
{com}. 
. *Firm size
. *a) Categorical
. gen size_2014=P11_0CAL
{txt}
{com}. 
. *b) Numerical
. gen nworkers_2014= P11_0
{txt}
{com}. 
. *Change in inventories
. *Final total inventories
. gen invtot_fin_2014=P26B
{txt}
{com}. 
. *Merchandise for resales final inventory
. gen invmprev_fin_2014=P27B
{txt}
{com}. 
. *Initial inventory
. gen invtot_ini_2014=P26A
{txt}
{com}. 
. *Merchandise for resales initial inventory
. gen invmprev_ini_2014=P27A
{txt}
{com}. 
. *Changes in inventories
. gen Var_inven_2014=(invtot_fin_2014+invmprev_fin_2014)-(invtot_ini_2014+invmprev_ini_2014)*1.047
{txt}
{com}. *Note: For first wave inventories are separated, for second wave the total includes merchandise for resale
. 
. *Regions
. gen region_2014=1 if inlist(E03,"02","03","05", "08", "10","19","25","26","28")
{txt}(10,040 missing values generated)

{com}. replace region_2014=2 if inlist(E03,"01","06","11","14","16","18","24","32")
{txt}(10,040 real changes made)

{com}. replace region_2014=3 if inlist(E03, "09","13","15","17","22","29")
{txt}(0 real changes made)

{com}. replace region_2014=4 if inlist(E03,"04","07","12","20","21","23","27","30","31")
{txt}(0 real changes made)

{com}. 
. *Age of the firm
. destring P5, gen(inicio_op_2014)
{txt}P5: all characters numeric; inicio_op_2014 {res}generated {txt}as {res}int
{txt}
{com}. label var inicio_op_2014 "Año de inicio de operaciones"
{txt}
{com}. gen edad_emp_2014=2014-inicio_op_2014
{txt}
{com}. label var edad_emp_2014 "Age of the firm"
{txt}
{com}. 
. * Summarize selected variables with and without weights
. sum months* rem_dir_month* rem_op_supp* rem_tot_month* rem_tot_anual* g_train* g_insum* ingre_tot* activ_fijos* FAC_EXPA

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}months_2014 {c |}{res}     10,040          12           0         12         12
{txt}rem_dir~2014 {c |}{res}     10,040      888.55    808.7544          0       2951
{txt}rem_op_~2014 {c |}{res}     10,040     1951.95    2293.447          0       8688
{txt}rem_tot_mo~4 {c |}{res}     10,040      2840.5    2661.812          0       9889
{txt}rem~ual_2014 {c |}{res}     10,040       34086    31941.74          0     118668
{txt}{hline 13}{c +}{hline 57}
rem~eal_2014 {c |}{res}     10,040    33040.43    31141.38          0   116451.6
{txt}g_train_2014 {c |}{res}     10,040       256.3    575.1799          0       2487
{txt}g_train_2013 {c |}{res}     10,040      227.55    512.0976          0       2231
{txt}g_insum_2014 {c |}{res}     10,040    554379.8    680909.4        180    2908121
{txt}g_insum_cu~4 {c |}{res}     10,040    291547.6    396547.2        135    1449882
{txt}{hline 13}{c +}{hline 57}
g_insum_to~4 {c |}{res}     10,040    554636.1    681032.2        251    2909171
{txt}ingre~t_2014 {c |}{res}     10,040     1611102     1863469       2073    7360451
{txt}ingre~l_2014 {c |}{res}     10,040     1552010     1788110   2034.282    7231179
{txt}activ~s_2014 {c |}{res}     10,040    77712.35    114611.4         45     477633
{txt}activ~l_2014 {c |}{res}     10,040    74159.54    109361.3   42.97059   457075.3
{txt}{hline 13}{c +}{hline 57}
{space 4}FAC_EXPA {c |}{res}     10,040     4.63095    3.313742     1.8333         14
{txt}
{com}. sum months* rem_dir_month* rem_op_supp* rem_tot_month* rem_tot_anual* g_train* g_insum* ingre_tot* activ_fijos* [w=FAC_EXPA]
{txt}(analytic weights assumed)

    Variable {c |}     Obs      Weight        Mean   Std. dev.       Min        Max
{hline 13}{c +}{hline 65}
{space 1}months_2014 {c |}{res}  10,040   46494.738          12   1.78e-15         12         12
{txt}rem_dir~2014 {c |}{res}  10,040   46494.738    786.1279   792.2348          0       2951
{txt}rem_op_~2014 {c |}{res}  10,040   46494.738    2090.454   2576.139          0       8688
{txt}rem_tot_mo~4 {c |}{res}  10,040   46494.738    2876.582   2980.245          0       9889
{txt}rem~ual_2014 {c |}{res}  10,040   46494.738    34518.99   35762.94          0     118668
{txt}{hline 13}{c +}{hline 65}
rem~eal_2014 {c |}{res}  10,040   46494.738    33482.52   34851.41          0   116451.6
{txt}g_train_2014 {c |}{res}  10,040   46494.738    240.9085   530.2632          0       2487
{txt}g_train_2013 {c |}{res}  10,040   46494.738    224.7499    468.906          0       2231
{txt}g_insum_2014 {c |}{res}  10,040   46494.738    507967.1   706317.7        180    2908121
{txt}g_insum_cu~4 {c |}{res}  10,040   46494.738    258482.3   357183.9        135    1449882
{txt}{hline 13}{c +}{hline 65}
g_insum_to~4 {c |}{res}  10,040   46494.738      508208   706489.4        251    2909171
{txt}ingre~t_2014 {c |}{res}  10,040   46494.738     1474547    2028759       2073    7360451
{txt}ingre~l_2014 {c |}{res}  10,040   46494.738     1420127    1947096   2034.282    7231179
{txt}activ~s_2014 {c |}{res}  10,040   46494.738     79199.9   135527.8         45     477633
{txt}activ~l_2014 {c |}{res}  10,040   46494.738    75594.09   129366.3   42.97059   457075.3
{txt}
{com}. 
. ************************************************************************************************************************************************************
. *ICT and innovations indicator
. ************************************************************************************************************************************************************
. 
. *Computer use (dummy and share of labor with computer)
. gen dum_comp_2014=(P68=="1")
{txt}
{com}. gen shlabcomp_2014=P70/nworkers_2014
{txt}
{com}. 
. *Main reasons for not using a computer
. destring P69, gen(reasnotcomp_2014)
{txt}P69: all characters numeric; reasnotcomp_2014 {res}generated {txt}as {res}byte
{txt}(10040 missing values generated)
{res}{txt}
{com}. 
. *Use of Internet
. gen dum_inter_2014=(P71=="1")
{txt}
{com}. 
. *Main reasons for not using  Internet
. destring P72, gen(reasnotinter_2014)
{txt}P72: all characters numeric; reasnotinter_2014 {res}generated {txt}as {res}byte
{txt}(10040 missing values generated)
{res}{txt}
{com}. 
. *Use of internet
. destring P73, gen(inter_use_2014)
{txt}P73: all characters numeric; inter_use_2014 {res}generated {txt}as {res}byte
{txt}
{com}. 
. *Capital IT/workers
. gen ITcap_worker_2014=P28_2/nworkers
{txt}
{com}. 
. *Score number of certifications
. foreach var of varlist P76_1 P76_2 P76_3 P76_4 P76_5 {c -(}
{txt}  2{com}. gen `var'b=(`var'=="1")
{txt}  3{com}. {c )-}
{txt}
{com}. egen scor_cert_2014=rsum(P76_1b P76_2b P76_3b P76_4b P76_5b)
{txt}
{com}. 
. gen certif_2014=(scor_cert>=1)
{txt}
{com}. 
. ******Innovation******
. *R&D
. gen dum_IDT_2014=(P77_0ACAL>0 & P77_0ACAL!=.)|( P77_0BCAL>0 & P77_0ACAL!=. )
{txt}
{com}. gen IDT13_real_2014=P77_0ACAL*1.047
{txt}
{com}. egen IDT_1314_2014=rsum(IDT13_real P77_0BCAL)
{txt}
{com}. gen IDT_1314_sal_2014=IDT_1314_2014/ingre_tot_2014
{txt}
{com}. 
. 
. 
. gen innov13_real_2014=P78A*1.047 
{txt}
{com}. egen innov_13_14_2014=rsum(innov13_real_2014 P78B)
{txt}
{com}. gen innov_13_14_sal_2014=innov_13_14_2014/ingre_tot_2014
{txt}
{com}. 
. gen dum_ginnov_2014=(innov_13_14_2014>0 & innov_13_14_2014!=.)
{txt}
{com}. 
. 
. foreach var of varlist P74_1 P74_2 P74_3 P74_4 P74_5 P74_6 {c -(}
{txt}  2{com}. destring `var', gen(`var'b)
{txt}  3{com}. recode `var'b (1=4) (2=3) (3=2) (4=1) (5=0)
{txt}  4{com}. {c )-}
{txt}P74_1: all characters numeric; P74_1b {res}generated {txt}as {res}byte
{txt}(10,040 changes made to {bf:P74_1b})
P74_2: all characters numeric; P74_2b {res}generated {txt}as {res}byte
{txt}(10,040 changes made to {bf:P74_2b})
P74_3: all characters numeric; P74_3b {res}generated {txt}as {res}byte
{txt}(10,040 changes made to {bf:P74_3b})
P74_4: all characters numeric; P74_4b {res}generated {txt}as {res}byte
{txt}(10,040 changes made to {bf:P74_4b})
P74_5: all characters numeric; P74_5b {res}generated {txt}as {res}byte
{txt}(10,040 changes made to {bf:P74_5b})
P74_6: all characters numeric; P74_6b {res}generated {txt}as {res}byte
{txt}(10,040 changes made to {bf:P74_6b})

{com}. 
. *Innovation score
. egen score_innov_2014= rsum(P74_1b P74_2b P74_3b P74_4b P74_5b P74_6b) 
{txt}
{com}. 
. *Patents
. gen dum_pat=(P75A =="1" | P75B =="1")
{txt}
{com}. 
. *Innovation expenses per worker
. gen innov_13_14_work_2014= innov_13_14_2014/nworkers_2014
{txt}
{com}. 
. *R&D expenses per worker
. gen IDT_13_14_work_2014=IDT_1314_2014/nworkers_2014
{txt}
{com}. 
. gen innov_real_2014=P78A*(PRICE_IND_INVEST_MX_B20132014/PRICE_IND_INVEST_MX_B20132014)
{txt}
{com}. 
. 
. **************************************************
. *FDI and exports
. **************************************************
. 
. *FDI
. gen IED_dum_2014=(P6=="1")
{txt}
{com}. label var IED_dum_2014 "=1 si tiene IED"
{txt}
{com}. 
. gen sh_IED_2014=P6_1
{txt}(10,040 missing values generated)

{com}. replace sh_IED_2014=0 if P6=="2"        
{txt}(10,040 real changes made)

{com}. label var sh_IED_2014 "Porcentaje de IED que tiene la empresa"
{txt}
{com}. gen FDI1_2014=IED_dum_2014
{txt}
{com}. 
. *Share of exports
. gen dum_exp_2014=P23_0>0 & P23_0!=.
{txt}
{com}. gen shexp_2014=P23_0/ingre_tot_2014
{txt}
{com}. 
. 
. *Ownership
. gen own_direc2_2014=.
{txt}(10,040 missing values generated)

{com}. replace own_direc2_2014=1 if (P3=="1" | P3=="2") & (P4=="1" | P4=="2") & FDI1_2014==0
{txt}(6,024 real changes made)

{com}. replace own_direc2_2014=2 if (P3=="1" | P3=="2") & (P4=="3") & FDI1_2014==0
{txt}(1,506 real changes made)

{com}. replace own_direc2_2014=3 if (P3=="3" | P3=="4")  & FDI1_2014==0
{txt}(1,004 real changes made)

{com}. replace own_direc2_2014=4 if (P3=="5" | P3=="6" | P3=="9" )  & FDI1_2014==0
{txt}(1,506 real changes made)

{com}. replace own_direc2_2014=5 if FDI1_2014==1
{txt}(0 real changes made)

{com}. 
. 
. label define owner2 1 "Founder & Family owned & directed" 2 "Family owned-external manager" 3 "Private individuals" 4 "Small shareholders" 5 "Foreign"
{txt}
{com}. label values  own_direc2_2014 owner2
{txt}
{com}. 
. 
. *****************************************************
. *Educational level and labor structure
. *****************************************************
. *Share educación
. gen sh_sup_2014=P13_4/P13_0
{txt}
{com}. gen sh_pre_2014= (P13_3+P13_4)/P13_0
{txt}
{com}. 
. *Share estructura dirección
. gen sh_direc_2014=P12_1A/nworkers_2014
{txt}
{com}. 
. *Quintiles white collar workers
. xtile quin_sh_direc_2014=sh_direc_2014, nq(5)
{txt}
{com}. 
. 
. 
. *************************************************************************************************************************************************************
. *Management practices
. ************************************************************************************************************************************************************
. *************************************************************************************************************************************************************
. *Management scoring
. **************************************************************************************************************************************************************
. 
. foreach var of varlist P31 P32 P35 P36 P37 P38 P40 P42 P43 P44 P45 P46 {c -(} 
{txt}  2{com}. gen n_`var'=1 if `var'!=""
{txt}  3{com}. {c )-}
{txt}(2,510 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(5,020 missing values generated)
(5,522 missing values generated)

{com}. 
. foreach var of varlist P33* P34* P39* P41* {c -(}
{txt}  2{com}. gen nn_`var'=1 if `var'!=""
{txt}  3{com}. {c )-}
{txt}(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(10,040 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(2,510 missing values generated)
(10,040 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)
(1,004 missing values generated)

{com}. egen n_P33=rmean(nn_P33*)
{txt}(2,510 missing values generated)

{com}. egen n_P34=rmean(nn_P34*)
{txt}(2,510 missing values generated)

{com}. egen n_P39=rmean(nn_P39*)
{txt}(1,004 missing values generated)

{com}. egen n_P41=rmean(nn_P41*)
{txt}(1,004 missing values generated)

{com}. 
. egen resp_manag=rowtotal(n_P*)
{txt}
{com}. 
. drop n_P* nn_P*
{txt}
{com}. *Revise that resp_manag is at least 7 for all firms, as adding the 3 questions that are zero if excluded will mean at least 10 questions answered
. 
. 
. 
. *Question 1 from MOPS
. gen val_sco_P31=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P31=1/3 if P31=="1"
{txt}(3,514 real changes made)

{com}. replace val_sco_P31=2/3 if P31=="2"
{txt}(3,012 real changes made)

{com}. replace val_sco_P31=1 if P31=="3"
{txt}(2,510 real changes made)

{com}. replace val_sco_P31=0 if P31=="4"
{txt}(1,004 real changes made)

{com}. 
. *Question 2 from MOPS
. gen val_sco_P32=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P32=1/3 if P32=="1"
{txt}(2,008 real changes made)

{com}. replace val_sco_P32=2/3 if P32=="2"
{txt}(3,514 real changes made)

{com}. replace val_sco_P32=2/3 if P32=="3"
{txt}(1,004 real changes made)

{com}. replace val_sco_P32=1 if P32=="4"
{txt}(1,004 real changes made)

{com}. replace val_sco_P32=0 if P32=="5"
{txt}(2,510 real changes made)

{com}. 
. *Question 3 from MOPS-(Mark all that apply-use the median)
. gen val_sco_P33_1=1/6 if P33_1=="1"
{txt}(6,024 missing values generated)

{com}. gen val_sco_P33_2=2/6 if P33_2=="1"
{txt}(6,526 missing values generated)

{com}. gen val_sco_P33_3=3/6 if P33_3=="1"
{txt}(4,518 missing values generated)

{com}. gen val_sco_P33_4=4/6 if P33_4=="1"
{txt}(9,036 missing values generated)

{com}. gen val_sco_P33_5=5/6 if P33_5=="1"
{txt}(9,538 missing values generated)

{com}. gen val_sco_P33_6=1   if P33_6=="1"
{txt}(9,538 missing values generated)

{com}. gen val_sco_P33_7=0   if P33_7=="1"
{txt}(10,040 missing values generated)

{com}. 
. egen val_sco_P33=rowmedian(val_sco_P33_*)
{res}{txt}(2,510 missing values generated)

{com}. replace val_sco_P33=0 if P33_1=="2" & P33_2=="2" & P33_3=="2"  & P33_4=="2"  & P33_5=="2"  & P33_6=="2"  & P33_7=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P33=0 if P32=="5"
{txt}(2,510 real changes made)

{com}. 
. *Question 4 from MOPS-(Mark all that apply-use the median)
. 
. gen val_sco_P34_1=1/6 if P34_1=="1"
{txt}(7,530 missing values generated)

{com}. gen val_sco_P34_2=2/6 if P34_2=="1"
{txt}(8,032 missing values generated)

{com}. gen val_sco_P34_3=3/6 if P34_3=="1"
{txt}(5,020 missing values generated)

{com}. gen val_sco_P34_4=4/6 if P34_4=="1"
{txt}(8,032 missing values generated)

{com}. gen val_sco_P34_5=5/6 if P34_5=="1"
{txt}(9,538 missing values generated)

{com}. gen val_sco_P34_6=1   if P34_6=="1"
{txt}(10,040 missing values generated)

{com}. gen val_sco_P34_7=0   if P34_7=="1"
{txt}(10,040 missing values generated)

{com}. 
. egen val_sco_P34=rowmedian(val_sco_P34_*)
{res}{txt}(2,510 missing values generated)

{com}. replace val_sco_P34=0 if P34_1=="2" & P34_2=="2" & P34_3=="2"  & P34_4=="2"  & P34_5=="2"  & P34_6=="2"  & P34_7=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P34=0 if P32=="5"
{txt}(2,510 real changes made)

{com}. 
. *Question 5 from MOPS
. gen val_sco_P35=1/2 if P35=="1"
{txt}(7,530 missing values generated)

{com}. replace val_sco_P35=1 if P35=="2"
{txt}(502 real changes made)

{com}. replace val_sco_P35=0 if P35=="3"
{txt}(4,518 real changes made)

{com}. 
. replace val_sco_P35=0 if P32=="5"
{txt}(2,510 real changes made)

{com}. 
. 
. *Question 6 from MOPS
. gen val_sco_P36=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P36=1/3 if P36=="1"
{txt}(5,020 real changes made)

{com}. replace val_sco_P36=2/3 if P36=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P36=1   if P36=="3"
{txt}(4,016 real changes made)

{com}. replace val_sco_P36=0   if P36=="4"
{txt}(1,004 real changes made)

{com}. 
. *Question 7 from MOPS
. gen val_sco_P37=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P37=0   if P37=="1"
{txt}(0 real changes made)

{com}. replace val_sco_P37=0.5 if P37=="2"
{txt}(3,012 real changes made)

{com}. replace val_sco_P37=0.75 if P37=="3"
{txt}(3,514 real changes made)

{com}. replace val_sco_P37=1 if P37=="4"
{txt}(2,008 real changes made)

{com}. replace val_sco_P37=0.25   if P37=="5"
{txt}(502 real changes made)

{com}. 
. *Question 8 from MOPS
. gen val_sco_P38=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P38=0 if P38=="1"
{txt}(2,008 real changes made)

{com}. replace val_sco_P38=1/3 if P38=="2"
{txt}(2,008 real changes made)

{com}. replace val_sco_P38=2/3   if P38=="3"
{txt}(502 real changes made)

{com}. replace val_sco_P38=1   if P38=="4"
{txt}(4,518 real changes made)

{com}. 
. *Question 9 from MOPS-(Mark all that apply-use the median)
. gen val_sco_P39_1=1   if P39_1=="1"
{txt}(6,024 missing values generated)

{com}. gen val_sco_P39_2=3/4 if P39_2=="1"
{txt}(9,036 missing values generated)

{com}. gen val_sco_P39_3=1/2 if P39_3=="1"
{txt}(9,036 missing values generated)

{com}. gen val_sco_P39_4=1/4 if P39_4=="1"
{txt}(8,534 missing values generated)

{com}. gen val_sco_P39_5=0   if P39_5=="1"
{txt}(6,024 missing values generated)

{com}. 
. 
. egen val_sco_P39=rowmedian(val_sco_P39_*)
{res}{txt}(1,004 missing values generated)

{com}. replace val_sco_P39=0 if P39_1=="2" & P39_2=="2" & P39_3=="2" & P39_4=="2" & P39_5=="2"
{txt}(0 real changes made)

{com}. 
. sum val_sco*

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}val_sco_P31 {c |}{res}     10,040    .5666667    .3179956          0          1
{txt}{space 1}val_sco_P32 {c |}{res}     10,040    .4666667    .3231948          0          1
{txt}val_sco_~3_1 {c |}{res}      4,016    .1666667           0   .1666667   .1666667
{txt}val_sco_~3_2 {c |}{res}      3,514    .3333333           0   .3333333   .3333333
{txt}val_sco_~3_3 {c |}{res}      5,522          .5           0         .5         .5
{txt}{hline 13}{c +}{hline 57}
val_sco_~3_4 {c |}{res}      1,004    .6666667           0   .6666667   .6666667
{txt}val_sco_~3_5 {c |}{res}        502    .8333333           0   .8333333   .8333333
{txt}val_sco_~3_6 {c |}{res}        502           1           0          1          1
{txt}val_sco_~3_7 {c |}{res}          0
{txt}{space 1}val_sco_P33 {c |}{res}     10,040    .2958333    .1962859          0   .5833334
{txt}{hline 13}{c +}{hline 57}
val_sco_~4_1 {c |}{res}      2,510    .1666667           0   .1666667   .1666667
{txt}val_sco_~4_2 {c |}{res}      2,008    .3333333           0   .3333333   .3333333
{txt}val_sco_~4_3 {c |}{res}      5,020          .5           0         .5         .5
{txt}val_sco_~4_4 {c |}{res}      2,008    .6666667           0   .6666667   .6666667
{txt}val_sco_~4_5 {c |}{res}        502    .8333333           0   .8333333   .8333333
{txt}{hline 13}{c +}{hline 57}
val_sco_~4_6 {c |}{res}          0
{txt}val_sco_~4_7 {c |}{res}          0
{txt}{space 1}val_sco_P34 {c |}{res}     10,040    .3291667    .2319643          0   .6666667
{txt}{space 1}val_sco_P35 {c |}{res}     10,040        .175    .2861523          0          1
{txt}{space 1}val_sco_P36 {c |}{res}     10,040    .5666667    .3666849          0          1
{txt}{hline 13}{c +}{hline 57}
{space 1}val_sco_P37 {c |}{res}      9,036    .6944444     .213377        .25          1
{txt}{space 1}val_sco_P38 {c |}{res}      9,036    .6111111    .4194585          0          1
{txt}val_sco_~9_1 {c |}{res}      4,016           1           0          1          1
{txt}val_sco_~9_2 {c |}{res}      1,004         .75           0        .75        .75
{txt}val_sco_~9_3 {c |}{res}      1,004          .5           0         .5         .5
{txt}{hline 13}{c +}{hline 57}
val_sco_~9_4 {c |}{res}      1,506         .25           0        .25        .25
{txt}val_sco_~9_5 {c |}{res}      4,016           0           0          0          0
{txt}{space 1}val_sco_P39 {c |}{res}      9,036    .4652778     .446472          0          1
{txt}
{com}. 
. *Question 10 from MOPS
. gen     val_sco_P40=1/5 if P40=="1"
{txt}(9,538 missing values generated)

{com}. replace val_sco_P40=2/5 if P40=="2"
{txt}(1,004 real changes made)

{com}. replace val_sco_P40=3/5 if P40=="3"
{txt}(2,008 real changes made)

{com}. replace val_sco_P40=4/5 if P40=="4"
{txt}(1,506 real changes made)

{com}. replace val_sco_P40=1   if P40=="5"
{txt}(0 real changes made)

{com}. replace val_sco_P40=0   if P40=="6"
{txt}(0 real changes made)

{com}. 
. *Question 11 from MOPS (Mark all that apply-use the median)
. gen val_sco_P41_1=1   if P41_1=="1"
{txt}(6,526 missing values generated)

{com}. gen val_sco_P41_2=3/4 if P41_2=="1"
{txt}(8,534 missing values generated)

{com}. gen val_sco_P41_3=1/2 if P41_3=="1"
{txt}(9,036 missing values generated)

{com}. gen val_sco_P41_4=1/4 if P41_4=="1"
{txt}(9,036 missing values generated)

{com}. gen val_sco_P41_5=0   if P41_5=="1"
{txt}(5,522 missing values generated)

{com}. 
. 
. egen val_sco_P41=rowmedian(val_sco_P41_*)
{res}{txt}(1,004 missing values generated)

{com}. replace val_sco_P41=0 if P41_1=="2" & P41_2=="2" & P41_3=="2" & P41_4=="2" & P41_5=="2"
{txt}(0 real changes made)

{com}. 
. 
. *Question 12 from MOPS
. gen     val_sco_P42=1/5 if P42=="1"
{txt}(10,040 missing values generated)

{com}. replace val_sco_P42=2/5 if P42=="2"
{txt}(1,004 real changes made)

{com}. replace val_sco_P42=3/5 if P42=="3"
{txt}(1,004 real changes made)

{com}. replace val_sco_P42=4/5 if P42=="4"
{txt}(1,506 real changes made)

{com}. replace val_sco_P42=1   if P42=="5"
{txt}(1,004 real changes made)

{com}. replace val_sco_P42=0   if P42=="6"
{txt}(0 real changes made)

{com}. 
. *Question 13 from MOPS
. gen val_sco_P43=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P43=1   if P43=="1"
{txt}(2,510 real changes made)

{com}. replace val_sco_P43=2/3 if P43=="2"
{txt}(1,004 real changes made)

{com}. replace val_sco_P43=1/3 if P43=="3"
{txt}(0 real changes made)

{com}. replace val_sco_P43=0   if P43=="4"
{txt}(6,526 real changes made)

{com}. 
. *Question 14 from MOPS
. gen val_sco_P44=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P44=1   if P44=="1"
{txt}(3,012 real changes made)

{com}. replace val_sco_P44=2/3 if P44=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P44=1/3 if P44=="3"
{txt}(0 real changes made)

{com}. replace val_sco_P44=0   if P44=="4"
{txt}(7,028 real changes made)

{com}. 
. *Question 15 from MOPS
. gen val_sco_P45=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P45=1   if P45=="1"
{txt}(3,514 real changes made)

{com}. replace val_sco_P45=1/2 if P45=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P45=0   if P45=="3"
{txt}(6,526 real changes made)

{com}. 
. *Question 16 from MOPS
. gen val_sco_P46=.
{txt}(10,040 missing values generated)

{com}. replace val_sco_P46=1   if P46=="1"
{txt}(3,514 real changes made)

{com}. replace val_sco_P46=1/2 if P46=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P46=0   if P46=="3"
{txt}(6,526 real changes made)

{com}. 
. 
. *Calculate score and subindices according to Management in America
. egen score_manag_2014=rmean(val_sco_P31 val_sco_P32 val_sco_P33 val_sco_P34 val_sco_P35 val_sco_P36 val_sco_P37 val_sco_P38 val_sco_P39 val_sco_P40 val_sco_P41 val_sco_P42 val_sco_P43 val_sco_P44 val_sco_P45 val_sco_P46)
{txt}
{com}. egen score_man_dat_2014=rmean(val_sco_P31 val_sco_P32 val_sco_P33 val_sco_P34 val_sco_P35 val_sco_P38)
{txt}
{com}. egen score_man_inc_2014=rmean( val_sco_P36 val_sco_P37 val_sco_P39 val_sco_P40 val_sco_P41 val_sco_P42 val_sco_P43 val_sco_P44 val_sco_P45 val_sco_P46)
{txt}
{com}. egen score_manfirst8_2014=rmean(val_sco_P31 val_sco_P32 val_sco_P33 val_sco_P34 val_sco_P35 val_sco_P36 val_sco_P37 val_sco_P38)
{txt}
{com}. egen score_manlast8_2014= rmean(val_sco_P39 val_sco_P40 val_sco_P41 val_sco_P42 val_sco_P43 val_sco_P44 val_sco_P45 val_sco_P46)
{txt}
{com}. 
. 
. 
. 
. *Replace if resp_manag<10
. replace score_manag_2014=. if resp_manag<10
{txt}(1,004 real changes made, 1,004 to missing)

{com}. replace score_man_dat_2014=. if resp_manag<10
{txt}(1,004 real changes made, 1,004 to missing)

{com}. replace score_man_inc_2014=. if resp_manag<10
{txt}(1,004 real changes made, 1,004 to missing)

{com}. replace score_manfirst8_2014=. if resp_manag<10
{txt}(1,004 real changes made, 1,004 to missing)

{com}. replace score_manlast8_2014=. if resp_manag<10
{txt}(1,004 real changes made, 1,004 to missing)

{com}. 
. 
. *Calculate deciles and quintiles of management
. xtile dec_manag_2014=score_manag_2014 , nq(10)
{txt}
{com}. xtile quin_manag_2014=score_manag_2014, nq(5)
{txt}
{com}. 
. ************************************************************************************************************************************************************
. *Global value chains
. ************************************************************************************************************************************************************
. 
. *Participates in gvcs
. gen dum_gvc_2014=(P63=="1")
{txt}
{com}. 
. *Reasons for not participating in gvcs
. destring P64A, gen(raz_nogvc_2013)
{txt}P64A: all characters numeric; raz_nogvc_2013 {res}generated {txt}as {res}byte
{txt}(502 missing values generated)
{res}{txt}
{com}. destring P64B, gen(raz_nogvc_2014)
{txt}P64B: all characters numeric; raz_nogvc_2014 {res}generated {txt}as {res}byte
{txt}(502 missing values generated)
{res}{txt}
{com}. 
. *Year started participating in gvcs
. destring P65, gen(inicio_gvc_2014)
{txt}P65: all characters numeric; inicio_gvc_2014 {res}generated {txt}as {res}int
{txt}(9538 missing values generated)
{res}{txt}
{com}. 
. *# of years participating in gvcs
. gen anios_gvc_2014=2014-inicio_gvc if dum_gvc==1
{txt}(9,538 missing values generated)

{com}. 
. 
. *Link in the value chain
. destring P66_1, gen(eslab_1_2014)
{txt}P66_1: all characters numeric; eslab_1_2014 {res}generated {txt}as {res}byte
{txt}(9538 missing values generated)
{res}{txt}
{com}. destring P66_2, gen(eslab_2_2014)
{txt}P66_2: all characters numeric; eslab_2_2014 {res}generated {txt}as {res}byte
{txt}(9538 missing values generated)
{res}{txt}
{com}. destring P66_3, gen(eslab_3_2014)
{txt}P66_3: all characters numeric; eslab_3_2014 {res}generated {txt}as {res}byte
{txt}(9538 missing values generated)
{res}{txt}
{com}. recode eslab_1_2014 eslab_2_2014 eslab_3_2014 (2=0)
{txt}(502 changes made to {bf:eslab_1_2014})
(502 changes made to {bf:eslab_2_2014})
(0 changes made to {bf:eslab_3_2014})

{com}. 
. *# of links in the value chain
. egen eslabones_gvc_2014=rsum(eslab_1_2014 eslab_2_2014 eslab_3_2014)
{txt}
{com}. 
. *Main benefit value chain
. destring P67, gen(beneficio_gvc_2014)
{txt}P67: all characters numeric; beneficio_gvc_2014 {res}generated {txt}as {res}byte
{txt}(9538 missing values generated)
{res}{txt}
{com}. save "$working/BasePyMETFP_2015.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/BasePyMETFP_2015.dta{rm}
saved
{p_end}

{com}. 
. 
. use "$working/BasePyMETFP_2015.dta", clear
{txt}
{com}. 
. *1) Generate variables used in Management in America as outcomes
. 
. *Value added per worker (Real= Prices 2014)
. gen VA_2014=(ingre_tot_2014 + Var_inven_2014)-(g_materials_2014+g_energy_2014)
{txt}
{com}. gen VA_real_2014=(ingre_tot_2014 + Var_inven_2014)*(100/INPP_prodB20132014)-(g_materials_2014+g_energy_2014)*(100/impatp_base20132014)
{txt}
{com}. gen VA_wk_2014=VA_2014/nworkers_2014
{txt}
{com}. gen VA_real_wk_2014=VA_real_2014/nworkers_2014
{txt}
{com}. gen VA_real_wk_mil_2014=(VA_real_2014/1000)/nworkers_2014
{txt}
{com}. 
. 
. *Operating profits
. gen oper_profit_2014=(VA_real_2014-rem_tot_anual_2014)
{txt}
{com}. gen oper_profit_real_2014=(VA_2014-rem_tot_anual_2014*(100/INPP_prodB20132014))
{txt}
{com}. 
. *Profitability
. gen profitab_2014=(VA_2014-rem_tot_anual_2014)/ingre_tot_2014
{txt}
{com}. gen profitabrea_2014=(VA_real_2014-rem_tot_anual_2014*(100/INPP_prodB20132014))/(ingre_tot_2014*(100/INPP_prodB20132014))
{txt}
{com}. 
. 
. gen sh_rem_2014=rem_tot_anual_2014/ingre_tot_2014
{txt}
{com}. gen sh_insum_2014=(g_train_2014+g_insum_2014+g_energy_2014)/ingre_tot_2014
{txt}
{com}. gen sh_af_2014=1-sh_rem-sh_insum
{txt}
{com}. 
. 
. foreach var of varlist VA_real_wk_mil_2014 VA_real_2014 rem_tot_anual_2014 rem_tot_anual_real_2014 g_train_2014 g_insum_2014 g_energy_2014  activ_fijos_real_2014 ingre_tot_2014 ingre_tot_real_2014 oper_profit_real_2014 g_insum_cut_2014 activ_fijos_2014 g_materials_2014 g_insum_tot_2014 Var_inven_2014 nworkers_2014 innov_13_14_work_2014 IDT_13_14_work_2014 sh_sup_2014 sh_direc_2014{c -(}
{txt}  2{com}.                 local newvar = substr("`var'", 1, length("`var'") - 5)   // Remove 2014
{txt}  3{com}.                 gen `var'b=`var'
{txt}  4{com}.                 ren `var'b `newvar'
{txt}  5{com}.                 winsor2 `newvar', s(_WA_2014) 
{txt}  6{com}.                 drop `newvar'
{txt}  7{com}. {c )-}
{res}{txt}
{com}. 
. gen lVA_real_wk_mil_2014=ln(VA_real_wk_mil_2014 )
{txt}
{com}. gen lVA_real_wk_mil_WA_2014=ln(VA_real_wk_mil_WA_2014 )
{txt}
{com}. 
. gen ln_rem_real_2014=ln(rem_tot_anual_real_2014+sqrt(rem_tot_anual_real_2014^2+1))
{txt}
{com}. gen ln_rem_real_WA_2014=ln(rem_tot_anual_real_WA_2014+sqrt(rem_tot_anual_real_WA_2014^2+1))
{txt}
{com}. gen ln_rem_2014=ln(rem_tot_anual_2014+sqrt(rem_tot_anual_2014^2+1))
{txt}
{com}. 
. gen ln_insum_2014=ln(g_train_WA_2014+g_insum_WA_2014)
{txt}
{com}. gen ln_insum_real_2014=ln((g_train_2014+g_insum_2014+g_energy_2014)*(100/INPP_prodB20132014)) 
{txt}
{com}. gen ln_insum_real_WA_2014=ln((g_train_WA_2014+g_insum_WA_2014+g_energy_WA_2014)*(100/INPP_prodB20132014))
{txt}
{com}. 
. gen ln_af_real_2014=ln(activ_fijos_real_2014+sqrt(activ_fijos_real_2014^2+1))
{txt}
{com}. gen ln_af_2014=ln(activ_fijos_2014+sqrt(activ_fijos_2014^2+1))
{txt}
{com}. 
. gen ln_af_WA_2014=ln(activ_fijos_WA_2014+sqrt(activ_fijos_WA_2014^2+1))
{txt}
{com}. gen ln_af_real_WA_2014=ln(activ_fijos_real_WA_2014+sqrt(activ_fijos_real_WA_2014^2+1))
{txt}
{com}. gen lnY_real_WA_2014=ln(ingre_tot_WA_2014+ Var_inven_WA_2014)*(100/INPP_prodB20132014) 
{txt}
{com}. gen lnY_real_2014=ln(ingre_tot_2014+ Var_inven_2014)*(100/INPP_prodB20132014) 
{txt}
{com}. gen lnY_WA_2014=ln(ingre_tot_WA_2014+ Var_inven_WA_2014)
{txt}
{com}. gen lnY_2014=ln(ingre_tot_2014+ Var_inven_2014) 
{txt}
{com}. 
. 
. 
. 
. ////LTFP INDEX AW & ROBERTS///////
> levelsof sector_2014, local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. foreach var of varlist sh_rem_2014 sh_insum_2014 sh_af_2014 lnY_real_2014 ln_rem_real_2014 ln_insum_real_2014 ln_af_real_2014 lnY_2014 ln_rem_2014 ln_insum_2014 ln_af_2014 lnY_real_WA_2014 ln_rem_real_WA_2014 ln_insum_real_WA_2014 ln_af_real_WA_2014{c -(}
{txt}  3{com}. sum `var' if sector_2014 =="`x'" , d
{txt}  4{com}. sum `var' if (`var'>r(p1) & `var'<r(p99)) & sector_2014 =="`x'"  [aw=FAC_EXPA], d
{txt}  5{com}. scalar `var'_bar`x'=r(mean)
{txt}  6{com}. {c )-}
{txt}  7{com}. {c )-}

                         {txt}sh_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .0116786              0
{txt} 5%    {res} .0116786              0
{txt}10%    {res} .0116786              0       {txt}Obs         {res}      1,000
{txt}25%    {res} .0116786       .0069105       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} .0240838                      {txt}Mean          {res} .2616146
                        {txt}Largest       Std. dev.     {res} .2578868
{txt}75%    {res} .5277526       .5277526
{txt}90%    {res} .5277526       .5277526       {txt}Variance      {res} .0665056
{txt}95%    {res} .5277526       .5277526       {txt}Skewness      {res} .0771193
{txt}99%    {res} .5277526       .8683068       {txt}Kurtosis      {res}  1.02721

                         {txt}sh_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .0184997       .0184997
{txt} 5%    {res} .0195668       .0195668
{txt}10%    {res} .0213384       .0213384       {txt}Obs         {res}         40
{txt}25%    {res} .0240838       .0226488       {txt}Sum of wgt. {res}    99.5164

{txt}50%    {res} .0240838                      {txt}Mean          {res} .0472553
                        {txt}Largest       Std. dev.     {res} .0485466
{txt}75%    {res} .0320309       .0421949
{txt}90%    {res} .1398887        .107559       {txt}Variance      {res} .0023568
{txt}95%    {res} .1398887       .1398887       {txt}Skewness      {res} 1.755521
{txt}99%    {res} .1980737       .1980737       {txt}Kurtosis      {res}  4.53245

                        {txt}sh_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .1938838       .1283164
{txt} 5%    {res} .1938838       .1286306
{txt}10%    {res} .1938838       .1326257       {txt}Obs         {res}      1,000
{txt}25%    {res} .1938838       .1938838       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} .3606245                      {txt}Mean          {res} .2949112
                        {txt}Largest       Std. dev.     {res} .1034004
{txt}75%    {res} .3885413       .7006511
{txt}90%    {res} .3885413       .8400632       {txt}Variance      {res} .0106916
{txt}95%    {res} .3885413       .9174623       {txt}Skewness      {res} .5844167
{txt}99%    {res} .3885413       .9189189       {txt}Kurtosis      {res} 4.562962

                        {txt}sh_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .2183234       .2183234
{txt} 5%    {res} .2282545       .2282545
{txt}10%    {res} .3004586       .3004586       {txt}Obs         {res}         36
{txt}25%    {res} .3415467       .3328581       {txt}Sum of wgt. {res}    79.6909

{txt}50%    {res} .3606245                      {txt}Mean          {res} .3419209
                        {txt}Largest       Std. dev.     {res} .0414339
{txt}75%    {res} .3606245       .3606245
{txt}90%    {res} .3606245       .3606245       {txt}Variance      {res} .0017168
{txt}95%    {res} .3606245       .3606245       {txt}Skewness      {res}-2.253303
{txt}99%    {res} .3786443       .3786443       {txt}Kurtosis      {res} 6.694525

                         {txt}sh_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .2783636       .0033767
{txt} 5%    {res} .2783636       .0200481
{txt}10%    {res} .2783636       .0490502       {txt}Obs         {res}      1,000
{txt}25%    {res} .2783636       .0611993       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} .5997801                      {txt}Mean          {res} .4434742
                        {txt}Largest       Std. dev.     {res} .1644783
{txt}75%    {res} .5997801       .6995413
{txt}90%    {res} .5997801       .7521788       {txt}Variance      {res} .0270531
{txt}95%    {res} .5997801       .8408143       {txt}Skewness      {res}-.0706946
{txt}99%    {res} .6152917       .8426327       {txt}Kurtosis      {res} 1.186581

                         {txt}sh_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .5997801        .361829
{txt} 5%    {res} .5997801       .5248163
{txt}10%    {res} .5997801       .5791607       {txt}Obs         {res}        477
{txt}25%    {res} .5997801       .5944515       {txt}Sum of wgt. {res} 1,590.1414

{txt}50%    {res} .5997801                      {txt}Mean          {res} .5988983
                        {txt}Largest       Std. dev.     {res} .0127006
{txt}75%    {res} .5997801       .5997801
{txt}90%    {res} .5997801       .5997801       {txt}Variance      {res} .0001613
{txt}95%    {res} .5997801       .5997801       {txt}Skewness      {res}-17.07984
{txt}99%    {res} .5997801       .5997801       {txt}Kurtosis      {res} 310.5424

                        {txt}lnY_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  11.9924       7.499865
{txt} 5%    {res}  11.9924        11.9924
{txt}10%    {res}  11.9924        11.9924       {txt}Obs         {res}      1,000
{txt}25%    {res}  11.9924        11.9924       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res}  13.4605                      {txt}Mean          {res} 12.75309
                        {txt}Largest       Std. dev.     {res}  .768074
{txt}75%    {res}  13.4605       14.39408
{txt}90%    {res}  13.4605       14.51512       {txt}Variance      {res} .5899376
{txt}95%    {res}  13.4605       15.32472       {txt}Skewness      {res}-.2606223
{txt}99%    {res} 13.56965        15.5134       {txt}Kurtosis      {res} 3.410797

                        {txt}lnY_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.40945        12.0308
{txt} 5%    {res}  13.4605       12.21454
{txt}10%    {res}  13.4605       12.40945       {txt}Obs         {res}        508
{txt}25%    {res}  13.4605       12.48475       {txt}Sum of wgt. {res} 1,663.5659

{txt}50%    {res}  13.4605                      {txt}Mean          {res} 13.44546
                        {txt}Largest       Std. dev.     {res} .1349463
{txt}75%    {res}  13.4605       13.50292
{txt}90%    {res}  13.4605       13.50292       {txt}Variance      {res} .0182105
{txt}95%    {res}  13.4605       13.50292       {txt}Skewness      {res}-8.168743
{txt}99%    {res} 13.50292       13.51742       {txt}Kurtosis      {res} 69.75159

                      {txt}ln_rem_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  10.6501              0
{txt} 5%    {res}  10.6501              0
{txt}10%    {res}  10.6501              0       {txt}Obs         {res}      1,000
{txt}25%    {res}  10.6501       8.169835       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 11.37857                      {txt}Mean          {res} 11.41402
                        {txt}Largest       Std. dev.     {res} 1.013458
{txt}75%    {res} 12.25581       12.25581
{txt}90%    {res} 12.25581       12.25581       {txt}Variance      {res} 1.027098
{txt}95%    {res} 12.25581       12.25581       {txt}Skewness      {res} -4.25935
{txt}99%    {res} 12.25581       12.35838       {txt}Kurtosis      {res} 48.86428

                      {txt}ln_rem_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.70406       10.70406
{txt} 5%    {res} 10.73016       10.73016
{txt}10%    {res} 10.80937       10.80937       {txt}Obs         {res}         39
{txt}25%    {res} 11.37857       10.89507       {txt}Sum of wgt. {res}    93.3114

{txt}50%    {res} 11.37857                      {txt}Mean          {res}  11.3114
                        {txt}Largest       Std. dev.     {res} .2477624
{txt}75%    {res} 11.37857       11.37857
{txt}90%    {res} 11.51223       11.43254       {txt}Variance      {res} .0613862
{txt}95%    {res} 11.51223       11.51223       {txt}Skewness      {res} -1.21459
{txt}99%    {res} 11.78561       11.78561       {txt}Kurtosis      {res} 4.123832

                     {txt}ln_insum_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  10.5613       5.564642
{txt} 5%    {res}  10.5613        10.5613
{txt}10%    {res}  10.5613        10.5613       {txt}Obs         {res}      1,000
{txt}25%    {res}  10.5613        10.5613       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 13.39172                      {txt}Mean          {res} 12.04752
                        {txt}Largest       Std. dev.     {res} 1.460562
{txt}75%    {res} 13.46159       14.12186
{txt}90%    {res} 13.46159       14.17129       {txt}Variance      {res}  2.13324
{txt}95%    {res} 13.46159       14.31244       {txt}Skewness      {res}-.1231462
{txt}99%    {res} 13.46159       15.04872       {txt}Kurtosis      {res} 1.376183

                     {txt}ln_insum_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 11.20081       11.20081
{txt} 5%    {res} 11.64513       11.64513
{txt}10%    {res} 11.87803       11.86301       {txt}Obs         {res}         41
{txt}25%    {res} 12.61171       11.87803       {txt}Sum of wgt. {res}    104.269

{txt}50%    {res} 13.39172                      {txt}Mean          {res} 12.93445
                        {txt}Largest       Std. dev.     {res} .5805559
{txt}75%    {res} 13.39172       13.39172
{txt}90%    {res} 13.39172       13.39172       {txt}Variance      {res} .3370452
{txt}95%    {res} 13.39172       13.39172       {txt}Skewness      {res}-1.174375
{txt}99%    {res} 13.44569       13.44569       {txt}Kurtosis      {res} 3.723631

                       {txt}ln_af_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 9.399677        4.45595
{txt} 5%    {res} 9.399677       6.198233
{txt}10%    {res} 9.399677        8.58938       {txt}Obs         {res}      1,000
{txt}25%    {res} 9.399677       9.399677       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res}    12.13                      {txt}Mean          {res} 11.29423
                        {txt}Largest       Std. dev.     {res} 1.886785
{txt}75%    {res} 13.19842       13.19842
{txt}90%    {res} 13.19842       13.19842       {txt}Variance      {res} 3.559956
{txt}95%    {res} 13.19842       13.20409       {txt}Skewness      {res} -.066452
{txt}99%    {res} 13.19842       13.72575       {txt}Kurtosis      {res} 1.216826

                       {txt}ln_af_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.05875       10.05875
{txt} 5%    {res} 10.19925       10.19925
{txt}10%    {res} 10.67087       10.36069       {txt}Obs         {res}         42
{txt}25%    {res} 11.14037       10.55803       {txt}Sum of wgt. {res}    95.5069

{txt}50%    {res}    12.13                      {txt}Mean          {res} 11.75966
                        {txt}Largest       Std. dev.     {res} .6911188
{txt}75%    {res}    12.13          12.13
{txt}90%    {res} 12.13027       12.13027       {txt}Variance      {res} .4776451
{txt}95%    {res} 12.77447       12.13567       {txt}Skewness      {res}-1.027678
{txt}99%    {res} 12.77447       12.77447       {txt}Kurtosis      {res} 3.000453

                          {txt}lnY_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.22065       7.642609
{txt} 5%    {res} 12.22065       12.22065
{txt}10%    {res} 12.22065       12.22065       {txt}Obs         {res}      1,000
{txt}25%    {res} 12.22065       12.22065       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 14.47724                      {txt}Mean          {res} 13.37616
                        {txt}Largest       Std. dev.     {res}  1.14375
{txt}75%    {res} 14.47724       14.66804
{txt}90%    {res} 14.47724       14.79139       {txt}Variance      {res} 1.308163
{txt}95%    {res} 14.47724       15.61639       {txt}Skewness      {res}-.1512878
{txt}99%    {res} 14.52286       15.80866       {txt}Kurtosis      {res}  1.61291

                          {txt}lnY_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.64564       12.25978
{txt} 5%    {res} 14.47724       12.44702
{txt}10%    {res} 14.47724       12.64564       {txt}Obs         {res}        485
{txt}25%    {res} 14.47724       12.72237       {txt}Sum of wgt. {res} 1,626.6084

{txt}50%    {res} 14.47724                      {txt}Mean          {res} 14.43922
                        {txt}Largest       Std. dev.     {res} .2433794
{txt}75%    {res} 14.47724       14.47724
{txt}90%    {res} 14.47724       14.47724       {txt}Variance      {res} .0592335
{txt}95%    {res} 14.47724       14.47724       {txt}Skewness      {res}-7.026559
{txt}99%    {res} 14.47724       14.47724       {txt}Kurtosis      {res} 53.08251

                         {txt}ln_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.72291              0
{txt} 5%    {res} 10.72291              0
{txt}10%    {res} 10.72291              0       {txt}Obs         {res}      1,000
{txt}25%    {res} 10.72291       8.188689       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 11.45139                      {txt}Mean          {res}  11.4598
                        {txt}Largest       Std. dev.     {res} .9950182
{txt}75%    {res} 12.27467       12.27467
{txt}90%    {res} 12.27467       12.27467       {txt}Variance      {res} .9900611
{txt}95%    {res} 12.27467       12.27467       {txt}Skewness      {res}-4.562118
{txt}99%    {res} 12.27467       12.37723       {txt}Kurtosis      {res}  53.3812

                         {txt}ln_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.74901       10.74901
{txt} 5%    {res} 10.82822       10.82822
{txt}10%    {res} 10.91392       10.91392       {txt}Obs         {res}         38
{txt}25%    {res} 11.45139       11.12219       {txt}Sum of wgt. {res}    89.9781

{txt}50%    {res} 11.45139                      {txt}Mean          {res} 11.38354
                        {txt}Largest       Std. dev.     {res} .2295835
{txt}75%    {res} 11.45139       11.45139
{txt}90%    {res} 11.53108       11.45139       {txt}Variance      {res} .0527086
{txt}95%    {res} 11.53108       11.53108       {txt}Skewness      {res}-1.517253
{txt}99%    {res} 11.80446       11.80446       {txt}Kurtosis      {res} 5.029811

                        {txt}ln_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.51434       5.525453
{txt} 5%    {res} 10.51434       10.51434
{txt}10%    {res} 10.51434       10.51434       {txt}Obs         {res}      1,000
{txt}25%    {res} 10.51434       10.51434       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 13.12851                      {txt}Mean          {res} 12.04601
                        {txt}Largest       Std. dev.     {res} 1.506212
{txt}75%    {res} 13.52628       13.94519
{txt}90%    {res} 13.52628       14.13721       {txt}Variance      {res} 2.268675
{txt}95%    {res} 13.52628       14.18715       {txt}Skewness      {res}-.1128732
{txt}99%    {res} 13.52628       14.88338       {txt}Kurtosis      {res} 1.340644

                        {txt}ln_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 11.08042       11.08042
{txt} 5%    {res} 11.41397       11.41397
{txt}10%    {res} 11.84657       11.82684       {txt}Obs         {res}         41
{txt}25%    {res}  12.6023       11.84657       {txt}Sum of wgt. {res}    104.269

{txt}50%    {res} 13.12851                      {txt}Mean          {res} 12.75919
                        {txt}Largest       Std. dev.     {res} .5112465
{txt}75%    {res} 13.12851       13.12851
{txt}90%    {res} 13.12851       13.12851       {txt}Variance      {res}  .261373
{txt}95%    {res} 13.12851       13.12851       {txt}Skewness      {res}-1.657755
{txt}99%    {res} 13.12851       13.12851       {txt}Kurtosis      {res} 5.374623

                         {txt}ln_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 9.443672       4.499933
{txt} 5%    {res} 9.443672       6.242227
{txt}10%    {res} 9.443672       8.633375       {txt}Obs         {res}      1,000
{txt}25%    {res} 9.443672       9.443672       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 12.17967                      {txt}Mean          {res} 11.34106
                        {txt}Largest       Std. dev.     {res}  1.88956
{txt}75%    {res} 13.24809       13.24809
{txt}90%    {res} 13.24809       13.24809       {txt}Variance      {res} 3.570437
{txt}95%    {res} 13.24809       13.24809       {txt}Skewness      {res}-.0662791
{txt}99%    {res} 13.24809       13.76974       {txt}Kurtosis      {res} 1.215946

                         {txt}ln_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.10275       10.10275
{txt} 5%    {res} 10.24324       10.24324
{txt}10%    {res} 10.71486       10.40469       {txt}Obs         {res}         42
{txt}25%    {res} 11.18437       10.60202       {txt}Sum of wgt. {res}    95.5069

{txt}50%    {res} 12.17967                      {txt}Mean          {res}  11.8067
                        {txt}Largest       Std. dev.     {res} .6927966
{txt}75%    {res} 12.17967       12.17967
{txt}90%    {res} 12.17967       12.17967       {txt}Variance      {res} .4799671
{txt}95%    {res} 12.81847       12.17967       {txt}Skewness      {res}-1.029623
{txt}99%    {res} 12.81847       12.81847       {txt}Kurtosis      {res}  2.99261

                      {txt}lnY_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  11.9924       7.499865
{txt} 5%    {res}  11.9924        11.9924
{txt}10%    {res}  11.9924        11.9924       {txt}Obs         {res}      1,000
{txt}25%    {res}  11.9924        11.9924       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res}  13.4605                      {txt}Mean          {res} 12.75309
                        {txt}Largest       Std. dev.     {res}  .768074
{txt}75%    {res}  13.4605       14.39408
{txt}90%    {res}  13.4605       14.51512       {txt}Variance      {res} .5899376
{txt}95%    {res}  13.4605       15.32472       {txt}Skewness      {res}-.2606223
{txt}99%    {res} 13.56965        15.5134       {txt}Kurtosis      {res} 3.410797

                      {txt}lnY_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.40945        12.0308
{txt} 5%    {res}  13.4605       12.21454
{txt}10%    {res}  13.4605       12.40945       {txt}Obs         {res}        508
{txt}25%    {res}  13.4605       12.48475       {txt}Sum of wgt. {res} 1,663.5659

{txt}50%    {res}  13.4605                      {txt}Mean          {res} 13.44546
                        {txt}Largest       Std. dev.     {res} .1349463
{txt}75%    {res}  13.4605       13.50292
{txt}90%    {res}  13.4605       13.50292       {txt}Variance      {res} .0182105
{txt}95%    {res}  13.4605       13.50292       {txt}Skewness      {res}-8.168743
{txt}99%    {res} 13.50292       13.51742       {txt}Kurtosis      {res} 69.75159

                     {txt}ln_rem_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  10.6501              0
{txt} 5%    {res}  10.6501              0
{txt}10%    {res}  10.6501              0       {txt}Obs         {res}      1,000
{txt}25%    {res}  10.6501       8.169835       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 11.37857                      {txt}Mean          {res} 11.41401
                        {txt}Largest       Std. dev.     {res} 1.013445
{txt}75%    {res} 12.25581       12.25581
{txt}90%    {res} 12.25581       12.25581       {txt}Variance      {res} 1.027072
{txt}95%    {res} 12.25581       12.25581       {txt}Skewness      {res}-4.259506
{txt}99%    {res} 12.25581       12.34452       {txt}Kurtosis      {res} 48.86647

                     {txt}ln_rem_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.70406       10.70406
{txt} 5%    {res} 10.73016       10.73016
{txt}10%    {res} 10.80937       10.80937       {txt}Obs         {res}         39
{txt}25%    {res} 11.37857       10.89507       {txt}Sum of wgt. {res}    93.3114

{txt}50%    {res} 11.37857                      {txt}Mean          {res}  11.3114
                        {txt}Largest       Std. dev.     {res} .2477624
{txt}75%    {res} 11.37857       11.37857
{txt}90%    {res} 11.51223       11.43254       {txt}Variance      {res} .0613862
{txt}95%    {res} 11.51223       11.51223       {txt}Skewness      {res} -1.21459
{txt}99%    {res} 11.78561       11.78561       {txt}Kurtosis      {res} 4.123832

                    {txt}ln_insum_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  10.5613       5.564642
{txt} 5%    {res}  10.5613        10.5613
{txt}10%    {res}  10.5613        10.5613       {txt}Obs         {res}      1,000
{txt}25%    {res}  10.5613        10.5613       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res} 13.39172                      {txt}Mean          {res} 12.04752
                        {txt}Largest       Std. dev.     {res} 1.460562
{txt}75%    {res} 13.46159       14.12186
{txt}90%    {res} 13.46159       14.17129       {txt}Variance      {res}  2.13324
{txt}95%    {res} 13.46159       14.31244       {txt}Skewness      {res}-.1231462
{txt}99%    {res} 13.46159       15.04872       {txt}Kurtosis      {res} 1.376183

                    {txt}ln_insum_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 11.20081       11.20081
{txt} 5%    {res} 11.64513       11.64513
{txt}10%    {res} 11.87803       11.86301       {txt}Obs         {res}         41
{txt}25%    {res} 12.61171       11.87803       {txt}Sum of wgt. {res}    104.269

{txt}50%    {res} 13.39172                      {txt}Mean          {res} 12.93445
                        {txt}Largest       Std. dev.     {res} .5805559
{txt}75%    {res} 13.39172       13.39172
{txt}90%    {res} 13.39172       13.39172       {txt}Variance      {res} .3370452
{txt}95%    {res} 13.39172       13.39172       {txt}Skewness      {res}-1.174375
{txt}99%    {res} 13.44569       13.44569       {txt}Kurtosis      {res} 3.723631

                     {txt}ln_af_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 9.399677        4.45595
{txt} 5%    {res} 9.399677       6.198233
{txt}10%    {res} 9.399677        8.58938       {txt}Obs         {res}      1,000
{txt}25%    {res} 9.399677       9.399677       {txt}Sum of wgt. {res}      1,000

{txt}50%    {res}    12.13                      {txt}Mean          {res} 11.29423
                        {txt}Largest       Std. dev.     {res} 1.886782
{txt}75%    {res} 13.19842       13.19842
{txt}90%    {res} 13.19842       13.19842       {txt}Variance      {res} 3.559946
{txt}95%    {res} 13.19842       13.20409       {txt}Skewness      {res}-.0664545
{txt}99%    {res} 13.19842        13.7236       {txt}Kurtosis      {res} 1.216823

                     {txt}ln_af_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 10.05875       10.05875
{txt} 5%    {res} 10.19925       10.19925
{txt}10%    {res} 10.67087       10.36069       {txt}Obs         {res}         42
{txt}25%    {res} 11.14037       10.55803       {txt}Sum of wgt. {res}    95.5069

{txt}50%    {res}    12.13                      {txt}Mean          {res} 11.75966
                        {txt}Largest       Std. dev.     {res} .6911188
{txt}75%    {res}    12.13          12.13
{txt}90%    {res} 12.13027       12.13027       {txt}Variance      {res} .4776451
{txt}95%    {res} 12.77447       12.13567       {txt}Skewness      {res}-1.027678
{txt}99%    {res} 12.77447       12.77447       {txt}Kurtosis      {res} 3.000453

                         {txt}sh_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      9,040
{txt}25%    {res} .0109045              0       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} .0226488                      {txt}Mean          {res} .0880494
                        {txt}Largest       Std. dev.     {res} .1973764
{txt}75%    {res} .0421949       .8683068
{txt}90%    {res} .1980737       .8683068       {txt}Variance      {res} .0389574
{txt}95%    {res} .8683068       .8683068       {txt}Skewness      {res}   3.4227
{txt}99%    {res} .8683068       .8683068       {txt}Kurtosis      {res} 13.61494

                         {txt}sh_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .0069105       .0069105
{txt} 5%    {res} .0069105       .0069105
{txt}10%    {res} .0109045       .0069105       {txt}Obs         {res}      7,036
{txt}25%    {res} .0195668       .0069105       {txt}Sum of wgt. {res} 27,423.122

{txt}50%    {res} .0287368                      {txt}Mean          {res} .0669147
                        {txt}Largest       Std. dev.     {res} .0679896
{txt}75%    {res} .1398887       .5277526
{txt}90%    {res} .1398887       .5277526       {txt}Variance      {res} .0046226
{txt}95%    {res} .1980737       .5277526       {txt}Skewness      {res} 2.014821
{txt}99%    {res} .1980737       .5277526       {txt}Kurtosis      {res} 11.72401

                        {txt}sh_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .1283164       .1283164
{txt} 5%    {res} .1283164       .1283164
{txt}10%    {res} .1286306       .1283164       {txt}Obs         {res}      9,040
{txt}25%    {res} .2282545       .1283164       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} .3471126                      {txt}Mean          {res} .4315052
                        {txt}Largest       Std. dev.     {res} .2544508
{txt}75%    {res} .6312605       .9189189
{txt}90%    {res} .9174623       .9189189       {txt}Variance      {res} .0647452
{txt}95%    {res} .9189189       .9189189       {txt}Skewness      {res} .7409678
{txt}99%    {res} .9189189       .9189189       {txt}Kurtosis      {res} 2.330061

                        {txt}sh_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .1286306       .1286306
{txt} 5%    {res} .1286306       .1286306
{txt}10%    {res} .1326257       .1286306       {txt}Obs         {res}      8,038
{txt}25%    {res} .3004586       .1286306       {txt}Sum of wgt. {res} 32,995.896

{txt}50%    {res} .3606245                      {txt}Mean          {res} .4731968
                        {txt}Largest       Std. dev.     {res} .2541163
{txt}75%    {res} .7006511       .9174623
{txt}90%    {res} .8400632       .9174623       {txt}Variance      {res} .0645751
{txt}95%    {res} .8400632       .9174623       {txt}Skewness      {res} .4350087
{txt}99%    {res} .9174623       .9174623       {txt}Kurtosis      {res} 1.757716

                         {txt}sh_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .0033767       .0033767
{txt} 5%    {res} .0033767       .0033767
{txt}10%    {res} .0200481       .0033767       {txt}Obs         {res}      9,040
{txt}25%    {res} .1012752       .0033767       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} .5944515                      {txt}Mean          {res} .4804454
                        {txt}Largest       Std. dev.     {res}  .288994
{txt}75%    {res} .6741175       .8426327
{txt}90%    {res} .8408143       .8426327       {txt}Variance      {res} .0835175
{txt}95%    {res} .8426327       .8426327       {txt}Skewness      {res}-.6100533
{txt}99%    {res} .8426327       .8426327       {txt}Kurtosis      {res} 1.822979

                         {txt}sh_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .0200481       .0200481
{txt} 5%    {res} .0200481       .0200481
{txt}10%    {res} .0200481       .0200481       {txt}Obs         {res}      8,038
{txt}25%    {res} .0490502       .0200481       {txt}Sum of wgt. {res} 33,447.798

{txt}50%    {res} .5791607                      {txt}Mean          {res} .4278965
                        {txt}Largest       Std. dev.     {res} .2991647
{txt}75%    {res} .6671419       .8408143
{txt}90%    {res} .7521788       .8408143       {txt}Variance      {res} .0894995
{txt}95%    {res} .8408143       .8408143       {txt}Skewness      {res}-.3673543
{txt}99%    {res} .8408143       .8408143       {txt}Kurtosis      {res} 1.444404

                        {txt}lnY_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 7.512661       7.512661
{txt} 5%    {res} 7.512661       7.512661
{txt}10%    {res}  11.8652       7.512661       {txt}Obs         {res}      9,040
{txt}25%    {res} 12.31291       7.512661       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 13.33247                      {txt}Mean          {res} 13.11224
                        {txt}Largest       Std. dev.     {res} 1.642072
{txt}75%    {res} 14.05545       15.53101
{txt}90%    {res} 15.07174       15.53101       {txt}Variance      {res} 2.696402
{txt}95%    {res} 15.11378       15.53101       {txt}Skewness      {res}-1.992674
{txt}99%    {res} 15.11378       15.53101       {txt}Kurtosis      {res} 7.818391

                        {txt}lnY_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  11.8652       11.82733
{txt} 5%    {res} 12.04642       11.82733
{txt}10%    {res} 12.04642       11.82733       {txt}Obs         {res}      8,005
{txt}25%    {res} 12.19535       11.82733       {txt}Sum of wgt. {res} 32,285.979

{txt}50%    {res} 13.27988                      {txt}Mean          {res} 13.20278
                        {txt}Largest       Std. dev.     {res} .9393104
{txt}75%    {res} 13.80238       15.07174
{txt}90%    {res} 14.43109       15.07174       {txt}Variance      {res} .8823041
{txt}95%    {res} 15.07174       15.07174       {txt}Skewness      {res} .2852284
{txt}99%    {res} 15.07174       15.07174       {txt}Kurtosis      {res} 2.086723

                      {txt}ln_rem_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      9,040
{txt}25%    {res} 9.721809              0       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 10.81101                      {txt}Mean          {res} 9.040652
                        {txt}Largest       Std. dev.     {res} 4.130482
{txt}75%    {res} 11.27893       12.34452
{txt}90%    {res} 11.78817       12.34452       {txt}Variance      {res} 17.06088
{txt}95%    {res} 12.34452       12.34452       {txt}Skewness      {res}  -1.6286
{txt}99%    {res} 12.34452       12.34452       {txt}Kurtosis      {res} 3.887112

                      {txt}ln_rem_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  8.17154        8.17154
{txt} 5%    {res}  8.17154        8.17154
{txt}10%    {res}  8.17154        8.17154       {txt}Obs         {res}      7,036
{txt}25%    {res} 9.721809        8.17154       {txt}Sum of wgt. {res} 31,279.319

{txt}50%    {res} 10.88121                      {txt}Mean          {res} 10.42119
                        {txt}Largest       Std. dev.     {res} 1.267982
{txt}75%    {res} 11.49482       12.24195
{txt}90%    {res} 11.49482       12.24195       {txt}Variance      {res} 1.607778
{txt}95%    {res} 11.78817       12.24195       {txt}Skewness      {res}-.9053898
{txt}99%    {res} 11.78817       12.24195       {txt}Kurtosis      {res} 2.311507

                     {txt}ln_insum_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.566347       5.566347
{txt} 5%    {res} 5.566347       5.566347
{txt}10%    {res} 11.18695       5.566347       {txt}Obs         {res}      9,040
{txt}25%    {res} 11.87718       5.566347       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 12.64774                      {txt}Mean          {res} 12.49383
                        {txt}Largest       Std. dev.     {res} 1.942282
{txt}75%    {res} 13.43183       15.04986
{txt}90%    {res} 14.28356       15.04986       {txt}Variance      {res} 3.772459
{txt}95%    {res} 15.01984       15.04986       {txt}Skewness      {res}-2.301376
{txt}99%    {res} 15.01984       15.04986       {txt}Kurtosis      {res} 9.246873

                     {txt}ln_insum_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 11.18695       10.54744
{txt} 5%    {res} 11.63136       10.54744
{txt}10%    {res} 11.84915       10.54744       {txt}Obs         {res}      8,038
{txt}25%    {res} 12.59379       10.54744       {txt}Sum of wgt. {res} 32,460.978

{txt}50%    {res} 12.61341                      {txt}Mean          {res} 12.77451
                        {txt}Largest       Std. dev.     {res} .8041334
{txt}75%    {res} 13.10379       14.31501
{txt}90%    {res}   14.108       14.31501       {txt}Variance      {res} .6466306
{txt}95%    {res} 14.28356       14.31501       {txt}Skewness      {res} .3034022
{txt}99%    {res} 14.28356       14.31501       {txt}Kurtosis      {res} 2.894699

                       {txt}ln_af_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 4.453798       4.453798
{txt} 5%    {res} 4.453798       4.453798
{txt}10%    {res} 6.196081       4.453798       {txt}Obs         {res}      9,040
{txt}25%    {res} 10.19694       4.453798       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 11.01347                      {txt}Mean          {res}  10.5813
                        {txt}Largest       Std. dev.     {res} 2.189366
{txt}75%    {res} 12.04055        13.7236
{txt}90%    {res} 12.77217        13.7236       {txt}Variance      {res} 4.793325
{txt}95%    {res}  13.7236        13.7236       {txt}Skewness      {res}-1.405207
{txt}99%    {res}  13.7236        13.7236       {txt}Kurtosis      {res}  4.67822

                       {txt}ln_af_real_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.196081       6.196081
{txt} 5%    {res} 6.196081       6.196081
{txt}10%    {res} 6.196081       6.196081       {txt}Obs         {res}      8,038
{txt}25%    {res} 8.587229       6.196081       {txt}Sum of wgt. {res} 35,841.425

{txt}50%    {res} 10.55572                      {txt}Mean          {res} 9.855006
                        {txt}Largest       Std. dev.     {res} 2.125851
{txt}75%    {res} 11.30061       13.20194
{txt}90%    {res} 12.13337       13.20194       {txt}Variance      {res} 4.519241
{txt}95%    {res} 12.77217       13.20194       {txt}Skewness      {res}-.5551391
{txt}99%    {res} 12.77217       13.20194       {txt}Kurtosis      {res} 2.095186

                          {txt}lnY_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 7.642609       7.642609
{txt} 5%    {res} 7.642609       7.642609
{txt}10%    {res} 12.25978       7.642609       {txt}Obs         {res}      9,040
{txt}25%    {res} 12.72237       7.642609       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res}  13.7747                      {txt}Mean          {res} 13.53332
                        {txt}Largest       Std. dev.     {res} 1.729332
{txt}75%    {res} 14.52286       15.80866
{txt}90%    {res} 15.61639       15.80866       {txt}Variance      {res} 2.990589
{txt}95%    {res} 15.80866       15.80866       {txt}Skewness      {res}-1.962657
{txt}99%    {res} 15.80866       15.80866       {txt}Kurtosis      {res} 7.813468

                          {txt}lnY_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.25978       12.22065
{txt} 5%    {res} 12.44702       12.22065
{txt}10%    {res} 12.44702       12.22065       {txt}Obs         {res}      8,038
{txt}25%    {res} 12.64564       12.22065       {txt}Sum of wgt. {res} 32,460.978

{txt}50%    {res}  13.7747                      {txt}Mean          {res} 13.63689
                        {txt}Largest       Std. dev.     {res} .9746944
{txt}75%    {res} 14.27201       15.61639
{txt}90%    {res} 14.79139       15.61639       {txt}Variance      {res} .9500291
{txt}95%    {res} 15.61639       15.61639       {txt}Skewness      {res} .3855411
{txt}99%    {res} 15.61639       15.61639       {txt}Kurtosis      {res} 2.193827

                         {txt}ln_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      9,040
{txt}25%    {res} 9.754523              0       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 10.82822                      {txt}Mean          {res} 9.067075
                        {txt}Largest       Std. dev.     {res} 4.142458
{txt}75%    {res} 11.31164       12.37723
{txt}90%    {res} 11.80446       12.37723       {txt}Variance      {res} 17.15996
{txt}95%    {res} 12.37723       12.37723       {txt}Skewness      {res}-1.628937
{txt}99%    {res} 12.37723       12.37723       {txt}Kurtosis      {res}  3.88721

                         {txt}ln_rem_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 8.188689       8.188689
{txt} 5%    {res} 8.188689       8.188689
{txt}10%    {res} 8.188689       8.188689       {txt}Obs         {res}      7,036
{txt}25%    {res} 9.754523       8.188689       {txt}Sum of wgt. {res} 31,279.319

{txt}50%    {res} 10.91392                      {txt}Mean          {res} 10.45144
                        {txt}Largest       Std. dev.     {res} 1.273587
{txt}75%    {res} 11.53108       12.27467
{txt}90%    {res} 11.53108       12.27467       {txt}Variance      {res} 1.622024
{txt}95%    {res} 11.80446       12.27467       {txt}Skewness      {res}-.9130935
{txt}99%    {res} 11.80446       12.27467       {txt}Kurtosis      {res} 2.318435

                        {txt}ln_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.525453       5.525453
{txt} 5%    {res} 5.525453       5.525453
{txt}10%    {res} 11.08042       5.525453       {txt}Obs         {res}      9,040
{txt}25%    {res} 11.84657       5.525453       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res}  12.6023                      {txt}Mean          {res} 12.39034
                        {txt}Largest       Std. dev.     {res} 1.923337
{txt}75%    {res} 13.12851       14.88338
{txt}90%    {res} 14.18715       14.88338       {txt}Variance      {res} 3.699226
{txt}95%    {res} 14.88338       14.88338       {txt}Skewness      {res}-2.307564
{txt}99%    {res} 14.88338       14.88338       {txt}Kurtosis      {res} 9.277108

                        {txt}ln_insum_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 11.08042       10.51434
{txt} 5%    {res} 11.41397       10.51434
{txt}10%    {res} 11.82684       10.51434       {txt}Obs         {res}      8,038
{txt}25%    {res} 12.50481       10.51434       {txt}Sum of wgt. {res} 32,460.978

{txt}50%    {res} 12.60756                      {txt}Mean          {res} 12.68912
                        {txt}Largest       Std. dev.     {res}  .788399
{txt}75%    {res} 12.84006       14.18715
{txt}90%    {res} 13.94519       14.18715       {txt}Variance      {res}  .621573
{txt}95%    {res} 14.13721       14.18715       {txt}Skewness      {res} .1984358
{txt}99%    {res} 14.18715       14.18715       {txt}Kurtosis      {res} 3.081395

                         {txt}ln_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 4.499933       4.499933
{txt} 5%    {res} 4.499933       4.499933
{txt}10%    {res} 6.242227       4.499933       {txt}Obs         {res}      9,040
{txt}25%    {res} 10.24324       4.499933       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 11.05961                      {txt}Mean          {res} 10.62751
                        {txt}Largest       Std. dev.     {res} 2.189391
{txt}75%    {res} 12.08669       13.76974
{txt}90%    {res} 12.81847       13.76974       {txt}Variance      {res} 4.793432
{txt}95%    {res} 13.76974       13.76974       {txt}Skewness      {res}-1.405223
{txt}99%    {res} 13.76974       13.76974       {txt}Kurtosis      {res} 4.678212

                         {txt}ln_af_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.242227       6.242227
{txt} 5%    {res} 6.242227       6.242227
{txt}10%    {res} 6.242227       6.242227       {txt}Obs         {res}      8,038
{txt}25%    {res} 8.633375       6.242227       {txt}Sum of wgt. {res} 35,841.425

{txt}50%    {res} 10.60202                      {txt}Mean          {res} 9.901199
                        {txt}Largest       Std. dev.     {res} 2.125888
{txt}75%    {res} 11.34675       13.24809
{txt}90%    {res} 12.17967       13.24809       {txt}Variance      {res} 4.519398
{txt}95%    {res} 12.81847       13.24809       {txt}Skewness      {res}-.5551228
{txt}99%    {res} 12.81847       13.24809       {txt}Kurtosis      {res} 2.095171

                      {txt}lnY_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 7.512661       7.512661
{txt} 5%    {res} 7.512661       7.512661
{txt}10%    {res}  11.8652       7.512661       {txt}Obs         {res}      9,040
{txt}25%    {res} 12.31291       7.512661       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 13.33247                      {txt}Mean          {res} 13.11224
                        {txt}Largest       Std. dev.     {res} 1.642072
{txt}75%    {res} 14.05545       15.53101
{txt}90%    {res} 15.07174       15.53101       {txt}Variance      {res} 2.696402
{txt}95%    {res} 15.11378       15.53101       {txt}Skewness      {res}-1.992674
{txt}99%    {res} 15.11378       15.53101       {txt}Kurtosis      {res} 7.818391

                      {txt}lnY_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  11.8652       11.82733
{txt} 5%    {res} 12.04642       11.82733
{txt}10%    {res} 12.04642       11.82733       {txt}Obs         {res}      8,005
{txt}25%    {res} 12.19535       11.82733       {txt}Sum of wgt. {res} 32,285.979

{txt}50%    {res} 13.27988                      {txt}Mean          {res} 13.20278
                        {txt}Largest       Std. dev.     {res} .9393104
{txt}75%    {res} 13.80238       15.07174
{txt}90%    {res} 14.43109       15.07174       {txt}Variance      {res} .8823041
{txt}95%    {res} 15.07174       15.07174       {txt}Skewness      {res} .2852284
{txt}99%    {res} 15.07174       15.07174       {txt}Kurtosis      {res} 2.086723

                     {txt}ln_rem_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      9,040
{txt}25%    {res} 9.721809              0       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 10.81101                      {txt}Mean          {res} 9.040652
                        {txt}Largest       Std. dev.     {res} 4.130482
{txt}75%    {res} 11.27893       12.34452
{txt}90%    {res} 11.78817       12.34452       {txt}Variance      {res} 17.06088
{txt}95%    {res} 12.34452       12.34452       {txt}Skewness      {res}  -1.6286
{txt}99%    {res} 12.34452       12.34452       {txt}Kurtosis      {res} 3.887112

                     {txt}ln_rem_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  8.17154        8.17154
{txt} 5%    {res}  8.17154        8.17154
{txt}10%    {res}  8.17154        8.17154       {txt}Obs         {res}      7,036
{txt}25%    {res} 9.721809        8.17154       {txt}Sum of wgt. {res} 31,279.319

{txt}50%    {res} 10.88121                      {txt}Mean          {res} 10.42119
                        {txt}Largest       Std. dev.     {res} 1.267982
{txt}75%    {res} 11.49482       12.24195
{txt}90%    {res} 11.49482       12.24195       {txt}Variance      {res} 1.607778
{txt}95%    {res} 11.78817       12.24195       {txt}Skewness      {res}-.9053898
{txt}99%    {res} 11.78817       12.24195       {txt}Kurtosis      {res} 2.311507

                    {txt}ln_insum_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.566347       5.566347
{txt} 5%    {res} 5.566347       5.566347
{txt}10%    {res} 11.18695       5.566347       {txt}Obs         {res}      9,040
{txt}25%    {res} 11.87718       5.566347       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 12.64774                      {txt}Mean          {res} 12.49383
                        {txt}Largest       Std. dev.     {res} 1.942282
{txt}75%    {res} 13.43183       15.04986
{txt}90%    {res} 14.28356       15.04986       {txt}Variance      {res} 3.772459
{txt}95%    {res} 15.01984       15.04986       {txt}Skewness      {res}-2.301376
{txt}99%    {res} 15.01984       15.04986       {txt}Kurtosis      {res} 9.246873

                    {txt}ln_insum_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 11.18695       10.54744
{txt} 5%    {res} 11.63136       10.54744
{txt}10%    {res} 11.84915       10.54744       {txt}Obs         {res}      8,038
{txt}25%    {res} 12.59379       10.54744       {txt}Sum of wgt. {res} 32,460.978

{txt}50%    {res} 12.61341                      {txt}Mean          {res} 12.77451
                        {txt}Largest       Std. dev.     {res} .8041334
{txt}75%    {res} 13.10379       14.31501
{txt}90%    {res}   14.108       14.31501       {txt}Variance      {res} .6466306
{txt}95%    {res} 14.28356       14.31501       {txt}Skewness      {res} .3034022
{txt}99%    {res} 14.28356       14.31501       {txt}Kurtosis      {res} 2.894699

                     {txt}ln_af_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 4.453798       4.453798
{txt} 5%    {res} 4.453798       4.453798
{txt}10%    {res} 6.196081       4.453798       {txt}Obs         {res}      9,040
{txt}25%    {res} 10.19694       4.453798       {txt}Sum of wgt. {res}      9,040

{txt}50%    {res} 11.01347                      {txt}Mean          {res}  10.5813
                        {txt}Largest       Std. dev.     {res} 2.189366
{txt}75%    {res} 12.04055        13.7236
{txt}90%    {res} 12.77217        13.7236       {txt}Variance      {res} 4.793325
{txt}95%    {res}  13.7236        13.7236       {txt}Skewness      {res}-1.405207
{txt}99%    {res}  13.7236        13.7236       {txt}Kurtosis      {res}  4.67822

                     {txt}ln_af_real_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.196081       6.196081
{txt} 5%    {res} 6.196081       6.196081
{txt}10%    {res} 6.196081       6.196081       {txt}Obs         {res}      8,038
{txt}25%    {res} 8.587229       6.196081       {txt}Sum of wgt. {res} 35,841.425

{txt}50%    {res} 10.55572                      {txt}Mean          {res} 9.855006
                        {txt}Largest       Std. dev.     {res} 2.125851
{txt}75%    {res} 11.30061       13.20194
{txt}90%    {res} 12.13337       13.20194       {txt}Variance      {res} 4.519241
{txt}95%    {res} 12.77217       13.20194       {txt}Skewness      {res}-.5551391
{txt}99%    {res} 12.77217       13.20194       {txt}Kurtosis      {res} 2.095186
{txt}
{com}. 
. 
. gen ltfp_sec_2014=.
{txt}(10,040 missing values generated)

{com}. levelsof sector_2014, local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_2014=(lnY_2014-lnY_2014_bar`x')-(0.5)*(sh_rem_2014+sh_rem_2014_bar`x')*(ln_rem_2014-ln_rem_2014_bar`x')-(0.5)*(sh_insum_2014+sh_insum_2014_bar`x')*(ln_insum_2014-ln_insum_2014_bar`x')-(0.5)*(sh_af_2014+sh_af_2014_bar`x')*(ln_af_2014-ln_af_2014_bar`x') if sector_2014=="`x'"
{txt}  3{com}. {c )-}
{txt}(1,000 real changes made)
(9,040 real changes made)

{com}. 
. 
. gen ltfp_sec_real_2014=.
{txt}(10,040 missing values generated)

{com}. levelsof sector_2014 , local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_real_2014=(lnY_real_2014-lnY_real_2014_bar`x')-(0.5)*(sh_rem_2014+sh_rem_2014_bar`x')*(ln_rem_real_2014-ln_rem_real_2014_bar`x')-(0.5)*(sh_insum_2014+sh_insum_2014_bar`x')*(ln_insum_real_2014-ln_insum_real_2014_bar`x')-(0.5)*(sh_af_2014 +sh_af_2014_bar`x')*(ln_af_real_2014-ln_af_real_2014_bar`x') if sector_2014 =="`x'"
{txt}  3{com}. {c )-}
{txt}(1,000 real changes made)
(9,040 real changes made)

{com}. 
. 
. gen ltfp_sec_real_WA_2014=.
{txt}(10,040 missing values generated)

{com}. levelsof sector_2014 , local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_real_WA_2014=(lnY_real_WA_2014-lnY_real_WA_2014_bar`x')-(0.5)*(sh_rem_2014+sh_rem_2014_bar`x')*(ln_rem_real_WA_2014-ln_rem_real_WA_2014_bar`x')-(0.5)*(sh_insum_2014+sh_insum_2014_bar`x')*(ln_insum_real_WA_2014-ln_insum_real_WA_2014_bar`x')-(0.5)*(sh_af_2014 +sh_af_2014_bar`x')*(ln_af_real_WA_2014-ln_af_real_WA_2014_bar`x') if sector_2014 =="`x'"
{txt}  3{com}. {c )-}
{txt}(1,000 real changes made)
(9,040 real changes made)

{com}. 
. 
. 
. *************************************************************************************************************************************************************
. * Aggregated sector variables
. label variable sector_2014 "Aggregated sector (manufacturing , services, and commerce) classification for 2014"
{txt}note: label truncated to 80 characters

{com}. label variable sector2_2014 "Aggregated sector (manufacturing and services) for 2014"
{txt}
{com}. 
. * Grouped sector and state
. label variable sec_ent_2014 "Grouped sector and state for 2014"
{txt}
{com}. 
. * Months of operation
. label variable months_2014 "Months firm operated in 2014"
{txt}
{com}. 
. * Monthly salaries (MX pesos)
. label variable rem_dir_month_2014 "Monthly salary of of white-collar employees in 2014 (MX pesos)"
{txt}
{com}. label variable rem_op_supp_2014 "Monthly salary of of blue-collar employees in 2014 (MX pesos)"
{txt}
{com}. 
. * Monthly remunerations (MX pesos)
. label variable rem_tot_month_2014 "Total monthly remuneration in 2014 (MX pesos)"
{txt}
{com}. 
. * Total annual remunerations (MX pesos)
. label variable rem_tot_anual_2014 "Total annual remuneration in 2014 (MX pesos)"
{txt}
{com}. 
. * Total expenses in training (MX pesos)
. label variable g_train_2014 "Total training expenses in 2014 (MX pesos)"
{txt}
{com}. 
. 
. * Energy expenses (MX pesos)
. label variable g_energy_2014 "Energy expenses in 2014 (MX pesos)"
{txt}
{com}. 
. * Materials expenses (MX pesos)
. label variable g_materials_2014 "Materials expenses in 2014 (MX pesos)"
{txt}
{com}. 
. * Total inputs (MX pesos)
. label variable g_insum_2014 "Total input expenses in 2014 (MX pesos)"
{txt}
{com}. label variable g_insum_cut_2014 "Total inputs excluding specific adjustments in 2014 (MX pesos)"
{txt}
{com}. label variable g_insum_tot_2014 "Total inputs plus training expenses in 2014 (MX pesos)"
{txt}
{com}. 
. * Total revenues (MX pesos)
. label variable ingre_tot_2014 "Total revenue in 2014 (MX pesos)"
{txt}
{com}. 
. * Fixed assets (MX pesos)
. label variable activ_fijos_2014 "Fixed assets value in 2014 (MX pesos)"
{txt}
{com}. 
. * Firm size
. label variable size_2014 "Categorical size of the firm in 2014"
{txt}
{com}. label variable nworkers_2014 "Number of workers in 2014"
{txt}
{com}. 
. * Inventories (MX pesos)
. label variable invtot_fin_2014 "Final total inventory in 2014 (MX pesos)"
{txt}
{com}. label variable invmprev_fin_2014 "Final inventory for merchandise for resale in 2014 (MX pesos)"
{txt}
{com}. label variable invtot_ini_2014 "Initial total inventory in 2014 (MX pesos)"
{txt}
{com}. label variable invmprev_ini_2014 "Initial inventory for merchandise for resale in 2014 (MX pesos)"
{txt}
{com}. label variable Var_inven_2014 "Change in inventories (deflated to 2014 prices) (MX pesos)"
{txt}
{com}. 
. * Regions
. label variable region_2014 "Region of firm operation in 2014"
{txt}
{com}. 
. * Age of the firm
. label variable inicio_op_2014 "Year the firm started operations"
{txt}
{com}. label variable edad_emp_2014 "Firm's age in 2014"
{txt}
{com}. 
. 
. *IT use
. label variable dum_comp_2014 "Computer use dummy 2014"
{txt}
{com}. label variable shlabcomp_2014 "Share of labor with computer 2014"
{txt}
{com}. label variable reasnotcomp_2014 "Reasons for not using a computer 2014"
{txt}
{com}. label variable dum_inter_2014 "Internet use dummy 2014"
{txt}
{com}. label variable reasnotinter_2014 "Reasons for not using Internet 2014"
{txt}
{com}. label variable inter_use_2014 "Internet use type 2014"
{txt}
{com}. label variable ITcap_worker_2014 "IT capital per worker 2014"
{txt}
{com}. 
. *Certifications
. label variable scor_cert_2014 "Score of certifications 2014"
{txt}
{com}. label variable certif_2014 "Certification obtained (dummy) 2014"
{txt}
{com}. 
. 
. label variable dum_IDT_2014 "Dummy for R&D (2014)"
{txt}
{com}. label variable IDT_1314_2014 "R&D (2013-2014) (MX Pesos, 2014)"
{txt}
{com}. label variable IDT_1314_sal_2014 "R&D/Revenues (2013-2014) (MX Pesos, 2014)"
{txt}
{com}. label variable innov_real_2014 "Real Innovation Expenditure (MX Pesos, 2014)"
{txt}
{com}. label variable innov_13_14_2014 "Innovation Expenditure (2013-2014) (MX Pesos, 2014)"
{txt}
{com}. label variable innov_13_14_sal_2014 "Innovation Expenditure/Revenues (2013-2014) (MX Pesos, 2014)"
{txt}
{com}. label variable dum_ginnov_2014 "Dummy for Expenditure in Innovation (2014)"
{txt}
{com}. label variable score_innov_2014 "Innovation Score (2014)"
{txt}
{com}. label variable dum_pat "Dummy for Patents (2014)"
{txt}
{com}. label variable innov_13_14_work_2014 "Innovation (2013-2014) per Worker (MX Pesos, 2014)"
{txt}
{com}. label variable IDT_13_14_work_2014 "R&D per worker (2013-2014) (MX Pesos, 2014)"
{txt}
{com}. label variable IED_dum_2014 "Dummy for Foreign Direct Investment (2014)"
{txt}
{com}. label variable sh_IED_2014 "Share of Foreign Direct Investment (2014)"
{txt}
{com}. label variable FDI1_2014 "FDI Dummy"
{txt}
{com}. label variable dum_exp_2014 "Dummy for Exports (2014)"
{txt}
{com}. label variable shexp_2014 "Share of Exports (2014)"
{txt}
{com}. label variable own_direc2_2014 "Ownership (2014)"
{txt}
{com}. label variable sh_sup_2014 "Share of employees with college (2014)"
{txt}
{com}. label variable sh_sup_WA_2014 "Share of employees with college (2014, winsorized)"
{txt}
{com}. label variable sh_pre_2014 "Share ofemployees with High School (2014)"
{txt}
{com}. label variable sh_direc_2014 "Share of White collar  (2014)"
{txt}
{com}. label variable sh_direc_WA_2014 "Share of White collar (2014, winsorized)"
{txt}
{com}. label variable quin_sh_direc_2014 "Quintile Share of Management (2014)"
{txt}
{com}. label variable resp_manag "# of responses to management questions (2014)"
{txt}
{com}. label variable score_manag_2014 "Management Score (2014)"
{txt}
{com}. label variable score_man_dat_2014 "Management Data Score (2014)"
{txt}
{com}. label variable score_man_inc_2014 "Management Incentives Score (2014)"
{txt}
{com}. label variable score_manfirst8_2014 "Management score first 8 questions"
{txt}
{com}. label variable score_manlast8_2014 "Management score last questions"
{txt}
{com}. 
. 
. label variable dum_gvc_2014 "Dummy for Global Value Chains"
{txt}
{com}. label variable raz_nogvc_2013 "Reason No GVC (2013)"
{txt}
{com}. label variable raz_nogvc_2014 "Reason No GVC (2014)"
{txt}
{com}. label variable anios_gvc_2014 "Years in Global Value Chains"
{txt}
{com}. label variable eslabones_gvc_2014 "Links in Global Value Chains"
{txt}
{com}. label variable VA_2014 "Value Added (MX Pesos, 2014)"
{txt}
{com}. label variable VA_real_2014 "Real Value Added (MX Pesos, 2014)"
{txt}
{com}. label variable VA_wk_2014 "Value Added per Worker (MX Pesos, 2014)"
{txt}
{com}. label variable VA_real_wk_2014 "Real Value Added per Worker (MX Pesos, 2014)"
{txt}
{com}. label variable VA_real_wk_mil_2014 "Real Value Added per Worker (Thousand MX Pesos, 2014)"
{txt}
{com}. label variable oper_profit_2014 "Operating Profit (MX Pesos, 2014)"
{txt}
{com}. label variable oper_profit_real_2014 "Real Operating Profit (MX Pesos, 2014)"
{txt}
{com}. label variable profitab_2014 "Profitability (2014)"
{txt}
{com}. label variable profitabrea_2014 "Profitability Ratio (2014)"
{txt}
{com}. label variable sh_rem_2014 "Share of Revenue (2014)"
{txt}
{com}. label variable sh_insum_2014 "Share of Input Costs (2014)"
{txt}
{com}. label variable sh_af_2014 "Share of Fixed Assets (2014)"
{txt}
{com}. 
. *Labels winsorized variables
. label variable activ_fijos_real_2014 "Real Fixed Assets (2014) in MX pesos"
{txt}
{com}. label variable ingre_tot_real_2014 "Real Total Income (2014) in MX pesos"
{txt}
{com}. label variable rem_tot_anual_real_2014 "Real Annual Remunerations (2014) in MX pesos"
{txt}
{com}. label variable VA_real_wk_mil_WA_2014 "Value added per worker (mil) 2014 (Winsorized)"
{txt}
{com}. label variable VA_real_WA_2014 "Real value added 2014 (Winsorized)"
{txt}
{com}. label variable rem_tot_anual_WA_2014 "Total annual remuneration 2014 (Winsorized)"
{txt}
{com}. label variable rem_tot_anual_real_WA_2014 "Real total annual remuneration 2014 (Winsorized)"
{txt}
{com}. label variable g_train_WA_2014 "Training expenses 2014 (Winsorized)"
{txt}
{com}. label variable g_insum_WA_2014 "Inputs expenses 2014 (Winsorized)"
{txt}
{com}. label variable g_energy_WA_2014 "Energy expenses 2014 (Winsorized)"
{txt}
{com}. label variable activ_fijos_real_WA_2014 "Real fixed assets 2014 (Winsorized)"
{txt}
{com}. label variable ingre_tot_WA_2014 "Total income 2014 (Winsorized)"
{txt}
{com}. label variable ingre_tot_real_WA_2014 "Real total income 2014 (Winsorized)"
{txt}
{com}. label variable oper_profit_real_WA_2014 "Real operational profit 2014 (Winsorized)"
{txt}
{com}. label variable g_insum_cut_WA_2014 "Cut input expenses 2014 (Winsorized)"
{txt}
{com}. label variable activ_fijos_WA_2014 "Fixed assets 2014 (Winsorized)"
{txt}
{com}. label variable g_materials_WA_2014 "Materials expenses 2014 (Winsorized)"
{txt}
{com}. label variable g_insum_tot_WA_2014 "Total input expenses 2014 (Winsorized)"
{txt}
{com}. label variable Var_inven_WA_2014 "Inventory variation 2014 (Winsorized)"
{txt}
{com}. label variable nworkers_WA_2014 "Number of workers 2014 (Winsorized)"
{txt}
{com}. label variable lVA_real_wk_mil_2014 "Log of Real Value Added per Worker"
{txt}
{com}. label variable lVA_real_wk_mil_WA_2014 "Log of Real Value Added per Worker (Winsorized)"
{txt}
{com}. label variable ln_rem_real_2014 "Log of Real Remunerations"
{txt}
{com}. label variable ln_rem_real_WA_2014 "Log of Real Remunerations (Winsorized)"
{txt}
{com}. label variable ln_rem_2014 "Log of Remunerations"
{txt}
{com}. label variable ln_insum_2014 "Log of Input Costs"
{txt}
{com}. label variable ln_insum_real_2014 "Log of Real Input Costs"
{txt}
{com}. label variable ln_insum_real_WA_2014 "Log of Real Input Costs (Winsorized)"
{txt}
{com}. label variable ln_af_real_2014 "Log of Real Fixed Assets"
{txt}
{com}. label variable ln_af_2014 "Log of Fixed Assets"
{txt}
{com}. label variable ln_af_WA_2014 "Log of Fixed Assets (Winsorized)"
{txt}
{com}. label variable ln_af_real_WA_2014 "Log of Real Fixed Assets (Winsorized)"
{txt}
{com}. label variable lnY_real_WA_2014 "Log of Real Output (Winsorized)"
{txt}
{com}. label variable lnY_real_2014 "Log of Real Output"
{txt}
{com}. label variable lnY_WA_2014 "Log of Output (Winsorized)"
{txt}
{com}. label variable lnY_2014 "Log of Output"
{txt}
{com}. label variable ltfp_sec_2014 "Log of Total Factor Productivity (Sector)"
{txt}
{com}. label variable ltfp_sec_real_2014 "Log of Real Total Factor Productivity (Sector)"
{txt}
{com}. label variable ltfp_sec_real_WA_2014 "Log of Real Total Factor Productivity (Sector) (Winsorized)"
{txt}
{com}. label variable E03 "State"
{txt}
{com}. label variable clase "NAICS 6 digits code according to ENAPROCE"  
{txt}
{com}. label var dum_man2 "Dummy Sector =1 if Manufacturing, O if Services"
{txt}
{com}. 
. 
. *ren CLEE clee
. foreach var of varlist E03 FAC_EXPA clave clase resp_manag dum_pat {c -(}
{txt}  2{com}.         ren `var' `var'_2014
{txt}  3{com}. {c )-}
{res}{txt}
{com}. 
. 
. ren innov_13_14_2014 innov_2yr_2014 
{res}{txt}
{com}. ren innov_13_14_work_2014 innov_2yr_work_2014 
{res}{txt}
{com}. ren innov_13_14_work_WA_2014 innov_2yr_work_WA_2014 
{res}{txt}
{com}. ren innov_13_14_sal_2014 innov_2yr_sal_2014
{res}{txt}
{com}. ren IDT_1314_2014  IDT_2yr_2014
{res}{txt}
{com}. ren IDT_13_14_work_2014 IDT_2yr_work_2014
{res}{txt}
{com}. ren IDT_13_14_work_WA_2014 IDT_2yr_work_WA_2014
{res}{txt}
{com}. ren IDT_1314_sal_2014 IDT_2yr_sal_2014
{res}{txt}
{com}. 
. 
. 
. 
. keep id_mun clee E03  inicio_gvc_2014 eslab_1_2014 eslab_2_2014 eslab_3_2014 beneficio_gvc_2014 FAC_EXPA clave_2014  clase resp_manag dec_manag_2014 quin_manag_2014 sector_2014 sector2_2014 size_2014 certif_2014 Var_inven_2014 dum_comp_2014 g_insum_cut_WA_2014  dum_ginnov_2014 dum_exp_2014 IED_dum_2014 dum_gvc_2014 dum_pat_2014  dum_IDT_2014 g_energy_WA_2014 g_energy_2014  FDI1_2014 invmprev_fin_2014 invtot_fin_2014 edad_emp_2014 activ_fijos_WA_2014 activ_fijos_2014 sec_ent_2014 IDT_2yr_work_WA_2014 ITcap_worker_2014 invmprev_ini_2014 invtot_ini_2014 innov_2yr_work_2014 innov_2yr_2014 innov_2yr_sal_2014 score_innov_2014 g_insum_WA_2014 dum_inter_2014 inter_use_2014 Var_inven_WA_2014 eslabones_gvc_2014 ln_af_2014 ln_af_WA_2014 ln_insum_2014 lnY_2014 lnY_WA_2014 ln_af_real_2014 ln_af_real_WA_2014 ln_insum_real_2014 ln_insum_real_WA_2014 lnY_real_2014 lnY_real_WA_2014 ln_rem_real_2014 ln_rem_real_WA_2014 ltfp_sec_real_2014 ltfp_sec_real_WA_2014 lVA_real_wk_mil_2014 lVA_real_wk_mil_WA_2014 ln_rem_2014 ltfp_sec_2014 score_man_dat_2014 score_man_inc_2014 score_manag_2014 score_manfirst8_2014 score_manlast8_2014 g_materials_WA_2014 g_materials_2014 rem_op_supp_2014 rem_dir_month_2014  months_2014 nworkers_WA_2014 nworkers_2014 oper_profit_2014 own_direc2_2014 profitab_2014 profitabrea_2014  quin_sh_direc_2014 IDT_2yr_2014 IDT_2yr_work_2014 IDT_2yr_sal_2014 rem_tot_anual_real_2014 activ_fijos_real_2014 innov_real_2014 oper_profit_real_2014 ingre_tot_real_2014 VA_real_2014 VA_real_wk_2014 VA_real_wk_mil_2014 activ_fijos_real_WA_2014 oper_profit_real_WA_2014 rem_tot_anual_real_WA_2014 ingre_tot_real_WA_2014 VA_real_WA_2014 raz_nogvc_2013 raz_nogvc_2014 reasnotinter_2014 reasnotcomp_2014 region_2014  scor_cert_2014 shexp_2014 sh_af_2014 sh_IED_2014 sh_insum_2014  sh_rem_2014 sh_direc_2014 sh_sup_2014 shlabcomp_2014 sh_pre_2014 rem_tot_anual_WA_2014 rem_tot_anual_2014 ingre_tot_WA_2014 g_insum_tot_WA_2014 g_insum_2014 g_insum_cut_2014 g_insum_tot_2014 rem_tot_month_2014 ingre_tot_2014 g_train_2013 g_train_2014 g_train_WA_2014 VA_2014 VA_wk_2014 VA_real_wk_mil_WA_2014 inicio_op_2014 anios_gvc_2014 innov_2yr_work_WA_2014 sh_sup_WA_2014 dum_man2 sh_direc_WA_2014
{txt}
{com}. 
. duplicates drop clee, force

{p 0 4}{txt}Duplicates in terms of {res} clee{p_end}

{txt}(0 observations are duplicates)

{com}. 
. save "$working/Data_for_estimates_ENAPROCE_2015.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Data_for_estimates_ENAPROCE_2015.dta{rm}
saved
{p_end}

{com}. 
. 
{txt}end of do-file

{com}. do "$programs/Data prepare 2018 mock data.do"
{txt}
{com}. /***********************************************************************************************************************************************************
> * Inputs: ENAPROCE 2018 Small Business (PYMES) Database
> *
> * Program Description: This program generates descriptive statistics for the ENAPROCE 2018 databases,
> * including means, percentiles, and standard deviations, as well as some charts that will be used in
> * a descriptive publication.
> *
> * Note: This program was developed within the premises of INEGI's Microdata Data Laboratory.
> * Last Update: 09/26/2021
> 
> ************************************************************************************************************************************************************/
. *ENAPROCE 2018
. *************************************************************************************************************************************************************
. 
. use "$data\ejem_base_pyme_ciega_2018.dta", clear
{txt}
{com}. *Get NAICS 6 digits code
. gen clase=substr(clee,6,6)
{txt}
{com}. *If included in the data
. *gen clase=e17
. gen clase_ce=clase
{txt}
{com}. gen clase_clee=clase
{txt}
{com}. 
. *Add price deflators
. cap drop _merge
{txt}
{com}. merge m:1 clase using "$data/Deflators.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           6,158
{txt}{col 9}from master{col 30}{res}           5,500{txt}  (_merge==1)
{col 9}from using{col 30}{res}             658{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           5,000{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge==2
{txt}(658 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. 
. ************************************************************************************************************************************************************
. *Performance measures and general characteristics
. ************************************************************************************************************************************************************
. tostring P1_1B, replace
{txt}P1_1B was {res:byte} now {res:str2}

{com}. *Calculate a more aggregated version of sector
. gen str sector_2017="MAN" if inlist(P1_1B,"31","32","33")
{txt}(7,804 missing values generated)

{com}. replace sector_2017="COM" if inlist(P1_1B,"43","46")
{txt}(3,496 real changes made)

{com}. replace sector_2017="SER" if sector==""
{txt}(4,308 real changes made)

{com}. 
. gen str sector2_2017="MAN" if sector_2017=="MAN"
{txt}(7,804 missing values generated)

{com}. replace sector2_2017="SERCOM" if sector_2017=="COM"
{txt}variable {bf}{res}sector2_2017{sf}{txt} was {bf}{res}str3{sf}{txt} now {bf}{res}str6{sf}
{txt}(3,496 real changes made)

{com}. replace sector2_2017="SERCOM" if sector_2017=="SER"
{txt}(4,308 real changes made)

{com}. 
. gen dum_man2=sector_2017=="MAN"
{txt}
{com}. *Group sector state
. egen sec_ent_2017=group(sector_2017 E03)
{txt}
{com}. 
. *Months the firm operates
. gen months_2016=P10A
{txt}
{com}. gen months_2017=P10B
{txt}
{com}. 
. *Monthly salaries
. gen rem_dir_month_2016=P15_1A/months_2016
{txt}
{com}. gen rem_dir_month_2017=P15_1B/months_2017
{txt}
{com}. gen rem_op_supp_2016=P15_2A/months_2016
{txt}
{com}. gen rem_op_supp_2017=P15_2B/months_2017
{txt}
{com}. 
. 
. *Monthly remunerations (Calculated according to the months the firm operates)
. foreach x in 2016 2017 {c -(}
{txt}  2{com}. gen rem_tot_month_`x'=rem_dir_month_`x'+rem_op_supp_`x'
{txt}  3{com}. {c )-}
{txt}
{com}. 
. 
. *Total annual remunerations
. gen rem_tot_anual_2016=P15_1A+P15_2A
{txt}
{com}. gen rem_tot_anual_2017=P15_1B+P15_2B
{txt}
{com}. gen rem_tot_anual_real_2017=rem_tot_anual_2017*(100/INPP_prodB20132017)
{txt}(5,500 missing values generated)

{com}. 
. *Total expenses in training
. gen g_train_2016=P19_2A
{txt}(4,000 missing values generated)

{com}. gen g_train_2017=P19_2B
{txt}(2,500 missing values generated)

{com}. replace g_train_2016=0 if g_train_2016==.
{txt}(4,000 real changes made)

{com}. replace g_train_2017=0 if g_train_2017==.
{txt}(2,500 real changes made)

{com}. 
. *Expenses in merchandise for resales (important for commerce)
. gen g_resales_2017=P20_2
{txt}
{com}. 
. *Energy expenses
. gen g_energy_2017=P20_4
{txt}
{com}. 
. *Materials
. gen g_materials_2017=P20_1
{txt}
{com}. 
. *Total Inputs
. gen g_insum_2017=P20_0
{txt}
{com}. gen g_insum_cut_2017=P20_0-P20_6-P20_9
{txt}
{com}. gen g_insum_tot_2017=g_insum_2017+g_train_2017
{txt}
{com}. 
. *Total revenues
. gen ingre_tot_2016=P21A
{txt}
{com}. gen ingre_tot_2017=P21B
{txt}
{com}. gen ingre_tot_real_2017=ingre_tot_2017*(100/INPP_prodB20132017)
{txt}(5,500 missing values generated)

{com}. 
. *Fixed assets
. gen activ_fijos_2017=P29_0
{txt}
{com}. gen activ_fijos_real_2017=activ_fijos_2017*(100/PRICE_IND_INVEST_MX_B20132017)
{txt}(5,500 missing values generated)

{com}. 
. *Firm size
. *a) Categorical
. gen size_2017=P12_0CAL
{txt}
{com}. 
. *b) Numerical
. gen nworkers_2016= P12_0A
{txt}
{com}. gen nworkers_2017= P12_0B
{txt}
{com}. 
. *Change in inventories
. *Final total inventories
. gen invtot_fin_2017=P27B
{txt}
{com}. *Merchandise for resales final inventory
. gen invmprev_fin_2017=P28B
{txt}(1,000 missing values generated)

{com}. *Initial inventories
. gen invtot_ini_2017=P27A
{txt}
{com}. *Merchandise for resales initial inventory
. gen invmprev_ini_2017=P28A
{txt}(1,000 missing values generated)

{com}. 
. replace invmprev_fin_2017=0 if invmprev_fin_2017==.
{txt}(1,000 real changes made)

{com}. replace invmprev_ini_2017=0 if invmprev_ini_2017==.
{txt}(1,000 real changes made)

{com}. 
. *Deflate inventories (Prices of 2017)
. gen Var_inven_2017=(invtot_fin_2017)-(invtot_ini_2017)*(INPP_prodB20132017/INPP_prodB20132016)
{txt}(5,500 missing values generated)

{com}. 
. *Regions
. gen region_2017=1 if inlist(E03,"02","03","05", "08", "10","19","25","26","28")
{txt}(6,000 missing values generated)

{com}. replace region_2017=2 if inlist(E03,"01","06","11","14","16","18","24","32")
{txt}(3,500 real changes made)

{com}. replace region_2017=3 if inlist(E03, "09","13","15","17","22","29")
{txt}(500 real changes made)

{com}. replace region_2017=4 if inlist(E03,"04","07","12","20","21","23","27","30","31")
{txt}(2,000 real changes made)

{com}. 
. *Age of the firm
. *destring P5, gen(inicio_op_2017)
. gen inicio_op_2017=P5
{txt}
{com}. label var inicio_op_2017 "Year the firm started operations"
{txt}
{com}. gen edad_emp_2017=2017-inicio_op_2017
{txt}
{com}. label var edad_emp_2017 "Age of the firm"
{txt}
{com}. 
. * Summarize selected variables with and without weights
. sum months* rem_dir_month* rem_op_supp* rem_tot_month* rem_tot_anual* g_train* g_insum* ingre_tot* activ_fijos* FAC_EXPA

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}months_2016 {c |}{res}     10,500          12           0         12         12
{txt}{space 1}months_2017 {c |}{res}     10,500          12           0         12         12
{txt}rem_dir~2016 {c |}{res}     10,500    127761.8    268662.7          0    1239052
{txt}rem_dir~2017 {c |}{res}     10,500    143531.9    314779.8          0    1451694
{txt}rem_op_~2016 {c |}{res}     10,500    252871.5    466336.1          0    2212476
{txt}{hline 13}{c +}{hline 57}
rem_op_~2017 {c |}{res}     10,500    266514.8    505312.3          0    2395616
{txt}rem_tot_mo~6 {c |}{res}     10,500    380633.3    722448.3          0    3451528
{txt}rem_tot_mo~7 {c |}{res}     10,500    410046.7      806490          0    3847310
{txt}rem_tot_an~6 {c |}{res}     10,500     4567599     8669380          0   4.14e+07
{txt}rem~ual_2017 {c |}{res}     10,500     4920560     9677879          0   4.62e+07
{txt}{hline 13}{c +}{hline 57}
rem~eal_2017 {c |}{res}      5,000     5727986    1.22e+07          0   4.20e+07
{txt}g_train_2016 {c |}{res}     10,500    45302.52    124091.5          0     555806
{txt}g_train_2017 {c |}{res}     10,500    47115.76    111552.1          0     475370
{txt}g_insum_2017 {c |}{res}     10,500    1.07e+08    1.08e+08     169566   4.34e+08
{txt}g_insum_cu~7 {c |}{res}     10,500    1.04e+08    1.05e+08     139607   4.16e+08
{txt}{hline 13}{c +}{hline 57}
g_insum_to~7 {c |}{res}     10,500    1.07e+08    1.08e+08     170735   4.34e+08
{txt}ingre_t~2016 {c |}{res}     10,500    1.25e+08    1.17e+08     193567   4.68e+08
{txt}ingre~t_2017 {c |}{res}     10,500    1.31e+08    1.32e+08     187492   5.08e+08
{txt}ingre~l_2017 {c |}{res}      5,000    1.23e+08    1.29e+08    8043890   4.62e+08
{txt}activ~s_2017 {c |}{res}     10,500     7678906    1.38e+07      16580   6.57e+07
{txt}{hline 13}{c +}{hline 57}
activ~l_2017 {c |}{res}      5,000     2354920     2568187     130224    8335794
{txt}{space 4}FAC_EXPA {c |}{res}     10,500     2.69732    2.420164          1       11.5
{txt}
{com}. sum months* rem_dir_month* rem_op_supp* rem_tot_month* rem_tot_anual* g_train* g_insum* ingre_tot* activ_fijos* [w=FAC_EXPA]
{txt}(analytic weights assumed)

    Variable {c |}     Obs      Weight        Mean   Std. dev.       Min        Max
{hline 13}{c +}{hline 65}
{space 1}months_2016 {c |}{res}  10,500  28321.8587          12          0         12         12
{txt}{space 1}months_2017 {c |}{res}  10,500  28321.8587          12          0         12         12
{txt}rem_dir~2016 {c |}{res}  10,500  28321.8587     79869.3   179602.7          0    1239052
{txt}rem_dir~2017 {c |}{res}  10,500  28321.8587    86627.29   208753.2          0    1451694
{txt}rem_op_~2016 {c |}{res}  10,500  28321.8587    157220.5   303922.6          0    2212476
{txt}{hline 13}{c +}{hline 65}
rem_op_~2017 {c |}{res}  10,500  28321.8587      165353   328391.5          0    2395616
{txt}rem_tot_mo~6 {c |}{res}  10,500  28321.8587    237089.8   471466.4          0    3451528
{txt}rem_tot_mo~7 {c |}{res}  10,500  28321.8587    251980.3   524097.7          0    3847310
{txt}rem_tot_an~6 {c |}{res}  10,500  28321.8587     2845078    5657597          0   4.14e+07
{txt}rem~ual_2017 {c |}{res}  10,500  28321.8587     3023763    6289172          0   4.62e+07
{txt}{hline 13}{c +}{hline 65}
rem~eal_2017 {c |}{res}   5,000  12641.3031     3143801    7997475          0   4.20e+07
{txt}g_train_2016 {c |}{res}  10,500  28321.8587    23207.46   78840.09          0     555806
{txt}g_train_2017 {c |}{res}  10,500  28321.8587    23978.02    72024.5          0     475370
{txt}g_insum_2017 {c |}{res}  10,500  28321.8587    6.34e+07   8.52e+07     169566   4.34e+08
{txt}g_insum_cu~7 {c |}{res}  10,500  28321.8587    6.15e+07   8.30e+07     139607   4.16e+08
{txt}{hline 13}{c +}{hline 65}
g_insum_to~7 {c |}{res}  10,500  28321.8587    6.34e+07   8.52e+07     170735   4.34e+08
{txt}ingre_t~2016 {c |}{res}  10,500  28321.8587    7.39e+07   9.36e+07     193567   4.68e+08
{txt}ingre~t_2017 {c |}{res}  10,500  28321.8587    7.67e+07   1.02e+08     187492   5.08e+08
{txt}ingre~l_2017 {c |}{res}   5,000  12641.3031    7.84e+07   1.00e+08    8043890   4.62e+08
{txt}activ~s_2017 {c |}{res}  10,500  28321.8587     4167804    9230398      16580   6.57e+07
{txt}{hline 13}{c +}{hline 65}
activ~l_2017 {c |}{res}   5,000  12641.3031     1884834    2041546     130224    8335794
{txt}
{com}. 
. ************************************************************************************************************************************************************
. *ICT and innovations indicator
. ************************************************************************************************************************************************************
. 
. *Computer use (dummy and share of labor with computer)
. gen dum_comp_2017=(P70==1)
{txt}
{com}. gen shlabcomp_2017=P72/nworkers_2017
{txt}
{com}. 
. *Main reasons for not using a computer
. *destring P71, gen(reasnotcomp_2017)
. gen reasnotcomp_2017=P71
{txt}(10,500 missing values generated)

{com}. *Use of Internet
. gen dum_inter_2017=(P73==1)
{txt}
{com}. 
. *Main reasons for not using  Internet
. *destring P74, gen(reasnotinter_2017)
. gen reasnotinter_2017=P74
{txt}(10,500 missing values generated)

{com}. 
. *Use of internet
. *destring P75, gen(inter_use_2017)
. gen inter_use_2017=P75
{txt}
{com}. 
. *Capital IT/workers
. gen ITcap_worker_2017=P29_2/nworkers_2017
{txt}
{com}. 
. *Score number of certifications
. foreach var of varlist P78_1 P78_2 P78_3 P78_4 P78_5 {c -(}
{txt}  2{com}. gen `var'b=(`var'==1)
{txt}  3{com}. {c )-}
{txt}
{com}. egen scor_cert_2017=rsum(P78_1b P78_2b P78_3b P78_4b P78_5b)
{txt}
{com}. 
. gen certif_2017=(scor_cert_2017>=1)
{txt}
{com}. 
. ******Innovation******
. 
. gen P79_0ACAL= P79_1A+P79_2A
{txt}(10,500 missing values generated)

{com}. gen P79_0BCAL= P79_1B+P79_2B
{txt}(10,500 missing values generated)

{com}. 
. 
. gen dum_IDT_2017=(P79_0ACAL>0 & P79_0ACAL!=.)|( P79_0BCAL>0 & P79_0BCAL!=. )
{txt}
{com}. gen IDT_real_2016=P79_0ACAL*(PRICE_IND_INVEST_MX_B20132017/PRICE_IND_INVEST_MX_B20132016)
{txt}(10,500 missing values generated)

{com}. egen IDT_1617_2017=rsum(IDT_real_2016 P79_0BCAL)
{txt}
{com}. gen IDT_1617_sal_2017=IDT_1617_2017/ingre_tot_2017
{txt}
{com}. 
. gen innov_real_2016=P80_5A*(PRICE_IND_INVEST_MX_B20132017/PRICE_IND_INVEST_MX_B20132016)
{txt}(10,500 missing values generated)

{com}. gen innov_real_2017=P80_5B
{txt}(10,500 missing values generated)

{com}. 
. egen innov_16_17_2017=rsum(innov_real_2016 innov_real_2017)
{txt}
{com}. gen innov_16_17_sal_2017=innov_16_17_2017/ingre_tot_2017
{txt}
{com}. 
. gen dum_ginnov_2017=(innov_16_17_2017>0 &  innov_16_17_2017!=.)
{txt}
{com}. 
. foreach var of varlist P76_1 P76_2 P76_3 P76_4 P76_5 P76_6 {c -(}
{txt}  2{com}. *destring `var', gen(`var'b)
. gen `var'b=`var'
{txt}  3{com}. recode `var'b (1=4) (2=3) (3=2) (4=1) (5=0)
{txt}  4{com}. {c )-}
{txt}(10,500 changes made to {bf:P76_1b})
(10,500 changes made to {bf:P76_2b})
(10,500 changes made to {bf:P76_3b})
(10,500 changes made to {bf:P76_4b})
(10,500 changes made to {bf:P76_5b})
(10,500 changes made to {bf:P76_6b})

{com}. 
. *Score innovation
. egen score_innov_2017= rsum(P76_1b P76_2b P76_3b P76_4b P76_5b P76_6b)
{txt}
{com}. 
. 
. *Innovation product, process, and marketing
. gen dumm_innov_prod_2017=(P80_1==1)
{txt}
{com}. gen dumm_innov_proc_2017=(P80_2==1)
{txt}
{com}. gen dumm_innov_org_2017= (P80_3==1)
{txt}
{com}. gen dumm_innov_merc_2017=(P80_4==1)
{txt}
{com}. 
. *Dummy innovation
. gen dumm_innov_any_2017=(dumm_innov_prod_2017==1 | dumm_innov_proc_2017==1 | dumm_innov_org_2017==1 | dumm_innov_merc_2017==1)
{txt}
{com}. 
. *Score innovation
. egen score_innovcuali_2017=rsum(dumm_innov_prod_2017 dumm_innov_proc_2017 dumm_innov_org_2017 dumm_innov_merc_2017)
{txt}
{com}. 
. *Patents
. gen dum_pat=(P77_2A ==1 | P77_2B ==1)
{txt}
{com}. 
. *Innovation expenses per worker
. gen innov_16_17_work_2017=innov_16_17_2017/nworkers_2017
{txt}
{com}. 
. *R&D expenses per worker
. gen IDT_16_17_work_2017=IDT_1617_2017/nworkers_2017
{txt}
{com}. 
. 
. 
. *************************************************************************************************************************************************************
. *FDI and Exports
. *************************************************************************************************************************************************************
. 
. *FDI dummy and share
. gen IED_dum_2017=(P6==1)
{txt}
{com}. label var IED_dum_2017 "=1 if it has FDI"
{txt}
{com}. 
. gen sh_IED_2017=P6_1
{txt}(10,500 missing values generated)

{com}. replace sh_IED_2017=0 if P6==2
{txt}(10,500 real changes made)

{com}. label var sh_IED_2017 "Share of FDI"
{txt}
{com}. gen FDI1_2017=IED_dum_2017
{txt}
{com}. 
. *Share of exports
. gen dum_exp_2017=P24_0>0 & P24_0!=.
{txt}
{com}. gen shexp_2017=P24_0/ingre_tot_2017
{txt}
{com}. 
. 
. *Ownership
. gen own_direc2_2017=.
{txt}(10,500 missing values generated)

{com}. replace own_direc2_2017=1 if (P3==1 | P3==2) & (P4==1 | P4==2) & FDI1_2017==0
{txt}(5,000 real changes made)

{com}. replace own_direc2_2017=2 if (P3==1 | P3==2) & (P4==3) & FDI1_2017==0
{txt}(3,000 real changes made)

{com}. replace own_direc2_2017=3 if (P3==3 | P3==4)  & FDI1_2017==0
{txt}(2,000 real changes made)

{com}. replace own_direc2_2017=4 if (P3==5 | P3==6 | P3==9 )  & FDI1_2017==0
{txt}(500 real changes made)

{com}. replace own_direc2_2017=5 if FDI1_2017==1
{txt}(0 real changes made)

{com}. 
. 
. label define owner2 1 "Founder & Family owned & directed" 2 "Family owned-external manager" 3 "Private individuals" 4 "Small shareholders" 5 "Foreign"
{txt}
{com}. label values  own_direc2_2017 owner2
{txt}
{com}. 
. ********************************************************************************************************************************************************
. *Education
. ********************************************************************************************************************************************************
. *Share education
. 
. *Share of college education
. gen sh_sup_2016=P14_4A/P14_0A
{txt}
{com}. gen sh_sup_2017=P14_4B/P14_0B
{txt}
{com}. 
. *Share of high-school education
. gen sh_pre_2016= (P14_3A+P14_4A)/P14_0A
{txt}
{com}. gen sh_pre_2017= (P14_3B+P14_4B)/P14_0B
{txt}
{com}. 
. 
. *Share of white-collar workers
. gen sh_direc_2016=P13_1A/nworkers_2016
{txt}
{com}. gen sh_direc_2017=P13_1C/nworkers_2017
{txt}
{com}. 
. *Share of white-collar workers (female)
. gen sh_direc_muj_2016=P13_1_1A/nworkers_2016
{txt}
{com}. gen sh_direc_muj_2017=P13_1_1C/nworkers_2017
{txt}
{com}. 
. *Share of white-collar workers (male)
. gen sh_direc_hom_2016=(nworkers_2016-P13_1_1A)/nworkers_2016
{txt}
{com}. gen sh_direc_hom_2017=(nworkers_2017-P13_1_1C)/nworkers_2017
{txt}
{com}. 
. *Share of blue-collar workers (female)
. gen sh_oper_muj_2016=P13_1_1B/nworkers_2016
{txt}
{com}. gen sh_oper_muj_2017=P13_1_1D/nworkers_2017
{txt}
{com}. 
. *Quintiles of white-collar workers
. xtile quin_sh_direc_2017=sh_direc_2017, nq(5)
{txt}
{com}. 
. 
. *************************************************************************************************************************************************************
. *Management practices
. ************************************************************************************************************************************************************
. 
. *************************************************************************************************************************************************************
. *Management scoring
. *************************************************************************************************************************************************************
. foreach var of varlist P32 P33 P36 P37 P38 P39 P41 P43 P44 P45 P46 P47 {c -(}
{txt}  2{com}.         tostring `var', replace
{txt}  3{com}. gen n_`var'=1 if `var'!=""
{txt}  4{com}. {c )-}
{txt}P32 was {res:byte} now {res:str1}
P33 was {res:byte} now {res:str1}
P36 was {res:byte} now {res:str1}
P37 was {res:byte} now {res:str1}
P38 was {res:byte} now {res:str1}
P39 was {res:byte} now {res:str1}
P41 was {res:byte} now {res:str1}
P43 was {res:byte} now {res:str1}
P44 was {res:byte} now {res:str1}
P45 was {res:byte} now {res:str1}
P46 was {res:byte} now {res:str1}
P47 was {res:byte} now {res:str1}

{com}. 
. foreach var of varlist P34* P35* P40* P42* {c -(}
{txt}  2{com}.                 tostring `var', replace
{txt}  3{com}. gen nn_`var'=1 if `var'!=""
{txt}  4{com}. {c )-}
{txt}P34_1 was {res:byte} now {res:str1}
P34_2 was {res:byte} now {res:str1}
P34_3 was {res:byte} now {res:str1}
P34_4 was {res:byte} now {res:str1}
P34_5 was {res:byte} now {res:str1}
P34_6 was {res:byte} now {res:str1}
P34_7 was {res:byte} now {res:str1}
P35_1 was {res:byte} now {res:str1}
P35_2 was {res:byte} now {res:str1}
P35_3 was {res:byte} now {res:str1}
P35_4 was {res:byte} now {res:str1}
P35_5 was {res:byte} now {res:str1}
P35_6 was {res:byte} now {res:str1}
P35_7 was {res:byte} now {res:str1}
P40_1 was {res:byte} now {res:str1}
P40_2 was {res:byte} now {res:str1}
P40_3 was {res:byte} now {res:str1}
P40_4 was {res:byte} now {res:str1}
P40_5 was {res:byte} now {res:str1}
P42_1 was {res:byte} now {res:str1}
P42_2 was {res:byte} now {res:str1}
P42_3 was {res:byte} now {res:str1}
P42_4 was {res:byte} now {res:str1}
P42_5 was {res:byte} now {res:str1}

{com}. egen n_P34=rmean(nn_P34*)
{txt}
{com}. egen n_P35=rmean(nn_P35*)
{txt}
{com}. egen n_P40=rmean(nn_P40*)
{txt}
{com}. egen n_P42=rmean(nn_P42*)
{txt}
{com}. 
. egen resp_manag=rowtotal(n_P*)
{txt}
{com}. 
. drop n_P* nn_P*
{txt}
{com}. *Revise that resp_manag is at least 7 for all firms, as adding the 3 questions that are zero if excluded will mean at least 10 questions answered
. 
. 
. 
. *Question 1 from MOPS
. gen val_sco_P32=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P32=1/3 if P32=="1"
{txt}(2,500 real changes made)

{com}. replace val_sco_P32=2/3 if P32=="2"
{txt}(3,500 real changes made)

{com}. replace val_sco_P32=1   if P32=="3"
{txt}(3,500 real changes made)

{com}. replace val_sco_P32=0   if P32=="4"
{txt}(1,000 real changes made)

{com}. 
. *Question 2 from MOPS
. gen val_sco_P33=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P33=1/3 if P33=="1"
{txt}(1,000 real changes made)

{com}. replace val_sco_P33=2/3 if P33=="2"
{txt}(2,000 real changes made)

{com}. replace val_sco_P33=2/3 if P33=="3"
{txt}(2,500 real changes made)

{com}. replace val_sco_P33=1 if P33=="4"
{txt}(1,000 real changes made)

{com}. replace val_sco_P33=0 if P33=="5"
{txt}(4,000 real changes made)

{com}. 
. 
. *Question 3 from MOPS-(Mark all that apply-use the median)
. gen val_sco_P34_1=1/6 if P34_1=="1"
{txt}(4,500 missing values generated)

{com}. gen val_sco_P34_2=2/6 if P34_2=="1"
{txt}(7,000 missing values generated)

{com}. gen val_sco_P34_3=3/6 if P34_3=="1"
{txt}(5,500 missing values generated)

{com}. gen val_sco_P34_4=4/6 if P34_4=="1"
{txt}(9,000 missing values generated)

{com}. gen val_sco_P34_5=5/6 if P34_5=="1"
{txt}(10,000 missing values generated)

{com}. gen val_sco_P34_6=1   if P34_6=="1"
{txt}(10,500 missing values generated)

{com}. gen val_sco_P34_7=0   if P34_7=="1"
{txt}(10,500 missing values generated)

{com}. 
. 
. egen val_sco_P34=rowmedian(val_sco_P34_*)
{res}{txt}(4,000 missing values generated)

{com}. replace val_sco_P34=0 if P34_1=="2" & P34_2=="2" & P34_3=="2"  & P34_4=="2"  & P34_5=="2"  & P34_6=="2"  & P34_7=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P34=0 if P33=="5"
{txt}(4,000 real changes made)

{com}. 
. *Question 4 from MOPS-(Mark all that apply-use the median)
. 
. gen val_sco_P35_1=1/6 if P35_1=="1"
{txt}(7,500 missing values generated)

{com}. gen val_sco_P35_2=2/6 if P35_2=="1"
{txt}(9,000 missing values generated)

{com}. gen val_sco_P35_3=3/6 if P35_3=="1"
{txt}(8,000 missing values generated)

{com}. gen val_sco_P35_4=4/6 if P35_4=="1"
{txt}(8,500 missing values generated)

{com}. gen val_sco_P35_5=5/6 if P35_5=="1"
{txt}(9,500 missing values generated)

{com}. gen val_sco_P35_6=1   if P35_6=="1"
{txt}(10,500 missing values generated)

{com}. gen val_sco_P35_7=0   if P35_7=="1"
{txt}(7,500 missing values generated)

{com}. 
. egen val_sco_P35=rowmedian(val_sco_P35_*)
{res}{txt}(4,000 missing values generated)

{com}. replace val_sco_P35=0 if P35_1=="2" & P35_2=="2" & P35_3=="2"  & P35_4=="2"  & P35_5=="2"  & P35_6=="2"  & P35_7=="2"
{txt}(0 real changes made)

{com}. replace val_sco_P35=0 if P33=="5"
{txt}(4,000 real changes made)

{com}. 
. *Question 5 from MOPS
. gen val_sco_P36=1/2 if P36=="1"
{txt}(10,500 missing values generated)

{com}. replace val_sco_P36=1 if P36=="2"
{txt}(1,000 real changes made)

{com}. replace val_sco_P36=0 if P36=="3"
{txt}(5,500 real changes made)

{com}. 
. replace val_sco_P36=0 if P33=="5"
{txt}(4,000 real changes made)

{com}. 
. 
. *Question 6 from MOPS
. gen val_sco_P37=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P37=1/3 if P37=="1"
{txt}(5,000 real changes made)

{com}. replace val_sco_P37=2/3 if P37=="2"
{txt}(500 real changes made)

{com}. replace val_sco_P37=1   if P37=="3"
{txt}(3,000 real changes made)

{com}. replace val_sco_P37=0   if P37=="4"
{txt}(2,000 real changes made)

{com}. 
. 
. *Question 7 from MOPS
. gen val_sco_P38=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P38=0   if P38=="1"
{txt}(500 real changes made)

{com}. replace val_sco_P38=0.5 if P38=="2"
{txt}(1,000 real changes made)

{com}. replace val_sco_P38=0.75 if P38=="3"
{txt}(2,500 real changes made)

{com}. replace val_sco_P38=1 if P38=="4"
{txt}(4,000 real changes made)

{com}. replace val_sco_P38=0.25   if P38=="5"
{txt}(500 real changes made)

{com}. 
. 
. *Question 8 from MOPS
. gen val_sco_P39=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P39=0 if P39=="1"
{txt}(2,000 real changes made)

{com}. replace val_sco_P39=1/3 if P39=="2"
{txt}(2,500 real changes made)

{com}. replace val_sco_P39=2/3   if P39=="3"
{txt}(500 real changes made)

{com}. replace val_sco_P39=1   if P39=="4"
{txt}(3,500 real changes made)

{com}. 
. 
. *Question 9 from MOPS-(Mark all that apply-use the median)
. gen val_sco_P40_1=1   if P40_1=="1"
{txt}(7,000 missing values generated)

{com}. gen val_sco_P40_2=3/4 if P40_2=="1"
{txt}(8,000 missing values generated)

{com}. gen val_sco_P40_3=1/2 if P40_3=="1"
{txt}(9,000 missing values generated)

{com}. gen val_sco_P40_4=1/4 if P40_4=="1"
{txt}(8,500 missing values generated)

{com}. gen val_sco_P40_5=0   if P40_5=="1"
{txt}(6,000 missing values generated)

{com}. 
. 
. egen val_sco_P40=rowmedian(val_sco_P40_*)
{res}{txt}(2,000 missing values generated)

{com}. replace val_sco_P40=0 if P40_1=="2" & P40_2=="2" & P40_3=="2" & P40_4=="2" & P40_5=="2"
{txt}(0 real changes made)

{com}. 
. sum val_sco*

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}val_sco_P32 {c |}{res}     10,500    .6349206    .3237627          0          1
{txt}{space 1}val_sco_P33 {c |}{res}     10,500    .4126984    .3549483          0          1
{txt}val_sco_~4_1 {c |}{res}      6,000    .1666667           0   .1666667   .1666667
{txt}val_sco_~4_2 {c |}{res}      3,500    .3333333           0   .3333333   .3333333
{txt}val_sco_~4_3 {c |}{res}      5,000          .5           0         .5         .5
{txt}{hline 13}{c +}{hline 57}
val_sco_~4_4 {c |}{res}      1,500    .6666667           0   .6666667   .6666667
{txt}val_sco_~4_5 {c |}{res}        500    .8333333           0   .8333333   .8333333
{txt}val_sco_~4_6 {c |}{res}          0
{txt}val_sco_~4_7 {c |}{res}          0
{txt}{space 1}val_sco_P34 {c |}{res}     10,500     .218254    .1880724          0         .5
{txt}{hline 13}{c +}{hline 57}
val_sco_~5_1 {c |}{res}      3,000    .1666667           0   .1666667   .1666667
{txt}val_sco_~5_2 {c |}{res}      1,500    .3333333           0   .3333333   .3333333
{txt}val_sco_~5_3 {c |}{res}      2,500          .5           0         .5         .5
{txt}val_sco_~5_4 {c |}{res}      2,000    .6666667           0   .6666667   .6666667
{txt}val_sco_~5_5 {c |}{res}      1,000    .8333333           0   .8333333   .8333333
{txt}{hline 13}{c +}{hline 57}
val_sco_~5_6 {c |}{res}          0
{txt}val_sco_~5_7 {c |}{res}      3,000           0           0          0          0
{txt}{space 1}val_sco_P35 {c |}{res}     10,500    .1269841    .2002393          0   .5833334
{txt}{space 1}val_sco_P36 {c |}{res}     10,500    .0952381    .2935575          0          1
{txt}{space 1}val_sco_P37 {c |}{res}     10,500    .4761905    .3647515          0          1
{txt}{hline 13}{c +}{hline 57}
{space 1}val_sco_P38 {c |}{res}      8,500    .7647059     .290435          0          1
{txt}{space 1}val_sco_P39 {c |}{res}      8,500    .5490196    .4113218          0          1
{txt}val_sco_P4~1 {c |}{res}      3,500           1           0          1          1
{txt}val_sco_P4~2 {c |}{res}      2,500         .75           0        .75        .75
{txt}val_sco_P4~3 {c |}{res}      1,500          .5           0         .5         .5
{txt}{hline 13}{c +}{hline 57}
val_sco_P4~4 {c |}{res}      2,000         .25           0        .25        .25
{txt}val_sco_P4~5 {c |}{res}      4,500           0           0          0          0
{txt}{space 1}val_sco_P40 {c |}{res}      8,500    .3602941    .3996611          0          1
{txt}
{com}. 
. 
. *Question 10 from MOPS
. gen     val_sco_P41=1/5 if P41=="1"
{txt}(10,000 missing values generated)

{com}. replace val_sco_P41=2/5 if P41=="2"
{txt}(1,500 real changes made)

{com}. replace val_sco_P41=3/5 if P41=="3"
{txt}(1,000 real changes made)

{com}. replace val_sco_P41=4/5 if P41=="4"
{txt}(500 real changes made)

{com}. replace val_sco_P41=1   if P41=="5"
{txt}(500 real changes made)

{com}. replace val_sco_P41=0   if P41=="6"
{txt}(0 real changes made)

{com}. 
. 
. *Question 11 from MOPS (Mark all that apply-use the median)
. gen val_sco_P42_1=1   if P42_1=="1"
{txt}(7,500 missing values generated)

{com}. gen val_sco_P42_2=3/4 if P42_2=="1"
{txt}(7,500 missing values generated)

{com}. gen val_sco_P42_3=1/2 if P42_3=="1"
{txt}(8,500 missing values generated)

{com}. gen val_sco_P42_4=1/4 if P42_4=="1"
{txt}(8,000 missing values generated)

{com}. gen val_sco_P42_5=0   if P42_5=="1"
{txt}(5,500 missing values generated)

{com}. 
. 
. egen val_sco_P42=rowmedian(val_sco_P42_*)
{res}{txt}(2,000 missing values generated)

{com}. replace val_sco_P42=0 if P42_1=="2" & P42_2=="2" & P42_3=="2" & P42_4=="2" & P42_5=="2"
{txt}(0 real changes made)

{com}. 
. 
. *Question 12 from MOPS
. gen     val_sco_P43=1/5 if P43=="1"
{txt}(10,000 missing values generated)

{com}. replace val_sco_P43=2/5 if P43=="2"
{txt}(1,500 real changes made)

{com}. replace val_sco_P43=3/5 if P43=="3"
{txt}(0 real changes made)

{com}. replace val_sco_P43=4/5 if P43=="4"
{txt}(1,000 real changes made)

{com}. replace val_sco_P43=1   if P43=="5"
{txt}(500 real changes made)

{com}. replace val_sco_P43=0   if P43=="6"
{txt}(0 real changes made)

{com}. 
. 
. *Question 13 from MOPS
. gen val_sco_P44=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P44=1   if P44=="1"
{txt}(4,500 real changes made)

{com}. replace val_sco_P44=2/3 if P44=="2"
{txt}(1,000 real changes made)

{com}. replace val_sco_P44=1/3 if P44=="3"
{txt}(0 real changes made)

{com}. replace val_sco_P44=0   if P44=="4"
{txt}(5,000 real changes made)

{com}. 
. 
. *Question 14 from MOPS
. gen val_sco_P45=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P45=1   if P45=="1"
{txt}(2,500 real changes made)

{com}. replace val_sco_P45=2/3 if P45=="2"
{txt}(500 real changes made)

{com}. replace val_sco_P45=1/3 if P45=="3"
{txt}(0 real changes made)

{com}. replace val_sco_P45=0   if P45=="4"
{txt}(7,500 real changes made)

{com}. 
. 
. *Question 15 from MOPS
. gen val_sco_P46=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P46=1   if P46=="1"
{txt}(3,500 real changes made)

{com}. replace val_sco_P46=1/2 if P46=="2"
{txt}(1,000 real changes made)

{com}. replace val_sco_P46=0   if P46=="3"
{txt}(6,000 real changes made)

{com}. 
. *Question 16 from MOPS
. gen val_sco_P47=.
{txt}(10,500 missing values generated)

{com}. replace val_sco_P47=1   if P47=="1"
{txt}(0 real changes made)

{com}. replace val_sco_P47=1/2 if P47=="2"
{txt}(1,500 real changes made)

{com}. replace val_sco_P47=0   if P47=="3"
{txt}(9,000 real changes made)

{com}. 
. 
. *Calculate score and subindices according to Management in America
. egen score_manag=rmean(val_sco_P32 val_sco_P33 val_sco_P34 val_sco_P35 val_sco_P36 val_sco_P37 val_sco_P38 val_sco_P39 val_sco_P40 val_sco_P41 val_sco_P42 val_sco_P43 val_sco_P44 val_sco_P45 val_sco_P46 val_sco_P47)
{txt}
{com}. egen score_man_dat=rmean(val_sco_P32 val_sco_P33 val_sco_P34 val_sco_P35 val_sco_P36 val_sco_P39)
{txt}
{com}. egen score_man_inc=rmean( val_sco_P37 val_sco_P38 val_sco_P40 val_sco_P41 val_sco_P42 val_sco_P43 val_sco_P44 val_sco_P45 val_sco_P46 val_sco_P47)
{txt}
{com}. egen score_manfirst8=rmean(val_sco_P32 val_sco_P33 val_sco_P34 val_sco_P35 val_sco_P36 val_sco_P37 val_sco_P38 val_sco_P39)
{txt}
{com}. egen score_manlast8= rmean(val_sco_P40 val_sco_P41 val_sco_P42 val_sco_P43 val_sco_P44 val_sco_P45 val_sco_P46 val_sco_P47)
{txt}
{com}. 
. 
. 
. 
. *Replace if resp_manag<10
. replace score_manag=. if resp_manag<10
{txt}(0 real changes made)

{com}. replace score_man_dat=. if resp_manag<10
{txt}(0 real changes made)

{com}. replace score_man_inc=. if resp_manag<10
{txt}(0 real changes made)

{com}. replace score_manfirst8=. if resp_manag<10
{txt}(0 real changes made)

{com}. replace score_manlast8=. if resp_manag<10
{txt}(0 real changes made)

{com}. 
. 
. 
. *Keep only management variables and rename (Optional)
. ren score_manag score_manag_2017
{res}{txt}
{com}. ren score_man_dat score_man_dat_2017
{res}{txt}
{com}. ren score_man_inc score_man_inc_2017
{res}{txt}
{com}. ren score_manfirst8 score_manfirst8_2017
{res}{txt}
{com}. ren score_manlast8  score_manlast8_2017
{res}{txt}
{com}. 
. 
. *Calculate deciles and quintiles of management
. xtile dec_manag_2017=score_manag_2017 , nq(10)
{txt}
{com}. xtile quin_manag_2017=score_manag_2017, nq(5)
{txt}
{com}. 
. drop val_sco*
{txt}
{com}. ************************************************************************************************************************************************************
. *Global value chains
. ************************************************************************************************************************************************************
. 
. *Participates in gvcs
. gen dum_gvc_2017=(P65==1)
{txt}
{com}. 
. *Reasons for not participating in gvcs
. gen raz_nogvc_2016=P66A
{txt}(500 missing values generated)

{com}. gen raz_nogvc_2017=P66B
{txt}(500 missing values generated)

{com}. 
. *Year started participating in gvcs
. *destring P67, gen(inicio_gvc_2017)
. gen inicio_gvc_2017=P67
{txt}(10,000 missing values generated)

{com}. *# of years participating in gvcs
. gen anios_gvc_2017=2017-inicio_gvc if dum_gvc_2017==1
{txt}(10,000 missing values generated)

{com}. 
. *Link in the value chain
. *destring P68_1, gen(eslab_1_2017)
. *destring P68_2, gen(eslab_2_2017)
. *destring P68_3, gen(eslab_3_2017)
. 
. gen eslab_1_2017=P68_1
{txt}(10,000 missing values generated)

{com}. gen eslab_2_2017=P68_2
{txt}(10,000 missing values generated)

{com}. gen eslab_3_2017=P68_3
{txt}(10,000 missing values generated)

{com}. 
. recode eslab_1_2017 eslab_2_2017 eslab_3_2017 (2=0)
{txt}(500 changes made to {bf:eslab_1_2017})
(500 changes made to {bf:eslab_2_2017})
(0 changes made to {bf:eslab_3_2017})

{com}. 
. *# of links in the value chain
. egen eslabones_gvc_2017=rsum( eslab_1_2017 eslab_2_2017 eslab_3_2017)
{txt}
{com}. 
. *Main benefit value chain
. *destring P69, gen(beneficio_gvc_2017)
. gen beneficio_gvc_2017=P69
{txt}(10,000 missing values generated)

{com}. 
. save "$working/BasePyMETFP_2018.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/BasePyMETFP_2018.dta{rm}
saved
{p_end}

{com}. *************************************************************************************************************************************************************
. 
. 
. *************************************************************************************************************************************************************
. use "$working/BasePyMETFP_2018.dta", clear
{txt}
{com}. 
. *1) Generate variables used in Management in America as outcomes
. 
. *Value added per worker (Real= Prices 2013)
. gen VA_2017=(ingre_tot_2017 + Var_inven_2017)-(g_materials_2017+g_energy_2017+g_resales_2017)
{txt}(5,500 missing values generated)

{com}. gen VA_real_2017=(ingre_tot_2017 + Var_inven_2017)*(100/INPP_prodB20132017)-(g_materials_2017+g_energy_2017+g_resales_2017)*(100/impatp_base20132017)
{txt}(5,500 missing values generated)

{com}. gen VA_wk_2017=VA_2017/nworkers_2017
{txt}(5,500 missing values generated)

{com}. gen VA_real_wk_2017=VA_real_2017/nworkers_2017
{txt}(5,500 missing values generated)

{com}. gen VA_real_wk_mil_2017=(VA_real_2017/1000)/nworkers_2017
{txt}(5,500 missing values generated)

{com}. 
. 
. *Operating profits
. gen oper_profit_2017=(VA_real_2017-rem_tot_anual_2017)
{txt}(5,500 missing values generated)

{com}. gen oper_profit_real_2017=(VA_2017-rem_tot_anual_2017*(100/INPP_prodB20132017))
{txt}(5,500 missing values generated)

{com}. 
. *Profitability
. gen profitab_2017=(VA_2017-rem_tot_anual_2017)/ingre_tot_2017
{txt}(5,500 missing values generated)

{com}. gen profitabrea_2017=(VA_real_2017-rem_tot_anual_2017*(100/INPP_prodB20132017))/(ingre_tot_2017*(100/INPP_prodB20132017))
{txt}(5,500 missing values generated)

{com}. 
. 
. gen sh_rem_2017=rem_tot_anual_2017/ingre_tot_2017
{txt}
{com}. gen sh_insum_2017=(g_train_2017+g_insum_2017+g_energy_2017+g_resales_2017)/ingre_tot_2017
{txt}
{com}. gen sh_af_2017=1-sh_rem-sh_insum
{txt}
{com}. 
. 
. foreach var of varlist VA_real_wk_mil_2017 VA_real_2017 rem_tot_anual_2017 rem_tot_anual_real_2017 g_train_2017 g_insum_2017 g_energy_2017 g_resales_2017 activ_fijos_real_2017 ingre_tot_2017 ingre_tot_real_2017 oper_profit_real_2017 g_insum_cut_2017 activ_fijos_2017 g_materials_2017 g_insum_tot_2017 Var_inven_2017 nworkers_2017 innov_16_17_work_2017 IDT_16_17_work_2017 sh_sup_2017 sh_direc_2017{c -(}
{txt}  2{com}.                 local newvar = substr("`var'", 1, length("`var'") - 5)   // Remove 2017
{txt}  3{com}.                 gen `var'b=`var'
{txt}  4{com}.                 ren `var'b `newvar'
{txt}  5{com}.                 winsor2 `newvar', s(_WA_2017) 
{txt}  6{com}.                 drop `newvar'
{txt}  7{com}. {c )-}
{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}
{com}. 
. gen lVA_real_wk_mil_2017=ln(VA_real_wk_mil_2017 )
{txt}(6,500 missing values generated)

{com}. gen lVA_real_wk_mil_WA_2017=ln(VA_real_wk_mil_WA_2017 )
{txt}(6,500 missing values generated)

{com}. 
. gen ln_rem_real_2017=ln(rem_tot_anual_real_WA_2017+sqrt(rem_tot_anual_real_WA_2017^2+1))
{txt}(5,500 missing values generated)

{com}. gen ln_rem_real_WA_2017=ln(rem_tot_anual_real_WA_2017+sqrt(rem_tot_anual_real_WA_2017^2+1))
{txt}(5,500 missing values generated)

{com}. gen ln_rem_2017=ln(rem_tot_anual_2017+sqrt(rem_tot_anual_2017^2+1))
{txt}
{com}. 
. gen ln_insum_2017=ln(g_train_WA_2017+g_insum_WA_2017)
{txt}
{com}. gen ln_insum_real_2017=ln((g_train_2017+g_insum_2017+g_energy_2017+g_resales_2017)*(100/INPP_prodB20132017)) 
{txt}(5,500 missing values generated)

{com}. gen ln_insum_real_WA_2017=ln((g_train_WA_2017+g_insum_WA_2017+g_energy_WA_2017+g_resales_WA_2017)*(100/INPP_prodB20132017))
{txt}(5,500 missing values generated)

{com}. 
. gen ln_af_real_2017=ln(activ_fijos_real_2017+sqrt(activ_fijos_real_2017^2+1))
{txt}(5,500 missing values generated)

{com}. gen ln_af_2017=ln(activ_fijos_2017+sqrt(activ_fijos_2017^2+1))
{txt}
{com}. 
. gen ln_af_WA_2017=ln(activ_fijos_WA_2017+sqrt(activ_fijos_WA_2017^2+1))
{txt}
{com}. gen ln_af_real_WA_2017=ln(activ_fijos_real_WA_2017+sqrt(activ_fijos_real_WA_2017^2+1))
{txt}(5,500 missing values generated)

{com}. gen lnY_real_WA_2017=ln(ingre_tot_WA_2017+ Var_inven_WA_2017)*(100/INPP_prodB20132017) 
{txt}(5,500 missing values generated)

{com}. gen lnY_real_2017=ln(ingre_tot_2017+ Var_inven_2017)*(100/INPP_prodB20132017) 
{txt}(5,500 missing values generated)

{com}. gen lnY_WA_2017=ln(ingre_tot_WA_2017+ Var_inven_WA_2017)
{txt}(5,500 missing values generated)

{com}. gen lnY_2017=ln(ingre_tot_2017+ Var_inven_2017) 
{txt}(5,500 missing values generated)

{com}. 
. 
. 
. 
. ////LTFP INDEX AW & ROBERTS///////
> levelsof sector_2017, local(sectores)
{res}{txt}`"COM"' `"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. foreach var of varlist sh_rem_2017 sh_insum_2017 sh_af_2017 lnY_real_2017 ln_rem_real_2017 ln_insum_real_2017 ln_af_real_2017 lnY_2017 ln_rem_2017 ln_insum_2017 ln_af_2017 lnY_real_WA_2017 ln_rem_real_WA_2017 ln_insum_real_WA_2017 ln_af_real_WA_2017{c -(}
{txt}  3{com}. sum `var' if sector_2017 =="`x'" , d
{txt}  4{com}. sum `var' if (`var'>r(p1) & `var'<r(p99)) & sector_2017 =="`x'"  [aw=FAC_EXPA], d
{txt}  5{com}. scalar `var'_bar`x'=r(mean)
{txt}  6{com}. {c )-}
{txt}  7{com}. {c )-}

                         {txt}sh_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .0006478       .0006478
{txt} 5%    {res} .0006478       .0006478
{txt}10%    {res} .0006478       .0006478       {txt}Obs         {res}      3,496
{txt}25%    {res} .0015997       .0006478       {txt}Sum of wgt. {res}      3,496

{txt}50%    {res} .0154234                      {txt}Mean          {res} .0248911
                        {txt}Largest       Std. dev.     {res} .0259722
{txt}75%    {res} .0406404         .08062
{txt}90%    {res}   .08062         .08062       {txt}Variance      {res} .0006746
{txt}95%    {res}   .08062         .08062       {txt}Skewness      {res} 1.201803
{txt}99%    {res}   .08062         .08062       {txt}Kurtosis      {res} 3.257704

                         {txt}sh_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .0015997       .0015997
{txt} 5%    {res} .0015997       .0015997
{txt}10%    {res} .0015997       .0015997       {txt}Obs         {res}      2,498
{txt}25%    {res} .0110812       .0015997       {txt}Sum of wgt. {res} 4,113.1944

{txt}50%    {res} .0196573                      {txt}Mean          {res} .0209567
                        {txt}Largest       Std. dev.     {res} .0126347
{txt}75%    {res} .0236279       .0406404
{txt}90%    {res} .0406404       .0406404       {txt}Variance      {res} .0001596
{txt}95%    {res} .0406404       .0406404       {txt}Skewness      {res} .4075763
{txt}99%    {res} .0406404       .0482078       {txt}Kurtosis      {res} 2.164435

                        {txt}sh_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .2225667       .2225667
{txt} 5%    {res} .2225667       .2225667
{txt}10%    {res} .2225667       .2225667       {txt}Obs         {res}      3,496
{txt}25%    {res} 1.348383       .2225667       {txt}Sum of wgt. {res}      3,496

{txt}50%    {res} 1.520319                      {txt}Mean          {res} 1.388353
                        {txt}Largest       Std. dev.     {res}  .520808
{txt}75%    {res} 1.674824       1.930181
{txt}90%    {res} 1.930181       1.930181       {txt}Variance      {res}  .271241
{txt}95%    {res} 1.930181       1.930181       {txt}Skewness      {res} -1.38913
{txt}99%    {res} 1.930181       1.930181       {txt}Kurtosis      {res} 3.788529

                        {txt}sh_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.160556       1.160556
{txt} 5%    {res} 1.160556       1.160556
{txt}10%    {res} 1.348383       1.160556       {txt}Obs         {res}      2,498
{txt}25%    {res} 1.520319       1.160556       {txt}Sum of wgt. {res} 6,251.7659

{txt}50%    {res} 1.658459                      {txt}Mean          {res} 1.560471
                        {txt}Largest       Std. dev.     {res} .1688326
{txt}75%    {res} 1.674824       1.718276
{txt}90%    {res} 1.674824       1.718276       {txt}Variance      {res} .0285044
{txt}95%    {res} 1.718276       1.718276       {txt}Skewness      {res}-1.209493
{txt}99%    {res} 1.718276       1.727049       {txt}Kurtosis      {res} 3.182208

                         {txt}sh_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}-.9317808      -.9317808
{txt} 5%    {res}-.9317808      -.9317808
{txt}10%    {res}-.9317808      -.9317808       {txt}Obs         {res}      3,496
{txt}25%    {res} -.755444      -.9317808       {txt}Sum of wgt. {res}      3,496

{txt}50%    {res}-.5609592                      {txt}Mean          {res}-.4132441
                        {txt}Largest       Std. dev.     {res} .5304709
{txt}75%    {res}-.3680399       .7767855
{txt}90%    {res} .7767855       .7767855       {txt}Variance      {res} .2813994
{txt}95%    {res} .7767855       .7767855       {txt}Skewness      {res} 1.416653
{txt}99%    {res} .7767855       .7767855       {txt}Kurtosis      {res} 3.793093

                         {txt}sh_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} -.755444      -.7752572
{txt} 5%    {res} -.755444       -.755444
{txt}10%    {res} -.755444       -.755444       {txt}Obs         {res}      2,498
{txt}25%    {res} -.755444       -.755444       {txt}Sum of wgt. {res} 6,251.7659

{txt}50%    {res}-.6738821                      {txt}Mean          {res}-.6081444
                        {txt}Largest       Std. dev.     {res} .1884999
{txt}75%    {res}-.5609592      -.1716377
{txt}90%    {res}-.3680399      -.1716377       {txt}Variance      {res} .0355322
{txt}95%    {res}-.1716377      -.1716377       {txt}Skewness      {res} 1.124507
{txt}99%    {res}-.1716377      -.1716377       {txt}Kurtosis      {res} 2.996209

                        {txt}lnY_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}  14.6885        14.6885
{txt} 5%    {res}  14.6885        14.6885
{txt}10%    {res}  14.6885        14.6885       {txt}Obs         {res}      1,180
{txt}25%    {res}  14.6885        14.6885       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 16.15626                      {txt}Mean          {res} 15.72202
                        {txt}Largest       Std. dev.     {res} .8974096
{txt}75%    {res} 16.59716       16.59716
{txt}90%    {res} 16.59716       16.59716       {txt}Variance      {res}  .805344
{txt}95%    {res} 16.59716       16.59716       {txt}Skewness      {res}-.2366082
{txt}99%    {res} 16.59716       16.59716       {txt}Kurtosis      {res}  1.13675

                        {txt}lnY_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.15626       16.15626
{txt} 5%    {res} 16.15626       16.15626
{txt}10%    {res} 16.15626       16.15626       {txt}Obs         {res}        182
{txt}25%    {res} 16.15626       16.15626       {txt}Sum of wgt. {res}        364

{txt}50%    {res} 16.15626                      {txt}Mean          {res} 16.15626
                        {txt}Largest       Std. dev.     {res}        0
{txt}75%    {res} 16.15626       16.15626
{txt}90%    {res} 16.15626       16.15626       {txt}Variance      {res}        0
{txt}95%    {res} 16.15626       16.15626       {txt}Skewness      {res}        .
{txt}99%    {res} 16.15626       16.15626       {txt}Kurtosis      {res}        .

                      {txt}ln_rem_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.14685       13.14685
{txt} 5%    {res} 13.14685       13.14685
{txt}10%    {res} 13.14685       13.14685       {txt}Obs         {res}      1,180
{txt}25%    {res} 13.14685       13.14685       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 15.00059                      {txt}Mean          {res} 14.21696
                        {txt}Largest       Std. dev.     {res} .9164026
{txt}75%    {res} 15.00059       15.00238
{txt}90%    {res} 15.00238       15.00238       {txt}Variance      {res} .8397937
{txt}95%    {res} 15.00238       15.00238       {txt}Skewness      {res}-.3122092
{txt}99%    {res} 15.00238       15.00238       {txt}Kurtosis      {res} 1.097476

                      {txt}ln_rem_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.00059       15.00059
{txt} 5%    {res} 15.00059       15.00059
{txt}10%    {res} 15.00059       15.00059       {txt}Obs         {res}        499
{txt}25%    {res} 15.00059       15.00059       {txt}Sum of wgt. {res} 2,637.5714

{txt}50%    {res} 15.00059                      {txt}Mean          {res} 15.00059
                        {txt}Largest       Std. dev.     {res} 1.78e-15
{txt}75%    {res} 15.00059       15.00059
{txt}90%    {res} 15.00059       15.00059       {txt}Variance      {res} 3.16e-30
{txt}95%    {res} 15.00059       15.00059       {txt}Skewness      {res}       -1
{txt}99%    {res} 15.00059       15.00059       {txt}Kurtosis      {res}        1

                     {txt}ln_insum_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 17.34116       17.34116
{txt} 5%    {res} 17.34116       17.34116
{txt}10%    {res} 17.34116       17.34116       {txt}Obs         {res}      1,180
{txt}25%    {res} 17.34116       17.34116       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 18.59588                      {txt}Mean          {res} 18.46846
                        {txt}Largest       Std. dev.     {res} 1.017241
{txt}75%    {res} 19.54928       19.54928
{txt}90%    {res} 19.54928       19.54928       {txt}Variance      {res} 1.034779
{txt}95%    {res} 19.54928       19.54928       {txt}Skewness      {res}-.0680676
{txt}99%    {res} 19.54928       19.54928       {txt}Kurtosis      {res} 1.178765

                     {txt}ln_insum_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.59588       18.59588
{txt} 5%    {res} 18.59588       18.59588
{txt}10%    {res} 18.59588       18.59588       {txt}Obs         {res}        182
{txt}25%    {res} 18.59588       18.59588       {txt}Sum of wgt. {res}        364

{txt}50%    {res} 18.59588                      {txt}Mean          {res} 18.59588
                        {txt}Largest       Std. dev.     {res}        0
{txt}75%    {res} 18.59588       18.59588
{txt}90%    {res} 18.59588       18.59588       {txt}Variance      {res}        0
{txt}95%    {res} 18.59588       18.59588       {txt}Skewness      {res}        .
{txt}99%    {res} 18.59588       18.59588       {txt}Kurtosis      {res}        .

                       {txt}ln_af_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.47016       12.47016
{txt} 5%    {res} 12.47016       12.47016
{txt}10%    {res} 12.47016       12.47016       {txt}Obs         {res}      1,180
{txt}25%    {res} 14.42864       12.47016       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 14.42864                      {txt}Mean          {res}  14.3947
                        {txt}Largest       Std. dev.     {res} .8724083
{txt}75%    {res} 15.06268       15.06268
{txt}90%    {res} 15.06268       15.06268       {txt}Variance      {res} .7610963
{txt}95%    {res} 15.06268       15.06268       {txt}Skewness      {res}-1.467816
{txt}99%    {res} 15.06268       15.06268       {txt}Kurtosis      {res} 3.804512

                       {txt}ln_af_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 14.42864       14.42864
{txt} 5%    {res} 14.42864       14.42864
{txt}10%    {res} 14.42864       14.42864       {txt}Obs         {res}        499
{txt}25%    {res} 14.42864       14.42864       {txt}Sum of wgt. {res}        499

{txt}50%    {res} 14.42864                      {txt}Mean          {res} 14.42864
                        {txt}Largest       Std. dev.     {res}        0
{txt}75%    {res} 14.42864       14.42864
{txt}90%    {res} 14.42864       14.42864       {txt}Variance      {res}        0
{txt}95%    {res} 14.42864       14.42864       {txt}Skewness      {res}        .
{txt}99%    {res} 14.42864       14.42864       {txt}Kurtosis      {res}        .

                          {txt}lnY_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.96983       16.96983
{txt} 5%    {res} 16.96983       16.96983
{txt}10%    {res} 16.96983       16.96983       {txt}Obs         {res}      1,180
{txt}25%    {res} 16.96983       16.96983       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 18.18875                      {txt}Mean          {res} 18.03278
                        {txt}Largest       Std. dev.     {res} .9541218
{txt}75%    {res} 19.03884       19.03884
{txt}90%    {res} 19.03884       19.03884       {txt}Variance      {res} .9103485
{txt}95%    {res} 19.03884       19.03884       {txt}Skewness      {res}-.0883838
{txt}99%    {res} 19.03884       19.03884       {txt}Kurtosis      {res} 1.176281

                          {txt}lnY_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.18875       18.18875
{txt} 5%    {res} 18.18875       18.18875
{txt}10%    {res} 18.18875       18.18875       {txt}Obs         {res}        182
{txt}25%    {res} 18.18875       18.18875       {txt}Sum of wgt. {res}        364

{txt}50%    {res} 18.18875                      {txt}Mean          {res} 18.18875
                        {txt}Largest       Std. dev.     {res}        0
{txt}75%    {res} 18.18875       18.18875
{txt}90%    {res} 18.18875       18.18875       {txt}Variance      {res}        0
{txt}95%    {res} 18.18875       18.18875       {txt}Skewness      {res}        .
{txt}99%    {res} 18.18875       18.18875       {txt}Kurtosis      {res}        .

                         {txt}ln_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 11.18442       11.18442
{txt} 5%    {res} 11.18442       11.18442
{txt}10%    {res} 11.18442       11.18442       {txt}Obs         {res}      3,496
{txt}25%    {res}  13.2841       11.18442       {txt}Sum of wgt. {res}      3,496

{txt}50%    {res} 15.14365                      {txt}Mean          {res} 14.41452
                        {txt}Largest       Std. dev.     {res} 1.500344
{txt}75%    {res} 15.29893        15.7161
{txt}90%    {res}  15.7161        15.7161       {txt}Variance      {res} 2.251033
{txt}95%    {res}  15.7161        15.7161       {txt}Skewness      {res}-1.314511
{txt}99%    {res}  15.7161       16.77322       {txt}Kurtosis      {res} 3.237106

                         {txt}ln_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}  13.2841        13.2841
{txt} 5%    {res}  13.2841        13.2841
{txt}10%    {res} 15.12087        13.2841       {txt}Obs         {res}      2,497
{txt}25%    {res} 15.13009        13.2841       {txt}Sum of wgt. {res} 5,751.0159

{txt}50%    {res} 15.14497                      {txt}Mean          {res} 15.00346
                        {txt}Largest       Std. dev.     {res} .5331722
{txt}75%    {res} 15.14497       15.29893
{txt}90%    {res} 15.29893       15.29893       {txt}Variance      {res} .2842726
{txt}95%    {res} 15.29893       15.29893       {txt}Skewness      {res}-2.872299
{txt}99%    {res} 15.29893       15.29893       {txt}Kurtosis      {res} 9.408481

                        {txt}ln_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.85896       15.85896
{txt} 5%    {res} 15.85896       15.85896
{txt}10%    {res} 15.85896       15.85896       {txt}Obs         {res}      3,496
{txt}25%    {res} 16.81014       15.85896       {txt}Sum of wgt. {res}      3,496

{txt}50%    {res} 18.14692                      {txt}Mean          {res}  17.7813
                        {txt}Largest       Std. dev.     {res} .9981248
{txt}75%    {res} 18.42203       19.00113
{txt}90%    {res} 19.00113       19.00113       {txt}Variance      {res} .9962532
{txt}95%    {res} 19.00113       19.00113       {txt}Skewness      {res}-.8214532
{txt}99%    {res} 19.00113       19.00113       {txt}Kurtosis      {res} 2.453599

                        {txt}ln_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.81014       16.81014
{txt} 5%    {res} 16.81014       16.81014
{txt}10%    {res} 16.81014       16.81014       {txt}Obs         {res}      2,498
{txt}25%    {res} 16.81014       16.81014       {txt}Sum of wgt. {res} 6,251.7659

{txt}50%    {res} 17.95684                      {txt}Mean          {res} 17.60449
                        {txt}Largest       Std. dev.     {res} .6947429
{txt}75%    {res} 18.14692       18.42203
{txt}90%    {res} 18.42203       18.42203       {txt}Variance      {res} .4826677
{txt}95%    {res} 18.42203       18.42203       {txt}Skewness      {res}-.1785146
{txt}99%    {res} 18.42203       18.97812       {txt}Kurtosis      {res} 1.205472

                         {txt}ln_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.92275       12.92275
{txt} 5%    {res} 12.92275       12.92275
{txt}10%    {res} 14.13134       12.92275       {txt}Obs         {res}      3,496
{txt}25%    {res} 14.88123       12.92275       {txt}Sum of wgt. {res}      3,496

{txt}50%    {res} 16.05324                      {txt}Mean          {res} 15.90255
                        {txt}Largest       Std. dev.     {res} 1.546974
{txt}75%    {res} 16.36261       18.69342
{txt}90%    {res} 18.69342       18.69342       {txt}Variance      {res}  2.39313
{txt}95%    {res} 18.69342       18.69342       {txt}Skewness      {res} .2734724
{txt}99%    {res} 18.69342       18.69342       {txt}Kurtosis      {res} 2.557545

                         {txt}ln_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 14.13134       14.13134
{txt} 5%    {res} 14.13134       14.13134
{txt}10%    {res} 14.13134       14.13134       {txt}Obs         {res}      2,815
{txt}25%    {res} 15.51527       14.13134       {txt}Sum of wgt. {res} 6,386.7659

{txt}50%    {res} 15.51527                      {txt}Mean          {res} 15.63227
                        {txt}Largest       Std. dev.     {res} .8150816
{txt}75%    {res} 16.05324         17.255
{txt}90%    {res} 16.36261         17.255       {txt}Variance      {res} .6643581
{txt}95%    {res}   17.255         17.255       {txt}Skewness      {res}-.0970904
{txt}99%    {res}   17.255         17.255       {txt}Kurtosis      {res} 2.964349

                      {txt}lnY_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}  14.6885        14.6885
{txt} 5%    {res}  14.6885        14.6885
{txt}10%    {res}  14.6885        14.6885       {txt}Obs         {res}      1,180
{txt}25%    {res}  14.6885        14.6885       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 16.15626                      {txt}Mean          {res} 15.72202
                        {txt}Largest       Std. dev.     {res} .8974096
{txt}75%    {res} 16.59716       16.59716
{txt}90%    {res} 16.59716       16.59716       {txt}Variance      {res}  .805344
{txt}95%    {res} 16.59716       16.59716       {txt}Skewness      {res}-.2366082
{txt}99%    {res} 16.59716       16.59716       {txt}Kurtosis      {res}  1.13675

                      {txt}lnY_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.15626       16.15626
{txt} 5%    {res} 16.15626       16.15626
{txt}10%    {res} 16.15626       16.15626       {txt}Obs         {res}        182
{txt}25%    {res} 16.15626       16.15626       {txt}Sum of wgt. {res}        364

{txt}50%    {res} 16.15626                      {txt}Mean          {res} 16.15626
                        {txt}Largest       Std. dev.     {res}        0
{txt}75%    {res} 16.15626       16.15626
{txt}90%    {res} 16.15626       16.15626       {txt}Variance      {res}        0
{txt}95%    {res} 16.15626       16.15626       {txt}Skewness      {res}        .
{txt}99%    {res} 16.15626       16.15626       {txt}Kurtosis      {res}        .

                     {txt}ln_rem_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.14685       13.14685
{txt} 5%    {res} 13.14685       13.14685
{txt}10%    {res} 13.14685       13.14685       {txt}Obs         {res}      1,180
{txt}25%    {res} 13.14685       13.14685       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 15.00059                      {txt}Mean          {res} 14.21696
                        {txt}Largest       Std. dev.     {res} .9164026
{txt}75%    {res} 15.00059       15.00238
{txt}90%    {res} 15.00238       15.00238       {txt}Variance      {res} .8397937
{txt}95%    {res} 15.00238       15.00238       {txt}Skewness      {res}-.3122092
{txt}99%    {res} 15.00238       15.00238       {txt}Kurtosis      {res} 1.097476

                     {txt}ln_rem_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.00059       15.00059
{txt} 5%    {res} 15.00059       15.00059
{txt}10%    {res} 15.00059       15.00059       {txt}Obs         {res}        499
{txt}25%    {res} 15.00059       15.00059       {txt}Sum of wgt. {res} 2,637.5714

{txt}50%    {res} 15.00059                      {txt}Mean          {res} 15.00059
                        {txt}Largest       Std. dev.     {res} 1.78e-15
{txt}75%    {res} 15.00059       15.00059
{txt}90%    {res} 15.00059       15.00059       {txt}Variance      {res} 3.16e-30
{txt}95%    {res} 15.00059       15.00059       {txt}Skewness      {res}       -1
{txt}99%    {res} 15.00059       15.00059       {txt}Kurtosis      {res}        1

                    {txt}ln_insum_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 17.34116       17.34116
{txt} 5%    {res} 17.34116       17.34116
{txt}10%    {res} 17.34116       17.34116       {txt}Obs         {res}      1,180
{txt}25%    {res} 17.34116       17.34116       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 18.59588                      {txt}Mean          {res} 18.46846
                        {txt}Largest       Std. dev.     {res} 1.017241
{txt}75%    {res} 19.54928       19.54928
{txt}90%    {res} 19.54928       19.54928       {txt}Variance      {res} 1.034779
{txt}95%    {res} 19.54928       19.54928       {txt}Skewness      {res}-.0680676
{txt}99%    {res} 19.54928       19.54928       {txt}Kurtosis      {res} 1.178765

                    {txt}ln_insum_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.59588       18.59588
{txt} 5%    {res} 18.59588       18.59588
{txt}10%    {res} 18.59588       18.59588       {txt}Obs         {res}        182
{txt}25%    {res} 18.59588       18.59588       {txt}Sum of wgt. {res}        364

{txt}50%    {res} 18.59588                      {txt}Mean          {res} 18.59588
                        {txt}Largest       Std. dev.     {res}        0
{txt}75%    {res} 18.59588       18.59588
{txt}90%    {res} 18.59588       18.59588       {txt}Variance      {res}        0
{txt}95%    {res} 18.59588       18.59588       {txt}Skewness      {res}        .
{txt}99%    {res} 18.59588       18.59588       {txt}Kurtosis      {res}        .

                     {txt}ln_af_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.47016       12.47016
{txt} 5%    {res} 12.47016       12.47016
{txt}10%    {res} 12.47016       12.47016       {txt}Obs         {res}      1,180
{txt}25%    {res} 14.42864       12.47016       {txt}Sum of wgt. {res}      1,180

{txt}50%    {res} 14.42864                      {txt}Mean          {res}  14.3947
                        {txt}Largest       Std. dev.     {res} .8724083
{txt}75%    {res} 15.06268       15.06268
{txt}90%    {res} 15.06268       15.06268       {txt}Variance      {res} .7610963
{txt}95%    {res} 15.06268       15.06268       {txt}Skewness      {res}-1.467816
{txt}99%    {res} 15.06268       15.06268       {txt}Kurtosis      {res} 3.804512

                     {txt}ln_af_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 14.42864       14.42864
{txt} 5%    {res} 14.42864       14.42864
{txt}10%    {res} 14.42864       14.42864       {txt}Obs         {res}        499
{txt}25%    {res} 14.42864       14.42864       {txt}Sum of wgt. {res}        499

{txt}50%    {res} 14.42864                      {txt}Mean          {res} 14.42864
                        {txt}Largest       Std. dev.     {res}        0
{txt}75%    {res} 14.42864       14.42864
{txt}90%    {res} 14.42864       14.42864       {txt}Variance      {res}        0
{txt}95%    {res} 14.42864       14.42864       {txt}Skewness      {res}        .
{txt}99%    {res} 14.42864       14.42864       {txt}Kurtosis      {res}        .

                         {txt}sh_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .0110812              0
{txt} 5%    {res} .0110812              0
{txt}10%    {res}  .014681       .0006478       {txt}Obs         {res}      2,696
{txt}25%    {res}  .014681       .0015997       {txt}Sum of wgt. {res}      2,696

{txt}50%    {res} .0246326                      {txt}Mean          {res} .0264767
                        {txt}Largest       Std. dev.     {res} .0139687
{txt}75%    {res}  .033183         .08062
{txt}90%    {res} .0401501       .0909267       {txt}Variance      {res} .0001951
{txt}95%    {res} .0401501       .1753219       {txt}Skewness      {res} 18.44939
{txt}99%    {res} .0401501       .5354895       {txt}Kurtosis      {res} 659.9186

                         {txt}sh_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}  .014681        .014681
{txt} 5%    {res}  .014681        .014681
{txt}10%    {res}  .014681        .014681       {txt}Obs         {res}      2,004
{txt}25%    {res} .0196573        .014681       {txt}Sum of wgt. {res} 3,345.2821

{txt}50%    {res} .0239001                      {txt}Mean          {res} .0241711
                        {txt}Largest       Std. dev.     {res} .0066445
{txt}75%    {res}  .033183        .033183
{txt}90%    {res}  .033183        .033183       {txt}Variance      {res} .0000441
{txt}95%    {res}  .033183        .033183       {txt}Skewness      {res}-.0642416
{txt}99%    {res}  .033183        .033183       {txt}Kurtosis      {res} 1.939388

                        {txt}sh_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.160556       .2225667
{txt} 5%    {res} 1.160556       .9596664
{txt}10%    {res} 1.733276       1.160556       {txt}Obs         {res}      2,696
{txt}25%    {res} 1.733276       1.160556       {txt}Sum of wgt. {res}      2,696

{txt}50%    {res} 1.838549                      {txt}Mean          {res} 1.804043
                        {txt}Largest       Std. dev.     {res} .1933225
{txt}75%    {res}  1.87886       1.978843
{txt}90%    {res} 1.978843       1.978843       {txt}Variance      {res} .0373736
{txt}95%    {res} 1.978843       1.978843       {txt}Skewness      {res}-2.591972
{txt}99%    {res} 1.978843       1.978843       {txt}Kurtosis      {res} 10.22917

                        {txt}sh_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.733276       1.276219
{txt} 5%    {res} 1.733276       1.292967
{txt}10%    {res} 1.733276       1.348383       {txt}Obs         {res}      2,013
{txt}25%    {res} 1.733276       1.520319       {txt}Sum of wgt. {res}  5,088.939

{txt}50%    {res} 1.733276                      {txt}Mean          {res} 1.790556
                        {txt}Largest       Std. dev.     {res} .0649189
{txt}75%    {res} 1.838549        1.87886
{txt}90%    {res}  1.87886        1.87886       {txt}Variance      {res} .0042145
{txt}95%    {res}  1.87886        1.87886       {txt}Skewness      {res}-.5923225
{txt}99%    {res}  1.87886       1.930181       {txt}Kurtosis      {res} 6.795408

                         {txt}sh_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}-.9935236      -1.177592
{txt} 5%    {res}-.9935236      -.9935236
{txt}10%    {res}-.9935236      -.9935236       {txt}Obs         {res}      2,696
{txt}25%    {res}-.9120431      -.9935236       {txt}Sum of wgt. {res}      2,696

{txt}50%    {res}-.8624488                      {txt}Mean          {res}-.8305197
                        {txt}Largest       Std. dev.     {res} .1944445
{txt}75%    {res}-.7734259      -.1716377
{txt}90%    {res}-.7734259      -.1716377       {txt}Variance      {res} .0378087
{txt}95%    {res}-.1716377       .0403336       {txt}Skewness      {res} 2.748783
{txt}99%    {res}-.1716377       .7767855       {txt}Kurtosis      {res} 10.87332

                         {txt}sh_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}-.9120431      -.9317808
{txt} 5%    {res}-.9120431      -.9120431
{txt}10%    {res}-.9120431      -.9120431       {txt}Obs         {res}      2,012
{txt}25%    {res}-.8624488      -.9120431       {txt}Sum of wgt. {res}  5,077.439

{txt}50%    {res}-.7734259                      {txt}Mean          {res}-.8248972
                        {txt}Largest       Std. dev.     {res} .0582908
{txt}75%    {res}-.7734259      -.5609592
{txt}90%    {res}-.7734259      -.4682885       {txt}Variance      {res} .0033978
{txt}95%    {res}-.7734259      -.3680399       {txt}Skewness      {res} .2689526
{txt}99%    {res}-.7734259      -.3671461       {txt}Kurtosis      {res} 5.168652

                        {txt}lnY_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.20765       13.20765
{txt} 5%    {res} 13.20765       13.20765
{txt}10%    {res} 13.20765       13.20765       {txt}Obs         {res}      2,006
{txt}25%    {res} 13.98928       13.20765       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 14.56314                      {txt}Mean          {res} 14.84296
                        {txt}Largest       Std. dev.     {res} 1.663873
{txt}75%    {res} 16.68843       17.59621
{txt}90%    {res} 17.59621       17.59621       {txt}Variance      {res} 2.768473
{txt}95%    {res} 17.59621       17.59621       {txt}Skewness      {res} .8642403
{txt}99%    {res} 17.59621       18.24208       {txt}Kurtosis      {res} 2.131137

                        {txt}lnY_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.98928       13.98928
{txt} 5%    {res} 13.98928       13.98928
{txt}10%    {res} 13.98928       13.98928       {txt}Obs         {res}      1,005
{txt}25%    {res} 13.98928       13.98928       {txt}Sum of wgt. {res} 1,735.7387

{txt}50%    {res} 14.56314                      {txt}Mean          {res} 14.29356
                        {txt}Largest       Std. dev.     {res} .3109827
{txt}75%    {res} 14.56314        14.6885
{txt}90%    {res} 14.56314       16.15626       {txt}Variance      {res} .0967102
{txt}95%    {res} 14.56314       16.59716       {txt}Skewness      {res} .9605065
{txt}99%    {res} 14.56314       16.68843       {txt}Kurtosis      {res} 8.491977

                      {txt}ln_rem_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.37844              0
{txt} 5%    {res} 13.37844       13.14685
{txt}10%    {res} 13.37844       13.37844       {txt}Obs         {res}      2,006
{txt}25%    {res} 13.37844       13.37844       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 15.00148                      {txt}Mean          {res} 14.91519
                        {txt}Largest       Std. dev.     {res} 1.057392
{txt}75%    {res} 15.52071       16.10405
{txt}90%    {res} 16.10405       16.10405       {txt}Variance      {res} 1.118077
{txt}95%    {res} 16.10405       16.10405       {txt}Skewness      {res}-1.775793
{txt}99%    {res} 16.10405       18.24576       {txt}Kurtosis      {res} 21.32981

                      {txt}ln_rem_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 14.81467       14.81467
{txt} 5%    {res} 14.81467       14.81467
{txt}10%    {res} 14.81467       14.81467       {txt}Obs         {res}      1,003
{txt}25%    {res} 14.81467       14.81467       {txt}Sum of wgt. {res} 1,731.7387

{txt}50%    {res} 15.38876                      {txt}Mean          {res} 15.11104
                        {txt}Largest       Std. dev.     {res} .2867141
{txt}75%    {res} 15.38876       15.38876
{txt}90%    {res} 15.38876       15.38876       {txt}Variance      {res}  .082205
{txt}95%    {res} 15.38876       15.38876       {txt}Skewness      {res}-.0615699
{txt}99%    {res} 15.38876       15.52071       {txt}Kurtosis      {res} 1.008689

                     {txt}ln_insum_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.45044       16.34483
{txt} 5%    {res} 16.45044       16.45044
{txt}10%    {res} 16.45044       16.45044       {txt}Obs         {res}      2,006
{txt}25%    {res} 17.34116       16.45044       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 18.87862                      {txt}Mean          {res} 18.49052
                        {txt}Largest       Std. dev.     {res} 1.235691
{txt}75%    {res} 19.54928       19.75375
{txt}90%    {res} 19.75375       19.75375       {txt}Variance      {res} 1.526933
{txt}95%    {res} 19.75375       19.75375       {txt}Skewness      {res}-.8351939
{txt}99%    {res} 19.75375       20.19422       {txt}Kurtosis      {res} 2.142303

                     {txt}ln_insum_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.73199       17.34116
{txt} 5%    {res} 18.73199       18.59588
{txt}10%    {res} 18.73199       18.73199       {txt}Obs         {res}      1,004
{txt}25%    {res} 18.73199       18.73199       {txt}Sum of wgt. {res} 1,732.7387

{txt}50%    {res} 18.73199                      {txt}Mean          {res} 18.86976
                        {txt}Largest       Std. dev.     {res} .1704629
{txt}75%    {res} 19.02524       19.02524
{txt}90%    {res} 19.02524       19.02524       {txt}Variance      {res} .0290576
{txt}95%    {res} 19.02524       19.24919       {txt}Skewness      {res}-2.062729
{txt}99%    {res} 19.02524       19.54928       {txt}Kurtosis      {res} 20.50309

                       {txt}ln_af_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.24454       12.47016
{txt} 5%    {res} 13.24454       13.24454
{txt}10%    {res} 13.24454       13.24454       {txt}Obs         {res}      2,006
{txt}25%    {res} 13.43978       13.24454       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 15.03501                      {txt}Mean          {res} 14.54037
                        {txt}Largest       Std. dev.     {res} 1.266407
{txt}75%    {res} 15.27985       16.31041
{txt}90%    {res} 16.31041       16.31041       {txt}Variance      {res} 1.603786
{txt}95%    {res} 16.31041       16.31041       {txt}Skewness      {res} .2800396
{txt}99%    {res} 16.31041       16.62922       {txt}Kurtosis      {res} 1.389954

                       {txt}ln_af_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.43978       13.43978
{txt} 5%    {res} 13.43978       13.43978
{txt}10%    {res} 13.43978       13.43978       {txt}Obs         {res}      1,004
{txt}25%    {res} 13.43978       13.43978       {txt}Sum of wgt. {res} 1,733.7387

{txt}50%    {res} 13.43978                      {txt}Mean          {res} 14.27901
                        {txt}Largest       Std. dev.     {res} .8614856
{txt}75%    {res} 15.16349       15.16349
{txt}90%    {res} 15.16349       15.16349       {txt}Variance      {res} .7421575
{txt}95%    {res} 15.16349       15.16349       {txt}Skewness      {res} .0520578
{txt}99%    {res} 15.16349       15.27985       {txt}Kurtosis      {res} 1.003695

                          {txt}lnY_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.09834       16.09834
{txt} 5%    {res} 16.09834       16.09834
{txt}10%    {res} 16.09834       16.09834       {txt}Obs         {res}      2,006
{txt}25%    {res} 16.96983       16.09834       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 18.49101                      {txt}Mean          {res} 18.09087
                        {txt}Largest       Std. dev.     {res} 1.199464
{txt}75%    {res} 19.03884       19.28098
{txt}90%    {res} 19.28098       19.28098       {txt}Variance      {res} 1.438713
{txt}95%    {res} 19.28098       19.28098       {txt}Skewness      {res}-.8725133
{txt}99%    {res} 19.28098       20.06364       {txt}Kurtosis      {res} 2.158943

                          {txt}lnY_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.34208       16.51996
{txt} 5%    {res} 18.34208       16.96983
{txt}10%    {res} 18.34208       18.18875       {txt}Obs         {res}      1,005
{txt}25%    {res} 18.34208       18.34208       {txt}Sum of wgt. {res} 1,735.7387

{txt}50%    {res} 18.34208                      {txt}Mean          {res}  18.4786
                        {txt}Largest       Std. dev.     {res} .1899259
{txt}75%    {res} 18.63993       18.63993
{txt}90%    {res} 18.63993       18.63993       {txt}Variance      {res} .0360718
{txt}95%    {res} 18.63993       18.82629       {txt}Skewness      {res}-3.304505
{txt}99%    {res} 18.63993       19.03884       {txt}Kurtosis      {res}  32.1912

                         {txt}ln_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.57636              0
{txt} 5%    {res} 13.57636              0
{txt}10%    {res} 13.57636       11.18442       {txt}Obs         {res}      2,696
{txt}25%    {res} 15.10169       12.21006       {txt}Sum of wgt. {res}      2,696

{txt}50%    {res} 15.61946                      {txt}Mean          {res} 15.44996
                        {txt}Largest       Std. dev.     {res} 1.180604
{txt}75%    {res} 16.19548       16.93947
{txt}90%    {res} 16.93947       16.93947       {txt}Variance      {res} 1.393827
{txt}95%    {res} 16.93947       16.93947       {txt}Skewness      {res}-2.012595
{txt}99%    {res} 16.93947       18.34094       {txt}Kurtosis      {res} 23.55457

                         {txt}ln_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.10169       15.10169
{txt} 5%    {res} 15.10169       15.10169
{txt}10%    {res} 15.10169       15.10169       {txt}Obs         {res}      1,690
{txt}25%    {res} 15.10169       15.10169       {txt}Sum of wgt. {res} 3,163.9844

{txt}50%    {res} 15.61946                      {txt}Mean          {res} 15.64091
                        {txt}Largest       Std. dev.     {res} .4589688
{txt}75%    {res} 16.19548       16.19548
{txt}90%    {res} 16.19548       16.19548       {txt}Variance      {res} .2106524
{txt}95%    {res} 16.19548       16.19548       {txt}Skewness      {res} .0790777
{txt}99%    {res} 16.19548       16.77322       {txt}Kurtosis      {res} 1.412019

                        {txt}ln_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.03598       12.04787
{txt} 5%    {res} 16.03598       15.83467
{txt}10%    {res} 16.03598       15.85896       {txt}Obs         {res}      2,696
{txt}25%    {res}  18.2697       16.03598       {txt}Sum of wgt. {res}      2,696

{txt}50%    {res} 18.62221                      {txt}Mean          {res} 18.39069
                        {txt}Largest       Std. dev.     {res} 1.261268
{txt}75%    {res} 19.15803       19.88958
{txt}90%    {res} 19.88958       19.88958       {txt}Variance      {res} 1.590797
{txt}95%    {res} 19.88958       19.88958       {txt}Skewness      {res}-.9081692
{txt}99%    {res} 19.88958       19.88958       {txt}Kurtosis      {res} 2.903113

                        {txt}ln_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}  18.2697       16.76536
{txt} 5%    {res}  18.2697       16.81014
{txt}10%    {res}  18.2697       17.00872       {txt}Obs         {res}      1,693
{txt}25%    {res}  18.2697       17.95684       {txt}Sum of wgt. {res} 3,166.9844

{txt}50%    {res} 18.62221                      {txt}Mean          {res} 18.68395
                        {txt}Largest       Std. dev.     {res} .3880472
{txt}75%    {res} 19.15803       19.15803
{txt}90%    {res} 19.15803       19.15803       {txt}Variance      {res} .1505806
{txt}95%    {res} 19.15803       19.15803       {txt}Skewness      {res}-.1813409
{txt}99%    {res} 19.15803       19.62043       {txt}Kurtosis      {res} 3.353437

                         {txt}ln_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.69713        10.4091
{txt} 5%    {res} 13.69713       12.68132
{txt}10%    {res} 13.69713       12.92275       {txt}Obs         {res}      2,696
{txt}25%    {res} 13.89237       13.69713       {txt}Sum of wgt. {res}      2,696

{txt}50%    {res} 15.62599                      {txt}Mean          {res}  15.5349
                        {txt}Largest       Std. dev.     {res} 1.440399
{txt}75%    {res} 17.08402         17.255
{txt}90%    {res} 17.08402         17.255       {txt}Variance      {res} 2.074749
{txt}95%    {res}   17.255         17.255       {txt}Skewness      {res}-.2177087
{txt}99%    {res}   17.255       18.69342       {txt}Kurtosis      {res} 1.367821

                         {txt}ln_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.89237       13.89237
{txt} 5%    {res} 13.89237       13.89237
{txt}10%    {res} 13.89237       13.89237       {txt}Obs         {res}      2,011
{txt}25%    {res} 13.89237       13.89237       {txt}Sum of wgt. {res} 3,360.3178

{txt}50%    {res} 15.62599                      {txt}Mean          {res} 15.76245
                        {txt}Largest       Std. dev.     {res} 1.235471
{txt}75%    {res}   16.763       17.08402
{txt}90%    {res} 17.08402       17.08402       {txt}Variance      {res} 1.526389
{txt}95%    {res} 17.08402       17.08402       {txt}Skewness      {res}-.5601924
{txt}99%    {res} 17.08402       17.08402       {txt}Kurtosis      {res}  1.73068

                      {txt}lnY_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.20765       13.20765
{txt} 5%    {res} 13.20765       13.20765
{txt}10%    {res} 13.20765       13.20765       {txt}Obs         {res}      2,006
{txt}25%    {res} 13.98928       13.20765       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 14.56314                      {txt}Mean          {res} 14.84296
                        {txt}Largest       Std. dev.     {res} 1.663873
{txt}75%    {res} 16.68843       17.59621
{txt}90%    {res} 17.59621       17.59621       {txt}Variance      {res} 2.768473
{txt}95%    {res} 17.59621       17.59621       {txt}Skewness      {res} .8642403
{txt}99%    {res} 17.59621       18.24208       {txt}Kurtosis      {res} 2.131137

                      {txt}lnY_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.98928       13.98928
{txt} 5%    {res} 13.98928       13.98928
{txt}10%    {res} 13.98928       13.98928       {txt}Obs         {res}      1,005
{txt}25%    {res} 13.98928       13.98928       {txt}Sum of wgt. {res} 1,735.7387

{txt}50%    {res} 14.56314                      {txt}Mean          {res} 14.29356
                        {txt}Largest       Std. dev.     {res} .3109827
{txt}75%    {res} 14.56314        14.6885
{txt}90%    {res} 14.56314       16.15626       {txt}Variance      {res} .0967102
{txt}95%    {res} 14.56314       16.59716       {txt}Skewness      {res} .9605065
{txt}99%    {res} 14.56314       16.68843       {txt}Kurtosis      {res} 8.491977

                     {txt}ln_rem_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.37844              0
{txt} 5%    {res} 13.37844       13.14685
{txt}10%    {res} 13.37844       13.37844       {txt}Obs         {res}      2,006
{txt}25%    {res} 13.37844       13.37844       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 15.00148                      {txt}Mean          {res} 14.91519
                        {txt}Largest       Std. dev.     {res} 1.057392
{txt}75%    {res} 15.52071       16.10405
{txt}90%    {res} 16.10405       16.10405       {txt}Variance      {res} 1.118077
{txt}95%    {res} 16.10405       16.10405       {txt}Skewness      {res}-1.775793
{txt}99%    {res} 16.10405       18.24576       {txt}Kurtosis      {res} 21.32981

                     {txt}ln_rem_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 14.81467       14.81467
{txt} 5%    {res} 14.81467       14.81467
{txt}10%    {res} 14.81467       14.81467       {txt}Obs         {res}      1,003
{txt}25%    {res} 14.81467       14.81467       {txt}Sum of wgt. {res} 1,731.7387

{txt}50%    {res} 15.38876                      {txt}Mean          {res} 15.11104
                        {txt}Largest       Std. dev.     {res} .2867141
{txt}75%    {res} 15.38876       15.38876
{txt}90%    {res} 15.38876       15.38876       {txt}Variance      {res}  .082205
{txt}95%    {res} 15.38876       15.38876       {txt}Skewness      {res}-.0615699
{txt}99%    {res} 15.38876       15.52071       {txt}Kurtosis      {res} 1.008689

                    {txt}ln_insum_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.45044       16.34483
{txt} 5%    {res} 16.45044       16.45044
{txt}10%    {res} 16.45044       16.45044       {txt}Obs         {res}      2,006
{txt}25%    {res} 17.34116       16.45044       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 18.87862                      {txt}Mean          {res} 18.49052
                        {txt}Largest       Std. dev.     {res} 1.235691
{txt}75%    {res} 19.54928       19.75375
{txt}90%    {res} 19.75375       19.75375       {txt}Variance      {res} 1.526933
{txt}95%    {res} 19.75375       19.75375       {txt}Skewness      {res}-.8351939
{txt}99%    {res} 19.75375       20.19422       {txt}Kurtosis      {res} 2.142303

                    {txt}ln_insum_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.73199       17.34116
{txt} 5%    {res} 18.73199       18.59588
{txt}10%    {res} 18.73199       18.73199       {txt}Obs         {res}      1,004
{txt}25%    {res} 18.73199       18.73199       {txt}Sum of wgt. {res} 1,732.7387

{txt}50%    {res} 18.73199                      {txt}Mean          {res} 18.86976
                        {txt}Largest       Std. dev.     {res} .1704629
{txt}75%    {res} 19.02524       19.02524
{txt}90%    {res} 19.02524       19.02524       {txt}Variance      {res} .0290576
{txt}95%    {res} 19.02524       19.24919       {txt}Skewness      {res}-2.062729
{txt}99%    {res} 19.02524       19.54928       {txt}Kurtosis      {res} 20.50309

                     {txt}ln_af_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.24454       12.47016
{txt} 5%    {res} 13.24454       13.24454
{txt}10%    {res} 13.24454       13.24454       {txt}Obs         {res}      2,006
{txt}25%    {res} 13.43978       13.24454       {txt}Sum of wgt. {res}      2,006

{txt}50%    {res} 15.03501                      {txt}Mean          {res} 14.54037
                        {txt}Largest       Std. dev.     {res} 1.266407
{txt}75%    {res} 15.27985       16.31041
{txt}90%    {res} 16.31041       16.31041       {txt}Variance      {res} 1.603786
{txt}95%    {res} 16.31041       16.31041       {txt}Skewness      {res} .2800396
{txt}99%    {res} 16.31041       16.62922       {txt}Kurtosis      {res} 1.389954

                     {txt}ln_af_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 13.43978       13.43978
{txt} 5%    {res} 13.43978       13.43978
{txt}10%    {res} 13.43978       13.43978       {txt}Obs         {res}      1,004
{txt}25%    {res} 13.43978       13.43978       {txt}Sum of wgt. {res} 1,733.7387

{txt}50%    {res} 13.43978                      {txt}Mean          {res} 14.27901
                        {txt}Largest       Std. dev.     {res} .8614856
{txt}75%    {res} 15.16349       15.16349
{txt}90%    {res} 15.16349       15.16349       {txt}Variance      {res} .7421575
{txt}95%    {res} 15.16349       15.16349       {txt}Skewness      {res} .0520578
{txt}99%    {res} 15.16349       15.27985       {txt}Kurtosis      {res} 1.003695

                         {txt}sh_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      4,308
{txt}25%    {res} .0210249              0       {txt}Sum of wgt. {res}      4,308

{txt}50%    {res} .0482078                      {txt}Mean          {res} .1082644
                        {txt}Largest       Std. dev.     {res}  .163261
{txt}75%    {res} .0909267       .5354895
{txt}90%    {res} .5354895       .5354895       {txt}Variance      {res} .0266541
{txt}95%    {res} .5354895       .5354895       {txt}Skewness      {res} 1.977361
{txt}99%    {res} .5354895       .5354895       {txt}Kurtosis      {res} 5.501109

                         {txt}sh_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .0210249       .0210249
{txt} 5%    {res} .0210249       .0210249
{txt}10%    {res} .0210249       .0210249       {txt}Obs         {res}      2,811
{txt}25%    {res} .0482078       .0210249       {txt}Sum of wgt. {res} 7,109.6538

{txt}50%    {res} .0487916                      {txt}Mean          {res} .0983701
                        {txt}Largest       Std. dev.     {res}  .067731
{txt}75%    {res} .1753219       .1753219
{txt}90%    {res} .1753219       .1753219       {txt}Variance      {res} .0045875
{txt}95%    {res} .1753219       .1753219       {txt}Skewness      {res} .1597395
{txt}99%    {res} .1753219       .1753219       {txt}Kurtosis      {res} 1.178178

                        {txt}sh_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .9596664       .9596664
{txt} 5%    {res} .9596664       .9596664
{txt}10%    {res} .9596664       .9596664       {txt}Obs         {res}      4,308
{txt}25%    {res} 1.292967       .9596664       {txt}Sum of wgt. {res}      4,308

{txt}50%    {res} 1.642102                      {txt}Mean          {res} 1.530898
                        {txt}Largest       Std. dev.     {res} .2805691
{txt}75%    {res} 1.727049       1.847348
{txt}90%    {res} 1.847348       1.847348       {txt}Variance      {res}  .078719
{txt}95%    {res} 1.847348       1.847348       {txt}Skewness      {res}-.8395525
{txt}99%    {res} 1.847348       1.847348       {txt}Kurtosis      {res} 2.407593

                        {txt}sh_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.276219       1.276219
{txt} 5%    {res} 1.292967       1.276219
{txt}10%    {res} 1.292967       1.276219       {txt}Obs         {res}      3,310
{txt}25%    {res} 1.292967       1.276219       {txt}Sum of wgt. {res} 12,349.154

{txt}50%    {res} 1.642102                      {txt}Mean          {res}  1.56194
                        {txt}Largest       Std. dev.     {res} .1743684
{txt}75%    {res} 1.642102       1.749946
{txt}90%    {res} 1.727049       1.749946       {txt}Variance      {res} .0304043
{txt}95%    {res} 1.749946       1.749946       {txt}Skewness      {res}-.8102241
{txt}99%    {res} 1.749946       1.749946       {txt}Kurtosis      {res} 1.898796

                         {txt}sh_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.177592      -1.177592
{txt} 5%    {res}-1.177592      -1.177592
{txt}10%    {res}-1.177592      -1.177592       {txt}Obs         {res}      4,308
{txt}25%    {res}-.7752572      -1.177592       {txt}Sum of wgt. {res}      4,308

{txt}50%    {res}-.7419043                      {txt}Mean          {res}-.6391626
                        {txt}Largest       Std. dev.     {res} .3341003
{txt}75%    {res}-.4682885       .0403336
{txt}90%    {res} .0403336       .0403336       {txt}Variance      {res}  .111623
{txt}95%    {res} .0403336       .0403336       {txt}Skewness      {res} .4980322
{txt}99%    {res} .0403336       .0403336       {txt}Kurtosis      {res} 2.870492

                         {txt}sh_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}-.8961393      -.8961393
{txt} 5%    {res}-.8961393      -.8961393
{txt}10%    {res}-.8961393      -.8961393       {txt}Obs         {res}      3,310
{txt}25%    {res}-.7709709      -.8961393       {txt}Sum of wgt. {res} 7,608.6538

{txt}50%    {res}-.6332445                      {txt}Mean          {res}-.6308358
                        {txt}Largest       Std. dev.     {res} .1746107
{txt}75%    {res}-.4682885      -.3671461
{txt}90%    {res}-.4682885      -.3671461       {txt}Variance      {res} .0304889
{txt}95%    {res}-.3671461      -.3671461       {txt}Skewness      {res}-.0786881
{txt}99%    {res}-.3671461      -.3671461       {txt}Kurtosis      {res} 1.464495

                        {txt}lnY_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 14.45401       14.45401
{txt} 5%    {res} 14.45401       14.45401
{txt}10%    {res} 14.45401       14.45401       {txt}Obs         {res}      1,814
{txt}25%    {res} 14.45401       14.45401       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 16.68843                      {txt}Mean          {res} 16.40816
                        {txt}Largest       Std. dev.     {res} 1.417548
{txt}75%    {res} 18.24208       18.24208
{txt}90%    {res} 18.24208       18.24208       {txt}Variance      {res} 2.009442
{txt}95%    {res} 18.24208       18.24208       {txt}Skewness      {res}-.1239669
{txt}99%    {res} 18.24208       18.24208       {txt}Kurtosis      {res} 1.766602

                        {txt}lnY_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.15626       16.15626
{txt} 5%    {res} 16.15626       16.15626
{txt}10%    {res} 16.15626       16.15626       {txt}Obs         {res}        816
{txt}25%    {res} 16.15626       16.15626       {txt}Sum of wgt. {res} 1,747.1538

{txt}50%    {res} 16.68843                      {txt}Mean          {res} 16.49532
                        {txt}Largest       Std. dev.     {res} .2560415
{txt}75%    {res} 16.68843       16.68843
{txt}90%    {res} 16.68843       16.68843       {txt}Variance      {res} .0655572
{txt}95%    {res} 16.68843       16.68843       {txt}Skewness      {res}-.5703649
{txt}99%    {res} 16.68843       16.68843       {txt}Kurtosis      {res} 1.325316

                      {txt}ln_rem_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,814
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 15.52071                      {txt}Mean          {res} 11.91026
                        {txt}Largest       Std. dev.     {res} 7.439773
{txt}75%    {res} 18.24576       18.24576
{txt}90%    {res} 18.24576       18.24576       {txt}Variance      {res} 55.35022
{txt}95%    {res} 18.24576       18.24576       {txt}Skewness      {res}-.9155202
{txt}99%    {res} 18.24576       18.24576       {txt}Kurtosis      {res} 1.974107

                      {txt}ln_rem_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.00238       15.00238
{txt} 5%    {res} 15.00238       15.00238
{txt}10%    {res} 15.00238       15.00238       {txt}Obs         {res}        816
{txt}25%    {res} 15.00238       15.00238       {txt}Sum of wgt. {res} 1,747.1538

{txt}50%    {res} 15.52071                      {txt}Mean          {res} 15.33262
                        {txt}Largest       Std. dev.     {res} .2493815
{txt}75%    {res} 15.52071       15.52071
{txt}90%    {res} 15.52071       15.52071       {txt}Variance      {res} .0621911
{txt}95%    {res} 15.52071       15.52071       {txt}Skewness      {res}-.5703649
{txt}99%    {res} 15.52071       15.52071       {txt}Kurtosis      {res} 1.325316

                     {txt}ln_insum_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.34483       16.34483
{txt} 5%    {res} 16.34483       16.34483
{txt}10%    {res} 16.34483       16.34483       {txt}Obs         {res}      1,814
{txt}25%    {res} 16.34483       16.34483       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 19.24919                      {txt}Mean          {res} 18.59604
                        {txt}Largest       Std. dev.     {res} 1.488386
{txt}75%    {res} 20.19422       20.19422
{txt}90%    {res} 20.19422       20.19422       {txt}Variance      {res} 2.215292
{txt}95%    {res} 20.19422       20.19422       {txt}Skewness      {res}-.5885356
{txt}99%    {res} 20.19422       20.19422       {txt}Kurtosis      {res} 1.817579

                     {txt}ln_insum_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.59588       18.59588
{txt} 5%    {res} 18.59588       18.59588
{txt}10%    {res} 18.59588       18.59588       {txt}Obs         {res}        816
{txt}25%    {res} 18.59588       18.59588       {txt}Sum of wgt. {res} 1,747.1538

{txt}50%    {res} 19.24919                      {txt}Mean          {res} 19.01212
                        {txt}Largest       Std. dev.     {res} .3143265
{txt}75%    {res} 19.24919       19.24919
{txt}90%    {res} 19.24919       19.24919       {txt}Variance      {res} .0988011
{txt}95%    {res} 19.24919       19.24919       {txt}Skewness      {res}-.5703649
{txt}99%    {res} 19.24919       19.24919       {txt}Kurtosis      {res} 1.325316

                       {txt}ln_af_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.47016       12.47016
{txt} 5%    {res} 12.47016       12.47016
{txt}10%    {res} 12.47016       12.47016       {txt}Obs         {res}      1,814
{txt}25%    {res} 15.00733       12.47016       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 15.27985                      {txt}Mean          {res} 15.08507
                        {txt}Largest       Std. dev.     {res} 1.365263
{txt}75%    {res} 16.62922       16.62922
{txt}90%    {res} 16.62922       16.62922       {txt}Variance      {res} 1.863942
{txt}95%    {res} 16.62922       16.62922       {txt}Skewness      {res}-.8297938
{txt}99%    {res} 16.62922       16.62922       {txt}Kurtosis      {res} 2.805078

                       {txt}ln_af_real_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.00733       15.00733
{txt} 5%    {res} 15.00733       15.00733
{txt}10%    {res} 15.00733       15.00733       {txt}Obs         {res}        998
{txt}25%    {res} 15.00733       15.00733       {txt}Sum of wgt. {res} 2,610.1538

{txt}50%    {res} 15.27985                      {txt}Mean          {res} 15.16363
                        {txt}Largest       Std. dev.     {res} .1348414
{txt}75%    {res} 15.27985       15.27985
{txt}90%    {res} 15.27985       15.27985       {txt}Variance      {res} .0181822
{txt}95%    {res} 15.27985       15.27985       {txt}Skewness      {res}-.2973505
{txt}99%    {res} 15.27985       15.27985       {txt}Kurtosis      {res} 1.088417

                          {txt}lnY_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.51996       16.51996
{txt} 5%    {res} 16.51996       16.51996
{txt}10%    {res} 16.51996       16.51996       {txt}Obs         {res}      1,814
{txt}25%    {res} 16.51996       16.51996       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 18.82629                      {txt}Mean          {res} 18.42082
                        {txt}Largest       Std. dev.     {res} 1.338649
{txt}75%    {res} 20.06364       20.06364
{txt}90%    {res} 20.06364       20.06364       {txt}Variance      {res} 1.791982
{txt}95%    {res} 20.06364       20.06364       {txt}Skewness      {res} -.272673
{txt}99%    {res} 20.06364       20.06364       {txt}Kurtosis      {res} 1.746756

                          {txt}lnY_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.18875       18.18875
{txt} 5%    {res} 18.18875       18.18875
{txt}10%    {res} 18.18875       18.18875       {txt}Obs         {res}        816
{txt}25%    {res} 18.18875       18.18875       {txt}Sum of wgt. {res} 1,747.1538

{txt}50%    {res} 18.82629                      {txt}Mean          {res} 18.59494
                        {txt}Largest       Std. dev.     {res} .3067383
{txt}75%    {res} 18.82629       18.82629
{txt}90%    {res} 18.82629       18.82629       {txt}Variance      {res} .0940884
{txt}95%    {res} 18.82629       18.82629       {txt}Skewness      {res}-.5703649
{txt}99%    {res} 18.82629       18.82629       {txt}Kurtosis      {res} 1.325316

                         {txt}ln_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      4,308
{txt}25%    {res} 12.21006              0       {txt}Sum of wgt. {res}      4,308

{txt}50%    {res} 15.64124                      {txt}Mean          {res} 12.11061
                        {txt}Largest       Std. dev.     {res} 6.828504
{txt}75%    {res} 16.02835       18.34094
{txt}90%    {res} 18.34094       18.34094       {txt}Variance      {res} 46.62846
{txt}95%    {res} 18.34094       18.34094       {txt}Skewness      {res}-1.102544
{txt}99%    {res} 18.34094       18.34094       {txt}Kurtosis      {res} 2.434193

                         {txt}ln_rem_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.21006       12.21006
{txt} 5%    {res} 12.21006       12.21006
{txt}10%    {res} 12.21006       12.21006       {txt}Obs         {res}      2,811
{txt}25%    {res} 12.21006       12.21006       {txt}Sum of wgt. {res} 12,349.154

{txt}50%    {res} 15.12087                      {txt}Mean          {res} 14.21536
                        {txt}Largest       Std. dev.     {res} 1.893278
{txt}75%    {res} 15.98808       16.77322
{txt}90%    {res} 16.02835       16.77322       {txt}Variance      {res} 3.584503
{txt}95%    {res} 16.77322       16.77322       {txt}Skewness      {res} -.064047
{txt}99%    {res} 16.77322       16.77322       {txt}Kurtosis      {res} 1.106702

                        {txt}ln_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.04787       12.04787
{txt} 5%    {res} 12.04787       12.04787
{txt}10%    {res} 12.04787       12.04787       {txt}Obs         {res}      4,308
{txt}25%    {res} 16.76536       12.04787       {txt}Sum of wgt. {res}      4,308

{txt}50%    {res} 18.02218                      {txt}Mean          {res} 17.21677
                        {txt}Largest       Std. dev.     {res} 2.191848
{txt}75%    {res} 18.69346       19.62043
{txt}90%    {res} 19.62043       19.62043       {txt}Variance      {res} 4.804195
{txt}95%    {res} 19.62043       19.62043       {txt}Skewness      {res}-1.284767
{txt}99%    {res} 19.62043       19.62043       {txt}Kurtosis      {res} 3.850037

                        {txt}ln_insum_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.83467       15.83467
{txt} 5%    {res} 15.83467       15.83467
{txt}10%    {res} 15.83467       15.83467       {txt}Obs         {res}      3,310
{txt}25%    {res} 16.76536       15.83467       {txt}Sum of wgt. {res} 8,606.6538

{txt}50%    {res} 16.76536                      {txt}Mean          {res} 17.35894
                        {txt}Largest       Std. dev.     {res} 1.077125
{txt}75%    {res} 18.27745       18.97812
{txt}90%    {res} 18.97812       18.97812       {txt}Variance      {res} 1.160199
{txt}95%    {res} 18.97812       18.97812       {txt}Skewness      {res} .1259673
{txt}99%    {res} 18.97812       18.97812       {txt}Kurtosis      {res} 1.624984

                         {txt}ln_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}  10.4091        10.4091
{txt} 5%    {res}  10.4091        10.4091
{txt}10%    {res}  10.4091        10.4091       {txt}Obs         {res}      4,308
{txt}25%    {res} 12.92275        10.4091       {txt}Sum of wgt. {res}      4,308

{txt}50%    {res} 15.45606                      {txt}Mean          {res} 14.74591
                        {txt}Largest       Std. dev.     {res} 2.123759
{txt}75%    {res} 16.11623        17.0818
{txt}90%    {res}  17.0818        17.0818       {txt}Variance      {res} 4.510353
{txt}95%    {res}  17.0818        17.0818       {txt}Skewness      {res} -.805844
{txt}99%    {res}  17.0818        17.0818       {txt}Kurtosis      {res} 2.493823

                         {txt}ln_af_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.68132       12.68132
{txt} 5%    {res} 12.68132       12.68132
{txt}10%    {res} 12.68132       12.68132       {txt}Obs         {res}      3,310
{txt}25%    {res} 12.68132       12.68132       {txt}Sum of wgt. {res} 8,606.6538

{txt}50%    {res} 15.45606                      {txt}Mean          {res} 14.54961
                        {txt}Largest       Std. dev.     {res} 1.639265
{txt}75%    {res} 15.73243       16.96766
{txt}90%    {res} 16.96766       16.96766       {txt}Variance      {res} 2.687188
{txt}95%    {res} 16.96766       16.96766       {txt}Skewness      {res} -.039216
{txt}99%    {res} 16.96766       16.96766       {txt}Kurtosis      {res} 1.351916

                      {txt}lnY_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 14.45401       14.45401
{txt} 5%    {res} 14.45401       14.45401
{txt}10%    {res} 14.45401       14.45401       {txt}Obs         {res}      1,814
{txt}25%    {res} 14.45401       14.45401       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 16.68843                      {txt}Mean          {res} 16.40816
                        {txt}Largest       Std. dev.     {res} 1.417548
{txt}75%    {res} 18.24208       18.24208
{txt}90%    {res} 18.24208       18.24208       {txt}Variance      {res} 2.009442
{txt}95%    {res} 18.24208       18.24208       {txt}Skewness      {res}-.1239669
{txt}99%    {res} 18.24208       18.24208       {txt}Kurtosis      {res} 1.766602

                      {txt}lnY_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.15626       16.15626
{txt} 5%    {res} 16.15626       16.15626
{txt}10%    {res} 16.15626       16.15626       {txt}Obs         {res}        816
{txt}25%    {res} 16.15626       16.15626       {txt}Sum of wgt. {res} 1,747.1538

{txt}50%    {res} 16.68843                      {txt}Mean          {res} 16.49532
                        {txt}Largest       Std. dev.     {res} .2560415
{txt}75%    {res} 16.68843       16.68843
{txt}90%    {res} 16.68843       16.68843       {txt}Variance      {res} .0655572
{txt}95%    {res} 16.68843       16.68843       {txt}Skewness      {res}-.5703649
{txt}99%    {res} 16.68843       16.68843       {txt}Kurtosis      {res} 1.325316

                     {txt}ln_rem_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,814
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 15.52071                      {txt}Mean          {res} 11.91026
                        {txt}Largest       Std. dev.     {res} 7.439773
{txt}75%    {res} 18.24576       18.24576
{txt}90%    {res} 18.24576       18.24576       {txt}Variance      {res} 55.35022
{txt}95%    {res} 18.24576       18.24576       {txt}Skewness      {res}-.9155202
{txt}99%    {res} 18.24576       18.24576       {txt}Kurtosis      {res} 1.974107

                     {txt}ln_rem_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.00238       15.00238
{txt} 5%    {res} 15.00238       15.00238
{txt}10%    {res} 15.00238       15.00238       {txt}Obs         {res}        816
{txt}25%    {res} 15.00238       15.00238       {txt}Sum of wgt. {res} 1,747.1538

{txt}50%    {res} 15.52071                      {txt}Mean          {res} 15.33262
                        {txt}Largest       Std. dev.     {res} .2493815
{txt}75%    {res} 15.52071       15.52071
{txt}90%    {res} 15.52071       15.52071       {txt}Variance      {res} .0621911
{txt}95%    {res} 15.52071       15.52071       {txt}Skewness      {res}-.5703649
{txt}99%    {res} 15.52071       15.52071       {txt}Kurtosis      {res} 1.325316

                    {txt}ln_insum_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.34483       16.34483
{txt} 5%    {res} 16.34483       16.34483
{txt}10%    {res} 16.34483       16.34483       {txt}Obs         {res}      1,814
{txt}25%    {res} 16.34483       16.34483       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 19.24919                      {txt}Mean          {res} 18.59604
                        {txt}Largest       Std. dev.     {res} 1.488386
{txt}75%    {res} 20.19422       20.19422
{txt}90%    {res} 20.19422       20.19422       {txt}Variance      {res} 2.215292
{txt}95%    {res} 20.19422       20.19422       {txt}Skewness      {res}-.5885356
{txt}99%    {res} 20.19422       20.19422       {txt}Kurtosis      {res} 1.817579

                    {txt}ln_insum_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 18.59588       18.59588
{txt} 5%    {res} 18.59588       18.59588
{txt}10%    {res} 18.59588       18.59588       {txt}Obs         {res}        816
{txt}25%    {res} 18.59588       18.59588       {txt}Sum of wgt. {res} 1,747.1538

{txt}50%    {res} 19.24919                      {txt}Mean          {res} 19.01212
                        {txt}Largest       Std. dev.     {res} .3143265
{txt}75%    {res} 19.24919       19.24919
{txt}90%    {res} 19.24919       19.24919       {txt}Variance      {res} .0988011
{txt}95%    {res} 19.24919       19.24919       {txt}Skewness      {res}-.5703649
{txt}99%    {res} 19.24919       19.24919       {txt}Kurtosis      {res} 1.325316

                     {txt}ln_af_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 12.47016       12.47016
{txt} 5%    {res} 12.47016       12.47016
{txt}10%    {res} 12.47016       12.47016       {txt}Obs         {res}      1,814
{txt}25%    {res} 15.00733       12.47016       {txt}Sum of wgt. {res}      1,814

{txt}50%    {res} 15.27985                      {txt}Mean          {res} 15.08507
                        {txt}Largest       Std. dev.     {res} 1.365263
{txt}75%    {res} 16.62922       16.62922
{txt}90%    {res} 16.62922       16.62922       {txt}Variance      {res} 1.863942
{txt}95%    {res} 16.62922       16.62922       {txt}Skewness      {res}-.8297938
{txt}99%    {res} 16.62922       16.62922       {txt}Kurtosis      {res} 2.805078

                     {txt}ln_af_real_WA_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} 15.00733       15.00733
{txt} 5%    {res} 15.00733       15.00733
{txt}10%    {res} 15.00733       15.00733       {txt}Obs         {res}        998
{txt}25%    {res} 15.00733       15.00733       {txt}Sum of wgt. {res} 2,610.1538

{txt}50%    {res} 15.27985                      {txt}Mean          {res} 15.16363
                        {txt}Largest       Std. dev.     {res} .1348414
{txt}75%    {res} 15.27985       15.27985
{txt}90%    {res} 15.27985       15.27985       {txt}Variance      {res} .0181822
{txt}95%    {res} 15.27985       15.27985       {txt}Skewness      {res}-.2973505
{txt}99%    {res} 15.27985       15.27985       {txt}Kurtosis      {res} 1.088417
{txt}
{com}. 
. 
. gen ltfp_sec_2017=.
{txt}(10,500 missing values generated)

{com}. levelsof sector_2017, local(sectores)
{res}{txt}`"COM"' `"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_2017=(lnY_2017-lnY_2017_bar`x')-(0.5)*(sh_rem_2017+sh_rem_2017_bar`x')*(ln_rem_2017-ln_rem_2017_bar`x')-(0.5)*(sh_insum_2017+sh_insum_2017_bar`x')*(ln_insum_2017-ln_insum_2017_bar`x')-(0.5)*(sh_af_2017+sh_af_2017_bar`x')*(ln_af_2017-ln_af_2017_bar`x') if sector_2017=="`x'"
{txt}  3{com}. {c )-}
{txt}(1,180 real changes made)
(2,006 real changes made)
(1,814 real changes made)

{com}. 
. 
. gen ltfp_sec_real_2017=.
{txt}(10,500 missing values generated)

{com}. levelsof sector_2017 , local(sectores)
{res}{txt}`"COM"' `"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_real_2017=(lnY_real_2017-lnY_real_2017_bar`x')-(0.5)*(sh_rem_2017+sh_rem_2017_bar`x')*(ln_rem_real_2017-ln_rem_real_2017_bar`x')-(0.5)*(sh_insum_2017+sh_insum_2017_bar`x')*(ln_insum_real_2017-ln_insum_real_2017_bar`x')-(0.5)*(sh_af_2017 +sh_af_2017_bar`x')*(ln_af_real_2017-ln_af_real_2017_bar`x') if sector_2017 =="`x'"
{txt}  3{com}. {c )-}
{txt}(1,180 real changes made)
(2,006 real changes made)
(1,814 real changes made)

{com}. 
. 
. gen ltfp_sec_real_WA_2017=.
{txt}(10,500 missing values generated)

{com}. levelsof sector_2017 , local(sectores)
{res}{txt}`"COM"' `"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_real_WA_2017=(lnY_real_WA_2017-lnY_real_WA_2017_bar`x')-(0.5)*(sh_rem_2017+sh_rem_2017_bar`x')*(ln_rem_real_WA_2017-ln_rem_real_WA_2017_bar`x')-(0.5)*(sh_insum_2017+sh_insum_2017_bar`x')*(ln_insum_real_WA_2017-ln_insum_real_WA_2017_bar`x')-(0.5)*(sh_af_2017 +sh_af_2017_bar`x')*(ln_af_real_WA_2017-ln_af_real_WA_2017_bar`x') if sector_2017 =="`x'"
{txt}  3{com}. {c )-}
{txt}(1,180 real changes made)
(2,006 real changes made)
(1,814 real changes made)

{com}. 
. 
. *************************************************************************************************************************************************************
. * Aggregated sector variables
. label variable sector_2017 "Aggregated sector (manufacturing , services, and commerce) classification for 2017"
{txt}note: label truncated to 80 characters

{com}. label variable sector2_2017 "Aggregated sector (manufacturing and services) for 2017"
{txt}
{com}. 
. * Grouped sector and state
. label variable sec_ent_2017 "Grouped sector and state for 2017"
{txt}
{com}. 
. * Months of operation
. label variable months_2016 "Months firm operated in 2016"
{txt}
{com}. label variable months_2017 "Months firm operated in 2017"
{txt}
{com}. 
. * Monthly salaries (MX pesos)
. label variable rem_dir_month_2016 "Monthly salary of white-collar employees in 2016 (MX pesos)"
{txt}
{com}. label variable rem_dir_month_2017 "Monthly salary of of white-collar employees in 2017 (MX pesos)"
{txt}
{com}. label variable rem_op_supp_2016 "Monthly salary of blue-collar employees in 2016 (MX pesos)"
{txt}
{com}. label variable rem_op_supp_2017 "Monthly salary of of blue-collar employees in 2017 (MX pesos)"
{txt}
{com}. 
. * Monthly remunerations (MX pesos)
. label variable rem_tot_month_2016 "Total monthly remuneration in 2016 (MX pesos)"
{txt}
{com}. label variable rem_tot_month_2017 "Total monthly remuneration in 2017 (MX pesos)"
{txt}
{com}. 
. * Total annual remunerations (MX pesos)
. label variable rem_tot_anual_2016 "Total annual remuneration in 2016 (MX pesos)"
{txt}
{com}. label variable rem_tot_anual_2017 "Total annual remuneration in 2017 (MX pesos)"
{txt}
{com}. 
. * Total expenses in training (MX pesos)
. label variable g_train_2016 "Total training expenses in 2016 (MX pesos)"
{txt}
{com}. label variable g_train_2017 "Total training expenses in 2017 (MX pesos)"
{txt}
{com}. 
. * Expenses in merchandise for resales (MX pesos)
. label variable g_resales_2017 "Expenses in merchandise for resales in 2017 (MX pesos)"
{txt}
{com}. 
. * Energy expenses (MX pesos)
. label variable g_energy_2017 "Energy expenses in 2017 (MX pesos)"
{txt}
{com}. 
. * Materials expenses (MX pesos)
. label variable g_materials_2017 "Materials expenses in 2017 (MX pesos)"
{txt}
{com}. 
. * Total inputs (MX pesos)
. label variable g_insum_2017 "Total input expenses in 2017 (MX pesos)"
{txt}
{com}. label variable g_insum_cut_2017 "Total inputs excluding specific adjustments in 2017 (MX pesos)"
{txt}
{com}. label variable g_insum_tot_2017 "Total inputs plus training expenses in 2017 (MX pesos)"
{txt}
{com}. 
. * Total revenues (MX pesos)
. label variable ingre_tot_2016 "Total revenue in 2016 (MX pesos)"
{txt}
{com}. label variable ingre_tot_2017 "Total revenue in 2017 (MX pesos)"
{txt}
{com}. 
. * Fixed assets (MX pesos)
. label variable activ_fijos_2017 "Fixed assets value in 2017 (MX pesos)"
{txt}
{com}. 
. * Firm size
. label variable size_2017 "Categorical size of the firm in 2017"
{txt}
{com}. label variable nworkers_2016 "Number of workers in 2016"
{txt}
{com}. label variable nworkers_2017 "Number of workers in 2017"
{txt}
{com}. 
. * Inventories (MX pesos)
. label variable invtot_fin_2017 "Final total inventory in 2017 (MX pesos)"
{txt}
{com}. label variable invmprev_fin_2017 "Final inventory for merchandise for resale in 2017 (MX pesos)"
{txt}
{com}. label variable invtot_ini_2017 "Initial total inventory in 2017 (MX pesos)"
{txt}
{com}. label variable invmprev_ini_2017 "Initial inventory for merchandise for resale in 2017 (MX pesos)"
{txt}
{com}. label variable Var_inven_2017 "Change in inventories (deflated to 2017 prices) (MX pesos)"
{txt}
{com}. 
. * Regions
. label variable region_2017 "Region of firm operation in 2017"
{txt}
{com}. 
. * Age of the firm
. label variable inicio_op_2017 "Year the firm started operations"
{txt}
{com}. label variable edad_emp_2017 "Firm's age in 2017"
{txt}
{com}. 
. 
. *IT use
. label variable dum_comp_2017 "Computer use dummy 2017"
{txt}
{com}. label variable shlabcomp_2017 "Share of labor with computer 2017"
{txt}
{com}. label variable reasnotcomp_2017 "Reasons for not using a computer 2017"
{txt}
{com}. label variable dum_inter_2017 "Internet use dummy 2017"
{txt}
{com}. label variable reasnotinter_2017 "Reasons for not using Internet 2017"
{txt}
{com}. label variable inter_use_2017 "Internet use type 2017"
{txt}
{com}. label variable ITcap_worker_2017 "IT capital per worker 2017"
{txt}
{com}. 
. *Certifications
. label variable scor_cert_2017 "Score of certifications 2017"
{txt}
{com}. label variable certif_2017 "Certification obtained (dummy) 2017"
{txt}
{com}. 
. 
. label variable dum_IDT_2017 "Dummy for R&D (2017)"
{txt}
{com}. label variable IDT_real_2016 "Real R&D  (MX Pesos, 2016)"
{txt}
{com}. label variable IDT_1617_2017 "R&D (2016-2017) (MX Pesos, 2017)"
{txt}
{com}. label variable IDT_1617_sal_2017 "R&D/Revenues (2016-2017) (MX Pesos, 2017)"
{txt}
{com}. label variable innov_real_2016 "Real Innovation Expenditure (MX Pesos, 2016)"
{txt}
{com}. label variable innov_real_2017 "Real Innovation Expenditure (MX Pesos, 2017)"
{txt}
{com}. label variable innov_16_17_2017 "Innovation Expenditure (2016-2017) (MX Pesos, 2017)"
{txt}
{com}. label variable innov_16_17_sal_2017 "Innovation Expenditure/Revenues (2016-2017) (MX Pesos, 2017)"
{txt}
{com}. label variable dum_ginnov_2017 "Dummy for Expenditure in Innovation (2017)"
{txt}
{com}. label variable score_innov_2017 "Innovation Score (2017)"
{txt}
{com}. label variable dumm_innov_prod_2017 "Dummy for Product Innovation (2017)"
{txt}
{com}. label variable dumm_innov_proc_2017 "Dummy for Process Innovation (2017)"
{txt}
{com}. label variable dumm_innov_org_2017 "Dummy for Organizational Innovation (2017)"
{txt}
{com}. label variable dumm_innov_merc_2017 "Dummy for Marketing Innovation (2017)"
{txt}
{com}. label variable dumm_innov_any_2017 "Dummy for Any Innovation (2017)"
{txt}
{com}. label variable score_innovcuali_2017 "Qualitative Innovation Score (2017)"
{txt}
{com}. label variable dum_pat "Dummy for Patents (2017)"
{txt}
{com}. label variable innov_16_17_work_2017 "Innovation (2016-2017) per Worker (MX Pesos, 2017)"
{txt}
{com}. label variable IDT_16_17_work_2017 "R&D per worker (2016-2017) (MX Pesos, 2017)"
{txt}
{com}. label variable IED_dum_2017 "Dummy for Foreign Direct Investment (2017)"
{txt}
{com}. label variable sh_IED_2017 "Share of Foreign Direct Investment (2017)"
{txt}
{com}. label variable FDI1_2017 "FDI Dummy"
{txt}
{com}. label variable dum_exp_2017 "Dummy for Exports (2017)"
{txt}
{com}. label variable shexp_2017 "Share of Exports (2017)"
{txt}
{com}. label variable own_direc2_2017 "Ownership (2017)"
{txt}
{com}. label variable sh_sup_2016 "Share of employees with college (2016)"
{txt}
{com}. label variable sh_sup_2017 "Share of employees with college (2017)"
{txt}
{com}. label variable sh_sup_WA_2017 "Share of employees with college (2017, winsorized)"
{txt}
{com}. label variable sh_pre_2016 "Share of employees with High School (2016)"
{txt}
{com}. label variable sh_pre_2017 "Share ofemployees with High School (2017)"
{txt}
{com}. label variable sh_direc_2016 "Share of White collar (2016)"
{txt}
{com}. label variable sh_direc_2017 "Share of White collar  (2017)"
{txt}
{com}. label variable sh_direc_WA_2017 "Share of White collar (2017, winsorized)"
{txt}
{com}. label variable sh_direc_muj_2016 "Share of Women among white collar workers (2016)"
{txt}
{com}. label variable sh_direc_muj_2017 "Share of Women among white collar workers (2017)"
{txt}
{com}. label variable sh_direc_hom_2016 "Share of Men among white collar workers (2016)"
{txt}
{com}. label variable sh_direc_hom_2017 "Share of Men among white collar workers(2017)"
{txt}
{com}. label variable sh_oper_muj_2016 "Share of Women among blue collar workers (2016)"
{txt}
{com}. label variable sh_oper_muj_2017 "Share of Women among blue collar workers(2017)"
{txt}
{com}. label variable quin_sh_direc_2017 "Quintile Share of Management (2017)"
{txt}
{com}. label variable resp_manag "# of responses to management questions (2017)"
{txt}
{com}. label variable score_manag_2017 "Management Score (2017)"
{txt}
{com}. label variable score_man_dat_2017 "Management Data Score (2017)"
{txt}
{com}. label variable score_man_inc_2017 "Management Incentives Score (2017)"
{txt}
{com}. label variable score_manfirst8_2017 "Management score first 8 questions"
{txt}
{com}. label variable score_manlast8_2017 "Management score last questions"
{txt}
{com}. 
. label variable dum_gvc_2017 "Dummy for Global Value Chains"
{txt}
{com}. label variable raz_nogvc_2016 "Reason No GVC (2016)"
{txt}
{com}. label variable raz_nogvc_2017 "Reason No GVC (2017)"
{txt}
{com}. label variable anios_gvc_2017 "Years in Global Value Chains"
{txt}
{com}. label variable eslabones_gvc_2017 "Links in Global Value Chains"
{txt}
{com}. label variable VA_2017 "Value Added (MX Pesos, 2017)"
{txt}
{com}. label variable VA_real_2017 "Real Value Added (MX Pesos, 2017)"
{txt}
{com}. label variable VA_wk_2017 "Value Added per Worker (MX Pesos, 2017)"
{txt}
{com}. label variable VA_real_wk_2017 "Real Value Added per Worker (MX Pesos, 2017)"
{txt}
{com}. label variable VA_real_wk_mil_2017 "Real Value Added per Worker (Thousand MX Pesos, 2017)"
{txt}
{com}. label variable oper_profit_2017 "Operating Profit (MX Pesos, 2017)"
{txt}
{com}. label variable oper_profit_real_2017 "Real Operating Profit (MX Pesos, 2017)"
{txt}
{com}. label variable profitab_2017 "Profitability (2017)"
{txt}
{com}. label variable profitabrea_2017 "Profitability Ratio (2017)"
{txt}
{com}. label variable sh_rem_2017 "Share of Revenue (2017)"
{txt}
{com}. label variable sh_insum_2017 "Share of Input Costs (2017)"
{txt}
{com}. label variable sh_af_2017 "Share of Fixed Assets (2017)"
{txt}
{com}. 
. *Labels winsorized variables
. label variable activ_fijos_real_2017 "Real Fixed Assets (2017) in MX pesos"
{txt}
{com}. label variable ingre_tot_real_2017 "Real Total Income (2017) in MX pesos"
{txt}
{com}. label variable rem_tot_anual_real_2017 "Real Annual Remunerations (2017) in MX pesos"
{txt}
{com}. label variable VA_real_wk_mil_WA_2017 "Value added per worker (mil) 2017 (Winsorized)"
{txt}
{com}. label variable VA_real_WA_2017 "Real value added 2017 (Winsorized)"
{txt}
{com}. label variable rem_tot_anual_WA_2017 "Total annual remuneration 2017 (Winsorized)"
{txt}
{com}. label variable rem_tot_anual_real_WA_2017 "Real total annual remuneration 2017 (Winsorized)"
{txt}
{com}. label variable g_train_WA_2017 "Training expenses 2017 (Winsorized)"
{txt}
{com}. label variable g_insum_WA_2017 "Inputs expenses 2017 (Winsorized)"
{txt}
{com}. label variable g_energy_WA_2017 "Energy expenses 2017 (Winsorized)"
{txt}
{com}. label variable g_resales_WA_2017 "Resales expenses 2017 (Winsorized)"
{txt}
{com}. label variable activ_fijos_real_WA_2017 "Real fixed assets 2017 (Winsorized)"
{txt}
{com}. label variable ingre_tot_WA_2017 "Total income 2017 (Winsorized)"
{txt}
{com}. label variable ingre_tot_real_WA_2017 "Real total income 2017 (Winsorized)"
{txt}
{com}. label variable oper_profit_real_WA_2017 "Real operational profit 2017 (Winsorized)"
{txt}
{com}. label variable g_insum_cut_WA_2017 "Cut input expenses 2017 (Winsorized)"
{txt}
{com}. label variable activ_fijos_WA_2017 "Fixed assets 2017 (Winsorized)"
{txt}
{com}. label variable g_materials_WA_2017 "Materials expenses 2017 (Winsorized)"
{txt}
{com}. label variable g_insum_tot_WA_2017 "Total input expenses 2017 (Winsorized)"
{txt}
{com}. label variable Var_inven_WA_2017 "Inventory variation 2017 (Winsorized)"
{txt}
{com}. label variable nworkers_WA_2017 "Number of workers 2017 (Winsorized)"
{txt}
{com}. label variable lVA_real_wk_mil_2017 "Log of Real Value Added per Worker"
{txt}
{com}. label variable lVA_real_wk_mil_WA_2017 "Log of Real Value Added per Worker (Winsorized)"
{txt}
{com}. label variable ln_rem_real_2017 "Log of Real Remunerations"
{txt}
{com}. label variable ln_rem_real_WA_2017 "Log of Real Remunerations (Winsorized)"
{txt}
{com}. label variable ln_rem_2017 "Log of Remunerations"
{txt}
{com}. label variable ln_insum_2017 "Log of Input Costs"
{txt}
{com}. label variable ln_insum_real_2017 "Log of Real Input Costs"
{txt}
{com}. label variable ln_insum_real_WA_2017 "Log of Real Input Costs (Winsorized)"
{txt}
{com}. label variable ln_af_real_2017 "Log of Real Fixed Assets"
{txt}
{com}. label variable ln_af_2017 "Log of Fixed Assets"
{txt}
{com}. label variable ln_af_WA_2017 "Log of Fixed Assets (Winsorized)"
{txt}
{com}. label variable ln_af_real_WA_2017 "Log of Real Fixed Assets (Winsorized)"
{txt}
{com}. label variable lnY_real_WA_2017 "Log of Real Output (Winsorized)"
{txt}
{com}. label variable lnY_real_2017 "Log of Real Output"
{txt}
{com}. label variable lnY_WA_2017 "Log of Output (Winsorized)"
{txt}
{com}. label variable lnY_2017 "Log of Output"
{txt}
{com}. label variable ltfp_sec_2017 "Log of Total Factor Productivity (Sector)"
{txt}
{com}. label variable ltfp_sec_real_2017 "Log of Real Total Factor Productivity (Sector)"
{txt}
{com}. label variable ltfp_sec_real_WA_2017 "Log of Real Total Factor Productivity (Sector) (Winsorized)"
{txt}
{com}. label variable E03 "State"
{txt}
{com}. label variable clase_ce "NAICS 6 digits code according to CENSUS" 
{txt}
{com}. label variable clase "NAICS 6 digits code according to ENAPROCE"  
{txt}
{com}. label variable clase_clee "NAICS 6 digits code according to unique ID INEGI" 
{txt}
{com}. label var dum_man2 "Dummy Sector =1 if Manufacturing, O if Services"
{txt}
{com}. 
. ren CLAVE clave
{res}{txt}
{com}. foreach var of varlist  E03 FAC_EXPA clave clase_ce clase clase_clee resp_manag dum_pat {c -(}
{txt}  2{com}.         ren `var' `var'_2017
{txt}  3{com}. {c )-}
{res}{txt}
{com}. gen clee_18=clee
{txt}
{com}. gen sample_2018=1
{txt}
{com}. *merge 1:1 clee_18 using  "$data\Registros liberados 2018.dta"  /*Only if some ID is missing*/
. *drop _merge
. tostring CLAVE15, gen(clave_2014)
{txt}clave_2014 generated as {res:str2}

{com}. gen clave_2015=clave_2014
{txt}
{com}. replace clave_2014="1000"*clave_2017 if clave_2014==""
{txt}(0 real changes made)

{com}. ren PANEL panel
{res}{txt}
{com}. ren innov_16_17_2017 innov_2yr_2017 
{res}{txt}
{com}. ren innov_16_17_work_2017 innov_2yr_work_2017 
{res}{txt}
{com}. ren innov_16_17_work_WA_2017 innov_2yr_work_WA_2017 
{res}{txt}
{com}. ren innov_16_17_sal_2017 innov_2yr_sal_2017
{res}{txt}
{com}. ren IDT_1617_2017  IDT_2yr_2017
{res}{txt}
{com}. ren IDT_16_17_work_2017 IDT_2yr_work_2017
{res}{txt}
{com}. ren IDT_16_17_work_WA_2017 IDT_2yr_work_WA_2017
{res}{txt}
{com}. ren IDT_1617_sal_2017 IDT_2yr_sal_2017
{res}{txt}
{com}. 
. keep clee E03  clave_2017 panel inicio_gvc_2017 eslab_1_2017 eslab_2_2017 eslab_3_2017 beneficio_gvc_2017 FAC_EXPA clave_2015 clase_ce clase_2017 clase_clee resp_manag dec_manag_2017 quin_manag_2017 sector_2017 sector2_2017 size_2017 certif_2017 Var_inven_2017 dum_comp_2017 g_insum_cut_WA_2017 dumm_innov_any_2017 dum_ginnov_2017 dum_exp_2017 IED_dum_2017 dum_gvc_2017 dumm_innov_merc_2017 dumm_innov_org_2017 dum_pat_2017 dumm_innov_proc_2017 dumm_innov_prod_2017 dum_IDT_2017 g_energy_WA_2017 g_energy_2017 g_resales_2017 FDI1_2017 invmprev_fin_2017 invtot_fin_2017 edad_emp_2017 activ_fijos_WA_2017 activ_fijos_2017 sec_ent_2017 IDT_2yr_work_WA_2017 ITcap_worker_2017 invmprev_ini_2017 invtot_ini_2017 innov_2yr_work_2017 innov_2yr_2017 innov_2yr_sal_2017 score_innov_2017 g_insum_WA_2017 dum_inter_2017 inter_use_2017 Var_inven_WA_2017 eslabones_gvc_2017 ln_af_2017 ln_af_WA_2017 ln_insum_2017 lnY_2017 lnY_WA_2017 ln_af_real_2017 ln_af_real_WA_2017 ln_insum_real_2017 ln_insum_real_WA_2017 lnY_real_2017 lnY_real_WA_2017 ln_rem_real_2017 ln_rem_real_WA_2017 ltfp_sec_real_2017 ltfp_sec_real_WA_2017 lVA_real_wk_mil_2017 lVA_real_wk_mil_WA_2017 ln_rem_2017 ltfp_sec_2017 score_man_dat_2017 score_man_inc_2017 score_manag_2017  score_manfirst8_2017 score_manlast8_2017 g_materials_WA_2017 g_materials_2017 rem_op_supp_2016 rem_op_supp_2017 rem_dir_month_2017 rem_dir_month_2016 months_2016 months_2017 nworkers_WA_2017 nworkers_2016 nworkers_2017 oper_profit_2017 own_direc2_2017 profitab_2017 profitabrea_2017 score_innovcuali_2017 quin_sh_direc_2017 IDT_2yr_2017 IDT_2yr_work_2017 IDT_2yr_sal_2017 rem_tot_anual_real_2017 activ_fijos_real_2017 innov_real_2016 innov_real_2017 oper_profit_real_2017 IDT_real_2016 ingre_tot_real_2017 VA_real_2017 VA_real_wk_2017 VA_real_wk_mil_2017 activ_fijos_real_WA_2017 oper_profit_real_WA_2017 rem_tot_anual_real_WA_2017 ingre_tot_real_WA_2017 VA_real_WA_2017 raz_nogvc_2016 raz_nogvc_2017 reasnotinter_2017 reasnotcomp_2017 region_2017 g_resales_WA_2017 scor_cert_2017 shexp_2017 sh_af_2017 sh_IED_2017 sh_insum_2017 sh_direc_hom_2016 sh_direc_hom_2017 sh_rem_2017 sh_direc_2017 sh_direc_2016 sh_oper_muj_2016 sh_oper_muj_2017 sh_direc_muj_2016 sh_direc_muj_2017 sh_pre_2016 sh_sup_2016 sh_sup_2017 shlabcomp_2017 sh_pre_2017 rem_tot_anual_WA_2017 rem_tot_anual_2016 rem_tot_anual_2017 ingre_tot_WA_2017 g_insum_tot_WA_2017 g_insum_2017 g_insum_cut_2017 g_insum_tot_2017 rem_tot_month_2016 rem_tot_month_2017 ingre_tot_2016 ingre_tot_2017 g_train_2016 g_train_2017 g_train_WA_2017 VA_2017 VA_wk_2017 VA_real_wk_mil_WA_2017 inicio_op_2017 anios_gvc_2017 innov_2yr_work_WA_2017 sh_sup_WA_2017 dum_man2 sh_direc_WA_2017  sample_2018 clave_2014
{txt}
{com}. drop clave_2015
{txt}
{com}. save "$working/Data_for_estimates_ENAPROCE_2018.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Data_for_estimates_ENAPROCE_2018.dta{rm}
saved
{p_end}

{com}. 
{txt}end of do-file

{com}. do "$programs/Municipality variables.do"
{txt}
{com}. /********************************************************************************************
> * Project: Management and Misallocation in Mexico
> * Purpose: Generate municipality-level variables for analysis
> * Date: 2024-12-01
> ********************************************************************************************/
. 
. *--------------------------------------------------------------------------------------------
. * 1. Load and Merge Datasets
. *--------------------------------------------------------------------------------------------
. * Load municipality-level variables and merge with ENAPROCE municipalities
. * Note: Municipality inclusion is identified, but frequency data is confidential 
. * and only accessible within the Microdata Lab premises.
. 
. use "$working/Data_for_estimates_ENAPROCE_2015.dta", clear
{txt}
{com}. merge 1:1 clee using "$working/Data_for_estimates_ENAPROCE_2018.dta"
{res}{txt}(label {bf:{txt}owner2} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             460
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}             460{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          10,040{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep id_mun
{txt}
{com}. duplicates drop

{p 0 4}{txt}Duplicates in terms of {txt} all variables{p_end}

(10,467 observations deleted)

{com}. save "$working/Municipalities ENAPROCE.dta", replace 
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Municipalities ENAPROCE.dta{rm}
saved
{p_end}

{com}. 
. use "$data\Municipality_data_all.dta", clear
{txt}
{com}. merge 1:1 id_mun using "$working\Municipalities ENAPROCE.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           2,436
{txt}{col 9}from master{col 30}{res}           2,434{txt}  (_merge==1)
{col 9}from using{col 30}{res}               2{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              31{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3
{txt}(2,436 observations deleted)

{com}. 
. 
. *Drive time below the median*
. sum durat_border_hrs5, d

                      {txt}durat_border_hrs5
{hline 61}
      Percentiles      Smallest
 1%    {res} 2.306944       2.306944
{txt} 5%    {res} 4.618611       4.618611
{txt}10%    {res}   8.4725       8.229445       {txt}Obs         {res}         31
{txt}25%    {res} 11.85583         8.4725       {txt}Sum of wgt. {res}         31

{txt}50%    {res} 16.29806                      {txt}Mean          {res} 18.85124
                        {txt}Largest       Std. dev.     {res} 8.664395
{txt}75%    {res} 26.33889       30.13028
{txt}90%    {res} 30.13028       31.84167       {txt}Variance      {res} 75.07174
{txt}95%    {res} 31.92639       31.92639       {txt}Skewness      {res}-.0089836
{txt}99%    {res} 33.22222       33.22222       {txt}Kurtosis      {res} 1.852274
{txt}
{com}. gen BM_durat_border_hrs=(durat_border_hrs5 <= r(p50)) & (durat_border_hrs5 != .)
{txt}
{com}. tab BM_durat_border_hrs

{txt}BM_durat_bo {c |}
   rder_hrs {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         15       48.39       48.39
{txt}          1 {c |}{res}         16       51.61      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         31      100.00
{txt}
{com}. gen AM_durat_border_hrs=BM_durat_border_hrs
{txt}
{com}. recode AM_durat_border_hrs (1=0) (0=1)
{txt}(31 changes made to {bf:AM_durat_border_hrs})

{com}. 
. *Population density Above the median
. gen lpopdensMAincome=ln(popdens_MA*income_MA)
{txt}
{com}. sum lpopdensMAincome, d

                      {txt}lpopdensMAincome
{hline 61}
      Percentiles      Smallest
 1%    {res} 9.382539       9.382539
{txt} 5%    {res} 9.476868       9.476868
{txt}10%    {res} 10.10129       9.956244       {txt}Obs         {res}         31
{txt}25%    {res} 10.48324       10.10129       {txt}Sum of wgt. {res}         31

{txt}50%    {res} 11.00788                      {txt}Mean          {res} 11.50209
                        {txt}Largest       Std. dev.     {res} 1.557059
{txt}75%    {res} 12.16803       13.54718
{txt}90%    {res} 13.54718       14.32722       {txt}Variance      {res} 2.424431
{txt}95%    {res} 15.40448       15.40448       {txt}Skewness      {res} 1.362177
{txt}99%    {res} 15.93451       15.93451       {txt}Kurtosis      {res} 4.452756
{txt}
{com}. gen AMlpopdensMAincome=(lpopdensMAincome > r(p50)) & (lpopdensMAincome != .)
{txt}
{com}. tab AMlpopdensMAincome

{txt}AMlpopdensM {c |}
    Aincome {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         19       61.29       61.29
{txt}          1 {c |}{res}         12       38.71      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         31      100.00
{txt}
{com}. 
. *Institutional variables
. foreach var of varlist shd_contract_proc_2014 shd_contract_proc_2017 rate_kid_2014 rate_kid_2017 shfrec_corrupc_2014 shfrec_corrupc_2017  informal2w_2014 informal2w_2017   {c -(}
{txt}  2{com}.         * High 10% (90th percentile)
.     sum `var', d
{txt}  3{com}.     gen H10_`var' = (`var' >= r(p90)) if `var' != .
{txt}  4{com}.     
.     * High 5% (95th percentile)
.     gen H5_`var' = (`var' >= r(p95)) if `var' != .
{txt}  5{com}. {c )-}

                   {txt}shd_contract_proc_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .0248496       .0248496
{txt}10%    {res} .1224337        .091094       {txt}Obs         {res}         31
{txt}25%    {res} .1638015       .1224337       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .1669036                      {txt}Mean          {res} .1623289
                        {txt}Largest       Std. dev.     {res} .0516068
{txt}75%    {res} .1698263       .1958214
{txt}90%    {res} .1958214       .2283266       {txt}Variance      {res} .0026633
{txt}95%    {res} .2506849       .2506849       {txt}Skewness      {res}-1.142508
{txt}99%    {res}  .280407        .280407       {txt}Kurtosis      {res} 6.582457

                   {txt}shd_contract_proc_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .0650517       .0650517
{txt} 5%    {res} .0749275       .0749275
{txt}10%    {res} .0835839       .0825471       {txt}Obs         {res}         31
{txt}25%    {res} .0855767       .0835839       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .1023307                      {txt}Mean          {res}  .108354
                        {txt}Largest       Std. dev.     {res} .0324398
{txt}75%    {res} .1133942       .1583711
{txt}90%    {res} .1583711       .1779695       {txt}Variance      {res} .0010523
{txt}95%    {res} .1807287       .1807287       {txt}Skewness      {res} 1.425433
{txt}99%    {res} .2007646       .2007646       {txt}Kurtosis      {res} 4.335516

                        {txt}rate_kid_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}         31
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}         31

{txt}50%    {res}        0                      {txt}Mean          {res} 2.823835
                        {txt}Largest       Std. dev.     {res} 6.021321
{txt}75%    {res} 2.408633       6.679283
{txt}90%    {res} 6.679283       12.20604       {txt}Variance      {res} 36.25631
{txt}95%    {res} 22.15166       22.15166       {txt}Skewness      {res} 2.600784
{txt}99%    {res} 23.65017       23.65017       {txt}Kurtosis      {res} 8.832658

                        {txt}rate_kid_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}         31
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}         31

{txt}50%    {res}        0                      {txt}Mean          {res} 1.970943
                        {txt}Largest       Std. dev.     {res} 3.854027
{txt}75%    {res} 2.610477       7.116312
{txt}90%    {res} 7.116312       8.897589       {txt}Variance      {res} 14.85352
{txt}95%    {res} 13.22635       13.22635       {txt}Skewness      {res} 1.960345
{txt}99%    {res} 13.32117       13.32117       {txt}Kurtosis      {res} 5.696817

                     {txt}shfrec_corrupc_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .5363942       .5363942
{txt} 5%    {res} .5605205       .5605205
{txt}10%    {res} .7856773       .7518165       {txt}Obs         {res}         31
{txt}25%    {res} .7883934       .7856773       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .7914899                      {txt}Mean          {res} .7936223
                        {txt}Largest       Std. dev.     {res} .0823116
{txt}75%    {res} .7946905       .8571648
{txt}90%    {res} .8571648       .8803478       {txt}Variance      {res} .0067752
{txt}95%    {res} .9442935       .9442935       {txt}Skewness      {res}-1.000662
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.311474

                     {txt}shfrec_corrupc_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .5538157       .5538157
{txt} 5%    {res}  .594312        .594312
{txt}10%    {res} .5990239       .5946435       {txt}Obs         {res}         31
{txt}25%    {res} .6016009       .5990239       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .7566071                      {txt}Mean          {res} .7223197
                        {txt}Largest       Std. dev.     {res} .0843531
{txt}75%    {res}  .783889       .8115699
{txt}90%    {res} .8115699        .817754       {txt}Variance      {res} .0071154
{txt}95%    {res}  .825571        .825571       {txt}Skewness      {res}-.6673571
{txt}99%    {res} .8406455       .8406455       {txt}Kurtosis      {res} 2.048043

                      {txt}No contributions
{hline 61}
      Percentiles      Smallest
 1%    {res} .4221631       .4221631
{txt} 5%    {res} .4472576       .4472576
{txt}10%    {res} .5314996       .4587323       {txt}Obs         {res}         31
{txt}25%    {res} .6644306       .5314996       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .9356802                      {txt}Mean          {res} .8234394
                        {txt}Largest       Std. dev.     {res} .1973651
{txt}75%    {res} .9961315              1
{txt}90%    {res}        1              1       {txt}Variance      {res}  .038953
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.7466872
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.116883

                      {txt}(mean) informal2
{hline 61}
      Percentiles      Smallest
 1%    {res} .3948256       .3948256
{txt} 5%    {res} .4055131       .4055131
{txt}10%    {res} .4909519       .4082245       {txt}Obs         {res}         31
{txt}25%    {res} .6630435       .4909519       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .9163265                      {txt}Mean          {res} .8146535
                        {txt}Largest       Std. dev.     {res} .2096893
{txt}75%    {res} .9869707              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0439696
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.8841441
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.278659
{txt}
{com}. 
. 
. 
. *Composite index normalized
.         foreach var of varlist shd_contract_proc_2014 shfrec_corrupc_2014 rate_kid_2014 shd_contract_proc_2017 shfrec_corrupc_2017 rate_kid_2017{c -(}
{txt}  2{com}.         sum `var', d
{txt}  3{com}.         gen `var'_norm=(`var'-r(mean))/(r(sd))
{txt}  4{com}.         {c )-}

                   {txt}shd_contract_proc_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .0248496       .0248496
{txt}10%    {res} .1224337        .091094       {txt}Obs         {res}         31
{txt}25%    {res} .1638015       .1224337       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .1669036                      {txt}Mean          {res} .1623289
                        {txt}Largest       Std. dev.     {res} .0516068
{txt}75%    {res} .1698263       .1958214
{txt}90%    {res} .1958214       .2283266       {txt}Variance      {res} .0026633
{txt}95%    {res} .2506849       .2506849       {txt}Skewness      {res}-1.142508
{txt}99%    {res}  .280407        .280407       {txt}Kurtosis      {res} 6.582457

                     {txt}shfrec_corrupc_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .5363942       .5363942
{txt} 5%    {res} .5605205       .5605205
{txt}10%    {res} .7856773       .7518165       {txt}Obs         {res}         31
{txt}25%    {res} .7883934       .7856773       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .7914899                      {txt}Mean          {res} .7936223
                        {txt}Largest       Std. dev.     {res} .0823116
{txt}75%    {res} .7946905       .8571648
{txt}90%    {res} .8571648       .8803478       {txt}Variance      {res} .0067752
{txt}95%    {res} .9442935       .9442935       {txt}Skewness      {res}-1.000662
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.311474

                        {txt}rate_kid_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}         31
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}         31

{txt}50%    {res}        0                      {txt}Mean          {res} 2.823835
                        {txt}Largest       Std. dev.     {res} 6.021321
{txt}75%    {res} 2.408633       6.679283
{txt}90%    {res} 6.679283       12.20604       {txt}Variance      {res} 36.25631
{txt}95%    {res} 22.15166       22.15166       {txt}Skewness      {res} 2.600784
{txt}99%    {res} 23.65017       23.65017       {txt}Kurtosis      {res} 8.832658

                   {txt}shd_contract_proc_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .0650517       .0650517
{txt} 5%    {res} .0749275       .0749275
{txt}10%    {res} .0835839       .0825471       {txt}Obs         {res}         31
{txt}25%    {res} .0855767       .0835839       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .1023307                      {txt}Mean          {res}  .108354
                        {txt}Largest       Std. dev.     {res} .0324398
{txt}75%    {res} .1133942       .1583711
{txt}90%    {res} .1583711       .1779695       {txt}Variance      {res} .0010523
{txt}95%    {res} .1807287       .1807287       {txt}Skewness      {res} 1.425433
{txt}99%    {res} .2007646       .2007646       {txt}Kurtosis      {res} 4.335516

                     {txt}shfrec_corrupc_2017
{hline 61}
      Percentiles      Smallest
 1%    {res} .5538157       .5538157
{txt} 5%    {res}  .594312        .594312
{txt}10%    {res} .5990239       .5946435       {txt}Obs         {res}         31
{txt}25%    {res} .6016009       .5990239       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .7566071                      {txt}Mean          {res} .7223197
                        {txt}Largest       Std. dev.     {res} .0843531
{txt}75%    {res}  .783889       .8115699
{txt}90%    {res} .8115699        .817754       {txt}Variance      {res} .0071154
{txt}95%    {res}  .825571        .825571       {txt}Skewness      {res}-.6673571
{txt}99%    {res} .8406455       .8406455       {txt}Kurtosis      {res} 2.048043

                        {txt}rate_kid_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}         31
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}         31

{txt}50%    {res}        0                      {txt}Mean          {res} 1.970943
                        {txt}Largest       Std. dev.     {res} 3.854027
{txt}75%    {res} 2.610477       7.116312
{txt}90%    {res} 7.116312       8.897589       {txt}Variance      {res} 14.85352
{txt}95%    {res} 13.22635       13.22635       {txt}Skewness      {res} 1.960345
{txt}99%    {res} 13.32117       13.32117       {txt}Kurtosis      {res} 5.696817
{txt}
{com}. 
.         * Create composite index using normalized variables
.         egen compos_ind_2014=rmean(shd_contract_proc_2014_norm shfrec_corrupc_2014_norm rate_kid_2014_norm)
{txt}
{com}.         egen compos_ind_2017=rmean(shd_contract_proc_2017_norm shfrec_corrupc_2017_norm rate_kid_2017_norm)
{txt}
{com}.         
. * Generate percentile indicators for the composite index
. sum compos_ind_2014, d

                       {txt}compos_ind_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.374121      -1.374121
{txt} 5%    {res}-1.343678      -1.343678
{txt}10%    {res}-.6180376      -1.093363       {txt}Obs         {res}         31
{txt}25%    {res}-.1517472      -.6180376       {txt}Sum of wgt. {res}         31

{txt}50%    {res}-.1322863                      {txt}Mean          {res}-3.36e-09
                        {txt}Largest       Std. dev.     {res} .6970031
{txt}75%    {res}  .200246       .6825818
{txt}90%    {res} .6825818       .8636789       {txt}Variance      {res} .4858134
{txt}95%    {res} 1.181585       1.181585       {txt}Skewness      {res} .9116139
{txt}99%    {res} 2.332009       2.332009       {txt}Kurtosis      {res} 5.976051
{txt}
{com}. gen H10_compos_ind_2014 = (compos_ind_2014 >= r(p90)) if compos_ind_2014 != .
{txt}
{com}. gen H5_compos_ind_2014 = (compos_ind_2014 >= r(p95)) if compos_ind_2014 != .
{txt}
{com}. 
. sum compos_ind_2017, d

                       {txt}compos_ind_2017
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.179806      -1.179806
{txt} 5%    {res}-.9414851      -.9414851
{txt}10%    {res}-.9040639      -.9295214       {txt}Obs         {res}         31
{txt}25%    {res}-.8634218      -.9040639       {txt}Sum of wgt. {res}         31

{txt}50%    {res} .0313832                      {txt}Mean          {res} 6.91e-10
                        {txt}Largest       Std. dev.     {res} .6911026
{txt}75%    {res} .5748908       .8262807
{txt}90%    {res} .8262807       .9731815       {txt}Variance      {res} .4776228
{txt}95%    {res}  1.18901        1.18901       {txt}Skewness      {res} -.038163
{txt}99%    {res} 1.262553       1.262553       {txt}Kurtosis      {res} 2.011392
{txt}
{com}. gen H10_compos_ind_2017 = (compos_ind_2017 >= r(p90)) if compos_ind_2017 != .
{txt}
{com}. gen H5_compos_ind_2017 = (compos_ind_2017 >= r(p95)) if compos_ind_2017 != .
{txt}
{com}. 
. *Rename variables
. ren H10_shd_contract_proc_2014 H10_contr_2014
{res}{txt}
{com}. ren H5_shd_contract_proc_2014 H5_contr_2014
{res}{txt}
{com}. ren H10_shfrec_corrupc_2014 H10_shfrec_corrup_2014
{res}{txt}
{com}. ren H5_shfrec_corrupc_2014 H5_shfrec_corrup_2014
{res}{txt}
{com}. ren H10_informal2w_2014 H10_informal_2014
{res}{txt}
{com}. ren H5_informal2w_2014 H5_informal_2014
{res}{txt}
{com}. 
. ren H10_shd_contract_proc_2017 H10_contr_2017
{res}{txt}
{com}. ren H5_shd_contract_proc_2017 H5_contr_2017
{res}{txt}
{com}. ren H10_shfrec_corrupc_2017 H10_shfrec_corrup_2017
{res}{txt}
{com}. ren H5_shfrec_corrupc_2017 H5_shfrec_corrup_2017
{res}{txt}
{com}. ren H10_informal2w_2017 H10_informal_2017
{res}{txt}
{com}. ren H5_informal2w_2017 H5_informal_2017
{res}{txt}
{com}. 
. foreach var of varlist BM_durat_border_hrs AM_durat_border_hrs AMlpopdensMAincome {c -(}
{txt}  2{com}.         gen `var'2014=`var'
{txt}  3{com}.         gen `var'2017=`var'
{txt}  4{com}. {c )-}
{txt}
{com}. 
. drop BM_durat_border_hrs AM_durat_border_hrs AMlpopdensMAincome 
{txt}
{com}. *Reshape
. reshape long H10_rate_kid_ H5_rate_kid_  rate_kid_ BM_durat_border_hrs AM_durat_border_hrs AMlpopdensMAincome H10_contr_ H5_contr_ H10_shfrec_corrup_ H5_shfrec_corrup_ H10_informal_ H5_informal_ compos_ind_ H10_compos_ind_ H5_compos_ind_ informal2w_ shfrec_corrupc_ shd_contract_proc_, i(id_mun )
{txt}(j = 2014 2017)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}          31   {txt}->   {res}62          
{txt}Number of variables        {res}          56   {txt}->   {res}39          
{txt}j variable (2 values)                     ->   {res}_j
{txt}xij variables:
    {res}H10_rate_kid_2014 H10_rate_kid_2017   {txt}->   {res}H10_rate_kid_
      H5_rate_kid_2014 H5_rate_kid_2017   {txt}->   {res}H5_rate_kid_
            rate_kid_2014 rate_kid_2017   {txt}->   {res}rate_kid_
BM_durat_border_hrs2014 BM_durat_border_hrs2017{txt}->{res}BM_durat_border_hrs
AM_durat_border_hrs2014 AM_durat_border_hrs2017{txt}->{res}AM_durat_border_hrs
AMlpopdensMAincome2014 AMlpopdensMAincome2017{txt}->{res}AMlpopdensMAincome
          H10_contr_2014 H10_contr_2017   {txt}->   {res}H10_contr_
            H5_contr_2014 H5_contr_2017   {txt}->   {res}H5_contr_
H10_shfrec_corrup_2014 H10_shfrec_corrup_2017{txt}->{res}H10_shfrec_corrup_
H5_shfrec_corrup_2014 H5_shfrec_corrup_2017{txt}->  {res}H5_shfrec_corrup_
    H10_informal_2014 H10_informal_2017   {txt}->   {res}H10_informal_
      H5_informal_2014 H5_informal_2017   {txt}->   {res}H5_informal_
        compos_ind_2014 compos_ind_2017   {txt}->   {res}compos_ind_
H10_compos_ind_2014 H10_compos_ind_2017   {txt}->   {res}H10_compos_ind_
  H5_compos_ind_2014 H5_compos_ind_2017   {txt}->   {res}H5_compos_ind_
        informal2w_2014 informal2w_2017   {txt}->   {res}informal2w_
shfrec_corrupc_2014 shfrec_corrupc_2017   {txt}->   {res}shfrec_corrupc_
shd_contract_proc_2014 shd_contract_proc_2017{txt}->{res}shd_contract_proc_
{txt}{hline 77}

{com}. ren id_mun id_mun2
{res}{txt}
{com}. 
. save "$working/Municipality_merge_temp.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Municipality_merge_temp.dta{rm}
saved
{p_end}

{com}. 
. 
. /********************************************************************************************
> * END OF DO FILE
> ********************************************************************************************/
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. do "$programs/Data assembly and merge.do"
{txt}
{com}. /********************************************************************************************
> * Project: Management and Misallocation in Mexico
> * Purpose:  Integrate multiple datasets required for analysis, generate key variables, and 
> *           produce summary datasets for further estimation and visualization.
> * Date: 2024-12-01
> ********************************************************************************************/
. *--------------------------------------------------------------------------------------------
. * 1. Load and merge data
. *--------------------------------------------------------------------------------------------
. 
. use "$working/Data_for_estimates_ENAPROCE_2015.dta", clear
{txt}
{com}. merge 1:1 clee using "$working/Data_for_estimates_ENAPROCE_2018.dta"
{res}{txt}(label {bf:{txt}owner2} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             460
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}             460{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          10,040{txt}  (_merge==3)
{col 5}{hline 41}

{com}. tostring clave_2017, replace
{txt}clave_2017 was {res:byte} now {res:str2}

{com}. 
. 
. *--------------------------------------------------------------------------------------------
. * 2. Reshape data long
. *--------------------------------------------------------------------------------------------
. reshape long  clase_ inicio_gvc_ eslab_1_ eslab_2_ eslab_3_ beneficio_gvc_ clave_ dec_manag_ quin_manag_ sector_ sector2_ size_ certif_ Var_inven_ dum_comp_ g_insum_cut_WA_ dum_ginnov_ dum_exp_ IED_dum_ dum_gvc_ dum_pat  dum_IDT_ g_energy_WA_ g_energy_  FDI1_ invmprev_fin_ invtot_fin_ edad_emp_ activ_fijos_WA_ activ_fijos_ sec_ent_  ITcap_worker_ invmprev_ini_ invtot_ini_  score_innov_ g_insum_WA_ dum_inter_ inter_use_ Var_inven_WA_ eslabones_gvc_ ln_af_ ln_af_WA_ ln_insum_ lnY_ lnY_WA_ ln_af_real_ ln_af_real_WA_ ln_insum_real_ ln_insum_real_WA_ lnY_real_ lnY_real_WA_ ln_rem_real_ ln_rem_real_WA_ ltfp_sec_real_ ltfp_sec_real_WA_ lVA_real_wk_mil_ lVA_real_wk_mil_WA_ ln_rem_ ltfp_sec_ score_man_dat_ score_man_inc_ score_manag_ score_manfirst8_ score_manlast8_ g_materials_WA_ g_materials_ rem_op_supp_ rem_dir_month_  months_ nworkers_WA_ nworkers_ oper_profit_ own_direc2_ profitab_ profitabrea_  quin_sh_direc_   rem_tot_anual_real_ activ_fijos_real_ innov_real_ oper_profit_real_ ingre_tot_real_ VA_real_ VA_real_wk_ VA_real_wk_mil_ activ_fijos_real_WA_ oper_profit_real_WA_ rem_tot_anual_real_WA_ ingre_tot_real_WA_ VA_real_WA_ raz_nogvc_ reasnotinter_ reasnotcomp_ region_  scor_cert_ shexp_ sh_af_ sh_IED_ sh_insum_  sh_rem_ sh_direc_ sh_sup_ shlabcomp_ sh_pre_ rem_tot_anual_WA_ rem_tot_anual_ ingre_tot_WA_ g_insum_tot_WA_ g_insum_ g_insum_cut_ g_insum_tot_ rem_tot_month_ ingre_tot_ g_train_ g_train_WA_ VA_ VA_wk_ VA_real_wk_mil_WA_ inicio_op_ anios_gvc_ sh_sup_WA_  sh_direc_WA_ innov_2yr_ innov_2yr_work_ innov_2yr_work_WA_ innov_2yr_sal_ IDT_2yr_ IDT_2yr_work_ IDT_2yr_work_WA_ IDT_2yr_sal_ dum_pat_, i(clee)
{txt}(j = 2013 2014 2016 2017)
(variable {bf:clase_2013} not found)
(variable {bf:inicio_gvc_2013} not found)
(variable {bf:eslab_1_2013} not found)
(variable {bf:eslab_2_2013} not found)
(variable {bf:eslab_3_2013} not found)
(variable {bf:beneficio_gvc_2013} not found)
(variable {bf:clave_2013} not found)
(variable {bf:dec_manag_2013} not found)
(variable {bf:quin_manag_2013} not found)
(variable {bf:sector_2013} not found)
(variable {bf:sector2_2013} not found)
(variable {bf:size_2013} not found)
(variable {bf:certif_2013} not found)
(variable {bf:Var_inven_2013} not found)
(variable {bf:dum_comp_2013} not found)
(variable {bf:g_insum_cut_WA_2013} not found)
(variable {bf:dum_ginnov_2013} not found)
(variable {bf:dum_exp_2013} not found)
(variable {bf:IED_dum_2013} not found)
(variable {bf:dum_gvc_2013} not found)
(variable {bf:dum_pat2013} not found)
(variable {bf:dum_IDT_2013} not found)
(variable {bf:g_energy_WA_2013} not found)
(variable {bf:g_energy_2013} not found)
(variable {bf:FDI1_2013} not found)
(variable {bf:invmprev_fin_2013} not found)
(variable {bf:invtot_fin_2013} not found)
(variable {bf:edad_emp_2013} not found)
(variable {bf:activ_fijos_WA_2013} not found)
(variable {bf:activ_fijos_2013} not found)
(variable {bf:sec_ent_2013} not found)
(variable {bf:ITcap_worker_2013} not found)
(variable {bf:invmprev_ini_2013} not found)
(variable {bf:invtot_ini_2013} not found)
(variable {bf:score_innov_2013} not found)
(variable {bf:g_insum_WA_2013} not found)
(variable {bf:dum_inter_2013} not found)
(variable {bf:inter_use_2013} not found)
(variable {bf:Var_inven_WA_2013} not found)
(variable {bf:eslabones_gvc_2013} not found)
(variable {bf:ln_af_2013} not found)
(variable {bf:ln_af_WA_2013} not found)
(variable {bf:ln_insum_2013} not found)
(variable {bf:lnY_2013} not found)
(variable {bf:lnY_WA_2013} not found)
(variable {bf:ln_af_real_2013} not found)
(variable {bf:ln_af_real_WA_2013} not found)
(variable {bf:ln_insum_real_2013} not found)
(variable {bf:ln_insum_real_WA_2013} not found)
(variable {bf:lnY_real_2013} not found)
(variable {bf:lnY_real_WA_2013} not found)
(variable {bf:ln_rem_real_2013} not found)
(variable {bf:ln_rem_real_WA_2013} not found)
(variable {bf:ltfp_sec_real_2013} not found)
(variable {bf:ltfp_sec_real_WA_2013} not found)
(variable {bf:lVA_real_wk_mil_2013} not found)
(variable {bf:lVA_real_wk_mil_WA_2013} not found)
(variable {bf:ln_rem_2013} not found)
(variable {bf:ltfp_sec_2013} not found)
(variable {bf:score_man_dat_2013} not found)
(variable {bf:score_man_inc_2013} not found)
(variable {bf:score_manag_2013} not found)
(variable {bf:score_manfirst8_2013} not found)
(variable {bf:score_manlast8_2013} not found)
(variable {bf:g_materials_WA_2013} not found)
(variable {bf:g_materials_2013} not found)
(variable {bf:rem_op_supp_2013} not found)
(variable {bf:rem_dir_month_2013} not found)
(variable {bf:months_2013} not found)
(variable {bf:nworkers_WA_2013} not found)
(variable {bf:nworkers_2013} not found)
(variable {bf:oper_profit_2013} not found)
(variable {bf:own_direc2_2013} not found)
(variable {bf:profitab_2013} not found)
(variable {bf:profitabrea_2013} not found)
(variable {bf:quin_sh_direc_2013} not found)
(variable {bf:rem_tot_anual_real_2013} not found)
(variable {bf:activ_fijos_real_2013} not found)
(variable {bf:innov_real_2013} not found)
(variable {bf:oper_profit_real_2013} not found)
(variable {bf:ingre_tot_real_2013} not found)
(variable {bf:VA_real_2013} not found)
(variable {bf:VA_real_wk_2013} not found)
(variable {bf:VA_real_wk_mil_2013} not found)
(variable {bf:activ_fijos_real_WA_2013} not found)
(variable {bf:oper_profit_real_WA_2013} not found)
(variable {bf:rem_tot_anual_real_WA_2013} not found)
(variable {bf:ingre_tot_real_WA_2013} not found)
(variable {bf:VA_real_WA_2013} not found)
(variable {bf:reasnotinter_2013} not found)
(variable {bf:reasnotcomp_2013} not found)
(variable {bf:region_2013} not found)
(variable {bf:scor_cert_2013} not found)
(variable {bf:shexp_2013} not found)
(variable {bf:sh_af_2013} not found)
(variable {bf:sh_IED_2013} not found)
(variable {bf:sh_insum_2013} not found)
(variable {bf:sh_rem_2013} not found)
(variable {bf:sh_direc_2013} not found)
(variable {bf:sh_sup_2013} not found)
(variable {bf:shlabcomp_2013} not found)
(variable {bf:sh_pre_2013} not found)
(variable {bf:rem_tot_anual_WA_2013} not found)
(variable {bf:rem_tot_anual_2013} not found)
(variable {bf:ingre_tot_WA_2013} not found)
(variable {bf:g_insum_tot_WA_2013} not found)
(variable {bf:g_insum_2013} not found)
(variable {bf:g_insum_cut_2013} not found)
(variable {bf:g_insum_tot_2013} not found)
(variable {bf:rem_tot_month_2013} not found)
(variable {bf:ingre_tot_2013} not found)
(variable {bf:g_train_WA_2013} not found)
(variable {bf:VA_2013} not found)
(variable {bf:VA_wk_2013} not found)
(variable {bf:VA_real_wk_mil_WA_2013} not found)
(variable {bf:inicio_op_2013} not found)
(variable {bf:anios_gvc_2013} not found)
(variable {bf:sh_sup_WA_2013} not found)
(variable {bf:sh_direc_WA_2013} not found)
(variable {bf:innov_2yr_2013} not found)
(variable {bf:innov_2yr_work_2013} not found)
(variable {bf:innov_2yr_work_WA_2013} not found)
(variable {bf:innov_2yr_sal_2013} not found)
(variable {bf:IDT_2yr_2013} not found)
(variable {bf:IDT_2yr_work_2013} not found)
(variable {bf:IDT_2yr_work_WA_2013} not found)
(variable {bf:IDT_2yr_sal_2013} not found)
(variable {bf:dum_pat_2013} not found)
(variable {bf:dum_pat2014} not found)
(variable {bf:clase_2016} not found)
(variable {bf:inicio_gvc_2016} not found)
(variable {bf:eslab_1_2016} not found)
(variable {bf:eslab_2_2016} not found)
(variable {bf:eslab_3_2016} not found)
(variable {bf:beneficio_gvc_2016} not found)
(variable {bf:clave_2016} not found)
(variable {bf:dec_manag_2016} not found)
(variable {bf:quin_manag_2016} not found)
(variable {bf:sector_2016} not found)
(variable {bf:sector2_2016} not found)
(variable {bf:size_2016} not found)
(variable {bf:certif_2016} not found)
(variable {bf:Var_inven_2016} not found)
(variable {bf:dum_comp_2016} not found)
(variable {bf:g_insum_cut_WA_2016} not found)
(variable {bf:dum_ginnov_2016} not found)
(variable {bf:dum_exp_2016} not found)
(variable {bf:IED_dum_2016} not found)
(variable {bf:dum_gvc_2016} not found)
(variable {bf:dum_pat2016} not found)
(variable {bf:dum_IDT_2016} not found)
(variable {bf:g_energy_WA_2016} not found)
(variable {bf:g_energy_2016} not found)
(variable {bf:FDI1_2016} not found)
(variable {bf:invmprev_fin_2016} not found)
(variable {bf:invtot_fin_2016} not found)
(variable {bf:edad_emp_2016} not found)
(variable {bf:activ_fijos_WA_2016} not found)
(variable {bf:activ_fijos_2016} not found)
(variable {bf:sec_ent_2016} not found)
(variable {bf:ITcap_worker_2016} not found)
(variable {bf:invmprev_ini_2016} not found)
(variable {bf:invtot_ini_2016} not found)
(variable {bf:score_innov_2016} not found)
(variable {bf:g_insum_WA_2016} not found)
(variable {bf:dum_inter_2016} not found)
(variable {bf:inter_use_2016} not found)
(variable {bf:Var_inven_WA_2016} not found)
(variable {bf:eslabones_gvc_2016} not found)
(variable {bf:ln_af_2016} not found)
(variable {bf:ln_af_WA_2016} not found)
(variable {bf:ln_insum_2016} not found)
(variable {bf:lnY_2016} not found)
(variable {bf:lnY_WA_2016} not found)
(variable {bf:ln_af_real_2016} not found)
(variable {bf:ln_af_real_WA_2016} not found)
(variable {bf:ln_insum_real_2016} not found)
(variable {bf:ln_insum_real_WA_2016} not found)
(variable {bf:lnY_real_2016} not found)
(variable {bf:lnY_real_WA_2016} not found)
(variable {bf:ln_rem_real_2016} not found)
(variable {bf:ln_rem_real_WA_2016} not found)
(variable {bf:ltfp_sec_real_2016} not found)
(variable {bf:ltfp_sec_real_WA_2016} not found)
(variable {bf:lVA_real_wk_mil_2016} not found)
(variable {bf:lVA_real_wk_mil_WA_2016} not found)
(variable {bf:ln_rem_2016} not found)
(variable {bf:ltfp_sec_2016} not found)
(variable {bf:score_man_dat_2016} not found)
(variable {bf:score_man_inc_2016} not found)
(variable {bf:score_manag_2016} not found)
(variable {bf:score_manfirst8_2016} not found)
(variable {bf:score_manlast8_2016} not found)
(variable {bf:g_materials_WA_2016} not found)
(variable {bf:g_materials_2016} not found)
(variable {bf:nworkers_WA_2016} not found)
(variable {bf:oper_profit_2016} not found)
(variable {bf:own_direc2_2016} not found)
(variable {bf:profitab_2016} not found)
(variable {bf:profitabrea_2016} not found)
(variable {bf:quin_sh_direc_2016} not found)
(variable {bf:rem_tot_anual_real_2016} not found)
(variable {bf:activ_fijos_real_2016} not found)
(variable {bf:oper_profit_real_2016} not found)
(variable {bf:ingre_tot_real_2016} not found)
(variable {bf:VA_real_2016} not found)
(variable {bf:VA_real_wk_2016} not found)
(variable {bf:VA_real_wk_mil_2016} not found)
(variable {bf:activ_fijos_real_WA_2016} not found)
(variable {bf:oper_profit_real_WA_2016} not found)
(variable {bf:rem_tot_anual_real_WA_2016} not found)
(variable {bf:ingre_tot_real_WA_2016} not found)
(variable {bf:VA_real_WA_2016} not found)
(variable {bf:reasnotinter_2016} not found)
(variable {bf:reasnotcomp_2016} not found)
(variable {bf:region_2016} not found)
(variable {bf:scor_cert_2016} not found)
(variable {bf:shexp_2016} not found)
(variable {bf:sh_af_2016} not found)
(variable {bf:sh_IED_2016} not found)
(variable {bf:sh_insum_2016} not found)
(variable {bf:sh_rem_2016} not found)
(variable {bf:shlabcomp_2016} not found)
(variable {bf:rem_tot_anual_WA_2016} not found)
(variable {bf:ingre_tot_WA_2016} not found)
(variable {bf:g_insum_tot_WA_2016} not found)
(variable {bf:g_insum_2016} not found)
(variable {bf:g_insum_cut_2016} not found)
(variable {bf:g_insum_tot_2016} not found)
(variable {bf:g_train_WA_2016} not found)
(variable {bf:VA_2016} not found)
(variable {bf:VA_wk_2016} not found)
(variable {bf:VA_real_wk_mil_WA_2016} not found)
(variable {bf:inicio_op_2016} not found)
(variable {bf:anios_gvc_2016} not found)
(variable {bf:sh_sup_WA_2016} not found)
(variable {bf:sh_direc_WA_2016} not found)
(variable {bf:innov_2yr_2016} not found)
(variable {bf:innov_2yr_work_2016} not found)
(variable {bf:innov_2yr_work_WA_2016} not found)
(variable {bf:innov_2yr_sal_2016} not found)
(variable {bf:IDT_2yr_2016} not found)
(variable {bf:IDT_2yr_work_2016} not found)
(variable {bf:IDT_2yr_work_WA_2016} not found)
(variable {bf:IDT_2yr_sal_2016} not found)
(variable {bf:dum_pat_2016} not found)
(variable {bf:dum_pat2017} not found)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}      10,500   {txt}->   {res}42,000      
{txt}Number of variables        {res}         302   {txt}->   {res}160         
{txt}j variable (4 values)                     ->   {res}_j
{txt}xij variables:
   {res}clase_2013 clase_2014 ... clase_2017   {txt}->   {res}clase_
inicio_gvc_2013 inicio_gvc_2014 ... inicio_gvc_2017{txt}->{res}inicio_gvc_
eslab_1_2013 eslab_1_2014 ... eslab_1_2017{txt}->   {res}eslab_1_
eslab_2_2013 eslab_2_2014 ... eslab_2_2017{txt}->   {res}eslab_2_
eslab_3_2013 eslab_3_2014 ... eslab_3_2017{txt}->   {res}eslab_3_
beneficio_gvc_2013 beneficio_gvc_2014 ... beneficio_gvc_2017{txt}->{res}beneficio_gvc_
   clave_2013 clave_2014 ... clave_2017   {txt}->   {res}clave_
dec_manag_2013 dec_manag_2014 ... dec_manag_2017{txt}->{res}dec_manag_
quin_manag_2013 quin_manag_2014 ... quin_manag_2017{txt}->{res}quin_manag_
sector_2013 sector_2014 ... sector_2017   {txt}->   {res}sector_
sector2_2013 sector2_2014 ... sector2_2017{txt}->   {res}sector2_
      size_2013 size_2014 ... size_2017   {txt}->   {res}size_
certif_2013 certif_2014 ... certif_2017   {txt}->   {res}certif_
Var_inven_2013 Var_inven_2014 ... Var_inven_2017{txt}->{res}Var_inven_
dum_comp_2013 dum_comp_2014 ... dum_comp_2017{txt}->{res}dum_comp_
g_insum_cut_WA_2013 g_insum_cut_WA_2014 ... g_insum_cut_WA_2017{txt}->{res}g_insum_cut_WA_
dum_ginnov_2013 dum_ginnov_2014 ... dum_ginnov_2017{txt}->{res}dum_ginnov_
dum_exp_2013 dum_exp_2014 ... dum_exp_2017{txt}->   {res}dum_exp_
IED_dum_2013 IED_dum_2014 ... IED_dum_2017{txt}->   {res}IED_dum_
dum_gvc_2013 dum_gvc_2014 ... dum_gvc_2017{txt}->   {res}dum_gvc_
dum_pat2013 dum_pat2014 ... dum_pat2017   {txt}->   {res}dum_pat
dum_IDT_2013 dum_IDT_2014 ... dum_IDT_2017{txt}->   {res}dum_IDT_
g_energy_WA_2013 g_energy_WA_2014 ... g_energy_WA_2017{txt}->{res}g_energy_WA_
g_energy_2013 g_energy_2014 ... g_energy_2017{txt}->{res}g_energy_
      FDI1_2013 FDI1_2014 ... FDI1_2017   {txt}->   {res}FDI1_
invmprev_fin_2013 invmprev_fin_2014 ... invmprev_fin_2017{txt}->{res}invmprev_fin_
invtot_fin_2013 invtot_fin_2014 ... invtot_fin_2017{txt}->{res}invtot_fin_
edad_emp_2013 edad_emp_2014 ... edad_emp_2017{txt}->{res}edad_emp_
activ_fijos_WA_2013 activ_fijos_WA_2014 ... activ_fijos_WA_2017{txt}->{res}activ_fijos_WA_
activ_fijos_2013 activ_fijos_2014 ... activ_fijos_2017{txt}->{res}activ_fijos_
sec_ent_2013 sec_ent_2014 ... sec_ent_2017{txt}->   {res}sec_ent_
ITcap_worker_2013 ITcap_worker_2014 ... ITcap_worker_2017{txt}->{res}ITcap_worker_
invmprev_ini_2013 invmprev_ini_2014 ... invmprev_ini_2017{txt}->{res}invmprev_ini_
invtot_ini_2013 invtot_ini_2014 ... invtot_ini_2017{txt}->{res}invtot_ini_
score_innov_2013 score_innov_2014 ... score_innov_2017{txt}->{res}score_innov_
g_insum_WA_2013 g_insum_WA_2014 ... g_insum_WA_2017{txt}->{res}g_insum_WA_
dum_inter_2013 dum_inter_2014 ... dum_inter_2017{txt}->{res}dum_inter_
inter_use_2013 inter_use_2014 ... inter_use_2017{txt}->{res}inter_use_
Var_inven_WA_2013 Var_inven_WA_2014 ... Var_inven_WA_2017{txt}->{res}Var_inven_WA_
eslabones_gvc_2013 eslabones_gvc_2014 ... eslabones_gvc_2017{txt}->{res}eslabones_gvc_
   ln_af_2013 ln_af_2014 ... ln_af_2017   {txt}->   {res}ln_af_
ln_af_WA_2013 ln_af_WA_2014 ... ln_af_WA_2017{txt}->{res}ln_af_WA_
ln_insum_2013 ln_insum_2014 ... ln_insum_2017{txt}->{res}ln_insum_
         lnY_2013 lnY_2014 ... lnY_2017   {txt}->   {res}lnY_
lnY_WA_2013 lnY_WA_2014 ... lnY_WA_2017   {txt}->   {res}lnY_WA_
ln_af_real_2013 ln_af_real_2014 ... ln_af_real_2017{txt}->{res}ln_af_real_
ln_af_real_WA_2013 ln_af_real_WA_2014 ... ln_af_real_WA_2017{txt}->{res}ln_af_real_WA_
ln_insum_real_2013 ln_insum_real_2014 ... ln_insum_real_2017{txt}->{res}ln_insum_real_
ln_insum_real_WA_2013 ln_insum_real_WA_2014 ... ln_insum_real_WA_2017{txt}->{res}ln_insum_real_WA_
lnY_real_2013 lnY_real_2014 ... lnY_real_2017{txt}->{res}lnY_real_
lnY_real_WA_2013 lnY_real_WA_2014 ... lnY_real_WA_2017{txt}->{res}lnY_real_WA_
ln_rem_real_2013 ln_rem_real_2014 ... ln_rem_real_2017{txt}->{res}ln_rem_real_
ln_rem_real_WA_2013 ln_rem_real_WA_2014 ... ln_rem_real_WA_2017{txt}->{res}ln_rem_real_WA_
ltfp_sec_real_2013 ltfp_sec_real_2014 ... ltfp_sec_real_2017{txt}->{res}ltfp_sec_real_
ltfp_sec_real_WA_2013 ltfp_sec_real_WA_2014 ... ltfp_sec_real_WA_2017{txt}->{res}ltfp_sec_real_WA_
lVA_real_wk_mil_2013 lVA_real_wk_mil_2014 ... lVA_real_wk_mil_2017{txt}->{res}lVA_real_wk_mil_
lVA_real_wk_mil_WA_2013 lVA_real_wk_mil_WA_2014 ... lVA_real_wk_mil_WA_2017{txt}->{res}lVA_real_wk_mil_WA_
ln_rem_2013 ln_rem_2014 ... ln_rem_2017   {txt}->   {res}ln_rem_
ltfp_sec_2013 ltfp_sec_2014 ... ltfp_sec_2017{txt}->{res}ltfp_sec_
score_man_dat_2013 score_man_dat_2014 ... score_man_dat_2017{txt}->{res}score_man_dat_
score_man_inc_2013 score_man_inc_2014 ... score_man_inc_2017{txt}->{res}score_man_inc_
score_manag_2013 score_manag_2014 ... score_manag_2017{txt}->{res}score_manag_
score_manfirst8_2013 score_manfirst8_2014 ... score_manfirst8_2017{txt}->{res}score_manfirst8_
score_manlast8_2013 score_manlast8_2014 ... score_manlast8_2017{txt}->{res}score_manlast8_
g_materials_WA_2013 g_materials_WA_2014 ... g_materials_WA_2017{txt}->{res}g_materials_WA_
g_materials_2013 g_materials_2014 ... g_materials_2017{txt}->{res}g_materials_
rem_op_supp_2013 rem_op_supp_2014 ... rem_op_supp_2017{txt}->{res}rem_op_supp_
rem_dir_month_2013 rem_dir_month_2014 ... rem_dir_month_2017{txt}->{res}rem_dir_month_
months_2013 months_2014 ... months_2017   {txt}->   {res}months_
nworkers_WA_2013 nworkers_WA_2014 ... nworkers_WA_2017{txt}->{res}nworkers_WA_
nworkers_2013 nworkers_2014 ... nworkers_2017{txt}->{res}nworkers_
oper_profit_2013 oper_profit_2014 ... oper_profit_2017{txt}->{res}oper_profit_
own_direc2_2013 own_direc2_2014 ... own_direc2_2017{txt}->{res}own_direc2_
profitab_2013 profitab_2014 ... profitab_2017{txt}->{res}profitab_
profitabrea_2013 profitabrea_2014 ... profitabrea_2017{txt}->{res}profitabrea_
quin_sh_direc_2013 quin_sh_direc_2014 ... quin_sh_direc_2017{txt}->{res}quin_sh_direc_
rem_tot_anual_real_2013 rem_tot_anual_real_2014 ... rem_tot_anual_real_2017{txt}->{res}rem_tot_anual_real_
activ_fijos_real_2013 activ_fijos_real_2014 ... activ_fijos_real_2017{txt}->{res}activ_fijos_real_
innov_real_2013 innov_real_2014 ... innov_real_2017{txt}->{res}innov_real_
oper_profit_real_2013 oper_profit_real_2014 ... oper_profit_real_2017{txt}->{res}oper_profit_real_
ingre_tot_real_2013 ingre_tot_real_2014 ... ingre_tot_real_2017{txt}->{res}ingre_tot_real_
VA_real_2013 VA_real_2014 ... VA_real_2017{txt}->   {res}VA_real_
VA_real_wk_2013 VA_real_wk_2014 ... VA_real_wk_2017{txt}->{res}VA_real_wk_
VA_real_wk_mil_2013 VA_real_wk_mil_2014 ... VA_real_wk_mil_2017{txt}->{res}VA_real_wk_mil_
activ_fijos_real_WA_2013 activ_fijos_real_WA_2014 ... activ_fijos_real_WA_2017{txt}->{res}activ_fijos_real_WA_
oper_profit_real_WA_2013 oper_profit_real_WA_2014 ... oper_profit_real_WA_2017{txt}->{res}oper_profit_real_WA_
rem_tot_anual_real_WA_2013 rem_tot_anual_real_WA_2014 ... rem_tot_anual_real_WA_2017{txt}->{res}rem_tot_anual_real_WA_
ingre_tot_real_WA_2013 ingre_tot_real_WA_2014 ... ingre_tot_real_WA_2017{txt}->{res}ingre_tot_real_WA_
VA_real_WA_2013 VA_real_WA_2014 ... VA_real_WA_2017{txt}->{res}VA_real_WA_
raz_nogvc_2013 raz_nogvc_2014 ... raz_nogvc_2017{txt}->{res}raz_nogvc_
reasnotinter_2013 reasnotinter_2014 ... reasnotinter_2017{txt}->{res}reasnotinter_
reasnotcomp_2013 reasnotcomp_2014 ... reasnotcomp_2017{txt}->{res}reasnotcomp_
region_2013 region_2014 ... region_2017   {txt}->   {res}region_
scor_cert_2013 scor_cert_2014 ... scor_cert_2017{txt}->{res}scor_cert_
   shexp_2013 shexp_2014 ... shexp_2017   {txt}->   {res}shexp_
   sh_af_2013 sh_af_2014 ... sh_af_2017   {txt}->   {res}sh_af_
sh_IED_2013 sh_IED_2014 ... sh_IED_2017   {txt}->   {res}sh_IED_
sh_insum_2013 sh_insum_2014 ... sh_insum_2017{txt}->{res}sh_insum_
sh_rem_2013 sh_rem_2014 ... sh_rem_2017   {txt}->   {res}sh_rem_
sh_direc_2013 sh_direc_2014 ... sh_direc_2017{txt}->{res}sh_direc_
sh_sup_2013 sh_sup_2014 ... sh_sup_2017   {txt}->   {res}sh_sup_
shlabcomp_2013 shlabcomp_2014 ... shlabcomp_2017{txt}->{res}shlabcomp_
sh_pre_2013 sh_pre_2014 ... sh_pre_2017   {txt}->   {res}sh_pre_
rem_tot_anual_WA_2013 rem_tot_anual_WA_2014 ... rem_tot_anual_WA_2017{txt}->{res}rem_tot_anual_WA_
rem_tot_anual_2013 rem_tot_anual_2014 ... rem_tot_anual_2017{txt}->{res}rem_tot_anual_
ingre_tot_WA_2013 ingre_tot_WA_2014 ... ingre_tot_WA_2017{txt}->{res}ingre_tot_WA_
g_insum_tot_WA_2013 g_insum_tot_WA_2014 ... g_insum_tot_WA_2017{txt}->{res}g_insum_tot_WA_
g_insum_2013 g_insum_2014 ... g_insum_2017{txt}->   {res}g_insum_
g_insum_cut_2013 g_insum_cut_2014 ... g_insum_cut_2017{txt}->{res}g_insum_cut_
g_insum_tot_2013 g_insum_tot_2014 ... g_insum_tot_2017{txt}->{res}g_insum_tot_
rem_tot_month_2013 rem_tot_month_2014 ... rem_tot_month_2017{txt}->{res}rem_tot_month_
ingre_tot_2013 ingre_tot_2014 ... ingre_tot_2017{txt}->{res}ingre_tot_
g_train_2013 g_train_2014 ... g_train_2017{txt}->   {res}g_train_
g_train_WA_2013 g_train_WA_2014 ... g_train_WA_2017{txt}->{res}g_train_WA_
            VA_2013 VA_2014 ... VA_2017   {txt}->   {res}VA_
   VA_wk_2013 VA_wk_2014 ... VA_wk_2017   {txt}->   {res}VA_wk_
VA_real_wk_mil_WA_2013 VA_real_wk_mil_WA_2014 ... VA_real_wk_mil_WA_2017{txt}->{res}VA_real_wk_mil_WA_
inicio_op_2013 inicio_op_2014 ... inicio_op_2017{txt}->{res}inicio_op_
anios_gvc_2013 anios_gvc_2014 ... anios_gvc_2017{txt}->{res}anios_gvc_
sh_sup_WA_2013 sh_sup_WA_2014 ... sh_sup_WA_2017{txt}->{res}sh_sup_WA_
sh_direc_WA_2013 sh_direc_WA_2014 ... sh_direc_WA_2017{txt}->{res}sh_direc_WA_
innov_2yr_2013 innov_2yr_2014 ... innov_2yr_2017{txt}->{res}innov_2yr_
innov_2yr_work_2013 innov_2yr_work_2014 ... innov_2yr_work_2017{txt}->{res}innov_2yr_work_
innov_2yr_work_WA_2013 innov_2yr_work_WA_2014 ... innov_2yr_work_WA_2017{txt}->{res}innov_2yr_work_WA_
innov_2yr_sal_2013 innov_2yr_sal_2014 ... innov_2yr_sal_2017{txt}->{res}innov_2yr_sal_
IDT_2yr_2013 IDT_2yr_2014 ... IDT_2yr_2017{txt}->   {res}IDT_2yr_
IDT_2yr_work_2013 IDT_2yr_work_2014 ... IDT_2yr_work_2017{txt}->{res}IDT_2yr_work_
IDT_2yr_work_WA_2013 IDT_2yr_work_WA_2014 ... IDT_2yr_work_WA_2017{txt}->{res}IDT_2yr_work_WA_
IDT_2yr_sal_2013 IDT_2yr_sal_2014 ... IDT_2yr_sal_2017{txt}->{res}IDT_2yr_sal_
dum_pat_2013 dum_pat_2014 ... dum_pat_2017{txt}->   {res}dum_pat_
{txt}{hline 77}

{com}. 
. 
. *Drop unnecessary years
. drop if inlist(_j, 2013, 2016)
{txt}(21,000 observations deleted)

{com}. 
. 
. *--------------------------------------------------------------------------------------------
. * 3. Create additional variables
. *--------------------------------------------------------------------------------------------
. gen dum_time=_j==2017
{txt}
{com}. destring id_mun, replace
{txt}id_mun: all characters numeric; {res}replaced {txt}as {res}int
{txt}(920 missing values generated)
{res}{txt}
{com}. 
. gen lcapworkWA_=ln(activ_fijos_real_WA_/nworkers_WA_)
{txt}(5,960 missing values generated)

{com}. gen lnworkers_WA_=ln(nworkers_WA_)
{txt}(460 missing values generated)

{com}. destring clase_, gen(clase)
{txt}clase_: all characters numeric; clase {res}generated {txt}as {res}long
{txt}(460 missing values generated)
{res}{txt}
{com}. 
. gen id_munclee= substr(clee,1,5)
{txt}
{com}. destring id_munclee, replace
{txt}id_munclee: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. replace id_mun=id_munclee if id_mun==.
{txt}(920 real changes made)

{com}. tostring id_mun, gen(id_mun2)
{txt}id_mun2 generated as {res:str5}

{com}. replace id_mun2="0"+id_mun2 if id_mun<10000
{txt}(5,028 real changes made)

{com}. gen clase_clee=substr(clee,6,6)
{txt}
{com}. destring clase_clee, replace
{txt}clase_clee: all characters numeric; {res}replaced {txt}as {res}long
{txt}
{com}. replace clase=clase_clee if clase==.
{txt}(460 real changes made)

{com}. 
. 
. *--------------------------------------------------------------------------------------------
. * 4. Merge municipality- and sectoral-level variables
. *--------------------------------------------------------------------------------------------
. merge m:1 id_mun2 _j using "$working/Municipality_merge_temp.dta", gen(mergemundata)
{res}{txt}(label {bf:{txt}size} already defined)
(label {bf:{txt}_merge} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             994
{txt}{col 9}from master{col 30}{res}             994{txt}  (mergemundata==1)
{col 9}from using{col 30}{res}               0{txt}  (mergemundata==2)

{col 5}Matched{col 30}{res}          20,006{txt}  (mergemundata==3)
{col 5}{hline 41}

{com}. merge m:1 clase using "$data\Share exports clase.dta", gen(mergeclase)
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}          10,873
{txt}{col 9}from master{col 30}{res}          10,000{txt}  (mergeclase==1)
{col 9}from using{col 30}{res}             873{txt}  (mergeclase==2)

{col 5}Matched{col 30}{res}          11,000{txt}  (mergeclase==3)
{col 5}{hline 41}

{com}. 
. *--------------------------------------------------------------------------------------------
. * 5. Save dataset for estimations
. *--------------------------------------------------------------------------------------------
. save "$working/BASE LONG FOR ESTIMATES.dta", replace 
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/BASE LONG FOR ESTIMATES.dta{rm}
saved
{p_end}

{com}. 
. *--------------------------------------------------------------------------------------------
. * 6. Additional summary datasets for graphs
. *--------------------------------------------------------------------------------------------
. use "$working/BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. *Keep relevant observations (with management score)
. keep if score_manag_!=.
{txt}(2,337 observations deleted)

{com}. 
. 
. *Generate additional variables for graphs
. gen  r_d_mil=IDT_2yr_/1000
{txt}
{com}. gen r_d_worker=r_d_mil/nworkers_
{txt}
{com}. 
. *Winsorize
. winsor2 r_d_worker profitabrea_, by(_j)
{txt}
{com}. 
. drop r_d_worker profitabrea_
{txt}
{com}. ren r_d_worker_w r_d_worker_
{res}{txt}
{com}. ren profitabrea__w profitabrea_
{res}{txt}
{com}. 
. collapse (mean) ltfp_sec_real_WA_ profitabrea_ dum_exp IDT_2yr_work_WA_ dum_pat_ nworkers_WA_ shexp_ r_d_worker_, by(dec_manag_ dum_man2)
{res}{txt}
{com}. gen tfp=exp(ltfp_sec_real_WA_)
{txt}
{com}. bysort dum_man2: gen tfp_d1=tfp if dec_manag==1
{txt}(18 missing values generated)

{com}. bysort dum_man2: egen mean_tfp_d1=mean(tfp_d1)
{txt}
{com}. replace tfp=100*tfp/mean_tfp
{txt}(20 real changes made)

{com}. drop mean_tfp* tfp_d1
{txt}
{com}. 
. reshape wide ltfp_sec_real_WA_ profitabrea_ dum_exp shexp_ IDT_2yr_work_WA_ dum_pat_ nworkers_WA_ tfp r_d_worker_, i(dec_manag_) j(dum_man2)
{txt}(j = 0 1)

Data{col 36}Long{col 43}->{col 48}Wide
{hline 77}
Number of observations     {res}          20   {txt}->   {res}10          
{txt}Number of variables        {res}          11   {txt}->   {res}19          
{txt}j variable (2 values)          {res}dum_man2   {txt}->   (dropped)
xij variables:
                      {res}ltfp_sec_real_WA_   {txt}->   {res}ltfp_sec_real_WA_0 ltfp_sec_real_WA_1
                           profitabrea_   {txt}->   {res}profitabrea_0 profitabrea_1
                               dum_exp_   {txt}->   {res}dum_exp_0 dum_exp_1
                                 shexp_   {txt}->   {res}shexp_0 shexp_1
                       IDT_2yr_work_WA_   {txt}->   {res}IDT_2yr_work_WA_0 IDT_2yr_work_WA_1
                               dum_pat_   {txt}->   {res}dum_pat_0 dum_pat_1
                           nworkers_WA_   {txt}->   {res}nworkers_WA_0 nworkers_WA_1
                                    tfp   {txt}->   {res}tfp0 tfp1
                            r_d_worker_   {txt}->   {res}r_d_worker_0 r_d_worker_1
{txt}{hline 77}

{com}. 
. ren dec_manag_ decile
{res}{txt}
{com}. ren tfp1 tfp_man
{res}{txt}
{com}. ren tfp0 tfp_serv
{res}{txt}
{com}. ren profitabrea_1 profitab_man
{res}{txt}
{com}. ren profitabrea_0 profitab_serv
{res}{txt}
{com}. ren shexp_1 shexp_man
{res}{txt}
{com}. ren shexp_0 shexp_serv
{res}{txt}
{com}. ren r_d_worker_1 r_d_worker_man
{res}{txt}
{com}. ren r_d_worker_0 r_d_worker_serv
{res}{txt}
{com}. ren dum_pat_1 dum_pat_man
{res}{txt}
{com}. ren dum_pat_0 dum_pat_serv
{res}{txt}
{com}. ren nworkers_WA_1  nworkers_man
{res}{txt}
{com}. ren nworkers_WA_0  nworkers_serv
{res}{txt}
{com}. 
. keep tfp_man tfp_serv profitab_serv profitab_man shexp_man shexp_serv r_d_worker_man r_d_worker_ser dum_pat_man dum_pat_serv nworkers_man nworkers_serv decile
{txt}
{com}. 
. save "$working/Management and performance.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Management and performance.dta{rm}
saved
{p_end}

{com}. 
. 
. use "$working/BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. drop if score_manag_==.
{txt}(2,337 observations deleted)

{com}. gen bin_score_manag20 = .
{txt}(19,536 missing values generated)

{com}. replace bin_score_manag20 = 1 if score_manag_>= 0 & score_manag_< 0.05 
{txt}(500 real changes made)

{com}. replace bin_score_manag20 = 2 if score_manag_>= 0.05 & score_manag_< 0.1 
{txt}(500 real changes made)

{com}. replace bin_score_manag20 = 3 if score_manag_>= 0.1 & score_manag_< 0.15 
{txt}(0 real changes made)

{com}. replace bin_score_manag20 = 4 if score_manag_>= 0.15 & score_manag_< 0.2 
{txt}(2,002 real changes made)

{com}. replace bin_score_manag20 = 5 if score_manag_>= 0.2 & score_manag_< 0.25 
{txt}(4,508 real changes made)

{com}. replace bin_score_manag20 = 6 if score_manag_>= 0.25 & score_manag_< 0.3 
{txt}(0 real changes made)

{com}. replace bin_score_manag20 = 7 if score_manag_>= 0.3 & score_manag_< 0.35 
{txt}(2,004 real changes made)

{com}. replace bin_score_manag20 = 8 if score_manag_>= 0.35 & score_manag_< 0.4 
{txt}(1,502 real changes made)

{com}. replace bin_score_manag20 = 9 if score_manag_>= 0.4 & score_manag_< 0.45 
{txt}(1,002 real changes made)

{com}. replace bin_score_manag20 = 10 if score_manag_>= 0.45 & score_manag_< 0.5 
{txt}(2,006 real changes made)

{com}. replace bin_score_manag20 = 11 if score_manag_>= 0.5 & score_manag_< 0.55 
{txt}(500 real changes made)

{com}. replace bin_score_manag20 = 12 if score_manag_>= 0.55 & score_manag_< 0.6 
{txt}(500 real changes made)

{com}. replace bin_score_manag20 = 13 if score_manag_>= 0.6 & score_manag_< 0.65 
{txt}(1,504 real changes made)

{com}. replace bin_score_manag20 = 14 if score_manag_>= 0.65 & score_manag_< 0.7 
{txt}(1,506 real changes made)

{com}. replace bin_score_manag20 = 15 if score_manag_>= 0.7 & score_manag_< 0.75 
{txt}(0 real changes made)

{com}. replace bin_score_manag20 = 16 if score_manag_>= 0.75 & score_manag_< 0.8 
{txt}(1,502 real changes made)

{com}. replace bin_score_manag20 = 17 if score_manag_>= 0.8 & score_manag_< 0.85 
{txt}(0 real changes made)

{com}. replace bin_score_manag20 = 18 if score_manag_>= 0.85 & score_manag_< 0.9 
{txt}(0 real changes made)

{com}. replace bin_score_manag20 = 19 if score_manag_>= 0.9 & score_manag_< 0.95 
{txt}(0 real changes made)

{com}. replace bin_score_manag20 = 20 if score_manag_>= 0.95 & score_manag_<= 1
{txt}(0 real changes made)

{com}. 
. gen number=1
{txt}
{com}. collapse (sum) number, by(dum_man2 bin_score_manag20)
{res}{txt}
{com}. drop if dum_man2==.
{txt}(0 observations deleted)

{com}. 
. bysort dum_man2: egen tot_number=total(number)
{txt}
{com}. gen share=number/tot_number
{txt}
{com}. 
. drop number tot_number
{txt}
{com}. reshape wide share, i(bin_score_manag20) j(dum_man2)
{txt}(j = 0 1)

Data{col 36}Long{col 43}->{col 48}Wide
{hline 77}
Number of observations     {res}          22   {txt}->   {res}13          
{txt}Number of variables        {res}           3   {txt}->   {res}3           
{txt}j variable (2 values)          {res}dum_man2   {txt}->   (dropped)
xij variables:
                                  {res}share   {txt}->   {res}share0 share1
{txt}{hline 77}

{com}. 
. ren share0 serv_
{res}{txt}
{com}. ren share1 manuf_
{res}{txt}
{com}. ren bin_score_manag20 bin 
{res}{txt}
{com}. 
. merge 1:1 bin using "$data\Data_US_bins.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}               7
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}               7{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              13{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. gen zero=0
{txt}
{com}. 
. save "$working/Densities MX vs US.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Densities MX vs US.dta{rm}
saved
{p_end}

{com}. 
. 
. use "$working/BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. binscatter lnworkers_WA_ score_manag, by(dum_man2) nq(50) controls(dum_time) savedata("$working/Binscatter_MX.csv")    replace
{res}{txt}(file C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Binscatter_MX.csv.csv written containing saved data)
(file C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Binscatter_MX.csv.do written containing commands to process saved data)

{com}. 
. 
. import delimited "$working\Binscatter_MX.csv.csv", clear
{res}{txt}(encoding automatically selected: ISO-8859-1)
{res}{text}(4 vars, 37 obs)

{com}. gen point=_n
{txt}
{com}. ren lnworkers_wa__by1 lnworkers_wa_serpool 
{res}{txt}
{com}. ren lnworkers_wa__by2 lnworkers_wa_manpool 
{res}{txt}
{com}. ren score_manag__by1  score_manag_finwa_serpool
{res}{txt}
{com}. ren score_manag__by2  score_manag_finwa_manpool 
{res}{txt}
{com}. 
. merge 1:1 point using "$working_interm\Data Binscatter US.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              13
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}              13{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              37{txt}  (_merge==3)
{col 5}{hline 41}

{com}. 
. save  "$working/Binscatters size management MS vs US_mock.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Binscatters size management MS vs US_mock.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. use "$working/BASE LONG FOR ESTIMATES.dta", replace 
{txt}
{com}. gen age_bins=.
{txt}(21,873 missing values generated)

{com}. replace age_bins=1 if edad_emp_<=5 
{txt}(0 real changes made)

{com}. replace age_bins=2 if edad_emp_>=6 &  edad_emp_<=10 
{txt}(502 real changes made)

{com}. replace age_bins=3 if edad_emp_>=11 &  edad_emp_<=15 
{txt}(1,500 real changes made)

{com}. replace age_bins=4 if edad_emp_>=16 &  edad_emp_<=20 
{txt}(4,510 real changes made)

{com}. replace age_bins=5 if edad_emp_>=21 &  edad_emp_<=25 
{txt}(6,016 real changes made)

{com}. replace age_bins=6 if edad_emp_>=26 &  edad_emp_<=30
{txt}(5,006 real changes made)

{com}. replace age_bins=7 if edad_emp_>=31 &  edad_emp_<=35 
{txt}(1,002 real changes made)

{com}. replace age_bins=8 if edad_emp_>=36 
{txt}(3,337 real changes made)

{com}. gen sdscore_manag=score_manag_
{txt}(2,337 missing values generated)

{com}. 
. version 16: table age_bins dum_man2, c(mean score_manag_)

{txt}{hline 10}{c TT}{hline 19}
          {c |} Dummy Sector =1 if
          {c |}Manufacturing, O if
          {c |}      Services     
 age_bins {c |}        0         1
{hline 10}{c +}{hline 19}
        2 {c |}         {res}          
        {txt}3 {c |} {res}.3709921  .0666667
        {txt}4 {c |} {res}.4759355  .2952487
        {txt}5 {c |} {res}.4162975  .4344494
        {txt}6 {c |} {res}.3926985  .5390873
        {txt}7 {c |}  {res}.324282  .6479167
        {txt}8 {c |} {res}.4898272  .2489274
{txt}{hline 10}{c BT}{hline 19}

{com}. version 16: table age_bins dum_man2, c(sd score_manag_)

{txt}{hline 10}{c TT}{hline 19}
          {c |} Dummy Sector =1 if
          {c |}Manufacturing, O if
          {c |}      Services     
 age_bins {c |}        0         1
{hline 10}{c +}{hline 19}
        2 {c |}         {res}          
        {txt}3 {c |} {res}.1329633         0
        {txt}4 {c |} {res}.2255536  .1414658
        {txt}5 {c |} {res}.1773086   .134531
        {txt}6 {c |}  {res}.182916  .2608353
        {txt}7 {c |} {res}.3241201          
        {txt}8 {c |} {res}.1980086  .0193771
{txt}{hline 10}{c BT}{hline 19}

{com}. 
. collapse (mean) score_manag_ (sd) sdscore_manag, by(age_bins dum_man2)
{res}{txt}
{com}. drop if dum_man2==.
{txt}(1 observation deleted)

{com}. 
. reshape wide score_manag_ sdscore_manag, i(age_bins) j(dum_man2)
{txt}(j = 0 1)

Data{col 36}Long{col 43}->{col 48}Wide
{hline 77}
Number of observations     {res}          14   {txt}->   {res}7           
{txt}Number of variables        {res}           4   {txt}->   {res}5           
{txt}j variable (2 values)          {res}dum_man2   {txt}->   (dropped)
xij variables:
                           {res}score_manag_   {txt}->   {res}score_manag_0 score_manag_1
                          sdscore_manag   {txt}->   {res}sdscore_manag0 sdscore_manag1
{txt}{hline 77}

{com}. foreach var of varlist  score_manag_* sdscore_manag* {c -(}
{txt}  2{com}.         sum `var'
{txt}  3{com}.         gen n`var'=`var'-r(mean)
{txt}  4{com}. {c )-}

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
score_mana~0 {c |}{res}          6    .4116721    .0631052    .324282   .4898272
{txt}(1 missing value generated)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
score_mana~1 {c |}{res}          6    .3720494    .2108147   .0666667   .6479167
{txt}(1 missing value generated)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
sdscore_ma~0 {c |}{res}          6    .2068117    .0649385   .1329633   .3241201
{txt}(1 missing value generated)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
sdscore_ma~1 {c |}{res}          5    .1112418    .1056503          0   .2608353
{txt}(2 missing values generated)

{com}. ren age_bins age_bin
{res}{txt}
{com}. ren nscore_manag_1 mean_man
{res}{txt}
{com}. ren nscore_manag_0 mean_serv 
{res}{txt}
{com}. ren sdscore_manag0 sd_serv
{res}{txt}
{com}. ren sdscore_manag1 sd_manuf
{res}{txt}
{com}. 
. merge 1:1 age_bin using "$data\Age_bins_US.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}               2
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}               2{txt}  (_merge==2)

{col 5}Matched{col 30}{res}               7{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if age_bin==.
{txt}(1 observation deleted)

{com}. drop _merge
{txt}
{com}. 
. save  "$working/Age bin mean sd manufacturing.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Age bin mean sd manufacturing.dta{rm}
saved
{p_end}

{com}. 
. 
. use "$working/BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. gen ent=substr(id_mun2,1,2)
{txt}(873 missing values generated)

{com}. drop if dum_man2==.
{txt}(873 observations deleted)

{com}. collapse (mean) score_manag_,by(ent dum_man2)
{res}{txt}
{com}. 
. 
. reshape wide  score_manag_, i(ent) j(dum_man2)
{txt}(j = 0 1)

Data{col 36}Long{col 43}->{col 48}Wide
{hline 77}
Number of observations     {res}          25   {txt}->   {res}22          
{txt}Number of variables        {res}           3   {txt}->   {res}3           
{txt}j variable (2 values)          {res}dum_man2   {txt}->   (dropped)
xij variables:
                           {res}score_manag_   {txt}->   {res}score_manag_0 score_manag_1
{txt}{hline 77}

{com}. ren score_manag_0 score_ser 
{res}{txt}
{com}. ren score_manag_1 score_man
{res}{txt}
{com}. 
. gen class_man=.
{txt}(22 missing values generated)

{com}. replace class_man=1 if score_man<0.415
{txt}(5 real changes made)

{com}. replace class_man=2 if score_man>=0.415 & score_man<0.445
{txt}(0 real changes made)

{com}. replace class_man=3 if score_man>=0.445 & score_man<0.475
{txt}(0 real changes made)

{com}. replace class_man=4 if score_man>=0.475 & score_man<0.495
{txt}(0 real changes made)

{com}. replace class_man=5 if score_man>=0.495
{txt}(17 real changes made)

{com}. 
. gen class_ser=.
{txt}(22 missing values generated)

{com}. replace class_ser=1 if score_ser<0.425
{txt}(11 real changes made)

{com}. replace class_ser=2 if score_ser>=0.425 & score_ser<0.445
{txt}(0 real changes made)

{com}. replace class_ser=3 if score_ser>=0.445 & score_ser<0.455
{txt}(0 real changes made)

{com}. replace class_ser=4 if score_ser>=0.455 
{txt}(11 real changes made)

{com}. 
. destring ent, replace
{txt}ent: all characters numeric; {res}replaced {txt}as {res}byte
{txt}
{com}. 
. save  "$working/Map management state.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Map management state.dta{rm}
saved
{p_end}

{com}. 
. /********************************************************************************************
> * END OF DO FILE
> ********************************************************************************************/
. 
. 
{txt}end of do-file

{com}. *Robustness datasets for appendix
. do "$programs/Microenterprisesdata.do"
{txt}
{com}. /********************************************************************************************
> * Project: Management and Misallocation in Mexico
> * Purpose: Assemble management scores and performance variables for Microenterprises
> * Date: 2024-12-01
> ********************************************************************************************/
. 
. ******************************************************************
. *2014
. ******************************************************************
. 
. 
. use "$data\ejem_base_micro_ciega2015.dta" , clear
{txt}
{com}. ren clave clave_15
{res}{txt}
{com}. 
. 
. gen clase=substr(clee,6,6)
{txt}
{com}. *gen clase=E17
. gen id_mun=substr(clee,1,5)
{txt}
{com}. *gen id_mun=E03+E04
. 
. *Add price deflators
. cap drop _merge
{txt}
{com}. merge m:1 clase using "$data/Deflators.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           6,159
{txt}{col 9}from master{col 30}{res}           5,500{txt}  (_merge==1)
{col 9}from using{col 30}{res}             659{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           4,500{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge==2
{txt}(659 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. 
. //////Questions MOPS Microenterprises/////
> 
. gen val_scoM28=.
{txt}(10,000 missing values generated)

{com}. replace val_scoM28=1/3 if M28=="1"
{txt}(2,000 real changes made)

{com}. replace val_scoM28=2/3 if M28=="2"
{txt}(2,500 real changes made)

{com}. replace val_scoM28=1   if M28=="3"
{txt}(1,500 real changes made)

{com}. replace val_scoM28=0   if M28=="4"
{txt}(4,000 real changes made)

{com}. 
. 
. gen val_scoM29=.
{txt}(10,000 missing values generated)

{com}. replace val_scoM29=1/3  if M29=="1"
{txt}(2,000 real changes made)

{com}. replace val_scoM29=2/3  if M29=="2"
{txt}(2,000 real changes made)

{com}. replace val_scoM29=2/3  if M29=="3"
{txt}(500 real changes made)

{com}. replace val_scoM29=1    if M29=="4"
{txt}(0 real changes made)

{com}. replace val_scoM29=0    if M29=="5"
{txt}(5,500 real changes made)

{com}. 
. 
. gen val_scoM30=.
{txt}(10,000 missing values generated)

{com}. replace val_scoM30=0 if M30=="1"
{txt}(2,500 real changes made)

{com}. replace val_scoM30=1/3 if M30=="2"
{txt}(1,500 real changes made)

{com}. replace val_scoM30=2/3  if M30=="3"
{txt}(0 real changes made)

{com}. replace val_scoM30=1   if M30=="4"
{txt}(500 real changes made)

{com}. 
. gen val_scoM31=.
{txt}(10,000 missing values generated)

{com}. replace val_scoM31=1   if M31=="1"
{txt}(500 real changes made)

{com}. replace val_scoM31=2/3 if M31=="2"
{txt}(500 real changes made)

{com}. replace val_scoM31=1/3 if M31=="3"
{txt}(0 real changes made)

{com}. replace val_scoM31=0   if M31=="4"
{txt}(9,000 real changes made)

{com}. 
. 
. egen score_manag_2014=rmean(val_scoM28 val_scoM29 val_scoM30 val_scoM31)
{txt}
{com}. 
. *Performance questions
. gen meses=M13
{txt}
{com}. 
. gen g_insum_2014=M17_0*meses
{txt}
{com}. gen g_insum_cut=g_insum-M17_9*meses
{txt}
{com}. 
. gen nworkers_2014= M10_0A
{txt}
{com}. 
. gen ingre_tot_2014= M18_0B
{txt}
{com}. gen ingre_tot_real_2014=ingre_tot_2014*(100/INPP_prodB20132014)
{txt}(5,500 missing values generated)

{com}. gen ingre_totaprox=M19_0B*meses
{txt}
{com}. gen activ_fijos_2014=M22
{txt}
{com}. gen activ_fijos_real_2014=activ_fijos_2014*(100/PRICE_IND_INVEST_MX_B20132014)
{txt}(5,500 missing values generated)

{com}. 
. 
. gen sh_direc_2014=M10_1A/M10_0A
{txt}
{com}. gen sh_sup_2014= M11_4/M11_0
{txt}
{com}. 
. 
. gen VA_real_2014=(ingre_tot_2014)*(100/INPP_prodB20132014)-(g_insum_2014)*(100/impatp_base20132014)
{txt}(5,500 missing values generated)

{com}. gen va_worker_2014=(ingre_tot_2014-g_insum_2014)/nworkers_2014
{txt}
{com}. gen VA_real_wk_mil_2014=(VA_real_2014/1000)/nworkers_2014
{txt}(5,500 missing values generated)

{com}. 
. 
. foreach var of varlist va_worker_2014 nworkers_2014 sh_sup_2014 sh_direc_2014  ingre_tot_real_2014 activ_fijos_real_2014 VA_real_2014  VA_real_wk_mil_2014{c -(}
{txt}  2{com}.                 local newvar = substr("`var'", 1, length("`var'") - 5)   // Remove 2014
{txt}  3{com}.                 gen `var'b=`var'
{txt}  4{com}.                 ren `var'b `newvar'
{txt}  5{com}.                 winsor2 `newvar', s(_WA_2014) 
{txt}  6{com}.                 drop `newvar'
{txt}  7{com}. {c )-}
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}
{com}. 
. gen lVA_real_wk_mil_2014=ln(VA_real_wk_mil_2014)
{txt}(5,500 missing values generated)

{com}. gen lVA_real_wk_mil_WA_2014=ln(VA_real_wk_mil_WA_2014)
{txt}(5,500 missing values generated)

{com}. 
. keep lVA_real_wk_mil_WA_2014 score_manag_2014 sh_sup* sh_direc* nwor* clave* clee ingre* g_insum* clase* id_mun activ_fijos_real*
{txt}
{com}. 
. save "$working/Microenterprises_manag2014.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Microenterprises_manag2014.dta{rm}
saved
{p_end}

{com}. 
. 
. ******************************************************************
. *2017
. ******************************************************************
. 
. use "$data\ejem_base_micro_ciega2018.dta", clear
{txt}
{com}. 
. gen clase=substr(clee,6,6)
{txt}
{com}. *gen clase=E17
. gen id_mun=substr(clee,1,5)
{txt}
{com}. *gen id_mun=E03+E04
. 
. cap drop _merge
{txt}
{com}. merge m:1 clase using "$data/Deflators.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           6,158
{txt}{col 9}from master{col 30}{res}           5,500{txt}  (_merge==1)
{col 9}from using{col 30}{res}             658{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           5,000{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge==2
{txt}(658 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. //////Questions MOPS Microenterprises/////
> 
. foreach var of varlist M29 M30 M31 M32 {c -(}
{txt}  2{com}.         tostring `var', replace
{txt}  3{com}. {c )-}
{txt}M29 was {res:byte} now {res:str1}
M30 was {res:byte} now {res:str1}
M31 was {res:byte} now {res:str1}
M32 was {res:byte} now {res:str1}

{com}. 
. gen val_scoM29=.
{txt}(10,500 missing values generated)

{com}. replace val_scoM29=1/3 if M29=="1"
{txt}(1,000 real changes made)

{com}. replace val_scoM29=2/3 if M29=="2"
{txt}(4,500 real changes made)

{com}. replace val_scoM29=1   if M29=="3"
{txt}(3,500 real changes made)

{com}. replace val_scoM29=0   if M29=="4"
{txt}(1,500 real changes made)

{com}. 
. 
. gen val_scoM30=.
{txt}(10,500 missing values generated)

{com}. replace val_scoM30=1/3  if M30=="1"
{txt}(1,500 real changes made)

{com}. replace val_scoM30=2/3  if M30=="2"
{txt}(2,500 real changes made)

{com}. replace val_scoM30=2/3  if M30=="3"
{txt}(1,000 real changes made)

{com}. replace val_scoM30=1    if M30=="4"
{txt}(500 real changes made)

{com}. replace val_scoM30=0    if M30=="5"
{txt}(5,000 real changes made)

{com}. 
. 
. gen val_scoM31=.
{txt}(10,500 missing values generated)

{com}. replace val_scoM31=0 if M31=="1"
{txt}(3,500 real changes made)

{com}. replace val_scoM31=1/3 if M31=="2"
{txt}(500 real changes made)

{com}. replace val_scoM31=2/3  if M31=="3"
{txt}(500 real changes made)

{com}. replace val_scoM31=1   if M31=="4"
{txt}(1,000 real changes made)

{com}. 
. gen val_scoM32=.
{txt}(10,500 missing values generated)

{com}. replace val_scoM32=1   if M32=="1"
{txt}(2,000 real changes made)

{com}. replace val_scoM32=2/3 if M32=="2"
{txt}(500 real changes made)

{com}. replace val_scoM32=1/3 if M32=="3"
{txt}(0 real changes made)

{com}. replace val_scoM32=0   if M32=="4"
{txt}(8,000 real changes made)

{com}. 
. 
. egen score_manag_2017=rmean(val_scoM29 val_scoM30 val_scoM31 val_scoM32)
{txt}
{com}. 
. *Performance questions
. gen meses=M13B
{txt}
{com}. gen g_insum_2017=M18_0*meses
{txt}
{com}. gen g_insum_cut_2017=g_insum-M18_9*meses
{txt}
{com}. 
. gen nworkers_2017= M11_0C
{txt}
{com}. 
. gen ingre_tot_2017= M19_0B
{txt}
{com}. gen ingre_tot_real_2017=ingre_tot_2017*(100/INPP_prodB20132017)
{txt}(5,500 missing values generated)

{com}. gen activ_fijos_2017=M23
{txt}
{com}. gen activ_fijos_real_2017=activ_fijos_2017*(100/PRICE_IND_INVEST_MX_B20132017)
{txt}(5,500 missing values generated)

{com}. 
. gen sh_direc_2017=M11_1C/M11_0C
{txt}
{com}. gen sh_sup_2017= M12_4B/M12_0B
{txt}
{com}. 
. gen VA_2017=(ingre_tot_2017)-(g_insum_2017)
{txt}
{com}. gen VA_real_2017=(ingre_tot_2017)*(100/INPP_prodB20132017)-(g_insum_2017)*(100/impatp_base20132017)
{txt}(5,500 missing values generated)

{com}. gen VA_wk_2017=VA_2017/nworkers_2017
{txt}
{com}. gen VA_real_wk_2017=VA_real_2017/nworkers_2017
{txt}(5,500 missing values generated)

{com}. gen VA_real_wk_mil_2017=(VA_real_2017/1000)/nworkers_2017
{txt}(5,500 missing values generated)

{com}. 
. 
. ren CLAVE clave
{res}{txt}
{com}. 
. foreach var of varlist nworkers_2017 sh_sup_2017 sh_direc_2017  ingre_tot_real_2017 activ_fijos_real_2017 VA_real_2017  VA_real_wk_mil_2017{c -(}
{txt}  2{com}.                 local newvar = substr("`var'", 1, length("`var'") - 5)   // Remove 2017
{txt}  3{com}.                 gen `var'b=`var'
{txt}  4{com}.                 ren `var'b `newvar'
{txt}  5{com}.                 winsor2 `newvar', s(_WA_2017) 
{txt}  6{com}.                 drop `newvar'
{txt}  7{com}. {c )-}
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}(5,500 missing values generated)
{res}{txt}
{com}. 
. gen lVA_real_wk_mil_2017=ln(VA_real_wk_mil_2017)
{txt}(5,993 missing values generated)

{com}. gen lVA_real_wk_mil_WA_2017=ln(VA_real_wk_mil_WA_2017)
{txt}(5,993 missing values generated)

{com}. 
. 
. keep lVA_real_wk_mil_* score_manag_* sh_sup* sh_direc* nwor* clave* clee ingre* g_insum* clase* id_mun activ_fijos_real*
{txt}
{com}. 
. save "$working/Microenterprises_manag2017.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Microenterprises_manag2017.dta{rm}
saved
{p_end}

{com}. 
. 
. **********************************************************************
. *Data assembly
. **********************************************************************
. use "$working/Microenterprises_manag2014.dta", clear
{txt}
{com}. merge m:1 clee using "$working/Microenterprises_manag2017.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             500
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}             500{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          10,000{txt}  (_merge==3)
{col 5}{hline 41}

{com}. 
. gen cleenew=clee
{txt}
{com}. replace cleenew="noclee"+clave_15 if clee==""
{txt}(0 real changes made)

{com}. reshape long ingre_tot_real_ sh_direc_ sh_sup_ score_manag_ nworkers_WA_ sh_sup_WA_ sh_direc_WA_ ingre_tot_real_WA_ lVA_real_wk_mil_WA_ activ_fijos_real_WA_, i(cleenew id_mun clase)
{txt}(j = 2014 2017)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}      10,500   {txt}->   {res}21,000      
{txt}Number of variables        {res}          39   {txt}->   {res}30          
{txt}j variable (2 values)                     ->   {res}_j
{txt}xij variables:
{res}ingre_tot_real_2014 ingre_tot_real_2017   {txt}->   {res}ingre_tot_real_
            sh_direc_2014 sh_direc_2017   {txt}->   {res}sh_direc_
                sh_sup_2014 sh_sup_2017   {txt}->   {res}sh_sup_
      score_manag_2014 score_manag_2017   {txt}->   {res}score_manag_
      nworkers_WA_2014 nworkers_WA_2017   {txt}->   {res}nworkers_WA_
          sh_sup_WA_2014 sh_sup_WA_2017   {txt}->   {res}sh_sup_WA_
      sh_direc_WA_2014 sh_direc_WA_2017   {txt}->   {res}sh_direc_WA_
ingre_tot_real_WA_2014 ingre_tot_real_WA_2017{txt}->{res}ingre_tot_real_WA_
lVA_real_wk_mil_WA_2014 lVA_real_wk_mil_WA_2017{txt}->{res}lVA_real_wk_mil_WA_
activ_fijos_real_WA_2014 activ_fijos_real_WA_2017{txt}->{res}activ_fijos_real_WA_
{txt}{hline 77}

{com}. 
. 
. gen lcapworkWA_=ln(activ_fijos_real_WA_/nworkers_WA_)
{txt}(11,500 missing values generated)

{com}. gen lnworkers_WA_=ln(nworkers_WA_)
{txt}(500 missing values generated)

{com}. 
. gen dum_time=_j==2017
{txt}
{com}. destring clase, gen(claseb)
{txt}clase: all characters numeric; claseb {res}generated {txt}as {res}long
{txt}
{com}. 
. save "$working/Base_long_microenterprises.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Base_long_microenterprises.dta{rm}
saved
{p_end}

{com}. 
. 
. /********************************************************************************************
> * END OF DO FILE
> ********************************************************************************************/
. 
{txt}end of do-file

{com}. do "$programs/Data prepare CE 2014 mock data.do"
{txt}
{com}.  
.  /***********************************************************************************************************************************************************
> * Inputs: Census 2014 (Economic Census 2014, INEGI)    
> *
> * Program Description: This program generates performance indicators for all the firms in the Economic Census 2014 that also appear in ENAPROCE 2015.
> *
> * Note: This program was developed within the premises of INEGI's Microdata Data Laboratory.
> * Last Update: 09/26/2021 */
. 
. ************************************************************************************************************************************************************
. 
. *Share of exports
. 
. use "$data/ejem_CE_2014.dta" , clear 
{txt}
{com}. 
. *gen clase=e17
. gen ingresos=m000a
{txt}
{com}. gen exports=m000c
{txt}
{com}. 
. collapse (sum) ingresos exports, by(clase)
{res}{txt}
{com}. 
. gen shexp_clase=exports/ingresos
{txt}
{com}. 
. keep clase shexp_clase
{txt}
{com}. destring clase, replace
{txt}clase: all characters numeric; {res}replaced {txt}as {res}long
{txt}
{com}. 
. save "$working\Share exports clase_mock.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp\Share exports clase_mock.dta{rm}
saved
{p_end}

{com}. 
. 
. **********************************************************************************************
. *Obtain IDs for ENAPROCE 2015 firms
. 
. use "$working\BASE LONG FOR ESTIMATES.dta" , clear
{txt}
{com}. keep if _j==2014 & nworkers_!=. & score_manag_!=.
{txt}(12,837 observations deleted)

{com}. keep clee
{txt}
{com}. duplicates drop

{p 0 4}{txt}Duplicates in terms of {txt} all variables{p_end}

(0 observations are duplicates)

{com}. save "$working/IDs_firms_2014", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/IDs_firms_2014.dta{rm}
saved
{p_end}

{com}. 
. 
. use "$data/ejem_CE_2014.dta" , clear 
{txt}
{com}. *gen clase=e17
. gen scian2=substr(clase,1,2)
{txt}
{com}. 
. gen str sector_2014=""
{txt}(50,000 missing values generated)

{com}. replace sector_2014="MAN" if inlist(scian2,"31","32","33")
{txt}variable {bf}{res}sector_2014{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str3{sf}
{txt}(4,903 real changes made)

{com}. replace sector_2014="COM" if inlist(scian2,"43","46")
{txt}(7,882 real changes made)

{com}. replace sector_2014="SER" if sector_2014==""
{txt}(37,215 real changes made)

{com}. 
. *merge m:1 clase using "$data/Deflators.dta"
. merge m:1 clase using "$data\Deflators.dta" 
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}          33,870
{txt}{col 9}from master{col 30}{res}          33,215{txt}  (_merge==1)
{col 9}from using{col 30}{res}             655{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          16,785{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. drop if clee==""
{txt}(655 observations deleted)

{com}. *1) Generate variables used in Management in America as outcomes
. 
. *Value added per worker (Real= Prices 2014)
. gen ingre_totCE_2014=m000a
{txt}
{com}. gen ingre_tot_realCE_2014=ingre_totCE_2014*(100/INPP_prodB20132014)
{txt}(33,215 missing values generated)

{com}. gen Var_invenCE_2014=p000b-p000a
{txt}
{com}. egen g_materialsCE_2014=rsum(k010a k020a k030a)
{txt}
{com}. egen g_energyCE_2014=rsum(k040a k041a)
{txt}
{com}. gen g_insumCE_2014=k000a
{txt}
{com}. gen nworkersCE_2014=h001a
{txt}
{com}. *gen VA_realCE_2014=(ingre_totCE_2014 + Var_invenCE_2014)*(100/INPP_prodB20132014)-(g_materialsCE_2014+g_energyCE_2014)*(100/impatp_base20132014)
. 
. 
. gen VA_realCE_2014=a131a*(100/INPP_prodB20132014)
{txt}(33,215 missing values generated)

{com}. 
. gen VA_wk_CE_2014=a131a/nworkersCE_2014
{txt}
{com}. gen VA_real_wkCE_2014=VA_realCE_2014/nworkersCE_2014
{txt}(33,215 missing values generated)

{com}. gen VA_real_wk_milCE_2014=(VA_realCE_2014/1000)/nworkersCE_2014
{txt}(33,215 missing values generated)

{com}. 
. 
. *Fixed assets
. gen activ_fijosCE_2014=q000a
{txt}
{com}. gen activ_fijos_realCE_2014=activ_fijosCE_2014*(100/PRICE_IND_INVEST_MX_B20132014)
{txt}(33,215 missing values generated)

{com}. 
. gen rem_tot_anualCE_2014=j000a
{txt}
{com}. gen rem_tot_anual_realCE_2014=rem_tot_anualCE_2014*(100/INPP_prodB20132014)
{txt}(33,215 missing values generated)

{com}. 
. gen sh_remCE_2014=rem_tot_anualCE_2014/ingre_totCE_2014
{txt}
{com}. gen sh_insumCE_2014=k000a/ingre_totCE_2014
{txt}
{com}. gen sh_afCE_2014=1-sh_rem-sh_insum
{txt}
{com}. gen Y_2014=a111a
{txt}
{com}. 
. foreach var of varlist VA_realCE_2014 rem_tot_anualCE_2014 rem_tot_anual_realCE_2014  g_insumCE_2014 g_energyCE_2014  activ_fijos_realCE_2014 ingre_totCE_2014 ingre_tot_realCE_2014   activ_fijosCE_2014 g_materialsCE_2014 Var_invenCE_2014 nworkersCE_2014 Y_2014{c -(}
{txt}  2{com}.         bysort f01: egen t`var'=total(`var')
{txt}  3{com}.         replace t`var'=. if f01==""
{txt}  4{com}. {c )-}
{txt}(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)
(47,982 real changes made, 47,982 to missing)

{com}. gen tVA_real_wk_milCE_2014= tVA_realCE_2014/tnworkersCE_2014 
{txt}(47,982 missing values generated)

{com}. 
. merge 1:1 clee using "$working/IDs_firms_2014"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}          40,964
{txt}{col 9}from master{col 30}{res}          40,964{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           9,036{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge==1
{txt}(40,964 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. foreach var of varlist VA_realCE_2014 VA_real_wk_milCE_2014 rem_tot_anualCE_2014 rem_tot_anual_realCE_2014  g_insumCE_2014 g_energyCE_2014  activ_fijos_realCE_2014 ingre_totCE_2014 ingre_tot_realCE_2014   activ_fijosCE_2014 g_materialsCE_2014 Var_invenCE_2014 nworkersCE_2014 Y_2014{c -(}
{txt}  2{com}.                 replace `var'=t`var' if f01!=""
{txt}  3{com}. {c )-}
{txt}(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

{com}. 
. 
. foreach var of varlist VA_real_wk_milCE_2014 VA_realCE_2014 rem_tot_anualCE_2014 rem_tot_anual_realCE_2014  g_insumCE_2014 g_energyCE_2014  activ_fijos_realCE_2014 ingre_totCE_2014 ingre_tot_realCE_2014   activ_fijosCE_2014 g_materialsCE_2014 Var_invenCE_2014 nworkersCE_2014 Y_2014{c -(}
{txt}  2{com}.                 local newvar = substr("`var'", 1, length("`var'") - 5)   // Remove 2014
{txt}  3{com}.                 gen `var'b=`var'
{txt}  4{com}.                 ren `var'b `newvar'
{txt}  5{com}.                 winsor2 `newvar', s(_WA_2014) 
{txt}  6{com}.                 drop `newvar'
{txt}  7{com}. {c )-}
{txt}(4,528 missing values generated)
{res}{txt}(4,528 missing values generated)
{res}{txt}(4,528 missing values generated)
{res}{txt}(4,528 missing values generated)
{res}{txt}(4,528 missing values generated)
{res}{txt}
{com}. 
. gen lVA_real_wk_milCE_2014=ln(VA_real_wk_milCE_2014 )
{txt}(4,528 missing values generated)

{com}. gen lVA_real_wk_milCE_WA_2014=ln(VA_real_wk_milCE_WA_2014 )
{txt}(4,528 missing values generated)

{com}. 
. 
. gen ln_rem_realCE_2014=ln(rem_tot_anual_realCE_2014+sqrt(rem_tot_anual_realCE_2014^2+1))
{txt}(4,528 missing values generated)

{com}. gen ln_rem_real_CEWA_2014=ln(rem_tot_anual_realCE_WA_2014+sqrt(rem_tot_anual_realCE_WA_2014^2+1))
{txt}(4,528 missing values generated)

{com}. gen ln_remCE_2014=ln(rem_tot_anualCE_2014+sqrt(rem_tot_anualCE_2014^2+1))
{txt}
{com}. 
. gen ln_insumCE_2014=ln(k000a)
{txt}
{com}. gen ln_insum_realCE_2014=ln((k000a)*(100/INPP_prodB20132014)) 
{txt}(4,528 missing values generated)

{com}. gen ln_insum_realCE_WA_2014=ln((g_insumCE_WA_2014)*(100/INPP_prodB20132014))
{txt}(4,528 missing values generated)

{com}. 
. gen ln_af_realCE_2014=ln(activ_fijos_realCE_2014+sqrt(activ_fijos_realCE_2014^2+1))
{txt}(4,528 missing values generated)

{com}. gen ln_afCE_2014=ln(activ_fijosCE_2014+sqrt(activ_fijosCE_2014^2+1))
{txt}
{com}. 
. gen ln_afCE_WA_2014=ln(activ_fijosCE_WA_2014+sqrt(activ_fijosCE_WA_2014^2+1))
{txt}
{com}. gen ln_af_realCE_WA_2014=ln(activ_fijos_realCE_WA_2014+sqrt(activ_fijos_realCE_WA_2014^2+1))
{txt}(4,528 missing values generated)

{com}. gen lnY_realCE_WA_2014=ln((Y_WA_2014)*(100/INPP_prodB20132014)) 
{txt}(4,528 missing values generated)

{com}. gen lnY_realCE_2014=ln((Y_WA_2014)*(100/INPP_prodB20132014)) 
{txt}(4,528 missing values generated)

{com}. gen lnYCE_WA_2014=ln(Y_WA_2014)
{txt}
{com}. gen lnYCE_2014=ln(Y_WA_2014) 
{txt}
{com}. 
. gen FAC_EXPA=1
{txt}
{com}. 
. 
. ////LTFP INDEX AW & ROBERTS///////
> levelsof sector_2014, local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. foreach var of varlist sh_remCE_2014 sh_insumCE_2014 sh_afCE_2014 lnY_realCE_2014 ln_rem_realCE_2014 ln_insum_realCE_2014 ln_af_realCE_2014 lnYCE_2014 ln_remCE_2014 ln_insumCE_2014 ln_afCE_2014 lnY_realCE_WA_2014 ln_rem_real_CEWA_2014 ln_insum_realCE_WA_2014 ln_af_realCE_WA_2014{c -(}
{txt}  3{com}. sum `var' if sector_2014 =="`x'" , d
{txt}  4{com}. sum `var' if (`var'>r(p1) & `var'<r(p99)) & sector_2014 =="`x'"  [aw=FAC_EXPA], d
{txt}  5{com}. scalar `var'_bar`x'=r(mean)
{txt}  6{com}. {c )-}
{txt}  7{com}. {c )-}

                        {txt}sh_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .3742857       .3742857
{txt} 5%    {res} .3742857       .3742857
{txt}10%    {res} .4105794       .3742857       {txt}Obs         {res}        998
{txt}25%    {res} .4889706       .3742857       {txt}Sum of wgt. {res}        998

{txt}50%    {res} .6108696                      {txt}Mean          {res} .6372439
                        {txt}Largest       Std. dev.     {res} .1906258
{txt}75%    {res} .7326389       .9703947
{txt}90%    {res} .9703947       .9703947       {txt}Variance      {res} .0363382
{txt}95%    {res} .9703947       .9703947       {txt}Skewness      {res} .4952855
{txt}99%    {res} .9703947       .9703947       {txt}Kurtosis      {res} 2.149273

                        {txt}sh_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .4105794       .4105794
{txt} 5%    {res} .4105794       .4105794
{txt}10%    {res} .4105794       .4105794       {txt}Obs         {res}        789
{txt}25%    {res}  .556701       .4105794       {txt}Sum of wgt. {res}        789

{txt}50%    {res} .5700197                      {txt}Mean          {res}  .616992
                        {txt}Largest       Std. dev.     {res}  .144592
{txt}75%    {res} .6676471       .9212329
{txt}90%    {res} .9212329       .9212329       {txt}Variance      {res} .0209068
{txt}95%    {res} .9212329       .9212329       {txt}Skewness      {res} .6983936
{txt}99%    {res} .9212329       .9212329       {txt}Kurtosis      {res} 2.978114

                       {txt}sh_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  2.00789        2.00789
{txt} 5%    {res}  2.00789        2.00789
{txt}10%    {res}  2.00789        2.00789       {txt}Obs         {res}        998
{txt}25%    {res} 2.389175        2.00789       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 2.670588                      {txt}Mean          {res} 2.740094
                        {txt}Largest       Std. dev.     {res} .4797789
{txt}75%    {res} 3.080882       3.626712
{txt}90%    {res} 3.204861       3.626712       {txt}Variance      {res} .2301878
{txt}95%    {res} 3.626712       3.626712       {txt}Skewness      {res} .1259151
{txt}99%    {res} 3.626712       3.626712       {txt}Kurtosis      {res} 2.040498

                       {txt}sh_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 2.171739       2.171739
{txt} 5%    {res} 2.171739       2.171739
{txt}10%    {res} 2.171739       2.171739       {txt}Obs         {res}        801
{txt}25%    {res} 2.389175       2.171739       {txt}Sum of wgt. {res}        801

{txt}50%    {res} 2.670588                      {txt}Mean          {res} 2.732222
                        {txt}Largest       Std. dev.     {res} .3545846
{txt}75%    {res} 3.039474       3.204861
{txt}90%    {res} 3.204861       3.204861       {txt}Variance      {res} .1257302
{txt}95%    {res} 3.204861       3.204861       {txt}Skewness      {res}-.2176762
{txt}99%    {res} 3.204861       3.204861       {txt}Kurtosis      {res} 1.586896

                        {txt}sh_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.547945      -3.547945
{txt} 5%    {res}-3.547945      -3.547945
{txt}10%    {res}-3.009869      -3.547945       {txt}Obs         {res}        998
{txt}25%    {res}  -2.9375      -3.547945       {txt}Sum of wgt. {res}        998

{txt}50%    {res}-2.314286                      {txt}Mean          {res}-2.377338
                        {txt}Largest       Std. dev.     {res} .5986757
{txt}75%    {res}-1.889169      -1.577909
{txt}90%    {res}-1.577909      -1.577909       {txt}Variance      {res} .3584126
{txt}95%    {res}-1.577909      -1.577909       {txt}Skewness      {res}-.4652624
{txt}99%    {res}-1.577909      -1.577909       {txt}Kurtosis      {res} 2.100706

                        {txt}sh_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.009869      -3.009869
{txt} 5%    {res}-3.009869      -3.009869
{txt}10%    {res}-3.009869      -3.009869       {txt}Obs         {res}        801
{txt}25%    {res}  -2.9375      -3.009869       {txt}Sum of wgt. {res}        801

{txt}50%    {res}-2.314286                      {txt}Mean          {res}-2.345221
                        {txt}Largest       Std. dev.     {res} .4508412
{txt}75%    {res}-1.889169      -1.782609
{txt}90%    {res}-1.782609      -1.782609       {txt}Variance      {res} .2032578
{txt}95%    {res}-1.782609      -1.782609       {txt}Skewness      {res}-.2611019
{txt}99%    {res}-1.782609      -1.782609       {txt}Kurtosis      {res} 1.609118

                       {txt}lnY_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.873092       1.873092
{txt} 5%    {res} 1.873092       1.873092
{txt}10%    {res} 2.006623       1.873092       {txt}Obs         {res}        998
{txt}25%    {res} 3.062676       1.873092       {txt}Sum of wgt. {res}        998

{txt}50%    {res}  4.24467                      {txt}Mean          {res} 4.548625
                        {txt}Largest       Std. dev.     {res} 1.930217
{txt}75%    {res} 6.417296       7.253544
{txt}90%    {res}  7.19958       7.253544       {txt}Variance      {res} 3.725739
{txt}95%    {res} 7.253544       7.253544       {txt}Skewness      {res} .0602541
{txt}99%    {res} 7.253544       7.253544       {txt}Kurtosis      {res} 1.470112

                       {txt}lnY_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.927056       1.927056
{txt} 5%    {res} 1.927056       1.927056
{txt}10%    {res} 2.006623       1.927056       {txt}Obs         {res}        891
{txt}25%    {res}  3.11664       1.927056       {txt}Sum of wgt. {res}        891

{txt}50%    {res}  4.24467                      {txt}Mean          {res} 4.525725
                        {txt}Largest       Std. dev.     {res} 1.817247
{txt}75%    {res} 6.363332        7.19958
{txt}90%    {res} 6.945282        7.19958       {txt}Variance      {res} 3.302388
{txt}95%    {res}  7.19958        7.19958       {txt}Skewness      {res}  .086789
{txt}99%    {res}  7.19958        7.19958       {txt}Kurtosis      {res} 1.520836

                     {txt}ln_rem_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.495543       5.495543
{txt} 5%    {res} 5.510695       5.495543
{txt}10%    {res} 5.549506       5.495543       {txt}Obs         {res}        998
{txt}25%    {res} 5.768053       5.495543       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.049577                      {txt}Mean          {res} 6.026472
                        {txt}Largest       Std. dev.     {res}  .291858
{txt}75%    {res} 6.286759       6.361271
{txt}90%    {res} 6.340723       6.361271       {txt}Variance      {res} .0851811
{txt}95%    {res} 6.340723       6.361271       {txt}Skewness      {res} -.623872
{txt}99%    {res} 6.361271       6.361271       {txt}Kurtosis      {res} 1.992068

                     {txt}ln_rem_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.510695       5.510695
{txt} 5%    {res} 5.549506       5.510695
{txt}10%    {res} 5.564657       5.510695       {txt}Obs         {res}        900
{txt}25%    {res} 5.768053       5.510695       {txt}Sum of wgt. {res}        900

{txt}50%    {res} 6.049577                      {txt}Mean          {res}  6.03715
                        {txt}Largest       Std. dev.     {res} .2699608
{txt}75%    {res} 6.286759       6.340723
{txt}90%    {res} 6.312651       6.340723       {txt}Variance      {res} .0728788
{txt}95%    {res} 6.340723       6.340723       {txt}Skewness      {res}-.6822843
{txt}99%    {res} 6.340723       6.340723       {txt}Kurtosis      {res} 2.171564

                    {txt}ln_insum_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}   6.6582         6.6582
{txt} 5%    {res} 6.712164         6.6582
{txt}10%    {res} 6.738426         6.6582       {txt}Obs         {res}        998
{txt}25%    {res} 6.755894         6.6582       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.818808                      {txt}Mean          {res} 6.820554
                        {txt}Largest       Std. dev.     {res}  .071259
{txt}75%    {res} 6.887901       6.946226
{txt}90%    {res} 6.906741       6.946226       {txt}Variance      {res} .0050778
{txt}95%    {res} 6.946226       6.946226       {txt}Skewness      {res} -.253849
{txt}99%    {res} 6.946226       6.946226       {txt}Kurtosis      {res} 2.458939

                    {txt}ln_insum_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.712164       6.712164
{txt} 5%    {res} 6.738426       6.712164
{txt}10%    {res} 6.738426       6.712164       {txt}Obs         {res}        905
{txt}25%    {res} 6.759135       6.712164       {txt}Sum of wgt. {res}        905

{txt}50%    {res} 6.813099                      {txt}Mean          {res} 6.821325
                        {txt}Largest       Std. dev.     {res} .0589263
{txt}75%    {res} 6.872772       6.917489
{txt}90%    {res} 6.906741       6.917489       {txt}Variance      {res} .0034723
{txt}95%    {res} 6.906741       6.917489       {txt}Skewness      {res}-.0758113
{txt}99%    {res} 6.917489       6.917489       {txt}Kurtosis      {res} 1.913525

                      {txt}ln_af_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.819638       5.819638
{txt} 5%    {res} 5.825311       5.819638
{txt}10%    {res} 6.055131       5.819638       {txt}Obs         {res}        998
{txt}25%    {res}  6.19256       5.819638       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.269557                      {txt}Mean          {res} 6.245024
                        {txt}Largest       Std. dev.     {res} .1746954
{txt}75%    {res} 6.367067       6.469238
{txt}90%    {res} 6.408057       6.469238       {txt}Variance      {res} .0305185
{txt}95%    {res} 6.463565       6.469238       {txt}Skewness      {res}-1.116811
{txt}99%    {res} 6.469238       6.469238       {txt}Kurtosis      {res} 3.593049

                      {txt}ln_af_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.825311       5.825311
{txt} 5%    {res} 6.055131       5.825311
{txt}10%    {res} 6.055131       5.825311       {txt}Obs         {res}        905
{txt}25%    {res} 6.198233       5.825311       {txt}Sum of wgt. {res}        905

{txt}50%    {res} 6.269557                      {txt}Mean          {res} 6.257155
                        {txt}Largest       Std. dev.     {res} .1457932
{txt}75%    {res} 6.367067       6.463565
{txt}90%    {res} 6.408057       6.463565       {txt}Variance      {res} .0212556
{txt}95%    {res} 6.408057       6.463565       {txt}Skewness      {res}-1.199506
{txt}99%    {res} 6.463565       6.463565       {txt}Kurtosis      {res} 4.322373

                         {txt}lnYCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  1.94591        1.94591
{txt} 5%    {res}  1.94591        1.94591
{txt}10%    {res} 2.079442        1.94591       {txt}Obs         {res}        998
{txt}25%    {res} 3.135494        1.94591       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 4.317488                      {txt}Mean          {res} 4.594515
                        {txt}Largest       Std. dev.     {res} 1.931159
{txt}75%    {res} 6.436151       7.272398
{txt}90%    {res} 7.272398       7.272398       {txt}Variance      {res} 3.729374
{txt}95%    {res} 7.272398       7.272398       {txt}Skewness      {res} .0601554
{txt}99%    {res} 7.272398       7.272398       {txt}Kurtosis      {res} 1.468193

                         {txt}lnYCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 2.079442       2.079442
{txt} 5%    {res} 2.079442       2.079442
{txt}10%    {res} 2.079442       2.079442       {txt}Obs         {res}        800
{txt}25%    {res} 3.135494       2.079442       {txt}Sum of wgt. {res}        800

{txt}50%    {res} 4.317488                      {txt}Mean          {res} 4.570918
                        {txt}Largest       Std. dev.     {res} 1.701373
{txt}75%    {res} 6.436151       6.964136
{txt}90%    {res} 6.964136       6.964136       {txt}Variance      {res}  2.89467
{txt}95%    {res} 6.964136       6.964136       {txt}Skewness      {res} .1069127
{txt}99%    {res} 6.964136       6.964136       {txt}Kurtosis      {res} 1.555463

                        {txt}ln_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.568359       5.568359
{txt} 5%    {res} 5.568359       5.568359
{txt}10%    {res}  5.58351       5.568359       {txt}Obs         {res}        998
{txt}25%    {res} 5.786907       5.568359       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.118102                      {txt}Mean          {res} 6.072361
                        {txt}Largest       Std. dev.     {res} .2901372
{txt}75%    {res} 6.331505       6.380126
{txt}90%    {res} 6.380126       6.380126       {txt}Variance      {res} .0841796
{txt}95%    {res} 6.380126       6.380126       {txt}Skewness      {res}-.6261064
{txt}99%    {res} 6.380126       6.380126       {txt}Kurtosis      {res} 1.974326

                        {txt}ln_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  5.58351        5.58351
{txt} 5%    {res}  5.58351        5.58351
{txt}10%    {res}  5.58351        5.58351       {txt}Obs         {res}        789
{txt}25%    {res} 6.045011        5.58351       {txt}Sum of wgt. {res}        789

{txt}50%    {res} 6.118102                      {txt}Mean          {res} 6.083434
                        {txt}Largest       Std. dev.     {res} .2511322
{txt}75%    {res} 6.331505       6.359577
{txt}90%    {res} 6.359577       6.359577       {txt}Variance      {res} .0630674
{txt}95%    {res} 6.359577       6.359577       {txt}Skewness      {res}-.7084965
{txt}99%    {res} 6.359577       6.359577       {txt}Kurtosis      {res} 2.368606

                       {txt}ln_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.731018       6.731018
{txt} 5%    {res} 6.731018       6.731018
{txt}10%    {res} 6.811244       6.731018       {txt}Obs         {res}        998
{txt}25%    {res} 6.827629       6.731018       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.891626                      {txt}Mean          {res} 6.866444
                        {txt}Largest       Std. dev.     {res} .0654747
{txt}75%    {res} 6.925595        6.96508
{txt}90%    {res} 6.936343        6.96508       {txt}Variance      {res} .0042869
{txt}95%    {res}  6.96508        6.96508       {txt}Skewness      {res}-.3495692
{txt}99%    {res}  6.96508        6.96508       {txt}Kurtosis      {res} 2.366262

                       {txt}ln_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.811244       6.811244
{txt} 5%    {res} 6.811244       6.811244
{txt}10%    {res} 6.811244       6.811244       {txt}Obs         {res}        818
{txt}25%    {res} 6.828712       6.811244       {txt}Sum of wgt. {res}        818

{txt}50%    {res} 6.831954                      {txt}Mean          {res} 6.869633
                        {txt}Largest       Std. dev.     {res} .0464841
{txt}75%    {res} 6.906755       6.936343
{txt}90%    {res} 6.936343       6.936343       {txt}Variance      {res} .0021608
{txt}95%    {res} 6.936343       6.936343       {txt}Skewness      {res} .1480954
{txt}99%    {res} 6.936343       6.936343       {txt}Kurtosis      {res} 1.308058

                        {txt}ln_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.869305       5.869305
{txt} 5%    {res} 5.869305       5.869305
{txt}10%    {res} 6.104798       5.869305       {txt}Obs         {res}        998
{txt}25%    {res} 6.242227       5.869305       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.313551                      {txt}Mean          {res}  6.29186
                        {txt}Largest       Std. dev.     {res} .1744873
{txt}75%    {res} 6.416735       6.513232
{txt}90%    {res} 6.452052       6.513232       {txt}Variance      {res} .0304458
{txt}95%    {res} 6.513232       6.513232       {txt}Skewness      {res}-1.118183
{txt}99%    {res} 6.513232       6.513232       {txt}Kurtosis      {res} 3.598859

                        {txt}ln_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.104798       6.104798
{txt} 5%    {res} 6.104798       6.104798
{txt}10%    {res} 6.104798       6.104798       {txt}Obs         {res}        821
{txt}25%    {res} 6.242227       6.104798       {txt}Sum of wgt. {res}        821

{txt}50%    {res} 6.313551                      {txt}Mean          {res} 6.314723
                        {txt}Largest       Std. dev.     {res} .1081556
{txt}75%    {res}  6.38351       6.452052
{txt}90%    {res} 6.452052       6.452052       {txt}Variance      {res} .0116976
{txt}95%    {res} 6.452052       6.452052       {txt}Skewness      {res} -.714333
{txt}99%    {res} 6.452052       6.452052       {txt}Kurtosis      {res}  2.51449

                     {txt}lnY_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.873092       1.873092
{txt} 5%    {res} 1.873092       1.873092
{txt}10%    {res} 2.006623       1.873092       {txt}Obs         {res}        998
{txt}25%    {res} 3.062676       1.873092       {txt}Sum of wgt. {res}        998

{txt}50%    {res}  4.24467                      {txt}Mean          {res} 4.548625
                        {txt}Largest       Std. dev.     {res} 1.930217
{txt}75%    {res} 6.417296       7.253544
{txt}90%    {res}  7.19958       7.253544       {txt}Variance      {res} 3.725739
{txt}95%    {res} 7.253544       7.253544       {txt}Skewness      {res} .0602541
{txt}99%    {res} 7.253544       7.253544       {txt}Kurtosis      {res} 1.470112

                     {txt}lnY_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.927056       1.927056
{txt} 5%    {res} 1.927056       1.927056
{txt}10%    {res} 2.006623       1.927056       {txt}Obs         {res}        891
{txt}25%    {res}  3.11664       1.927056       {txt}Sum of wgt. {res}        891

{txt}50%    {res}  4.24467                      {txt}Mean          {res} 4.525725
                        {txt}Largest       Std. dev.     {res} 1.817247
{txt}75%    {res} 6.363332        7.19958
{txt}90%    {res} 6.945282        7.19958       {txt}Variance      {res} 3.302388
{txt}95%    {res}  7.19958        7.19958       {txt}Skewness      {res}  .086789
{txt}99%    {res}  7.19958        7.19958       {txt}Kurtosis      {res} 1.520836

                    {txt}ln_rem_real_CEWA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.495543       5.495543
{txt} 5%    {res} 5.510695       5.495543
{txt}10%    {res} 5.549506       5.495543       {txt}Obs         {res}        998
{txt}25%    {res} 5.768053       5.495543       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.049577                      {txt}Mean          {res} 6.026472
                        {txt}Largest       Std. dev.     {res}  .291858
{txt}75%    {res} 6.286759       6.361271
{txt}90%    {res} 6.340723       6.361271       {txt}Variance      {res} .0851811
{txt}95%    {res} 6.340723       6.361271       {txt}Skewness      {res} -.623872
{txt}99%    {res} 6.361271       6.361271       {txt}Kurtosis      {res} 1.992068

                    {txt}ln_rem_real_CEWA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.510695       5.510695
{txt} 5%    {res} 5.549506       5.510695
{txt}10%    {res} 5.564657       5.510695       {txt}Obs         {res}        900
{txt}25%    {res} 5.768053       5.510695       {txt}Sum of wgt. {res}        900

{txt}50%    {res} 6.049577                      {txt}Mean          {res}  6.03715
                        {txt}Largest       Std. dev.     {res} .2699608
{txt}75%    {res} 6.286759       6.340723
{txt}90%    {res} 6.312651       6.340723       {txt}Variance      {res} .0728788
{txt}95%    {res} 6.340723       6.340723       {txt}Skewness      {res}-.6822843
{txt}99%    {res} 6.340723       6.340723       {txt}Kurtosis      {res} 2.171564

                   {txt}ln_insum_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}   6.6582         6.6582
{txt} 5%    {res} 6.712164         6.6582
{txt}10%    {res} 6.738426         6.6582       {txt}Obs         {res}        998
{txt}25%    {res} 6.755894         6.6582       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.818808                      {txt}Mean          {res} 6.820554
                        {txt}Largest       Std. dev.     {res}  .071259
{txt}75%    {res} 6.887901       6.946226
{txt}90%    {res} 6.906741       6.946226       {txt}Variance      {res} .0050778
{txt}95%    {res} 6.946226       6.946226       {txt}Skewness      {res} -.253849
{txt}99%    {res} 6.946226       6.946226       {txt}Kurtosis      {res} 2.458939

                   {txt}ln_insum_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.712164       6.712164
{txt} 5%    {res} 6.738426       6.712164
{txt}10%    {res} 6.738426       6.712164       {txt}Obs         {res}        905
{txt}25%    {res} 6.759135       6.712164       {txt}Sum of wgt. {res}        905

{txt}50%    {res} 6.813099                      {txt}Mean          {res} 6.821325
                        {txt}Largest       Std. dev.     {res} .0589263
{txt}75%    {res} 6.872772       6.917489
{txt}90%    {res} 6.906741       6.917489       {txt}Variance      {res} .0034723
{txt}95%    {res} 6.906741       6.917489       {txt}Skewness      {res}-.0758113
{txt}99%    {res} 6.917489       6.917489       {txt}Kurtosis      {res} 1.913525

                    {txt}ln_af_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.819638       5.819638
{txt} 5%    {res} 5.825311       5.819638
{txt}10%    {res} 6.055131       5.819638       {txt}Obs         {res}        998
{txt}25%    {res}  6.19256       5.819638       {txt}Sum of wgt. {res}        998

{txt}50%    {res} 6.269557                      {txt}Mean          {res} 6.244929
                        {txt}Largest       Std. dev.     {res}  .174574
{txt}75%    {res} 6.367067       6.467086
{txt}90%    {res} 6.408057       6.467086       {txt}Variance      {res} .0304761
{txt}95%    {res} 6.463565       6.467086       {txt}Skewness      {res}-1.120179
{txt}99%    {res} 6.467086       6.467086       {txt}Kurtosis      {res} 3.596065

                    {txt}ln_af_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.825311       5.825311
{txt} 5%    {res} 6.055131       5.825311
{txt}10%    {res} 6.055131       5.825311       {txt}Obs         {res}        905
{txt}25%    {res} 6.198233       5.825311       {txt}Sum of wgt. {res}        905

{txt}50%    {res} 6.269557                      {txt}Mean          {res} 6.257155
                        {txt}Largest       Std. dev.     {res} .1457932
{txt}75%    {res} 6.367067       6.463565
{txt}90%    {res} 6.408057       6.463565       {txt}Variance      {res} .0212556
{txt}95%    {res} 6.408057       6.463565       {txt}Skewness      {res}-1.199506
{txt}99%    {res} 6.463565       6.463565       {txt}Kurtosis      {res} 4.322373

                        {txt}sh_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .3742857       .3742857
{txt} 5%    {res} .3742857       .3742857
{txt}10%    {res} .3742857       .3742857       {txt}Obs         {res}      8,038
{txt}25%    {res} .4889706       .3742857       {txt}Sum of wgt. {res}      8,038

{txt}50%    {res} .6108696                      {txt}Mean          {res} .6296489
                        {txt}Largest       Std. dev.     {res} .1888344
{txt}75%    {res} .7326389       .9703947
{txt}90%    {res} .9212329       .9703947       {txt}Variance      {res} .0356584
{txt}95%    {res} .9703947       .9703947       {txt}Skewness      {res}  .500651
{txt}99%    {res} .9703947       .9703947       {txt}Kurtosis      {res} 2.188791

                        {txt}sh_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} .4105794       .4105794
{txt} 5%    {res} .4105794       .4105794
{txt}10%    {res} .4105794       .4105794       {txt}Obs         {res}      6,409
{txt}25%    {res} .4889706       .4105794       {txt}Sum of wgt. {res}      6,409

{txt}50%    {res} .6108696                      {txt}Mean          {res} .6207046
                        {txt}Largest       Std. dev.     {res} .1475104
{txt}75%    {res} .7326389       .9212329
{txt}90%    {res} .9212329       .9212329       {txt}Variance      {res} .0217593
{txt}95%    {res} .9212329       .9212329       {txt}Skewness      {res} .6481336
{txt}99%    {res} .9212329       .9212329       {txt}Kurtosis      {res} 2.808705

                       {txt}sh_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  2.00789        2.00789
{txt} 5%    {res}  2.00789        2.00789
{txt}10%    {res} 2.171739        2.00789       {txt}Obs         {res}      8,038
{txt}25%    {res} 2.389175        2.00789       {txt}Sum of wgt. {res}      8,038

{txt}50%    {res}     2.94                      {txt}Mean          {res} 2.764499
                        {txt}Largest       Std. dev.     {res} .4775621
{txt}75%    {res} 3.080882       3.626712
{txt}90%    {res} 3.204861       3.626712       {txt}Variance      {res} .2280655
{txt}95%    {res} 3.626712       3.626712       {txt}Skewness      {res} .0733873
{txt}99%    {res} 3.626712       3.626712       {txt}Kurtosis      {res} 2.063862

                       {txt}sh_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 2.171739       2.171739
{txt} 5%    {res} 2.171739       2.171739
{txt}10%    {res} 2.171739       2.171739       {txt}Obs         {res}      6,465
{txt}25%    {res}  2.47859       2.171739       {txt}Sum of wgt. {res}      6,465

{txt}50%    {res}     2.94                      {txt}Mean          {res}  2.74777
                        {txt}Largest       Std. dev.     {res} .3503826
{txt}75%    {res} 3.039474       3.204861
{txt}90%    {res} 3.204861       3.204861       {txt}Variance      {res} .1227679
{txt}95%    {res} 3.204861       3.204861       {txt}Skewness      {res}-.2734694
{txt}99%    {res} 3.204861       3.204861       {txt}Kurtosis      {res} 1.646055

                        {txt}sh_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.547945      -3.547945
{txt} 5%    {res}-3.547945      -3.547945
{txt}10%    {res}-3.009869      -3.547945       {txt}Obs         {res}      8,038
{txt}25%    {res}  -2.9375      -3.547945       {txt}Sum of wgt. {res}      8,038

{txt}50%    {res}-2.338235                      {txt}Mean          {res}-2.394148
                        {txt}Largest       Std. dev.     {res} .5930049
{txt}75%    {res}-1.889169      -1.577909
{txt}90%    {res}-1.782609      -1.577909       {txt}Variance      {res} .3516548
{txt}95%    {res}-1.577909      -1.577909       {txt}Skewness      {res}-.4599574
{txt}99%    {res}-1.577909      -1.577909       {txt}Kurtosis      {res} 2.162753

                        {txt}sh_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.009869      -3.009869
{txt} 5%    {res}-3.009869      -3.009869
{txt}10%    {res}-3.009869      -3.009869       {txt}Obs         {res}      6,465
{txt}25%    {res}-2.569853      -3.009869       {txt}Sum of wgt. {res}      6,465

{txt}50%    {res}-2.314286                      {txt}Mean          {res}-2.348359
                        {txt}Largest       Std. dev.     {res}  .436457
{txt}75%    {res}-1.889169      -1.782609
{txt}90%    {res}-1.782609      -1.782609       {txt}Variance      {res} .1904948
{txt}95%    {res}-1.782609      -1.782609       {txt}Skewness      {res}-.2382166
{txt}99%    {res}-1.782609      -1.782609       {txt}Kurtosis      {res} 1.678188

                       {txt}lnY_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.898174       1.898174
{txt} 5%    {res} 1.913279       1.898174
{txt}10%    {res} 2.031705       1.898174       {txt}Obs         {res}      3,510
{txt}25%    {res} 3.102863       1.898174       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 4.269752                      {txt}Mean          {res} 4.519559
                        {txt}Largest       Std. dev.     {res} 1.881045
{txt}75%    {res} 6.403354       7.256112
{txt}90%    {res} 6.947849       7.256112       {txt}Variance      {res}  3.53833
{txt}95%    {res} 7.239768       7.256112       {txt}Skewness      {res} .0868862
{txt}99%    {res} 7.256112       7.256112       {txt}Kurtosis      {res} 1.530427

                       {txt}lnY_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.909652       1.909652
{txt} 5%    {res}  1.92621       1.909652
{txt}10%    {res} 2.031705       1.909652       {txt}Obs         {res}      3,412
{txt}25%    {res} 3.115794       1.909652       {txt}Sum of wgt. {res}      3,412

{txt}50%    {res} 4.269752                      {txt}Mean          {res} 4.522616
                        {txt}Largest       Std. dev.     {res} 1.853168
{txt}75%    {res} 6.403354       7.255249
{txt}90%    {res} 6.947849       7.255249       {txt}Variance      {res}  3.43423
{txt}95%    {res} 7.239603       7.255249       {txt}Skewness      {res} .0882515
{txt}99%    {res} 7.255185       7.255249       {txt}Kurtosis      {res} 1.545508

                     {txt}ln_rem_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.520624       5.520624
{txt} 5%    {res} 5.535564       5.520624
{txt}10%    {res} 5.547254       5.520624       {txt}Obs         {res}      3,510
{txt}25%    {res} 5.754111       5.520624       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 6.052145                      {txt}Mean          {res} 6.018197
                        {txt}Largest       Std. dev.     {res} .2965327
{txt}75%    {res} 6.298874       6.363839
{txt}90%    {res} 6.342363       6.363839       {txt}Variance      {res} .0879316
{txt}95%    {res} 6.347495       6.363839       {txt}Skewness      {res}-.5126401
{txt}99%    {res} 6.363839       6.363839       {txt}Kurtosis      {res} 1.807888

                     {txt}ln_rem_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.532102       5.532102
{txt} 5%    {res} 5.535775       5.532102
{txt}10%    {res} 5.550715       5.532102       {txt}Obs         {res}      3,386
{txt}25%    {res} 5.754277       5.532102       {txt}Sum of wgt. {res}      3,386

{txt}50%    {res} 6.052145                      {txt}Mean          {res} 6.022473
                        {txt}Largest       Std. dev.     {res} .2901457
{txt}75%    {res} 6.298709       6.362912
{txt}90%    {res} 6.339877       6.362912       {txt}Variance      {res} .0841845
{txt}95%    {res}  6.34733       6.362977       {txt}Skewness      {res}-.5381609
{txt}99%    {res} 6.362912       6.362977       {txt}Kurtosis      {res} 1.867988

                    {txt}ln_insum_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.683281       6.683281
{txt} 5%    {res} 6.698387       6.683281
{txt}10%    {res} 6.763508       6.683281       {txt}Obs         {res}      3,510
{txt}25%    {res} 6.794833       6.683281       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 6.855368                      {txt}Mean          {res} 6.838196
                        {txt}Largest       Std. dev.     {res} .0684461
{txt}75%    {res} 6.892799       6.948794
{txt}90%    {res} 6.920056       6.948794       {txt}Variance      {res} .0046849
{txt}95%    {res} 6.932449       6.948794       {txt}Skewness      {res}-.3905615
{txt}99%    {res} 6.948794       6.948794       {txt}Kurtosis      {res} 2.370748

                    {txt}ln_insum_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  6.69476        6.69476
{txt} 5%    {res} 6.711318        6.69476
{txt}10%    {res} 6.774986        6.69476       {txt}Obs         {res}      3,399
{txt}25%    {res} 6.794958        6.69476       {txt}Sum of wgt. {res}      3,399

{txt}50%    {res} 6.855368                      {txt}Mean          {res} 6.839505
                        {txt}Largest       Std. dev.     {res} .0649498
{txt}75%    {res} 6.892799       6.947931
{txt}90%    {res} 6.919129       6.947931       {txt}Variance      {res} .0042185
{txt}95%    {res} 6.932284       6.947931       {txt}Skewness      {res}-.3506771
{txt}99%    {res} 6.947867       6.947931       {txt}Kurtosis      {res} 2.326337

                      {txt}ln_af_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.823159       5.823159
{txt} 5%    {res} 5.823159       5.823159
{txt}10%    {res} 5.823159       5.823159       {txt}Obs         {res}      3,510
{txt}25%    {res} 6.196081       5.823159       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 6.267405                      {txt}Mean          {res}  6.24432
                        {txt}Largest       Std. dev.     {res} .1832808
{txt}75%    {res} 6.370589       6.467086
{txt}90%    {res} 6.405905       6.467086       {txt}Variance      {res} .0335919
{txt}95%    {res} 6.467086       6.467086       {txt}Skewness      {res}-1.137111
{txt}99%    {res} 6.467086       6.467086       {txt}Kurtosis      {res} 3.471415

                      {txt}ln_af_realCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.058652       6.058652
{txt} 5%    {res} 6.058652       6.058652
{txt}10%    {res} 6.058652       6.058652       {txt}Obs         {res}      2,784
{txt}25%    {res} 6.226734       6.058652       {txt}Sum of wgt. {res}      2,784

{txt}50%    {res} 6.327177                      {txt}Mean          {res} 6.275507
                        {txt}Largest       Std. dev.     {res} .1035245
{txt}75%    {res} 6.337363       6.405905
{txt}90%    {res} 6.405905       6.405905       {txt}Variance      {res} .0107173
{txt}95%    {res} 6.405905       6.405905       {txt}Skewness      {res}-.7646925
{txt}99%    {res} 6.405905       6.405905       {txt}Kurtosis      {res}   2.7464

                         {txt}lnYCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  1.94591        1.94591
{txt} 5%    {res}  1.94591        1.94591
{txt}10%    {res}  1.94591        1.94591       {txt}Obs         {res}      8,038
{txt}25%    {res} 3.135494        1.94591       {txt}Sum of wgt. {res}      8,038

{txt}50%    {res} 4.317488                      {txt}Mean          {res} 4.544511
                        {txt}Largest       Std. dev.     {res} 1.888545
{txt}75%    {res} 6.436151       7.272398
{txt}90%    {res} 6.964136       7.272398       {txt}Variance      {res} 3.566603
{txt}95%    {res} 7.272398       7.272398       {txt}Skewness      {res} .0906293
{txt}99%    {res} 7.272398       7.272398       {txt}Kurtosis      {res} 1.526127

                         {txt}lnYCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 2.079442       2.079442
{txt} 5%    {res} 2.079442       2.079442
{txt}10%    {res} 2.079442       2.079442       {txt}Obs         {res}      6,457
{txt}25%    {res} 3.218876       2.079442       {txt}Sum of wgt. {res}      6,457

{txt}50%    {res} 4.317488                      {txt}Mean          {res} 4.555492
                        {txt}Largest       Std. dev.     {res} 1.644805
{txt}75%    {res} 6.077642       6.964136
{txt}90%    {res} 6.964136       6.964136       {txt}Variance      {res} 2.705384
{txt}95%    {res} 6.964136       6.964136       {txt}Skewness      {res} .1178502
{txt}99%    {res} 6.964136       6.964136       {txt}Kurtosis      {res} 1.615893

                        {txt}ln_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.568359       5.568359
{txt} 5%    {res} 5.568359       5.568359
{txt}10%    {res} 5.568359       5.568359       {txt}Obs         {res}      8,038
{txt}25%    {res} 5.786907       5.568359       {txt}Sum of wgt. {res}      8,038

{txt}50%    {res} 6.068431                      {txt}Mean          {res} 6.050471
                        {txt}Largest       Std. dev.     {res} .2947234
{txt}75%    {res} 6.331505       6.380126
{txt}90%    {res} 6.359577       6.380126       {txt}Variance      {res} .0868619
{txt}95%    {res} 6.380126       6.380126       {txt}Skewness      {res} -.528225
{txt}99%    {res} 6.380126       6.380126       {txt}Kurtosis      {res} 1.828855

                        {txt}ln_remCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  5.58351        5.58351
{txt} 5%    {res}  5.58351        5.58351
{txt}10%    {res}  5.58351        5.58351       {txt}Obs         {res}      6,409
{txt}25%    {res} 5.786907        5.58351       {txt}Sum of wgt. {res}      6,409

{txt}50%    {res} 6.118102                      {txt}Mean          {res} 6.072442
                        {txt}Largest       Std. dev.     {res} .2543776
{txt}75%    {res} 6.287862       6.359577
{txt}90%    {res} 6.359577       6.359577       {txt}Variance      {res}  .064708
{txt}95%    {res} 6.359577       6.359577       {txt}Skewness      {res}-.6727167
{txt}99%    {res} 6.359577       6.359577       {txt}Kurtosis      {res} 2.289942

                       {txt}ln_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.731018       6.731018
{txt} 5%    {res} 6.731018       6.731018
{txt}10%    {res} 6.811244       6.731018       {txt}Obs         {res}      8,038
{txt}25%    {res} 6.827629       6.731018       {txt}Sum of wgt. {res}      8,038

{txt}50%    {res} 6.891626                      {txt}Mean          {res} 6.865969
                        {txt}Largest       Std. dev.     {res} .0676283
{txt}75%    {res} 6.925595        6.96508
{txt}90%    {res} 6.936343        6.96508       {txt}Variance      {res} .0045736
{txt}95%    {res}  6.96508        6.96508       {txt}Skewness      {res}-.3603925
{txt}99%    {res}  6.96508        6.96508       {txt}Kurtosis      {res} 2.290175

                       {txt}ln_insumCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.811244       6.811244
{txt} 5%    {res} 6.811244       6.811244
{txt}10%    {res} 6.811244       6.811244       {txt}Obs         {res}      6,443
{txt}25%    {res} 6.827629       6.811244       {txt}Sum of wgt. {res}      6,443

{txt}50%    {res} 6.891626                      {txt}Mean          {res} 6.870242
                        {txt}Largest       Std. dev.     {res} .0471482
{txt}75%    {res} 6.906755       6.936343
{txt}90%    {res} 6.936343       6.936343       {txt}Variance      {res} .0022229
{txt}95%    {res} 6.936343       6.936343       {txt}Skewness      {res} .1307456
{txt}99%    {res} 6.936343       6.936343       {txt}Kurtosis      {res} 1.303405

                        {txt}ln_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.869305       5.869305
{txt} 5%    {res} 5.869305       5.869305
{txt}10%    {res} 5.869305       5.869305       {txt}Obs         {res}      8,038
{txt}25%    {res} 6.242227       5.869305       {txt}Sum of wgt. {res}      8,038

{txt}50%    {res} 6.313551                      {txt}Mean          {res}   6.2919
                        {txt}Largest       Std. dev.     {res} .1809688
{txt}75%    {res} 6.416735       6.513232
{txt}90%    {res} 6.452052       6.513232       {txt}Variance      {res} .0327497
{txt}95%    {res} 6.513232       6.513232       {txt}Skewness      {res}-1.131032
{txt}99%    {res} 6.513232       6.513232       {txt}Kurtosis      {res} 3.517609

                        {txt}ln_afCE_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.104798       6.104798
{txt} 5%    {res} 6.104798       6.104798
{txt}10%    {res} 6.104798       6.104798       {txt}Obs         {res}      6,410
{txt}25%    {res} 6.242227       6.104798       {txt}Sum of wgt. {res}      6,410

{txt}50%    {res} 6.373322                      {txt}Mean          {res} 6.319567
                        {txt}Largest       Std. dev.     {res}  .104759
{txt}75%    {res}  6.38351       6.452052
{txt}90%    {res} 6.452052       6.452052       {txt}Variance      {res} .0109744
{txt}95%    {res} 6.452052       6.452052       {txt}Skewness      {res}-.7363991
{txt}99%    {res} 6.452052       6.452052       {txt}Kurtosis      {res} 2.666518

                     {txt}lnY_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.898174       1.898174
{txt} 5%    {res} 1.913279       1.898174
{txt}10%    {res} 2.031705       1.898174       {txt}Obs         {res}      3,510
{txt}25%    {res} 3.102863       1.898174       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 4.269752                      {txt}Mean          {res} 4.519559
                        {txt}Largest       Std. dev.     {res} 1.881045
{txt}75%    {res} 6.403354       7.256112
{txt}90%    {res} 6.947849       7.256112       {txt}Variance      {res}  3.53833
{txt}95%    {res} 7.239768       7.256112       {txt}Skewness      {res} .0868862
{txt}99%    {res} 7.256112       7.256112       {txt}Kurtosis      {res} 1.530427

                     {txt}lnY_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 1.909652       1.909652
{txt} 5%    {res}  1.92621       1.909652
{txt}10%    {res} 2.031705       1.909652       {txt}Obs         {res}      3,412
{txt}25%    {res} 3.115794       1.909652       {txt}Sum of wgt. {res}      3,412

{txt}50%    {res} 4.269752                      {txt}Mean          {res} 4.522616
                        {txt}Largest       Std. dev.     {res} 1.853168
{txt}75%    {res} 6.403354       7.255249
{txt}90%    {res} 6.947849       7.255249       {txt}Variance      {res}  3.43423
{txt}95%    {res} 7.239603       7.255249       {txt}Skewness      {res} .0882515
{txt}99%    {res} 7.255185       7.255249       {txt}Kurtosis      {res} 1.545508

                    {txt}ln_rem_real_CEWA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.520624       5.520624
{txt} 5%    {res} 5.535564       5.520624
{txt}10%    {res} 5.547254       5.520624       {txt}Obs         {res}      3,510
{txt}25%    {res} 5.754111       5.520624       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 6.052145                      {txt}Mean          {res} 6.018197
                        {txt}Largest       Std. dev.     {res} .2965327
{txt}75%    {res} 6.298874       6.363839
{txt}90%    {res} 6.342363       6.363839       {txt}Variance      {res} .0879316
{txt}95%    {res} 6.347495       6.363839       {txt}Skewness      {res}-.5126401
{txt}99%    {res} 6.363839       6.363839       {txt}Kurtosis      {res} 1.807888

                    {txt}ln_rem_real_CEWA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.532102       5.532102
{txt} 5%    {res} 5.535775       5.532102
{txt}10%    {res} 5.550715       5.532102       {txt}Obs         {res}      3,386
{txt}25%    {res} 5.754277       5.532102       {txt}Sum of wgt. {res}      3,386

{txt}50%    {res} 6.052145                      {txt}Mean          {res} 6.022473
                        {txt}Largest       Std. dev.     {res} .2901457
{txt}75%    {res} 6.298709       6.362912
{txt}90%    {res} 6.339877       6.362912       {txt}Variance      {res} .0841845
{txt}95%    {res}  6.34733       6.362977       {txt}Skewness      {res}-.5381609
{txt}99%    {res} 6.362912       6.362977       {txt}Kurtosis      {res} 1.867988

                   {txt}ln_insum_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.683281       6.683281
{txt} 5%    {res} 6.698387       6.683281
{txt}10%    {res} 6.763508       6.683281       {txt}Obs         {res}      3,510
{txt}25%    {res} 6.794833       6.683281       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 6.855368                      {txt}Mean          {res} 6.838196
                        {txt}Largest       Std. dev.     {res} .0684461
{txt}75%    {res} 6.892799       6.948794
{txt}90%    {res} 6.920056       6.948794       {txt}Variance      {res} .0046849
{txt}95%    {res} 6.932449       6.948794       {txt}Skewness      {res}-.3905615
{txt}99%    {res} 6.948794       6.948794       {txt}Kurtosis      {res} 2.370748

                   {txt}ln_insum_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res}  6.69476        6.69476
{txt} 5%    {res} 6.711318        6.69476
{txt}10%    {res} 6.774986        6.69476       {txt}Obs         {res}      3,399
{txt}25%    {res} 6.794958        6.69476       {txt}Sum of wgt. {res}      3,399

{txt}50%    {res} 6.855368                      {txt}Mean          {res} 6.839505
                        {txt}Largest       Std. dev.     {res} .0649498
{txt}75%    {res} 6.892799       6.947931
{txt}90%    {res} 6.919129       6.947931       {txt}Variance      {res} .0042185
{txt}95%    {res} 6.932284       6.947931       {txt}Skewness      {res}-.3506771
{txt}99%    {res} 6.947867       6.947931       {txt}Kurtosis      {res} 2.326337

                    {txt}ln_af_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 5.823159       5.823159
{txt} 5%    {res} 5.823159       5.823159
{txt}10%    {res} 5.823159       5.823159       {txt}Obs         {res}      3,510
{txt}25%    {res} 6.196081       5.823159       {txt}Sum of wgt. {res}      3,510

{txt}50%    {res} 6.267405                      {txt}Mean          {res}  6.24432
                        {txt}Largest       Std. dev.     {res} .1832808
{txt}75%    {res} 6.370589       6.467086
{txt}90%    {res} 6.405905       6.467086       {txt}Variance      {res} .0335919
{txt}95%    {res} 6.467086       6.467086       {txt}Skewness      {res}-1.137111
{txt}99%    {res} 6.467086       6.467086       {txt}Kurtosis      {res} 3.471415

                    {txt}ln_af_realCE_WA_2014
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.058652       6.058652
{txt} 5%    {res} 6.058652       6.058652
{txt}10%    {res} 6.058652       6.058652       {txt}Obs         {res}      2,784
{txt}25%    {res} 6.226734       6.058652       {txt}Sum of wgt. {res}      2,784

{txt}50%    {res} 6.327177                      {txt}Mean          {res} 6.275507
                        {txt}Largest       Std. dev.     {res} .1035245
{txt}75%    {res} 6.337363       6.405905
{txt}90%    {res} 6.405905       6.405905       {txt}Variance      {res} .0107173
{txt}95%    {res} 6.405905       6.405905       {txt}Skewness      {res}-.7646925
{txt}99%    {res} 6.405905       6.405905       {txt}Kurtosis      {res}   2.7464
{txt}
{com}. 
. 
. gen ltfp_secCE_2014=.
{txt}(9,036 missing values generated)

{com}. levelsof sector_2014, local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_secCE_2014=(lnYCE_2014-lnYCE_2014_bar`x')-(0.5)*(sh_remCE_2014+sh_remCE_2014_bar`x')*(ln_remCE_2014-ln_remCE_2014_bar`x')-(0.5)*(sh_insumCE_2014+sh_insumCE_2014_bar`x')*(ln_insumCE_2014-ln_insumCE_2014_bar`x')-(0.5)*(sh_afCE_2014+sh_afCE_2014_bar`x')*(ln_afCE_2014-ln_afCE_2014_bar`x') if sector_2014=="`x'"
{txt}  3{com}. {c )-}
{txt}(998 real changes made)
(8,038 real changes made)

{com}. 
. 
. gen ltfp_sec_realCE_2014=.
{txt}(9,036 missing values generated)

{com}. levelsof sector_2014 , local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_realCE_2014=(lnY_realCE_2014-lnY_realCE_2014_bar`x')-(0.5)*(sh_remCE_2014+sh_remCE_2014_bar`x')*(ln_rem_realCE_2014-ln_rem_realCE_2014_bar`x')-(0.5)*(sh_insumCE_2014+sh_insumCE_2014_bar`x')*(ln_insum_realCE_2014-ln_insum_realCE_2014_bar`x')-(0.5)*(sh_afCE_2014 +sh_afCE_2014_bar`x')*(ln_af_realCE_2014-ln_af_realCE_2014_bar`x') if sector_2014 =="`x'"
{txt}  3{com}. {c )-}
{txt}(998 real changes made)
(3,510 real changes made)

{com}. 
. gen ltfp_sec_realCE_WA_2014=.
{txt}(9,036 missing values generated)

{com}. levelsof sector_2014 , local(sectores)
{res}{txt}`"MAN"' `"SER"'

{com}. foreach x of local sectores {c -(}
{txt}  2{com}. replace ltfp_sec_realCE_WA_2014=(lnY_realCE_WA_2014-lnY_realCE_WA_2014_bar`x')-(0.5)*(sh_remCE_2014+sh_remCE_2014_bar`x')*(ln_rem_real_CEWA_2014-ln_rem_real_CEWA_2014_bar`x')-(0.5)*(sh_insumCE_2014+sh_insumCE_2014_bar`x')*(ln_insum_realCE_WA_2014-ln_insum_realCE_WA_2014_bar`x')-(0.5)*(sh_afCE_2014 +sh_afCE_2014_bar`x')*(ln_af_realCE_WA_2014-ln_af_realCE_WA_2014_bar`x') if sector_2014 =="`x'"
{txt}  3{com}. {c )-}
{txt}(998 real changes made)
(3,510 real changes made)

{com}. 
. gen multiplant=1 if f01!=""
{txt}(9,036 missing values generated)

{com}. gen _j=2014
{txt}
{com}. gen lcapworkCEWA_2014=ln(activ_fijos_realCE_WA_2014/nworkersCE_WA_2014)
{txt}(4,528 missing values generated)

{com}. gen lnworkersCE_WA_2014=ln(nworkersCE_WA_2014)
{txt}
{com}. destring clase, gen(claseCE)
{txt}clase: all characters numeric; claseCE {res}generated {txt}as {res}long
{txt}
{com}. 
. 
. keep clee lVA_real_wk_milCE_WA_2014 ltfp_sec_realCE_WA_2014 multiplant _j  lcapworkCEWA_2014  lnworkersCE_WA_2014 claseCE
{txt}
{com}. save "$working/data_CE_2014estimates.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/data_CE_2014estimates.dta{rm}
saved
{p_end}

{com}. 
. 
. 
{txt}end of do-file

{com}. 
. * ---------------------------------------------------------------------------
. * 2: Main Analysis
. *This step produces the final Figures and Regressions of the main paper
. do "$programs/Figures.do"
{txt}
{com}. /********************************************************************************************
> * Project: Management and Misallocation in Mexico
> * Purpose: Generate main figures of the paper
> * Date: 2024-12-01
> ********************************************************************************************/
.  
.  
. 
.  
. /******************************************************************** 
>  * Figure 1: Firm Performance and Management Practices in Mexico  
>  ********************************************************************/
.  use "$working_interm/Management and performance.dta", clear
{txt}
{com}. 
.  *a) Productivity
. graph bar tfp_man tfp_serv, over(decile) ///
>     bar(1, color(navy)) ///
>     bar(2, color(eltblue)) ///
>     ytitle("Productivity (normed to 100 in the first decile)", size(medium)) ///
>     graphregion(color(white)) ///
>     plotregion(color(white)) ///
>     legend(off) ///
>     ylabel(, labsize(medium)) 
{res}{txt}
{com}.         
.         graph save "$figures/Figure_1a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1a.gph} saved

{com}. 
.  
.  *b) Profitability
.  
.  gen decile_offset= decile + 0.4  // Shift second variable slightly for visibility
{txt}
{com}. 
. twoway ///
>     (bar profitab_man decile, barwidth(0.4) color(navy)) ///
>     (bar profitab_serv decile_offset, barwidth(0.4) color(eltblue)), ///
>     ytitle("Profitability (operating profits/sales)", size(medium)) ///
>     graphregion(color(white)) ///
>     plotregion(color(white)) ///
>     legend(off) ///
>         xtitle("") ///
>     ylabel(0.25(0.1)0.6, labsize(medium)) /// 
>     yscale(range(0.25 0.6)) ///
>     xlabel(1.2 "1" 2.2 "2" 3.2 "3" 4.2 "4" 5.2 "5" 6.2 "6" 7.2 "7" 8.2 "8" 9.2 "9" 10.2 "10")
{res}{txt}
{com}.  
. 
.         graph save "$figures/Figure_1b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1b.gph} saved

{com}. 
.         
.  *c) Exports
.  graph bar shexp_man, over(decile) ///
>     bar(1, color(navy)) ///
>     ytitle("Exporters (share in each decile)", size(medium)) ///
>     graphregion(color(white)) ///
>     plotregion(color(white)) ///
>     legend(off) ///
>     ylabel(, labsize(medium)) 
{res}{txt}
{com}.         
.         graph save "$figures/Figure_1c.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1c.gph} saved

{com}. 
.  
.  *d) R&D expenditure/workers
. graph bar r_d_worker_man r_d_worker_serv, over(decile) ///
>     bar(1, color(navy)) ///
>     bar(2, color(eltblue)) ///
>     ytitle("R&D expenditure/worker '000s", size(medium)) ///
>     graphregion(color(white)) ///
>     plotregion(color(white)) ///
>     legend(off) ///
>     ylabel(, labsize(medium)) 
{res}{txt}
{com}.  
.         graph save "$figures/Figure_1d.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1d.gph} saved

{com}. 
.  
.  *e) Patents
.  graph bar dum_pat_man dum_pat_serv, over(decile) ///
>     bar(1, color(navy)) ///
>     bar(2, color(eltblue)) ///
>     ytitle("Has patents (share of firms in each decile)", size(medium)) ///
>     graphregion(color(white)) ///
>     plotregion(color(white)) ///
>     legend(off) ///
>     ylabel(, labsize(medium)) 
{res}{txt}
{com}.         
.         graph save "$figures/Figure_1e.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1e.gph} saved

{com}. 
.  
.   *f) Number of employees
.  graph bar nworkers_man nworkers_serv, over(decile) ///
>     bar(1, color(navy)) ///
>     bar(2, color(eltblue)) ///
>     ytitle("Number of employees", size(medium)) ///
>     graphregion(color(white)) ///
>     plotregion(color(white)) ///
>     legend(off) ///
>     ylabel(, labsize(medium)) 
{res}{txt}
{com}.         
.         graph save "$figures/Figure_1f.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1f.gph} saved

{com}. 
.  
.  
. graph combine "$figures/Figure_1a.gph" "$figures/Figure_1b.gph" "$figures/Figure_1c.gph" ///
>               "$figures/Figure_1d.gph" "$figures/Figure_1e.gph" "$figures/Figure_1f.gph", ///
>               graphregion(color(white)) plotregion(color(white)) ///
>               note("Manufacturing = Navy Blue; Services = Light Blue", size(small))
{res}{txt}
{com}. graph save "$figures/Figure_1.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1.gph} saved

{com}. graph export "$figures/Figure_1.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1.jpg} written in JPEG format

{com}. graph export "$figures/Figure_1.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_1.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. /******************************************************************** 
> *Figure 2- Management distribution across firms
> ********************************************************************/
. *Data for Mexico was obtanined from pooled dataset of ENAPROCE 2015 & 2017
. *Data for US was obtained from Bloom, N., Brynjolfsson, E., Foster, L., Jarmin, R., Patnaik, M., Saporta-Eksten, I., & Van Reenen, J. (2019). What drives differences in management practices?. American Economic Review, 109(5), 1648-83. Figure 1 using  PlotDigitizer
. *This plot runs in Stata 16-18 (not for previous versions)
. 
. 
. *Settings
. use "$working_interm/Densities MX vs US.dta", clear
{txt}
{com}. 
.    
.    twoway rarea manuf_ zero bin, fcolor("none") lc(black) lw(thick)   ///
>    ||  rarea us_manuf zero bin, fcolor("none") lc(black)      ///
>    ||  rarea serv_ zero bin, fcolor("none")  lc(gs10) lw(thick) lp(dash) ///
>    || function y=0, range(0.9 17.9) lcolor(white) lwidth(vthick)  ///
>    || function y=0.0005, range(1.1 18) lcolor(white) lwidth(vthick)  ///
>           ,  xlabel(0.5 "0" 5.5 "0.25" 10.5 "0.5" 15.5 "0.75" 20.5 "1") xtitle("Management score") ///
>    ytitle("Share of firms") graphregion(color(white)) plotregion(color(white)) ///
>    , legend(order(1 "Manufacturing MX" 2 "Manufacturing US" 3 "Services MX") pos(6) col(2) region(lwidth(none)))
{res}{txt}
{com}.    
.    * .gph file
. graph save "$figures/Figure_2.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_2.gph} saved

{com}. * .jpg file
. graph export "$figures/Figure_2.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_2.jpg} written in JPEG format

{com}. graph export "$figures/Figure_2.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures/Figure_2.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
.    
.  /********************************************************************
>  *Figure 3: Reallocation - manufacturing and Services 2014 and 2017
> ********************************************************************/
. *Data for Mexico was obtained using Stata's binscatter command separately for Manufacturing and Services
. *Data for US was obtained from Bloom, N., Brynjolfsson, E., Foster, L., Jarmin, R., Patnaik, M., Saporta-Eksten, I., & Van Reenen, J. (2019). What drives differences in management practices?. American Economic Review, 109(5), 1648-83 using  PlotDigitizer
. 
. use "$working_interm/Binscatters size management MS vs US.dta" , clear
{txt}
{com}. 
. 
. twoway (scatter lnworkers_wa_manpool score_manag_finwa_manpool, msymbol(circle) mc(navy)) ///
>        (scatter lnworkers_wa_serpool score_manag_finwa_serpool, msymbol(triangle) mc(green)) ///
>            (scatter lnworkers_us management_score_us, msymbol(square) mc(eltblue)) ///
>        (lfit lnworkers_wa_manpool score_manag_finwa_manpool, lc(navy)) ///
>        (lfit lnworkers_wa_serpool score_manag_finwa_serpool, lc(green)) ///
>                   (lfit lnworkers_us management_score_us, lc(eltblue)), ///
>        legend(order(1   "Manufacturing MX          2.752" 2 "Services MX                   1.621" 3 "Manufacturing U.S.        3.360") ///
>               cols(1) region(lwidth(none))) ///
>        ylabel(3 "20" 3.55 "35" 4 "55" 4.5 "90" 5 "150" 5.52 "250", angle(0)) ///
>        ytitle("Employees (log scale)") ///
>        graphregion(color(white))
{res}{txt}
{com}.  
. * .gph file
. graph save "$figures\Figure_3.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_3.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_3.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_3.jpg} written in JPEG format

{com}. graph export "$figures\Figure_3.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_3.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
. /********************************************************************
>  *Figure 4: Age and management score: mean
> ********************************************************************/
. use "$working_interm/Age bin mean sd manufacturing.dta" , clear
{txt}
{com}. 
. *Means
.  ** US
. twoway scatter mean_us age_bin, ytitle("Management mean" "Normalized to zero over the sample of each sector") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) || connected mean_us age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) xlabel( 1 "1-5" 2 "6-10" 3 "11-15" 4 "16-20" 5 "21-25" 6 "26-30" 7 "31-35" 8 "35+", angle(0)) yscale(range(-0.03(0.01)0.02))
{res}{txt}
{com}. 
. * .gph file
. graph save "$figures\Figure_4a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_4a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4a.jpg} written in JPEG format

{com}. graph export "$figures\Figure_4a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
.  ** Manufacturing
. twoway scatter mean_man age_bin, ytitle("Management mean" "Normalized to zero over the sample of each sector") ///
> xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) ///
> || connected mean_man age_bin, graphregion(color(white)) msymbol(X) msize(large) ///
> mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off)  ///
> xlabel( 1 "1-5" 2 "6-10" 3 "11-15" 4 "16-20" 5 "21-25" 6 "26-30" 7 "31-35" 8 "35+", angle(0)) ///
> yscale(range(-0.03(0.01)0.02))  ylabel(-0.03(0.01)0.02, labsize(medium))
{res}{txt}
{com}. 
. * .gph file
. graph save "$figures\Figure_4b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_4b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4b.jpg} written in JPEG format

{com}. graph export "$figures\Figure_4b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
.  ** Services
. twoway scatter mean_serv age_bin, ytitle("Management mean" "Normalized to zero over the sample of each sector") ///
> xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) ///
> || connected mean_serv age_bin, graphregion(color(white)) msymbol(X) msize(large) ///
> mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) ///
> xlabel( 1 "1-5" 2 "6-10" 3 "11-15" 4 "16-20" 5 "21-25" 6 "26-30" 7 "31-35" 8 "35+", angle(0)) ///
> yscale(range(-0.03(0.01)0.02))      ylabel(-0.03(0.01)0.02, labsize(medium))
{res}{txt}
{com}.  
.  * .gph file
. graph save "$figures\Figure_4c.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4c.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_4c.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4c.jpg} written in JPEG format

{com}. graph export "$figures\Figure_4c.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_4c.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. /********************************************************************
>  *Figure 5: Age and management score: spread
> ********************************************************************/
.  
. egen mean_sd_ser=mean(sd_serv)
{txt}
{com}. egen mean_sd_man=mean(sd_manuf)
{txt}
{com}. egen mean_sd_us=mean(sd_us)
{txt}
{com}. 
. gen dev_sd_ser=sd_ser- mean_sd_ser
{txt}(1 missing value generated)

{com}. gen dev_sd_man=sd_man- mean_sd_man
{txt}(1 missing value generated)

{com}. gen dev_sd_us=sd_us- mean_sd_us
{txt}(1 missing value generated)

{com}.  
.  
.  *US
. twoway scatter  dev_sd_us age_bin , graphregion(color(white)) ytitle("Management standard deviation" "Normalized to zero over the sample of each sector") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) || connected dev_sd_us  age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) xlabel( 1 "1-5" 2 "6-10" 3"11-15" 4 "16-20" 5 "21-25" 6 "26-30" 7 "31-35" 8 "35+", angle(0)) yscale(range(-0.01(0.005)0.015))
{res}{txt}
{com}. 
. 
.  * .gph file
. graph save "$figures\Figure_5a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_5a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5a.jpg} written in JPEG format

{com}. graph export "$figures\Figure_5a.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
.  *MX Manufacturing
.  twoway scatter  dev_sd_man age_bin , graphregion(color(white)) ytitle("Management standard deviation" "Normalized to zero over the sample of each sector") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) || connected dev_sd_man  age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) xlabel( 1 "1-5" 2 "6-10" 3 "11-15" 4 "16-20" 5 "21-25" 6 "26-30" 7 "31-35" 8 "35+", angle(0)) yscale(range(-0.01(0.005)0.015)) ylabel(-0.01(0.005)0.015, labsize(medium))
{res}{txt}
{com}. 
. 
.  * .gph file
. graph save "$figures\Figure_5b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_5b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5b.jpg} written in JPEG format

{com}. graph export "$figures\Figure_5b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
.  
. *MX Services 
.  twoway scatter  dev_sd_ser age_bin , graphregion(color(white)) ytitle("Management standard deviation" "Normalized to zero over the sample of each sector") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) || connected dev_sd_ser age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) xlabel( 1 "1-5" 2 "6-10" 3 "11-15" 4 "16-20" 5 "21-25" 6 "26-30" 7 "31-35" 8 "35+", angle(0)) yscale(range(-0.01(0.005)0.015)) ylabel(-0.01(0.005)0.015, labsize(medium))
{res}{txt}
{com}. 
.  
.  * .gph file
. graph save "$figures\Figure_5c.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5c.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_5c.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5c.jpg} written in JPEG format

{com}. graph export "$figures\Figure_5c.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_5c.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.    
.  /********************************************************************
>  *Figure 6: Management scores by state 
>  ********************************************************************/
. clear
{txt}
{com}. cd "$working"
{res}C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp
{txt}
{com}. 
. 
. cap erase "ent-d.dta"
{txt}
{com}. cap erase "ent-c.dta"
{txt}
{com}. 
. *Load the shapefile
. shp2dta using "$maps\Entidades_2010_5A", data("ent-d") coor("ent-c") genid(aid) gencentroids(c)
{res}type: 5
{txt}
{com}. 
. 
. use ent-d, clear
{txt}
{com}. 
. 
. gen str e03=CVE_ENT
{txt}
{com}. sort e03
{txt}
{com}. gen ent=CVE_ENT
{txt}
{com}. destring ent, replace
{txt}ent: all characters numeric; {res}replaced {txt}as {res}byte
{txt}
{com}. 
. merge 1:1 ent using   "$working_interm\Map management state.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}               0
{txt}{col 5}Matched{col 30}{res}              32{txt}  (_merge==3)
{col 5}{hline 41}

{com}. tab _merge

   {txt}Matching result from {c |}
                  merge {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
            Matched (3) {c |}{res}         32      100.00      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}         32      100.00
{txt}
{com}. 
. *recode class_man (1=5) (2=4) (4=2) (5=1)
. *recode class_ser (1=5) (2=4) (4=2) (5=1)
. tabulate class_man, generate(class_)

  {txt}Class_man {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          7       21.88       21.88
{txt}          2 {c |}{res}          5       15.62       37.50
{txt}          3 {c |}{res}          6       18.75       56.25
{txt}          4 {c |}{res}          5       15.62       71.88
{txt}          5 {c |}{res}          9       28.12      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         32      100.00
{txt}
{com}. 
. * Count observations in each class and store as scalar
. count if class_1 == 1
  {res}7
{txt}
{com}. scalar n_class1 = r(N)
{txt}
{com}. 
. count if class_2 == 1
  {res}5
{txt}
{com}. scalar n_class2 = r(N)
{txt}
{com}. 
. count if class_3 == 1
  {res}6
{txt}
{com}. scalar n_class3 = r(N)
{txt}
{com}. 
. count if class_4 == 1
  {res}5
{txt}
{com}. scalar n_class4 = r(N)
{txt}
{com}. 
. count if class_5== 1
  {res}9
{txt}
{com}. scalar n_class5 = r(N)
{txt}
{com}. 
. spmap class_man using "ent-c.dta" , id(aid) clm(u) fc(Purples) legc legtitle("States") polygon(data("ent-c.dta") os(.1) fc(none)) ///
> legend(order(1 "<0.41 (`=n_class1')" 2 "[0.41-0.44] (`=n_class2')" ///
> 3 "[0.44-0.47] (`=n_class3')" 4 "[0.47-0.49] (`=n_class4')" 5 ">=0.49 (`=n_class5')"))
{res}{txt}
{com}. 
.  * .gph file
. graph save "$figures\Figure_6a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_6a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_6a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_6a.jpg} written in JPEG format

{com}. graph export "$figures\Figure_6a.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_6a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. drop class_1 class_2 class_3 class_4 class_5
{txt}
{com}. tab class_ser, generate(class_)

  {txt}Class_ser {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          7       21.88       21.88
{txt}          2 {c |}{res}          9       28.12       50.00
{txt}          3 {c |}{res}          8       25.00       75.00
{txt}          4 {c |}{res}          8       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         32      100.00
{txt}
{com}. 
. * Count observations in each class and store as scalar
. count if class_1 == 1
  {res}7
{txt}
{com}. scalar n_class1 = r(N)
{txt}
{com}. 
. count if class_2 == 1
  {res}9
{txt}
{com}. scalar n_class2 = r(N)
{txt}
{com}. 
. count if class_3 == 1
  {res}8
{txt}
{com}. scalar n_class3 = r(N)
{txt}
{com}. 
. count if class_4 == 1
  {res}8
{txt}
{com}. scalar n_class4 = r(N)
{txt}
{com}. 
. 
. spmap class_ser using "ent-c.dta" , id(aid) clm(u) fc(Greens) legc legtitle("States") polygon(data("ent-c.dta") os(.1) fc(none))   ///
> legend(order(1 "<0.42 (`=n_class1')" 2 "[0.42-0.44] (`=n_class2')" ///
> 3 "[0.44-0.45] (`=n_class3')" 4 ">=0.45 (`=n_class4')"))
{res}{txt}
{com}. 
.    * .gph file
. graph save "$figures\Figure_6b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_6b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures\Figure_6b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_6b.jpg} written in JPEG format

{com}. graph export "$figures\Figure_6b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\Figure_6b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
.  /********************************************************************
>  *Figure 7: CDFs Management score drive time to the border 
>  ********************************************************************/
.  use "$working/BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}.  
.  *a) Manufacturing
. ksmirnov score_manag_ if dum_man2==1, by(AM_durat_border_hrs)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0922       0.550
{txt}1                  {res} -0.2294       0.025
{txt}Combined K-S       {res}  0.2294       0.050

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 1466 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  [aw=nworkers_WA_] if dum_man2==1 & AM_durat_border_hrs!=., by(AM_durat_border_hrs) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below median drive time" 2 "Above median drive time") pos(6) cols(2)  region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.   * .gph file
. graph save "$figures_mock\Figure_7a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_7a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_7a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_7a.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_7a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_7a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
.  *b) Services
. ksmirnov score_manag_ if dum_man2==0, by(AM_durat_border_hrs)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.4321       0.000
{txt}1                  {res} -0.1238       0.000
{txt}Combined K-S       {res}  0.4321       0.000

{txt}Note: Ties exist in combined dataset;
      there are 33 unique values out of 17078 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval 
{res}{txt}
{com}.  
.  
. cdfplot score_manag_  [aw=nworkers_WA_] if dum_man2==0 & AM_durat_border_hrs!=., by(AM_durat_border_hrs) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below median drive time" 2 "Above median drive time") pos (6) cols(2)  region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.   * .gph file
. graph save "$figures_mock\Figure_7b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_7b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_7b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_7b.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_7b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_7b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.    
.  /********************************************************************
>  *Figure 8: Sources of misallocation: Drive time to the border 
>  ********************************************************************/
.  
. *a) Manufacturing
. binscatter lnworkers_WA_ score_manag_ if dum_man2==1, by(AM_durat_border_hrs) nq(50) controls(dum_time) ///
> msymbol(o triangle_hollow) mcolor(navy eltblue) ///
> lcolor(navy eltblue) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel( 3 "20" 3.55 "35" 4 "55" 4.5 "90" 5 "150" 5.52 "250", angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Below median drive time" 2 "Above median drive time") pos(6) cols(2)  region(lcolor(white)) )
{res}{txt}
{com}.    * .gph file
. graph save "$figures_mock\Figure_8a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_8a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_8a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_8a.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_8a.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_8a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
. *b) Services
. binscatter lnworkers_WA_ score_manag_ if dum_man2==0, by(AM_durat_border_hrs) nq(50) controls(dum_time) ///
> msymbol(o triangle_hollow) mcolor(navy eltblue) ///
> lcolor(navy eltblue) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel( 3 "20" 3.55 "35" 4 "55" 4.5 "90" , angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Below median drive time" 2 "Above median drive time") pos(6) cols(2)  region(lcolor(white)) )
{res}{txt}
{com}.  
.    * .gph file
. graph save "$figures_mock\Figure_8b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_8b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_8b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_8b.jpg} written in JPEG format

{com}.  graph export "$figures_mock\Figure_8b.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_8b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
.    
.  /********************************************************************
>  *Figure 9: CDFs Management score market size 
>  ********************************************************************/
.  *a) Manufacturing
. ksmirnov score_manag_ if dum_man2==1, by(AMlpopdensMAincome)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0198       0.774
{txt}1                  {res} -0.9137       0.000
{txt}Combined K-S       {res}  0.9137       0.000

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 1466 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  [aw=nworkers_WA_] if dum_man2==1 & AMlpopdensMAincome!=., by(AMlpopdensMAincome) graphregion(color(white)) opt1(lp(dash solid ) lc(eltblue navy ) ) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below the median market size" 2 "Above the median market size") pos(6) cols(2) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
. 
. 
.   * .gph file
. graph save "$figures_mock\Figure_9a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_9a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_9a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_9a.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_9a.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_9a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
.  
.  *b) Services
.  ksmirnov score_manag_ if dum_man2==0, by(AMlpopdensMAincome)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0208       0.028
{txt}1                  {res} -0.2497       0.000
{txt}Combined K-S       {res}  0.2497       0.000

{txt}Note: Ties exist in combined dataset;
      there are 33 unique values out of 17078 observations.

{com}.  scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. cdfplot score_manag_  [aw=nworkers_] if dum_man2==0 & AMlpopdensMAincome!=., by(AMlpopdensMAincome) graphregion(color(white)) opt1( lp( dash solid) lc(eltblue navy )) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below the median market size" 2 "Above the median market size") pos(6) cols(2) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.   * .gph file
. graph save "$figures_mock\Figure_9b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_9b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_9b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_9b.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_9b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_9b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.         
.    
.  /********************************************************************
>  *Figure 10: Sources of misallocation: market size 
>  ********************************************************************/
.  *a) Manufacturing
. binscatter lnworkers_WA_ score_manag_ if dum_man2==1, by(AMlpopdensMAincome) nq(50) controls(dum_time) ///
> msymbol(triangle_hollow o ) mcolor(eltblue navy ) ///
> lcolor( eltblue navy) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel( 3 "20" 3.55 "35" 4 "55" 4.5 "90" 5 "150" 5.52 "250", angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Below median market size" 2 "Above median market size") pos(6) region(lcolor(white)) )
{res}{txt}
{com}.         
.  * .gph file
. graph save "$figures_mock\Figure_10a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_10a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_10a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_10a.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_10a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_10a.png{rm}
saved as
PNG
format
{p_end}

{com}.         
.         
.  *) Services
. binscatter lnworkers_WA_ score_manag_ if dum_man2==0 , by(AMlpopdensMAincome) nq(50) controls(dum_time) ///
> msymbol(triangle_hollow o ) mcolor(eltblue navy ) ///
> lcolor( eltblue navy) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel( 3 "20" 3.55 "35" 4 "55" 4.5 "90" , angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Below median market size" 2 "Above median market size") pos(6) region(lcolor(white)) )
{res}{txt}
{com}.         
.  * .gph file
. graph save "$figures_mock\Figure_10b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_10b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_10b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_10b.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_10b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_10b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.         
.  /********************************************************************
>  *Figure 11: CDFs Management score according to institutional strengths 
>  ********************************************************************/
.   
. *Contract enforcement
. ksmirnov score_manag_ , by(H10_contr)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1229       0.000
{txt}1                  {res} -0.4049       0.000
{txt}Combined K-S       {res}  0.4049       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  [aw=nworkers_WA_] if H10_contr!=., by(H10_contr) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% contract enforcement problems") pos(6) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.  * .gph file
. graph save "$figures_mock\Figure_11a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_11a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11a.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_11a.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Corruption
. ksmirnov score_manag_ , by(H10_shfrec_corrup)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1206       0.000
{txt}1                  {res} -0.2426       0.000
{txt}Combined K-S       {res}  0.2426       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  [aw=nworkers_WA_] if H10_shfrec_corrup!=., by(H10_shfrec_corrup) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% frequency corruption problems") pos(6) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.   * .gph file
. graph save "$figures_mock\Figure_11c.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11c.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_11c.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11c.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_11c.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11c.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
. *Kidnapping
. ksmirnov score_manag_ , by(H10_rate_kid_)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1717       0.000
{txt}1                  {res} -0.1591       0.000
{txt}Combined K-S       {res}  0.1717       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  [aw=nworkers_WA_] if H10_rate_kid_!=., by(H10_rate_kid_) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% kidnapping") pos(6) region(lcolor(white)) ) ///
>  note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.   * .gph file
. graph save "$figures_mock\Figure_11b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_11b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11b.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_11b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Business Informality
. ksmirnov score_manag_ , by(H10_informal)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1895       0.000
{txt}1                  {res} -0.1384       0.000
{txt}Combined K-S       {res}  0.1895       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  [aw=nworkers_WA_] if H10_informal!=., by(H10_informal) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% of informality") pos(6) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.  * .gph file
. graph save "$figures_mock\Figure_11d.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11d.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_11d.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11d.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_11d.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_11d.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
.  /********************************************************************
>  *Figure 12:  Sources of misallocation: Institutional strengths 
>  ********************************************************************/
. 
. *Contract enforcement
. binscatter lnworkers_WA_ score_manag_ , by(H10_contr) nq(50) controls(dum_time) ///
> msymbol(o triangle_hollow) mcolor(navy eltblue) ///
> lcolor(navy eltblue) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel( 3 "20" 3.55 "35" 4 "55" 4.5 "90" , angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% contract enforcement problems") pos(6) region(lcolor(white)) )
{res}{txt}
{com}.  
.  * .gph file
. graph save "$figures_mock\Figure_12a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12a.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_12a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12a.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_12a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
. *Kidnapping
. binscatter lnworkers_WA_ score_manag_ , by(H10_rate_kid_) nq(50) controls(dum_time) ///
> msymbol(o triangle_hollow) mcolor(navy eltblue) ///
> lcolor(navy eltblue) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel( 3 "20" 3.55 "35" 4 "55" 4.5 "90", angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% kidnapping") pos(6) region(lcolor(white)) )
{res}{txt}
{com}. 
.  * .gph file
. graph save "$figures_mock\Figure_12b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12b.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_12b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12b.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_12b.png", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Corruption
. binscatter lnworkers_WA_ score_manag_, by(H10_shfrec_corrup) nq(50) controls(dum_time) ///
> msymbol(o triangle_hollow) mcolor(navy eltblue) ///
> lcolor(navy eltblue) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel(2.3 "10" 3 "20" 3.55 "35" 4 "55" 4.5 "90" , angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% frequency corruption problems") pos(6) region(lcolor(white)) )
{res}{txt}
{com}. 
.  * .gph file
. graph save "$figures_mock\Figure_12c.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12c.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_12c.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12c.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_12c.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12c.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
. *Informality
. binscatter lnworkers_WA_ score_manag_ , by(H10_informal) nq(50) controls(dum_time) ///
> msymbol(o triangle_hollow) mcolor(navy eltblue) ///
> lcolor(navy eltblue) linetype(lfit) ///
> xlabel(0(0.2)0.8) ///
> ylabel(2.3 "10" 3 "20" 3.55 "35" 4 "55" 4.5 "90" 5 "150", angle(0)) ///
> xtitle(Management score) ytitle(Employees (log scale)) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% of informality")  pos(6) region(lcolor(white)) )
{res}{txt}
{com}. 
.  * .gph file
. graph save "$figures_mock\Figure_12d.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12d.gph} saved

{com}. 
. * .jpg file
. graph export "$figures_mock\Figure_12d.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12d.jpg} written in JPEG format

{com}. graph export "$figures_mock\Figure_12d.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\figures\mock\Figure_12d.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. /********************************************************************************************
> * END OF DO FILE
> ********************************************************************************************/
. 
{txt}end of do-file

{com}. do "$programs/Regressions.do"
{txt}
{com}. /********************************************************************************************
> * Project: Management and Misallocation in Mexico
> * Purpose: Estimate main regressions of the paper
> * Date: 2024-12-01
> ********************************************************************************************/
. *--------------------------------------------------------------------------------------------
. * 0. Settings
. *--------------------------------------------------------------------------------------------
. cap clear matrix
{txt}
{com}. cap clear mata
{txt}
{com}. cap set matsize 11000
{txt}
{com}. cap set maxvar 20000
{txt}
{com}. *--------------------------------------------------------------------------------------------
. * 1. Load Datasets
. *--------------------------------------------------------------------------------------------
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. keep if sector_ != "COM"
{txt}(3,496 observations deleted)

{com}. 
. *--------------------------------------------------------------------------------------------
. * 3. Estimate regressions
. *--------------------------------------------------------------------------------------------
. 
. ************************************************************************************************************************************************************
. *Table 1- Management practices and firm performance
. ************************************************************************************************************************************************************
. 
. estimates drop _all
{txt}
{com}. 
. *Manufacturing and services
. qui reg lVA_real_wk_mil_WA_ dum_man2##(c.score_manag_ dum_time i.id_mun), cluster(id_mun) robust 
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reg lVA_real_wk_mil_WA_ dum_man2##(c.score_manag_ c.lcapworkWA_ c.lnworkers_WA_ c.sh_direc_WA_  c.sh_sup_WA_ i.clase dum_time i.id_mun), cluster(id_mun) robust
{txt}
{com}. estimates store e2
{txt}
{com}. 
. qui reg ltfp_sec_real_WA_ dum_man2##(c.score_manag_ i.clase dum_time i.id_mun),  cluster(id_mun) robust
{txt}
{com}. estimates store e3
{txt}
{com}. 
. *Manufacturing
. qui reghdfe ltfp_sec_real_WA_ score_manag_ dum_time i.id_mun if dum_man2==1, cluster(id_mun) absorb(clase)
{txt}
{com}. estimates store e4
{txt}
{com}. 
. *Services
. qui reghdfe ltfp_sec_real_WA_ score_manag_ dum_time i.id_mun if dum_man2!=1, cluster(id_mun) absorb(clase)
{txt}
{com}. estimates store e5
{txt}
{com}. 
. 
. *Notes*
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. 
. estadd local Mun_effects "Yes" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e5
{txt}
{com}. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e5
{txt}
{com}. 
. *Calculated manually using percentiles
. estadd local Rise_10_90_Man "120.2" , replace: e1
{txt}
{com}. estadd local Rise_10_90_Man "28.9" , replace: e2 
{txt}
{com}. estadd local Rise_10_90_Man "13.7" , replace: e3  
{txt}
{com}. estadd local Rise_10_90_Man "13.7"  , replace:e4
{txt}
{com}. estadd local Rise_10_90_Man " " , replace: e5
{txt}
{com}. 
. estadd local Rise_10_90_Ser "52.9" , replace: e1
{txt}
{com}. estadd local Rise_10_90_Ser "25.7" , replace: e2 
{txt}
{com}. estadd local Rise_10_90_Ser "15.7" , replace: e3  
{txt}
{com}. estadd local Rise_10_90_Ser ""  , replace:e4
{txt}
{com}. estadd local Rise_10_90_Ser "15.7 " , replace: e5
{txt}
{com}. 
. 
. * Exporting results
. *csv
. esttab e* using "$tables_mock\Table1.csv", keep(1.dum_man2#c.score_manag_ score* lcap* lnwork* sh_direc* sh_sup*) ///
>     scalars("clase 6-digits NAICS") b(%9.4f) stats(Time_effects Mun_effects NAICS_6_digits_effects  Rise_10_90_Man Rise_10_90_Ser  N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "% Rise 10th to 90th management percentile Manufacturing" "Services" "Observations")) star(* 0.10 ** 0.05 *** 0.01) se  title("Management Practices and Firm Performance") ///
>                 mtitle("log(VA per employee)" "log(VA per employee)" "log(TFP)" "log(TFP)" "log(TFP)" ) ///
>     label ///
>     coeflabels( ///
>         1.dum_man2#c.score_manag_ "Manufacturing Dummy*Management Score" ///
>         score_manag_ "Management score" ///
>         lcapworkWA_ "log(capital/employee)" ///
>         lnworkers_WA_ "log(employees)" ///
>         sh_direc_WA_ "Share of white-collar workers" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>     ) ///
>     replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock\Table1.csv"'})

{com}. *tex
.         esttab e* using "$tables_mock/Table1.tex", ///
>     keep(1.dum_man2#c.score_manag_ score* lcap* lnwork* sh_direc* sh_sup*) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>     stats(Time_effects Mun_effects NAICS_6_digits_effects  Rise_10_90_Man Rise_10_90_Ser  N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "\% Rise 10th to 90th management percentile Manufacturing" "Services" "Observations")) ///
>     scalars("clase 6-digits NAICS") ///
>     title("Management Practices and Firm Performance") ///
>                 mtitle("log(VA per employee)" "log(VA per employee)" "log(TFP)" "log(TFP)" "log(TFP)" ) ///
>     label ///
>     coeflabels( ///
>         1.dum_man2#c.score_manag_ "Manufacturing Dummy × Management Score" ///
>         score_manag_ "Management score" ///
>         lcapworkWA_ "log(capital/employee)" ///
>         lnworkers_WA_ "log(employees)" ///
>         sh_direc_WA_ "Share of white-collar workers" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock/Table1.tex"'})

{com}. 
. ************************************************************************************************************************************************************
. *Table 2 - Management practices and firm size
. ************************************************************************************************************************************************************
. 
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. estimates drop _all
{res}{txt}
{com}. 
. *Manufacturing and services
. qui reg lnworkers_WA_ dum_man2##(c.score_manag_ dum_time), cluster(id_mun) robust
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reg lnworkers_WA_ dum_man2##(c.score_manag_ c.sh_sup_WA_ dum_time i.id_mun i.clase), cluster(id_mun) robust
{txt}
{com}. estimates store e2
{txt}
{com}. 
. *Manufacturing
. qui reg lnworkers_WA_ score_manag_ dum_time if  dum_man2==1,  cluster(id_mun) robust
{txt}
{com}. estimates store e3
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ score_manag_ sh_sup_WA_  dum_time if dum_man2==1, cluster(id_mun) absorb(id_mun clase)
{txt}
{com}. estimates store e4
{txt}
{com}. 
. *Services
. qui reg lnworkers_WA_ score_manag_ dum_time if dum_man2!=1,  cluster(id_mun) robust
{txt}
{com}. estimates store e5
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ score_manag_ sh_sup_WA_  dum_time if dum_man2!=1, cluster(id_mun) absorb(id_mun clase)
{txt}
{com}. estimates store e6
{txt}
{com}. 
. 
. *Notes*
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. estadd local Time_effects "Yes" , replace: e6
{txt}
{com}. 
. 
. estadd local Mun_effects "No" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Mun_effects "No" , replace: e3  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Mun_effects "No" , replace: e5
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e6
{txt}
{com}. 
. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e5
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e6
{txt}
{com}. 
. 
. estadd local Skills_effects "No" , replace: e1
{txt}
{com}. estadd local Skills_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Skills_effects "No" , replace: e3  
{txt}
{com}. estadd local Skills_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Skills_effects "No" , replace: e5
{txt}
{com}. estadd local Skills_effects "Yes" , replace: e6
{txt}
{com}. 
. *Exporting results
. esttab e* using "$tables_mock/Table2.csv", keep(score_manag_ 1.dum_man2#c.score_manag_) starlevels(* 0.10 ** 0.05 *** 0.01) ///
>         mtitle("Manufacturing and Services" "Manufacturing and Services"  "Manufacturing" "Manufacturing" "Services" "Services" ) ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects  Skills_effects  N r2, fmt(%13.3f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Skills controls" "Observations" "R squared"))  ///
>     label ///
>     coeflabels( ///
>         1.dum_man2#c.score_manag_ "Manufacturing Dummy*Management Score" ///
>         score_manag_ "Management score" ///
>     ) ///
> se  replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock/Table2.csv"'})

{com}. 
. *tex
.         esttab e* using "$tables_mock/Table2.tex", ///
>     keep(score_manag_ 1.dum_man2#c.score_manag_) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects  Skills_effects  N r2, fmt(%13.3f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Skills controls" "Observations" "R squared"))  ///
>     title("Management Practices and Firm Size") ///
>                 mtitle("" "" "" "" "" "") ///
>     label ///
>     coeflabels( ///
>         1.dum_man2#c.score_manag_ "Manufacturing Dummy × Management Score" ///
>         score_manag_ "Management score" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock/Table2.tex"'})

{com}.         
. ***********************************************************************************************************************************************************
. *Table 3 Sources of misallocation: Competition & Market size
. ***********************************************************************************************************************************************************
. estimates drop _all
{res}{txt}
{com}. 
. *Drive time
. **Manufacturing
. qui reghdfe  lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2==1, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e1
{txt}
{com}. **Services
. qui reghdfe  lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2!=1, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e2
{txt}
{com}. **Exports-Manufacturing
. qui reghdfe  lnworkers_WA_  c.shexp_clase##i.BM_durat_border_hrs##c.score_manag_  dum_time if  dum_man2==1, cluster(id_mun) absorb(clase id_mun) 
{txt}
{com}. estimates store e4
{txt}
{com}. *Market size
. **Manufacturing
. qui reghdfe  lnworkers_WA_  i.AMlpopdensMAincome##c.score_manag_   dum_time  if dum_man2==1, cluster(id_mun) absorb(clase id_mun)  
{txt}
{com}. estimates store e3
{txt}
{com}. 
. **Services
. qui reghdfe  lnworkers_WA_  i.AMlpopdensMAincome##c.score_manag_   dum_time  if dum_man2!=1, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e5
{txt}
{com}. 
. *Notes
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. 
. 
. estadd local Mun_effects "Yes" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e5
{txt}
{com}. 
. estadd local NAICS_6_digits_effects "Yes" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e5
{txt}
{com}. 
. 
. *Exporting results
. esttab e* using "$tables_mock/Table3.csv", keep(1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ score_manag_ 1.BM_durat_border_hrs#c.score_manag_ 1.AMlpopdensMAincome#c.score_manag_  c.shexp_clase#c.score_manag_ 1.BM_durat_border_hrs#c.shexp_clase) starlevels(* 0.10 ** 0.05 *** 0.01)  ///
> order(1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ score_manag_ 1.BM_durat_border_hrs#c.score_manag_   c.shexp_clase#c.score_manag_ 1.BM_durat_border_hrs#c.shexp_clase 1.AMlpopdensMAincome#c.score_manag_) ///
> stats(Time_effects Mun_effects NAICS_6_digits_effects  N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects"  "Observations" ))  /// 
> mtitle("Manufacturing" "Services"  "Manufacturing" "Manufacturing" "Services") ///
>     label ///
>     coeflabels( ///
>                 1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ "Management score*Industry export share*Drive time below the median" ///
>         score_manag_ "Management score" ///]
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 c.shexp_clase#c.score_manag_ "Management score*Industry export share" ///
>                 1.BM_durat_border_hrs#c.shexp_clase  "Drive time below the median*Industry export share" ///
>                 1.AMlpopdensMAincome#c.score_manag_  "Market size above the median*Management score" ///
>     ) ///
> se  replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock/Table3.csv"'})

{com}. 
. *tex
.         esttab e* using "$tables_mock/Table3.tex", ///
>     keep(1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ score_manag_ 1.BM_durat_border_hrs#c.score_manag_ 1.AMlpopdensMAincome#c.score_manag_  c.shexp_clase#c.score_manag_ 1.BM_durat_border_hrs#c.shexp_clase) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>     order(1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ score_manag_ 1.BM_durat_border_hrs#c.score_manag_   c.shexp_clase#c.score_manag_ 1.BM_durat_border_hrs#c.shexp_clase 1.AMlpopdensMAincome#c.score_manag_) ///
> stats(Time_effects Mun_effects NAICS_6_digits_effects  N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects"  "Observations" ))  /// 
>     scalars("clase 6-digits NAICS") ///
>     title("Sources of Misallocation: Competition & market size") ///
>         mtitle("Manufacturing" "Services"  "Manufacturing" "Manufacturing" "Services") ///
>     label ///
>     coeflabels( ///
>                 1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ "Management score*Industry export share*Drive time below the median" ///
>         score_manag_ "Management score" ///]
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 c.shexp_clase#c.score_manag_ "Management score*Industry export share" ///
>                 1.BM_durat_border_hrs#c.shexp_clase  "Drive time below the median*Industry export share" ///
>                 1.AMlpopdensMAincome#c.score_manag_  "Market size above the median*Management score" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock/Table3.tex"'})

{com}. 
. 
. ***********************************************************************************************************************************************************
. *Table 4 Sources of misallocation: Institutional Environment
. ***********************************************************************************************************************************************************
. estimates drop _all 
{res}{txt}
{com}. *Contract enforcement
. qui xi: reghdfe lnworkers_WA_  i.H10_contr*score_manag_        dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}.         estimates store e1
{txt}
{com}. 
. *Kidnapping
. qui xi: reghdfe lnworkers_WA_  i.H10_rate_kid*score_manag_      dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}.         estimates store e2
{txt}
{com}. 
. *Corruption
. qui xi:reghdfe lnworkers_WA_   i.H10_shfrec_corrup*score_manag_ dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}.         estimates store e3
{txt}
{com}. 
. *Business crime composite index
. qui xi:reghdfe lnworkers_WA_   i.H10_compos_ind*score_manag_   dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}.         estimates store e4
{txt}
{com}. 
. *Informality
. qui xi: reghdfe lnworkers_WA_  i.H10_informal*score_manag_     dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}.         estimates store e5
{txt}
{com}. 
. 
. 
. /*
> * Exporting results
> esttab e* using "$tables/Table4.csv", ///
> keep(_IH10Xscore_1 _IH10* _IH10_shfre_1 _IH10_compo_1  score_manag_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> mtitle("Top 10% share firms contract enforcement problems" ///
>        "Top 10% kidnapping" ///
>        "Top 10% share firms with gov. corruption problems" ///
>        "Top 10% Business crime composite index" ///
>        "Top 10% Informality municipality") ///
> unstack nonotes replace ///
> coeflabels(_IH10Xscore_1 "High level*Management score" ///
>            _IH10_contr_1 "High level" ///
>            _IH10_rate__1 "High level" ///  // Fixed underscore issue
>            _IH10_shfre_1 "High level" ///
>            _IH10_compo_1 "High level" ///
>            _IH10_infor_1 "High level" ///
>            score_manag_ "Management score") 
> 
> *tex
> esttab e* using "$tables/Table4.tex", ///
> keep(_IH10Xscore_1 _IH10* _IH10_shfre_1 _IH10_compo_1 score_manag_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> unstack nonotes nolabel nonumber nogap ///
>         mtitle("" ""  "" "" "") ///
> coeflabels(_IH10Xscore_1 "High level*Management score" ///
>            _IH10_contr_1 "High level" ///
>            _IH10_rate__1 "High level" ///  
>            _IH10_shfre_1 "High level" ///
>            _IH10_compo_1 "High level" ///
>            _IH10_infor_1 "High level" ///
>            score_manag_ "Management score") ///
> replace fragment
> */
. ******************************************************************************************************
. //Formated for publication version
. 
. ////////////////////Contract enforcement///////////////////////////
> clear mata
{txt}
{com}.  xi: reghdfe lnworkers_WA_  i.H10_contr*score_manag_ dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}i.H10_contr_{col 19}_IH10_contr_0-1{col 39}(naturally coded; _IH10_contr_0 omitted)
i.H10~r_*sco~g_{col 19}_IH10Xscore_#{col 39}(coded as above)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}    18,544
{txt}Absorbing 2 HDFE groups{col 51}F({res}   4{txt},{res}     30{txt}){col 67}= {res}      5.11
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0029
{txt}{col 51}R-squared{col 67}= {res}    0.8001
{txt}{col 51}Adj R-squared{col 67}= {res}    0.7995
{txt}{col 51}Within R-sq.{col 67}= {res}    0.4917
{txt}{col 1}Number of clusters ({res}id_mun{txt}) {col 30}= {res}        31{txt}{col 51}Root MSE{col 67}= {res}    0.3250

{txt}{ralign 79:(Std. err. adjusted for {res:31} clusters in {res:id_mun})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}lnworkers_WA_{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_IH10_contr_1 {c |}{col 15}{res}{space 2} .9472933{col 27}{space 2} .7851929{col 38}{space 1}    1.21{col 47}{space 3}0.237{col 55}{space 4}-.6562845{col 68}{space 3} 2.550871
{txt}{space 1}score_manag_ {c |}{col 15}{res}{space 2} 2.120185{col 27}{space 2}  .544627{col 38}{space 1}    3.89{col 47}{space 3}0.001{col 55}{space 4} 1.007909{col 68}{space 3} 3.232462
{txt}_IH10Xscore_1 {c |}{col 15}{res}{space 2} .3139523{col 27}{space 2} 2.275841{col 38}{space 1}    0.14{col 47}{space 3}0.891{col 55}{space 4}-4.333936{col 68}{space 3}  4.96184
{txt}{space 5}dum_time {c |}{col 15}{res}{space 2} .0960886{col 27}{space 2} .1509455{col 38}{space 1}    0.64{col 47}{space 3}0.529{col 55}{space 4}-.2121832{col 68}{space 3} .4043605
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.737209{col 27}{space 2} .2774261{col 38}{space 1}    9.87{col 47}{space 3}0.000{col 55}{space 4} 2.170629{col 68}{space 3} 3.303789
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}      id_mun{col 14}{c |}{space 1}       31{col 27}{space 1}       31{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}       clase{col 14}{c |}{space 1}       21{col 27}{space 1}        1{col 39}{result}{space 1}       20{col 53}{text} {col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}.  estimates store e1
{txt}
{com}.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.9472933095   2.120185439   .3139522522{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.7851928767   .5446270167   2.275841382{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res}   0.947   2.120***      0.314{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.2370733952   .0005120712   .8912017296{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.785)   (0.545)   (2.276){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                         0.314{txt}  {c |}
  2 {c |}  {res}                                                  (2.276){txt}  {c |}
  3 {c |}  {res}                 High level                         0.947{txt}  {c |}
  4 {c |}  {res}                                                  (0.785){txt}  {c |}
  5 {c |}  {res}           Management score                      2.120***{txt}  {c |}
  6 {c |}  {res}                                                  (0.545){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res}   0.947   2.120***      0.314{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.785)   (0.545)   (2.276){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2.xlsx", replace
{res}{p}{txt}note: file will be replaced when the first {cmd:putexcel} command is issued.{p_end}

{com}. putexcel A5 = "$cell_1_1" B5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A6 = "$cell_2_1" B6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A7 = "$cell_3_1" B7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A8 = "$cell_4_1" B8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A9 = "$cell_5_1" B9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A10 = "$cell_6_1" B10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. 
. 
. 
. 
. putexcel B2 = "(1)" C2="(2)" D2="(3)" E2="(4)" F2="(5)"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel B3 = "Top 10% share firms contract enforcement problems" C3="Top 10% kidnapping" D3="Top 10% share firms with gov. corruption problems" E3="Top 10% Business crime composite index" F3="Top 10% Informality Municipality"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. 
. 
. putexcel A12 = "Share of firms in municipalities with high-levels"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. *calculated manually
. putexcel B12 = "9.58%" C12="7.13%" D12="8.72%" E12="8.76%" F12="12.82%"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A13 = "Time effects"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel B13 = "Yes" C13 = "Yes" D13 = "Yes" E13 = "Yes" F13 = "Yes"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A14 = "Municipality effects"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel B14 = "Yes" C14 = "Yes" D14 = "Yes" E14 = "Yes" F14 = "Yes"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A15 = "NAICS 6-digits effects"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel B15 = "Yes" C15 = "Yes" D15 = "Yes" E15 = "Yes" F15 = "Yes"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel A16 = "Observations"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel B16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. 
.         
.         
.         
. ////////////////////Kidnapping////////////////////
> clear mata      
{txt}
{com}. qui xi: reghdfe lnworkers_WA_  i.H10_rate_kid*score_manag_      dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}. estimates store e2
{txt}
{com}. 
.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}           1              2              3
    {c TLC}{hline 46}{c TRC}
  1 {c |}  {res}-.4182215239    1.340490834   -.3179683677{txt}  {c |}
    {c BLC}{hline 46}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.3180034531   .7011546986   .7762817579{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}     1        2        3
    {c TLC}{hline 28}{c TRC}
  1 {c |}  {res}-0.418   1.340*   -0.318{txt}  {c |}
    {c BLC}{hline 28}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.1984244676   .0654905726   .6850062894{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.318)   (0.701)   (0.776){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                        -0.318{txt}  {c |}
  2 {c |}  {res}                                                  (0.776){txt}  {c |}
  3 {c |}  {res}                 High level                        -0.418{txt}  {c |}
  4 {c |}  {res}                                                  (0.318){txt}  {c |}
  5 {c |}  {res}           Management score                        1.340*{txt}  {c |}
  6 {c |}  {res}                                                  (0.701){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}     1        2        3
    {c TLC}{hline 28}{c TRC}
  1 {c |}  {res}-0.418   1.340*   -0.318{txt}  {c |}
    {c BLC}{hline 28}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.318)   (0.701)   (0.776){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2.xlsx", modify
{res}{txt}
{com}. putexcel C5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel C6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel C7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel C8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel C9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel C10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel C16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. 
. 
. ////////////////////Corruption////////////////////
> clear mata      
{txt}
{com}. qui xi:reghdfe lnworkers_WA_   i.H10_shfrec_corrup*score_manag_ dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}. estimates store e3
{txt}
{com}. 
. scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}           1              2              3
    {c TLC}{hline 46}{c TRC}
  1 {c |}  {res}-.7629879757    1.242437988    2.326746893{txt}  {c |}
    {c BLC}{hline 46}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.3999646595   .6842074618   .7621401991{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res} -0.763*     1.242*   2.327***{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.0660512569    .079395826   .0047161201{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.400)   (0.684)   (0.762){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                      2.327***{txt}  {c |}
  2 {c |}  {res}                                                  (0.762){txt}  {c |}
  3 {c |}  {res}                 High level                       -0.763*{txt}  {c |}
  4 {c |}  {res}                                                  (0.400){txt}  {c |}
  5 {c |}  {res}           Management score                        1.242*{txt}  {c |}
  6 {c |}  {res}                                                  (0.684){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res} -0.763*     1.242*   2.327***{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.400)   (0.684)   (0.762){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2.xlsx", modify
{res}{txt}
{com}. putexcel D5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel D6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel D7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel D8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel D9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel D10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel D16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. 
. ////////////////////Business crime composite index////////////////////
> clear mata
{txt}
{com}. qui xi:reghdfe lnworkers_WA_   i.H10_compos_ind*score_manag_   dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}. estimates store e4
{txt}
{com}. 
.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}           1              2              3
    {c TLC}{hline 46}{c TRC}
  1 {c |}  {res}-.4175120777    1.232847223    2.061611151{txt}  {c |}
    {c BLC}{hline 46}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.3241760914    .660680118   .6953246421{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res}  -0.418     1.233*   2.062***{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.2076209169     .07184173   .0058853791{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.324)   (0.661)   (0.695){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                      2.062***{txt}  {c |}
  2 {c |}  {res}                                                  (0.695){txt}  {c |}
  3 {c |}  {res}                 High level                        -0.418{txt}  {c |}
  4 {c |}  {res}                                                  (0.324){txt}  {c |}
  5 {c |}  {res}           Management score                        1.233*{txt}  {c |}
  6 {c |}  {res}                                                  (0.661){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res}  -0.418     1.233*   2.062***{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.324)   (0.661)   (0.695){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2.xlsx", modify
{res}{txt}
{com}. putexcel E5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel E6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel E7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel E8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel E9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel E10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel E16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. 
.         
. ////////////////////Informality////////////////////
>         
. clear mata
{txt}
{com}. qui xi: reghdfe lnworkers_WA_  i.H10_informal*score_manag_     dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}.  estimates store e5
{txt}
{com}. 
.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}           1              2              3
    {c TLC}{hline 46}{c TRC}
  1 {c |}  {res} 1.964486655    1.960780535   -2.579416776{txt}  {c |}
    {c BLC}{hline 46}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.4888711819   .6311136085   .6499207608{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}        1           2           3
    {c TLC}{hline 37}{c TRC}
  1 {c |}  {res} 1.964***    1.961***   -2.579***{txt}  {c |}
    {c BLC}{hline 37}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.0003629937    .004111986   .0004159833{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.489)   (0.631)   (0.650){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                     -2.579***{txt}  {c |}
  2 {c |}  {res}                                                  (0.650){txt}  {c |}
  3 {c |}  {res}                 High level                      1.964***{txt}  {c |}
  4 {c |}  {res}                                                  (0.489){txt}  {c |}
  5 {c |}  {res}           Management score                      1.961***{txt}  {c |}
  6 {c |}  {res}                                                  (0.631){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}        1           2           3
    {c TLC}{hline 37}{c TRC}
  1 {c |}  {res} 1.964***    1.961***   -2.579***{txt}  {c |}
    {c BLC}{hline 37}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.489)   (0.631)   (0.650){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2.xlsx", modify
{res}{txt}
{com}. putexcel F5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel F6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel F7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel F8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel F9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel F10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}. putexcel F16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2.xlsx} saved

{com}.         
.         
.         clear
{txt}
{com}. import excel "$working\results_with_stars2.xlsx"
{res}{text}(6 vars, 15 obs)

{com}. replace B=char(160) + "(1)" in 1
{txt}(1 real change made)

{com}. replace C=char(160) + "(2)" in 1
{txt}(1 real change made)

{com}. replace D=char(160) + "(3)" in 1
{txt}(1 real change made)

{com}. replace E=char(160) + "(4)" in 1
{txt}(1 real change made)

{com}. replace F=char(160) + "(5)" in 1
{txt}(1 real change made)

{com}. foreach var of varlist A B C D E F {c -(}
{txt}  2{com}.         replace `var'=char(160)+`var' in 5 
{txt}  3{com}.         replace `var'=char(160)+`var' in 7
{txt}  4{com}.         replace `var'=char(160)+`var' in 9
{txt}  5{com}. 
. 
. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. export delimited using "$tables_mock\Table4.csv", quote novarnames replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock\Table4.csv} saved

{com}. 
. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. 
. estadd local Mun_effects "Yes" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e5
{txt}
{com}. 
. estadd local NAICS_6_digits_effects "Yes" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e5
{txt}
{com}. 
. *Check the table
. esttab e* using "$tables_mock/Table4.tex",  ///
> keep(_IH10Xscore_1 _IH10* _IH10_shfre_1 _IH10_compo_1  score_manag_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> stats(Time_effects Mun_effects NAICS_6_digits_effects  N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
> unstack nonotes replace ///
> coeflabels(_IH10Xscore_1 "High level*Management score" ///
>            _IH10_contr_1 "High level" ///
>            _IH10_rate__1 "High level" ///  // Fixed underscore issue
>            _IH10_shfre_1 "High level" ///
>            _IH10_compo_1 "High level" ///
>            _IH10_infor_1 "High level" ///
>            score_manag_ "Management score")    ///
>         fragment        ///
>         nomtitles ///
> nonumbers ///
> nolines ///
> nogaps
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\tables\mock/Table4.tex"'})

{com}. 
. 
. /********************************************************************************************
> * END OF DO FILE
> ********************************************************************************************/
. 
{txt}end of do-file

{com}. 
. * ---------------------------------------------------------------------------
. * 3: Appendix Figures and Tables 
. *This step produces the final Online Appendix Figures and Regressions
. do "$programs/Appendix Figures.do"
{txt}
{com}. /********************************************************************************************
> * Project: Management and Misallocation in Mexico
> * Purpose: Generate Appendix figures of the paper
> * Date: 2024-12-01
> ********************************************************************************************/
. 
. 
. *Figure A1 - Manually (Image from the questionnaire)
. *Figure A2 - Manually (From exit stats provided by INEGI)
. 
. ***************************
. set scheme s2gcolor
{txt}
{com}. 
. ******************************************************************** 
. *Figure A3 Correlation of the management score by firm in 2014 and 2017 (vingtiles)
. ********************************************************************/
. 
. use "$working_interm/Vingtiles management 2014 2017.dta", clear
{txt}
{com}. 
. twoway ///
> (scatter serv_2017 serv_2014, mc(green)) ///
> (function y = x, range(0 1)) ///
> (scatter man_2017 man_2014, mc(eltblue) msymbol(triangle)) ///
> , legend(order(1 "Services" 3 "Manufacturing") ///
>          region(lcolor(none) fcolor(none))) ///
>   xtitle("Vingtiles Management score 2014") ///
>   ytitle("Vingtiles Management score 2017") ///
>   graphregion(color(white)) ///
>   bgcolor(white)
{res}{txt}
{com}. 
. graph save "$app_figures/Figure_A3.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A3.gph} saved

{com}. graph export "$app_figures/Figure_A3.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A3.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A3.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A3.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
. 
. ******************************************************************** 
.  * Figures A4 and A5: Histograms comparison manufacturing and services 2014 and 2017 
. ********************************************************************/
. use "$working_interm/Datos histograma updated 2019.dta" 
{txt}
{com}. twoway ///
>     (bar manuf_2017 bin_score, barwidth(1) color(eltblue) lcolor(none)) ///
>     (bar manuf_2015 bin_score, barwidth(1) color(none) lcolor(black) lwidth(0.7)), ///
>     graphregion(color(white)) ///
>     bgcolor(white) ///
>         xtitle("Management score") ytitle("Share of firms") ///
>     legend(order(2 "2014" 1 "2017") region(lcolor(none) fcolor(none))) ///
>         xlabel(0 "0" 5 "0.25" 10 "0.5" 15 "0.75" 20 "1")
{res}{txt}
{com}. 
. 
. graph save "$app_figures/Figure_A4.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A4.gph} saved

{com}. graph export "$app_figures/Figure_A4.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A4.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A4.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A4.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. twoway ///
>     (bar sercom_2017 bin_score, barwidth(1) color(eltblue) lcolor(none)) ///
>     (bar sercom_2015 bin_score, barwidth(1) color(none) lcolor(black) lwidth(0.7)), ///
>     graphregion(color(white)) ///
>     bgcolor(white) ///
>         xtitle("Management score") ytitle("Share of firms") ///
>     legend(order(2 "2014" 1 "2017") region(lcolor(none) fcolor(none))) ///
>         xlabel(0 "0" 5 "0.25" 10 "0.5" 15 "0.75" 20 "1")
{res}{txt}
{com}. 
.         
. 
. graph save "$app_figures/Figure_A5.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A5.gph} saved

{com}. graph export "$app_figures/Figure_A5.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A5.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A5.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A5.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. ******************************************************************** 
. *Figure A6: Comparing the age distribution by year in which the firm was established
. ********************************************************************/
. 
. use "$working_interm\Distributions overlapping sample.dta", clear
{txt}
{com}. 
. *label define years 0 "1900-1907" 1 "1908-1912" 2 "1913-1917" 3 "1918-1922" 4 "1923-1927" 5 "1928-1932" 6 "1933-1937" 7 "1938-1942" 8 "1943-1947" 9 "1948-1952" 10 "1953-1957" 11 "1958-1962" 12 "1963-1967" 13 "1968-1972" 14 "1973-1977" 15 "1978-1982" 16 "1983-1987" 17 "1988-1992" 18 "1993-1997" 19 "1998-2002" 20 "2003-2007" 21 "2008-2012"
. 
. *label values bins years
. version 16: table bins, c(sum sh_employ_ce sum sh_employ_ceenap)

{txt}{hline 10}{c TT}{hline 29}
     bins {c |} sum(sh_emp~e)  sum(sh_emp~p)
{hline 10}{c +}{hline 29}
1900-1907 {c |}      {res}.0006311       .0007013
{txt}1908-1912 {c |}       {res}.000057       .0001086
{txt}1913-1917 {c |}      {res}.0001318       .0005484
{txt}1918-1922 {c |}       {res}.000152       .0000591
{txt}1923-1927 {c |}      {res}.0002537       .0002246
{txt}1928-1932 {c |}      {res}.0005043       .0005072
{txt}1933-1937 {c |}      {res}.0018176       .0011041
{txt}1938-1942 {c |}      {res}.0032194       .0051851
{txt}1943-1947 {c |}      {res}.0038248       .0018011
{txt}1948-1952 {c |}      {res}.0045759        .005222
{txt}1953-1957 {c |}       {res}.005912       .0049446
{txt}1958-1962 {c |}      {res}.0087568       .0075409
{txt}1963-1967 {c |}      {res}.0126695       .0206908
{txt}1968-1972 {c |}      {res}.0155013       .0176759
{txt}1973-1977 {c |}      {res}.0235168       .0275673
{txt}1978-1982 {c |}      {res}.0273052       .0327239
{txt}1983-1987 {c |}      {res}.0527631       .0621988
{txt}1988-1992 {c |}      {res}.0750458       .0860658
{txt}1993-1997 {c |}      {res}.0950812        .116668
{txt}1998-2002 {c |}       {res}.164552       .1637597
{txt}2003-2007 {c |}       {res}.172394       .1731428
{txt}2008-2012 {c |}      {res}.2033491       .1929952
{txt}{hline 10}{c BT}{hline 29}

{com}. version 16: table bins, c(sum sh_firms_ce sum sh_firms_ceenap)

{txt}{hline 10}{c TT}{hline 29}
     bins {c |} sum(sh_fir~e)  sum(sh_fir~p)
{hline 10}{c +}{hline 29}
1900-1907 {c |}       {res}.001362        .000818
{txt}1908-1912 {c |}      {res}.0001442       .0001753
{txt}1913-1917 {c |}      {res}.0001042       .0001169
{txt}1918-1922 {c |}      {res}.0002564       .0001753
{txt}1923-1927 {c |}      {res}.0002163       .0000584
{txt}1928-1932 {c |}      {res}.0004246       .0002921
{txt}1933-1937 {c |}      {res}.0008733       .0005259
{txt}1938-1942 {c |}      {res}.0019469        .001636
{txt}1943-1947 {c |}      {res}.0025317       .0014607
{txt}1948-1952 {c |}      {res}.0028762       .0031551
{txt}1953-1957 {c |}      {res}.0042382       .0039147
{txt}1958-1962 {c |}       {res}.006137       .0057844
{txt}1963-1967 {c |}       {res}.008164       .0091732
{txt}1968-1972 {c |}      {res}.0119616       .0119778
{txt}1973-1977 {c |}      {res}.0170892       .0200409
{txt}1978-1982 {c |}       {res}.025838       .0318434
{txt}1983-1987 {c |}      {res}.0415732       .0476775
{txt}1988-1992 {c |}      {res}.0670187       .0771838
{txt}1993-1997 {c |}      {res}.0933134       .1100789
{txt}1998-2002 {c |}      {res}.1445408       .1609115
{txt}2003-2007 {c |}       {res}.171212       .1834064
{txt}2008-2012 {c |}      {res}.2467071       .2352323
{txt}{hline 10}{c BT}{hline 29}

{com}. 
. graph hbar (sum) sh_employ_ce sh_employ_ceenap, over(bins) legend(pos(6) order(1 "Census" 2 "Sample ENAPROCE") region(lcolor(none) fcolor(none))) /// 
>  graphregion(color(white)) ///
>   bgcolor(white) title("(a) Employment Distribution", size(small))
{res}{txt}
{com}. 
. graph save "$app_figures/Figure_A6_a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A6_a.gph} saved

{com}. graph export "$app_figures/Figure_A6_a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A6_a.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A6_a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A6_a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. graph hbar (sum) sh_firms_ce sh_firms_ceenap , over(bins) legend(pos(6) order(1 "Firms Census" 2 "Sample ENAPROCE") region(lcolor(none) fcolor(none))) /// 
>  graphregion(color(white)) ///
> bgcolor(white) title("(b) Firms distribution", size(small))
{res}{txt}
{com}. graph save "$app_figures/Figure_A6_b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A6_b.gph} saved

{com}. graph export "$app_figures/Figure_A6_b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A6_b.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A6_b.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A6_b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. ******************************************************************** 
. *Figures A7, A8, A9, and A10
. ********************************************************************/
. 
. use "$working_interm\Data age TFP Value added.dta" , clear
{txt}
{com}. *Figure A7 (a)
. twoway scatter lvamean_man age_bin, ytitle("log(Value added per employee) mean" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8,valuelabel) msymbol(X) msize(large) mcolor(navy) ||connected lvamean_man   age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-1(0.5)0.5) title("(a) Manufacturing MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A7_a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A7_a.gph} saved

{com}. graph export "$app_figures/Figure_A7_a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A7_a.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A7_a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A7_a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. *Figure A7 (b)
. twoway scatter lvamean_ser age_bin, ytitle("log(Value added per employee) mean" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(ebblue) ||connected lvamean_ser   age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(ebblue) lcolor(ebblue) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-1(0.5)0.5) title("(b) Services MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A7_b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A7_b.gph} saved

{com}. graph export "$app_figures/Figure_A7_b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A7_b.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A7_b.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A7_b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Figure A8 (a)
. twoway scatter lvasd_man age_bin, ytitle("log(Value added per employee) standard deviation" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) ||connected lvasd_man   age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-0.06(0.02)0.04) title("(a) Manufacturing MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A8_a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A8_a.gph} saved

{com}. graph export "$app_figures/Figure_A8_a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A8_a.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A8_a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A8_a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Figure A8 (b)
. twoway scatter lvasd_ser age_bin, ytitle("log(Value added per employee) standard deviation" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(ebblue) ||connected lvasd_ser   age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(ebblue) lcolor(ebblue) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-0.06(0.02)0.04) title("(b) Services MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A8_b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A8_b.gph} saved

{com}. graph export "$app_figures/Figure_A8_b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A8_b.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A8_b.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A8_b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. *Figure A9 (a)
. twoway scatter ltfpmean_man age_bin, ytitle("log(TFP) mean" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) ||connected ltfpmean_man   age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-0.4(0.05)0.05) title("(a) Manufacturing MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A9_a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A9_a.gph} saved

{com}. graph export "$app_figures/Figure_A9_a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A9_a.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A9_a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A9_a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Figure A9 (b)
. twoway scatter ltfpmean_ser age_bin, ytitle("log(TFP) mean mean" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(ebblue) ||connected  ltfpmean_ser  age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(ebblue) lcolor(ebblue) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-0.4(0.05)0.05) title("(b) Services MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A9_b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A9_b.gph} saved

{com}. graph export "$app_figures/Figure_A9_b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A9_b.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A9_b.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A9_b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Figure A10 (a)
. twoway scatter ltfpsd_man age_bin, ytitle("log(TFP) standard deviation" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(navy) ||connected ltfpsd_man  age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(navy) lcolor(navy) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-0.15(0.05)0.1) title("(a) Manufacturing MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A10_a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A10_a.gph} saved

{com}. graph export "$app_figures/Figure_A10_a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A10_a.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A10_a.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A10_a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Figure A10 (b)
. twoway scatter  ltfpsd_ser age_bin, ytitle("log(TFP) standard deviation" "(normalized to zero over the sample of each sector)") xtitle(Age bins) xla(1/8, valuelabel) msymbol(X) msize(large) mcolor(ebblue) ||connected  ltfpsd_ser   age_bin, graphregion(color(white)) msymbol(X) msize(large) mcolor(ebblue) lcolor(ebblue) lwidth(medthick) lpattern(longdash) legend(off) ylabel(-0.15(0.05)0.1) title("(b) Services MX")
{res}{txt}
{com}. graph save "$app_figures/Figure_A10_b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A10_b.gph} saved

{com}. graph export "$app_figures/Figure_A10_b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A10_b.jpg} written in JPEG format

{com}. graph export "$app_figures/Figure_A10_b.png",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures/Figure_A10_b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.   
. ********************************************************************
.  *Figure A11: CDFs Management score drive time to the border (unweighted)
.  ********************************************************************/
.  use "$working/BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}.  
.  *a) Manufacturing
. ksmirnov score_manag_ if dum_man2==1, by(AM_durat_border_hrs)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0922       0.550
{txt}1                  {res} -0.2294       0.025
{txt}Combined K-S       {res}  0.2294       0.050

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 1466 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}.  
.  cdfplot score_manag_  if dum_man2==1 & AM_durat_border_hrs!=., by(AM_durat_border_hrs) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below median drive time" 2 "Above median drive time") pos(6) cols(2)  region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.   * .gph file
. graph save "$app_figures_mock\Figure_A11a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A11a.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A11a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A11a.jpg} written in JPEG format

{com}.  graph export "$app_figures_mock\Figure_A11a.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A11a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  *b) Services
. ksmirnov score_manag_ if dum_man2==0, by(AM_durat_border_hrs)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.4321       0.000
{txt}1                  {res} -0.1238       0.000
{txt}Combined K-S       {res}  0.4321       0.000

{txt}Note: Ties exist in combined dataset;
      there are 33 unique values out of 17078 observations.

{com}. ksmirnov score_manag_ if dum_man2==1, by(AM_durat_border_hrs)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0922       0.550
{txt}1                  {res} -0.2294       0.025
{txt}Combined K-S       {res}  0.2294       0.050

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 1466 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  if dum_man2==0 & AM_durat_border_hrs!=., by(AM_durat_border_hrs) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below median drive time" 2 "Above median drive time") pos (6) cols(2)  region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
. 
.   * .gph file
. graph save "$app_figures_mock\Figure_A11b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A11b.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A11b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A11b.jpg} written in JPEG format

{com}. graph export "$app_figures_mock\Figure_A11b.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A11b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. /********************************************************************
>  *Figure A12: CDFs Management score market size (unweighted)
>  ********************************************************************/
.  *a) Manufacturing
. ksmirnov score_manag_ if dum_man2==1, by(AMlpopdensMAincome)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0198       0.774
{txt}1                  {res} -0.9137       0.000
{txt}Combined K-S       {res}  0.9137       0.000

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 1466 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  if dum_man2==1 & AMlpopdensMAincome!=., by(AMlpopdensMAincome) graphregion(color(white)) opt1(lp(dash solid ) lc(eltblue navy ) ) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below the median market size" 2 "Above the median market size") pos(6) cols(2) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.   * .gph file
. graph save "$app_figures_mock\Figure_A12a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A12a.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A12a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A12a.jpg} written in JPEG format

{com}. graph export "$app_figures_mock\Figure_A12a.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A12a.png{rm}
saved as
PNG
format
{p_end}

{com}.         
. 
.  
.  *b) Services
. ksmirnov score_manag_ if dum_man2==0, by(AMlpopdensMAincome)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0208       0.028
{txt}1                  {res} -0.2497       0.000
{txt}Combined K-S       {res}  0.2497       0.000

{txt}Note: Ties exist in combined dataset;
      there are 33 unique values out of 17078 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. cdfplot score_manag_  if dum_man2==0 & AMlpopdensMAincome!=., by(AMlpopdensMAincome) graphregion(color(white)) opt1( lp( dash solid) lc(eltblue navy )) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Below the median market size" 2 "Above the median market size") pos(6) cols(2) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
. 
.   * .gph file
. graph save "$app_figures_mock\Figure_A12b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A12b.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A12b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A12b.jpg} written in JPEG format

{com}. graph export "$app_figures_mock\Figure_A12b.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A12b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.    
. /********************************************************************
>  *Figure A13: CDFs Management score according to institutional strengths (unweighted)
>  ********************************************************************/
.   
. *Contract enforcement
. ksmirnov score_manag_ , by(H10_contr)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1229       0.000
{txt}1                  {res} -0.4049       0.000
{txt}Combined K-S       {res}  0.4049       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_  if H10_contr!=., by(H10_contr) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% contract enforcement problems") pos(6) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
. 
.  * .gph file
. graph save "$app_figures_mock\Figure_A13a.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13a.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A13a.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13a.jpg} written in JPEG format

{com}. graph export "$app_figures_mock\Figure_A13a.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13a.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Corruption
. ksmirnov score_manag_ , by(H10_shfrec_corrup)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1206       0.000
{txt}1                  {res} -0.2426       0.000
{txt}Combined K-S       {res}  0.2426       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_ if H10_shfrec_corrup!=., by(H10_shfrec_corrup) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% frequency corruption problems") pos(6) region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
. 
.   * .gph file
. graph save "$app_figures_mock\Figure_A13c.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13c.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A13c.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13c.jpg} written in JPEG format

{com}. graph export "$app_figures_mock\Figure_A13c.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13c.png{rm}
saved as
PNG
format
{p_end}

{com}. 
.  
. *Kidnapping
. ksmirnov score_manag_ , by(H10_rate_kid_)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1717       0.000
{txt}1                  {res} -0.1591       0.000
{txt}Combined K-S       {res}  0.1717       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_ if H10_rate_kid_!=. , by(H10_rate_kid_) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% kidnapping") region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
.  
.   * .gph file
. graph save "$app_figures_mock\Figure_A13b.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13b.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A13b.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13b.jpg} written in JPEG format

{com}. graph export "$app_figures_mock\Figure_A13b.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13b.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. *Business Informality
. ksmirnov score_manag_ , by(H10_informal)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1895       0.000
{txt}1                  {res} -0.1384       0.000
{txt}Combined K-S       {res}  0.1895       0.000

{txt}Note: Ties exist in combined dataset;
      there are 35 unique values out of 18544 observations.

{com}. scalar pval = r(p)
{txt}
{com}. 
. *  p-value as a string
. local pvaltxt : display %5.3f pval
{res}{txt}
{com}. 
. cdfplot score_manag_ if H10_informal!=., by(H10_informal) graphregion(color(white)) opt1(lc(navy eltblue) lp(solid dash)) ///
> xtitle(Management score) ytitle(Cumulative Probability) ///
> legend(order(1 "Not in the top 10%" 2 "Top 10% of informality") region(lcolor(white)) ) ///
> note("p-value of difference (KS)=`pvaltxt'", size(small))
{txt}(0 observations deleted)

{com}. 
. 
.  * .gph file
. graph save "$app_figures_mock\Figure_A13d.gph", replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13d.gph} saved

{com}. 
. * .jpg file
. graph export "$app_figures_mock\Figure_A13d.jpg", as(jpg) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13d.jpg} written in JPEG format

{com}. graph export "$app_figures_mock\Figure_A13d.png", as(png) replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\figures\mock\Figure_A13d.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. /********************************************************************************************
> * END OF DO FILE
> ********************************************************************************************/
. 
{txt}end of do-file

{com}. do "$programs/Appendix Tables.do"
{txt}
{com}. /********************************************************************************************
> * Project: Management and Misallocation in Mexico
> * Purpose: Estimate Online Appendix Tables
> * Date: 2024-12-01
> ********************************************************************************************/
. 
. 
. ********************************************************************************************************************************************
. *Table A1- Descriptives
. ********************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. gen FDI2_=sh_IED_>=50
{txt}
{com}. 
. * Set up the Excel file
. putexcel set "$app_tables_mock/TableA1.xlsx", replace
{res}{p}{txt}note: file will be replaced when the first {cmd:putexcel} command is issued.{p_end}

{com}. 
. * Set up headers
. putexcel (A1:H1), merge
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel A1 = "Manufacturing"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. * Generate summary statistics for 2014
. tabstat score_manag_ score_manfirst8 score_manlast8 nworkers_WA_ edad_emp_ dum_exp_ shexp_ FDI1_ FDI2_ sh_IED_ sh_sup_WA_ sh_direc_WA_ durat_border_hrs popdens_MA income_MA shexp_clase ///
> if _j==2014 & sector_=="MAN" & score_manag_!=., ///
> stats(mean sd p10 p25 p50 p75 p90 n) col(stats) save

{txt}{ralign 12:Variable} {...}
{c |}      Mean        SD       p10       p25       p50       p75       p90         N
{hline 13}{c +}{hline 80}
{ralign 12:score_manag_} {...}
{c |}{...}
 {res} .2018499   .051694   .172619   .172619  .2142857  .2142857  .2142857       998
{txt}{ralign 12:score_manf~_} {...}
{c |}{...}
 {res} .3060078   .046271  .2916667  .2916667  .3020833  .3020833  .3020833       998
{txt}{ralign 12:score_manl~_} {...}
{c |}{...}
 {res} .0628159  .0822834         0         0  .0833333  .1111111  .1111111       998
{txt}{ralign 12:nworkers_WA_} {...}
{c |}{...}
 {res} 20.60421  13.07024        12        12        26        26        26       998
{txt}{ralign 12:edad_emp_} {...}
{c |}{...}
 {res} 17.78457  4.177924        16        16        18        18        18       998
{txt}{ralign 12:dum_exp_} {...}
{c |}{...}
 {res}  .002004  .0447437         0         0         0         0         0       998
{txt}{ralign 12:shexp_} {...}
{c |}{...}
 {res} .0161082  .3959251         0         0         0         0         0       998
{txt}{ralign 12:FDI1_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0       998
{txt}{ralign 12:FDI2_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0       998
{txt}{ralign 12:sh_IED_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0       998
{txt}{ralign 12:sh_sup_WA_} {...}
{c |}{...}
 {res} .1212889  .0402108  .0833333  .0833333  .1538462  .1538462  .1538462       998
{txt}{ralign 12:sh_direc_WA_} {...}
{c |}{...}
 {res} .0825557  .0176421  .0769231  .0769231  .0833333  .0833333  .0833333       998
{txt}{ralign 12:durat_bord~5} {...}
{c |}{...}
 {res} 12.33676  2.551023  11.85583  11.85583  11.85583  11.85583  11.85583       503
{txt}{ralign 12:popdens_MA} {...}
{c |}{...}
 {res} 137.8209   17.7822  141.2005  141.2005  141.2005  141.2005  141.2005       503
{txt}{ralign 12:income_MA} {...}
{c |}{...}
 {res} 1390.383  52.59001  1398.788  1398.788  1398.788  1398.788  1398.788       503
{txt}{ralign 12:shexp_clase} {...}
{c |}{...}
 {res} .1093338  .0246236  .0846731  .0846731  .1338958  .1338958  .1338958       998
{txt}{hline 13}{c BT}{hline 80}

{com}. 
. matrix stats_2014 = r(StatTotal)'
{txt}
{com}. local rows_2014 = rowsof(stats_2014)  // Count number of rows in 2014 data
{txt}
{com}. 
. * Export 2014 statistics
. putexcel A2 = "2014 Data"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B2 = matrix(stats_2014), names
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. * Generate summary statistics for 2017
. tabstat score_manag_ score_manfirst8 score_manlast8 nworkers_WA_ edad_emp_ dum_exp_ shexp_ FDI1_ FDI2_ sh_IED_ sh_sup_WA_ sh_direc_WA_ durat_border_hrs popdens_MA income_MA shexp_clase ///
> if _j==2017 & sector_=="MAN" & score_manag_!=., ///
> stats(mean sd p10 p25 p50 p75 p90 n) col(stats) save

{txt}{ralign 12:Variable} {...}
{c |}      Mean        SD       p10       p25       p50       p75       p90         N
{hline 13}{c +}{hline 80}
{ralign 12:score_manag_} {...}
{c |}{...}
 {res} .2905797  .2327511  .0666667  .1583333  .2380952  .2440476  .7588542      2696
{txt}{ralign 12:score_manf~_} {...}
{c |}{...}
 {res} .3728105  .2005466  .1111111  .2638889  .3333333  .4166667  .7395833      2696
{txt}{ralign 12:score_manl~_} {...}
{c |}{...}
 {res} .1771991  .2950544         0         0         0  .1666667   .778125      2696
{txt}{ralign 12:nworkers_WA_} {...}
{c |}{...}
 {res} 52.66283  54.18951        12        19        29        49       163      2696
{txt}{ralign 12:edad_emp_} {...}
{c |}{...}
 {res} 24.36721  12.24543        12        15        23        25        48      2696
{txt}{ralign 12:dum_exp_} {...}
{c |}{...}
 {res} .0011128  .0333457         0         0         0         0         0      2696
{txt}{ralign 12:shexp_} {...}
{c |}{...}
 {res} .0000714  .0027611         0         0         0         0         0      2696
{txt}{ralign 12:FDI1_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      2696
{txt}{ralign 12:FDI2_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      2696
{txt}{ralign 12:sh_IED_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      2696
{txt}{ralign 12:sh_sup_WA_} {...}
{c |}{...}
 {res} .1735335  .1039385  .0833333  .1034483  .1052632  .2208589  .3673469      2696
{txt}{ralign 12:sh_direc_WA_} {...}
{c |}{...}
 {res} .1313761  .1797583         0  .0344828  .0368098  .1428571        .5      2696
{txt}{ralign 12:durat_bord~5} {...}
{c |}{...}
 {res} 11.01862  2.203252    8.4725    8.4725  11.45889  11.85583  13.00583      2695
{txt}{ralign 12:popdens_MA} {...}
{c |}{...}
 {res} 521.5381  963.4083  14.22219  14.22219  112.5563  141.2005  2548.339      2695
{txt}{ralign 12:income_MA} {...}
{c |}{...}
 {res} 2109.859  696.7345  1398.788  1526.737  1710.566  2569.707  3265.978      2695
{txt}{ralign 12:shexp_clase} {...}
{c |}{...}
 {res} .0327664  .0518505         0         0         0  .0301173  .1338958      2506
{txt}{hline 13}{c BT}{hline 80}

{com}. 
. matrix stats_2017 = r(StatTotal)'
{txt}
{com}. local start_row = 3 + `rows_2014' /// Calculate the row where 2017 data should start
> 
{txt}
{com}. * Export 2017 statistics
. putexcel A`start_row' = "2017 Data"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B`start_row' = matrix(stats_2017), names
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. 
. 
. * Set up headers
. putexcel (A36:H36), merge
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel A36 = "Services"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. * Generate summary statistics for 2014
. tabstat score_manag_ score_manfirst8 score_manlast8 nworkers_WA_ edad_emp_ dum_exp_ shexp_ FDI1_ FDI2_ sh_IED_ sh_sup_WA_ sh_direc_WA_ durat_border_hrs popdens_MA income_MA shexp_clase ///
> if _j==2014 & sector_!="MAN" & score_manag_!=., ///
> stats(mean sd p10 p25 p50 p75 p90 n) col(stats) save

{txt}{ralign 12:Variable} {...}
{c |}      Mean        SD       p10       p25       p50       p75       p90         N
{hline 13}{c +}{hline 80}
{ralign 12:score_manag_} {...}
{c |}{...}
 {res} .4816121  .1815746  .2380952  .3214286  .4642857  .6479167  .6989583      8038
{txt}{ralign 12:score_manf~_} {...}
{c |}{...}
 {res} .5123761  .1342864  .2708333  .4270833        .5  .5729167  .6979167      8038
{txt}{ralign 12:score_manl~_} {...}
{c |}{...}
 {res} .4308488  .3135727         0  .1111111    .41875        .7   .840625      8038
{txt}{ralign 12:nworkers_WA_} {...}
{c |}{...}
 {res} 68.07651  61.43297        23        28        44        56       180      8038
{txt}{ralign 12:edad_emp_} {...}
{c |}{...}
 {res} 26.08323  7.481164        18        22        24        27        40      8038
{txt}{ralign 12:dum_exp_} {...}
{c |}{...}
 {res} .1246579  .3303511         0         0         0         0         1      8038
{txt}{ralign 12:shexp_} {...}
{c |}{...}
 {res} 1.001997  2.958783         0         0         0         0  4.341534      8038
{txt}{ralign 12:FDI1_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      8038
{txt}{ralign 12:FDI2_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      8038
{txt}{ralign 12:sh_IED_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      8038
{txt}{ralign 12:sh_sup_WA_} {...}
{c |}{...}
 {res} .1617391  .1165203  .0555556       .08  .1111111  .2051282  .3181818      8038
{txt}{ralign 12:sh_direc_WA_} {...}
{c |}{...}
 {res} .1598067  .0998482  .0434783  .0769231       .12  .2222222  .2777778      8038
{txt}{ralign 12:durat_bord~5} {...}
{c |}{...}
 {res} 15.21997  8.312259  4.618611  10.58583   13.6875  20.71917  31.92639      8038
{txt}{ralign 12:popdens_MA} {...}
{c |}{...}
 {res}  350.889  688.6658  5.382166  15.96507  52.36541  154.3663  1613.124      8038
{txt}{ralign 12:income_MA} {...}
{c |}{...}
 {res} 2170.909  666.6109  1356.135  1590.822  2213.424  2569.707  3265.978      8038
{txt}{ralign 12:shexp_clase} {...}
{c |}{...}
 {res} .0027995  .0071966         0         0         0  .0005242  .0207639      4010
{txt}{hline 13}{c BT}{hline 80}

{com}. 
. matrix stats_2014 = r(StatTotal)'
{txt}
{com}. local rows_2014 = rowsof(stats_2014)  // Count number of rows in 2014 data
{txt}
{com}. 
. * Export 2014 statistics
. putexcel A37 = "2014 Data"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B37 = matrix(stats_2014), names
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. * Generate summary statistics for 2017
. tabstat score_manag_ score_manfirst8 score_manlast8 nworkers_WA_ edad_emp_ dum_exp_ shexp_ FDI1_ FDI2_ sh_IED_ sh_sup_WA_ sh_direc_WA_ durat_border_hrs popdens_MA income_MA shexp_clase ///
> if _j==2017 & sector_!="MAN" & score_manag_!=., ///
> stats(mean sd p10 p25 p50 p75 p90 n) col(stats) save

{txt}{ralign 12:Variable} {...}
{c |}      Mean        SD       p10       p25       p50       p75       p90         N
{hline 13}{c +}{hline 80}
{ralign 12:score_manag_} {...}
{c |}{...}
 {res} .3693224  .1902174  .1583333  .2380952  .3630953  .5038889  .6145833      7804
{txt}{ralign 12:score_manf~_} {...}
{c |}{...}
 {res}  .390884  .1785722  .1979167  .2916667  .3854167  .5416667     .5625      7804
{txt}{ralign 12:score_manl~_} {...}
{c |}{...}
 {res} .3409995  .2297377         0  .1666667  .3333333        .5  .6979167      7804
{txt}{ralign 12:nworkers_WA_} {...}
{c |}{...}
 {res} 59.39531  48.19122        26        30        37        73       155      7804
{txt}{ralign 12:edad_emp_} {...}
{c |}{...}
 {res} 25.98744    9.1002        16        19        27        30        31      7804
{txt}{ralign 12:dum_exp_} {...}
{c |}{...}
 {res} .1918247  .3937612         0         0         0         0         1      7804
{txt}{ralign 12:shexp_} {...}
{c |}{...}
 {res} .0123046  .0341085         0         0         0         0  .0631625      7804
{txt}{ralign 12:FDI1_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      7804
{txt}{ralign 12:FDI2_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      7804
{txt}{ralign 12:sh_IED_} {...}
{c |}{...}
 {res}        0         0         0         0         0         0         0      7804
{txt}{ralign 12:sh_sup_WA_} {...}
{c |}{...}
 {res} .1250193  .0986021         0  .0638298  .0909091  .1725888  .2833333      7804
{txt}{ralign 12:sh_direc_WA_} {...}
{c |}{...}
 {res} .1254252  .0736836  .0547945  .0649351  .1153846  .1538462  .2333333      7804
{txt}{ralign 12:durat_bord~5} {...}
{c |}{...}
 {res} 15.98537  8.445373  4.618611  12.15889  14.49306  20.71917  31.92639      7308
{txt}{ralign 12:popdens_MA} {...}
{c |}{...}
 {res} 217.8234  412.4503  5.382166  32.95555  56.62106  154.3663  707.9922      7308
{txt}{ralign 12:income_MA} {...}
{c |}{...}
 {res} 2136.153  707.9331  1356.135  1529.619  2213.424  2473.417  3392.534      7308
{txt}{ralign 12:shexp_clase} {...}
{c |}{...}
 {res} .0176602  .0309073         0         0  .0002621  .0207639  .0846731      2994
{txt}{hline 13}{c BT}{hline 80}

{com}. 
. matrix stats_2017 = r(StatTotal)'
{txt}
{com}. local start_row = 38 + `rows_2014' /// Calculate the row where 2017 data should start
> 
{txt}
{com}. * Export 2017 statistics
. putexcel A`start_row' = "2017 Data"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B`start_row' = matrix(stats_2017), names
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. 
. putexcel B3 = "Management score"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B4 = "First 8 questions (non-incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B5 = "Last 8 questions (incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B6 = "Size (number of employees)" C6=117 D6=185
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B7 = "Firm age"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B8 = "Exporter"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B9 = "Share of exports"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B10 = "FDI1*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B11 = "FDI2*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B12 = "Share of FDI"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B13 = "Share college degree"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B14 = "Share white collar"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B15 = "Drive time to border (hrs)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B16 = "Population density (inhabitants/km2)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B17 = "MA average household monthly per capita income (pesos)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B18 = "Share of exports (NAICS 6-digits exports/sales)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. 
. putexcel B20 = "Management score"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B21 = "First 8 questions (non-incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B22 = "Last 8 questions (incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B23 = "Size (number of employees)" C23=133 D23=204
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B24 = "Firm age"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B25 = "Exporter"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B26 = "Share of exports"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B27 = "FDI1*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B28 = "FDI2*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B29 = "Share of FDI"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B30 = "Share college degree"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B31 = "Share white collar"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B32 = "Drive time to border (hrs)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B33 = "Population density (inhabitants/km2)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B34 = "MA average household monthly per capita income (pesos)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B35 = "Share of exports (NAICS 6-digits exports/sales)"        
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}.         
. 
. putexcel B38 = "Management score"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B39 = "First 8 questions (non-incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B40 = "Last 8 questions (incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B41 = "Size (number of employees)" C41=68 D41=147
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B42 = "Firm age"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B43 = "Exporter"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B44 = "Share of exports"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B45 = "FDI1*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B46 = "FDI2*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B47 = "Share of FDI"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B48 = "Share college degree"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B49 = "Share white collar"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B50 = "Drive time to border (hrs)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B51 = "Population density (inhabitants/km2)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B52 = "MA average household monthly per capita income (pesos)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B53 = "Share of exports (NAICS 6-digits exports/sales)"        
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. 
. putexcel B55 = "Management score"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B56 = "First 8 questions (non-incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B57 = "Last 8 questions (incentive)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B58 = "Size (number of employees)" C58=68 D58=136
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B59 = "Firm age"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B60 = "Exporter"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B61 = "Share of exports"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B62 = "FDI1*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B63 = "FDI2*"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B64 = "Share of FDI"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B65 = "Share college degree"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B66 = "Share white collar"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B67 = "Drive time to border (hrs)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B68 = "Population density (inhabitants/km2)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B69 = "MA average household monthly per capita income (pesos)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. putexcel B70 = "Share of exports (NAICS 6-digits exports/sales)"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA1.xlsx} saved

{com}. 
. * Generate summary statistics for Manufacturing (2014 and 2017)
. foreach year in 2014 2017 {c -(}
{txt}  2{com}.     quietly estpost tabstat score_manag_ score_manfirst8 score_manlast8 nworkers_WA_ edad_emp_ dum_exp_ shexp_ FDI1_ FDI2_ sh_IED_ sh_sup_WA_ sh_direc_WA_ durat_border_hrs popdens_MA income_MA shexp_clase ///
>     if _j==`year' & sector_=="MAN" & score_manag_!=., ///
>     stats(mean sd p10 p25 p50 p75 p90 n) columns(stats)
{txt}  3{com}.     est store man_`year'
{txt}  4{com}. {c )-}
{txt}
{com}. 
. * Generate summary statistics for Services (2014 and 2017)
. foreach year in 2014 2017 {c -(}
{txt}  2{com}.     quietly estpost tabstat score_manag_ score_manfirst8 score_manlast8 nworkers_WA_ edad_emp_ dum_exp_ shexp_ FDI1_ FDI2_ sh_IED_ sh_sup_WA_ sh_direc_WA_ durat_border_hrs popdens_MA income_MA shexp_clase ///
>     if _j==`year' & sector_!="MAN" & score_manag_!=., ///
>     stats(mean sd p10 p25 p50 p75 p90 n) columns(stats)
{txt}  3{com}.     est store serv_`year'
{txt}  4{com}. {c )-}
{txt}
{com}. 
. * Export to LaTeX
. esttab man_2014 using "$app_tables_mock\TableA1p1.tex", replace ///
>     cells("mean(fmt(3)) sd(fmt(3)) p10(fmt(3)) p25(fmt(3)) p50(fmt(3)) p75(fmt(3)) p90(fmt(3))") ///
>     nomtitles nonumbers ///
>          coeflabels( ///
>         score_manag_ "Management score" ///
>                 score_manfirst8_ "First 8 questions (non-incentive)" ///
>                 score_manlast8_ "Last 8 questions (incentive)" ///
>                 nworkers_WA_ "Size (Number of employees)" ///
>                 edad_emp_ "Firm age" ///
>                 dum_exp_ "Exporter" ///
>                 shexp_ "Share of exports" ///
>                 FDI1_ "FDI1*" ///
>                 FDI2_ "FDI2*" ///
>                 sh_IED_ "Share of FDI" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>                 sh_direc_WA_ "Share white collar" ///
>                 durat_border_hrs5 "Drive time to border (hrs)" ///
>                 popdens_MA "Population density (inhabitants/km2)" ///
>                 income_MA "MA average household monthly per capita income (pesos)" ///
>                 shexp_clase "Share of exports (NAICS 6-digits exports/sales)" ///
>                  ) ///
>           collabels(none) ///
>           nolines ///
>     label fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA1p1.tex"'})

{com}. 
. 
. 
. esttab man_2017 using "$app_tables_mock\TableA1p2.tex", replace ///
>     cells("mean(fmt(3)) sd(fmt(3)) p10(fmt(3)) p25(fmt(3)) p50(fmt(3)) p75(fmt(3)) p90(fmt(3))") ///
>     nomtitles nonumbers ///
>                  coeflabels( ///
>         score_manag_ "Management score" ///
>                 score_manfirst8_ "First 8 questions (non-incentive)" ///
>                 score_manlast8_ "Last 8 questions (incentive)" ///
>                 nworkers_WA_ "Size (Number of employees)" ///
>                 edad_emp_ "Firm age" ///
>                 dum_exp_ "Exporter" ///
>                 shexp_ "Share of exports" ///
>                 FDI1_ "FDI1*" ///
>                 FDI2_ "FDI2*" ///
>                 sh_IED_ "Share of FDI" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>                 sh_direc_WA_ "Share white collar" ///
>                 durat_border_hrs5 "Drive time to border (hrs)" ///
>                 popdens_MA "Population density (inhabitants/km2)" ///
>                 income_MA "MA average household monthly per capita income (pesos)" ///
>                 shexp_clase "Share of exports (NAICS 6-digits exports/sales)" ///
>                  ) ///
>           collabels(none) ///
>           nolines ///
>     label fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA1p2.tex"'})

{com}. 
. esttab serv_2014 using "$app_tables_mock\TableA1p3.tex", replace ///
>     cells("mean(fmt(3)) sd(fmt(3)) p10(fmt(3)) p25(fmt(3)) p50(fmt(3)) p75(fmt(3)) p90(fmt(3))") ///
>     nomtitles nonumbers ///
>                  coeflabels( ///
>         score_manag_ "Management score" ///
>                 score_manfirst8_ "First 8 questions (non-incentive)" ///
>                 score_manlast8_ "Last 8 questions (incentive)" ///
>                 nworkers_WA_ "Size (Number of employees)" ///
>                 edad_emp_ "Firm age" ///
>                 dum_exp_ "Exporter" ///
>                 shexp_ "Share of exports" ///
>                 FDI1_ "FDI1*" ///
>                 FDI2_ "FDI2*" ///
>                 sh_IED_ "Share of FDI" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>                 sh_direc_WA_ "Share white collar" ///
>                 durat_border_hrs5 "Drive time to border (hrs)" ///
>                 popdens_MA "Population density (inhabitants/km2)" ///
>                 income_MA "MA average household monthly per capita income (pesos)" ///
>                 shexp_clase "Share of exports (NAICS 6-digits exports/sales)" ///
>                  ) ///
>           collabels(none) ///
>           nolines ///
>     label fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA1p3.tex"'})

{com}. 
. 
. esttab serv_2017 using "$app_tables_mock\TableA1p4.tex", replace ///
>     cells("mean(fmt(3)) sd(fmt(3)) p10(fmt(3)) p25(fmt(3)) p50(fmt(3)) p75(fmt(3)) p90(fmt(3))") ///
>     nomtitles nonumbers ///
>                 coeflabels( ///
>         score_manag_ "Management score" ///
>                 score_manfirst8_ "First 8 questions (non-incentive)" ///
>                 score_manlast8_ "Last 8 questions (incentive)" ///
>                 nworkers_WA_ "Size (Number of employees)" ///
>                 edad_emp_ "Firm age" ///
>                 dum_exp_ "Exporter" ///
>                 shexp_ "Share of exports" ///
>                 FDI1_ "FDI1*" ///
>                 FDI2_ "FDI2*" ///
>                 sh_IED_ "Share of FDI" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>                 sh_direc_WA_ "Share white collar" ///
>                 durat_border_hrs5 "Drive time to border (hrs)" ///
>                 popdens_MA "Population density (inhabitants/km2)" ///
>                 income_MA "MA average household monthly per capita income (pesos)" ///
>                 shexp_clase "Share of exports (NAICS 6-digits exports/sales)" ///
>                  ) ///
>           collabels(none) ///
>           nolines  ///
>     label fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA1p4.tex"'})

{com}. 
. ********************************************************************************************************************************************
. *Table A2
. ********************************************************************************************************************************************
. 
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. drop if clee==""
{txt}(873 observations deleted)

{com}. merge 1:1 clee _j using "$working/data_CE_2014estimates.dta", gen(mergeCE)
{res}{txt}{p 0 7 2}
(variable
{bf:_j} was {bf:int}, now {bf:float} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}          11,964
{txt}{col 9}from master{col 30}{res}          11,964{txt}  (mergeCE==1)
{col 9}from using{col 30}{res}               0{txt}  (mergeCE==2)

{col 5}Matched{col 30}{res}           9,036{txt}  (mergeCE==3)
{col 5}{hline 41}

{com}. 
. 
. estimates drop _all
{res}{txt}
{com}. 
. qui reg lVA_real_wk_milCE_WA_2014 score_manag_  if dum_man2==1, cluster(id_mun) robust 
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reghdfe lVA_real_wk_milCE_WA_2014 score_manag_ lcapworkCEWA_2014  lnworkersCE_WA_2014 sh_sup_WA_ if dum_man2==1, absorb(claseCE) cluster(id_mun) 
{txt}
{com}. estimates store e2
{txt}
{com}. 
. qui reghdfe ltfp_sec_realCE_WA_2014 score_manag_  if dum_man2==1,  absorb(claseCE) cluster(id_mun) 
{txt}
{com}. estimates store e3
{txt}
{com}. 
. 
. qui reg lVA_real_wk_milCE_WA_2014 score_manag_  if dum_man2==0 & sector_!="COM", cluster(id_mun) robust 
{txt}
{com}. estimates store e4
{txt}
{com}. 
. qui reghdfe lVA_real_wk_milCE_WA_2014 score_manag_ lcapworkCEWA_2014 lnworkersCE_WA_2014 sh_sup_WA_  if dum_man2==0 & sector_!="COM", absorb(claseCE) cluster(id_mun) 
{txt}
{com}. estimates store e5
{txt}
{com}. 
. qui reghdfe ltfp_sec_realCE_WA_2014 score_manag_  if dum_man2==0 & sector_!="COM",  absorb(claseCE) cluster(id_mun) 
{txt}
{com}. estimates store e6
{txt}
{com}. 
. 
. *Add notes
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "No"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e5
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e6
{txt}
{com}. 
. 
. 
. * Exporting results
. *csv
. esttab e* using "$app_tables_mock\TableA2.csv", keep(score_manag*  lcap* lnwork*  sh_sup*) ///
>     scalars("clase 6-digits NAICS") b(%9.4f) stats(NAICS_6_digits_effects N, fmt(%13.0f) labels("NAICS 6-digits effects" "Observations")) star(* 0.10 ** 0.05 *** 0.01) se  title("Estimates of performance over management score using data from 2014 Economic Census") ///
>                 mtitle("log(VA per employee)" "log(VA per employee)" "log(TFP)" "log(VA per employee)" "log(VA per employee)" "log(TFP)" ) ///
>     label ///
>     coeflabels( ///
>         score_manag_ "Management score" ///
>         lcapworkCEWA_2014 "log(capital/employee)" ///
>         lnworkersCE_WA_2014 "log(employees)" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>     ) ///
>     replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA2.csv"'})

{com}. *tex
.         esttab e* using "$app_tables_mock\TableA2.tex", ///
>     keep(score_manag*  lcap* lnwork*  sh_sup*) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>     stats(NAICS_6_digits_effects N, fmt(%13.0f) labels("NAICS 6-digits effects" "Observations")) ///
>     scalars("clase 6-digits NAICS") ///
>                 mtitle("" "" "" "" "" "" ) ///
>     label ///
>     coeflabels( ///
>         score_manag_ "Management score" ///
>         lcapworkCEWA_2014 "log(capital/employee)" ///
>         lnworkersCE_WA_2014 "log(employees)" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA2.tex"'})

{com}. 
. 
. ************************************************************************************************************************************************************
. *Table A3
. ************************************************************************************************************************************************************
. 
. use "$working/Base_long_microenterprises.dta", clear
{txt}
{com}. estimates drop _all
{res}{txt}
{com}. 
. qui reg  lVA_real_wk_mil_WA_  score_manag_ dum_time , cluster(id_mun) robust
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reghdfe  lVA_real_wk_mil_WA_  score_manag_   lcapworkWA_ lnworkers_WA_  sh_direc_WA sh_sup_WA dum_time , absorb(claseb) cluster(id_mun)
{txt}
{com}. estimates store e2
{txt}
{com}. 
. *Add stats
. 
. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. 
. *Calculated manually
. estadd local Rise_10_90_Man "88.7" , replace: e1
{txt}
{com}. estadd local Rise_10_90_Man "21.8" , replace: e2 
{txt}
{com}. 
. 
. * Exporting results
. *csv
. esttab e* using "$app_tables_mock\TableA3.csv", keep(score_manag*  lcap* lnwork*  sh_sup* sh_direc*) ///
>     scalars("clase 6-digits NAICS") b(%9.4f) stats(Rise_10_90_Man Time_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("% Rise 10th to 90th management percentile" "Time effects"  "NAICS 6-digits effects" "Observations")) star(* 0.10 ** 0.05 *** 0.01) se  title("Management Practices and Firm Performance Microenterprises") ///
>                 mtitle("log(VA per employee)" "log(VA per employee)" ) ///
>     label ///
>     coeflabels( ///
>         score_manag_ "Management score" ///
>         lcapworkWA_ "log(capital/employee)" ///
>         lnworkers_WA_  "log(employees)" ///
>                 sh_direc_WA_ "Share white-collar workers" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>     ) ///
>     replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA3.csv"'})

{com}. *tex
.         esttab e* using "$app_tables_mock\TableA3.tex", ///
>     keep(score_manag*  lcap* lnwork*  sh_sup* sh_direc*) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>     stats(Rise_10_90_Man Time_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("% Rise 10th to 90th management percentile" "Time effects"  "NAICS 6-digits effects" "Observations")) ///
>     scalars("clase 6-digits NAICS") ///
>                 mtitle("" "" "" "" "" "" ) ///
>     label ///
>     coeflabels( ///
>         score_manag_ "Management score" ///
>         lcapworkWA_ "log(capital/employee)" ///
>         lnworkers_WA_  "log(employees)" ///
>                 sh_direc_WA_ "Share white-collar workers" ///
>         sh_sup_WA_ "Share of workers with a college degree" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA3.tex"'})

{com}. 
. 
. ************************************************************************************************************************************************************
. *Table A4
. ************************************************************************************************************************************************************
. 
. do "$programs/Appendix tables A4.do"
{txt}
{com}. 
. 
. use "$working/Data_for_estimates_ENAPROCE_2015.dta", clear
{txt}
{com}. merge 1:1 clee using "$working/Data_for_estimates_ENAPROCE_2018.dta"
{res}{txt}(label {bf:{txt}owner2} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             460
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}             460{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          10,040{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. merge 1:1 clee clave_2014 using "$data/ejem_operation_info2015.dta"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             460
{txt}{col 9}from master{col 30}{res}             460{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          10,040{txt}  (_merge==3)
{col 5}{hline 41}

{com}. 
. egen code=group(codigo_captura)
{txt}(460 missing values generated)

{com}. gen exit1=inlist(code,2,5,6)
{txt}
{com}. gen exit2=inlist(code,2,5)
{txt}
{com}. 
. 
. 
. *Generate alternative variable to ensure number of observations is the same
. gen lVA_real_wk_mil_WA_2014_EST=lVA_real_wk_mil_WA_2014
{txt}(460 missing values generated)

{com}. replace lVA_real_wk_mil_WA_2014_EST=-99999 if score_manag_2014!=. & lVA_real_wk_mil_WA_2014_EST==.
{txt}(0 real changes made)

{com}. gen dumlVA_miss=lVA_real_wk_mil_WA_2014==. &  score_manag_2014!=.
{txt}
{com}. 
. 
. *replace nworkers_WA_2014=0 if nworkers_WA_2014==.
. 
. foreach var of varlist exit1 exit2  {c -(}
{txt}  2{com}. gen nworkers_WA_2017`var'=nworkers_WA_2017
{txt}  3{com}. replace nworkers_WA_2017`var'=0 if `var'==1
{txt}  4{com}. 
. gen ch_haltw`var'=2*(nworkers_WA_2017`var'-nworkers_WA_2014)/(nworkers_WA_2017`var'+nworkers_WA_2014)
{txt}  5{com}. {c )-}
{txt}(1,839 real changes made)
(460 missing values generated)
(884 real changes made)
(460 missing values generated)

{com}. 
. 
. save "$working/Data_for_estimates_Wideexit.dta", replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/Data_for_estimates_Wideexit.dta{rm}
saved
{p_end}

{com}. 
.         
.         
. **************************************************************************************
. *Panel A1- Employment growth and management 
. 
. putexcel set "$app_tables_mock/combinedPanelA.xlsx", modify sheet("Combined", replace)
{res}{txt}
{com}. 
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg ch_haltwexit1  i.dum_man2##c.score_manag_2014  , robust

{txt}Linear regression                               Number of obs     = {res}     9,036
                                                {txt}F(3, 9032)        =  {res}    32.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0128
                                                {txt}Root MSE          =    {res} 1.0427

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}              ch_haltwexit1{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}1.dum_man2 {c |}{col 29}{res}{space 2} .0638691{col 41}{space 2} .1602089{col 52}{space 1}    0.40{col 61}{space 3}0.690{col 69}{space 4}-.2501765{col 82}{space 3} .3779148
{txt}{space 11}score_manag_2014 {c |}{col 29}{res}{space 2}   .26947{col 41}{space 2} .0677344{col 52}{space 1}    3.98{col 61}{space 3}0.000{col 69}{space 4} .1366952{col 82}{space 3} .4022447
{txt}{space 27} {c |}
dum_man2#c.score_manag_2014 {c |}
{space 25}1  {c |}{col 29}{res}{space 2} 1.702875{col 41}{space 2} .7952464{col 52}{space 1}    2.14{col 61}{space 3}0.032{col 69}{space 4} .1440116{col 82}{space 3} 3.261738
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}-.5659571{col 41}{space 2} .0360001{col 52}{space 1}  -15.72{col 61}{space 3}0.000{col 69}{space 4}-.6365254{col 82}{space 3}-.4953888
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.     matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix colnames pvals = `:colnames b'
{txt}
{com}. 
. matrix list pvals
{res}
{txt}pvals[1,6]
              0b.            1.                0b.dum_man2#   1.dum_man2#              
        dum_man2      dum_man2  score_mana~4  co.sc~g_2014  c.sco~g_2014         _cons
r1 {res}            .     .69015188     .00006994             .     .03227515     5.792e-55
{reset}
{com}. display pvals[1, 5] 
{res}.03227515
{txt}
{com}. 
. // Extract the specific p-value you want (5th element)
. scalar pval_interact = pvals[1,5]
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. // Manufacturing
. reg ch_haltwexit1 score_manag_2014 if dum_man2==1, robust

{txt}Linear regression                               Number of obs     = {res}       998
                                                {txt}F(1, 996)         =  {res}     6.19
                                                {txt}Prob > F          = {res}    0.0130
                                                {txt}R-squared         = {res}    0.0088
                                                {txt}Root MSE          =    {res}  1.081

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}   ch_haltwexit1{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_2014 {c |}{col 18}{res}{space 2} 1.972345{col 30}{space 2} .7929761{col 41}{space 1}    2.49{col 50}{space 3}0.013{col 58}{space 4} .4162492{col 71}{space 3}  3.52844
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -.502088{col 30}{space 2} .1562338{col 41}{space 1}   -3.21{col 50}{space 3}0.001{col 58}{space 4}-.8086732{col 71}{space 3}-.1955028
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e2
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, . \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "Management score" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
{txt}variable {bf}{res}star_value{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str2{sf}
{txt}(5 real changes made)
{com}. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(4 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Manufacturing
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. drop p_value_difference
{txt}
{com}. tostring Manufacturing, gen(Man2) force
{txt}Man2 generated as {res:str11}
Man2 was forced to string; some loss of information

{com}. replace Man2 = "(" + Man2 + ")" if _n == 2
{txt}variable {bf}{res}Man2{sf}{txt} was {bf}{res}str11{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Manufacturing
{txt}
{com}. ren Man2 Manufacturing
{res}{txt}
{com}. order Dependent_variable Manufacturing star_value
{txt}
{com}. save "$app_tables_mock/TableA4PanelA1Man.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA1Man.dta{rm}
saved
{p_end}

{com}. 
. 
. export excel using "$app_tables_mock/TableA4PanelA1Man.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA1Man.xls} saved

{com}. 
. 
. // Services
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg ch_haltwexit1 score_manag_2014 if dum_man2==0, robust

{txt}Linear regression                               Number of obs     = {res}     8,038
                                                {txt}F(1, 8036)        =  {res}    15.83
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0022
                                                {txt}Root MSE          =    {res} 1.0379

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}   ch_haltwexit1{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_2014 {c |}{col 18}{res}{space 2}   .26947{col 30}{space 2} .0677278{col 41}{space 1}    3.98{col 50}{space 3}0.000{col 58}{space 4} .1367058{col 71}{space 3} .4022341
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.5659571{col 30}{space 2} .0359966{col 41}{space 1}  -15.72{col 50}{space 3}0.000{col 58}{space 4}-.6365198{col 71}{space 3}-.4953945
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e3
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, pval_interact  \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "Management score" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
{txt}variable {bf}{res}star_value{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str3{sf}
{txt}(5 real changes made)
{com}. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(4 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Services
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. tostring Services, gen(Ser2) force
{txt}Ser2 generated as {res:str11}
Ser2 was forced to string; some loss of information

{com}. replace Ser2 = "(" + Ser2 + ")" if _n == 2
{txt}variable {bf}{res}Ser2{sf}{txt} was {bf}{res}str11{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Services
{txt}
{com}. ren Ser2 Services
{res}{txt}
{com}. order Dependent_variable Services star_value p_value_difference
{txt}
{com}. save "$app_tables_mock/TableA4PanelA1Ser.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA1Ser.dta{rm}
saved
{p_end}

{com}. 
. export excel using "$app_tables_mock/TableA4PanelA1ser.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA1ser.xls} saved

{com}. 
. 
. // Panel A
. putexcel A1 = ("Employment growth (annualized)"), bold
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelA.xlsx} saved

{com}. 
. // Export Panel A data
. use "$app_tables_mock/TableA4PanelA1Man.dta", clear
{txt}
{com}. 
. export excel using "$app_tables_mock/combinedPanelA.xlsx", ///
>     sheet("Combined") cell(A2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelA.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelA1Man.dta", clear
{txt}
{com}. listtab using "$app_tables_mock/TableA4PanelA1Man.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}.         
. // Count rows in Panel A to know where to start Panel B
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. 
. // Panel B header
. // Export Panel B data
. use "$app_tables_mock/TableA4PanelA1Ser.dta", clear
{txt}
{com}. outsheet using "$app_tables_mock/TableA4PanelA1ser.tex", comma replace
{txt}
{com}. export excel Services star_value p_value_difference using "$app_tables_mock/combinedPanelA.xlsx", ///
>     sheet("Combined") cell(D2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelA.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelA1Ser.dta", clear
{txt}
{com}. listtab  Services star_value p_value_difference  using "$app_tables_mock/TableA4PanelA1Ser.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}. 
. 
. 
. 
. 
. 
. **************************************************************************************
. *Panel A2- Exit and management 
. 
. *putexcel set "$app_tables_mock/combinedPanelA.xlsx", modify sheet("Combined", replace)
. 
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg exit1  i.dum_man2##c.score_manag_2014  , robust

{txt}Linear regression                               Number of obs     = {res}     9,036
                                                {txt}F(3, 9032)        =  {res}     0.50
                                                {txt}Prob > F          = {res}    0.6820
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} .38758

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      exit1{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}1.dum_man2 {c |}{col 29}{res}{space 2} .0177636{col 41}{space 2} .0391108{col 52}{space 1}    0.45{col 61}{space 3}0.650{col 69}{space 4}-.0589025{col 82}{space 3} .0944297
{txt}{space 11}score_manag_2014 {c |}{col 29}{res}{space 2}-.0147014{col 41}{space 2} .0238833{col 52}{space 1}   -0.62{col 61}{space 3}0.538{col 69}{space 4}-.0615182{col 82}{space 3} .0321153
{txt}{space 27} {c |}
dum_man2#c.score_manag_2014 {c |}
{space 25}1  {c |}{col 29}{res}{space 2} -.140041{col 41}{space 2} .1719967{col 52}{space 1}   -0.81{col 61}{space 3}0.416{col 69}{space 4}-.4771934{col 82}{space 3} .1971115
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} .1918278{col 41}{space 2} .0123354{col 52}{space 1}   15.55{col 61}{space 3}0.000{col 69}{space 4} .1676477{col 82}{space 3}  .216008
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.     matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix colnames pvals = `:colnames b'
{txt}
{com}. 
. matrix list pvals
{res}
{txt}pvals[1,6]
              0b.            1.                0b.dum_man2#   1.dum_man2#              
        dum_man2      dum_man2  score_mana~4  co.sc~g_2014  c.sco~g_2014         _cons
r1 {res}            .     .64970549     .53820588             .     .41554758     7.782e-54
{reset}
{com}. display pvals[1, 5] 
{res}.41554758
{txt}
{com}. 
. // Extract the specific p-value you want (5th element)
. scalar pval_interact = pvals[1,5]
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. // Manufacturing
. reg exit1 score_manag_2014 if dum_man2==1, robust

{txt}Linear regression                               Number of obs     = {res}       998
                                                {txt}F(1, 996)         =  {res}     0.82
                                                {txt}Prob > F          = {res}    0.3642
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res} .38311

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}           exit1{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_2014 {c |}{col 18}{res}{space 2}-.1547424{col 30}{space 2} .1704636{col 41}{space 1}   -0.91{col 50}{space 3}0.364{col 58}{space 4}-.4892514{col 71}{space 3} .1797666
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .2095914{col 30}{space 2} .0371437{col 41}{space 1}    5.64{col 50}{space 3}0.000{col 58}{space 4} .1367027{col 71}{space 3} .2824802
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e2
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, . \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "Management score" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
{txt}(0 real changes made)
{com}. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(0 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Manufacturing
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. drop p_value_difference
{txt}
{com}. tostring Manufacturing, gen(Man2) force
{txt}Man2 generated as {res:str12}
Man2 was forced to string; some loss of information

{com}. replace Man2 = "(" + Man2 + ")" if _n == 2
{txt}variable {bf}{res}Man2{sf}{txt} was {bf}{res}str12{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Manufacturing
{txt}
{com}. ren Man2 Manufacturing
{res}{txt}
{com}. order Dependent_variable Manufacturing star_value
{txt}
{com}. save "$app_tables_mock/TableA4PanelA2Man.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA2Man.dta{rm}
saved
{p_end}

{com}. 
. 
. export excel using "$app_tables_mock/TableA4PanelA2Man.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA2Man.xls} saved

{com}. 
. 
. // Services
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg exit1 score_manag_2014 if dum_man2==0, robust

{txt}Linear regression                               Number of obs     = {res}     8,038
                                                {txt}F(1, 8036)        =  {res}     0.38
                                                {txt}Prob > F          = {res}    0.5382
                                                {txt}R-squared         = {res}    0.0000
                                                {txt}Root MSE          =    {res} .38813

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}           exit1{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_2014 {c |}{col 18}{res}{space 2}-.0147014{col 30}{space 2}  .023881{col 41}{space 1}   -0.62{col 50}{space 3}0.538{col 58}{space 4}-.0615144{col 71}{space 3} .0321115
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .1918278{col 30}{space 2} .0123342{col 41}{space 1}   15.55{col 50}{space 3}0.000{col 58}{space 4} .1676497{col 71}{space 3}  .216006
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e3
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, pval_interact  \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "Management score" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
{txt}(0 real changes made)
{com}. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(0 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Services
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. tostring Services, gen(Ser2) force
{txt}Ser2 generated as {res:str12}
Ser2 was forced to string; some loss of information

{com}. replace Ser2 = "(" + Ser2 + ")" if _n == 2
{txt}variable {bf}{res}Ser2{sf}{txt} was {bf}{res}str12{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Services
{txt}
{com}. ren Ser2 Services
{res}{txt}
{com}. order Dependent_variable Services star_value p_value_difference
{txt}
{com}. save "$app_tables_mock/TableA4PanelA2Ser.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA2Ser.dta{rm}
saved
{p_end}

{com}. 
. export excel using "$app_tables_mock/TableA4PanelA2Ser.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelA2Ser.xls} saved

{com}. 
. 
. 
. 
. 
. // Panel A
. putexcel H1 = ("Exit(annualized)"), bold
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelA.xlsx} saved

{com}. 
. // Export Panel A data
. use "$app_tables_mock/TableA4PanelA2Man.dta", clear
{txt}
{com}. 
. export excel using "$app_tables_mock/combinedPanelA.xlsx", ///
>     sheet("Combined") cell(H2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelA.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelA2Man.dta", clear
{txt}
{com}. listtab Manufacturing star_value using "$app_tables_mock/TableA4PanelA2Man.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}.         
. // Count rows in Panel A to know where to start Panel B
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. 
. // Panel B header
. // Export Panel B data
. use "$app_tables_mock/TableA4PanelA2Ser.dta", clear
{txt}
{com}. outsheet using "$app_tables_mock/TableA4PanelA2Ser.tex", comma replace
{txt}
{com}. export excel Services star_value p_value_difference using "$app_tables_mock/combinedPanelA.xlsx", ///
>     sheet("Combined") cell(K2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelA.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelA2Ser.dta", clear
{txt}
{com}. listtab  Services star_value p_value_difference  using "$app_tables_mock/TableA4PanelA2Ser.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}. 
. 
. 
. **************************************************************************************
. *Panel B1- Employment growth and lVA/worker
. 
. putexcel set "$app_tables_mock/combinedPanelB.xlsx", modify sheet("Combined", replace)
{res}{txt}
{com}. 
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg ch_haltwexit1  i.dum_man2##c.lVA_real_wk_mil_WA_2014 if score_manag_2014!=. , robust

{txt}Linear regression                               Number of obs     = {res}     9,036
                                                {txt}F(3, 9032)        =  {res}   223.24
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0830
                                                {txt}Root MSE          =    {res}  1.005

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}            ch_haltwexit1{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.dum_man2 {c |}{col 27}{res}{space 2} 2.411962{col 39}{space 2}   .34237{col 50}{space 1}    7.04{col 59}{space 3}0.000{col 67}{space 4} 1.740839{col 80}{space 3} 3.083085
{txt}{space 2}lVA_real_wk_mil_WA_2014 {c |}{col 27}{res}{space 2} -.138038{col 39}{space 2} .0060947{col 50}{space 1}  -22.65{col 59}{space 3}0.000{col 67}{space 4}-.1499851{col 80}{space 3}-.1260909
{txt}{space 25} {c |}
{space 17}dum_man2#{c |}
c.lVA_real_wk_mil_WA_2014 {c |}
{space 23}1  {c |}{col 27}{res}{space 2}-.6047552{col 39}{space 2} .1023962{col 50}{space 1}   -5.91{col 59}{space 3}0.000{col 67}{space 4} -.805475{col 80}{space 3}-.4040354
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2}-.1333648{col 39}{space 2}  .019672{col 50}{space 1}   -6.78{col 59}{space 3}0.000{col 67}{space 4}-.1719263{col 80}{space 3}-.0948032
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.     matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix colnames pvals = `:colnames b'
{txt}
{com}. 
. matrix list pvals
{res}
{txt}pvals[1,6]
              0b.            1.                0b.dum_man2#   1.dum_man2#              
        dum_man2      dum_man2  lVA_r~A_2014  co.lV~A_2014  c.lVA~A_2014         _cons
r1 {res}            .     1.992e-12     1.65e-110             .     3.631e-09     1.282e-11
{reset}
{com}. display pvals[1, 5] 
{res}3.631e-09
{txt}
{com}. 
. // Extract the specific p-value you want (5th element)
. scalar pval_interact = pvals[1,5]
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. // Manufacturing
. reg ch_haltwexit1 lVA_real_wk_mil_WA_2014_EST dumlVA_miss if dum_man2==1 & score_manag_2014!=., robust
{txt}{p 0 6 2}note: {bf:dumlVA_miss} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       998
                                                {txt}F(1, 996)         =  {res}    52.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1838
                                                {txt}Root MSE          =    {res}   .981

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}              ch_haltwexit1{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
lVA_real_wk_mil_WA_2014_EST {c |}{col 29}{res}{space 2}-.7427932{col 41}{space 2} .1022946{col 52}{space 1}   -7.26{col 61}{space 3}0.000{col 69}{space 4}-.9435309{col 82}{space 3}-.5420555
{txt}{space 16}dumlVA_miss {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 22}_cons {c |}{col 29}{res}{space 2} 2.278597{col 41}{space 2} .3420717{col 52}{space 1}    6.66{col 61}{space 3}0.000{col 69}{space 4} 1.607333{col 82}{space 3} 2.949861
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e2
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, . \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "ln(VA/employee)" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
{txt}variable {bf}{res}star_value{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str3{sf}
{txt}(5 real changes made)
{com}. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(4 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Manufacturing
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. drop p_value_difference
{txt}
{com}. tostring Manufacturing, gen(Man2) force
{txt}Man2 generated as {res:str12}
Man2 was forced to string; some loss of information

{com}. replace Man2 = "(" + Man2 + ")" if _n == 2
{txt}variable {bf}{res}Man2{sf}{txt} was {bf}{res}str12{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Manufacturing
{txt}
{com}. ren Man2 Manufacturing
{res}{txt}
{com}. order Dependent_variable Manufacturing star_value
{txt}
{com}. save "$app_tables_mock/TableA4PanelB1Man.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB1Man.dta{rm}
saved
{p_end}

{com}. 
. 
. export excel using "$app_tables_mock/TableA4PanelB1Man.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB1Man.xls} saved

{com}. 
. 
. // Services
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg ch_haltwexit1 lVA_real_wk_mil_WA_2014_EST dumlVA_miss  if dum_man2==0 & score_manag_2014!=., robust
{txt}{p 0 6 2}note: {bf:dumlVA_miss} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     8,038
                                                {txt}F(1, 8036)        =  {res}   513.06
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0590
                                                {txt}Root MSE          =    {res} 1.0079

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}              ch_haltwexit1{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
lVA_real_wk_mil_WA_2014_EST {c |}{col 29}{res}{space 2} -.138038{col 41}{space 2} .0060942{col 52}{space 1}  -22.65{col 61}{space 3}0.000{col 69}{space 4}-.1499841{col 82}{space 3}-.1260919
{txt}{space 16}dumlVA_miss {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 22}_cons {c |}{col 29}{res}{space 2}-.1333648{col 41}{space 2} .0196701{col 52}{space 1}   -6.78{col 61}{space 3}0.000{col 69}{space 4}-.1719232{col 82}{space 3}-.0948063
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e3
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, pval_interact  \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "ln(VA/employee)" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
{txt}variable {bf}{res}star_value{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str3{sf}
{txt}(5 real changes made)
{com}. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(4 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Services
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. tostring Services, gen(Ser2) force
{txt}Ser2 generated as {res:str12}
Ser2 was forced to string; some loss of information

{com}. replace Ser2 = "(" + Ser2 + ")" if _n == 2
{txt}variable {bf}{res}Ser2{sf}{txt} was {bf}{res}str12{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Services
{txt}
{com}. ren Ser2 Services
{res}{txt}
{com}. order Dependent_variable Services star_value p_value_difference
{txt}
{com}. save "$app_tables_mock/TableA4PanelB1Ser.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB1Ser.dta{rm}
saved
{p_end}

{com}. 
. export excel using "$app_tables_mock/TableA4PanelB1ser.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB1ser.xls} saved

{com}. 
. 
. // Panel A
. putexcel A1 = ("Employment growth (annualized)"), bold
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelB.xlsx} saved

{com}. 
. // Export Panel A data
. use "$app_tables_mock/TableA4PanelB1Man.dta", clear
{txt}
{com}. 
. export excel using "$app_tables_mock/combinedPanelB.xlsx", ///
>     sheet("Combined") cell(A2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelB.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelB1Man.dta", clear
{txt}
{com}. listtab using "$app_tables_mock/TableA4PanelB1Man.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}.         
. // Count rows in Panel A to know where to start Panel B
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. 
. // Panel B header
. // Export Panel B data
. use "$app_tables_mock/TableA4PanelB1Ser.dta", clear
{txt}
{com}. outsheet using "$app_tables_mock/TableA4PanelB1ser.tex", comma replace
{txt}
{com}. export excel Services star_value p_value_difference using "$app_tables_mock/combinedPanelB.xlsx", ///
>     sheet("Combined") cell(D2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelB.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelB1Ser.dta", clear
{txt}
{com}. listtab  Services star_value p_value_difference  using "$app_tables_mock/TableA4PanelB1Ser.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}. 
. 
. 
. 
. 
. 
. **************************************************************************************
. *Panel A2- Exit and management 
. 
. *putexcel set "$app_tables_mock/combinedPanelA.xlsx", modify sheet("Combined", replace)
. 
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg exit1  i.dum_man2##c.lVA_real_wk_mil_WA_2014  , robust

{txt}Linear regression                               Number of obs     = {res}    10,040
                                                {txt}F(3, 10036)       =  {res}     0.31
                                                {txt}Prob > F          = {res}    0.8182
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} .38686

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                    exit1{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.dum_man2 {c |}{col 27}{res}{space 2} .0366657{col 39}{space 2} .0630561{col 50}{space 1}    0.58{col 59}{space 3}0.561{col 67}{space 4}-.0869368{col 80}{space 3} .1602682
{txt}{space 2}lVA_real_wk_mil_WA_2014 {c |}{col 27}{res}{space 2} .0012035{col 39}{space 2} .0022372{col 50}{space 1}    0.54{col 59}{space 3}0.591{col 67}{space 4}-.0031818{col 80}{space 3} .0055888
{txt}{space 25} {c |}
{space 17}dum_man2#{c |}
c.lVA_real_wk_mil_WA_2014 {c |}
{space 23}1  {c |}{col 27}{res}{space 2}-.0135259{col 39}{space 2} .0191674{col 50}{space 1}   -0.71{col 59}{space 3}0.480{col 67}{space 4}-.0510979{col 80}{space 3} .0240461
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} .1808758{col 39}{space 2} .0066721{col 50}{space 1}   27.11{col 59}{space 3}0.000{col 67}{space 4} .1677971{col 80}{space 3} .1939545
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.     matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix colnames pvals = `:colnames b'
{txt}
{com}. 
. matrix list pvals
{res}
{txt}pvals[1,6]
              0b.            1.                0b.dum_man2#   1.dum_man2#              
        dum_man2      dum_man2  lVA_r~A_2014  co.lV~A_2014  c.lVA~A_2014         _cons
r1 {res}            .     .56093137     .59061235             .     .48040895     2.98e-156
{reset}
{com}. display pvals[1, 5] 
{res}.48040895
{txt}
{com}. 
. // Extract the specific p-value you want (5th element)
. scalar pval_interact = pvals[1,5]
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. // Manufacturing
. reg exit1 lVA_real_wk_mil_WA_2014_EST dumlVA_miss  if dum_man2==1, robust
{txt}{p 0 6 2}note: {bf:dumlVA_miss} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,000
                                                {txt}F(1, 998)         =  {res}     0.42
                                                {txt}Prob > F          = {res}    0.5179
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res} .38282

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      exit1{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
lVA_real_wk_mil_WA_2014_EST {c |}{col 29}{res}{space 2}-.0123224{col 41}{space 2} .0190517{col 52}{space 1}   -0.65{col 61}{space 3}0.518{col 69}{space 4}-.0497083{col 82}{space 3} .0250636
{txt}{space 16}dumlVA_miss {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 22}_cons {c |}{col 29}{res}{space 2} .2175415{col 41}{space 2} .0627523{col 52}{space 1}    3.47{col 61}{space 3}0.001{col 69}{space 4} .0943998{col 82}{space 3} .3406832
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e2
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, . \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "ln(VA/employee)" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
{txt}(0 real changes made)
{com}. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(0 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Manufacturing
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. drop p_value_difference
{txt}
{com}. tostring Manufacturing, gen(Man2) force
{txt}Man2 generated as {res:str12}
Man2 was forced to string; some loss of information

{com}. replace Man2 = "(" + Man2 + ")" if _n == 2
{txt}variable {bf}{res}Man2{sf}{txt} was {bf}{res}str12{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Manufacturing
{txt}
{com}. ren Man2 Manufacturing
{res}{txt}
{com}. order Dependent_variable Manufacturing star_value
{txt}
{com}. save "$app_tables_mock/TableA4PanelB2Man.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB2Man.dta{rm}
saved
{p_end}

{com}. 
. 
. export excel using "$app_tables_mock/TableA4PanelB2Man.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB2Man.xls} saved

{com}. 
. 
. // Services
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. 
. reg exit1 lVA_real_wk_mil_WA_2014_EST dumlVA_miss  if dum_man2==0, robust
{txt}{p 0 6 2}note: {bf:dumlVA_miss} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     9,040
                                                {txt}F(1, 9038)        =  {res}     0.29
                                                {txt}Prob > F          = {res}    0.5906
                                                {txt}R-squared         = {res}    0.0000
                                                {txt}Root MSE          =    {res} .38731

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      exit1{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
lVA_real_wk_mil_WA_2014_EST {c |}{col 29}{res}{space 2} .0012035{col 41}{space 2}  .002237{col 52}{space 1}    0.54{col 61}{space 3}0.591{col 69}{space 4}-.0031815{col 82}{space 3} .0055885
{txt}{space 16}dumlVA_miss {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 22}_cons {c |}{col 29}{res}{space 2} .1808758{col 41}{space 2} .0066716{col 52}{space 1}   27.11{col 61}{space 3}0.000{col 69}{space 4}  .167798{col 82}{space 3} .1939536
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store e3
{txt}
{com}. 
. 
. matrix b = e(b)
{txt}
{com}. matrix V = e(V)
{txt}
{com}. matrix b3 = e(b)/3
{txt}
{com}. scalar r_squared = e(r2)
{txt}
{com}. scalar N = e(N)
{txt}
{com}. 
. local k = colsof(b)
{txt}
{com}. matrix se = J(1, `k', .)
{txt}
{com}. matrix t = J(1, `k', .)
{txt}
{com}. matrix pvals0 = J(1, `k', .)
{txt}
{com}. 
. forvalues i = 1/`k' {c -(}
{txt}  2{com}.     matrix se[1,`i'] = sqrt(V[`i',`i'])
{txt}  3{com}.         matrix t[1,`i'] = b[1,`i']/se[1,`i']
{txt}  4{com}.     matrix pvals0[1,`i'] = 2*ttail(e(df_r), abs(t[1,`i']))
{txt}  5{com}. {c )-}
{txt}
{com}. 
. matrix se3 = se/3
{txt}
{com}. 
. matrix b3_first = b3[1,1]
{txt}
{com}. matrix se3_first = se3[1,1]
{txt}
{com}. matrix pval_first=pvals0[1,1]
{txt}
{com}. 
. // Extract the first p-value from the matrix into a scalar
. scalar pval_first = pval_first[1, 1]
{txt}
{com}. 
. 
. * Combine coefficients and standard errors
. matrix results = (b3_first,pval_first, pval_interact  \ se3_first, ., .  \ r_squared, ., . \N, . , .)
{txt}
{com}. clear
{txt}
{com}. svmat results, names(col)
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. * Add row labels
. set obs 5
{txt}{p}
Number of observations ({bf:_N}) was 4,
now 5.
{p_end}

{com}. gen row = _n
{txt}
{com}. replace c1=r_squared in 4
{txt}(1 real change made)

{com}. replace c1=N in 5
{txt}(1 real change made)

{com}. 
. label define row_lbl 1 "ln(VA/employee)" 2 " " 3 " "  4 "R2" 5 "Observations"
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. 
. gen star_value=""
{txt}(5 missing values generated)

{com}. 
. 
. 
. // Assign stars based on p-value
. if c2 < 0.01 {c -(}
.     replace star_value= "***"
. {c )-}
{txt}
{com}. else if c2< 0.05 {c -(}
.     replace star_value=  "**"
. {c )-}
{txt}
{com}. else if c2 < 0.1 {c -(}
.     replace star_value=  "*"
. {c )-}
{txt}
{com}. else {c -(}
.     replace star_value=  ""
{txt}(0 real changes made)
{com}. {c )-}
{txt}
{com}. replace star_value="" in 2/5
{txt}(0 real changes made)

{com}. drop c2
{txt}
{com}. order row c1 star_value c3
{txt}
{com}. ren row Dependent_variable
{res}{txt}
{com}. ren c1 Services
{res}{txt}
{com}. ren c3 p_value_difference
{res}{txt}
{com}. 
. drop in 3
{txt}(1 observation deleted)

{com}. tostring Services, gen(Ser2) force
{txt}Ser2 generated as {res:str11}
Ser2 was forced to string; some loss of information

{com}. replace Ser2 = "(" + Ser2 + ")" if _n == 2
{txt}variable {bf}{res}Ser2{sf}{txt} was {bf}{res}str11{sf}{txt} now {bf}{res}str13{sf}
{txt}(1 real change made)

{com}. drop Services
{txt}
{com}. ren Ser2 Services
{res}{txt}
{com}. order Dependent_variable Services star_value p_value_difference
{txt}
{com}. save "$app_tables_mock/TableA4PanelB2Ser.dta",  replace
{txt}{p 0 4 2}
file {bf}
C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB2Ser.dta{rm}
saved
{p_end}

{com}. 
. export excel using "$app_tables_mock/TableA4PanelB2Ser.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA4PanelB2Ser.xls} saved

{com}. 
. 
. 
. 
. 
. // Panel B
. putexcel H1 = ("Exit(annualized)"), bold
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelB.xlsx} saved

{com}. 
. // Export Panel A data
. use "$app_tables_mock/TableA4PanelB2Man.dta", clear
{txt}
{com}. 
. export excel using "$app_tables_mock/combinedPanelB.xlsx", ///
>     sheet("Combined") cell(H2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelB.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelB2Man.dta", clear
{txt}
{com}. listtab Manufacturing star_value using "$app_tables_mock/TableA4PanelB2Man.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}.         
. // Count rows in Panel A to know where to start Panel B
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. 
. // Panel B header
. // Export Panel B data
. use "$app_tables_mock/TableA4PanelB2Ser.dta", clear
{txt}
{com}. outsheet using "$app_tables_mock/TableA4PanelB2Ser.tex", comma replace
{txt}
{com}. export excel Services star_value p_value_difference using "$app_tables_mock/combinedPanelB.xlsx", ///
>     sheet("Combined") cell(K2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedPanelB.xlsx} saved

{com}. 
. use "$app_tables_mock/TableA4PanelB2Ser.dta", clear
{txt}
{com}. listtab  Services star_value p_value_difference  using "$app_tables_mock/TableA4PanelB2Ser.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}. 
. 
. 
. 
. 
. 
. //Combine full table
. 
. // Panel A
. putexcel set "$app_tables_mock/combinedTablefull.xlsx", modify sheet("Combined", replace)
{res}{txt}
{com}. 
. 
. // Panel A
. putexcel A1 = ("Employment growth (annualized)"), bold
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. putexcel H1 = ("Exit (annualized)"), bold
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. 
. // Export Panel A data
. use "$app_tables_mock/TableA4PanelA1Man.dta", clear
{txt}
{com}. 
. export excel using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(A2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. 
. /*use "$app_tables_mock/TableA4PanelA1Man.dta", clear
> listtab using "$app_tables_mock/TableA4PanelA1Man.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
> */      
. // Count rows in Panel A to know where to start Panel B
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. 
. // Panel B header
. // Export Panel B data
. use "$app_tables_mock/TableA4PanelA1Ser.dta", clear
{txt}
{com}. *outsheet using "$app_tables_mock/TableA4PanelA1ser.tex", comma replace
. export excel Services star_value p_value_difference using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(D2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. /*
> use "$app_tables_mock/TableA4PanelA1Ser.dta", clear
> listtab  Services star_value p_value_difference  using "$app_tables_mock/TableA4PanelA1Ser.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
> */
. 
. // Export Panel A data
. use "$app_tables_mock/TableA4PanelA2Man.dta", clear
{txt}
{com}. drop Dependent_variable
{txt}
{com}. export excel using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(H2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. 
. /*use "$app_tables_mock/TableA4PanelA2Man.dta", clear
> listtab Manufacturing star_value using "$app_tables_mock/TableA4Tablefull.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
> */      
. // Count rows in Panel A to know where to start Panel B
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. 
. // Panel B header
. // Export Panel B data
. use "$app_tables_mock/TableA4PanelA2Ser.dta", clear
{txt}
{com}. *outsheet using "$app_tables_mock/TableA4PanelA2Ser.tex", comma replace
. export excel Services star_value p_value_difference using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(K2) sheetmodify firstrow(variables)
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. 
. /*use "$app_tables_mock/TableA4PanelA2Ser.dta", clear
> listtab  Services star_value p_value_difference  using "$app_tables_mock/TableA4PanelA2Ser.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
> */
. 
. // Export Panel A data
. use "$app_tables_mock/TableA4PanelB1Man.dta", clear
{txt}
{com}. 
. export excel using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(A8) sheetmodify 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. 
. /*use "$app_tables_mock/TableA4PanelB1Man.dta", clear
> listtab using "$app_tables_mock/TableA4PanelB1Man.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
> */      
. // Count rows in Panel A to know where to start Panel B
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. 
. // Panel B header
. // Export Panel B data
. use "$app_tables_mock/TableA4PanelB1Ser.dta", clear
{txt}
{com}. *outsheet using "$app_tables_mock/TableA4PanelB1ser.tex", comma replace
. export excel Services star_value p_value_difference using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(D8) sheetmodify 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. 
. *use "$app_tables_mock/TableA4PanelB1Ser.dta", clear
. *listtab  Services star_value p_value_difference  using "$app_tables_mock/TableA4PanelB1Ser.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
. 
.         
.         
. use "$app_tables_mock/TableA4PanelB2Man.dta", clear
{txt}
{com}. drop Dependent_variable
{txt}
{com}. export excel using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(H8) sheetmodify 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}. 
. local rows = _N + 3  // Data rows + header row + 2 space rows
{txt}
{com}. use "$app_tables_mock/TableA4PanelB2Ser.dta", clear
{txt}
{com}. *outsheet using "$app_tables_mock/TableA4PanelB2Ser.tex", comma replace
. export excel Services star_value p_value_difference using "$app_tables_mock/combinedTablefull.xlsx", ///
>     sheet("Combined") cell(K8) sheetmodify 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/combinedTablefull.xlsx} saved

{com}.         
.         
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. 
. ************************************************************************************************************************************************************
. *Table A5
. ************************************************************************************************************************************************************
. 
. use "$working/Data_for_estimates_Wideexit.dta", clear
{txt}
{com}. count if score_manag_2014!=. 
  {res}9,036
{txt}
{com}. scalar obs_count = r(N)  
{txt}
{com}. 
. count if exit1==1 & score_manag_2014!=. 
  {res}1,663
{txt}
{com}. scalar exit_count = r(N)  
{txt}
{com}. 
. scalar exit_rate3=exit_count/obs_count
{txt}
{com}. scalar exit_rate1=exit_rate3/3
{txt}
{com}. 
. matrix results_ENAP = (obs_count \ exit_count  \ exit_rate3*100 \ exit_rate1*100)
{txt}
{com}. 
. 
. use "$data/ejem_CE_2014", clear
{txt}
{com}. count if clee!=""
  {res}50,000
{txt}
{com}. scalar obs_countCE = r(N)  
{txt}
{com}. 
. gen nworkers=h001a
{txt}
{com}. gen scian2=substr(clase,1,2)
{txt}
{com}. destring scian2, replace
{txt}scian2: all characters numeric; {res}replaced {txt}as {res}byte
{txt}
{com}. gen samp_ENAPROCE=1 if inlist(scian2,31,32,33)
{txt}(45,097 missing values generated)

{com}. replace samp_ENAPROCE=1 if inlist(scian2,43,46)
{txt}(7,882 real changes made)

{com}. replace samp_ENAPROCE=1 if inlist(scian2,48,49)
{txt}(4,427 real changes made)

{com}. replace samp_ENAPROCE=1 if inlist(scian2,54,56,71,72,81)
{txt}(12,449 real changes made)

{com}. 
. replace samp_ENAPROCE=0 if nworkers<=10
{txt}(30,000 real changes made)

{com}. 
. keep clee samp_ENAPROCE
{txt}
{com}. merge 1:1 clee using "$data/ejem_CE_2019"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}          33,334
{txt}{col 9}from master{col 30}{res}          16,667{txt}  (_merge==1)
{col 9}from using{col 30}{res}          16,667{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          33,333{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge==2
{txt}(16,667 observations deleted)

{com}. gen exit=_merge==1
{txt}
{com}. 
. count if clee!=""
  {res}50,000
{txt}
{com}. count if exit==1
  {res}16,667
{txt}
{com}. scalar exit_countCE = r(N)  
{txt}
{com}. 
. scalar exit_rateCE5=exit_countCE/obs_countCE
{txt}
{com}. scalar exit_rateCE1=exit_rateCE5/5
{txt}
{com}. 
. matrix results_CE = (obs_countCE \ exit_countCE  \ exit_rateCE5*100 \ exit_rateCE1*100)
{txt}
{com}. 
. 
. count if samp_ENAPROCE==1
  {res}10,264
{txt}
{com}. scalar overlapping_count = r(N)  
{txt}
{com}. count if samp_ENAPROCE==1 & exit==1
  {res}3,325
{txt}
{com}. scalar overlapping_exit = r(N)  
{txt}
{com}. 
. scalar exit_rateOS5=overlapping_exit/overlapping_count
{txt}
{com}. scalar exit_rateOS1=exit_rateOS5/5
{txt}
{com}. 
. matrix results_OS = (overlapping_count \ overlapping_exit  \ exit_rateOS5*100 \ exit_rateOS1*100)
{txt}
{com}. 
. matrix matCE=(results_CE, results_OS, results_ENAP)
{txt}
{com}. clear
{txt}
{com}. svmat matCE
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. gen row=_n
{txt}
{com}. gen rowb=_n
{txt}
{com}. 
. label define row_lbl 1 "\# Establishments " 2 "Exit numbers " 3 "Exit rate (over 5 years) "  4 "Exit rate (annualized)" 
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. label define row_lblb 1 "# Establishments " 2 "Exit numbers " 3 "Exit rate (over 3 years) "  4 "Exit rate (annualized)" 
{txt}
{com}. label values rowb row_lblb
{txt}
{com}. 
. order row matCE1 matCE2 rowb matCE3
{txt}
{com}. 
. 
. 
. foreach var of varlist matCE1 matCE2 matCE3 {c -(}
{txt}  2{com}.         tostring `var', gen(`var'2) force
{txt}  3{com}. replace `var'2 = substr(`var'2,1,5) + "%" if _n == 3 | _n ==4
{txt}  4{com}. drop `var' 
{txt}  5{com}. ren `var'2 `var' 
{txt}  6{com}. {c )-}
{txt}matCE12 generated as {res:str11}
matCE12 was forced to string; some loss of information
(2 real changes made)
{res}{txt}matCE22 generated as {res:str11}
matCE22 was forced to string; some loss of information
(2 real changes made)
{res}{txt}matCE32 generated as {res:str11}
matCE32 was forced to string; some loss of information
(2 real changes made)
{res}{txt}
{com}. 
. 
. ren matCE1 Total_census_2014
{res}{txt}
{com}. ren matCE2 Census_Overlapping_Sample
{res}{txt}
{com}. ren matCE3 ENAPROCE_2015
{res}{txt}
{com}. export excel using "$app_tables_mock/TableA5.xls", sheet("A4") firstrow(variables) replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock/TableA5.xls} saved

{com}. 
. 
. 
. matrix results_OS = (overlapping_count \ overlapping_exit  \ exit_rateOS5*100 \ exit_rateOS1*100)
{txt}
{com}. 
. matrix matCE=(results_CE, results_OS, results_ENAP)
{txt}
{com}. clear
{txt}
{com}. svmat matCE
{txt}number of observations will be reset to 4
Press any key to continue, or Break to abort
{p}
Number of observations ({bf:_N}) was 0,
now 4.
{p_end}

{com}. 
. gen row=_n
{txt}
{com}. gen rowb=_n
{txt}
{com}. 
. label define row_lbl 1 "\# Establishments " 2 "Exit numbers " 3 "Exit rate (over 5 years) "  4 "Exit rate (annualized)" 
{txt}
{com}. label values row row_lbl
{txt}
{com}. 
. label define row_lblb 1 "\# Firms " 2 "Exit numbers " 3 "Exit rate (over 3 years) "  4 "Exit rate (annualized)" 
{txt}
{com}. label values rowb row_lblb
{txt}
{com}. 
. order row matCE1 matCE2 rowb matCE3
{txt}
{com}. 
. 
. foreach var of varlist matCE1 matCE2 matCE3 {c -(}
{txt}  2{com}.         tostring `var', gen(`var'2) force
{txt}  3{com}. replace `var'2 = substr(`var'2,1,5) + "\%" if _n == 3 | _n ==4
{txt}  4{com}. drop `var' 
{txt}  5{com}. ren `var'2 `var' 
{txt}  6{com}. {c )-}
{txt}matCE12 generated as {res:str11}
matCE12 was forced to string; some loss of information
(2 real changes made)
{res}{txt}matCE22 generated as {res:str11}
matCE22 was forced to string; some loss of information
(2 real changes made)
{res}{txt}matCE32 generated as {res:str11}
matCE32 was forced to string; some loss of information
(2 real changes made)
{res}{txt}
{com}. 
. ren matCE1 Total_census_2014
{res}{txt}
{com}. ren matCE2 Census_Overlapping_Sample
{res}{txt}
{com}. ren matCE3 ENAPROCE_2015
{res}{txt}
{com}. 
. 
. listtab using "$app_tables_mock/TableA5.tex", replace ///
>     rstyle(tabular)  ///
>     delimiter(&) 
{res}{txt}
{com}.         
.         
. ******************************************************************************************************************************************************
. *Table A6
. ******************************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. merge m:1 clase using "$data/Tradability and KIBS.dta", gen(mergetradab)
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}          12,247
{txt}{col 9}from master{col 30}{res}          12,243{txt}  (mergetradab==1)
{col 9}from using{col 30}{res}               4{txt}  (mergetradab==2)

{col 5}Matched{col 30}{res}           9,630{txt}  (mergetradab==3)
{col 5}{hline 41}

{com}. 
. 
. estimates drop _all
{res}{txt}
{com}. 
. qui reghdfe lnworkers_WA_ i.AMlpopdensMAincome##c.score_manag_     dum_time  if dum_man2==0  & tradability==1, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ i.AMlpopdensMAincome##c.score_manag_     dum_time  if dum_man2==0  & tradability==0, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e2
{txt}
{com}. 
. 
. qui reghdfe lnworkers_WA_ i.AMlpopdensMAincome##c.score_manag_     dum_time  if dum_man2==0  & KIBS==1, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e3
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ i.AMlpopdensMAincome##c.score_manag_     dum_time  if dum_man2==0  & KIBS==0, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e4
{txt}
{com}. 
. 
. qui reghdfe  lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2==0  & tradability==1, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e5
{txt}
{com}. 
. qui reghdfe  lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2==0  & tradability==0, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e6
{txt}
{com}. 
. 
. qui reghdfe  lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2==0  & KIBS==1, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e7
{txt}
{com}. 
. qui reghdfe  lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2==0  & KIBS==0, cluster(id_mun) absorb(clase id_mun)
{txt}
{com}. estimates store e8
{txt}
{com}. 
. 
. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. estadd local Time_effects "Yes" , replace: e6
{txt}
{com}. estadd local Time_effects "Yes" , replace: e7
{txt}
{com}. estadd local Time_effects "Yes" , replace: e8
{txt}
{com}. 
. estadd local Mun_effects "Yes" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e5
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e6
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e7
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e8
{txt}
{com}. 
. estadd local NAICS_6_digits_effects "Yes" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e5
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e6
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e7
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e8
{txt}
{com}. 
. *esttab e* using "$app_tables_mock/TableA6.csv" , se starlevel(* 0.10 ** 0.05 *** 0.01) keep(score*   _IAMlX* _IBM_X*   ) 
. 
. 
. 
. 
. * Exporting results
. *csv
. esttab e* using "$app_tables_mock\TableA6.csv", keep(score_manag_ 1.BM_durat_border_hrs#c.score_manag_ 1.AMlpopdensMAincome#c.score_manag_) ///
>     scalars("clase 6-digits NAICS") b(%9.4f)  stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations"))  star(* 0.10 ** 0.05 *** 0.01) se  title("Breaking down services sector to examine heterogeneous relationships in Table 4") ///
>                 mtitle("Tradable" "Non-tradable" "KIBS" "Non-KIBS""Tradable" "Non-tradable" "KIBS" "Non-KIBS") ///
>     label ///
>     coeflabels( ///
>         score_manag_ "Management score" ///
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 1.AMlpopdensMAincome#c.score_manag_  "Market size above the median*Management score" ///
>     ) ///
>     replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA6.csv"'})

{com}. *tex
.         esttab e* using "$app_tables_mock\TableA6.tex", ///
>         keep(score_manag_ 1.BM_durat_border_hrs#c.score_manag_ 1.AMlpopdensMAincome#c.score_manag_)  ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>      stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations"))  ///
>     scalars("clase 6-digits NAICS") ///
>                 mtitle("" "" "" "" "" "" "" "" ) ///
>     label ///
> coeflabels( ///
>         score_manag_ "Management score" ///
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 1.AMlpopdensMAincome#c.score_manag_  "Market size above the median*Management score" ///
>                     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA6.tex"'})

{com}. 
. 
. 
. ******************************************************************************************************************************************************
. *Table A7 Robustness sources of misallocation: Competition & Market size (no controls)
. ******************************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. estimates drop _all
{res}{txt}
{com}. 
. *Drive time
. **Manufacturing
. qui reg lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2==1, cluster(id_mun) 
{txt}
{com}. estimates store e1
{txt}
{com}. **Services
. qui reg  lnworkers_WA_  i.BM_durat_border_hrs##c.score_manag_  dum_time if dum_man2!=1, cluster(id_mun) 
{txt}
{com}. estimates store e2
{txt}
{com}. **Exports-Manufacturing
. qui reg  lnworkers_WA_  c.shexp_clase##i.BM_durat_border_hrs##c.score_manag_  dum_time if  dum_man2==1, cluster(id_mun) 
{txt}
{com}. estimates store e4
{txt}
{com}. *Market size
. **Manufacturing
. qui reg  lnworkers_WA_  i.AMlpopdensMAincome##c.score_manag_   dum_time  if dum_man2==1, cluster(id_mun) 
{txt}
{com}. estimates store e3
{txt}
{com}. 
. **Services
. qui reg  lnworkers_WA_  i.AMlpopdensMAincome##c.score_manag_   dum_time  if dum_man2!=1, cluster(id_mun) 
{txt}
{com}. estimates store e5
{txt}
{com}. 
. 
. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. 
. 
. estadd local Mun_effects "No" , replace: e1
{txt}
{com}. estadd local Mun_effects "No" , replace: e2 
{txt}
{com}. estadd local Mun_effects "No" , replace: e3  
{txt}
{com}. estadd local Mun_effects "No"  , replace:e4
{txt}
{com}. estadd local Mun_effects "No" , replace: e5
{txt}
{com}. 
. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "No"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e5
{txt}
{com}. 
. 
. *Exporting results
. esttab e* using "$app_tables_mock\TableA7.csv", keep(1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ score_manag_ 1.BM_durat_border_hrs#c.score_manag_ 1.AMlpopdensMAincome#c.score_manag_  c.shexp_clase#c.score_manag_ 1.BM_durat_border_hrs#c.shexp_clase) starlevels(* 0.10 ** 0.05 *** 0.01) indicate("Time=*dum_time") ///
>  stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
> mtitle("Manufacturing" "Services"  "Manufacturing" "Manufacturing" "Services") ///
>     label ///
>     coeflabels( ///
>                 1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ "Management score*Industry export share*Drive time below the median" ///
>         score_manag_ "Management score" ///]
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 c.shexp_clase#c.score_manag_ "Management score*Industry export share" ///
>                 1.BM_durat_border_hrs#c.shexp_clase  "Drive time below the median*Industry export share" ///
>                 1.AMlpopdensMAincome#c.score_manag_  "Market size above the median*Management score" ///
>     ) ///
> se  replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA7.csv"'})

{com}. 
. *tex
.         esttab e* using "$app_tables_mock\TableA7.tex", ///
>     keep(1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ score_manag_ 1.BM_durat_border_hrs#c.score_manag_ 1.AMlpopdensMAincome#c.score_manag_  c.shexp_clase#c.score_manag_ 1.BM_durat_border_hrs#c.shexp_clase) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
>     scalars("clase 6-digits NAICS") ///
>     title("Sources of Misallocation: Competition & market size") ///
>         mtitle("Manufacturing" "Services"  "Manufacturing" "Manufacturing" "Services") ///
>     label ///
>     coeflabels( ///
>                 1.BM_durat_border_hrs#c.shexp_clase#c.score_manag_ "Management score*Industry export share*Drive time below the median" ///
>         score_manag_ "Management score" ///]
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 c.shexp_clase#c.score_manag_ "Management score*Industry export share" ///
>                 1.BM_durat_border_hrs#c.shexp_clase  "Drive time below the median*Industry export share" ///
>                 1.AMlpopdensMAincome#c.score_manag_  "Market size above the median*Management score" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA7.tex"'})

{com}. 
. ******************************************************************************************************************************************************
. *Table A8
. ******************************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. destring id_mun2, gen(id_mun3)
{txt}id_mun2: all characters numeric; id_mun3 {res}generated {txt}as {res}int
{txt}(873 missing values generated)
{res}{txt}
{com}. estimates drop _all
{res}{txt}
{com}. qui xi: reg  lnworkers_WA_ c.score_manag_##c.durat_border_hrs5 dum_time if dum_man2==1 , cluster(id_mun2)
{txt}
{com}. estimates store e1
{txt}
{com}. *Services
. qui xi:reg  lnworkers_WA_ c.score_manag_##c.durat_border_hrs5 dum_time  if dum_man2==0, cluster(id_mun2) 
{txt}
{com}. estimates store e2
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ c.score_manag_##c.durat_border_hrs5 dum_time i.id_mun3 if dum_man2==1 , absorb(clase) cluster(id_mun2)
{txt}
{com}. estimates store e3
{txt}
{com}. *Services
. qui xi:areg  lnworkers_WA_ c.score_manag_##c.durat_border_hrs5 dum_time i.id_mun3 if dum_man2==0, cluster(id_mun2) absorb(clase)
{txt}
{com}. estimates store e4
{txt}
{com}. 
. qui xi: reg  lnworkers_WA_ c.score_manag_##c.durat_border_hrs5##c.shexp_clase   dum_time if  dum_man2==1 , cluster(id_mun2)
{txt}
{com}. estimates store e5
{txt}
{com}. qui xi: areg  lnworkers_WA_ c.score_manag_##c.durat_border_hrs5##c.shexp_clase     i.id_mun3       dum_time if  dum_man2==1, absorb(clase) cluster(id_mun2)
{txt}
{com}. estimates store e6 
{txt}
{com}. 
. qui xi: reg   lnworkers_ c.score_manag_##c.lpopdensMAincome  dum_time   if dum_man2==1 , cluster(id_mun2)
{txt}
{com}. estimates store e7
{txt}
{com}. qui xi: reg   lnworkers_ c.score_manag_##c.lpopdensMAincome   dum_time   if dum_man2==0 , cluster(id_mun2)
{txt}
{com}. estimates store e8
{txt}
{com}. 
. qui xi: areg   lnworkers_ c.score_manag_##c.lpopdensMAincome   dum_time  i.id_mun3  if dum_man2==1  , absorb(clase)  cluster(id_mun2)
{txt}
{com}. estimates store e9
{txt}
{com}. 
. qui xi: areg   lnworkers_ c.score_manag_##c.lpopdensMAincome   dum_time  i.id_mun3 if dum_man2==0   , absorb(clase)  cluster(id_mun2)
{txt}
{com}. estimates store e10
{txt}
{com}. 
. 
. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. estadd local Time_effects "Yes" , replace: e6
{txt}
{com}. estadd local Time_effects "Yes" , replace: e7 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e8  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e9
{txt}
{com}. estadd local Time_effects "Yes" , replace: e10
{txt}
{com}. 
. estadd local Mun_effects "No" , replace: e1
{txt}
{com}. estadd local Mun_effects "No" , replace: e2 
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Mun_effects "No" , replace: e5
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e6
{txt}
{com}. estadd local Mun_effects "No" , replace: e7
{txt}
{com}. estadd local Mun_effects "No" , replace: e8  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e9
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e10
{txt}
{com}. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e5
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e6
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e7
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e8  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes"  , replace:e9
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e10
{txt}
{com}. 
. 
. esttab e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 using "$app_tables_mock\TableA8.csv" , se starlevel(* 0.10 ** 0.05 *** 0.01)  keep(score_manag_ durat_border_hrs* lpopdensMAincome ///
>          c.score*)  ///
>         title("Robustness Sources of Misallocation: Competition & market size (continuous variables)") ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
>         mtitle("Manufacturing" "Services"  "Manufacturing"  "Services" "Manufacturing" "Manufacturing" "Manufacturing" "Services" "Manufacturing" "Services") ///
>  coeflabels( ///
>                 score_manag_ "Management score" ///
>                 durat_border_hrs5 "Drive time to border (hours)"  ///
>                 c.score_manag_#c.durat_border_hrs5 "Management score*Drive time to border"  ///
>                 c.score_manag_#c.shexp_clase "Management score*Share exports"  ///
>                 c.score_manag_#c.durat_border_hrs5#c.shexp_clase "Management score*Share exports*Drive time to border"  ///
>         lpopdensMAincome "ln(population density*average income)" ///
>                 c.score_manag_#c.lpopdensMAincome "Management score*ln(MA pop. density*av. income)" ///
>     ) ///
>         replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA8.csv"'})

{com}. 
.         *tex
.         esttab e* using "$app_tables_mock\TableA8.tex", ///
>     keep(score_manag_ durat_border_hrs* lpopdensMAincome ///
>          c.score*)  /// ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
>     scalars("clase 6-digits NAICS") ///
>     title("Robustness Sources of Misallocation: Competition & market size") ///
>         mtitle("Manufacturing" "Services"  "Manufacturing"  "Services" "Manufacturing" "Manufacturing" "Manufacturing" "Services" "Manufacturing" "Services") ///
>  coeflabels( ///
>                 score_manag_ "Management score" ///
>                 durat_border_hrs5 "Drive time to border (hours)"  ///
>                 c.score_manag_#c.durat_border_hrs5 "Management score*Drive time to border"  ///
>                 c.score_manag_#c.shexp_clase "Management score*Share exports"  ///
>                 c.score_manag_#c.durat_border_hrs5#c.shexp_clase "Management score*Share exports*Drive time to border"  ///
>         lpopdensMAincome "ln(population density*average income)" ///
>                 c.score_manag_#c.lpopdensMAincome "Management score*ln(MA pop. density*av. income)" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA8.tex"'})

{com}. 
. ******************************************************************************************************************************************************
. *Table A9
. ******************************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. destring id_mun2, gen(id_mun3)
{txt}id_mun2: all characters numeric; id_mun3 {res}generated {txt}as {res}int
{txt}(873 missing values generated)
{res}{txt}
{com}. destring clase_, gen(claseb_)
{txt}clase_: all characters numeric; claseb_ {res}generated {txt}as {res}long
{txt}(1333 missing values generated)
{res}{txt}
{com}. gen samp_start1990=(inicio_op_<=1990)
{txt}
{com}. 
. 
. estimates drop _all
{res}{txt}
{com}. 
. qui reg  score_manag_ i.dum_man2##c.durat_border_hrs5  dum_time, cluster(id_mun2)
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reg  score_manag_ i.dum_man2##( c.durat_border_hrs5 i.id_mun3 c.sh_direc_WA_ c.sh_sup_WA_ i.claseb_ c.lcapworkWA_  i.dum_time) , cluster(id_mun2)
{txt}
{com}. estimates store e2
{txt}
{com}. 
. qui reg  score_manag_  durat_border_hrs5 dum_time if dum_man2==1 & samp_start1990==1 , cluster(id_mun2)
{txt}
{com}. estimate store e3
{txt}
{com}. 
. qui reg score_manag_ durat_border_hrs5 dum_time if  dum_man2==0   & samp_start1990==1 , cluster(id_mun2)
{txt}
{com}. estimate store e4
{txt}
{com}. 
. qui reghdfe  score_manag_  c.shexp_clase##c.durat_border_hrs5 sh_direc_WA_ sh_sup_WA_  lcapworkWA_  dum_time  if dum_man2==1, cluster(id_mun2) absorb(id_mun2 clase)
{txt}
{com}. estimate store e5
{txt}
{com}. 
. qui reg ltfp_sec_real_WA_  durat_border_hrs5 dum_time if dum_man2==1, cluster(id_mun2)
{txt}
{com}. estimates store e6
{txt}
{com}. 
. qui reg ltfp_sec_real_WA_  durat_border_hrs5  dum_time if dum_man2==0, cluster(id_mun2)
{txt}
{com}. estimates store e7
{txt}
{com}. 
. qui reghdfe ltfp_sec_real_WA_  c.shexp_clase##c.durat_border_hrs5  dum_time  FDI1_  sh_direc_WA sh_sup_WA  if dum_man2==1, cluster(id_mun2) absorb(id_mun2)
{txt}
{com}. estimates store e8
{txt}
{com}. 
. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. estadd local Time_effects "Yes" , replace: e6  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e7
{txt}
{com}. estadd local Time_effects "Yes" , replace: e8
{txt}
{com}. 
. estadd local Mun_effects "No" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Mun_effects "No" , replace: e3  
{txt}
{com}. estadd local Mun_effects "No"  , replace:e4
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e5
{txt}
{com}. estadd local Mun_effects "No" , replace: e6  
{txt}
{com}. estadd local Mun_effects "No"  , replace:e7
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e8
{txt}
{com}. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "No"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e5
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e6  
{txt}
{com}. estadd local NAICS_6_digits_effects "No"  , replace:e7
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e8
{txt}
{com}. 
. 
. esttab e1 e2 e3 e4 e5 e6 e7 e8 using "$app_tables_mock\TableA9.csv" , se starlevel(* 0.10 ** 0.05 *** 0.01)  keep( 1.dum_man2 durat_border_hrs*  1.dum_man2#c.durat_border_hrs5 c.shexp* sh_direc_WA_ sh_sup_WA_ FDI1_ lcapworkWA_  ///
>          )  ///
>         title("Management and drivetime to the U.S. border") ///
>         mtitle("Manufacturing and Services" "Manufacturing and Services"   "Manufacturing"  "Services" "Manufacturing" "Manufacturing"  "Services" "Manufacturing") ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
>  coeflabels( ///
>                 1.dum_man2 "Manufacturing dummy" ///
>                 durat_border_hrs5 "Drive time to border (hours)"  ///
>                 1.dum_man2#c.durat_border_hrs5 "Manufacturing Dummy*Drive time"  ///
>                 c.shexp_clase#c.durat_border_hrs5 "Share exports*Drive time"  ///
>                 FDI1_ "=1 if any participation of FDI"    ///
>                 sh_direc_WA_ "Share of white-collar workers"  ///
>                 sh_sup_WA_ "Share of workers with college"  ///
>                 lcapworkWA_ "ln(capital/employee)" ///
>                 ///
>     ) ///
>         replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA9.csv"'})

{com}. 
.         *tex
.         esttab e* using "$app_tables_mock\TableA9.tex", ///
>     keep( 1.dum_man2 durat_border_hrs*  1.dum_man2#c.durat_border_hrs5 c.shexp* sh_direc_WA_ sh_sup_WA_ FDI1_ lcapworkWA_ ) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
>     scalars("clase 6-digits NAICS") ///
>         title("Management and drivetime to the U.S. border") ///
>         mtitle("" ""   ""  "" "" ""  "" "") ///
>   coeflabels( ///
>                 1.dum_man2 "Manufacturing dummy" ///
>                 durat_border_hrs5 "Drive time to border (hours)"  ///
>                 1.dum_man2#c.durat_border_hrs5 "Manufacturing Dummy*Drive time"  ///
>                 c.shexp_clase#c.durat_border_hrs5 "Share exports*Drive time"  ///
>                 FDI1_ "=1 if any participation of FDI"    ///
>                 sh_direc_WA_ "Share of white-collar workers"  ///
>                 sh_sup_WA_ "Share of workers with college"  ///
>                 lcapworkWA_ "ln(capital/employee)" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA9.tex"'})

{com}. 
. 
. 
. ******************************************************************************************************************************************************
. *Table A10
. ******************************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. estimates drop _all
{res}{txt}
{com}. qui reghdfe   lnworkers_WA_  c.shexp_##i.BM_durat_border_hrs##c.score_manag_  dum_time   if dum_man2==1, absorb(clase id_mun2) cluster(id_mun2) 
{txt}
{com}. estimates store e1
{txt}
{com}. 
. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e1
{txt}
{com}. 
. esttab e1  using "$app_tables_mock\TableA10.csv" , se starlevel(* 0.10 ** 0.05 *** 0.01)  keep( c.shexp* score*  1.BM_durat_border_hrs#c.shexp_ 1.BM_durat_border_hrs#c.score_manag_    1.BM_durat_border_hrs#c.shexp_#c.score_manag_ ) ///
>         title("Robustness management, drive time, and exports (using firm-level data from ENAPROCE for exports)") ///
>         mtitle("Manufacturing") ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
>         order(1.BM_durat_border_hrs#c.shexp_#c.score_manag_  score_manag_  1.BM_durat_border_hrs#c.score_manag_  c.shexp_#c.score_manag_  1.BM_durat_border_hrs#c.shexp_) ///
>     coeflabels( ///
>         score_manag_ "Management score" ///
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 c.shexp_#c.score_manag_ "Management score*Share of exports" ///
>                 1.BM_durat_border_hrs#c.shexp_#c.score_manag_  "Management score*Share of exports*Drive time below the median" ///
>                 1.BM_durat_border_hrs#c.shexp_ "Drive time below the median*Share of exports" ///
>     ) ///
>         replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA10.csv"'})

{com}. 
.         *tex
.         esttab e* using "$app_tables_mock\TableA10.tex", ///
>     keep( c.shexp* score*  1.BM_durat_border_hrs#c.shexp_ 1.BM_durat_border_hrs#c.score_manag_          1.BM_durat_border_hrs#c.shexp_#c.score_manag_ ) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Time_effects Mun_effects NAICS_6_digits_effects N, fmt(%13.0f) labels("Time effects" "Municipality Effects" "NAICS 6-digits effects" "Observations")) ///
>         order(1.BM_durat_border_hrs#c.shexp_#c.score_manag_  score_manag_  1.BM_durat_border_hrs#c.score_manag_  c.shexp_#c.score_manag_  1.BM_durat_border_hrs#c.shexp_) ///    scalars("clase 6-digits NAICS") ///
>         title("Robustness management, drive time, and exports (using firm-level data from ENAPROCE for exports)") ///
>         mtitle("" ""   ""  "" "" ""  "" "") ///
>  coeflabels( ///
>         score_manag_ "Management score" ///
>                 1.BM_durat_border_hrs#c.score_manag_ "Drive time below the median*Management Score" ///
>                 c.shexp_#c.score_manag_ "Management score*Share of exports" ///
>                 1.BM_durat_border_hrs#c.shexp_#c.score_manag_  "Management score*Share of exports*Drive time below the median" ///
>                 1.BM_durat_border_hrs#c.shexp_ "Drive time below the median*Share of exports" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA10.tex"'})

{com}. 
.         
. ******************************************************************************************************************************************************
. *Table A11
. ******************************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. gen dum_ser=dum_man2==0
{txt}
{com}. gen ent=substr(id_mun2,1,2)
{txt}(873 missing values generated)

{com}. gen region=.
{txt}(21,873 missing values generated)

{com}. replace region=1 if inlist(ent,"02","03","05","08","19","25","26","28")
{txt}(5,990 real changes made)

{com}. replace region=2 if  inlist(ent,"01","06","10","11","14","18","24","32")
{txt}(3,986 real changes made)

{com}. replace region=3 if  inlist(ent,"09","15")
{txt}(1,960 real changes made)

{com}. replace region=4 if  inlist(ent,"12","13", "16","17","21","22","29")
{txt}(4,990 real changes made)

{com}. replace region=5 if  inlist(ent,"04","07", "20","23","27","30","31")
{txt}(4,074 real changes made)

{com}. 
. 
. estimates drop _all
{res}{txt}
{com}. qui reg score_manag_ i.dum_ser##c.lpopdensMAincome  i.dum_ser##dum_time , cluster(id_mun2)
{txt}
{com}. estimate store e1
{txt}
{com}. 
. qui reg score_manag_  i.dum_ser##c.lpopdensMAincome  i.dum_ser##c.sh_direc_WA_ i.dum_ser##c.sh_sup_WA_ i.dum_ser##c.lcapworkWA_ i.dum_ser##i.clase  i.dum_ser##i.region i.dum_ser##i.dum_time, cluster(id_mun2)
{txt}
{com}. estimate store e2
{txt}
{com}. 
. qui reg score_manag_  lpopdensMAincome  sh_direc_WA_ sh_sup_WA_ lcapworkWA_ i.clase  i.region i.dum_time if dum_ser==0, cluster(id_mun2)
{txt}
{com}. estimates store e3
{txt}
{com}. 
. qui reg score_manag_  lpopdensMAincome  sh_direc_WA_ sh_sup_WA_ lcapworkWA_ i.clase  i.region i.dum_time if dum_ser==1, cluster(id_mun2)
{txt}
{com}. estimates store e4
{txt}
{com}. 
. qui reg score_manag_ i.city_us sh_direc_WA_ sh_sup_WA_ lcapworkWA_ i.clase  i.region i.dum_time  if  dum_ser==0, cluster(id_mun2)
{txt}
{com}. estimates store e5
{txt}
{com}. 
. qui reg score_manag_ i.city_us  sh_direc_WA_ sh_sup_WA_ lcapworkWA_ i.clase  i.region i.dum_time  if  dum_ser==1, cluster(id_mun2)
{txt}
{com}. estimates store e6
{txt}
{com}. 
. qui reghdfe ltfp_sec_real_WA_    lpopdensMAincome i.region i.dum_time if dum_ser==0, cluster(id_mun2) absorb(clase)
{txt}
{com}. estimates store e7
{txt}
{com}. 
. qui reghdfe ltfp_sec_real_WA_    lpopdensMAincome i.region i.dum_time if dum_ser==1, cluster(id_mun2) absorb(clase)
{txt}
{com}. estimates store e8
{txt}
{com}. 
. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3
{txt}
{com}. estadd local Time_effects "Yes" , replace: e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. estadd local Time_effects "Yes" , replace: e6
{txt}
{com}. estadd local Time_effects "Yes" , replace: e7
{txt}
{com}. estadd local Time_effects "Yes" , replace: e8
{txt}
{com}. 
. estadd local Region_effects "No" , replace: e1
{txt}
{com}. estadd local Region_effects "Yes" , replace: e2
{txt}
{com}. estadd local Region_effects "Yes" , replace: e3
{txt}
{com}. estadd local Region_effects "Yes" , replace: e4
{txt}
{com}. estadd local Region_effects "Yes" , replace: e5
{txt}
{com}. estadd local Region_effects "Yes" , replace: e6
{txt}
{com}. estadd local Region_effects "Yes" , replace: e7
{txt}
{com}. estadd local Region_effects "Yes" , replace: e8
{txt}
{com}. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e3
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e4
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e5
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e6
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e7
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e8
{txt}
{com}. 
. estadd local Controls "No" , replace: e1
{txt}
{com}. estadd local Controls "Yes" , replace: e2
{txt}
{com}. estadd local Controls "Yes" , replace: e3
{txt}
{com}. estadd local Controls "Yes" , replace: e4
{txt}
{com}. estadd local Controls "Yes" , replace: e5
{txt}
{com}. estadd local Controls "Yes" , replace: e6
{txt}
{com}. estadd local Controls "No" , replace: e7
{txt}
{com}. estadd local Controls "No" , replace: e8
{txt}
{com}. 
. 
. 
. 
. esttab e1 e2 e3 e4 e5 e6 e7 e8 using "$app_tables_mock\TableA11.csv" , se starlevel(* 0.10 ** 0.05 *** 0.01)  keep( 1.dum_ser lpopdensMAincome 1.dum_ser#c.lpopdensMAincome 2.city_us 3.city_us 4.city_us) ///
>         title("Management as a function of local market size") ///
>         mtitle("All" "All" "Manufacturing" "Services" "Manufacturing" "Services" "Dep. var log(TFP) Manufacturing" "Dep. var log(TFP) Services" ) ///
>         order(1.dum_ser lpopdensMAincome 1.dum_ser#c.lpopdensMAincome 2.city_us 3.city_us 4.city_us) ///
>         stats(Time_effects Region_effects NAICS_6_digits_effects Controls N, fmt(%13.0f) labels("Time effects" "Region Effects" "NAICS 6-digits effects" "Controls" "Observations")) ///
>     coeflabels( ///
>         1.dum_ser "Services dummy" ///
>                 lpopdensMAincome "ln(MA population density*average income)" ///
>                 1.dum_ser#c.lpopdensMAincome "Services dummy*ln(MA population density*average income)" ///
>                 2.city_us "Medium urban area" 3.city_us "Metropolitan area" 4.city_us "Large Metropolitan area" ///
>     ) ///
>         replace
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA11.csv"'})

{com}. 
.         *tex
.         esttab e* using "$app_tables_mock\TableA11.tex", ///
>         keep( 1.dum_ser lpopdensMAincome 1.dum_ser#c.lpopdensMAincome 2.city_us 3.city_us 4.city_us) ///
>     b(%9.4f) se star(* 0.10 ** 0.05 *** 0.01) ///
>         order(1.dum_ser lpopdensMAincome 1.dum_ser#c.lpopdensMAincome 2.city_us 3.city_us 4.city_us) ///
>         stats(Time_effects Region_effects NAICS_6_digits_effects Controls N, fmt(%13.0f) labels("Time effects" "Region Effects" "NAICS 6-digits effects" "Controls" "Observations")) ///    scalars("clase 6-digits NAICS") ///
>         title("Management as a function of local market size") ///
>         mtitle("" ""   ""  "" "" ""  "" "") ///
>  coeflabels( ///
>         1.dum_ser "Services dummy" ///
>                 lpopdensMAincome "ln(MA population density*average income)" ///
>                 1.dum_ser#c.lpopdensMAincome "Services dummy*ln(MA population density*average income)" ///
>                 2.city_us "Medium urban area" 3.city_us "Metropolitan area" 4.city_us "Large Metropolitan area" ///
>     ) ///
>     replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA11.tex"'})

{com}. 
.         
. ******************************************************************************************************************************************************
. *Table A12 Sources of misallocation: Institutional Environment: Robustness
. ******************************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. estimates drop _all 
{res}{txt}
{com}. *Contract enforcement
. qui xi: reghdfe lnworkers_WA_  i.H5_contr*score_manag_        dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}.         estimates store e1
{txt}
{com}. 
. *Kidnapping
. qui xi: reghdfe lnworkers_WA_  i.H5_rate_kid*score_manag_      dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}.         estimates store e2
{txt}
{com}. 
. *Corruption
. qui xi:reghdfe lnworkers_WA_   i.H5_shfrec_corrup*score_manag_ dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}.         estimates store e3
{txt}
{com}. 
. *Business crime composite index
. qui xi:reghdfe lnworkers_WA_   i.H5_compos_ind*score_manag_   dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}.         estimates store e4
{txt}
{com}. 
. *Informality
. qui xi: reghdfe lnworkers_WA_  i.H5_informal*score_manag_     dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}.         estimates store e5
{txt}
{com}. 
. 
. * Exporting results
. esttab e* using "$app_tables_mock\TableA12.csv", ///
> keep(_IH5_Xscore_1 _IH5* _IH5_shfrec_1 _IH5_compos_1  score_manag_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> mtitle("Top 5% share firms contract enforcement problems" ///
>        "Top 5% kidnapping" ///
>        "Top 5% share firms with gov. corruption problems" ///
>        "Top 5% Business crime composite index" ///
>        "Top 5% Informality municipality") ///
> unstack nonotes replace ///
> coeflabels(_IH5_Xscore_1 "High level*Management score" ///
>            _IH5_contr__1 "High level" ///
>            _IH5_rate_k_1 "High level" ///  // Fixed underscore issue
>            _IH5_shfrec_1 "High level" ///
>            _IH5_compos_1 "High level" ///
>            _IH5_inform_1 "High level" ///
>            score_manag_ "Management score") 
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA12.csv"'})

{com}. 
. *tex
. esttab e* using "$app_tables_mock\TableA12.tex", ///
> keep(_IH5_Xscore_1 _IH5* _IH5_shfrec_1 _IH5_compos_1  score_manag_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> unstack nonotes nolabel nonumber nogap ///
>         mtitle("" ""  "" "" "") ///
> coeflabels(_IH5_Xscore_1 "High level*Management score" ///
>            _IH5_contr__1 "High level" ///
>            _IH5_rate_k_1 "High level" ///  // Fixed underscore issue
>            _IH5_shfrec_1 "High level" ///
>            _IH5_compos_1 "High level" ///
>            _IH5_inform_1 "High level" ///
>            score_manag_ "Management score") ///
> replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA12.tex"'})

{com}. 
. 
. 
. ******************************************************************************************************
. //Formated for publication version
. 
. ////////////////////Contract enforcement///////////////////////////
> clear mata
{txt}
{com}.  xi: reghdfe lnworkers_WA_  i.H5_contr*score_manag_ dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}i.H5_contr_{col 19}_IH5_contr__0-1{col 39}(naturally coded; _IH5_contr__0 omitted)
i.H5_~r_*sco~g_{col 19}_IH5_Xscore_#{col 39}(coded as above)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}    18,544
{txt}Absorbing 2 HDFE groups{col 51}F({res}   4{txt},{res}     30{txt}){col 67}= {res}    208.14
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0000
{txt}{col 51}R-squared{col 67}= {res}    0.7634
{txt}{col 51}Adj R-squared{col 67}= {res}    0.7627
{txt}{col 51}Within R-sq.{col 67}= {res}    0.3983
{txt}{col 1}Number of clusters ({res}id_mun{txt}) {col 30}= {res}        31{txt}{col 51}Root MSE{col 67}= {res}    0.3537

{txt}{ralign 79:(Std. err. adjusted for {res:31} clusters in {res:id_mun})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}lnworkers_WA_{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_IH5_contr__1 {c |}{col 15}{res}{space 2} .9307046{col 27}{space 2} .5639737{col 38}{space 1}    1.65{col 47}{space 3}0.109{col 55}{space 4}-.2210833{col 68}{space 3} 2.082493
{txt}{space 1}score_manag_ {c |}{col 15}{res}{space 2} 1.935363{col 27}{space 2} .5325346{col 38}{space 1}    3.63{col 47}{space 3}0.001{col 55}{space 4} .8477819{col 68}{space 3} 3.022943
{txt}_IH5_Xscore_1 {c |}{col 15}{res}{space 2} .0914714{col 27}{space 2} 1.472563{col 38}{space 1}    0.06{col 47}{space 3}0.951{col 55}{space 4}-2.915904{col 68}{space 3} 3.098847
{txt}{space 5}dum_time {c |}{col 15}{res}{space 2} .1228737{col 27}{space 2} .1919513{col 38}{space 1}    0.64{col 47}{space 3}0.527{col 55}{space 4}-.2691431{col 68}{space 3} .5148905
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.890623{col 27}{space 2} .2765372{col 38}{space 1}   10.45{col 47}{space 3}0.000{col 55}{space 4} 2.325859{col 68}{space 3} 3.455387
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}      id_mun{col 14}{c |}{space 1}       31{col 27}{space 1}       31{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}       clase{col 14}{c |}{space 1}       21{col 27}{space 1}        1{col 39}{result}{space 1}       20{col 53}{text} {col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}.  estimates store e1
{txt}
{com}.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.9307046424    1.93536264   .0914713507{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.5639736736   .5325346067   1.472563431{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res}   0.931   1.935***      0.091{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.1093218927     .00103195   .9508816037{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.564)   (0.533)   (1.473){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                         0.091{txt}  {c |}
  2 {c |}  {res}                                                  (1.473){txt}  {c |}
  3 {c |}  {res}                 High level                         0.931{txt}  {c |}
  4 {c |}  {res}                                                  (0.564){txt}  {c |}
  5 {c |}  {res}           Management score                      1.935***{txt}  {c |}
  6 {c |}  {res}                                                  (0.533){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}       1          2          3
    {c TLC}{hline 34}{c TRC}
  1 {c |}  {res}   0.931   1.935***      0.091{txt}  {c |}
    {c BLC}{hline 34}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.564)   (0.533)   (1.473){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2b.xlsx", replace
{res}{p}{txt}note: file will be replaced when the first {cmd:putexcel} command is issued.{p_end}

{com}. putexcel A5 = "$cell_1_1" B5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A6 = "$cell_2_1" B6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A7 = "$cell_3_1" B7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A8 = "$cell_4_1" B8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A9 = "$cell_5_1" B9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A10 = "$cell_6_1" B10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. 
. 
. 
. 
. putexcel B2 = "(1)" C2="(2)" D2="(3)" E2="(4)" F2="(5)"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel B3 = "Top 10% share firms contract enforcement problems" C3="Top 10% kidnapping" D3="Top 10% share firms with gov. corruption problems" E3="Top 10% Business crime composite index" F3="Top 10% Informality Municipality"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. 
. 
. putexcel A12 = "Share of firms in municipalities with high-levels"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. *calculated manually
. putexcel B12 = "4.67%" C12="4.11%" D12="3.30%" E12="3.73%" F12="2.70%"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A13 = "Time effects"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel B13 = "Yes" C13 = "Yes" D13 = "Yes" E13 = "Yes" F13 = "Yes"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A14 = "Municipality effects"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel B14 = "Yes" C14 = "Yes" D14 = "Yes" E14 = "Yes" F14 = "Yes"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A15 = "NAICS 6-digits effects"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel B15 = "Yes" C15 = "Yes" D15 = "Yes" E15 = "Yes" F15 = "Yes"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel A16 = "Observations"
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel B16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. 
.         
.         
.         
. ////////////////////Kidnapping////////////////////
> clear mata      
{txt}
{com}. qui xi: reghdfe lnworkers_WA_  i.H5_rate_kid*score_manag_      dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}. estimates store e2
{txt}
{com}. 
.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}-.383685985   1.356090974   1.268397592{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.2470642525   .6593055182   .7014850344{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res} -0.384   1.356**    1.268*{txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.1309148772   .0484871951   .0806167853{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.247)   (0.659)   (0.701){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                        1.268*{txt}  {c |}
  2 {c |}  {res}                                                  (0.701){txt}  {c |}
  3 {c |}  {res}                 High level                        -0.384{txt}  {c |}
  4 {c |}  {res}                                                  (0.247){txt}  {c |}
  5 {c |}  {res}           Management score                       1.356**{txt}  {c |}
  6 {c |}  {res}                                                  (0.659){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res} -0.384   1.356**    1.268*{txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.247)   (0.659)   (0.701){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2b.xlsx", modify
{res}{txt}
{com}. putexcel C5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel C6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel C7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel C8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel C9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel C10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel C16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. 
. 
. ////////////////////Corruption////////////////////
> clear mata      
{txt}
{com}. qui xi:reghdfe lnworkers_WA_   i.H5_shfrec_corrup*score_manag_ dum_time, absorb(id_mun clase) cluster(id_mun)
{txt}
{com}. estimates store e3
{txt}
{com}. 
. scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}           1              2              3
    {c TLC}{hline 46}{c TRC}
  1 {c |}  {res}-.6029468437    1.362836016    1.541939365{txt}  {c |}
    {c BLC}{hline 46}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.4037530051   .6587335354   1.962802177{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res} -0.603   1.363**     1.542{txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res} .145787878   .0472695841    .438273996{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.404)   (0.659)   (1.963){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                         1.542{txt}  {c |}
  2 {c |}  {res}                                                  (1.963){txt}  {c |}
  3 {c |}  {res}                 High level                        -0.603{txt}  {c |}
  4 {c |}  {res}                                                  (0.404){txt}  {c |}
  5 {c |}  {res}           Management score                       1.363**{txt}  {c |}
  6 {c |}  {res}                                                  (0.659){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res} -0.603   1.363**     1.542{txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.404)   (0.659)   (1.963){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2b.xlsx", modify
{res}{txt}
{com}. putexcel D5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel D6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel D7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel D8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel D9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel D10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel D16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. 
. ////////////////////Business crime composite index////////////////////
> clear mata
{txt}
{com}. qui xi:reghdfe lnworkers_WA_   i.H5_compos_ind*score_manag_   dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}. estimates store e4
{txt}
{com}. 
.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}           1              2              3
    {c TLC}{hline 46}{c TRC}
  1 {c |}  {res}-.8792581759    1.312414901    1.825097127{txt}  {c |}
    {c BLC}{hline 46}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.5171176297   .6345327019    1.01360059{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}-0.879*   1.312**    1.825*{txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.0994197006   .0473252583   .0818270083{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.517)   (0.635)   (1.014){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                        1.825*{txt}  {c |}
  2 {c |}  {res}                                                  (1.014){txt}  {c |}
  3 {c |}  {res}                 High level                       -0.879*{txt}  {c |}
  4 {c |}  {res}                                                  (0.517){txt}  {c |}
  5 {c |}  {res}           Management score                       1.312**{txt}  {c |}
  6 {c |}  {res}                                                  (0.635){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}-0.879*   1.312**    1.825*{txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.517)   (0.635)   (1.014){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2b.xlsx", modify
{res}{txt}
{com}. putexcel E5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel E6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel E7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel E8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel E9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel E10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel E16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. 
.         
. ////////////////////Informality////////////////////
>         
. clear mata
{txt}
{com}. qui xi: reghdfe lnworkers_WA_  i.H5_informal*score_manag_     dum_time, absorb(id_mun clase) cluster(id_mun) 
{txt}
{com}.  estimates store e5
{txt}
{com}. 
.  scalar myN = e(N)
{txt}
{com}. mata: 
{txt}{hline 49} mata (type {cmd:end} to exit) {hline}
{com}: b = st_matrix("e(b)")
{res}
{com}: V = st_matrix("e(V)")
{res}
{com}: se = sqrt(diagonal(V))
{res}
{com}: b_row = b        
{res}
{com}: 
: 
: // Get first 3 coefficients
: b_row= b[1, 1..3]  // assuming b is a row vector
{res}
{com}: b_row
{res}       {txt}           1              2              3
    {c TLC}{hline 46}{c TRC}
  1 {c |}  {res} 1.964486655    1.960780535   -2.579416776{txt}  {c |}
    {c BLC}{hline 46}{c BRC}

{com}: 
: // Get first 3 standard errors
: 
: se_row = sqrt(diagonal(V))[1..3]'
{res}
{com}: se_row
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.4888711819   .6311136085   .6499207608{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: // Calculate t-statistics and p-values
: t_stats = b_row :/ se_row
{res}
{com}: df = st_numscalar("e(df_r)")
{res}
{com}: p_values = J(1, cols(t_stats), .)
{res}
{com}: 
: // Calculate p-values using correct t-distribution function
: for(i=1; i<=cols(t_stats); i++) {c -(}
>     p_values[1,i] = 2 * ttail(df, abs(t_stats[1,i]))
> {c )-}
{res}
{com}: 
: // Function to add significance stars (remove if already exists)
: string scalar add_stars(real scalar p_val) {c -(}
>     if (p_val < 0.01) return("***")
>     else if (p_val < 0.05) return("**") 
>     else if (p_val < 0.10) return("*")
>     else return("")
> {c )-}

: 
: // Initialize string matrices
: formatted_se = J(rows(se_row), cols(se_row), "")
{res}
{com}: formatted_b = J(rows(b_row), cols(b_row), "")
{res}
{com}: 
: // Format coefficients with 3 decimals AND significance stars
: for (c = 1; c <= cols(b_row); c++) {c -(}
>     stars = add_stars(p_values[1,c])
>     formatted_b[1,c] = strtrim(strofreal(b_row[1,c], "%9.3f")) + stars
> {c )-}
{res}
{com}: 
: // Format standard errors with parentheses
: for (c = 1; c <= cols(se_row); c++) {c -(}
>     formatted_se[1,c] = "(" + strtrim(strofreal(se_row[1,c], "%9.3f")) + ")"
> {c )-}
{res}
{com}: 
: // Display results with significance info
: "Coefficients with significance stars:"
{res}  Coefficients with significance stars:

{com}: formatted_b
{res}       {txt}        1           2           3
    {c TLC}{hline 37}{c TRC}
  1 {c |}  {res} 1.964***    1.961***   -2.579***{txt}  {c |}
    {c BLC}{hline 37}{c BRC}

{com}: "P-values:"
{res}  P-values:

{com}: p_values
{res}       {txt}          1             2             3
    {c TLC}{hline 43}{c TRC}
  1 {c |}  {res}.0003629937    .004111986   .0004159833{txt}  {c |}
    {c BLC}{hline 43}{c BRC}

{com}: "Standard Errors:"
{res}  Standard Errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.489)   (0.631)   (0.650){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: 
: // Create alternating format for export - CORRECTED
: variable_names = ("High-level*Management score"  \ "" \ "High level" \ "" \   "Management score" \ "")
{res}
{com}: values_column = (formatted_b[1,3] \ formatted_se[1,3] \   formatted_b[1,1] \ formatted_se[1,1] \ formatted_b[1,2] \ formatted_se[1,2] )
{res}
{com}: custom_alt = variable_names, values_column
{res}
{com}: 
: "Check formatted export matrix:"
{res}  Check formatted export matrix:

{com}: custom_alt
{res}       {txt}                          1                             2
    {c TLC}{hline 61}{c TRC}
  1 {c |}  {res}High-level*Management score                     -2.579***{txt}  {c |}
  2 {c |}  {res}                                                  (0.650){txt}  {c |}
  3 {c |}  {res}                 High level                      1.964***{txt}  {c |}
  4 {c |}  {res}                                                  (0.489){txt}  {c |}
  5 {c |}  {res}           Management score                      1.961***{txt}  {c |}
  6 {c |}  {res}                                                  (0.631){txt}  {c |}
    {c BLC}{hline 61}{c BRC}

{com}: 
: // Export matrix to Stata globals
: for(i=1; i<=rows(custom_alt); i++) {c -(}
>     for(j=1; j<=cols(custom_alt); j++) {c -(}
>         st_global("cell_" + strofreal(i) + "_" + strofreal(j), custom_alt[i,j])
>     {c )-}
> {c )-}
{res}
{com}: 
: "Formatted coefficients with stars:"
{res}  Formatted coefficients with stars:

{com}: formatted_b
{res}       {txt}        1           2           3
    {c TLC}{hline 37}{c TRC}
  1 {c |}  {res} 1.964***    1.961***   -2.579***{txt}  {c |}
    {c BLC}{hline 37}{c BRC}

{com}: 
: "Formatted standard errors:"
{res}  Formatted standard errors:

{com}: formatted_se
{res}       {txt}      1         2         3
    {c TLC}{hline 31}{c TRC}
  1 {c |}  {res}(0.489)   (0.631)   (0.650){txt}  {c |}
    {c BLC}{hline 31}{c BRC}

{com}: 
: end
{txt}{hline}

{com}. 
. // Export to Excel
. putexcel set "$working/results_with_stars2b.xlsx", modify
{res}{txt}
{com}. putexcel F5 = "$cell_1_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel F6 = "$cell_2_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel F7= "$cell_3_2"  
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel F8 = "$cell_4_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel F9 = "$cell_5_2" 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel F10 = "$cell_6_2"   
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}. putexcel F16 = myN 
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\working\Temp/results_with_stars2b.xlsx} saved

{com}.         
.         
.         clear
{txt}
{com}. import excel "$working\results_with_stars2b.xlsx"
{res}{text}(6 vars, 15 obs)

{com}. replace B=char(160) + "(1)" in 1
{txt}(1 real change made)

{com}. replace C=char(160) + "(2)" in 1
{txt}(1 real change made)

{com}. replace D=char(160) + "(3)" in 1
{txt}(1 real change made)

{com}. replace E=char(160) + "(4)" in 1
{txt}(1 real change made)

{com}. replace F=char(160) + "(5)" in 1
{txt}(1 real change made)

{com}. foreach var of varlist A B C D E F {c -(}
{txt}  2{com}.         replace `var'=char(160)+`var' in 5 
{txt}  3{com}.         replace `var'=char(160)+`var' in 7
{txt}  4{com}.         replace `var'=char(160)+`var' in 9
{txt}  5{com}. 
. 
. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. export delimited using "$app_tables_mock\TableA12.csv", quote novarnames replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA12.csv} saved

{com}. *dataout, save( "$tables/TableA12.tex") tex replace 
. 
. *Check the table
. esttab e* , ///
> keep(_IH5_Xscore_1 _IH5* _IH5_shfrec_1 _IH5_compos_1  score_manag_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> mtitle("Top 5% share firms contract enforcement problems" ///
>        "Top 5% kidnapping" ///
>        "Top 5% share firms with gov. corruption problems" ///
>        "Top 5% Business crime composite index" ///
>        "Top 5% Informality municipality") ///
> unstack nonotes replace ///
> coeflabels(_IH5_Xscore_1 "High level*Management score" ///
>            _IH5_contr_1 "High level" ///
>            _IH5_rate__1 "High level" ///  // Fixed underscore issue
>            _IH5_shfrec_1 "High level" ///
>            _IH5_compos_1 "High level" ///
>            _IH5_infor_1 "High level" ///
>            score_manag_ "Management score") 
{res}
{txt}{hline 92}
{txt}                      (1)             (2)             (3)             (4)             (5)   
{txt}             Top 5% sha~o    Top 5% kid~g    Top 5.. co~s    Top 5% Bus~     Top 5% Inf~y   
{txt}{hline 92}
{txt}_IH5_contr~1{res}        0.931                                                                   {txt}
            {res} {ralign 12:{txt:(}0.564{txt:)}}                                                                   {txt}

{txt}Management~e{res}        1.935***        1.356**         1.363**         1.312**         1.961***{txt}
            {res} {ralign 12:{txt:(}0.533{txt:)}}    {ralign 12:{txt:(}0.659{txt:)}}    {ralign 12:{txt:(}0.659{txt:)}}    {ralign 12:{txt:(}0.635{txt:)}}    {ralign 12:{txt:(}0.631{txt:)}}   {txt}

{txt}High level~e{res}       0.0915           1.268*          1.542           1.825*         -2.579***{txt}
            {res} {ralign 12:{txt:(}1.473{txt:)}}    {ralign 12:{txt:(}0.701{txt:)}}    {ralign 12:{txt:(}1.963{txt:)}}    {ralign 12:{txt:(}1.014{txt:)}}    {ralign 12:{txt:(}0.650{txt:)}}   {txt}

{txt}_IH5_rate_~1{res}                       -0.384                                                   {txt}
            {res}                 {ralign 12:{txt:(}0.247{txt:)}}                                                   {txt}

{txt}High level  {res}                                       -0.603                                   {txt}
            {res}                                 {ralign 12:{txt:(}0.404{txt:)}}                                   {txt}

{txt}High level  {res}                                                       -0.879*                  {txt}
            {res}                                                 {ralign 12:{txt:(}0.517{txt:)}}                   {txt}

{txt}_IH5_infor~1{res}                                                                        1.964***{txt}
            {res}                                                                 {ralign 12:{txt:(}0.489{txt:)}}   {txt}
{txt}{hline 92}
{txt}N           {res}        18544           18544           18544           18544           18544   {txt}
{txt}{hline 92}

{com}. 
. ********************************************************************************************************************************************
. *Table A13
. ********************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. estimates drop _all
{res}{txt}
{com}. 
. *Interacted models
. qui reg lnworkers_WA_ i.dum_man2##(c.ltfp_sec_real_WA_ i.dum_time) , cluster(id_mun2)
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reg lnworkers_WA_ i.dum_man2##(c.ltfp_sec_real_WA_ c.sh_sup_WA i.dum_time i.id_mun i.clase), cluster(id_mun2)
{txt}
{com}. estimates store e2
{txt}
{com}. 
. *Manufacturing
. qui reg lnworkers_WA_ ltfp_sec_real_WA_ dum_time if dum_man2==1,  cluster(id_mun2)
{txt}
{com}. estimates store e3
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ ltfp_sec_real_WA_ sh_sup_WA dum_time if dum_man2==1,  cluster(id_mun2) absorb(id_mun2 clase)
{txt}
{com}. estimates store e4
{txt}
{com}. 
. *Services
. qui reg lnworkers_WA_ ltfp_sec_real_WA_ dum_time if dum_man2==0,  cluster(id_mun2)
{txt}
{com}. estimates store e5
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ ltfp_sec_real_WA_ sh_sup_WA dum_time if dum_man2==0,  cluster(id_mun2) absorb(id_mun2 clase)
{txt}
{com}. estimates store e6
{txt}
{com}. 
. 
. * Exporting results
. esttab e* using "$app_tables_mock\TableA13p1.csv", ///
> keep(ltfp_sec_real_WA_ 1.dum_man2#c.ltfp_sec_real_WA_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> stats(N r2, fmt(%13.4f) labels( "Observations" "R2")) ///
> mtitle("Manufacturing and Services" "Manufacturing and Services" ///
>        "Manufacturing" ///
>        "Manufacturing" ///
>        "Services" ///
>        "Services") ///
> unstack nonotes replace ///
> coeflabels(ltfp_sec_real_WA_ "log(TFP)" ///
>             1.dum_man2#c.ltfp_sec_real_WA_ "Manufacturing dummy*log(TFP)" ///
>                         ) 
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA13p1.csv"'})

{com}. 
. *tex
. esttab e* using "$app_tables_mock\TableA13p1.tex", ///
> keep(ltfp_sec_real_WA_ 1.dum_man2#c.ltfp_sec_real_WA_) ///
> stats(N r2, fmt(%13.4f) labels( "Observations" "R2")) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> unstack nonotes nolabel nonumber nogap ///
>         mtitle("" ""  "" "" "" "") ///
> coeflabels(ltfp_sec_real_WA_ "log(TFP)" ///
>             1.dum_man2#c.ltfp_sec_real_WA_ "Manufacturing dummy*log(TFP)" ///
>                         ) ///
> replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA13p1.tex"'})

{com}. 
. 
. 
. estimates drop _all
{res}{txt}
{com}. 
. *Interacted models
. qui reg lnworkers_WA_ i.dum_man2##(c.ltfp_sec_real_WA_ i.dum_time) if sector_!="COM" , cluster(id_mun2)
{txt}
{com}. estimates store e1
{txt}
{com}. 
. qui reg lnworkers_WA_ i.dum_man2##(c.ltfp_sec_real_WA_ c.sh_sup_WA i.dum_time i.id_mun i.clase) if sector_!="COM", cluster(id_mun2)
{txt}
{com}. estimates store e2
{txt}
{com}. 
. *Manufacturing
. qui reg lnworkers_WA_ ltfp_sec_real_WA_ dum_time if dum_man2==1 & sector_!="COM",  cluster(id_mun2)
{txt}
{com}. estimates store e3
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ ltfp_sec_real_WA_ sh_sup_WA dum_time if dum_man2==1 & sector_!="COM",  cluster(id_mun2) absorb(id_mun2 clase)
{txt}
{com}. estimates store e4
{txt}
{com}. 
. *Services
. qui reg lnworkers_WA_ ltfp_sec_real_WA_ dum_time if dum_man2==0 & sector_!="COM",  cluster(id_mun2)
{txt}
{com}. estimates store e5
{txt}
{com}. 
. qui reghdfe lnworkers_WA_ ltfp_sec_real_WA_ sh_sup_WA dum_time if dum_man2==0 & sector_!="COM",  cluster(id_mun2) absorb(id_mun2 clase)
{txt}
{com}. estimates store e6
{txt}
{com}. 
. estadd local Time_effects "Yes" , replace: e1
{txt}
{com}. estadd local Time_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Time_effects "Yes" , replace: e3  
{txt}
{com}. estadd local Time_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Time_effects "Yes" , replace: e5
{txt}
{com}. estadd local Time_effects "Yes" , replace: e6
{txt}
{com}. 
. 
. estadd local Mun_effects "No" , replace: e1
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e2 
{txt}
{com}. estadd local Mun_effects "No" , replace: e3  
{txt}
{com}. estadd local Mun_effects "Yes"  , replace:e4
{txt}
{com}. estadd local Mun_effects "No" , replace: e5
{txt}
{com}. estadd local Mun_effects "Yes" , replace: e6
{txt}
{com}. 
. 
. estadd local NAICS_6_digits_effects "No" , replace: e1
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e2 
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e3  
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes"  , replace:e4
{txt}
{com}. estadd local NAICS_6_digits_effects "No" , replace: e5
{txt}
{com}. estadd local NAICS_6_digits_effects "Yes" , replace: e6
{txt}
{com}. 
. 
. estadd local Skill_controls "No" , replace: e1
{txt}
{com}. estadd local Skill_controls "Yes" , replace: e2 
{txt}
{com}. estadd local Skill_controls "No" , replace: e3  
{txt}
{com}. estadd local Skill_controls "Yes"  , replace:e4
{txt}
{com}. estadd local Skill_controls "No" , replace: e5
{txt}
{com}. estadd local Skill_controls "Yes" , replace: e6
{txt}
{com}. 
. 
. * Exporting results
. esttab e* using "$app_tables_mock\TableA13p2.csv", ///
> keep(ltfp_sec_real_WA_ 1.dum_man2#c.ltfp_sec_real_WA_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> stats(N r2 Time_effects Mun_effects NAICS_6_digits_effects Skill_controls, fmt(%13.4f) labels( "Observations" "R2" "Time effects" "Municipality effects" "NAICs 6-digits effects" "Skills controls")) ///
> mtitle("Manufacturing and Services" "Manufacturing and Services" ///
>        "Manufacturing" ///
>        "Manufacturing" ///
>        "Services" ///
>        "Services") ///
> unstack nonotes replace ///
> coeflabels(ltfp_sec_real_WA_ "log(TFP)" ///
>             1.dum_man2#c.ltfp_sec_real_WA_ "Manufacturing dummy*log(TFP)" ///
>                         ) 
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA13p2.csv"'})

{com}. 
. *tex
. esttab e* using "$app_tables_mock\TableA13p2.tex", ///
> keep(ltfp_sec_real_WA_ 1.dum_man2#c.ltfp_sec_real_WA_) ///
> se starlevel(* 0.10 ** 0.05 *** 0.01) ///
> stats(N r2 Time_effects Mun_effects NAICS_6_digits_effects Skill_controls, fmt(%13.4f) labels( "Observations" "R2" "Time effects" "Municipality effects" "NAICs 6-digits effects" "Skills controls")) ///
> unstack nonotes nolabel nonumber nogap ///
>         mtitle("" ""  "" "" "" "") ///
> coeflabels(ltfp_sec_real_WA_ "log(TFP)" ///
>             1.dum_man2#c.ltfp_sec_real_WA_ "Manufacturing dummy*log(TFP)" ///
>                         ) ///
> replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA13p2.tex"'})

{com}. 
. 
.         
.                         
. * Load and prepare first file
. import delimited "$app_tables_mock\TableA13p1.csv", clear
{res}{txt}(encoding automatically selected: ISO-8859-1)
{res}{text}(7 vars, 8 obs)

{com}. tempfile data1
{txt}
{com}. save `data1'
{txt}{p 0 4 2}
file {bf}
C:\Users\wb576945\AppData\Local\Temp\ST_7610_000001.tmp{rm}
saved
as .dta format
{p_end}

{com}. 
. * Load and prepare second file
. import delimited "$app_tables_mock\TableA13p2.csv", clear
{res}{txt}(encoding automatically selected: ISO-8859-1)
{res}{text}(7 vars, 12 obs)

{com}. tempfile data2  
{txt}
{com}. save `data2'
{txt}{p 0 4 2}
file {bf}
C:\Users\wb576945\AppData\Local\Temp\ST_7610_000002.tmp{rm}
saved
as .dta format
{p_end}

{com}. 
. * Combine the files
. use `data1', clear
{txt}
{com}. append using `data2'
{txt}
{com}. 
. 
. export delimited using "$app_tables_mock\TableA13.csv", quote novarnames replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA13.csv} saved

{com}. 
. ********************************************************************************************************************************************
. *Table A14
. ********************************************************************************************************************************************
. use "$working\BASE LONG FOR ESTIMATES.dta", clear
{txt}
{com}. 
. estimates drop _all
{res}{txt}
{com}. 
. gen dmanuf=sector_=="MAN"
{txt}
{com}. tab id_mun2, gen(dumid_m2)

     {txt}id_mun {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      03014 {c |}{res}        994        4.73        4.73
{txt}      05001 {c |}{res}        996        4.74        9.48
{txt}      06010 {c |}{res}      1,000        4.76       14.24
{txt}      07001 {c |}{res}          4        0.02       14.26
{txt}      07005 {c |}{res}          2        0.01       14.27
{txt}      07007 {c |}{res}         10        0.05       14.31
{txt}      07014 {c |}{res}          6        0.03       14.34
{txt}      07024 {c |}{res}          2        0.01       14.35
{txt}      07028 {c |}{res}          2        0.01       14.36
{txt}      07039 {c |}{res}          4        0.02       14.38
{txt}      07045 {c |}{res}         18        0.09       14.47
{txt}      08033 {c |}{res}      1,000        4.76       19.23
{txt}      09007 {c |}{res}        990        4.71       23.94
{txt}      11028 {c |}{res}        998        4.75       28.70
{txt}      12022 {c |}{res}        992        4.72       33.42
{txt}      14039 {c |}{res}        988        4.70       38.12
{txt}      15045 {c |}{res}        970        4.62       42.74
{txt}      16009 {c |}{res}      1,000        4.76       47.50
{txt}      17025 {c |}{res}      1,000        4.76       52.27
{txt}      19012 {c |}{res}      1,000        4.76       57.03
{txt}      20035 {c |}{res}      1,000        4.76       61.79
{txt}      22007 {c |}{res}      1,000        4.76       66.55
{txt}      23005 {c |}{res}        998        4.75       71.30
{txt}      25015 {c |}{res}      1,000        4.76       76.07
{txt}      26050 {c |}{res}      1,000        4.76       80.83
{txt}      27011 {c |}{res}      1,000        4.76       85.59
{txt}      29024 {c |}{res}        998        4.75       90.34
{txt}      31008 {c |}{res}      1,000        4.76       95.10
{txt}      31022 {c |}{res}          8        0.04       95.14
{txt}      31039 {c |}{res}          8        0.04       95.18
{txt}      31045 {c |}{res}         12        0.06       95.24
{txt}      32010 {c |}{res}      1,000        4.76      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     21,000      100.00
{txt}
{com}. tab clase, gen(dclase)

      {txt}clase {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     112511 {c |}{res}          1        0.00        0.00
{txt}     112512 {c |}{res}          1        0.00        0.01
{txt}     114111 {c |}{res}          1        0.00        0.01
{txt}     114112 {c |}{res}          1        0.00        0.02
{txt}     114113 {c |}{res}          1        0.00        0.02
{txt}     114119 {c |}{res}          1        0.00        0.03
{txt}     115111 {c |}{res}          1        0.00        0.03
{txt}     115112 {c |}{res}          1        0.00        0.04
{txt}     115113 {c |}{res}          1        0.00        0.04
{txt}     115119 {c |}{res}          1        0.00        0.05
{txt}     115210 {c |}{res}          1        0.00        0.05
{txt}     115310 {c |}{res}          1        0.00        0.05
{txt}     211110 {c |}{res}          1        0.00        0.06
{txt}     212110 {c |}{res}          1        0.00        0.06
{txt}     212210 {c |}{res}          1        0.00        0.07
{txt}     212221 {c |}{res}          1        0.00        0.07
{txt}     212222 {c |}{res}          1        0.00        0.08
{txt}     212231 {c |}{res}          1        0.00        0.08
{txt}     212232 {c |}{res}          1        0.00        0.09
{txt}     212291 {c |}{res}          1        0.00        0.09
{txt}     212292 {c |}{res}          1        0.00        0.10
{txt}     212299 {c |}{res}          1        0.00        0.10
{txt}     212311 {c |}{res}          1        0.00        0.11
{txt}     212312 {c |}{res}          1        0.00        0.11
{txt}     212319 {c |}{res}          1        0.00        0.11
{txt}     212321 {c |}{res}          1        0.00        0.12
{txt}     212322 {c |}{res}          1        0.00        0.12
{txt}     212323 {c |}{res}          1        0.00        0.13
{txt}     212324 {c |}{res}          1        0.00        0.13
{txt}     212325 {c |}{res}          1        0.00        0.14
{txt}     212329 {c |}{res}          1        0.00        0.14
{txt}     212391 {c |}{res}          1        0.00        0.15
{txt}     212392 {c |}{res}          1        0.00        0.15
{txt}     212393 {c |}{res}          1        0.00        0.16
{txt}     212394 {c |}{res}          1        0.00        0.16
{txt}     212395 {c |}{res}          1        0.00        0.16
{txt}     212396 {c |}{res}          1        0.00        0.17
{txt}     212398 {c |}{res}          1        0.00        0.17
{txt}     212399 {c |}{res}          1        0.00        0.18
{txt}     213111 {c |}{res}          1        0.00        0.18
{txt}     213119 {c |}{res}          1        0.00        0.19
{txt}     221110 {c |}{res}          1        0.00        0.19
{txt}     222111 {c |}{res}          1        0.00        0.20
{txt}     222112 {c |}{res}          1        0.00        0.20
{txt}     222210 {c |}{res}          1        0.00        0.21
{txt}     236111 {c |}{res}          1        0.00        0.21
{txt}     236112 {c |}{res}          1        0.00        0.21
{txt}     236113 {c |}{res}          1        0.00        0.22
{txt}     236211 {c |}{res}          1        0.00        0.22
{txt}     236212 {c |}{res}          1        0.00        0.23
{txt}     236220 {c |}{res}      1,000        4.57        4.80
{txt}     236221 {c |}{res}          1        0.00        4.81
{txt}     236222 {c |}{res}          1        0.00        4.81
{txt}     237111 {c |}{res}          1        0.00        4.81
{txt}     237112 {c |}{res}          1        0.00        4.82
{txt}     237113 {c |}{res}          1        0.00        4.82
{txt}     237121 {c |}{res}          1        0.00        4.83
{txt}     237122 {c |}{res}          1        0.00        4.83
{txt}     237123 {c |}{res}          1        0.00        4.84
{txt}     237131 {c |}{res}          1        0.00        4.84
{txt}     237132 {c |}{res}          1        0.00        4.85
{txt}     237133 {c |}{res}          1        0.00        4.85
{txt}     237211 {c |}{res}          1        0.00        4.86
{txt}     237212 {c |}{res}          1        0.00        4.86
{txt}     237213 {c |}{res}          1        0.00        4.86
{txt}     237311 {c |}{res}          1        0.00        4.87
{txt}     237312 {c |}{res}          1        0.00        4.87
{txt}     237313 {c |}{res}          1        0.00        4.88
{txt}     237991 {c |}{res}          1        0.00        4.88
{txt}     237992 {c |}{res}          1        0.00        4.89
{txt}     237993 {c |}{res}          1        0.00        4.89
{txt}     237994 {c |}{res}          1        0.00        4.90
{txt}     237999 {c |}{res}          1        0.00        4.90
{txt}     238110 {c |}{res}          1        0.00        4.91
{txt}     238121 {c |}{res}          1        0.00        4.91
{txt}     238122 {c |}{res}          1        0.00        4.91
{txt}     238130 {c |}{res}          1        0.00        4.92
{txt}     238190 {c |}{res}          1        0.00        4.92
{txt}     238210 {c |}{res}          1        0.00        4.93
{txt}     238221 {c |}{res}          1        0.00        4.93
{txt}     238222 {c |}{res}          1        0.00        4.94
{txt}     238290 {c |}{res}          1        0.00        4.94
{txt}     238311 {c |}{res}          1        0.00        4.95
{txt}     238312 {c |}{res}          1        0.00        4.95
{txt}     238320 {c |}{res}          1        0.00        4.96
{txt}     238330 {c |}{res}          1        0.00        4.96
{txt}     238340 {c |}{res}          1        0.00        4.97
{txt}     238350 {c |}{res}          1        0.00        4.97
{txt}     238390 {c |}{res}          1        0.00        4.97
{txt}     238910 {c |}{res}          1        0.00        4.98
{txt}     238990 {c |}{res}          1        0.00        4.98
{txt}     311110 {c |}{res}          1        0.00        4.99
{txt}     311211 {c |}{res}          1        0.00        4.99
{txt}     311212 {c |}{res}          1        0.00        5.00
{txt}     311213 {c |}{res}          1        0.00        5.00
{txt}     311214 {c |}{res}          1        0.00        5.01
{txt}     311215 {c |}{res}          1        0.00        5.01
{txt}     311221 {c |}{res}          1        0.00        5.02
{txt}     311222 {c |}{res}          1        0.00        5.02
{txt}     311230 {c |}{res}          1        0.00        5.02
{txt}     311311 {c |}{res}          1        0.00        5.03
{txt}     311319 {c |}{res}          1        0.00        5.03
{txt}     311340 {c |}{res}          1        0.00        5.04
{txt}     311350 {c |}{res}          1        0.00        5.04
{txt}     311411 {c |}{res}          1        0.00        5.05
{txt}     311412 {c |}{res}          1        0.00        5.05
{txt}     311421 {c |}{res}          1        0.00        5.06
{txt}     311422 {c |}{res}          1        0.00        5.06
{txt}     311423 {c |}{res}          1        0.00        5.07
{txt}     311511 {c |}{res}          1        0.00        5.07
{txt}     311512 {c |}{res}          1        0.00        5.07
{txt}     311513 {c |}{res}          1        0.00        5.08
{txt}     311520 {c |}{res}          1        0.00        5.08
{txt}     311611 {c |}{res}          1        0.00        5.09
{txt}     311612 {c |}{res}      1,000        4.57        9.66
{txt}     311613 {c |}{res}          1        0.00        9.66
{txt}     311614 {c |}{res}          1        0.00        9.67
{txt}     311710 {c |}{res}          1        0.00        9.67
{txt}     311811 {c |}{res}          1        0.00        9.68
{txt}     311812 {c |}{res}          1        0.00        9.68
{txt}     311820 {c |}{res}          1        0.00        9.69
{txt}     311830 {c |}{res}          1        0.00        9.69
{txt}     311910 {c |}{res}          1        0.00        9.70
{txt}     311921 {c |}{res}          1        0.00        9.70
{txt}     311922 {c |}{res}          1        0.00        9.71
{txt}     311923 {c |}{res}          1        0.00        9.71
{txt}     311924 {c |}{res}          1        0.00        9.72
{txt}     311930 {c |}{res}          1        0.00        9.72
{txt}     311940 {c |}{res}          1        0.00        9.72
{txt}     311991 {c |}{res}          1        0.00        9.73
{txt}     311992 {c |}{res}          1        0.00        9.73
{txt}     311993 {c |}{res}          1        0.00        9.74
{txt}     311999 {c |}{res}          1        0.00        9.74
{txt}     312111 {c |}{res}          1        0.00        9.75
{txt}     312112 {c |}{res}          1        0.00        9.75
{txt}     312113 {c |}{res}          1        0.00        9.76
{txt}     312120 {c |}{res}          1        0.00        9.76
{txt}     312131 {c |}{res}          1        0.00        9.77
{txt}     312132 {c |}{res}          1        0.00        9.77
{txt}     312139 {c |}{res}          1        0.00        9.77
{txt}     312141 {c |}{res}          1        0.00        9.78
{txt}     312142 {c |}{res}          1        0.00        9.78
{txt}     312149 {c |}{res}          1        0.00        9.79
{txt}     312210 {c |}{res}          1        0.00        9.79
{txt}     312221 {c |}{res}          1        0.00        9.80
{txt}     312222 {c |}{res}          1        0.00        9.80
{txt}     313111 {c |}{res}          1        0.00        9.81
{txt}     313112 {c |}{res}          1        0.00        9.81
{txt}     313113 {c |}{res}          1        0.00        9.82
{txt}     313210 {c |}{res}          1        0.00        9.82
{txt}     313220 {c |}{res}          1        0.00        9.82
{txt}     313230 {c |}{res}          1        0.00        9.83
{txt}     313240 {c |}{res}          1        0.00        9.83
{txt}     313310 {c |}{res}          1        0.00        9.84
{txt}     313320 {c |}{res}          1        0.00        9.84
{txt}     314110 {c |}{res}          1        0.00        9.85
{txt}     314120 {c |}{res}          1        0.00        9.85
{txt}     314911 {c |}{res}          1        0.00        9.86
{txt}     314912 {c |}{res}          1        0.00        9.86
{txt}     314991 {c |}{res}          1        0.00        9.87
{txt}     314992 {c |}{res}          1        0.00        9.87
{txt}     314993 {c |}{res}          1        0.00        9.88
{txt}     314999 {c |}{res}          1        0.00        9.88
{txt}     315110 {c |}{res}          1        0.00        9.88
{txt}     315191 {c |}{res}          1        0.00        9.89
{txt}     315192 {c |}{res}          1        0.00        9.89
{txt}     315210 {c |}{res}          1        0.00        9.90
{txt}     315221 {c |}{res}          1        0.00        9.90
{txt}     315222 {c |}{res}          1        0.00        9.91
{txt}     315223 {c |}{res}          1        0.00        9.91
{txt}     315224 {c |}{res}          1        0.00        9.92
{txt}     315225 {c |}{res}          1        0.00        9.92
{txt}     315229 {c |}{res}          1        0.00        9.93
{txt}     315991 {c |}{res}          1        0.00        9.93
{txt}     315999 {c |}{res}          1        0.00        9.93
{txt}     316110 {c |}{res}          1        0.00        9.94
{txt}     316211 {c |}{res}          1        0.00        9.94
{txt}     316212 {c |}{res}          1        0.00        9.95
{txt}     316213 {c |}{res}          1        0.00        9.95
{txt}     316214 {c |}{res}          1        0.00        9.96
{txt}     316219 {c |}{res}          1        0.00        9.96
{txt}     316991 {c |}{res}          1        0.00        9.97
{txt}     316999 {c |}{res}          1        0.00        9.97
{txt}     321111 {c |}{res}          1        0.00        9.98
{txt}     321112 {c |}{res}          1        0.00        9.98
{txt}     321113 {c |}{res}          1        0.00        9.98
{txt}     321210 {c |}{res}          1        0.00        9.99
{txt}     321910 {c |}{res}          1        0.00        9.99
{txt}     321920 {c |}{res}          1        0.00       10.00
{txt}     321991 {c |}{res}          1        0.00       10.00
{txt}     321992 {c |}{res}          1        0.00       10.01
{txt}     321993 {c |}{res}          1        0.00       10.01
{txt}     321999 {c |}{res}          1        0.00       10.02
{txt}     322121 {c |}{res}          1        0.00       10.02
{txt}     322122 {c |}{res}          1        0.00       10.03
{txt}     322131 {c |}{res}          1        0.00       10.03
{txt}     322132 {c |}{res}          1        0.00       10.04
{txt}     322210 {c |}{res}          1        0.00       10.04
{txt}     322220 {c |}{res}          1        0.00       10.04
{txt}     322230 {c |}{res}          1        0.00       10.05
{txt}     322291 {c |}{res}          1        0.00       10.05
{txt}     322299 {c |}{res}          1        0.00       10.06
{txt}     323111 {c |}{res}          1        0.00       10.06
{txt}     323119 {c |}{res}          1        0.00       10.07
{txt}     323120 {c |}{res}          1        0.00       10.07
{txt}     324110 {c |}{res}          1        0.00       10.08
{txt}     324120 {c |}{res}          1        0.00       10.08
{txt}     324191 {c |}{res}          1        0.00       10.09
{txt}     324199 {c |}{res}          1        0.00       10.09
{txt}     325110 {c |}{res}          1        0.00       10.09
{txt}     325120 {c |}{res}          1        0.00       10.10
{txt}     325130 {c |}{res}          1        0.00       10.10
{txt}     325180 {c |}{res}          1        0.00       10.11
{txt}     325190 {c |}{res}          1        0.00       10.11
{txt}     325211 {c |}{res}          1        0.00       10.12
{txt}     325212 {c |}{res}          1        0.00       10.12
{txt}     325220 {c |}{res}          1        0.00       10.13
{txt}     325310 {c |}{res}          1        0.00       10.13
{txt}     325320 {c |}{res}          1        0.00       10.14
{txt}     325411 {c |}{res}          1        0.00       10.14
{txt}     325412 {c |}{res}      1,000        4.57       14.71
{txt}     325510 {c |}{res}          1        0.00       14.72
{txt}     325520 {c |}{res}          1        0.00       14.72
{txt}     325610 {c |}{res}          1        0.00       14.73
{txt}     325620 {c |}{res}          1        0.00       14.73
{txt}     325910 {c |}{res}          1        0.00       14.74
{txt}     325920 {c |}{res}          1        0.00       14.74
{txt}     325991 {c |}{res}          1        0.00       14.74
{txt}     325992 {c |}{res}          1        0.00       14.75
{txt}     325993 {c |}{res}          1        0.00       14.75
{txt}     325999 {c |}{res}          1        0.00       14.76
{txt}     326110 {c |}{res}          1        0.00       14.76
{txt}     326120 {c |}{res}          1        0.00       14.77
{txt}     326130 {c |}{res}          1        0.00       14.77
{txt}     326140 {c |}{res}          1        0.00       14.78
{txt}     326150 {c |}{res}          1        0.00       14.78
{txt}     326160 {c |}{res}          1        0.00       14.79
{txt}     326191 {c |}{res}          1        0.00       14.79
{txt}     326192 {c |}{res}          1        0.00       14.79
{txt}     326193 {c |}{res}          1        0.00       14.80
{txt}     326194 {c |}{res}          1        0.00       14.80
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{txt}     488190 {c |}{res}          1        0.00       25.46
{txt}     488210 {c |}{res}          1        0.00       25.47
{txt}     488310 {c |}{res}          1        0.00       25.47
{txt}     488320 {c |}{res}          1        0.00       25.47
{txt}     488330 {c |}{res}          1        0.00       25.48
{txt}     488390 {c |}{res}          1        0.00       25.48
{txt}     488410 {c |}{res}          1        0.00       25.49
{txt}     488491 {c |}{res}          1        0.00       25.49
{txt}     488492 {c |}{res}          1        0.00       25.50
{txt}     488493 {c |}{res}          1        0.00       25.50
{txt}     488511 {c |}{res}          1        0.00       25.51
{txt}     488519 {c |}{res}          1        0.00       25.51
{txt}     488990 {c |}{res}          1        0.00       25.52
{txt}     491110 {c |}{res}          1        0.00       25.52
{txt}     492110 {c |}{res}          1        0.00       25.52
{txt}     492210 {c |}{res}          1        0.00       25.53
{txt}     493110 {c |}{res}      1,000        4.57       30.10
{txt}     493111 {c |}{res}          1        0.00       30.11
{txt}     493119 {c |}{res}          1        0.00       30.11
{txt}     493120 {c |}{res}          1        0.00       30.11
{txt}     493130 {c |}{res}          1        0.00       30.12
{txt}     493190 {c |}{res}          1        0.00       30.12
{txt}     511111 {c |}{res}          1        0.00       30.13
{txt}     511112 {c |}{res}          1        0.00       30.13
{txt}     511121 {c |}{res}          1        0.00       30.14
{txt}     511122 {c |}{res}          1        0.00       30.14
{txt}     511131 {c |}{res}          1        0.00       30.15
{txt}     511132 {c |}{res}          1        0.00       30.15
{txt}     511141 {c |}{res}          1        0.00       30.16
{txt}     511142 {c |}{res}          1        0.00       30.16
{txt}     511191 {c |}{res}          1        0.00       30.17
{txt}     511192 {c |}{res}          1        0.00       30.17
{txt}     511210 {c |}{res}          1        0.00       30.17
{txt}     512111 {c |}{res}          1        0.00       30.18
{txt}     512112 {c |}{res}          1        0.00       30.18
{txt}     512113 {c |}{res}          1        0.00       30.19
{txt}     512120 {c |}{res}          1        0.00       30.19
{txt}     512130 {c |}{res}          1        0.00       30.20
{txt}     512190 {c |}{res}          1        0.00       30.20
{txt}     512210 {c |}{res}          1        0.00       30.21
{txt}     512220 {c |}{res}          1        0.00       30.21
{txt}     512230 {c |}{res}          1        0.00       30.22
{txt}     512240 {c |}{res}          1        0.00       30.22
{txt}     512290 {c |}{res}          1        0.00       30.22
{txt}     515110 {c |}{res}          1        0.00       30.23
{txt}     515120 {c |}{res}          1        0.00       30.23
{txt}     515210 {c |}{res}          1        0.00       30.24
{txt}     517110 {c |}{res}          1        0.00       30.24
{txt}     517210 {c |}{res}          1        0.00       30.25
{txt}     517410 {c |}{res}          1        0.00       30.25
{txt}     517910 {c |}{res}          1        0.00       30.26
{txt}     518210 {c |}{res}          1        0.00       30.26
{txt}     519110 {c |}{res}          1        0.00       30.27
{txt}     519121 {c |}{res}          1        0.00       30.27
{txt}     519130 {c |}{res}          1        0.00       30.27
{txt}     519190 {c |}{res}          1        0.00       30.28
{txt}     521110 {c |}{res}          1        0.00       30.28
{txt}     522110 {c |}{res}          1        0.00       30.29
{txt}     522210 {c |}{res}          1        0.00       30.29
{txt}     522220 {c |}{res}          1        0.00       30.30
{txt}     522310 {c |}{res}          1        0.00       30.30
{txt}     522320 {c |}{res}          1        0.00       30.31
{txt}     522390 {c |}{res}          1        0.00       30.31
{txt}     522440 {c |}{res}          1        0.00       30.32
{txt}     522451 {c |}{res}          1        0.00       30.32
{txt}     522452 {c |}{res}          1        0.00       30.33
{txt}     522460 {c |}{res}          1        0.00       30.33
{txt}     522490 {c |}{res}          1        0.00       30.33
{txt}     522510 {c |}{res}          1        0.00       30.34
{txt}     523110 {c |}{res}          1        0.00       30.34
{txt}     523121 {c |}{res}          1        0.00       30.35
{txt}     523122 {c |}{res}          1        0.00       30.35
{txt}     523210 {c |}{res}          1        0.00       30.36
{txt}     523910 {c |}{res}          1        0.00       30.36
{txt}     523990 {c |}{res}          1        0.00       30.37
{txt}     524110 {c |}{res}          1        0.00       30.37
{txt}     524120 {c |}{res}          1        0.00       30.38
{txt}     524126 {c |}{res}      1,000        4.57       34.95
{txt}     524130 {c |}{res}          1        0.00       34.95
{txt}     524210 {c |}{res}          1        0.00       34.96
{txt}     524220 {c |}{res}          1        0.00       34.96
{txt}     531111 {c |}{res}          1        0.00       34.97
{txt}     531112 {c |}{res}          1        0.00       34.97
{txt}     531113 {c |}{res}          1        0.00       34.97
{txt}     531114 {c |}{res}          1        0.00       34.98
{txt}     531115 {c |}{res}          1        0.00       34.98
{txt}     531116 {c |}{res}          1        0.00       34.99
{txt}     531119 {c |}{res}          1        0.00       34.99
{txt}     531210 {c |}{res}          1        0.00       35.00
{txt}     531311 {c |}{res}      1,000        4.57       39.57
{txt}     531319 {c |}{res}          1        0.00       39.57
{txt}     532110 {c |}{res}          1        0.00       39.58
{txt}     532121 {c |}{res}          1        0.00       39.58
{txt}     532122 {c |}{res}          1        0.00       39.59
{txt}     532210 {c |}{res}          1        0.00       39.59
{txt}     532220 {c |}{res}          1        0.00       39.60
{txt}     532230 {c |}{res}          1        0.00       39.60
{txt}     532291 {c |}{res}          1        0.00       39.61
{txt}     532292 {c |}{res}          1        0.00       39.61
{txt}     532299 {c |}{res}          1        0.00       39.62
{txt}     532310 {c |}{res}          1        0.00       39.62
{txt}     532411 {c |}{res}          1        0.00       39.62
{txt}     532412 {c |}{res}          1        0.00       39.63
{txt}     532420 {c |}{res}          1        0.00       39.63
{txt}     532491 {c |}{res}          1        0.00       39.64
{txt}     532492 {c |}{res}          1        0.00       39.64
{txt}     532493 {c |}{res}          1        0.00       39.65
{txt}     533110 {c |}{res}          1        0.00       39.65
{txt}     541110 {c |}{res}          1        0.00       39.66
{txt}     541120 {c |}{res}          1        0.00       39.66
{txt}     541190 {c |}{res}          1        0.00       39.67
{txt}     541211 {c |}{res}          1        0.00       39.67
{txt}     541219 {c |}{res}          1        0.00       39.67
{txt}     541310 {c |}{res}          1        0.00       39.68
{txt}     541320 {c |}{res}          1        0.00       39.68
{txt}     541330 {c |}{res}      1,000        4.57       44.26
{txt}     541340 {c |}{res}          1        0.00       44.26
{txt}     541350 {c |}{res}          1        0.00       44.26
{txt}     541360 {c |}{res}          1        0.00       44.27
{txt}     541370 {c |}{res}          1        0.00       44.27
{txt}     541380 {c |}{res}          1        0.00       44.28
{txt}     541410 {c |}{res}          1        0.00       44.28
{txt}     541420 {c |}{res}          1        0.00       44.29
{txt}     541430 {c |}{res}          1        0.00       44.29
{txt}     541490 {c |}{res}          1        0.00       44.30
{txt}     541510 {c |}{res}          1        0.00       44.30
{txt}     541610 {c |}{res}          1        0.00       44.31
{txt}     541620 {c |}{res}          1        0.00       44.31
{txt}     541690 {c |}{res}          1        0.00       44.31
{txt}     541711 {c |}{res}          1        0.00       44.32
{txt}     541721 {c |}{res}          1        0.00       44.32
{txt}     541810 {c |}{res}          1        0.00       44.33
{txt}     541820 {c |}{res}          1        0.00       44.33
{txt}     541830 {c |}{res}          1        0.00       44.34
{txt}     541840 {c |}{res}          1        0.00       44.34
{txt}     541850 {c |}{res}          1        0.00       44.35
{txt}     541860 {c |}{res}          1        0.00       44.35
{txt}     541870 {c |}{res}          1        0.00       44.36
{txt}     541890 {c |}{res}          1        0.00       44.36
{txt}     541910 {c |}{res}          1        0.00       44.37
{txt}     541920 {c |}{res}          1        0.00       44.37
{txt}     541921 {c |}{res}      1,000        4.57       48.94
{txt}     541930 {c |}{res}          1        0.00       48.95
{txt}     541941 {c |}{res}          1        0.00       48.95
{txt}     541943 {c |}{res}          1        0.00       48.96
{txt}     541990 {c |}{res}          1        0.00       48.96
{txt}     551111 {c |}{res}          1        0.00       48.96
{txt}     551112 {c |}{res}      1,000        4.57       53.54
{txt}     561110 {c |}{res}          1        0.00       53.54
{txt}     561210 {c |}{res}          1        0.00       53.55
{txt}     561310 {c |}{res}          1        0.00       53.55
{txt}     561320 {c |}{res}          1        0.00       53.55
{txt}     561330 {c |}{res}          1        0.00       53.56
{txt}     561410 {c |}{res}          1        0.00       53.56
{txt}     561421 {c |}{res}          1        0.00       53.57
{txt}     561422 {c |}{res}          1        0.00       53.57
{txt}     561431 {c |}{res}          1        0.00       53.58
{txt}     561432 {c |}{res}          1        0.00       53.58
{txt}     561440 {c |}{res}          1        0.00       53.59
{txt}     561450 {c |}{res}          1        0.00       53.59
{txt}     561490 {c |}{res}          1        0.00       53.60
{txt}     561510 {c |}{res}          1        0.00       53.60
{txt}     561520 {c |}{res}          1        0.00       53.60
{txt}     561590 {c |}{res}          1        0.00       53.61
{txt}     561610 {c |}{res}          1        0.00       53.61
{txt}     561620 {c |}{res}          1        0.00       53.62
{txt}     561710 {c |}{res}          1        0.00       53.62
{txt}     561720 {c |}{res}          1        0.00       53.63
{txt}     561730 {c |}{res}      1,000        4.57       58.20
{txt}     561740 {c |}{res}          1        0.00       58.20
{txt}     561790 {c |}{res}          1        0.00       58.21
{txt}     561910 {c |}{res}          1        0.00       58.21
{txt}     561920 {c |}{res}          1        0.00       58.22
{txt}     561990 {c |}{res}          1        0.00       58.22
{txt}     562111 {c |}{res}          1        0.00       58.23
{txt}     562112 {c |}{res}          1        0.00       58.23
{txt}     611111 {c |}{res}          1        0.00       58.24
{txt}     611121 {c |}{res}          1        0.00       58.24
{txt}     611131 {c |}{res}          1        0.00       58.25
{txt}     611141 {c |}{res}          1        0.00       58.25
{txt}     611151 {c |}{res}          1        0.00       58.25
{txt}     611161 {c |}{res}          1        0.00       58.26
{txt}     611171 {c |}{res}          1        0.00       58.26
{txt}     611181 {c |}{res}          1        0.00       58.27
{txt}     611211 {c |}{res}          1        0.00       58.27
{txt}     611311 {c |}{res}          1        0.00       58.28
{txt}     611411 {c |}{res}          1        0.00       58.28
{txt}     611421 {c |}{res}          1        0.00       58.29
{txt}     611431 {c |}{res}          1        0.00       58.29
{txt}     611511 {c |}{res}          1        0.00       58.30
{txt}     611611 {c |}{res}          1        0.00       58.30
{txt}     611621 {c |}{res}          1        0.00       58.30
{txt}     611631 {c |}{res}          1        0.00       58.31
{txt}     611691 {c |}{res}          1        0.00       58.31
{txt}     611698 {c |}{res}          1        0.00       58.32
{txt}     611710 {c |}{res}          1        0.00       58.32
{txt}     621111 {c |}{res}      1,000        4.57       62.89
{txt}     621113 {c |}{res}          1        0.00       62.90
{txt}     621115 {c |}{res}          1        0.00       62.90
{txt}     621211 {c |}{res}          1        0.00       62.91
{txt}     621311 {c |}{res}          1        0.00       62.91
{txt}     621320 {c |}{res}          1        0.00       62.92
{txt}     621331 {c |}{res}          1        0.00       62.92
{txt}     621341 {c |}{res}          1        0.00       62.93
{txt}     621391 {c |}{res}          1        0.00       62.93
{txt}     621398 {c |}{res}          1        0.00       62.94
{txt}     621411 {c |}{res}          1        0.00       62.94
{txt}     621421 {c |}{res}          1        0.00       62.95
{txt}     621491 {c |}{res}          1        0.00       62.95
{txt}     621511 {c |}{res}          1        0.00       62.95
{txt}     621610 {c |}{res}          1        0.00       62.96
{txt}     621910 {c |}{res}          1        0.00       62.96
{txt}     621991 {c |}{res}          1        0.00       62.97
{txt}     622111 {c |}{res}          1        0.00       62.97
{txt}     622211 {c |}{res}          1        0.00       62.98
{txt}     622311 {c |}{res}          1        0.00       62.98
{txt}     623111 {c |}{res}          1        0.00       62.99
{txt}     623211 {c |}{res}          1        0.00       62.99
{txt}     623221 {c |}{res}          1        0.00       63.00
{txt}     623311 {c |}{res}          1        0.00       63.00
{txt}     623991 {c |}{res}          1        0.00       63.00
{txt}     624111 {c |}{res}          1        0.00       63.01
{txt}     624121 {c |}{res}          1        0.00       63.01
{txt}     624191 {c |}{res}          1        0.00       63.02
{txt}     624198 {c |}{res}          1        0.00       63.02
{txt}     624211 {c |}{res}          1        0.00       63.03
{txt}     624221 {c |}{res}          1        0.00       63.03
{txt}     624231 {c |}{res}          1        0.00       63.04
{txt}     624311 {c |}{res}          1        0.00       63.04
{txt}     624410 {c |}{res}      1,000        4.57       67.61
{txt}     624411 {c |}{res}          1        0.00       67.62
{txt}     711111 {c |}{res}          1        0.00       67.62
{txt}     711121 {c |}{res}          1        0.00       67.63
{txt}     711130 {c |}{res}      1,000        4.57       72.20
{txt}     711131 {c |}{res}          1        0.00       72.20
{txt}     711191 {c |}{res}          1        0.00       72.21
{txt}     711211 {c |}{res}          1        0.00       72.21
{txt}     711212 {c |}{res}          1        0.00       72.22
{txt}     711311 {c |}{res}          1        0.00       72.22
{txt}     711320 {c |}{res}          1        0.00       72.23
{txt}     711410 {c |}{res}          1        0.00       72.23
{txt}     711510 {c |}{res}          1        0.00       72.24
{txt}     711511 {c |}{res}      1,000        4.57       76.81
{txt}     712111 {c |}{res}          1        0.00       76.81
{txt}     712120 {c |}{res}          1        0.00       76.82
{txt}     712131 {c |}{res}          1        0.00       76.82
{txt}     712190 {c |}{res}          1        0.00       76.83
{txt}     713111 {c |}{res}          1        0.00       76.83
{txt}     713113 {c |}{res}          1        0.00       76.83
{txt}     713120 {c |}{res}          1        0.00       76.84
{txt}     713291 {c |}{res}          1        0.00       76.84
{txt}     713299 {c |}{res}          1        0.00       76.85
{txt}     713910 {c |}{res}          1        0.00       76.85
{txt}     713930 {c |}{res}          1        0.00       76.86
{txt}     713941 {c |}{res}          1        0.00       76.86
{txt}     713943 {c |}{res}          1        0.00       76.87
{txt}     713950 {c |}{res}          1        0.00       76.87
{txt}     713991 {c |}{res}          1        0.00       76.88
{txt}     713992 {c |}{res}          1        0.00       76.88
{txt}     713998 {c |}{res}          1        0.00       76.88
{txt}     721111 {c |}{res}          1        0.00       76.89
{txt}     721112 {c |}{res}          1        0.00       76.89
{txt}     721113 {c |}{res}          1        0.00       76.90
{txt}     721190 {c |}{res}          1        0.00       76.90
{txt}     721210 {c |}{res}          1        0.00       76.91
{txt}     721311 {c |}{res}          1        0.00       76.91
{txt}     721312 {c |}{res}          1        0.00       76.92
{txt}     722310 {c |}{res}          1        0.00       76.92
{txt}     722320 {c |}{res}          1        0.00       76.93
{txt}     722411 {c |}{res}          1        0.00       76.93
{txt}     722412 {c |}{res}          1        0.00       76.94
{txt}     722511 {c |}{res}      1,000        4.57       81.51
{txt}     722512 {c |}{res}          1        0.00       81.51
{txt}     722513 {c |}{res}          1        0.00       81.52
{txt}     722514 {c |}{res}          1        0.00       81.52
{txt}     722515 {c |}{res}          1        0.00       81.53
{txt}     722516 {c |}{res}          1        0.00       81.53
{txt}     722517 {c |}{res}          1        0.00       81.53
{txt}     722518 {c |}{res}          1        0.00       81.54
{txt}     722519 {c |}{res}          1        0.00       81.54
{txt}     811111 {c |}{res}      1,000        4.57       86.12
{txt}     811112 {c |}{res}          1        0.00       86.12
{txt}     811113 {c |}{res}          1        0.00       86.12
{txt}     811114 {c |}{res}          1        0.00       86.13
{txt}     811115 {c |}{res}          1        0.00       86.13
{txt}     811116 {c |}{res}          1        0.00       86.14
{txt}     811119 {c |}{res}          1        0.00       86.14
{txt}     811121 {c |}{res}          1        0.00       86.15
{txt}     811122 {c |}{res}          1        0.00       86.15
{txt}     811129 {c |}{res}          1        0.00       86.16
{txt}     811191 {c |}{res}          1        0.00       86.16
{txt}     811192 {c |}{res}          1        0.00       86.17
{txt}     811199 {c |}{res}          1        0.00       86.17
{txt}     811211 {c |}{res}          1        0.00       86.17
{txt}     811219 {c |}{res}          1        0.00       86.18
{txt}     811311 {c |}{res}          1        0.00       86.18
{txt}     811312 {c |}{res}          1        0.00       86.19
{txt}     811313 {c |}{res}          1        0.00       86.19
{txt}     811314 {c |}{res}          1        0.00       86.20
{txt}     811410 {c |}{res}          1        0.00       86.20
{txt}     811420 {c |}{res}          1        0.00       86.21
{txt}     811430 {c |}{res}          1        0.00       86.21
{txt}     811491 {c |}{res}          1        0.00       86.22
{txt}     811492 {c |}{res}          1        0.00       86.22
{txt}     811493 {c |}{res}          1        0.00       86.23
{txt}     811499 {c |}{res}          1        0.00       86.23
{txt}     812110 {c |}{res}          1        0.00       86.23
{txt}     812120 {c |}{res}          1        0.00       86.24
{txt}     812130 {c |}{res}          1        0.00       86.24
{txt}     812210 {c |}{res}      1,000        4.57       90.82
{txt}     812310 {c |}{res}          1        0.00       90.82
{txt}     812321 {c |}{res}          1        0.00       90.82
{txt}     812410 {c |}{res}          1        0.00       90.83
{txt}     812910 {c |}{res}          1        0.00       90.83
{txt}     812990 {c |}{res}          1        0.00       90.84
{txt}     813110 {c |}{res}      1,000        4.57       95.41
{txt}     813120 {c |}{res}          1        0.00       95.41
{txt}     813130 {c |}{res}          1        0.00       95.42
{txt}     813140 {c |}{res}          1        0.00       95.42
{txt}     813230 {c |}{res}          1        0.00       95.43
{txt}     931181 {c |}{res}      1,000        4.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     21,873      100.00
{txt}
{com}. 
. oaxaca lnworkers_WA_ score_manag_ dum_time  , by(dmanuf) relax noisily vce(robust) cluster(id_mun2)

{txt}Model for group 1

      Source {c |}       SS           df       MS      Number of obs   ={res}    15,842
{txt}{hline 13}{c +}{hline 34}   F(2, 15839)     = {res}  1040.17
{txt}       Model {c |} {res}   850.5156         2    425.2578   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 6475.54897    15,839   .40883572   {txt}R-squared       ={res}    0.1161
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1160
{txt}       Total {c |} {res} 7326.06457    15,841   .46247488   {txt}Root MSE        =   {res}  .6394

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lnworkers_~_{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_ {c |}{col 14}{res}{space 2} 1.234136{col 26}{space 2} .0273312{col 37}{space 1}   45.15{col 46}{space 3}0.000{col 54}{space 4} 1.180564{col 67}{space 3} 1.287709
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2} .0732205{col 26}{space 2} .0106146{col 37}{space 1}    6.90{col 46}{space 3}0.000{col 54}{space 4} .0524147{col 67}{space 3} .0940263
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.327442{col 26}{space 2} .0149709{col 37}{space 1}  222.26{col 46}{space 3}0.000{col 54}{space 4} 3.298097{col 67}{space 3} 3.356786
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Model for group 2

      Source {c |}       SS           df       MS      Number of obs   ={res}     3,694
{txt}{hline 13}{c +}{hline 34}   F(2, 3691)      = {res}  3575.20
{txt}       Model {c |} {res} 1661.00984         2  830.504918   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 857.403848     3,691  .232295814   {txt}R-squared       ={res}    0.6595
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.6594
{txt}       Total {c |} {res} 2518.41368     3,693  .681942509   {txt}Root MSE        =   {res} .48197

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lnworkers_~_{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_ {c |}{col 14}{res}{space 2}  3.03922{col 26}{space 2} .0395296{col 37}{space 1}   76.88{col 46}{space 3}0.000{col 54}{space 4} 2.961718{col 67}{space 3} 3.116722
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2} .3589791{col 26}{space 2} .0181996{col 37}{space 1}   19.72{col 46}{space 3}0.000{col 54}{space 4} .3232967{col 67}{space 3} .3946614
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.301066{col 26}{space 2}  .017217{col 37}{space 1}  133.65{col 46}{space 3}0.000{col 54}{space 4} 2.267311{col 67}{space 3} 2.334822
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{col 1}Blinder-Oaxaca decomposition{col 49}{lalign 17:Number of obs}{col 66} = {res}{ralign 10:19,536}
{txt}{col 49}Model{col 67}={col 69}    {res}linear
{txt}Group 1: dmanuf = {res}0{txt}{col 49}N of obs 1{col 67}={col 69}{res}    15,842
{txt}Group 2: dmanuf = {res}1{txt}{col 49}N of obs 2{col 67}={col 69}{res}     3,694

{txt}   endowments: (X1 - X2) * {bf:b2}
 coefficients: {bf:X2} * (b1 - b2)
  interaction: (X1 - X2) * (b1 - b2)

{res}{txt}{ralign 78:(Std. err. adjusted for {res:32} clusters in {res:id_mun2})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}lnworkers_~_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}overall      {txt}{c |}
{space 5}group_1 {c |}{col 14}{res}{space 2} 3.889619{col 26}{space 2} .1300737{col 37}{space 1}   29.90{col 46}{space 3}0.000{col 54}{space 4} 3.634679{col 67}{space 3} 4.144559
{txt}{space 5}group_2 {c |}{col 14}{res}{space 2} 3.373341{col 26}{space 2} .3199798{col 37}{space 1}   10.54{col 46}{space 3}0.000{col 54}{space 4} 2.746192{col 67}{space 3}  4.00049
{txt}{space 2}difference {c |}{col 14}{res}{space 2} .5162787{col 26}{space 2} .3491705{col 37}{space 1}    1.48{col 46}{space 3}0.139{col 54}{space 4}-.1680829{col 67}{space 3}  1.20064
{txt}{space 2}endowments {c |}{col 14}{res}{space 2} .4001733{col 26}{space 2} .3292488{col 37}{space 1}    1.22{col 46}{space 3}0.224{col 54}{space 4}-.2451426{col 67}{space 3} 1.045489
{txt}coefficients {c |}{col 14}{res}{space 2} .3365701{col 26}{space 2} .2660139{col 37}{space 1}    1.27{col 46}{space 3}0.206{col 54}{space 4}-.1848076{col 67}{space 3} .8579478
{txt}{space 1}interaction {c |}{col 14}{res}{space 2}-.2204647{col 26}{space 2} .2426747{col 37}{space 1}   -0.91{col 46}{space 3}0.364{col 54}{space 4}-.6960985{col 67}{space 3}  .255169
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}endowments   {txt}{c |}
score_manag_ {c |}{col 14}{res}{space 2} .4853294{col 26}{space 2} .2835959{col 37}{space 1}    1.71{col 46}{space 3}0.087{col 54}{space 4}-.0705082{col 67}{space 3} 1.041167
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2}-.0851561{col 26}{space 2} .1079492{col 37}{space 1}   -0.79{col 46}{space 3}0.430{col 54}{space 4}-.2967328{col 67}{space 3} .1264205
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}coefficients {txt}{c |}
score_manag_ {c |}{col 14}{res}{space 2}-.4812495{col 26}{space 2}  .259839{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-.9905245{col 67}{space 3} .0280256
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2}-.2085558{col 26}{space 2}  .340135{col 37}{space 1}   -0.61{col 46}{space 3}0.540{col 54}{space 4}-.8752081{col 67}{space 3} .4580965
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.026375{col 26}{space 2} .5309859{col 37}{space 1}    1.93{col 46}{space 3}0.053{col 54}{space 4}-.0143379{col 67}{space 3} 2.067089
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}interaction  {txt}{c |}
score_manag_ {c |}{col 14}{res}{space 2}-.2882517{col 26}{space 2} .2101793{col 37}{space 1}   -1.37{col 46}{space 3}0.170{col 54}{space 4}-.7001955{col 67}{space 3} .1236921
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2}  .067787{col 26}{space 2} .1196603{col 37}{space 1}    0.57{col 46}{space 3}0.571{col 54}{space 4} -.166743{col 67}{space 3} .3023169
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store e1
{txt}
{com}. 
. oaxaca lnworkers_WA_ score_manag_ sh_sup_WA dum_time dumid_m2* dclase* , by(dmanuf) relax noisily vce(robust) cluster(id_mun2)

{txt}Model for group 1

      Source {c |}       SS           df       MS      Number of obs   ={res}    15,842
{txt}{hline 13}{c +}{hline 34}   F(32, 15809)    = {res}  1218.48
{txt}       Model {c |} {res} 5212.61973        32  162.894366   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 2113.44485    15,809  .133686182   {txt}R-squared       ={res}    0.7115
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.7109
{txt}       Total {c |} {res} 7326.06457    15,841   .46247488   {txt}Root MSE        =   {res} .36563

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lnworkers_~_{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_ {c |}{col 14}{res}{space 2} 1.552874{col 26}{space 2} .0283299{col 37}{space 1}   54.81{col 46}{space 3}0.000{col 54}{space 4} 1.497344{col 67}{space 3} 1.608404
{txt}{space 2}sh_sup_WA_ {c |}{col 14}{res}{space 2}-2.934855{col 26}{space 2} .0478799{col 37}{space 1}  -61.30{col 46}{space 3}0.000{col 54}{space 4}-3.028705{col 67}{space 3}-2.841004
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2}-.0634737{col 26}{space 2} .0073537{col 37}{space 1}   -8.63{col 46}{space 3}0.000{col 54}{space 4}-.0778877{col 67}{space 3}-.0490596
{txt}{space 3}dumid_m21 {c |}{col 14}{res}{space 2} .0157267{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-11891.88{col 67}{space 3} 11891.91
{txt}{space 3}dumid_m22 {c |}{col 14}{res}{space 2}-.0094494{col 26}{space 2} .1831831{col 37}{space 1}   -0.05{col 46}{space 3}0.959{col 54}{space 4}-.3685092{col 67}{space 3} .3496105
{txt}{space 3}dumid_m23 {c |}{col 14}{res}{space 2} .2538632{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-11891.64{col 67}{space 3} 11892.15
{txt}{space 3}dumid_m24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m25 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m26 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m27 {c |}{col 14}{res}{space 2} .0157267{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-11891.88{col 67}{space 3} 11891.91
{txt}{space 3}dumid_m28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m29 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m210 {c |}{col 14}{res}{space 2} .0028815{col 26}{space 2}  .223909{col 37}{space 1}    0.01{col 46}{space 3}0.990{col 54}{space 4}-.4360056{col 67}{space 3} .4417686
{txt}{space 2}dumid_m211 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m212 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m213 {c |}{col 14}{res}{space 2} .1568575{col 26}{space 2} .1643477{col 37}{space 1}    0.95{col 46}{space 3}0.340{col 54}{space 4}-.1652828{col 67}{space 3} .4789978
{txt}{space 2}dumid_m214 {c |}{col 14}{res}{space 2} .0101679{col 26}{space 2} .3659975{col 37}{space 1}    0.03{col 46}{space 3}0.978{col 54}{space 4}-.7072289{col 67}{space 3} .7275647
{txt}{space 2}dumid_m215 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m216 {c |}{col 14}{res}{space 2} .0027919{col 26}{space 2}    .1298{col 37}{space 1}    0.02{col 46}{space 3}0.983{col 54}{space 4}-.2516309{col 67}{space 3} .2572147
{txt}{space 2}dumid_m217 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m218 {c |}{col 14}{res}{space 2} .0917588{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4} -11891.8{col 67}{space 3} 11891.98
{txt}{space 2}dumid_m219 {c |}{col 14}{res}{space 2}-.8641515{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.76{col 67}{space 3} 11891.03
{txt}{space 2}dumid_m220 {c |}{col 14}{res}{space 2}-1.125604{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11893.02{col 67}{space 3} 11890.77
{txt}{space 2}dumid_m221 {c |}{col 14}{res}{space 2} .1193003{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-11891.77{col 67}{space 3} 11892.01
{txt}{space 2}dumid_m222 {c |}{col 14}{res}{space 2} .7734865{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-11891.12{col 67}{space 3} 11892.67
{txt}{space 2}dumid_m223 {c |}{col 14}{res}{space 2} .3180428{col 26}{space 2} .3658647{col 37}{space 1}    0.87{col 46}{space 3}0.385{col 54}{space 4}-.3990937{col 67}{space 3} 1.035179
{txt}{space 2}dumid_m224 {c |}{col 14}{res}{space 2}-.3964873{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.29{col 67}{space 3}  11891.5
{txt}{space 2}dumid_m225 {c |}{col 14}{res}{space 2}-.3958687{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.29{col 67}{space 3}  11891.5
{txt}{space 2}dumid_m226 {c |}{col 14}{res}{space 2}  .342745{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-11891.55{col 67}{space 3} 11892.24
{txt}{space 2}dumid_m227 {c |}{col 14}{res}{space 2} .4262436{col 26}{space 2} .3658857{col 37}{space 1}    1.16{col 46}{space 3}0.244{col 54}{space 4}-.2909342{col 67}{space 3} 1.143421
{txt}{space 2}dumid_m228 {c |}{col 14}{res}{space 2}-.1311277{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.02{col 67}{space 3} 11891.76
{txt}{space 2}dumid_m229 {c |}{col 14}{res}{space 2} .0057941{col 26}{space 2} .1297912{col 37}{space 1}    0.04{col 46}{space 3}0.964{col 54}{space 4}-.2486113{col 67}{space 3} .2601996
{txt}{space 2}dumid_m230 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m231 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m232 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase1 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase2 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase4 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase6 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase7 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase9 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase10 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase11 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase12 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase13 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase14 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase15 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase16 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase17 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase18 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase19 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase20 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase21 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase22 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase23 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase25 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase26 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase27 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase29 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase30 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase31 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase32 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase33 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase34 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase35 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase36 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase37 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase38 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase39 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase40 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase41 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase42 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase43 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase44 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase45 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase46 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase47 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase48 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase49 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase50 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase51 {c |}{col 14}{res}{space 2}-.4467868{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.34{col 67}{space 3} 11891.45
{txt}{space 4}dclase52 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase53 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase54 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase55 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase56 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase57 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase58 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase59 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase60 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase61 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase62 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase63 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase64 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase65 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase66 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase67 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase68 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase69 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase70 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase71 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
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{space 3}dclase452 {c |}{col 14}{res}{space 2}-.7440023{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.64{col 67}{space 3} 11891.15
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{space 3}dclase760 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase761 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase762 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase763 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase764 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase765 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase766 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase767 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase768 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase769 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase770 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase771 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase772 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase773 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase774 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase775 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase776 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase777 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase778 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase779 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase780 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase781 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase782 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase783 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase784 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase785 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase786 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase787 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase788 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase789 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase790 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase791 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase792 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase793 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase794 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase795 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase796 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase797 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase798 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase799 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase800 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase801 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase802 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase803 {c |}{col 14}{res}{space 2}-.0341166{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11891.93{col 67}{space 3} 11891.86
{txt}{space 3}dclase804 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase805 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase806 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase807 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase808 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase809 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase810 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase811 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase812 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase813 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase814 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase815 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase816 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase817 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase818 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase819 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase820 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase821 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase822 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase823 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase824 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase825 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase826 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase827 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase828 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase829 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase830 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase831 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase832 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase833 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase834 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase835 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase836 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase837 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase838 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase839 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase840 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase841 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase842 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase843 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase844 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase845 {c |}{col 14}{res}{space 2} -.181003{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.07{col 67}{space 3} 11891.71
{txt}{space 3}dclase846 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase847 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase848 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase849 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase850 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase851 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase852 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase853 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase854 {c |}{col 14}{res}{space 2} -.388198{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.28{col 67}{space 3}  11891.5
{txt}{space 3}dclase855 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase856 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase857 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase858 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase859 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase860 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase861 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase862 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase863 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase864 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase865 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase866 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase867 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase868 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase869 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase870 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase871 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase872 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase873 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase874 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase875 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase876 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase877 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase878 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase879 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase880 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase881 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase882 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase883 {c |}{col 14}{res}{space 2} -.933558{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.83{col 67}{space 3} 11890.96
{txt}{space 3}dclase884 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase885 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase886 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase887 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase888 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase889 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase890 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase891 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase892 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase893 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase894 {c |}{col 14}{res}{space 2}-.9712909{col 26}{space 2} 6066.939{col 37}{space 1}   -0.00{col 46}{space 3}1.000{col 54}{space 4}-11892.86{col 67}{space 3} 11890.92
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.932804{col 26}{space 2} 6066.939{col 37}{space 1}    0.00{col 46}{space 3}0.999{col 54}{space 4}-11887.96{col 67}{space 3} 11895.83
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(model 1 has zero variance coefficients)

Model for group 2

      Source {c |}       SS           df       MS      Number of obs   ={res}     3,694
{txt}{hline 13}{c +}{hline 34}   F(28, 3665)     = {res} 17595.15
{txt}       Model {c |} {res} 2499.81718        28  89.2791851   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 18.5965004     3,665  .005074079   {txt}R-squared       ={res}    0.9926
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.9926
{txt}       Total {c |} {res} 2518.41368     3,693  .681942509   {txt}Root MSE        =   {res} .07123

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lnworkers_~_{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
score_manag_ {c |}{col 14}{res}{space 2} 4.618911{col 26}{space 2} .0708479{col 37}{space 1}   65.19{col 46}{space 3}0.000{col 54}{space 4} 4.480006{col 67}{space 3} 4.757816
{txt}{space 2}sh_sup_WA_ {c |}{col 14}{res}{space 2}-2.307266{col 26}{space 2} .1583548{col 37}{space 1}  -14.57{col 46}{space 3}0.000{col 54}{space 4}-2.617739{col 67}{space 3}-1.996794
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2} .0611638{col 26}{space 2} .0071344{col 37}{space 1}    8.57{col 46}{space 3}0.000{col 54}{space 4} .0471761{col 67}{space 3} .0751516
{txt}{space 3}dumid_m21 {c |}{col 14}{res}{space 2}-.1054936{col 26}{space 2} .0791787{col 37}{space 1}   -1.33{col 46}{space 3}0.183{col 54}{space 4}-.2607324{col 67}{space 3} .0497451
{txt}{space 3}dumid_m22 {c |}{col 14}{res}{space 2} .3667972{col 26}{space 2} .1050039{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .1609253{col 67}{space 3}  .572669
{txt}{space 3}dumid_m23 {c |}{col 14}{res}{space 2} 1.660253{col 26}{space 2} .1053159{col 37}{space 1}   15.76{col 46}{space 3}0.000{col 54}{space 4}  1.45377{col 67}{space 3} 1.866737
{txt}{space 3}dumid_m24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m25 {c |}{col 14}{res}{space 2} 1.531609{col 26}{space 2} .1079596{col 37}{space 1}   14.19{col 46}{space 3}0.000{col 54}{space 4} 1.319942{col 67}{space 3} 1.743276
{txt}{space 3}dumid_m26 {c |}{col 14}{res}{space 2} 1.92e-14{col 26}{space 2} .0320167{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-.0627722{col 67}{space 3} .0627722
{txt}{space 3}dumid_m27 {c |}{col 14}{res}{space 2}-.1210121{col 26}{space 2} .0893098{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4} -.296114{col 67}{space 3} .0540898
{txt}{space 3}dumid_m28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m29 {c |}{col 14}{res}{space 2} 1.33e-14{col 26}{space 2} .0713039{col 37}{space 1}    0.00{col 46}{space 3}1.000{col 54}{space 4}-.1397993{col 67}{space 3} .1397993
{txt}{space 2}dumid_m210 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m211 {c |}{col 14}{res}{space 2} .0040599{col 26}{space 2} .0265496{col 37}{space 1}    0.15{col 46}{space 3}0.878{col 54}{space 4}-.0479936{col 67}{space 3} .0561133
{txt}{space 2}dumid_m212 {c |}{col 14}{res}{space 2} 1.060898{col 26}{space 2} .0823633{col 37}{space 1}   12.88{col 46}{space 3}0.000{col 54}{space 4} .8994158{col 67}{space 3} 1.222381
{txt}{space 2}dumid_m213 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m214 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m215 {c |}{col 14}{res}{space 2} .5068435{col 26}{space 2} .1025377{col 37}{space 1}    4.94{col 46}{space 3}0.000{col 54}{space 4} .3058069{col 67}{space 3} .7078801
{txt}{space 2}dumid_m216 {c |}{col 14}{res}{space 2}-.1276104{col 26}{space 2} .1027577{col 37}{space 1}   -1.24{col 46}{space 3}0.214{col 54}{space 4}-.3290784{col 67}{space 3} .0738575
{txt}{space 2}dumid_m217 {c |}{col 14}{res}{space 2} .0020341{col 26}{space 2} .0206908{col 37}{space 1}    0.10{col 46}{space 3}0.922{col 54}{space 4}-.0385324{col 67}{space 3} .0426007
{txt}{space 2}dumid_m218 {c |}{col 14}{res}{space 2} .1095408{col 26}{space 2} .1049405{col 37}{space 1}    1.04{col 46}{space 3}0.297{col 54}{space 4}-.0962067{col 67}{space 3} .3152883
{txt}{space 2}dumid_m219 {c |}{col 14}{res}{space 2} .0896417{col 26}{space 2}  .102703{col 37}{space 1}    0.87{col 46}{space 3}0.383{col 54}{space 4}-.1117189{col 67}{space 3} .2910024
{txt}{space 2}dumid_m220 {c |}{col 14}{res}{space 2}-.2885964{col 26}{space 2} .1025358{col 37}{space 1}   -2.81{col 46}{space 3}0.005{col 54}{space 4}-.4896292{col 67}{space 3}-.0875636
{txt}{space 2}dumid_m221 {c |}{col 14}{res}{space 2} .6549121{col 26}{space 2} .1032199{col 37}{space 1}    6.34{col 46}{space 3}0.000{col 54}{space 4}  .452538{col 67}{space 3} .8572863
{txt}{space 2}dumid_m222 {c |}{col 14}{res}{space 2} 1.264879{col 26}{space 2} .1122515{col 37}{space 1}   11.27{col 46}{space 3}0.000{col 54}{space 4} 1.044797{col 67}{space 3}  1.48496
{txt}{space 2}dumid_m223 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m224 {c |}{col 14}{res}{space 2} .8294238{col 26}{space 2} .1071466{col 37}{space 1}    7.74{col 46}{space 3}0.000{col 54}{space 4}  .619351{col 67}{space 3} 1.039497
{txt}{space 2}dumid_m225 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m226 {c |}{col 14}{res}{space 2} .9731182{col 26}{space 2} .1014812{col 37}{space 1}    9.59{col 46}{space 3}0.000{col 54}{space 4} .7741529{col 67}{space 3} 1.172083
{txt}{space 2}dumid_m227 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m228 {c |}{col 14}{res}{space 2} .3565506{col 26}{space 2} .1015199{col 37}{space 1}    3.51{col 46}{space 3}0.000{col 54}{space 4} .1575095{col 67}{space 3} .5555917
{txt}{space 2}dumid_m229 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m230 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m231 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m232 {c |}{col 14}{res}{space 2}  .609156{col 26}{space 2}  .077653{col 37}{space 1}    7.84{col 46}{space 3}0.000{col 54}{space 4} .4569087{col 67}{space 3} .7614033
{txt}{space 5}dclase1 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase2 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase4 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase6 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase7 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
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{space 3}dclase115 {c |}{col 14}{res}{space 2} 1.008559{col 26}{space 2} .0859945{col 37}{space 1}   11.73{col 46}{space 3}0.000{col 54}{space 4} .8399571{col 67}{space 3} 1.177161
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{space 3}dclase700 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase701 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase702 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase703 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase704 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase705 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase706 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase707 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase708 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase709 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase710 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase711 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase712 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase713 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase714 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase715 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase716 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase717 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase718 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase719 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase720 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase721 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase722 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase723 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase724 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase725 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase726 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase727 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase728 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase729 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase730 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase731 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase732 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase733 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase734 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase735 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase736 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase737 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase738 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase739 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase740 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase741 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase742 {c |}{col 14}{res}{space 2} 1.990007{col 26}{space 2} .0992071{col 37}{space 1}   20.06{col 46}{space 3}0.000{col 54}{space 4}   1.7955{col 67}{space 3} 2.184513
{txt}{space 3}dclase743 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase744 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase745 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase746 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase747 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase748 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase749 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase750 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase751 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase752 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase753 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase754 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase755 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase756 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase757 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase758 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase759 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase760 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase761 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase762 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase763 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase764 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase765 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase766 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase767 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase768 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase769 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase770 {c |}{col 14}{res}{space 2} .2286623{col 26}{space 2}   .07199{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .0875179{col 67}{space 3} .3698068
{txt}{space 3}dclase771 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase772 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase773 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase774 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase775 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase776 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase777 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase778 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase779 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase780 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase781 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase782 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase783 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase784 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase785 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase786 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase787 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase788 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase789 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase790 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase791 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase792 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase793 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase794 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase795 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase796 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase797 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase798 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase799 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase800 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase801 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase802 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase803 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase804 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase805 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase806 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase807 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase808 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase809 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase810 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase811 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase812 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase813 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase814 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase815 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase816 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase817 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase818 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase819 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase820 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase821 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase822 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase823 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase824 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase825 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase826 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase827 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase828 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase829 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase830 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase831 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase832 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase833 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase834 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase835 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase836 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase837 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase838 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase839 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase840 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase841 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase842 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase843 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase844 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase845 {c |}{col 14}{res}{space 2}  -.29215{col 26}{space 2} .0770923{col 37}{space 1}   -3.79{col 46}{space 3}0.000{col 54}{space 4}-.4432982{col 67}{space 3}-.1410019
{txt}{space 3}dclase846 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase847 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase848 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase849 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase850 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase851 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase852 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase853 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase854 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase855 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase856 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase857 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase858 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase859 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase860 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase861 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase862 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase863 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase864 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase865 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase866 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase867 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase868 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase869 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase870 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase871 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase872 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase873 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase874 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase875 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase876 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase877 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase878 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase879 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase880 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase881 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase882 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase883 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase884 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase885 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase886 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase887 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase888 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase889 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase890 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase891 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase892 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase893 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase894 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} 1.808424{col 26}{space 2}  .083337{col 37}{space 1}   21.70{col 46}{space 3}0.000{col 54}{space 4} 1.645033{col 67}{space 3} 1.971816
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(model 2 has zero variance coefficients)
{res}
{txt}{col 1}Blinder-Oaxaca decomposition{col 49}{lalign 17:Number of obs}{col 66} = {res}{ralign 10:19,536}
{txt}{col 49}Model{col 67}={col 69}    {res}linear
{txt}Group 1: dmanuf = {res}0{txt}{col 49}N of obs 1{col 67}={col 69}{res}    15,842
{txt}Group 2: dmanuf = {res}1{txt}{col 49}N of obs 2{col 67}={col 69}{res}     3,694

{txt}   endowments: (X1 - X2) * {bf:b2}
 coefficients: {bf:X2} * (b1 - b2)
  interaction: (X1 - X2) * (b1 - b2)

{res}{txt}{ralign 78:(Std. err. adjusted for {res:32} clusters in {res:id_mun2})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}lnworkers_~_{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}overall      {txt}{c |}
{space 5}group_1 {c |}{col 14}{res}{space 2} 3.889619{col 26}{space 2} .1337078{col 37}{space 1}   29.09{col 46}{space 3}0.000{col 54}{space 4} 3.627557{col 67}{space 3} 4.151682
{txt}{space 5}group_2 {c |}{col 14}{res}{space 2} 3.373341{col 26}{space 2} .3055119{col 37}{space 1}   11.04{col 46}{space 3}0.000{col 54}{space 4} 2.774548{col 67}{space 3} 3.972133
{txt}{space 2}difference {c |}{col 14}{res}{space 2} .5162787{col 26}{space 2} .3346758{col 37}{space 1}    1.54{col 46}{space 3}0.123{col 54}{space 4}-.1396739{col 67}{space 3} 1.172231
{txt}{space 2}endowments {c |}{col 14}{res}{space 2} .5647637{col 26}{space 2} .3476366{col 37}{space 1}    1.62{col 46}{space 3}0.104{col 54}{space 4}-.1165914{col 67}{space 3} 1.246119
{txt}coefficients {c |}{col 14}{res}{space 2} .3159547{col 26}{space 2} .3722259{col 37}{space 1}    0.85{col 46}{space 3}0.396{col 54}{space 4}-.4135947{col 67}{space 3} 1.045504
{txt}{space 1}interaction {c |}{col 14}{res}{space 2}-.3644398{col 26}{space 2} .3589969{col 37}{space 1}   -1.02{col 46}{space 3}0.310{col 54}{space 4}-1.068061{col 67}{space 3} .3391812
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}endowments   {txt}{c |}
score_manag_ {c |}{col 14}{res}{space 2} .7375883{col 26}{space 2} .4234088{col 37}{space 1}    1.74{col 46}{space 3}0.082{col 54}{space 4}-.0922777{col 67}{space 3} 1.567454
{txt}{space 2}sh_sup_WA_ {c |}{col 14}{res}{space 2} .0363816{col 26}{space 2} .0916853{col 37}{space 1}    0.40{col 46}{space 3}0.692{col 54}{space 4}-.1433182{col 67}{space 3} .2160814
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2}-.0145091{col 26}{space 2} .0109027{col 37}{space 1}   -1.33{col 46}{space 3}0.183{col 54}{space 4}-.0358781{col 67}{space 3} .0068598
{txt}{space 3}dumid_m21 {c |}{col 14}{res}{space 2} .0108619{col 26}{space 2} .0160037{col 37}{space 1}    0.68{col 46}{space 3}0.497{col 54}{space 4}-.0205048{col 67}{space 3} .0422286
{txt}{space 3}dumid_m22 {c |}{col 14}{res}{space 2} .0229384{col 26}{space 2} .0232756{col 37}{space 1}    0.99{col 46}{space 3}0.324{col 54}{space 4}-.0226808{col 67}{space 3} .0685576
{txt}{space 3}dumid_m23 {c |}{col 14}{res}{space 2} .0541517{col 26}{space 2} .0548524{col 37}{space 1}    0.99{col 46}{space 3}0.324{col 54}{space 4}-.0533569{col 67}{space 3} .1616604
{txt}{space 3}dumid_m24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m25 {c |}{col 14}{res}{space 2}-.0003179{col 26}{space 2} .0003644{col 37}{space 1}   -0.87{col 46}{space 3}0.383{col 54}{space 4}-.0010321{col 67}{space 3} .0003962
{txt}{space 3}dumid_m26 {c |}{col 14}{res}{space 2}-1.99e-17{col 26}{space 2} 3.40e-17{col 37}{space 1}   -0.58{col 46}{space 3}0.559{col 54}{space 4}-8.66e-17{col 67}{space 3} 4.68e-17
{txt}{space 3}dumid_m27 {c |}{col 14}{res}{space 2} .0000754{col 26}{space 2}  .000113{col 37}{space 1}    0.67{col 46}{space 3}0.505{col 54}{space 4} -.000146{col 67}{space 3} .0002967
{txt}{space 3}dumid_m28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m29 {c |}{col 14}{res}{space 2}-2.75e-18{col 26}{space 2} 8.34e-18{col 37}{space 1}   -0.33{col 46}{space 3}0.741{col 54}{space 4}-1.91e-17{col 67}{space 3} 1.36e-17
{txt}{space 2}dumid_m210 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m211 {c |}{col 14}{res}{space 2}-.0000198{col 26}{space 2} .0000227{col 37}{space 1}   -0.87{col 46}{space 3}0.383{col 54}{space 4}-.0000642{col 67}{space 3} .0000247
{txt}{space 2}dumid_m212 {c |}{col 14}{res}{space 2} .0013238{col 26}{space 2} .0224563{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4}-.0426898{col 67}{space 3} .0453374
{txt}{space 2}dumid_m213 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m214 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m215 {c |}{col 14}{res}{space 2} .0315685{col 26}{space 2} .0311792{col 37}{space 1}    1.01{col 46}{space 3}0.311{col 54}{space 4}-.0295416{col 67}{space 3} .0926786
{txt}{space 2}dumid_m216 {c |}{col 14}{res}{space 2}-.0079159{col 26}{space 2} .0089441{col 37}{space 1}   -0.89{col 46}{space 3}0.376{col 54}{space 4} -.025446{col 67}{space 3} .0096141
{txt}{space 2}dumid_m217 {c |}{col 14}{res}{space 2}-.0005341{col 26}{space 2} .0004724{col 37}{space 1}   -1.13{col 46}{space 3}0.258{col 54}{space 4}-.0014599{col 67}{space 3} .0003917
{txt}{space 2}dumid_m218 {c |}{col 14}{res}{space 2} .0036212{col 26}{space 2} .0038773{col 37}{space 1}    0.93{col 46}{space 3}0.350{col 54}{space 4}-.0039782{col 67}{space 3} .0112207
{txt}{space 2}dumid_m219 {c |}{col 14}{res}{space 2} .0054588{col 26}{space 2} .0069483{col 37}{space 1}    0.79{col 46}{space 3}0.432{col 54}{space 4}-.0081596{col 67}{space 3} .0190773
{txt}{space 2}dumid_m220 {c |}{col 14}{res}{space 2}-.0181208{col 26}{space 2} .0182588{col 37}{space 1}   -0.99{col 46}{space 3}0.321{col 54}{space 4}-.0539074{col 67}{space 3} .0176657
{txt}{space 2}dumid_m221 {c |}{col 14}{res}{space 2} .0411216{col 26}{space 2} .0405944{col 37}{space 1}    1.01{col 46}{space 3}0.311{col 54}{space 4} -.038442{col 67}{space 3} .1206853
{txt}{space 2}dumid_m222 {c |}{col 14}{res}{space 2} .0794211{col 26}{space 2} .0781151{col 37}{space 1}    1.02{col 46}{space 3}0.309{col 54}{space 4}-.0736817{col 67}{space 3}  .232524
{txt}{space 2}dumid_m223 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m224 {c |}{col 14}{res}{space 2} .0520791{col 26}{space 2} .0513962{col 37}{space 1}    1.01{col 46}{space 3}0.311{col 54}{space 4}-.0486556{col 67}{space 3} .1528138
{txt}{space 2}dumid_m225 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m226 {c |}{col 14}{res}{space 2} .0611016{col 26}{space 2} .0598834{col 37}{space 1}    1.02{col 46}{space 3}0.308{col 54}{space 4}-.0562677{col 67}{space 3} .1784709
{txt}{space 2}dumid_m227 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m228 {c |}{col 14}{res}{space 2} .0223876{col 26}{space 2} .0221227{col 37}{space 1}    1.01{col 46}{space 3}0.312{col 54}{space 4} -.020972{col 67}{space 3} .0657473
{txt}{space 2}dumid_m229 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m230 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m231 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m232 {c |}{col 14}{res}{space 2} -.080914{col 26}{space 2} .0786924{col 37}{space 1}   -1.03{col 46}{space 3}0.304{col 54}{space 4}-.2351483{col 67}{space 3} .0733202
{txt}{space 5}dclase1 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase2 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase4 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase6 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase7 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase9 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase10 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase11 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase12 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase13 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase14 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase15 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase16 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase17 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase18 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase19 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase20 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase21 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase22 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase23 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase25 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase26 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase27 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase29 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase30 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase31 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase32 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase33 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase34 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase35 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase36 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase37 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase38 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase39 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase40 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase41 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase42 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase43 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase44 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase45 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase46 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase47 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase48 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase49 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase50 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase51 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase52 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase53 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase54 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase55 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase56 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase57 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase58 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase59 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase60 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase61 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase62 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase63 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase64 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase65 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase66 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase67 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase68 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase69 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase70 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase71 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase72 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase73 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase74 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase75 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase76 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase77 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase78 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase79 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase80 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase81 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase82 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase83 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase84 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase85 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase86 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase87 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase88 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase89 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase90 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase91 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase92 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase93 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase94 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase95 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase96 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase97 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase98 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase99 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase100 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase101 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase102 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase103 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase104 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase105 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase106 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase107 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase108 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase109 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase110 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase111 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase112 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase113 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase114 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase115 {c |}{col 14}{res}{space 2}-.2730262{col 26}{space 2}  .216654{col 37}{space 1}   -1.26{col 46}{space 3}0.208{col 54}{space 4}-.6976602{col 67}{space 3} .1516078
{txt}{space 3}dclase116 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase117 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase118 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase119 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase120 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase121 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase122 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase123 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase124 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase125 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase126 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase127 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase128 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase129 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase130 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase131 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase132 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase133 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase134 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase135 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase136 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase137 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase138 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase139 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase140 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase141 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase142 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase143 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase144 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase145 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase146 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase147 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase148 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase149 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase150 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase151 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase152 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase153 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase154 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase155 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase156 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase157 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase158 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase159 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase160 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase161 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase162 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
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{space 3}dclase850 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase851 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase852 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase853 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase854 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase855 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase856 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase857 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase858 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase859 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase860 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase861 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase862 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase863 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase864 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase865 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase866 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase867 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase868 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase869 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase870 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase871 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase872 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase873 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase874 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase875 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase876 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase877 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase878 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase879 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase880 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase881 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase882 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase883 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase884 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase885 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase886 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase887 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase888 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase889 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase890 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase891 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase892 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase893 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase894 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}coefficients {txt}{c |}
score_manag_ {c |}{col 14}{res}{space 2}-.8174293{col 26}{space 2} .3202365{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}-1.445081{col 67}{space 3}-.1897772
{txt}{space 2}sh_sup_WA_ {c |}{col 14}{res}{space 2}-.1000493{col 26}{space 2} .2093132{col 37}{space 1}   -0.48{col 46}{space 3}0.633{col 54}{space 4}-.5102956{col 67}{space 3}  .310197
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2}-.0909645{col 26}{space 2} .1962223{col 37}{space 1}   -0.46{col 46}{space 3}0.643{col 54}{space 4}-.4755532{col 67}{space 3} .2936243
{txt}{space 3}dumid_m21 {c |}{col 14}{res}{space 2} .0162765{col 26}{space 2}  .060314{col 37}{space 1}    0.27{col 46}{space 3}0.787{col 54}{space 4}-.1019367{col 67}{space 3} .1344897
{txt}{space 3}dumid_m22 {c |}{col 14}{res}{space 2}-.0001019{col 26}{space 2} .0001146{col 37}{space 1}   -0.89{col 46}{space 3}0.374{col 54}{space 4}-.0003265{col 67}{space 3} .0001228
{txt}{space 3}dumid_m23 {c |}{col 14}{res}{space 2}-.0003807{col 26}{space 2} .0004195{col 37}{space 1}   -0.91{col 46}{space 3}0.364{col 54}{space 4} -.001203{col 67}{space 3} .0004415
{txt}{space 3}dumid_m24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m25 {c |}{col 14}{res}{space 2}-.0004146{col 26}{space 2} .0004556{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4}-.0013075{col 67}{space 3} .0004783
{txt}{space 3}dumid_m26 {c |}{col 14}{res}{space 2}-2.59e-17{col 26}{space 2} 4.35e-17{col 37}{space 1}   -0.60{col 46}{space 3}0.551{col 54}{space 4}-1.11e-16{col 67}{space 3} 5.94e-17
{txt}{space 3}dumid_m27 {c |}{col 14}{res}{space 2}  .000111{col 26}{space 2}  .000373{col 37}{space 1}    0.30{col 46}{space 3}0.766{col 54}{space 4}-.0006201{col 67}{space 3} .0008422
{txt}{space 3}dumid_m28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m29 {c |}{col 14}{res}{space 2}-3.59e-18{col 26}{space 2} 1.08e-17{col 37}{space 1}   -0.33{col 46}{space 3}0.740{col 54}{space 4}-2.48e-17{col 67}{space 3} 1.76e-17
{txt}{space 2}dumid_m210 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m211 {c |}{col 14}{res}{space 2}-.0000198{col 26}{space 2} .0000227{col 37}{space 1}   -0.87{col 46}{space 3}0.383{col 54}{space 4}-.0000642{col 67}{space 3} .0000247
{txt}{space 2}dumid_m212 {c |}{col 14}{res}{space 2}-.0519823{col 26}{space 2} .0547752{col 37}{space 1}   -0.95{col 46}{space 3}0.343{col 54}{space 4}-.1593398{col 67}{space 3} .0553752
{txt}{space 2}dumid_m213 {c |}{col 14}{res}{space 2} .0210191{col 26}{space 2} .0211257{col 37}{space 1}    0.99{col 46}{space 3}0.320{col 54}{space 4}-.0203866{col 67}{space 3} .0624247
{txt}{space 2}dumid_m214 {c |}{col 14}{res}{space 2} .0013735{col 26}{space 2} .0016277{col 37}{space 1}    0.84{col 46}{space 3}0.399{col 54}{space 4}-.0018166{col 67}{space 3} .0045637
{txt}{space 2}dumid_m215 {c |}{col 14}{res}{space 2}-.0001372{col 26}{space 2} .0001512{col 37}{space 1}   -0.91{col 46}{space 3}0.364{col 54}{space 4}-.0004335{col 67}{space 3} .0001591
{txt}{space 2}dumid_m216 {c |}{col 14}{res}{space 2} .0000353{col 26}{space 2} .0000444{col 37}{space 1}    0.80{col 46}{space 3}0.426{col 54}{space 4}-.0000516{col 67}{space 3} .0001222
{txt}{space 2}dumid_m217 {c |}{col 14}{res}{space 2}-.0005341{col 26}{space 2} .0004724{col 37}{space 1}   -1.13{col 46}{space 3}0.258{col 54}{space 4}-.0014599{col 67}{space 3} .0003917
{txt}{space 2}dumid_m218 {c |}{col 14}{res}{space 2}-4.81e-06{col 26}{space 2} .0000926{col 37}{space 1}   -0.05{col 46}{space 3}0.959{col 54}{space 4}-.0001864{col 67}{space 3} .0001767
{txt}{space 2}dumid_m219 {c |}{col 14}{res}{space 2}-.0002582{col 26}{space 2} .0002842{col 37}{space 1}   -0.91{col 46}{space 3}0.364{col 54}{space 4}-.0008151{col 67}{space 3} .0002987
{txt}{space 2}dumid_m220 {c |}{col 14}{res}{space 2}-.0002266{col 26}{space 2} .0002492{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4}-.0007149{col 67}{space 3} .0002617
{txt}{space 2}dumid_m221 {c |}{col 14}{res}{space 2} -.000145{col 26}{space 2} .0001616{col 37}{space 1}   -0.90{col 46}{space 3}0.370{col 54}{space 4}-.0004617{col 67}{space 3} .0001717
{txt}{space 2}dumid_m222 {c |}{col 14}{res}{space 2} -.000133{col 26}{space 2} .0001577{col 37}{space 1}   -0.84{col 46}{space 3}0.399{col 54}{space 4}-.0004421{col 67}{space 3} .0001761
{txt}{space 2}dumid_m223 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m224 {c |}{col 14}{res}{space 2}-.0003319{col 26}{space 2} .0003649{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4} -.001047{col 67}{space 3} .0003833
{txt}{space 2}dumid_m225 {c |}{col 14}{res}{space 2}-.0535827{col 26}{space 2}  .057565{col 37}{space 1}   -0.93{col 46}{space 3}0.352{col 54}{space 4} -.166408{col 67}{space 3} .0592427
{txt}{space 2}dumid_m226 {c |}{col 14}{res}{space 2}-.0001706{col 26}{space 2} .0001935{col 37}{space 1}   -0.88{col 46}{space 3}0.378{col 54}{space 4}-.0005498{col 67}{space 3} .0002085
{txt}{space 2}dumid_m227 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m228 {c |}{col 14}{res}{space 2} -.000132{col 26}{space 2} .0001517{col 37}{space 1}   -0.87{col 46}{space 3}0.384{col 54}{space 4}-.0004294{col 67}{space 3} .0001654
{txt}{space 2}dumid_m229 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m230 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m231 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m232 {c |}{col 14}{res}{space 2}-.0824521{col 26}{space 2} .0801696{col 37}{space 1}   -1.03{col 46}{space 3}0.304{col 54}{space 4}-.2395816{col 67}{space 3} .0746774
{txt}{space 5}dclase1 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase2 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase4 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase6 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase7 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase9 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase10 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase11 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase12 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase13 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase14 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase15 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase16 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase17 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase18 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase19 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase20 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase21 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase22 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase23 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase25 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase26 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase27 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase29 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase30 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase31 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase32 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase33 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase34 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase35 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase36 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase37 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase38 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase39 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase40 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase41 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase42 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase43 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase44 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase45 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase46 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase47 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase48 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase49 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase50 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase51 {c |}{col 14}{res}{space 2}-.0001209{col 26}{space 2} .0001331{col 37}{space 1}   -0.91{col 46}{space 3}0.364{col 54}{space 4}-.0003818{col 67}{space 3} .0001399
{txt}{space 4}dclase52 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase53 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase54 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase55 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase56 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase57 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase58 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase59 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase60 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase61 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase62 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase63 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase64 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase65 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase66 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase67 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase68 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase69 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase70 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase71 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase72 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase73 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase74 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase75 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase76 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase77 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase78 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase79 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase80 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase81 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase82 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase83 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase84 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase85 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase86 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase87 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase88 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase89 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase90 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase91 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase92 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase93 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase94 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase95 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase96 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase97 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase98 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase99 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase100 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase101 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase102 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase103 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase104 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase105 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase106 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase107 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase108 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase109 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase110 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase111 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase112 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase113 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
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{space 3}dclase452 {c |}{col 14}{res}{space 2}-.0364549{col 26}{space 2} .0384055{col 37}{space 1}   -0.95{col 46}{space 3}0.343{col 54}{space 4}-.1117283{col 67}{space 3} .0388185
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{space 3}dclase798 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase799 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase800 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase801 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase802 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase803 {c |}{col 14}{res}{space 2}-9.24e-06{col 26}{space 2} .0000365{col 37}{space 1}   -0.25{col 46}{space 3}0.800{col 54}{space 4}-.0000808{col 67}{space 3} .0000624
{txt}{space 3}dclase804 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase805 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase806 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase807 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase808 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase809 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase810 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase811 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase812 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase813 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase814 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase815 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase816 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase817 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase818 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase819 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase820 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase821 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase822 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase823 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase824 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase825 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase826 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase827 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase828 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase829 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase830 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase831 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase832 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase833 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase834 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase835 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase836 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase837 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase838 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase839 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase840 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase841 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase842 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase843 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase844 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase845 {c |}{col 14}{res}{space 2} .0150443{col 26}{space 2} .0293167{col 37}{space 1}    0.51{col 46}{space 3}0.608{col 54}{space 4}-.0424154{col 67}{space 3} .0725039
{txt}{space 3}dclase846 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase847 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase848 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase849 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase850 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase851 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase852 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase853 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase854 {c |}{col 14}{res}{space 2}-.0001051{col 26}{space 2} .0001178{col 37}{space 1}   -0.89{col 46}{space 3}0.372{col 54}{space 4}-.0003359{col 67}{space 3} .0001258
{txt}{space 3}dclase855 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase856 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase857 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase858 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase859 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase860 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase861 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase862 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase863 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase864 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase865 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase866 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase867 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase868 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase869 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase870 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase871 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase872 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase873 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase874 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase875 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase876 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase877 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase878 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase879 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase880 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase881 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase882 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase883 {c |}{col 14}{res}{space 2}-.0002527{col 26}{space 2} .0002794{col 37}{space 1}   -0.90{col 46}{space 3}0.366{col 54}{space 4}-.0008003{col 67}{space 3} .0002948
{txt}{space 3}dclase884 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase885 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase886 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase887 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase888 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase889 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase890 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase891 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase892 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase893 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase894 {c |}{col 14}{res}{space 2}-.0002629{col 26}{space 2} .0002946{col 37}{space 1}   -0.89{col 46}{space 3}0.372{col 54}{space 4}-.0008403{col 67}{space 3} .0003144
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.124379{col 26}{space 2} .3969387{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} 1.346394{col 67}{space 3} 2.902365
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}interaction  {txt}{c |}
score_manag_ {c |}{col 14}{res}{space 2}-.4896117{col 26}{space 2} .3071778{col 37}{space 1}   -1.59{col 46}{space 3}0.111{col 54}{space 4}-1.091669{col 67}{space 3} .1124457
{txt}{space 2}sh_sup_WA_ {c |}{col 14}{res}{space 2}  .009896{col 26}{space 2} .0323397{col 37}{space 1}    0.31{col 46}{space 3}0.760{col 54}{space 4}-.0534887{col 67}{space 3} .0732807
{txt}{space 4}dum_time {c |}{col 14}{res}{space 2} .0295662{col 26}{space 2} .0668317{col 37}{space 1}    0.44{col 46}{space 3}0.658{col 54}{space 4}-.1014214{col 67}{space 3} .1605539
{txt}{space 3}dumid_m21 {c |}{col 14}{res}{space 2}-.0124812{col 26}{space 2} .0463204{col 37}{space 1}   -0.27{col 46}{space 3}0.788{col 54}{space 4}-.1032674{col 67}{space 3} .0783051
{txt}{space 3}dumid_m22 {c |}{col 14}{res}{space 2}-.0235293{col 26}{space 2} .0238451{col 37}{space 1}   -0.99{col 46}{space 3}0.324{col 54}{space 4}-.0702649{col 67}{space 3} .0232062
{txt}{space 3}dumid_m23 {c |}{col 14}{res}{space 2}-.0458716{col 26}{space 2} .0467175{col 37}{space 1}   -0.98{col 46}{space 3}0.326{col 54}{space 4}-.1374362{col 67}{space 3}  .045693
{txt}{space 3}dumid_m24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m25 {c |}{col 14}{res}{space 2} .0003179{col 26}{space 2} .0003644{col 37}{space 1}    0.87{col 46}{space 3}0.383{col 54}{space 4}-.0003962{col 67}{space 3} .0010321
{txt}{space 3}dumid_m26 {c |}{col 14}{res}{space 2} 1.99e-17{col 26}{space 2} 3.40e-17{col 37}{space 1}    0.58{col 46}{space 3}0.559{col 54}{space 4}-4.68e-17{col 67}{space 3} 8.66e-17
{txt}{space 3}dumid_m27 {c |}{col 14}{res}{space 2}-.0000852{col 26}{space 2} .0002874{col 37}{space 1}   -0.30{col 46}{space 3}0.767{col 54}{space 4}-.0006484{col 67}{space 3} .0004781
{txt}{space 3}dumid_m28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dumid_m29 {c |}{col 14}{res}{space 2} 2.75e-18{col 26}{space 2} 8.34e-18{col 37}{space 1}    0.33{col 46}{space 3}0.741{col 54}{space 4}-1.36e-17{col 67}{space 3} 1.91e-17
{txt}{space 2}dumid_m210 {c |}{col 14}{res}{space 2} 7.28e-07{col 26}{space 2} .0000116{col 37}{space 1}    0.06{col 46}{space 3}0.950{col 54}{space 4}-.0000221{col 67}{space 3} .0000235
{txt}{space 2}dumid_m211 {c |}{col 14}{res}{space 2} .0000198{col 26}{space 2} .0000227{col 37}{space 1}    0.87{col 46}{space 3}0.383{col 54}{space 4}-.0000247{col 67}{space 3} .0000642
{txt}{space 2}dumid_m212 {c |}{col 14}{res}{space 2}-.0013238{col 26}{space 2} .0224563{col 37}{space 1}   -0.06{col 46}{space 3}0.953{col 54}{space 4}-.0453374{col 67}{space 3} .0426898
{txt}{space 2}dumid_m213 {c |}{col 14}{res}{space 2}-.0161179{col 26}{space 2} .0165321{col 37}{space 1}   -0.97{col 46}{space 3}0.330{col 54}{space 4}-.0485203{col 67}{space 3} .0162845
{txt}{space 2}dumid_m214 {c |}{col 14}{res}{space 2}-.0010532{col 26}{space 2} .0012664{col 37}{space 1}   -0.83{col 46}{space 3}0.406{col 54}{space 4}-.0035353{col 67}{space 3} .0014288
{txt}{space 2}dumid_m215 {c |}{col 14}{res}{space 2}-.0315685{col 26}{space 2} .0311792{col 37}{space 1}   -1.01{col 46}{space 3}0.311{col 54}{space 4}-.0926786{col 67}{space 3} .0295416
{txt}{space 2}dumid_m216 {c |}{col 14}{res}{space 2} .0080891{col 26}{space 2} .0093674{col 37}{space 1}    0.86{col 46}{space 3}0.388{col 54}{space 4}-.0102707{col 67}{space 3}  .026449
{txt}{space 2}dumid_m217 {c |}{col 14}{res}{space 2} .0005341{col 26}{space 2} .0004724{col 37}{space 1}    1.13{col 46}{space 3}0.258{col 54}{space 4}-.0003917{col 67}{space 3} .0014599
{txt}{space 2}dumid_m218 {c |}{col 14}{res}{space 2}-.0005878{col 26}{space 2} .0113087{col 37}{space 1}   -0.05{col 46}{space 3}0.959{col 54}{space 4}-.0227525{col 67}{space 3} .0215768
{txt}{space 2}dumid_m219 {c |}{col 14}{res}{space 2} -.058082{col 26}{space 2} .0573645{col 37}{space 1}   -1.01{col 46}{space 3}0.311{col 54}{space 4}-.1705144{col 67}{space 3} .0543504
{txt}{space 2}dumid_m220 {c |}{col 14}{res}{space 2}-.0525553{col 26}{space 2} .0517475{col 37}{space 1}   -1.02{col 46}{space 3}0.310{col 54}{space 4}-.1539786{col 67}{space 3}  .048868
{txt}{space 2}dumid_m221 {c |}{col 14}{res}{space 2}-.0336308{col 26}{space 2} .0336702{col 37}{space 1}   -1.00{col 46}{space 3}0.318{col 54}{space 4}-.0996233{col 67}{space 3} .0323617
{txt}{space 2}dumid_m222 {c |}{col 14}{res}{space 2}-.0308543{col 26}{space 2} .0333158{col 37}{space 1}   -0.93{col 46}{space 3}0.354{col 54}{space 4} -.096152{col 67}{space 3} .0344434
{txt}{space 2}dumid_m223 {c |}{col 14}{res}{space 2} .0200358{col 26}{space 2} .0218043{col 37}{space 1}    0.92{col 46}{space 3}0.358{col 54}{space 4}-.0226999{col 67}{space 3} .0627714
{txt}{space 2}dumid_m224 {c |}{col 14}{res}{space 2}-.0769744{col 26}{space 2} .0757797{col 37}{space 1}   -1.02{col 46}{space 3}0.310{col 54}{space 4}-.2254998{col 67}{space 3}  .071551
{txt}{space 2}dumid_m225 {c |}{col 14}{res}{space 2} .0410884{col 26}{space 2} .0449238{col 37}{space 1}    0.91{col 46}{space 3}0.360{col 54}{space 4}-.0469606{col 67}{space 3} .1291374
{txt}{space 2}dumid_m226 {c |}{col 14}{res}{space 2}-.0395808{col 26}{space 2} .0404726{col 37}{space 1}   -0.98{col 46}{space 3}0.328{col 54}{space 4}-.1189057{col 67}{space 3}  .039744
{txt}{space 2}dumid_m227 {c |}{col 14}{res}{space 2} .0268521{col 26}{space 2} .0297502{col 37}{space 1}    0.90{col 46}{space 3}0.367{col 54}{space 4}-.0314572{col 67}{space 3} .0851614
{txt}{space 2}dumid_m228 {c |}{col 14}{res}{space 2}-.0306211{col 26}{space 2} .0318403{col 37}{space 1}   -0.96{col 46}{space 3}0.336{col 54}{space 4}-.0930269{col 67}{space 3} .0317848
{txt}{space 2}dumid_m229 {c |}{col 14}{res}{space 2} 2.93e-06{col 26}{space 2} 4.80e-06{col 37}{space 1}    0.61{col 46}{space 3}0.542{col 54}{space 4}-6.47e-06{col 67}{space 3} .0000123
{txt}{space 2}dumid_m230 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m231 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 2}dumid_m232 {c |}{col 14}{res}{space 2}  .080914{col 26}{space 2} .0786924{col 37}{space 1}    1.03{col 46}{space 3}0.304{col 54}{space 4}-.0733202{col 67}{space 3} .2351483
{txt}{space 5}dclase1 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase2 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase4 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase6 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase7 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}dclase9 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase10 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase11 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase12 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase13 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase14 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase15 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase16 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase17 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase18 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase19 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase20 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase21 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase22 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase23 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase24 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase25 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase26 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase27 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase28 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase29 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase30 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase31 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase32 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase33 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase34 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase35 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase36 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase37 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase38 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase39 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase40 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase41 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase42 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase43 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase44 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase45 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 4}dclase46 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
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{space 3}dclase734 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase735 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase736 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase737 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase738 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase739 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase740 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase741 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase742 {c |}{col 14}{res}{space 2} .2251927{col 26}{space 2} .2214392{col 37}{space 1}    1.02{col 46}{space 3}0.309{col 54}{space 4}-.2088201{col 67}{space 3} .6592056
{txt}{space 3}dclase743 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase744 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase745 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase746 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase747 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase748 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase749 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase750 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase751 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase752 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase753 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase754 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase755 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase756 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase757 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase758 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase759 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase760 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase761 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase762 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase763 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase764 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase765 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase766 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase767 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase768 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase769 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase770 {c |}{col 14}{res}{space 2} .0237335{col 26}{space 2} .0232785{col 37}{space 1}    1.02{col 46}{space 3}0.308{col 54}{space 4}-.0218916{col 67}{space 3} .0693587
{txt}{space 3}dclase771 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase772 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase773 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase774 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase775 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase776 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase777 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase778 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase779 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase780 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase781 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase782 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase783 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase784 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase785 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase786 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase787 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase788 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase789 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase790 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase791 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase792 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase793 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase794 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase795 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase796 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase797 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase798 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase799 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase800 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase801 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase802 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase803 {c |}{col 14}{res}{space 2}-.0021422{col 26}{space 2} .0084054{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4}-.0186164{col 67}{space 3} .0143321
{txt}{space 3}dclase804 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase805 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase806 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase807 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase808 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase809 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase810 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase811 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase812 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase813 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase814 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase815 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase816 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase817 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase818 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase819 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase820 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase821 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase822 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase823 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase824 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase825 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase826 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase827 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase828 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase829 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase830 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase831 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase832 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase833 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase834 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase835 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase836 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase837 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase838 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase839 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase840 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase841 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase842 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase843 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase844 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase845 {c |}{col 14}{res}{space 2}-.0115363{col 26}{space 2} .0226062{col 37}{space 1}   -0.51{col 46}{space 3}0.610{col 54}{space 4}-.0558437{col 67}{space 3} .0327711
{txt}{space 3}dclase846 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase847 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase848 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase849 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase850 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase851 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase852 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase853 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase854 {c |}{col 14}{res}{space 2}-.0243748{col 26}{space 2} .0246551{col 37}{space 1}   -0.99{col 46}{space 3}0.323{col 54}{space 4}-.0726978{col 67}{space 3} .0239482
{txt}{space 3}dclase855 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase856 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase857 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase858 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase859 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase860 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase861 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase862 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase863 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase864 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase865 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase866 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase867 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase868 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase869 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase870 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase871 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase872 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase873 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase874 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase875 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase876 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase877 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase878 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase879 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase880 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase881 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase882 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase883 {c |}{col 14}{res}{space 2}-.0586176{col 26}{space 2}  .058291{col 37}{space 1}   -1.01{col 46}{space 3}0.315{col 54}{space 4}-.1728659{col 67}{space 3} .0556306
{txt}{space 3}dclase884 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase885 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase886 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase887 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase888 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase889 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase890 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase891 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase892 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase893 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 3}dclase894 {c |}{col 14}{res}{space 2}-.0609869{col 26}{space 2} .0612343{col 37}{space 1}   -1.00{col 46}{space 3}0.319{col 54}{space 4}-.1810039{col 67}{space 3} .0590301
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store e2
{txt}
{com}. 
. esttab e* using "$app_tables_mock\TableA14.csv", replace se starlevels(* 0.10 ** 0.05 *** 0.01)  drop(dclase* dumid* _cons dum_time sh_sup_WA_) coeflabels(score_manag_ "Management score") 
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA14.csv"'})

{com}. 
. 
. esttab e* using "$app_tables_mock\TableA14.tex",  se starlevels(* 0.10 ** 0.05 *** 0.01)  drop(dclase* dumid* _cons dum_time sh_sup_WA_) ///
>  coeflabels(score_manag_ "Management score") ///
> replace fragment
{res}{txt}(output written to {browse  `"C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA14.tex"'})

{com}. 
. 
. 
. import delimited "$app_tables_mock\TableA14.csv", clear
{res}{txt}(encoding automatically selected: ISO-8859-1)
{res}{text}(3 vars, 27 obs)

{com}. 
. drop in 25
{txt}(1 observation deleted)

{com}. gen str v4=""
{txt}(26 missing values generated)

{com}. gen str v5=""
{txt}(26 missing values generated)

{com}. gen str v6=""
{txt}(26 missing values generated)

{com}. gen str v7=""
{txt}(26 missing values generated)

{com}. 
. replace v6=v3 in 4/9
{txt}variable {bf}{res}v6{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str11{sf}
{txt}(6 real changes made)

{com}. replace v3="" in 4/9
{txt}(6 real changes made)

{com}. replace v5=v3 in 10/11
{txt}variable {bf}{res}v5{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str10{sf}
{txt}(2 real changes made)

{com}. *replace v3="" in 10/11
. replace v3 = v2[_n+2] in 10/11
{txt}(2 real changes made)

{com}. replace v4 = v2[_n+4] in 10/11
{txt}variable {bf}{res}v4{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str10{sf}
{txt}(2 real changes made)

{com}. replace v6 = v3[_n+2] in 10/11
{txt}(2 real changes made)

{com}. replace v7 = v3[_n+4] in 10/11
{txt}variable {bf}{res}v7{sf}{txt} was {bf}{res}str1{sf}{txt} now {bf}{res}str10{sf}
{txt}(2 real changes made)

{com}. drop in 12/16
{txt}(5 observations deleted)

{com}. replace v5=v3 in 12/13
{txt}(2 real changes made)

{com}. replace v3 = v2[_n+3] in 12/13
{txt}(2 real changes made)

{com}. replace v4= v2[_n+6] in 12/13
{txt}(2 real changes made)

{com}. replace v6 = v3[_n+3] in 12/13
{txt}(2 real changes made)

{com}. replace v7= v3[_n+6] in 12/13
{txt}(2 real changes made)

{com}. 
. drop in 14/19
{txt}(6 observations deleted)

{com}. replace v3=v2 in 4/9
{txt}(6 real changes made)

{com}. replace v2="" in 4/9
{txt}(6 real changes made)

{com}. insobs 1
{txt}(1 observation added)

{com}. gen count=_n
{txt}
{com}. replace count=count+1 in 10/13
{txt}(4 real changes made)

{com}. replace count=10 in 14
{txt}(1 real change made)

{com}. sort count
{txt}
{com}. drop in 1
{txt}(1 observation deleted)

{com}. foreach var of varlist v2 v4 v5 v7 {c -(}
{txt}  2{com}.         replace `var'="" in 1
{txt}  3{com}. {c )-}
{txt}(1 real change made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

{com}. 
. replace v3="No controls" in 1
{txt}(1 real change made)

{com}. replace v6="Controls" in 1
{txt}(1 real change made)

{com}. 
. replace v1="" in 2
{txt}(1 real change made)

{com}. replace v1="Mean group 1 (services)" in 3
{txt}(1 real change made)

{com}. replace v1="Mean group 2 (manufacturing)" in 5
{txt}(1 real change made)

{com}. replace v1="Difference (services-manufacturing)" in 7
{txt}variable {bf}{res}v1{sf}{txt} was {bf}{res}str34{sf}{txt} now {bf}{res}str35{sf}
{txt}(1 real change made)

{com}. replace v1="All regresors" in 10
{txt}(1 real change made)

{com}. replace v1="Management score" in 12
{txt}(1 real change made)

{com}. 
. 
. 
. replace v2="Endowments" in 9
{txt}(1 real change made)

{com}. replace v3="Coefficients" in 9
{txt}(1 real change made)

{com}. replace v4="Interaction" in 9
{txt}variable {bf}{res}v4{sf}{txt} was {bf}{res}str10{sf}{txt} now {bf}{res}str11{sf}
{txt}(1 real change made)

{com}. replace v5="Endowments" in 9
{txt}(1 real change made)

{com}. replace v6="Coefficients" in 9
{txt}variable {bf}{res}v6{sf}{txt} was {bf}{res}str11{sf}{txt} now {bf}{res}str12{sf}
{txt}(1 real change made)

{com}. replace v7="Interaction" in 9
{txt}variable {bf}{res}v7{sf}{txt} was {bf}{res}str10{sf}{txt} now {bf}{res}str11{sf}
{txt}(1 real change made)

{com}. drop count
{txt}
{com}. 
. 
. export delimited "$app_tables_mock\TableA14.csv", quote novarnames replace
{res}{txt}file {bf:C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs\appendix\tables\mock\TableA14.csv} saved

{com}. 
. /********************************************************************************************
> * END OF DO FILE
> ********************************************************************************************/
. 
{txt}end of do-file

{com}. 
. 
. * ---------------------------------------------------------------------------
. *4: Close
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\WBG\github\299v2\OneDrive_2025-08-19\Updated Replication package\Replication package\Outputs/master_log.txt
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}19 Aug 2025, 22:23:02
{txt}{.-}
{smcl}
{txt}{sf}{ul off}