Packages and functions

Functions for tables

Load and prep data

Study 1

Descriptives

# must take others opinions into account
## Baseline
mean(df.asp.fu1$scs3_new_bl < 3, na.rm = TRUE) # 90% (survey error means needs to be reverse coded)
## [1] 0.9026688
# df.asp.bl$scs3_newbin <- if_else(df.asp.bl$scs3_new >2, 1,0)
# mean(df.asp.bl$scs3_newbin, na.rm=T) # 90% 

## Check: Control group at follow-up1
# mean(df.asp.fu1[df.asp.fu1$treat=="Control",]$scs3_new>2, na.rm=T) # 82% 

# must sacrifice self for others
## Baseline
mean(df.asp.fu1$tg1_new_bl < 3, na.rm = TRUE) # 79.8%
## [1] 0.7975736
# df.asp.bl$tg1_new_bin <- if_else(df.asp.bl$tg1_new >2, 1,0)
# mean(df.asp.bl$tg1_new_bin, na.rm=T) # 79%

## Check: Control group at follow-up1
# mean(df.asp.fu1[df.asp.fu1$treat=="Control",]$tg1_new>2, na.rm=T) # 71% 

# distance to nearest market
mean(df.asp.bl1$hou_mar_min, na.rm=T) # 73 min
## [1] 72.63368
# develop together
table(de.ctl$aut_dev_r)
## 
##    0    1 
##  176 1039
 #   0    1 
 # 176 1039 
mean(de.ctl$aut_dev_r, na.rm=T) # 86% 
## [1] 0.855144
vec <- c(176, 1039)
chisq.test(vec, p = c(1/2, 1/2)) # X-squared = 612.98, df = 1, p-value < 2.2e-16
## 
##  Chi-squared test for given probabilities
## 
## data:  vec
## X-squared = 612.98, df = 1, p-value < 2.2e-16

Descriptive Mental Models

Top Drivers of Success

# Success

### Niger
Hmisc::describe(de.ctl$TopSuccess)
## de.ctl$TopSuccess 
##        n  missing distinct 
##     1216        0        4 
##                                                                    
## Value      Good Relationships          Hard work              Peace
## Frequency                 268                280                517
## Proportion              0.220              0.230              0.425
##                              
## Value         Self-Initiative
## Frequency                 151
## Proportion              0.124
# Value      Good Relationships          Hard work              Peace    Self-Initiative
# Frequency                 268                280                517                151
# Proportion              0.220              0.230              0.425              0.124
vec <- c(268, 280, 517, 151)
chisq.test(vec, p = c(1/4, 1/4, 1/4, 1/4)) # X-squared = 232.4, df = 3, p<.001
## 
##  Chi-squared test for given probabilities
## 
## data:  vec
## X-squared = 232.4, df = 3, p-value < 2.2e-16
table(de.ctl$TopSuccess_Inter)
## 
##   0   1 
## 431 785
vec <- c(431, 785)
chisq.test(vec, p = c(1/2, 1/2)) # X-squared = 103.06, df = 1, p-value < 2.2e-16
## 
##  Chi-squared test for given probabilities
## 
## data:  vec
## X-squared = 103.06, df = 1, p-value < 2.2e-16
### USA
table(de.usa$important_quality_1)
## 
## Connections    Hardwork  Initiative    Peaceful 
##          26          81         112          83
# Connections    Hardwork  Initiative    Peaceful 
#          26          81         112          83
de.usa <- de.usa %>%
  mutate(TopSuccess_Inter = if_else(important_quality_1 %in% c("Peaceful", "Connections"), "Inter", "Ind",
      missing = NA_character_))
table(de.usa$TopSuccess_Inter)
## 
##   Ind Inter 
##   193   109
length(de.usa$TopSuccess_Inter)
## [1] 302
vec <- c(193, 109)
chisq.test(vec, p = c(1/2, 1/2)) # X-squared = 8.2781, df = 1, p-value = 0.004013
## 
##  Chi-squared test for given probabilities
## 
## data:  vec
## X-squared = 23.364, df = 1, p-value = 1.34e-06
## de.ctl$TopFail 
##        n  missing distinct 
##     1216        0        4 
##                                                                   
## Value             Not being persistent Not planning for the future
## Frequency                          221                         162
## Proportion                       0.182                       0.133
##                                                                   
## Value            Not respecting others               Tension in hh
## Frequency                          505                         328
## Proportion                       0.415                       0.270
## 
##        Not being persistent Not planning for the future 
##                         221                         162 
##       Not respecting others               Tension in hh 
##                         505                         328
## 
##  Chi-squared test for given probabilities
## 
## data:  vec
## X-squared = 223.78, df = 3, p-value < 2.2e-16
## de.ctl$TopFail_Inter 
##        n  missing distinct 
##     1216        0        2 
##                       
## Value          0     1
## Frequency    383   833
## Proportion 0.315 0.685
## 
##  Chi-squared test for given probabilities
## 
## data:  vec
## X-squared = 166.53, df = 1, p-value < 2.2e-16
## 
## NoPersevere      NoPlan   NoRespect     Tension 
##          30         146          39          87
## < table of extent 0 >
## 
## NoPersevere      NoPlan   NoRespect     Tension 
##          30         146          39          87
## 
##   Ind Inter 
##   176   126
## [1] 302
## 
##  Chi-squared test for given probabilities
## 
## data:  vec
## X-squared = 8.2781, df = 1, p-value = 0.004013
## de.ctl$TopSuccess 
##        n  missing distinct 
##     1216        0        4 
##                                                                    
## Value      Good Relationships          Hard work              Peace
## Frequency                 268                280                517
## Proportion              0.220              0.230              0.425
##                              
## Value         Self-Initiative
## Frequency                 151
## Proportion              0.124
## de.usa$important_quality_1 
##        n  missing distinct 
##      302        0        4 
##                                                           
## Value      Connections    Hardwork  Initiative    Peaceful
## Frequency           26          81         112          83
## Proportion       0.086       0.268       0.371       0.275
## [1] "Social \nconnections \n " "Hard work"               
## [3] "Peacefulness"             "Self-\ninitiative"

Top Barriers to Success

## de.ctl$TopFail 
##        n  missing distinct 
##     1216        0        4 
##                                                                   
## Value             Not being persistent Not planning for the future
## Frequency                          221                         162
## Proportion                       0.182                       0.133
##                                                                   
## Value            Not respecting others               Tension in hh
## Frequency                          505                         328
## Proportion                       0.415                       0.270
## de.usa$important_failure_1 
##        n  missing distinct 
##      302        0        4 
##                                                           
## Value      NoPersevere      NoPlan   NoRespect     Tension
## Frequency           30         146          39          87
## Proportion       0.099       0.483       0.129       0.288

Figure 2

Study 2

Cleaning

## [1] 0.04276937
## [1] 0.556179
## df.asp.fu1$intra_index 
##        n  missing distinct     Info     Mean  pMedian      Gmd      .05 
##     4476        0     4474        1   0.1318   0.1432   0.8154  -1.0796 
##      .10      .25      .50      .75      .90      .95 
##  -0.8208  -0.3494   0.1599   0.6441   1.0370   1.2605 
## 
## lowest : -2.77453 -2.66395 -2.58513 -2.41505 -2.31626
## highest: 2.05262  2.10235  2.12779  2.14315  2.23763
## df.asp.fu1$relation_index 
##        n  missing distinct     Info     Mean  pMedian      Gmd      .05 
##     4476        0     4476        1   0.1235   0.1237   0.5222  -0.6427 
##      .10      .25      .50      .75      .90      .95 
##  -0.4704  -0.1669   0.1268   0.4255   0.6895   0.8733 
## 
## lowest : -1.74375 -1.65096 -1.57832 -1.52133 -1.51129
## highest: 2.1498   2.2386   2.34226  2.56876  4.12898
## 
## t test of coefficients:
## 
##                 Estimate  Std. Error t value  Pr(>|t|)    
## (Intercept)  -0.12890410  0.11933023 -1.0802 0.2800987    
## treatCapital  0.10947186  0.04328024  2.5294 0.0114612 *  
## treatFull     0.24981480  0.04096582  6.0981 1.165e-09 ***
## treatSocial   0.22576859  0.04518033  4.9971 6.046e-07 ***
## strata2       0.11567998  0.15587184  0.7421 0.4580371    
## strata3       0.40154000  0.13438922  2.9879 0.0028246 ** 
## strata4       0.23734390  0.18356268  1.2930 0.1960837    
## strata5      -0.02140585  0.12309957 -0.1739 0.8619594    
## strata6      -0.09311476  0.13905615 -0.6696 0.5031351    
## strata7      -0.11755596  0.18108385 -0.6492 0.5162560    
## strata8       0.18679397  0.15394210  1.2134 0.2250403    
## strata9       0.01410085  0.23453704  0.0601 0.9520612    
## strata10      0.02759042  0.15070425  0.1831 0.8547463    
## strata11      0.49280188  0.14862912  3.3156 0.0009217 ***
## strata12      0.20892453  0.15677436  1.3326 0.1827171    
## strata13      0.23335120  0.19107147  1.2213 0.2220463    
## strata14      0.07880481  0.14966615  0.5265 0.5985414    
## strata15     -0.02821166  0.13333936 -0.2116 0.8324461    
## strata16      0.15083673  0.13458655  1.1207 0.2624589    
## strata17      0.24494596  0.12494420  1.9604 0.0500068 .  
## strata18      0.33020251  0.12546944  2.6317 0.0085245 ** 
## strata19      0.25190374  0.11819706  2.1312 0.0331262 *  
## strata20      0.44740127  0.12205480  3.6656 0.0002497 ***
## strata21      0.41618845  0.14491567  2.8719 0.0040991 ** 
## strata22      0.13943573  0.12022284  1.1598 0.2461886    
## strata23      0.04519132  0.15282040  0.2957 0.7674615    
## strata24     -0.31968499  0.14523976 -2.2011 0.0277816 *  
## strata25      0.21266460  0.12167742  1.7478 0.0805727 .  
## strata26     -0.08952602  0.16468766 -0.5436 0.5867366    
## strata27      0.12188378  0.13954457  0.8734 0.3824709    
## strata28      0.37277269  0.19097380  1.9520 0.0510064 .  
## strata29      0.01507848  0.14785398  0.1020 0.9187754    
## strata30     -0.02898681  0.16439528 -0.1763 0.8600476    
## strata31      0.10541644  0.11755091  0.8968 0.3698892    
## strata32      0.00671902  0.14954650  0.0449 0.9641657    
## strata33      0.39464908  0.15178663  2.6000 0.0093527 ** 
## strata34      0.20014425  0.14362335  1.3935 0.1635280    
## strata35     -0.17601090  0.18086778 -0.9731 0.3305336    
## strata36     -0.40074960  0.12060559 -3.3228 0.0008984 ***
## strata37      0.01409753  0.13104303  0.1076 0.9143343    
## strata38      0.10903092  0.15917070  0.6850 0.4933839    
## strata39      0.33283712  0.15983795  2.0823 0.0373687 *  
## strata40      0.26107155  0.16061493  1.6255 0.1041380    
## strata41      0.17175272  0.12429204  1.3818 0.1670882    
## strata42      0.46289195  0.18563354  2.4936 0.0126825 *  
## strata43      0.24106222  0.16431533  1.4671 0.1424280    
## strata44      0.29118046  0.17111228  1.7017 0.0888834 .  
## strata45     -0.02471374  0.16673433 -0.1482 0.8821741    
## strata46     -0.13481372  0.16389728 -0.8226 0.4108083    
## strata47      0.11096828  0.16316480  0.6801 0.4964772    
## strata48      0.30197872  0.14063445  2.1473 0.0318269 *  
## strata49      0.00050592  0.13601849  0.0037 0.9970324    
## strata50      0.57453567  0.17130538  3.3539 0.0008036 ***
## strata51      0.16883418  0.27038741  0.6244 0.5323868    
## strata52      0.18922456  0.17586024  1.0760 0.2819887    
## strata53     -0.23224192  0.11876984 -1.9554 0.0505995 .  
## strata54      0.01767957  0.15896849  0.1112 0.9114515    
## strata55      0.11233784  0.15350934  0.7318 0.4643306    
## strata56      0.18471889  0.13903398  1.3286 0.1840526    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##                Estimate Std. Error   t value     Pr(>|t|)
## (Intercept)  -0.1289041 0.11933023 -1.080230 2.800987e-01
## treatCapital  0.1094719 0.04328024  2.529373 1.146116e-02
## treatFull     0.2498148 0.04096582  6.098128 1.164865e-09
## treatSocial   0.2257686 0.04518033  4.997055 6.046318e-07
## 
## t test of coefficients:
## 
##                           Estimate Std. Error  t value  Pr(>|t|)    
## (Intercept)              0.2036620  0.0544144   3.7428 0.0001843 ***
## treatCapital             0.1315796  0.0402342   3.2703 0.0010824 ** 
## treatFull                0.1938722  0.0401068   4.8339 1.384e-06 ***
## treatSocial              0.1314348  0.0411730   3.1923 0.0014216 ** 
## strata2                 -0.0785358  0.1540804  -0.5097 0.6102824    
## strata3                 -0.2680798  0.0927975  -2.8889 0.0038851 ** 
## strata4                 -0.5373157  0.1318527  -4.0751 4.680e-05 ***
## strata5                 -0.4093844  0.0867222  -4.7206 2.424e-06 ***
## strata6                 -0.3770312  0.1252965  -3.0091 0.0026349 ** 
## strata7                 -0.2770261  0.0519600  -5.3315 1.023e-07 ***
## strata8                 -0.2217229  0.1169246  -1.8963 0.0579869 .  
## strata9                 -0.0863941  0.0799162  -1.0811 0.2797300    
## strata10                -0.0809895  0.0989185  -0.8188 0.4129732    
## strata11                 0.1207631  0.0946624   1.2757 0.2021200    
## strata12                -0.4420528  0.1038151  -4.2581 2.105e-05 ***
## strata13                -0.4429792  0.0640121  -6.9202 5.157e-12 ***
## strata14                -0.0939275  0.0865676  -1.0850 0.2779721    
## strata15                -0.0339687  0.0986823  -0.3442 0.7306954    
## strata16                -0.2895717  0.1809717  -1.6001 0.1096494    
## strata17                 0.0497608  0.0848229   0.5866 0.5574732    
## strata18                 0.0079101  0.0653259   0.1211 0.9036277    
## strata19                -0.0572119  0.0535642  -1.0681 0.2855340    
## strata20                -0.1740936  0.0584889  -2.9765 0.0029312 ** 
## strata21                -0.4372294  0.0574647  -7.6087 3.364e-14 ***
## strata22                -0.4079097  0.1108757  -3.6790 0.0002370 ***
## strata23                -0.3344640  0.1032347  -3.2398 0.0012048 ** 
## strata24                -0.4508580  0.1204072  -3.7444 0.0001831 ***
## strata25                -0.3779768  0.1167431  -3.2377 0.0012140 ** 
## strata26                -0.2817841  0.0853170  -3.3028 0.0009649 ***
## strata27                -0.0884223  0.0994611  -0.8890 0.3740441    
## strata28                 0.0546946  0.0959431   0.5701 0.5686565    
## strata29                -0.2897926  0.0455500  -6.3621 2.192e-10 ***
## strata30                -0.2056019  0.0731839  -2.8094 0.0049854 ** 
## strata31                -0.2144891  0.0521752  -4.1109 4.012e-05 ***
## strata32                -0.2428199  0.0808337  -3.0039 0.0026800 ** 
## strata33                -0.1928770  0.1363054  -1.4150 0.1571286    
## strata34                 0.0283802  0.0757937   0.3744 0.7080947    
## strata35                -0.5287779  0.0857547  -6.1662 7.622e-10 ***
## strata36                -0.6968004  0.0560332 -12.4355 < 2.2e-16 ***
## strata37                -0.1366538  0.1554512  -0.8791 0.3794064    
## strata38                -0.1525702  0.0694288  -2.1975 0.0280361 *  
## strata39                -0.2262751  0.1074141  -2.1066 0.0352113 *  
## strata40                 0.2049332  0.0762691   2.6870 0.0072372 ** 
## strata41                -0.0328352  0.0975017  -0.3368 0.7363102    
## strata42                 0.0231249  0.2073502   0.1115 0.9112044    
## strata43                -0.2153909  0.0964409  -2.2334 0.0255725 *  
## strata44                -0.5589932  0.0595763  -9.3828 < 2.2e-16 ***
## strata45                -0.3384161  0.1045162  -3.2379 0.0012129 ** 
## strata46                -0.4053394  0.0843065  -4.8079 1.576e-06 ***
## strata47                -0.5085063  0.0746295  -6.8137 1.079e-11 ***
## strata48                -0.1746003  0.2412289  -0.7238 0.4692299    
## strata49                -0.1961410  0.0851249  -2.3042 0.0212601 *  
## strata50                 0.2724663  0.0869966   3.1319 0.0017480 ** 
## strata51                -0.5938680  0.0520902 -11.4008 < 2.2e-16 ***
## strata52                -0.3405138  0.1568242  -2.1713 0.0299609 *  
## strata53                -0.2788629  0.1652866  -1.6871 0.0916456 .  
## strata54                 0.0090339  0.1121108   0.0806 0.9357794    
## strata55                -0.3333837  0.0884487  -3.7692 0.0001659 ***
## strata56                -0.0378489  0.0920085  -0.4114 0.6808265    
## ctrl_earn_index_trim_bl  0.1772717  0.0148427  11.9434 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##                          Estimate Std. Error   t value     Pr(>|t|)
## (Intercept)             0.2036620 0.05441443  3.742794 1.842993e-04
## treatCapital            0.1315796 0.04023424  3.270340 1.082423e-03
## treatFull               0.1938722 0.04010677  4.833902 1.384296e-06
## treatSocial             0.1314348 0.04117305  3.192254 1.421578e-03
## ctrl_earn_index_trim_bl 0.1772717 0.01484266 11.943393 2.205799e-32

Comparing Psychosocial arm treatment effects on personal and relational outcomes

##               Estimate Std. Error  t value     Pr(>|t|)
## (Intercept)  0.1377978 0.05423735 2.540645 1.109877e-02
## treatCapital 0.1406060 0.02284126 6.155790 8.133481e-10
## treatFull    0.2108712 0.02187315 9.640642 8.812249e-22
## treatSocial  0.1739722 0.02199428 7.909884 3.231104e-15
## [1] 4417
##                 Estimate Std. Error    t value     Pr(>|t|)
## (Intercept)  0.001905388 0.11193646 0.01702205 9.864198e-01
## treatCapital 0.122721141 0.03294703 3.72480119 1.979091e-04
## treatFull    0.265041298 0.03025665 8.75977088 2.743284e-18
## treatSocial  0.138486211 0.03171293 4.36686858 1.289263e-05
## [1] 4417
##              relation_index  intra_index
## (Intercept)      0.13779784  0.001905388
## treatCapital     0.14060602  0.122721141
## treatFull        0.21087118  0.265041298
## treatSocial      0.17397219  0.138486211
## strata2         -0.15145796 -0.324499034
## strata3         -0.06766020  0.040091786
## strata4         -0.18635754  0.008714773
## strata5         -0.33780405  0.124503865
## strata6         -0.23820839  0.086135365
## strata7         -0.21771534  0.076954489
## strata8         -0.13388533 -0.048319174
## strata9         -0.18337336 -0.015652933
## strata10        -0.06541951 -0.009816207
## strata11         0.02839679 -0.133751841
## strata12        -0.20222556  0.018644122
## strata13        -0.14617053 -0.125068046
## strata14        -0.06495828  0.044500512
## strata15        -0.26536291 -0.188707504
## strata16        -0.35307236 -0.387144803
## strata17         0.04372223  0.100266839
## strata18        -0.10154800 -0.231650766
## strata19        -0.03684306  0.011284747
## strata20        -0.12196863 -0.043801904
## strata21        -0.12726297 -0.070471699
## strata22        -0.20184334  0.207185782
## strata23        -0.14493216  0.131132546
## strata24        -0.35393408  0.291434946
## strata25        -0.26355409 -0.070874958
## strata26        -0.23874620 -0.106037251
## strata27        -0.08345293  0.039767535
## strata28         0.06014448  0.129804054
## strata29        -0.28291033 -0.058706762
## strata30        -0.05978318  0.248312959
## strata31        -0.10539708  0.221876350
## strata32        -0.26244861 -0.225685991
## strata33        -0.15996460 -0.236927843
## strata34        -0.02637661 -0.018505157
## strata35        -0.21207721  0.102726285
## strata36        -0.72473677 -0.445327406
## strata37        -0.11428656 -0.058736060
## strata38        -0.19902809 -0.135796956
## strata39        -0.11536247  0.101593327
## strata40         0.01582953 -0.078884444
## strata41        -0.04327362 -0.161775881
## strata42         0.02241206  0.071791281
## strata43        -0.17529669 -0.053494348
## strata44        -0.14324706  0.092973317
## strata45        -0.14057913  0.177494854
## strata46        -0.25545437  0.139348135
## strata47        -0.28977800 -0.223579442
## strata48        -0.18515421  0.075581592
## strata49        -0.19062130 -0.022666833
## strata50         0.11053017  0.092609938
## strata51        -0.12251615 -0.145051086
## strata52        -0.06379239  0.182235460
## strata53        -0.14303031  0.181044505
## strata54        -0.17420498 -0.165944605
## strata55        -0.23869787 -0.103374919
## strata56        -0.12593782  0.009630175
## [1] 1.414183
## [1] 0.2354277
## Linear hypothesis test (Theil's F test)
## 
## Hypothesis:
## rel_treatSocial - intra_treatSocial = 0
## 
## Model 1: restricted model
## Model 2: fit_sur
## 
##   Res.Df Df      F Pr(>F)
## 1   8835                 
## 2   8834  1 1.4955 0.2214

Comparing personal and relational composites as predictors of revenue gains in Psychosocial Arm

##   hhid tot_ben_rev12_ppp_std tot_ben_rev12_ppp_bl_std collective_action_index
## 1 1056                  1056                     1056                    1056
##   soc_stand_index gse_index future_expct_30_index ctrl_earn_index_new
## 1            1056      1056                  1056                1056
##   ment_hlth_index social_support_index soc_norms_index intrahh_vars_index
## 1            1056                 1056            1056               1056
##   fin_supp_index_2_new soc_cohsn_index ctrl_hh_index treat relation_index
## 1                 1056            1056           999  1056           1056
##   intra_index
## 1        1056
## 
##  Pearson's product-moment correlation
## 
## data:  df.asp.fu1.psy$intra_index and df.asp.fu1.psy$relation_index
## t = 13.318, df = 1054, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3266877 0.4300148
## sample estimates:
##       cor 
## 0.3795342
## 
## Call:
## lm(formula = tot_ben_rev12_ppp_std ~ intra_index + relation_index + 
##     tot_ben_rev12_ppp_bl_std, data = d.reg.T1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5080 -0.5441 -0.3232  0.1001 16.1089 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.17706    0.04086   4.333 1.61e-05 ***
## intra_index               0.17946    0.06062   2.960  0.00314 ** 
## relation_index            0.41050    0.09102   4.510 7.21e-06 ***
## tot_ben_rev12_ppp_bl_std  0.18395    0.04379   4.201 2.88e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.25 on 1052 degrees of freedom
## Multiple R-squared:  0.06319,    Adjusted R-squared:  0.06052 
## F-statistic: 23.65 on 3 and 1052 DF,  p-value: 8.08e-15
## 
## Linear hypothesis test:
## intra_index - relation_index = 0
## 
## Model 1: restricted model
## Model 2: tot_ben_rev12_ppp_std ~ intra_index + relation_index + tot_ben_rev12_ppp_bl_std
## 
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1   1053 1649.1                              
## 2   1052 1643.9  1    5.1857 3.3186 0.06879 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = tot_ben_rev12_ppp_std ~ intra_index + tot_ben_rev12_ppp_bl_std, 
##     data = d.reg.T1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6786 -0.5613 -0.3537  0.0871 16.0796 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.22930    0.03955   5.798 8.86e-09 ***
## intra_index               0.28167    0.05674   4.964 8.05e-07 ***
## tot_ben_rev12_ppp_bl_std  0.20145    0.04401   4.577 5.27e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.261 on 1053 degrees of freedom
## Multiple R-squared:  0.04508,    Adjusted R-squared:  0.04327 
## F-statistic: 24.86 on 2 and 1053 DF,  p-value: 2.835e-11
## Analysis of Variance Table
## 
## Model 1: tot_ben_rev12_ppp_std ~ intra_index + tot_ben_rev12_ppp_bl_std
## Model 2: tot_ben_rev12_ppp_std ~ intra_index + relation_index + tot_ben_rev12_ppp_bl_std
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1   1053 1675.7                                  
## 2   1052 1643.9  1    31.785 20.341 7.209e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Association of Psychosocial Processes with Gains in Women’s Business Revenues at 6 months
IV df beta SE pval
Collective Action 1053 0.09 0.04 0.021
Social Standing 1053 0.16 0.05 0.001
Self-Efficacy 1053 0.19 0.05 0.000
Future Expectations 1053 0.16 0.05 0.002
Control Over Earnings 1053 0.25 0.05 0.000
Mental Health 1053 0.10 0.04 0.015
Social Support 1053 0.13 0.03 0.000
Perceived Social Norms 1053 0.14 0.04 0.002
Intra-Household Dynamics 1053 0.04 0.05 0.379
Perceived Financial Support 1053 0.02 0.05 0.747
Social Cohesion in Community 1053 -0.05 0.06 0.402
Control Over Household Resources 996 0.12 0.04 0.005

Figure 3

Note that treatment effects are taken from Bossuroy et al. (2022) tables SI.14-SI.26, with slight modifications to the financial support and control over earnings indices.

Table S1. Exploratory factor analysis of relational and personal variables

## 
## Loadings:
##                          MR1   MR2  
## Self-Efficacy             0.40  0.32
## Future Expectations       0.72      
## Mental Health             0.54      
## Collective Action         0.16  0.21
## Social Standing           0.73      
## Social Support            0.15  0.15
## Social Norms                    0.27
## Intra-Household Dynamics        0.21
## Financial Support         0.10  0.21
## Social Cohesion           0.11  0.24
## Controls Earnings               0.66
## Control in HH                   0.67
## 
##                 MR1  MR2
## SS loadings    1.58 1.28
## Proportion Var 0.13 0.11
## Cumulative Var 0.13 0.24

Table S2. Correlation Matrix

## # A tibble: 1,056 × 12
##    `Collective Action` `Social Standing` `Controls Earnings` `Financial Support`
##                  <dbl>             <dbl>               <dbl>               <dbl>
##  1              -0.137           -0.684             -0.396                -1.50 
##  2               0.982            0.932              0.914                -0.175
##  3               2.04             0.285             -2.72                  0.603
##  4               1.28            -0.0379            -2.53                 -0.953
##  5              -0.231            0.124              1.23                 -0.953
##  6               0.982            1.42               0.515                -1.96 
##  7               0.286            0.932             -1.36                  0.376
##  8               0.846           -1.17              -0.00391               0.508
##  9               0.286            0.285             -0.152                 1.40 
## 10               0.286            0.770             -1.36                  0.603
## # ℹ 1,046 more rows
## # ℹ 8 more variables: `Social Support` <dbl>, `Social Norms` <dbl>,
## #   `Social Cohesion` <dbl>, `Intra-Household Dynamics` <dbl>,
## #   `Control in HH` <dbl>, `Self-Efficacy` <dbl>, `Future Expectations` <dbl>,
## #   `Mental Health` <dbl>
Collective Action Social Standing Controls Earnings Financial Support Social Support Social Norms Social Cohesion Intra-Household Dynamics Control in HH Self-Efficacy Future Expectations Mental Health
Collective Action NA 0.09** 0.03 0.15*** 0.15*** 0.03 0.18*** 0.03 0.09** 0.2*** 0.13*** 0.03
Social Standing 0.09** NA 0.07* -0.01 0.14*** 0.06† 0.05 0.04 0.06† 0.26*** 0.55*** 0.35***
Controls Earnings 0.03 0.07* NA 0.05 0.13*** 0.15*** 0.04 0.15*** 0.58*** 0.16*** 0.06† 0
Financial Support 0.15*** -0.01 0.05 NA 0.2*** -0.01 0.25*** 0.08** 0.01 0.19*** 0.03 0.1**
Social Support 0.15*** 0.14*** 0.13*** 0.2*** NA 0.05† 0.09** 0.02 0.08* 0.11*** 0.12*** -0.01
Social Norms 0.03 0.06† 0.15*** -0.01 0.05† NA 0.06† 0.03 0.21*** 0.12*** 0.02 0.06†
Social Cohesion 0.18*** 0.05 0.04 0.25*** 0.09** 0.06† NA 0.24*** 0.13*** 0.2*** 0.02 0.14***
Intra-Household Dynamics 0.03 0.04 0.15*** 0.08** 0.02 0.03 0.24*** NA 0.12*** 0.08** 0.09** 0.07*
Control in HH 0.09** 0.06† 0.58*** 0.01 0.08* 0.21*** 0.13*** 0.12*** NA 0.19*** 0.07* 0.01
Self-Efficacy 0.2*** 0.26*** 0.16*** 0.19*** 0.11*** 0.12*** 0.2*** 0.08** 0.19*** NA 0.27*** 0.24***
Future Expectations 0.13*** 0.55*** 0.06† 0.03 0.12*** 0.02 0.02 0.09** 0.07* 0.27*** NA 0.34***
Mental Health 0.03 0.35*** 0 0.1** -0.01 0.06† 0.14*** 0.07* 0.01 0.24*** 0.34*** NA

Study 3

Table S3: Descriptives and Randomization Check

Control (N=1296) T.ind (N=666) T.rel (N=666) Total (N=2628) p value
PMT poverty score 0.487
  • Mean (SD)
12.26 (0.31) 12.25 (0.33) 12.25 (0.31) 12.26 (0.31)
  • Range
11.19 - 12.93 11.15 - 12.93 11.02 - 12.93 11.02 - 12.93
Age 0.964
  • Mean (SD)
34.33 (14.10) 34.39 (13.78) 34.51 (14.01) 34.39 (13.99)
  • Range
18.00 - 100.00 18.00 - 90.00 18.00 - 100.00 18.00 - 100.00
Is head of household 0.660
  • Mean (SD)
0.12 (0.32) 0.13 (0.34) 0.12 (0.33) 0.12 (0.33)
  • Range
0.00 - 1.00 0.00 - 1.00 0.00 - 1.00 0.00 - 1.00
Is nomad 0.733
  • Mean (SD)
0.10 (0.30) 0.11 (0.32) 0.11 (0.31) 0.11 (0.31)
  • Range
0.00 - 1.00 0.00 - 1.00 0.00 - 1.00 0.00 - 1.00
Lives in hamlet 0.976
  • Mean (SD)
0.22 (0.42) 0.22 (0.42) 0.22 (0.41) 0.22 (0.41)
  • Range
0.00 - 1.00 0.00 - 1.00 0.00 - 1.00 0.00 - 1.00
ASP treatment arm 0.955
  • Complet
782 (60.3%) 398 (59.8%) 398 (59.8%) 1578 (60.0%)
  • Social
514 (39.7%) 268 (40.2%) 268 (40.2%) 1050 (40.0%)
ASP timing 0.549
  • Early
729 (56.2%) 361 (54.2%) 360 (54.1%) 1450 (55.2%)
  • Late
567 (43.8%) 305 (45.8%) 306 (45.9%) 1178 (44.8%)
Participant in ASP trial < 0.001
  • Mean (SD)
0.13 (0.34) 0.22 (0.42) 0.23 (0.42) 0.18 (0.38)
  • Range
0.00 - 1.00 0.00 - 1.00 0.00 - 1.00 0.00 - 1.00

Attrition analyses

##          
##              0    1
##   Control 1222   74
##   T.ind    626   40
##   T.rel    645   21

Table S4: Check for differential attrition

## 
## z test of coefficients:
## 
##                Estimate Std. Error  z value  Pr(>|z|)    
## (Intercept)    -3.67873    0.26881 -13.6854 < 2.2e-16 ***
## conditionT.ind  0.15528    0.20384   0.7618   0.44619    
## conditionT.rel -0.52480    0.25339  -2.0711   0.03835 *  
## typepaquet_c   -0.33052    0.17875  -1.8491   0.06445 .  
## timing_c        0.13751    0.18109   0.7594   0.44763    
## isbaseline_c   -2.04234    0.51123  -3.9950 6.471e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Association of Attrition with Baseline Sociodemographics
Outcome df beta SE pval
PMT 2623 0.55 0.35 0.121
Age 2623 0.01 0.01 0.025*
Is head of household 2623 0.5 0.23 0.030*
Is nomad 2623 0.05 0.29 0.865
Lives in a hamlet 2623 -0.27 0.23 0.243 
## Analysis of Variance Table
## 
## Model 1: attrited ~ condition + typepaquet_c + timing_c + isbaseline_c + 
##     pmt + age_benef + relation_head + nomade + hameau_bin
## Model 2: attrited ~ condition + condition * typepaquet_c + condition * 
##     timing_c + condition * isbaseline_c + condition * pmt + condition * 
##     age_benef + condition * relation_head + condition * nomade + 
##     condition * hameau_bin
##   Res.Df    RSS Df Sum of Sq     F Pr(>F)
## 1   2617 125.69                          
## 2   2601 124.94 16   0.75621 0.984 0.4714
## # A tibble: 2 × 3
##   condition total non_missing
##   <chr>     <int>       <int>
## 1 T.ind       666         642
## 2 T.rel       666         634
## # A tibble: 3 × 3
##   condition total no_consented
##   <fct>     <int>        <int>
## 1 Control    1296         1222
## 2 T.ind       666          626
## 3 T.rel       666          645

Treatment effects

Psychosocial Outcomes

Table 1

Treatment effects: Psychosocial outcome indices
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Psychosocial Composite Index 2487 0
(1) 		0.1
(0.05)
0.043*
0.062† 		0.12
(0.05)
0.015*
0.032*
Personal Composite Index 2487 0
(1) 		0.13
(0.05)
0.004**
0.011* 		0.12
(0.04)
0.010*
0.025*
Well-Being 2487 0
(1) 		0.09
(0.05)
0.049*
0.069† 		0.08
(0.05)
0.089†
0.096†
Self-Efficacy 2473 0
(1) 		0.09
(0.05)
0.068†
0.093† 		0.02
(0.05)
0.595
0.643
Future Expectations 2473 0
(1) 		0.12
(0.05)
0.019*
0.058† 		0.15
(0.05)
0.002**
0.012*
Relational Composite Index 2487 0
(1) 		0.05
(0.05)
0.338
0.344 		0.09
(0.05)
0.075†
0.128
Partner Dynamics 2226 0
(1) 		0.07
(0.05)
0.169
0.181 		0.08
(0.05)
0.102
0.157
Household Dynamics 2487 0
(1) 		0.03
(0.05)
0.458
0.507 		0.1
(0.04)
0.021*
0.045*
Control Over Earnings 2473 0
(1) 		-0.02
(0.05)
0.627
0.578 		-0.03
(0.05)
0.584
0.613
Social Standing 2473 0
(1) 		0.03
(0.05)
0.527
0.582 		0.05
(0.05)
0.318
0.394
Social Support 2487 0
(1) 		0.09
(0.05)
0.057†
0.114 		0.09
(0.05)
0.077†
0.125
Social Cohesion 2473 0
(1) 		-0.05
(0.05)
0.300
0.361 		0
(0.05)
0.982
0.985 
## [1] 0.4816199
Treatment effects: Psychosocial outcome indices
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Personal Composite Index 2487 0
(1) 		0.13
(0.05)
0.004**
0.011* 		0.12
(0.04)
0.010*
0.025*
Well-Being 2487 0
(1) 		0.09
(0.05)
0.049*
0.069† 		0.08
(0.05)
0.089†
0.096†
Self-Efficacy 2473 0
(1) 		0.09
(0.05)
0.068†
0.093† 		0.02
(0.05)
0.595
0.643
Future Expectations 2473 0
(1) 		0.12
(0.05)
0.019*
0.058† 		0.15
(0.05)
0.002**
0.012* 
## [1] 0.4931703
Treatment effects: Psychosocial outcome indices
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Relational Composite Index 2487 0
(1) 		0.05
(0.05)
0.338
0.344 		0.09
(0.05)
0.075†
0.128
Partner Dynamics 2226 0
(1) 		0.07
(0.05)
0.169
0.181 		0.08
(0.05)
0.102
0.157
Household Dynamics 2487 0
(1) 		0.03
(0.05)
0.458
0.507 		0.1
(0.04)
0.021*
0.045*
Control Over Earnings 2473 0
(1) 		-0.02
(0.05)
0.627
0.578 		-0.03
(0.05)
0.584
0.613
Social Standing 2473 0
(1) 		0.03
(0.05)
0.527
0.582 		0.05
(0.05)
0.318
0.394
Social Support 2487 0
(1) 		0.09
(0.05)
0.057†
0.114 		0.09
(0.05)
0.077†
0.125
Social Cohesion 2473 0
(1) 		-0.05
(0.05)
0.300
0.361 		0
(0.05)
0.982
0.985 
## [1] 0.7393353
Treatment effects: Psychosocial outcomes - Well-being
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Well-being 2487 0
(1) 		0.09
(0.05)
0.049*
0.069† 		0.08
(0.05)
0.089†
0.096†
cd1_r 2473 5.13
(1.9) 		0.16
(0.09)
0.089†
0.160 		0.19
(0.09)
0.035*
0.060†
cd2_r 2473 5.16
(1.78) 		0
(0.09)
0.968
0.971 		0.1
(0.09)
0.259
0.274
cd3_r 2473 5.44
(1.8) 		0.01
(0.09)
0.862
0.846 		0.01
(0.09)
0.950
0.950
cd4_r 2473 5
(1.86) 		0.1
(0.09)
0.279
0.322 		0
(0.09)
0.956
0.960
cd5 2473 3.92
(2.42) 		-0.03
(0.12)
0.799
0.840 		0.05
(0.12)
0.635
0.695
cd6_r 2473 5.16
(1.83) 		0.1
(0.09)
0.269
0.294 		0.22
(0.09)
0.012*
0.020*
cd7_r 2473 4.79
(1.97) 		0.16
(0.1)
0.099†
0.084† 		0.11
(0.09)
0.260
0.245
cd8 2473 3.91
(2.14) 		0.23
(0.1)
0.030*
0.056† 		0.03
(0.1)
0.751
0.777
cd9_r 2473 5.65
(1.77) 		0.02
(0.09)
0.800
0.827 		-0.06
(0.09)
0.502
0.545
cd10_r 2473 5.08
(1.79) 		0.07
(0.09)
0.402
0.408 		-0.04
(0.09)
0.659
0.677
stair_satis_today 2473 5.81
(1.72) 		0.17
(0.08)
0.047*
0.039* 		0.11
(0.08)
0.178
0.243
stairs_peace 2473 6.59
(1.74) 		0.1
(0.08)
0.223
0.242 		0.1
(0.09)
0.243
0.303
god_bless_r 2473 4.57
(0.66) 		0.02
(0.03)
0.438
0.512 		0.04
(0.03)
0.204
0.292
health_phys 2473 3.28
(1.01) 		0
(0.05)
0.961
0.961 		0.04
(0.05)
0.389
0.420 
## [1] 0.6950545
Treatment effects: Psychosocial outcomes - Self-efficacy
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Self-Efficacy 2473 0
(1) 		0.09
(0.05)
0.068†
0.093† 		0.02
(0.05)
0.595
0.643
gse2 2473 3.01
(0.82) 		0.02
(0.04)
0.664
0.687 		-0.02
(0.04)
0.646
0.692
gse3 2473 3.27
(0.64) 		0.04
(0.03)
0.259
0.290 		0
(0.03)
0.929
0.940
gse4 2473 3.01
(0.77) 		0.04
(0.04)
0.224
0.246 		0.03
(0.04)
0.388
0.424
gse7 2473 3.06
(0.77) 		0.06
(0.04)
0.068†
0.125 		0.01
(0.03)
0.836
0.864
ros4 2473 3.14
(0.75) 		0.05
(0.04)
0.137
0.163 		0.04
(0.04)
0.271
0.284 
## [1] 0.6823932
Treatment effects: Psychosocial outcomes - Social Standing
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Social Standing 2473 0
(1) 		0.03
(0.05)
0.527
0.582 		0.05
(0.05)
0.318
0.394
stair_status_today 2473 4.91
(1.88) 		0.19
(0.09)
0.045*
0.091† 		0.15
(0.09)
0.090†
0.141
stair_respect 2473 6.46
(1.72) 		0
(0.09)
0.990
0.992 		0.01
(0.09)
0.928
0.937
stair_good_today 2473 6.82
(1.69) 		-0.05
(0.08)
0.589
0.587 		0.05
(0.08)
0.542
0.578 
## [1] 0.6297192
Treatment effects: Psychosocial outcomes - Social Support
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Social Support 2487 0
(1) 		0.09
(0.05)
0.057†
0.114 		0.09
(0.05)
0.077†
0.125
soc_tips_in_psy 2473 4.8
(3.32) 		0.52
(0.17)
0.002**
0.015* 		0.37
(0.16)
0.020*
0.046*
soc_tips_psy 2473 5.25
(3.19) 		0.02
(0.15)
0.894
0.911 		0.3
(0.15)
0.048*
0.095†
soc_confl 2473 5.52
(3.19) 		0.2
(0.16)
0.212
0.278 		0.25
(0.16)
0.117
0.208
accesstofunds_r 2473 1.9
(1.05) 		0.03
(0.05)
0.542
0.572 		-0.05
(0.05)
0.345
0.393 
## Some items ( tg1 ) were negatively correlated with the first principal component and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## [1] 0.1406572
Treatment effects: Psychosocial outcomes - Social Cohesion and Trust
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Social Cohesion 2473 0
(1) 		-0.05
(0.05)
0.300
0.361 		0
(0.05)
0.982
0.985
for_trust_1 2473 4.9
(1.99) 		0
(0.1)
0.965
0.965 		-0.13
(0.09)
0.146
0.226
enemy_r 2473 2.87
(1.02) 		0
(0.05)
0.954
0.959 		0
(0.05)
0.975
0.979
los2 2473 2.42
(0.84) 		-0.02
(0.04)
0.700
0.729 		0.04
(0.04)
0.277
0.306
tg1 2437 3.24
(0.84) 		-0.07
(0.04)
0.084†
0.155 		0.01
(0.04)
0.725
0.740 
## Some items ( free_work_r ) were negatively correlated with the first principal component and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## [1] 0.6135229
Treatment effects: Psychosocial outcomes - Decision-making
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Control Over Earnings 2473 0
(1) 		-0.02
(0.05)
0.627
0.578 		-0.03
(0.05)
0.584
0.613
dec_pow_earn 2347 2.8
(0.48) 		0.01
(0.02)
0.531
0.517 		-0.02
(0.03)
0.450
0.483
dec_pow_bus 2280 2.59
(0.63) 		-0.02
(0.03)
0.469
0.436 		0
(0.03)
0.945
0.946
dec_pow_spend 2408 2.68
(0.54) 		-0.02
(0.03)
0.457
0.451 		-0.02
(0.03)
0.462
0.515
free_work_r 2473 0.84
(0.36) 		-0.01
(0.02)
0.763
0.777 		0.01
(0.02)
0.657
0.692 
## [1] 0.06819249
Treatment effects: Psychosocial outcomes - Partner Dynamics
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Partner Dynamics 2226 0
(1) 		0.07
(0.05)
0.169
0.181 		0.08
(0.05)
0.102
0.157
rel_disagree_r 2168 2.34
(1.17) 		0.05
(0.06)
0.379
0.369 		0.07
(0.06)
0.220
0.271
los3 2203 3.46
(0.65) 		0.04
(0.03)
0.278
0.321 		0.04
(0.03)
0.295
0.373 
## [1] 0.376014
Treatment effects: Psychosocial outcomes - Household Interpersonal Dynamics
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Household Dynamics 2487 0
(1) 		0.03
(0.05)
0.458
0.507 		0.1
(0.04)
0.021*
0.045*
los1 2473 2.9
(0.9) 		0.03
(0.04)
0.492
0.523 		0.11
(0.04)
0.008**
0.017*
tens_house_r 2473 3.64
(0.68) 		0
(0.03)
0.988
0.988 		0.01
(0.03)
0.780
0.802
rel_resp 2473 3.19
(1.06) 		0.03
(0.05)
0.538
0.571 		0.09
(0.05)
0.070†
0.124 
## [1] 0.001583861
Treatment effects: Psychosocial outcomes - Redistrbutive Preferences
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Redistributive
Preferences 		2473 0
(1) 		0.02
(0.05)
0.650
0.706 		0.03
(0.05)
0.550
0.585
aut_aum_comb_wins 2473 728.91
(718.54) 		22.8
(36.11)
0.528
0.558 		16.43
(35.95)
0.648
0.661
aut_rend_comb 2473 14.11
(16.25) 		1.08
(0.83)
0.194
0.240 		1.09
(0.82)
0.183
0.229
aut_dev_r 2472 0.86
(0.35) 		-0.02
(0.02)
0.278
0.340 		-0.01
(0.02)
0.499
0.597

Economic Outcomes

Table S5: Economic Outcomes

Treatment effects: Economic outcome indices
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Economic Composite Index 2474 0
(1) 		0.07
(0.05)
0.169
0.264 		0.12
(0.05)
0.013*
0.023*
Food Security Index 2473 0
(1) 		0.07
(0.05)
0.152
0.262 		0.11
(0.05)
0.029*
0.059†
Business Omnibus Index 2474 0
(1) 		0.04
(0.05)
0.386
0.466 		0.09
(0.05)
0.071†
0.108

Figure 5

Treatment effects: Food Security outcomes
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Food Security Index 2473 0
(1) 		0.07
(0.05)
0.152
0.262 		0.11
(0.05)
0.029*
0.059†
Food security (hh) 2473 6.83
(1.52) 		0.15
(0.07)
0.034*
0.074† 		0.16
(0.07)
0.032*
0.054†
Dietary diversity 2473 9.12
(8.75) 		0.07
(0.42)
0.870
0.898 		0.59
(0.43)
0.169
0.214
Treatment effects: Economic outcomes - Business outcomes
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Business Omnibus Index 2474 0
(1) 		0.04
(0.05)
0.386
0.466 		0.09
(0.05)
0.071†
0.108
Business Engagement Index 2474 0
(1) 		0.05
(0.05)
0.290
0.342 		0.09
(0.05)
0.055†
0.095†
Has a business 2474 0.82
(0.38) 		-0.01
(0.02)
0.778
0.779 		0.03
(0.02)
0.079†
0.118
No. businesses 2474 1.24
(1.21) 		0.01
(0.06)
0.848
0.856 		0.05
(0.06)
0.370
0.410
No. businesses past year 2474 0.48
(1.04) 		0.04
(0.05)
0.395
0.393 		0.05
(0.05)
0.295
0.331
Business investments (yearly, USD) 2474 106.12
(181.94) 		19.78
(9.17)
0.031*
0.059† 		14.22
(8.85)
0.108
0.163
Business asset value (USD) 2474 15.02
(22.19) 		0.62
(1.13)
0.585
0.629 		0.9
(1.09)
0.409
0.474
No. days worked 2474 17.47
(20.15) 		0.78
(0.94)
0.407
0.497 		0.82
(0.9)
0.366
0.402
Growth intentions 2474 1.06
(0.83) 		0.01
(0.04)
0.750
0.761 		0.07
(0.04)
0.087†
0.119
Healthy business practices 2457 0
(1) 		0.06
(0.05)
0.259
0.281 		0.06
(0.05)
0.215
0.253
Business Performance Index 2474 0
(1) 		0
(0.05)
0.943
0.955 		0.05
(0.05)
0.354
0.377
Business profits (monthly, USD) 2474 35.61
(50.24) 		-0.49
(2.44)
0.841
0.876 		2.91
(2.53)
0.249
0.297
Business revenues (monthly, USD) 2474 118.45
(162.2) 		2.68
(7.88)
0.734
0.783 		5.01
(7.86)
0.524
0.518

Table S6: Robustness

Robustness: Treatment effects controlling for age and head of household status: Outcome indices
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Economic Composite Index 2472 0
(1) 		0.07
(0.05)
0.168
0.263 		0.12
(0.05)
0.013*
0.022*
Food Security Index 2471 0
(1) 		0.07
(0.05)
0.150
0.260 		0.11
(0.05)
0.029*
0.059†
Business Omnibus Index 2472 0
(1) 		0.04
(0.05)
0.387
0.467 		0.09
(0.05)
0.071†
0.107
Psychosocial Composite Index 2485 0
(1) 		0.1
(0.05)
0.039*
0.058† 		0.12
(0.05)
0.014*
0.029*
Personal Composite Index 2485 0
(1) 		0.14
(0.05)
0.003**
0.010* 		0.12
(0.04)
0.009**
0.023*
Relational Composite Index 2485 0
(1) 		0.05
(0.05)
0.333
0.337 		0.09
(0.05)
0.072†
0.118

Other economic outcomes

Effects on severe food insecurity

## [1] 0.4625514
## [1] 0.4053208
## 
## Call:
## lm(formula = food_severe ~ condition + typepaquet_c + timing_c + 
##     isbaseline_c, data = de)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5573 -0.4098 -0.3552  0.5484  0.6988 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.45655    0.01702  26.824  < 2e-16 ***
## conditionT.ind -0.05461    0.02436  -2.241  0.02509 *  
## conditionT.rel -0.05403    0.02416  -2.236  0.02544 *  
## typepaquet_c   -0.09577    0.02029  -4.719  2.5e-06 ***
## timing_c       -0.05165    0.01989  -2.597  0.00946 ** 
## isbaseline_c   -0.05400    0.02577  -2.095  0.03627 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4922 on 2473 degrees of freedom
##   (149 observations deleted due to missingness)
## Multiple R-squared:  0.01579,    Adjusted R-squared:  0.0138 
## F-statistic: 7.936 on 5 and 2473 DF,  p-value: 1.983e-07
Outcome df T.ind_beta T.ind_SE T.ind_pval T.ind_pval_clus T.rel_beta T.rel_SE T.rel_pval T.rel_pval_clus
food_severe 2473 -0.05 (0.02) 0.025* 0.057† -0.05 (0.02) 0.025* 0.058†

Effects on number of businesses

## [1] 0.8238683
## [1] 0.8035144
## [1] 0.8419405
## 
## Call:
## lm(formula = ben_bus_has ~ condition + typepaquet_c + timing_c + 
##     isbaseline_c, data = de)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9057  0.1091  0.1405  0.1880  0.3514 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.764149   0.012997  58.795  < 2e-16 ***
## conditionT.ind -0.005428   0.018595  -0.292   0.7704    
## conditionT.rel  0.031381   0.018452   1.701   0.0891 .  
## typepaquet_c    0.062272   0.015495   4.019 6.02e-05 ***
## timing_c        0.014761   0.015186   0.972   0.3311    
## isbaseline_c   -0.143243   0.019681  -7.278 4.52e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3759 on 2474 degrees of freedom
##   (148 observations deleted due to missingness)
## Multiple R-squared:  0.03086,    Adjusted R-squared:  0.0289 
## F-statistic: 15.76 on 5 and 2474 DF,  p-value: 2.67e-15
Outcome df T.ind_beta T.ind_SE T.ind_pval T.ind_pval_clus T.rel_beta T.rel_SE T.rel_pval T.rel_pval_clus
ben_bus_has 2474 -0.01 (0.02) 0.778 0.779 0.03 (0.02) 0.079† 0.118 
## 
## t test of coefficients:
## 
##                 Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)     0.255000   0.030492  8.3628 < 2.2e-16 ***
## conditionT.ind  0.044054   0.046344  0.9506  0.341902    
## conditionT.rel -0.012427   0.042833 -0.2901  0.771748    
## typepaquet_c    0.110031   0.035854  3.0688  0.002172 ** 
## timing_c        0.046748   0.035982  1.2992  0.193993    
## isbaseline_c    0.031417   0.047626  0.6597  0.509526    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.2584362

Comparisons of agency interventions at endline

Comparing Personal and Relational Agency: Outcome indices
Outcome df T.relVT.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Economic Composite Index 2474 0.05
(0.05)
0.343
0.328
Food Security Index 2473 0.04
(0.05)
0.483
0.513
Business Omnibus Index 2474 0.04
(0.06)
0.434
0.356
Psychosocial Composite Index 2487 0.02
(0.05)
0.721
0.721
Personal Composite Index 2487 -0.02
(0.05)
0.693
0.707
Relational Composite Index 2487 0.04
(0.06)
0.446
0.471

Table S7: HH-owned businesses in subsample

Treatment effects: Household Businesses (subsample)
Outcome df Control
Mean
(SD) 		T.ind
Coefficient
(SE)
Robust p-value
Cluster robust p-value 		T.rel
Coefficient
(SE)
Robust p-value
Cluster robust p-value
Business Omnibus Index 452 0
(1) 		0.19
(0.13)
0.142
0.128 		0.3
(0.13)
0.023*
0.009**
Business Engagement Index 452 0
(1) 		0.19
(0.13)
0.151
0.154 		0.28
(0.14)
0.039*
0.021*
Has a business 452 0.51
(0.5) 		-0.02
(0.06)
0.769
0.757 		0.06
(0.06)
0.286
0.236
No. businesses 452 0.64
(0.75) 		0.07
(0.1)
0.435
0.403 		0.18
(0.1)
0.069†
0.053†
No. businesses past year 452 0.14
(0.42) 		0.05
(0.05)
0.312
0.334 		0.02
(0.05)
0.661
0.668
Business investments (yearly, USD) 452 0.5
(6.4) 		1.71
(1.11)
0.125
0.131 		1.65
(1.16)
0.156
0.157
Business asset value (USD) 452 50.9
(180.96) 		-10.64
(18.34)
0.562
0.574 		15.95
(24.56)
0.516
0.539
No. days worked 452 5.62
(10.88) 		1.1
(1.32)
0.406
0.434 		1.74
(1.3)
0.182
0.193
Growth intentions 452 0.01
(0.08) 		0.03
(0.02)
0.084†
0.086† 		0.03
(0.02)
0.083†
0.074†
Business Performance Index 452 0
(1) 		0.11
(0.13)
0.400
0.381 		0.21
(0.13)
0.099†
0.056†
Business profits (monthly, USD) 452 31.71
(80.83) 		10.68
(10.77)
0.322
0.301 		14.81
(10.18)
0.146
0.103
Business revenues (monthly, USD) 452 69.71
(184.59) 		13.66
(21.56)
0.527
0.512 		41.25
(24.53)
0.093†
0.053† 
## [1] 69.71275
## [1] 184.5859
## [1] 83.37178
## [1] 194.9694
## [1] 110.6907
## [1] 237.4377

Heterogeneity as pre-registered

## By timing of interventions (early vs late)
lm_het_tim_econ1 <- lm(ben_econ_omni_std ~ condition + timing_c + typepaquet_c + isbaseline_c, data = de)
lm_het_tim_econ2 <- lm(ben_econ_omni_std ~ condition*timing_c + typepaquet_c + isbaseline_c, data = de)
anova(lm_het_tim_econ1, lm_het_tim_econ2)
## Analysis of Variance Table
## 
## Model 1: ben_econ_omni_std ~ condition + timing_c + typepaquet_c + isbaseline_c
## Model 2: ben_econ_omni_std ~ condition * timing_c + typepaquet_c + isbaseline_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2474 2283.3                           
## 2   2472 2281.6  2    1.6942 0.9178 0.3995
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2474 2282.8                           
# 2   2472 2281.1  2     1.632 0.8843 0.4131

lm_het_tim_psy1 <- lm(psychosocial_omni_std ~ condition + timing_c + typepaquet_c + isbaseline_c, data = de)
lm_het_tim_psy2 <- lm(psychosocial_omni_std ~ condition*timing_c + typepaquet_c + isbaseline_c, data = de)
anova(lm_het_tim_psy1, lm_het_tim_psy2)
## Analysis of Variance Table
## 
## Model 1: psychosocial_omni_std ~ condition + timing_c + typepaquet_c + 
##     isbaseline_c
## Model 2: psychosocial_omni_std ~ condition * timing_c + typepaquet_c + 
##     isbaseline_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2487 2359.0                           
## 2   2485 2358.9  2  0.065204 0.0343 0.9662
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2487 2359.0                           
# 2   2485 2358.9  2  0.065204 0.0343 0.9662
lm_het_film_econ1 <- lm(ben_econ_omni_std ~ condition + saw_film + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_film_econ2 <- lm(ben_econ_omni_std ~ condition*saw_film + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_film_econ1, lm_het_film_econ2)
## Analysis of Variance Table
## 
## Model 1: ben_econ_omni_std ~ condition + saw_film + typepaquet_c + isbaseline_c + 
##     timing_c
## Model 2: ben_econ_omni_std ~ condition * saw_film + typepaquet_c + isbaseline_c + 
##     timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2459 2261.6                           
## 2   2457 2259.7  2    1.8538 1.0078 0.3652
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2459 2261.3                           
# 2   2457 2259.7  2    1.5958 0.8676 0.4201

lm_het_film_psy1 <- lm(psychosocial_omni_std ~ condition + saw_film + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_film_psy2 <- lm(psychosocial_omni_std ~ condition*saw_film + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_film_psy1, lm_het_film_psy2)
## Analysis of Variance Table
## 
## Model 1: psychosocial_omni_std ~ condition + saw_film + typepaquet_c + 
##     isbaseline_c + timing_c
## Model 2: psychosocial_omni_std ~ condition * saw_film + typepaquet_c + 
##     isbaseline_c + timing_c
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1   2472 2342.5                              
## 2   2470 2336.3  2    6.1526 3.2523 0.03885 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
# 1   2472 2342.5                              
# 2   2470 2336.3  2    6.1526 3.2523 0.03885 *
# institution: can you count on authorities to support in case of crime
lm_het_instit_econ1 <- lm(ben_econ_omni_std ~ condition + institution2 + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_instit_econ2 <- lm(ben_econ_omni_std ~ condition*institution2 + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_instit_econ1, lm_het_instit_econ2)
## Analysis of Variance Table
## 
## Model 1: ben_econ_omni_std ~ condition + institution2 + typepaquet_c + 
##     isbaseline_c + timing_c
## Model 2: ben_econ_omni_std ~ condition * institution2 + typepaquet_c + 
##     isbaseline_c + timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2457 2201.8                           
## 2   2455 2200.9  2   0.86921 0.4848 0.6159
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2457 2203.0                           
# 2   2455 2202.1  2   0.86742 0.4835 0.6167

lm_het_instit_psy1 <- lm(psychosocial_omni_std ~ condition + institution2 + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_instit_psy2 <- lm(psychosocial_omni_std ~ condition*institution2 + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_instit_psy1, lm_het_instit_psy2)
## Analysis of Variance Table
## 
## Model 1: psychosocial_omni_std ~ condition + institution2 + typepaquet_c + 
##     isbaseline_c + timing_c
## Model 2: psychosocial_omni_std ~ condition * institution2 + typepaquet_c + 
##     isbaseline_c + timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2457 2072.3                           
## 2   2455 2070.9  2     1.399 0.8292 0.4365
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2457 2072.3                           
# 2   2455 2070.9  2     1.399 0.8292 0.4365
# hamlet: live in hamlet outside of village
lm_het_hameau_bin_econ1 <- lm(ben_econ_omni_std ~ condition + hameau_bin + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_hameau_bin_econ2 <- lm(ben_econ_omni_std ~ condition*hameau_bin + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_hameau_bin_econ1, lm_het_hameau_bin_econ2)
## Analysis of Variance Table
## 
## Model 1: ben_econ_omni_std ~ condition + hameau_bin + typepaquet_c + isbaseline_c + 
##     timing_c
## Model 2: ben_econ_omni_std ~ condition * hameau_bin + typepaquet_c + isbaseline_c + 
##     timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2473 2214.6                           
## 2   2471 2213.9  2   0.75846 0.4233 0.6549
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2473 2213.1                           
# 2   2471 2212.4  2   0.72307 0.4038 0.6678

lm_het_hameau_bin_psy1 <- lm(psychosocial_omni_std ~ condition + hameau_bin + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_hameau_bin_psy2 <- lm(psychosocial_omni_std ~ condition*hameau_bin + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_hameau_bin_psy1, lm_het_hameau_bin_psy2)
## Analysis of Variance Table
## 
## Model 1: psychosocial_omni_std ~ condition + hameau_bin + typepaquet_c + 
##     isbaseline_c + timing_c
## Model 2: psychosocial_omni_std ~ condition * hameau_bin + typepaquet_c + 
##     isbaseline_c + timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2486 2334.8                           
## 2   2484 2334.2  2   0.63974 0.3404 0.7115
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2486 2334.8                           
# 2   2484 2334.2  2   0.63974 0.3404 0.7115
# Poverty score
lm_het_pmt_econ1 <- lm(ben_econ_omni_std ~ condition + pmt + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_pmt_econ2 <- lm(ben_econ_omni_std ~ condition*pmt + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_pmt_econ1, lm_het_pmt_econ2)
## Analysis of Variance Table
## 
## Model 1: ben_econ_omni_std ~ condition + pmt + typepaquet_c + isbaseline_c + 
##     timing_c
## Model 2: ben_econ_omni_std ~ condition * pmt + typepaquet_c + isbaseline_c + 
##     timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2473 2257.6                           
## 2   2471 2257.3  2    0.2747 0.1504 0.8604
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2473 2256.3                           
# 2   2471 2256.2  2   0.13928 0.0763 0.9266

lm_het_pmt_psy1 <- lm(psychosocial_omni_std ~ condition + pmt + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_pmt_psy2 <- lm(psychosocial_omni_std ~ condition*pmt + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_pmt_psy1, lm_het_pmt_psy2)
## Analysis of Variance Table
## 
## Model 1: psychosocial_omni_std ~ condition + pmt + typepaquet_c + isbaseline_c + 
##     timing_c
## Model 2: psychosocial_omni_std ~ condition * pmt + typepaquet_c + isbaseline_c + 
##     timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2486 2341.5                           
## 2   2484 2340.8  2   0.68424 0.3631 0.6956
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2486 2341.5                           
# 2   2484 2340.8  2   0.68424 0.3631 0.6956
# Head of hh
lm_het_relation_head_econ1 <- lm(ben_econ_omni_std ~ condition + relation_head + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_relation_head_econ2 <- lm(ben_econ_omni_std ~ condition*relation_head + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_relation_head_econ1, lm_het_relation_head_econ2)
## Analysis of Variance Table
## 
## Model 1: ben_econ_omni_std ~ condition + relation_head + typepaquet_c + 
##     isbaseline_c + timing_c
## Model 2: ben_econ_omni_std ~ condition * relation_head + typepaquet_c + 
##     isbaseline_c + timing_c
##   Res.Df    RSS Df Sum of Sq     F Pr(>F)  
## 1   2473 2283.3                            
## 2   2471 2278.1  2    5.1721 2.805 0.0607 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
# 1   2473 2282.8                              
# 2   2471 2277.5  2    5.2906 2.8701 0.05688 .

lm_het_relation_head_psy1 <- lm(psychosocial_omni_std ~ condition + relation_head + typepaquet_c + isbaseline_c + timing_c, data = de)
lm_het_relation_head_psy2 <- lm(psychosocial_omni_std ~ condition*relation_head + typepaquet_c + isbaseline_c + timing_c, data = de)
anova(lm_het_relation_head_psy1, lm_het_relation_head_psy2)
## Analysis of Variance Table
## 
## Model 1: psychosocial_omni_std ~ condition + relation_head + typepaquet_c + 
##     isbaseline_c + timing_c
## Model 2: psychosocial_omni_std ~ condition * relation_head + typepaquet_c + 
##     isbaseline_c + timing_c
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2486 2355.3                           
## 2   2484 2354.5  2    0.8086 0.4265 0.6528
#   Res.Df    RSS Df Sum of Sq      F Pr(>F)
# 1   2486 2355.3                           
# 2   2484 2354.5  2    0.8086 0.4265 0.6528

Table S9-S10: Effects of Within-Village Saturation

Treatment effects by Saturation Level: Psychosocial outcome indices
Outcome df T.25
Mean
(SD) 		T.50
Coefficient
(SE)
Cluster robust p-value 		T.75
Coefficient
(SE)
Cluster robust p-value
Psychosocial Composite Index 2487 -0.02
(1.02) 		0.08
0.09
0.382 		0.15
0.09
0.087
Personal Composite Index 2487 -0.03
(1.07) 		0.13
0.09
0.145 		0.14
0.08
0.075
Well-Being 2487 -0.03
(1.04) 		0.07
0.08
0.39 		0.14
0.08
0.086
Self-Efficacy 2473 -0.02
(1) 		0.05
0.09
0.57 		0.04
0.08
0.636
Future Expectations 2473 -0.03
(1.04) 		0.15
0.08
0.079 		0.13
0.08
0.079
Relational Composite Index 2487 0
(0.99) 		0.03
0.09
0.749 		0.11
0.08
0.176
Partner Dynamics 2226 0.06
(1.01) 		-0.03
0.06
0.674 		0.01
0.07
0.906
Household Dynamics 2487 -0.01
(0.99) 		0.04
0.08
0.648 		0.14
0.08
0.087
Control Over Earnings 2473 -0.02
(1.03) 		-0.03
0.08
0.693 		0.05
0.07
0.43
Social Standing 2473 -0.02
(1.04) 		0.06
0.09
0.522 		0.1
0.09
0.27
Social Support 2487 0
(0.98) 		0.07
0.07
0.323 		0.08
0.07
0.202
Social Cohesion 2473 -0.01
(0.98) 		-0.01
0.08
0.87 		-0.02
0.08
0.84
Treatment effects by Saturation Level: Economic outcome indices
Outcome df T.25
Mean
(SD) 		T.50
Coefficient
(SE)
Cluster robust p-value 		T.75
Coefficient
(SE)
Cluster robust p-value
Economic Composite Index 2474 -0.04
(0.95) 		0.1
0.12
0.382 		0.11
0.12
0.362
Food Security Index 2473 -0.03
(0.97) 		0.12
0.11
0.274 		0.09
0.11
0.427
Food security (hh) 2473 6.86
(1.52) 		0.11
0.14
0.446 		0.06
0.13
0.63
Dietary diversity 2473 8.48
(8.13) 		1.13
0.94
0.232 		0.9
1
0.37
Business Omnibus Index 2474 -0.03
(0.95) 		0.05
0.1
0.64 		0.09
0.1
0.388
Business Engagement Index 2474 -0.03
(0.94) 		0.05
0.09
0.583 		0.1
0.1
0.315
Has a business 2474 0.83
(0.38) 		0.01
0.03
0.798 		0
0.03
0.866
No. businesses 2474 1.19
(0.96) 		0.05
0.09
0.578 		0.12
0.1
0.248
No. businesses past year 2474 0.44
(0.79) 		0.06
0.05
0.225 		0.13
0.06
0.039
Business investments (yearly, USD) 2474 100.53
(178.05) 		21.65
20.15
0.283 		18.51
19.66
0.347
Business asset value (USD) 2474 15.9
(23.15) 		-0.88
1.75
0.618 		-0.59
1.69
0.727
No. days worked 2474 16.05
(18.36) 		1.24
1.89
0.511 		2.21
1.76
0.209
Growth intentions 2474 1.05
(0.81) 		0.01
0.07
0.886 		0.03
0.07
0.653
Healthy business practices 2457 -0.02
(1.02) 		0.01
0.08
0.914 		0.1
0.07
0.169
Business Performance Index 2474 -0.01
(0.95) 		0.02
0.1
0.856 		0.04
0.1
0.721
Business profits (monthly, USD) 2474 34.77
(47.69) 		1.36
4.8
0.777 		2.04
4.88
0.676
Business revenues (monthly, USD) 2474 117.32
(156.89) 		1.31
16.16
0.935 		4.5
15.67
0.774

Immediate post-intervention effects: Personal vs Relational Agency Intervention Comparisons

Table S8: Immediate Post-Intervention Effects

Treatment effects: Immediate Outcomes
Outcome df T.ind
Mean
(SD) 		T.rel
Coefficient
(SE)
Robust p-value
Economic Composite Index 1271 0.96
(0.15) 		0
(0.01)
0.657
Approach behaviors 1271 2.72
(0.94) 		0.06
(0.05)
0.277
Approach feelings 1271 3.7
(0.33) 		-0.01
(0.02)
0.656
Budget Allocation 1271 7.17
(2.04) 		-0.04
(0.12)
0.704
Relational Composite Index 1327 0.95
(0.15) 		0.01
(0.01)
0.176
Social Standing 1271 7.07
(1.59) 		0
(0.09)
0.985
Social Norms 1271 6.49
(1.37) 		-0.02
(0.08)
0.797
Social Support 1271 0.94
(0.24) 		0.01
(0.01)
0.252
Anticipation of Negative Reputation 1327 0.15
(0.36) 		-0.03
(0.02)
0.088†
Trust 1271 6.54
(2.49) 		0.03
(0.14)
0.813
Personal Composite Index 1327 8.65
(2.04) 		-0.08
(0.12)
0.498
Self-efficacy 1327 8.97
(2.53) 		-0.09
(0.14)
0.541
Future expectations (SES) 1271 8.64
(1.34) 		0.03
(0.08)
0.677
Future Expectations (Program) 1271 7.29
(1.63) 		-0.07
(0.09)
0.430
Prosocial Preferences 1271 0.93
(0.27) 		-0.02
(0.02)
0.306
Amina evaluation 1271 9.56
(0.79) 		-0.05
(0.05)
0.335

Additional: Administrative outcomes

# Life Skills sessions attended
median(da$presenceACV[da$condition=="Control"]) # 6 of 6
## [1] 6
median(da$presenceACV[da$condition=="T.ind"]) # 6 of 6
## [1] 6
median(da$presenceACV[da$condition=="T.rel"]) # 6 of 6
## [1] 6
lm_acv <- lm(presenceACV ~ condition + typepaquet + timing + isbaseline, data = da)
coeftest(lm_acv, vcov = vcovHC(lm_acv, type="HC1"))
## 
## t test of coefficients:
## 
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       5.351897   0.061364 87.2153   <2e-16 ***
## conditionT.ind   -0.059292   0.079887 -0.7422   0.4580    
## conditionT.rel   -0.043896   0.079445 -0.5525   0.5806    
## typepaquetSocial  0.019328   0.066037  0.2927   0.7698    
## timingTot         0.011126   0.064733  0.1719   0.8636    
## isbaseline       -0.121283   0.090620 -1.3384   0.1809    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Business sessions attended
median(da$presenceGERME[da$condition=="Control"]) # 6 of 6
## [1] 6
median(da$presenceGERME[da$condition=="T.ind"]) # 6 of 6
## [1] 6
median(da$presenceGERME[da$condition=="T.rel"]) # 6 of 6
## [1] 6
lm_germe <- lm(presenceGERME ~ condition + typepaquet + timing + isbaseline, data = da)
coeftest(lm_germe, vcov = vcovHC(lm_germe, type="HC1"))
## 
## t test of coefficients:
## 
##                   Estimate Std. Error  t value Pr(>|t|)    
## (Intercept)       5.482743   0.050884 107.7491   <2e-16 ***
## conditionT.ind   -0.026377   0.067777  -0.3892   0.6972    
## conditionT.rel   -0.030744   0.065500  -0.4694   0.6388    
## typepaquetSocial -0.070117   0.057215  -1.2255   0.2205    
## timingTot         0.017599   0.054370   0.3237   0.7462    
## isbaseline       -0.036926   0.076180  -0.4847   0.6279    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Total sessions attended (business + life skills)
median(da$presencetotal[da$condition=="Control"]) # 12 of 12
## [1] 12
mean(da$presencetotal[da$condition=="Control"]) # 10.81
## [1] 10.80941
sd(da$presencetotal[da$condition=="Control"]) # 2.492293
## [1] 2.492293
median(da$presencetotal[da$condition=="T.ind"]) # 12 of 12
## [1] 12
sd(da$presencetotal[da$condition=="Control"])
## [1] 2.492293
median(da$presencetotal[da$condition=="T.rel"]) # 12 of 12
## [1] 12
lm_tot <- lm(presencetotal ~ condition + typepaquet + timing + isbaseline, data = da)
summary(lm_tot)
## 
## Call:
## lm(formula = presencetotal ~ condition + typepaquet + timing + 
##     isbaseline, data = da)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.8634   0.1874   1.1654   1.2400   1.4600 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      10.83464    0.09897 109.470   <2e-16 ***
## conditionT.ind   -0.08567    0.12123  -0.707    0.480    
## conditionT.rel   -0.07464    0.12126  -0.616    0.538    
## typepaquetSocial -0.05079    0.10116  -0.502    0.616    
## timingTot         0.02872    0.09938   0.289    0.773    
## isbaseline       -0.15821    0.12979  -1.219    0.223    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.531 on 2622 degrees of freedom
## Multiple R-squared:  0.0011, Adjusted R-squared:  -0.000805 
## F-statistic: 0.5774 on 5 and 2622 DF,  p-value: 0.7174
coeftest(lm_tot, vcov = vcovHC(lm_tot, type="HC1"))
## 
## t test of coefficients:
## 
##                   Estimate Std. Error  t value Pr(>|t|)    
## (Intercept)      10.834640   0.092487 117.1477   <2e-16 ***
## conditionT.ind   -0.085669   0.124162  -0.6900   0.4903    
## conditionT.rel   -0.074640   0.119563  -0.6243   0.5325    
## typepaquetSocial -0.050789   0.104354  -0.4867   0.6265    
## timingTot         0.028725   0.099299   0.2893   0.7724    
## isbaseline       -0.158209   0.139917  -1.1307   0.2583    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Additional: Outcomes of saturation randomization

# Treatment effect of group-level randomization (25% vs 50%, 25% vs 75%)
lm_tot_int <- lm(presencetotal ~ intensity_cat + typepaquet + timing + isbaseline, data = da)
coeftest(lm_tot_int, cluster.vcov(lm_tot_int, da$groupe)) # ns
## 
## t test of coefficients:
## 
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       10.989655   0.198490 55.3664   <2e-16 ***
## intensity_cat0.5  -0.175987   0.226258 -0.7778   0.4367    
## intensity_cat0.75 -0.379771   0.303244 -1.2524   0.2105    
## typepaquetSocial  -0.044316   0.231474 -0.1915   0.8482    
## timingTot          0.016613   0.220613  0.0753   0.9400    
## isbaseline        -0.167338   0.190253 -0.8796   0.3792    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## z test of coefficients:
## 
##                           Estimate Std. Error z value Pr(>|z|)  
## (Intercept)               0.290466   0.297170  0.9774  0.32835  
## intensity_cat0.5         -0.061829   0.198566 -0.3114  0.75551  
## intensity_cat0.75        -0.241087   0.214241 -1.1253  0.26046  
## typepaquetSocial          0.249927   0.182515  1.3693  0.17089  
## timingTot                -0.101039   0.185318 -0.5452  0.58560  
## isbaseline                0.012421   0.138461  0.0897  0.92852  
## as.factor(communeid)450   0.345528   0.287151  1.2033  0.22886  
## as.factor(communeid)494   0.327668   0.356973  0.9179  0.35867  
## as.factor(communeid)566   0.682569   0.367680  1.8564  0.06339 .
## as.factor(communeid)3654  0.573849   0.368591  1.5569  0.11950  
## as.factor(communeid)3659 -0.137468   0.343503 -0.4002  0.68901  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1