{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DIME","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2025-07-08","version":"1"},"project_desc":{"title_statement":{"idno":"RR_SSA_2024_250","title":"Reproducibility package for The Welfare Cost of Drought in Sub-Saharan Africa"},"authoring_entity":[{"name":"Ruth Hill ","affiliation":"World Bank and Centre for Disaster Protection","email":"rhill@worldbank.org"},{"name":"Sandra Baquie ","affiliation":"World Bank","email":"sbaquie@worldbank.org"},{"name":"Katja Vinha ","affiliation":"World Bank","email":"kvinha@worldbank.org"},{"name":"Emmanuel Skoufias ","affiliation":"National University of Singapore and World Bank","email":"SKOUFIAS@nus.edu.sg"},{"name":"Evie Calcutt","affiliation":"World Bank","email":"ecalcutt@worldbank.org"},{"name":"Varun Kshirsagar ","email":"vkshirsagar@worldbank.org","affiliation":"World Bank"},{"name":"Conor Meenan","affiliation":"Centre for Disaster Protection"},{"name":"Jon Gascoigne ","affiliation":"Centre for Disaster Protection"}],"production_date":"2025-07","abstract":"This paper quantifies the impact of drought on household consumption for five main agroecological zones in Africa, developing vulnerability (or damage) functions of the relationship between rainfall deficits and poverty. Damage functions are a key element in models that quantify the risk of extreme weather and the impacts of climate change. Although these functions are commonly estimated for storm or flood damages to buildings, they are less often available for income losses from droughts. The paper takes a regional approach to the analysis, developing standardized hazard definitions and methods for matching hazard and household data, allowing survey data from close to 100,000 households to be used in the analysis. The damage functions are used to quantify the impact of historical weather conditions on poverty for eight countries, highlighting the risk to poverty outcomes that weather variability causes. National poverty rates are 1\u201312 percent higher, depending on the country, under the worst weather conditions relative to the best conditions observed in the past 13 years. This amounts to an increase in the total poverty gap that ranges from US$4 million to US$2.4 billion (2011 purchasing power parity). ","geographic_units":[{"code":"SSA","name":"Sub-Saharan Africa (excluding high income)"}],"keywords":[{"name":"Shocks and vulnerability to poverty"},{"name":"drought"}],"topics":[{"id":"Q34","vocabulary":"JEL Classifications","name":" Natural Resources and Domestic and International Conflicts ","url":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","parent_id":"Q3","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel"},{"id":"I32","vocabulary":"JEL Classifications","name":" Measurement and Analysis of Poverty ","url":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","parent_id":"I3","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel"}],"output":[{"type":"World Bank Policy Research Working Papers","title":"The Welfare Cost of Drought in Sub-Saharan Africa","description":"Policy Research Working Papers (PRWP)"}],"language":[{"name":"English","code":"EN"}],"software":[{"name":"Stata","version":"18 MP"}],"scripts":[{"file_name":"RR_SSA_2024_250","zip_package":"RR_SSA_2024_250.zip","title":"Reproducibility package for The Welfare Cost of Drought in Sub-Saharan Africa","date":"2025-07","dependencies":"Stata dependencies are listed in the ado folder.","instructions":"See README in reproducibility package.","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank."}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"technology_requirements":"~ 48 hours","technology_environment":"Paper exhibits were reproduced in a computer with the following specifications:\n\u2013 OS: Windows 11 Enterprise\n\u2013 Processor: INTEL(R) XEON(R) PLATINUM 8562Y+ 2.80 GHz (2 processors)\n\u2013 Memory available: 32.0 GB\n\u2013 Software version: Stata 18.0 MP","reproduction_instructions":"1. **Secure Access to Data:** Access the datasets not included in the package. See subsection Datasets and the README for more details.\n2. **Download and Place Data:** Once the data is downloaded, users should place it in the appropriate folder.\n3. **Run the Package:** After placing the data in the folder:\n      - Open the do-file \"Drought_paper_analyses\"\n      - Update the globals in line 14 to your folder's location and run the do-file\n \nSince all the data is not included, the package includes the results produced by replicators in the Results folder. These files can be used to review the results presented in the paper.","disclaimer":"The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development\/the World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package","license":[{"name":"Modified BSD3","uri":"https:\/\/opensource.org\/license\/bsd-3-clause\/"}],"contacts":[{"name":"Katja Vinha ","affiliation":"World Bank","email":"kvinha@worldbank.org"},{"name":"Ruth Hill","affiliation":"World Bank","email":"rhill@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"data_statement":"Some data is restricted and has not been included in the reproducibility package. For more details, please refer to the README file.","datasets":[{"name":"Gridded Global Administrative Areas (GADM) for Africa","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066860\/Gridded-Global-Administrative-Areas--GADM--for-Africa","note":"Source: Global Administrative Areas. 2012. GADM database of Global Administrative Areas, version 2.0.\nLocated at: Input_data\/Remote_Sensed\/SSA\/gadm_admin_urg005_mapping","access_type":"Publicly available at the link provided and included in the package."},{"name":"Gridded Global Administrative Unit Layers (GAUL) for Africa","note":"Source: Gridded GAUL designations at uniform grid resolution of 0.05\u00b0 by 0.05\u00b0 used in Gascoigne et al. (2024).\nLocated at: Input_data\/Remote_Sensed\/SSA\/country_mapping\n","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066859\/Gridded-Global-Administrative-Unit-Layers--GAUL--for-Africa","access_type":"Publicly available at the link provided and included in the package."},{"name":"Gridded Crop Coverage for Africa","note":"Source: Buchhorn, M., B. Smets, L. Bertels, B. De Roo, M. Lesiv, N.-E. Tsendbazar, M. Herold, and S. Fritz. Copernicus Global Land Service: Land Cover 100m: Collection 3: epoch 2019: Globe 2020. (https:\/\/land.copernicus.eu\/global\/products\/lc).\nLocated at: Input_data\/Remote_Sensed\/SSA\/crop_coverage\/country_mapping.\n","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066869\/Gridded-Crop-Coverage-for-Africa","access_type":"Publicly available at the link provided and included in the package."},{"name":"Crop calendar for Africa","note":"Source1 : Dimou, Maria (2018): Crop calendar dataset compatible with satellite-derived land surface phenology. European Commission, Joint Research Centre (JRC) [Dataset] PID: http:\/\/data.europa.eu\/89h\/jrc-10112-10003.\nSource2: https:\/\/geodata.ucdavis.edu\/gadm\/gadm3.6\/\nLocated at: \/Input_data\/Remote_Sensed\/SSA\/merge_variable_data; Input_data\/Data_Integration\/SSA\/crop_calendar_gaul_asap1.","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066842\/Crop-calendar-for-Africa","access_type":"Publicly available at the link provided and included in the package."},{"name":"Gridded Travel Time to Market for Africa","note":"Source: Gridded Travel Time to Market at uniform grid resolution of 0.05\u00b0 by 0.05\u00b0 used in Gascoigne et al. (2024).\nLocated at: Input_data\/Remote_Sensed\/SSA\/time_to_market\/country_mapping\n\n\n","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066865\/Gridded-Travel-Time-to-Market-for-Africa","access_type":"Publicly available at the link provided and included in the package."},{"name":"Gridded Global Agro-ecological Zones (GAEZ) for Africa","note":"Source: Gridded Global Agro-ecological Zones at uniform grid resolution of 0.05\u00b0 by 0.05\u00b0 used in Gascoigne et al. (2024). \nLocated at: Input_data\/Remote_Sensed\/SSA\/agro_ecological_zone\/country_mapping.\n","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066857\/Gridded-Global-Agro-ecological-Zones--GAEZ--for-Africa","access_type":"Publicly available at the link provided and included in the package."},{"name":"Gridded Livelihood Zones for Africa","note":"Source:  Dixon, John, Dennis P. Garrity, Jean-Marc Boffa, Timothy O. Williams, Tilahun Amede, Christopher Auricht, Rosemary Lott, and George Mburathi, eds. 2019. Farming Systems and Food Security in Africa: Priorities for Science and Policy Under Global Change. Routledge. (See figure 2.2b.).\nLocated at: Input_data\/Remote_Sensed\/SSA\/livelihood_zone\/country_mapping\n","access_type":"Data is restricted and not included in the package. World Bank internal users can access the data at the link provided. Users outside the Bank can contact the authors to access the dataset. ","uri":"https:\/\/datacatalog.worldbank.org\/int\/search\/dataset\/0066861\/Gridded-Livelihood-Zones-for-Africa"},{"name":"Gridded Dekadal Precipitation Data (CHIRPS) for Africa","note":"Source: Funk, C.C., P. J. Peterson, M. F. Landsfeld, D. H. Pedreros, J. P. Verdin, J. D. Rowland, B. E. Romero, G. J. Husak, J. C. Michaelsen, and A. P. Verdin. 2014. \u201cA Quasi-Global Precipitation Time Series for Drought monitoring.\u201d US Geological Survey Data Series 832, ftp:\/\/chg-ftpout.geog.ucsb.edu\/pub\/org\/chg\/products\/CHIRPS-2.0\/docs\/USGS-DS832.CHIRPS.pdf.\n\nLocated at: Input_data\/Remote_Sensed\/SSA\/CHIRPS","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066858\/Gridded-Dekadal-Precipitation-Data--CHIRPS--for-Africa","access_type":"Publicly available at the link provided, but not included in the package due to size restrictions."},{"name":"Gridded 3-month Standardized Precipitation-Evapotranspiration Index (SPEI) for Africa","note":"Source: Sergio M. Vicente-Serrano, Instituto Pirenaico de Ecolog\u00eda, Zaragoza, Spain. Santiago Beguer\u00eda, Estaci\u00f3n Experimental de Aula Dei, Zaragoza, Spain. (https:\/\/spei.csic.es\/).\nLocated at: Input_data\/Remote_Sensed\/SSA\/SPEI3","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066863\/Gridded-3-month-Standardized-Precipitation-Evapotranspiration-Index--SPEI--for-Africa","access_type":"Publicly available at the link provided and included in the package."},{"name":"Gridded Dekadal Water Requirement Satisfaction Index (WRSI) for Africa","note":"Source: Verdin, J., and R. Klaver. 2002. \u201cGrid\u2010cell\u2010based Crop Water Accounting for the Famine Early Warning System.\u201d Hydrological Processes 16 (8): 1617\u201330. DOI: 10.1002\/hyp.1025. (https:\/\/edcftp.cr.usgs.gov\/project\/fews\/dekadal\/ , https:\/\/earlywarning.usgs.gov\/fews\/product\/128).\nLocated at:  Input_data\/Remote_Sensed\/SSA\/WRSI","access_type":"Publicly available at the link provided and included in the package.","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066866\/Gridded-Dekadal-Water-Requirement-Satisfaction-Index--WRSI--for-Africa"},{"name":"Gridded Dekadal Soil Water Index (SWI) for Africa","note":"Source:  Copernicus Service information 2018. (https:\/\/land.copernicus.eu\/global\/products\/swi).\nLocated at:  Input_data\/Remote_Sensed\/SSA\/SWI","access_type":"Publicly available at the link provided and included in the package.","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066864\/Gridded-Dekadal-Soil-Water-Index--SWI--for-Africa"},{"name":"Gridded Monthly Normalized Difference Vegetation Index (NDVI) for Africa","note":"Source: Didan, K. 2021. MODIS\/Terra Vegetation Indices Monthly L3 Global 0.05Deg CMG V061 [data set]. NASA EOSDIS Land Processes DAAC. Accessed 2021-06-24 from https:\/\/doi.org\/10.5067\/MODIS\/MOD13C2.061. (https:\/\/lpdaac.usgs.gov\/products\/mod13c2v061\/).\nLocated at: Input_data\/Remote_Sensed\/SSA\/NDVI","access_type":"Publicly available at the link provided and included in the package.","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0066862\/Gridded-Monthly-Normalized-Difference-Vegetation-Index--NDVI--for-Africa"},{"name":"Simulated Dekadal Soil Water Index (SWI)","note":"Source: Blanchard, Antoine, and Luis Sousa. 2021. \u201cProbabilistic Hazard Analysis for Drought in Sub-Saharan Africa: Stochastic Precipitation, Soil Moisture, and Vegetation Index Datasets for Malawi.\u201d AIR Worldwide.\nLocated at: Input_Data\/Simulated\/Country\/Malawi","access_type":"Data is restricted and not included in the package. World Bank internal users can access the data at the link provided. Users outside the Bank can contact the authors to access the dataset. ","uri":"https:\/\/datacatalog.worldbank.org\/int\/search\/dataset\/0066871\/simulated_dekadal_soil_water_index_swi"},{"name":"Household Consumption and Expenditure Surveys","note":"Source: Household consumption surveys provided by country poverty economists in the World Bank\u2019s Poverty and Equity Global Practice. Table C in the README lists the names of the household surveys used. Countries included: Ethiopia, Lesotho, Malawi, Mauritania, Mozambique, Niger, Nigeria, Zambia, and Zimbabwe.\nLocation: Household_Survey\/<country> ; Data_Integration\\Country\\Ethiopia; Data_Integration\\Country\\Zimbabwe. \nA detailed list of datasets is available in the data_hash_report file included in the package.\n\n","access_type":"Data is restricted and not included in the package. Ruth Hill (rhill@worldbank.org) can be contacted for details."}]},"tags":[{"tag":"DOI"},{"tag":"Open Code"},{"tag":"Restricted Data"}],"schematype":"script"}