This repository provides code that was used to generate a synthetic population for each country in the world.
The outcome is a global synthetic population database, which we call GLOPOP-S. The database is stored on Harvard Dataverse https://doi.org/10.7910/DVN/KJC3RH. GLOPOP-S contains 1,999,227,130 households and 7,335,881,094 individuals for the year 2015 and includes the following attributes: age, education, economic situation, gender, settlement type (urban/rural), relationship to household head, household size and household type. We include ownership of agricultural land and dwelling characteristics for selected countries in the Global South.
The synthetic population is based on national microdata and scaled to regional statistics to account for socio-economic and demographic differences within a country. The synthetic household data can be downloaded per country or administrative unit.
- The DATA folder contains the data files needed to generate the database. We use microdata from the Luxembourg Income Study (LIS) and Demographic and Health Surveys (DHS). (We do not publish the original data files extracted from the LIS and DHS servers, for more information contact [email protected].). Data from the Global Data Lab (GDL) are used to generate a synthetic population when no microdata from LIS and DHS were available.
- The SCRIPTS folder contains the scripts to generate the database. Depending on which data were available, we created separate scripts. Nr_individuals_data_availability.csv provides details about the data that was available for each country. There are several options: LIS, LIS survey, LIS marginals, Modeled by LIS data, DHS, Modeled by DHS data, Excluded. To assess the goodness of fit of the synthetic population database, we evaluate how well the synthetic population resembles the observed survey data.
- The VALIDATION folder contains all the files necessary to evaluate the performance of our methods in handling missing data (missing micro data and/or missing regional statistics).
- The FIGURES folder contains the figures as published in -link to paper-.
- The READ_SYNTHPOP_DATA folder contains an R file and a python file that can be used to read the synthetic population data that is saved in binary format and stored on https://doi.org/10.7910/DVN/KJC3RH