Synthetic Data for an Imaginary Country, Full Population, 2023
A synthetic hierarchical dataset for simulation and training purposes
This dataset is part of a collection of fully synthetic data generated, for training and simulation purposes, for an imaginary middle-income country. The dataset is available in English and French. A subset of 8,000 households is also available in English and French as a "synthetic survey dataset".
The dataset is a relational dataset of 10,003,891 individuals (2,501,755 households), representing the entire population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.
A sample dataset of 8000 households was created out of this full-population dataset, and is also distributed as open data.
Kind of Data
Unit of Analysis
V. 2023-05-01 10M PP EN
Dataset generated using RealTabFormer (with 10,003,891 individuals and 2,501,755 households), with post-processing. English version.
The dataset is a synthetic dataset generated using a model that used data from IPUMS International, from the Demographic and Health Survey program, and from the World Bank Global Consumption Database (microdata) as training data. The detailed list of datasets used to create the training datasets is available in the Technical Documentation (document provided as an external resource).
water and sanitation
The dataset is a synthetic dataset for an imaginary country. It was created to represent the full national population of this country, by province and district (equivalent to admin1 and admin2 levels) and by urban/rural areas of residence.
province (admin1), district (admin2)
The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.
Producers and sponsors
Development Data Group, Data Analytics Unit
Sponsored research work for the development of synthetic data for the purpose of assessing statistical disclosure risk measures.
UNHCR-World Bank Joint Data Center on Forced Displacement
The dataset is the equivalent of a census dataset. No weighting applies.
Dates of Data Collection
Data Collection Mode
The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.
The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.
The dataset was generated as a fully-synthetic dataset. The model used to create the synthetic observations includes multiple procedures to avoid overfitting and data-copying. Also, the data used for training the model went through processes of sampling and recoding that make it impossible to link a synthetic observation to an actual observation. The dataset is thus safe for dissemination. It can be used with no restriction and is shared as open data.
World Bank, Microdata Library
Location of Data Collection
World Bank Microdata Library
Disclaimer and copyrights
The data are to be used for training or simulation purposes only. It is not intended to be representative of any particular country, and should not be used for inference purpose.