A parsimonious approach to predicting income distributions

Abstract

This paper develops a method to predict comparable income and consumption distributions for all countries in the world from a simple regression with a handful of country-level variables. To fit the model, the analysis uses around 2,000 distributions from household surveys covering 168 countries from the World Bank's Poverty and Inequality Platform. More than 1,000 economic, demographic, and remote sensing predictors from multiple databases are used to test the models. A model is selected that balances out-of-sample accuracy, simplicity, and the share of countries for which it can be applied. The paper finds that a parsimonious model relying on gross domestic product per capita, under-5 mortality rate, life expectancy, and rural population share gives almost the same accuracy as a complex machine learning model using 1,000 indicators jointly. This small set of basic indicators related to human development explains most cross-country variation in income distributions and can facilitate distributional analysis even in countries with extreme data deprivation.