Pover-T Tests: Predicting Poverty

Measuring poverty is hard. Thanks to the efforts of thousands of competitors, The World Bank can now build on open source machine learning tools to help predict poverty, optimize uses of survey data, and support work to end extreme poverty … #development

$15,000 in prizes
feb 2018
2,308 joined


Collecting household survey data on poverty is expensive and time-consuming, which means that policy makers are often making decisions based on old data. Machine learning could drastically change the game, making poverty measurement cheaper and much closer to real-time.

– Asli Demirguc-Kunt, Director of Research at the World Bank


The World Bank aims to end extreme poverty by 2030. To achieve this goal, they need efficient pipelines for measuring, tracking, and predicting poverty. But measuring poverty is currently hard, time consuming, and expensive. Estimates are typically collected through complex household consumption surveys with data on hundreds of different variables, each of which may be useful when assessing poverty levels.

The Solution

Machine learning offers new approaches for determining which variables are most predictive and how they can be most effectively combined. In this competition, data scientists from more than 130 countries around the world built algorithms to predict household-level poverty status using surveys data from three developing countries, each with a different distribution of wealth. 

The Results

The best algorithms pulled out all the stops, creating ensembles of neural networks, XGBoost, LightGBM, and even CatBoost (to leverage the mostly-categorical nature of the survey data) models. These approaches fed into a research paper publishing the winning solutions and contributing to the democratization of machine learning through resources for future application and training.