High-Resolution Poverty Maps in Sub-Saharan Africa

TL;DR

This paper introduces a machine learning-based method for creating high-resolution, accurate poverty maps at the village level across Sub-Saharan Africa, enabling cost-effective policy planning without extensive surveys.

Contribution

It presents a generalizable prediction methodology that improves the accuracy and feasibility of producing detailed poverty maps using geospatial data and machine learning.

Findings

Enhanced poverty map accuracy for 44 countries

Validated method across 25 countries with survey data

Enables poverty mapping without costly surveys

Abstract

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.