Machine Learning Approach Enables Poverty Measurement with Reduced Survey Data in Nigeria
Researchers applied a Random Forest machine learning technique to Nigeria's 2018/19 household survey data to determine which minimal set of variables can reliably classify poverty and inequality. The study found that income poverty status could be predicted with roughly 90% accuracy using just five predictors, while consumption-based quintile classification reached about 80% accuracy from a single seasonal visit. The findings suggest that costly full household surveys could potentially be streamlined without losing critical distributional information needed to monitor poverty.
A new preprint study on arXiv uses Random Forest Recursive Feature Elimination (RF-RFE) to analyze Nigeria's 2018/19 General Household Survey-Panel, identifying the smallest set of income, consumption, and household characteristics needed to accurately classify individuals within the welfare distribution. The researchers examined three outcomes: poverty status, quintile placement, and position relative to a Gini-based inequality line, testing performance across both post-planting and post-harvest seasonal periods. Income-based poverty status classification achieved approximately 90% accuracy with only five predictors, with labour earnings emerging as the dominant signal for inequality-line placement. Consumption-based models accurately predicted poverty status and inequality-line position from a small number of expenditure categories, while quintile classification reached around 80% accuracy for seasonal data but dropped to 60–65% when annual consumption was estimated from a single seasonal visit. The authors argue these results demonstrate that machine learning can guide the design of shorter, cheaper survey instruments that still capture the distributional information essential for poverty monitoring in low- and middle-income countries.
What's missing
The study is based on a single country (Nigeria) and a single survey wave (2018/19), so generalizability to other low- and middle-income countries or time periods is untested. The authors do not address how model performance might degrade when applied prospectively to new survey rounds or populations with different economic structures. Potential fairness or equity concerns — such as whether prediction errors are systematically concentrated among particular demographic or geographic subgroups — are not discussed.
What different sources said
- arXiv cs.LGCenter
Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data
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