Machine Learning Approach Enables Poverty Measurement with Reduced Survey Data in Nigeria
Researchers applied machine learning to Nigeria's household survey data to determine whether smaller, cheaper surveys could still accurately measure poverty and inequality. The study used Random Forest Recursive Feature Elimination to identify which income sources and consumption categories are most predictive of welfare status. The findings suggest that streamlined surveys could reduce costs while maintaining sufficient accuracy for poverty monitoring in low- and middle-income countries.
A new study published on arXiv examines whether reduced household survey instruments can preserve key distributional information needed to measure poverty and inequality in Nigeria. Using data from the 2018/19 Nigeria General Household Survey-Panel, researchers applied machine learning techniques to identify which income sources, consumption categories, and household characteristics best predict individuals' positions within the welfare distribution. The analysis evaluated three outcomes: poverty status, quintile distribution placement, and position relative to the Gini-based inequality line, with testing across different seasonal contexts. Results demonstrated strong classification accuracy with minimal predictors—poverty status reached approximately 90 percent accuracy using just five income-based predictors, while consumption-based poverty classification was similarly accurate with a small set of expenditure categories. Quintile classification achieved about 80 percent accuracy for seasonal consumption data but only 60-65 percent for annual consumption predicted from a single seasonal visit. The findings suggest machine learning methods could help improve survey design and reduce data collection costs while retaining sufficient information for poverty and inequality monitoring.
What's missing
The study's limitations regarding generalizability to other countries and contexts are not detailed in the abstract. Additionally, the specific machine learning model parameters, cross-validation methodology, and comparison to alternative statistical approaches are not discussed in the provided abstract.
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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