Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data

2026-05-29 · Source: Machine Learning · Field: Science & Research — Social Sciences & Behavioral Studies, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

A study utilizing Random Forest Recursive Feature Elimination (RF-RFE) on the 2018/19 Nigeria General Household Survey-Panel demonstrates that reduced survey instruments can effectively preserve key distributional information for measuring poverty and inequality. The analysis focused on predicting poverty status, location within the quintile distribution, and position relative to the Gini-based inequality line, assessing performance across post-planting and post-harvest seasonal contexts. Results indicate that RF-RFE achieves strong classification accuracy with minimal predictors. For consumption, poverty status and inequality-line position were accurately predicted using a small set of expenditure categories, while quintile classification reached approximately 80 percent accuracy for seasonal consumption and 60-65 percent for annual consumption derived from a single seasonal visit. Income-based poverty status achieved around 90 percent accuracy with just five predictors, and the inequality-line position was largely captured by labor earnings.

Key takeaway

For policy analysts and data scientists tasked with designing and implementing poverty monitoring surveys in low- and middle-income countries, you should consider integrating machine learning techniques like RF-RFE. This approach allows you to significantly reduce data collection requirements while retaining high accuracy for key welfare metrics, such as poverty status and inequality-line position. This can lead to more frequent and cost-effective data collection, improving the timeliness of your policy interventions.

Key insights

Machine learning, specifically RF-RFE, can accurately measure poverty and inequality using significantly reduced household survey data in low-income countries.

Principles

• Reduced data can maintain distributional info.
• RF-RFE effectively identifies key welfare predictors.
• Seasonal data impacts prediction accuracy.

Method

Apply Random Forest Recursive Feature Elimination (RF-RFE) to identify minimal predictors for poverty status, quintile distribution, and Gini-based inequality line from household survey data.

In practice

• Design more efficient household surveys.
• Monitor poverty with fewer data points.
• Tailor data collection to seasonal contexts.

Topics

• Poverty Measurement
• Inequality Monitoring
• Machine Learning
• Random Forest RFE
• Household Surveys
• Nigeria
• Data Reduction

Best for: AI Scientist, Data Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.