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

• URL: http://arxiv.org/abs/2606.07614v1
• Date: Fri, 29 May 2026 19:33:31 GMT
• Title: Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data
• Abstract summary: We apply Random Forest Recursive Feature Elimination to the 2018/19 Nigeria General Household Survey-Panel.<n>For consumption, poverty status and inequality-line position are accurately predicted using a small set of expenditure categories.<n>For income, poverty status reaches around 90 percent accuracy with five predictors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliable measurement of income and consumption is essential for monitoring poverty and inequality in low- and middle-income countries, yet full household surveys are costly and difficult to implement regularly. This paper examines whether reduced survey instruments can preserve key distributional information. We apply Random Forest Recursive Feature Elimination (RF-RFE) to the 2018/19 Nigeria General Household Survey-Panel to identify the income sources, consumption categories and household characteristics that best classify individuals within the welfare distribution. The analysis focuses on three outcomes: poverty status, location in the quintile distribution and position relative to the Gini-based inequality line. The survey's post-planting and post-harvest periods allow us to assess performance under different seasonal contexts. Results show that RF-RFE achieves strong classification accuracy with few predictors. For consumption, poverty status and inequality-line position are accurately predicted using a small set of expenditure categories, while quintile classification reaches about 80 percent accuracy for seasonal consumption and 60--65 percent for annual consumption predicted from a single seasonal visit. For income, poverty status reaches around 90 percent accuracy with five predictors, and inequality-line position is largely captured by labour earnings. The findings suggest that machine-learning methods can help improve survey design and reduce data requirements while retaining much of the distributional information needed to measure and monitor poverty and inequality.

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