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

arXiv:2606.07614v1 Announce Type: new 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 th