Machine learning predicts poverty with reduced Nigerian household data

Researchers have developed a machine learning approach using Random Forest Recursive Feature Elimination (RF-RFE) to identify key indicators for measuring poverty and inequality in Nigeria. By analyzing household survey data, the study found that a small set of income sources, consumption categories, and household characteristics can accurately predict poverty status and welfare distribution position. This method could significantly reduce the data requirements for future surveys, enabling more efficient monitoring of poverty and inequality in low- and middle-income countries. AI

IMPACT This research demonstrates how machine learning can optimize data collection for poverty and inequality metrics, potentially leading to more efficient and cost-effective monitoring in developing nations.

RANK_REASON Academic paper detailing a novel machine learning approach for data reduction in socio-economic surveys. [lever_c_demoted from research: ic=1 ai=1.0]

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• General Household Survey-Panel
• Nigeria
• Random Forest Recursive Feature Elimination

• paper
• other

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