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Which Model for Poverty Predictions?

Author

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  • Verme, Paolo

Abstract

OLS models are the predominant choice for poverty predictions in a variety of contexts such as proxy-means tests, poverty mapping or cross-survey impu- tations. This paper compares the performance of econometric and machine learning models in predicting poverty using alternative objective functions and stochastic dominance analysis based on coverage curves. It finds that the choice of an optimal model largely depends on the distribution of incomes and the poverty line. Comparing the performance of different econometric and machine learning models is therefore an important step in the process of opti- mizing poverty predictions and targeting ratios.

Suggested Citation

  • Verme, Paolo, 2020. "Which Model for Poverty Predictions?," GLO Discussion Paper Series 468, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:468
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    More about this item

    Keywords

    Welfare Modelling; Income Distributions; Poverty Predictions; Imputations;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • E64 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Incomes Policy; Price Policy
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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