Which Model for Poverty Predictions?

Paolo Verme

No 521, Working Papers from ECINEQ, Society for the Study of Economic Inequality

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 optimizing poverty predictions and targeting ratios.

Keywords: Welfare Modelling; Income Distributions; Poverty Predictions; Imputations. (search for similar items in EconPapers)
JEL-codes: D31 D63 E64 O15 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2020-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Which Model for Poverty Predictions? (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:inq:inqwps:ecineq2020-521

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