Predicting social assistance beneficiaries

Stephan Dietrich, Daniele Malerba and Franziska Gassmann
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Stephan Dietrich: RS: GSBE MGSoG, Maastricht Graduate School of Governance, RS: UNU-MERIT Theme 2

No 2023-007, MERIT Working Papers from United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT)

Abstract: Targeting error assessments for social transfers commonly rely on accuracy as a performance metric. This process is typically insensitive to the distributional position of incorrectly classified households. In this paper we develop an extended targeting assessment framework for proxy means tests that accounts for societal sensitivity to targeting errors. We use a social welfare framework to weight targeting errors depending on their position in the welfare distribution and for different levels of societal inequality aversion. While this provides a more comprehensive assessment of targeting performance, we show with two case studies that bias in the data, here in the form of label bias and unstable proxy means testing weights, leads to substantial underestimation of welfare losses that disadvantage some groups more than others.

JEL-codes: C53 H53 I32 I38 O12 (search for similar items in EconPapers)
Date: 2023-03-27
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Persistent link: https://EconPapers.repec.org/RePEc:unm:unumer:2023007

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