Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/326857 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
UNU-MERIT Working Papers No. 2023-007
Verlag: 
United Nations University (UNU), Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht
Zusammenfassung: 
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.
Schlagwörter: 
Proxy Means Test
Targeting
Cash Transfers
Social Protection
Fair Machine Learning
JEL: 
C53
I32
I38
H53
O12
Creative-Commons-Lizenz: 
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Dokumentart: 
Working Paper

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