Template-type: ReDif-Paper 1.0
Author-Name: Dietrich, Stephan
Author-workplace-name: RS: GSBE MGSoG, Maastricht Graduate School of Governance, RS: UNU-MERIT Theme 2
Author-Name: Malerba, Daniele
Author-Name: Gassmann, Franziska
Author-workplace-name: RS: GSBE UM-BIC, RS: GSBE MORSE, Maastricht Graduate School of Governance, RS: GSBE MGSoG, RS: UNU-MERIT Theme 2, RS: UNU-MERIT Theme 6
Title: Predicting social assistance beneficiaries
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.
Classification-JEL: c53,i32,i38,h53,o12
Series: UNU-MERIT Working Papers
Creation-Date: 20230327
Number: 2023-007
File-URL: https://cris.maastrichtuniversity.nl/ws/files/130427816/wp2023_007.pdf
File-Format: application/pdf
File-Size: 1068514
Handle: Repec:unm:unumer:2023007
DOI: