Social Policy Targeting and Binary Information Transfer between Surveys

Daniel Gottlieb () and Leonid Kushnir

MPRA Paper from University Library of Munich, Germany

Abstract: This paper deals with the optimal transfer of binary information (BIT) on group membership between different statistical surveys of an identical population, a need arising frequently in socio-economic surveys. The limited number of questions asked in any one survey may necessitate information transfer between surveys. We design a method for a a BIT between a source-survey originally including the information and a target survey in which it is needed. An efficient BIT depends on (1) efficient estimation of the statistical model explaining group-membership as estimated by the ROC-curve, (2) the choice of a cutoff value for translating the forecasted logistic probability back into a binary variable and (3) a statistically testable quality control of the transfer. We suggest an optimal cutoff point that minimizes the sum of squared errors instead of the well-known Hosmer-Lemeshow method. Our application illustrates how survey data can be enhanced, when repeated interviews are expensive or difficult to implement. We enhance the Household Expenditure Survey (HES) by transferring a binary variable of households' religious group membership from the Social Survey to the HES. This helps identify extremely poor groups for poverty calculations and improved targeting of anti-poverty policy.

Keywords: ROC curves; Binary Variables; Logistic Regression; Group Identification; Optimal Cutoff Value; Poverty Targeting; Poverty Mapping; Small Area Estimation (search for similar items in EconPapers)
JEL-codes: C46 (search for similar items in EconPapers)
Date: 2006-08
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:3127

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