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Social Policy Targeting and Binary Information Transfer between Surveys

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  • Gottlieb, Daniel
  • Kushnir, Leonid

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.

Suggested Citation

  • Gottlieb, Daniel & Kushnir, Leonid, 2006. "Social Policy Targeting and Binary Information Transfer between Surveys," MPRA Paper 3127, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:3127
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    File URL: https://mpra.ub.uni-muenchen.de/3127/1/MPRA_paper_3127.pdf
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    References listed on IDEAS

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    1. Glewwe, Paul & van der Gaag, Jacques, 1990. "Identifying the poor in developing countries: Do different definitions matter?," World Development, Elsevier, vol. 18(6), pages 803-814, June.
    2. Eli Berman, 2000. "Sect, Subsidy, and Sacrifice: An Economist's View of Ultra-Orthodox Jews," The Quarterly Journal of Economics, Oxford University Press, vol. 115(3), pages 905-953.
    3. Bigman, David & Srinivasan, P. V., 2002. "Geographical targeting of poverty alleviation programs: methodology and applications in rural India," Journal of Policy Modeling, Elsevier, vol. 24(3), pages 237-255, June.
    4. Hentschel, Jesko, et al, 2000. "Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador," World Bank Economic Review, World Bank Group, vol. 14(1), pages 147-165, January.
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    Cited by:

    1. García-Fernández, Rosa María & Gottlieb, Daniel & Palacios-González, Frederico, 2013. "Polarization, growth and social policy in the case of Israel, 1997-2008," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-40.

    More about this item

    Keywords

    ROC curves; Binary Variables; Logistic Regression; Group Identification; Optimal Cutoff Value; Poverty Targeting; Poverty Mapping; Small Area Estimation;

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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