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Social policy targeting and binary information transfer between surveys

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  • Gottlieb, Daniel
  • Kushnir, Leonid

Abstract

In this paper we develop a methodology for identifying a population group surveyed latently in the (target) survey relevant for further processing, for example poverty calculations, but surveyed explicitly in another (source) survey, not suitable for such processing. Identification is achieved by transferring the binary information from the source survey to the target survey by means of a logistic regression determining group affiliation in the source survey by use of variables available also in the target survey. In the proposed methodology we improve on common matching procedures by optimizing the cut-value of the probability which assigns group affiliation in the target survey. This contrasts with the commonly used Hosmer-Lemeshov cut-values for binary categorization, which equates between the sensitivity and specificity curves. Instead we improve group identification by minimizing the sum of total errors as a percent of total true outcomes. The Jewish ultra-orthodox population in Israel serves as a case study. This idiosyncratic community, committed to the observance of the Bible is only latently observed in the surveys typically used for poverty calculation. It is explicitly captured in the social survey, which is not suitable for poverty measurement. This procedure is useful for ex-post enhancement of survey data in general.

Suggested Citation

  • Gottlieb, Daniel & Kushnir, Leonid, 2009. "Social policy targeting and binary information transfer between surveys," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-16.
  • Handle: RePEc:zbw:ifweej:200930
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    File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2009-30
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    File URL: https://www.econstor.eu/bitstream/10419/27724/1/602501393.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

    Group identification; binary variables; optimal cutoff value; poverty; targeting;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • Z12 - Other Special Topics - - Cultural Economics - - - Religion

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