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Using matched substitutes to adjust for nonignorable nonresponse: an empirical investigation using labour market data

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  • Richard Dorsett

Abstract

This paper assesses the potential for reducing attrition bias by replacing survey dropouts with individuals from a refreshment sample, identified using propensity score matching. By linking administrative records with survey data, it is possible to observe outcomes for dropouts and therefore to test models of attrition. Doing so reveals the dropout process to be nonignorable such that the commonly-used method of reweighting non-dropouts is ineffective in overcoming attrition bias. By constructing artificial refreshment samples, the potential for replacing dropouts with individuals from a targeted refreshment sample is demonstrated. Where the targeting of the refreshment sample is imperfect, the success of the approach is more qualified. However, controlling for historic outcomes, the attrition bias can still be reduced.

Suggested Citation

  • Richard Dorsett, 2004. "Using matched substitutes to adjust for nonignorable nonresponse: an empirical investigation using labour market data," PSI Research Discussion Series 16, Policy Studies Institute, UK.
    • Handle: RePEc:psi:resdis:16
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    File URL: http://www.psi.org.uk/docs/rdp/rdp16-refreshment-samples-matching-and-attrition-bias.pdf
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    1. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    2. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    4. Ridder, Geert, 1992. "An empirical evaluation of some models for non-random attrition in panel data," Structural Change and Economic Dynamics, Elsevier, vol. 3(2), pages 337-355, December.
    5. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    6. Dolton, Peter, 2003. "Reducing Attrition Bias using Targeted Refreshment Sampling and Matching," Royal Economic Society Annual Conference 2003 65, Royal Economic Society.
    7. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
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    Cited by:

    1. Doreen Triebe, 2015. "The Added Worker Effect Differentiated by Gender and Partnership Status: Evidence from Involuntary Job Loss," SOEPpapers on Multidisciplinary Panel Data Research 740, DIW Berlin, The German Socio-Economic Panel (SOEP).

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