IDEAS home Printed from https://ideas.repec.org/p/psi/resdis/16.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.psi.org.uk/docs/rdp/rdp16-refreshment-samples-matching-and-attrition-bias.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. Dolton, Peter, 2003. "Reducing Attrition Bias using Targeted Refreshment Sampling and Matching," Royal Economic Society Annual Conference 2003 65, Royal Economic Society.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcel Das & Vera Toepoel & Arthur van Soest, 2011. "Nonparametric Tests of Panel Conditioning and Attrition Bias in Panel Surveys," Sociological Methods & Research, , vol. 40(1), pages 32-56, February.
    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. Terence C. Cheng & Pravin K. Trivedi, 2015. "Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1101-1117, September.
    4. 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.
    5. Shin, Jaeun & Moon, Sangho, 2006. "Fertility, relative wages, and labor market decisions: A case of female teachers," Economics of Education Review, Elsevier, vol. 25(6), pages 591-604, December.
    6. Harding, Matthew & Lamarche, Carlos, 2019. "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, vol. 211(1), pages 61-82.
    7. Yamana Kazufumi, 2020. "Monte Carlo Evidence on the Estimation Method for Industry Dynamics," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-12, January.
    8. Nevo, Aviv, 2003. "Using Weights to Adjust for Sample Selection When Auxiliary Information Is Available," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 43-52, January.
    9. Emre Ekinci, 2009. "Dealing with Attrition When Refreshment Samples are Available: An Application to the Turkish Household Labor Force Survey," 2009 Meeting Papers 353, Society for Economic Dynamics.
    10. Heng Chen & Marie-Hélène Felt & Kim P. Huynh, 2017. "Retail payment innovations and cash usage: accounting for attrition by using refreshment samples," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 503-530, February.
    11. Martin Huber, 2012. "Identification of Average Treatment Effects in Social Experiments Under Alternative Forms of Attrition," Journal of Educational and Behavioral Statistics, , vol. 37(3), pages 443-474, June.
    12. Kapteyn, Arie & Michaud, Pierre-Carl & Smith, James P. & van Soest, Arthur, 2006. "Effects of Attrition and Non-Response in the Health and Retirement Study," IZA Discussion Papers 2246, Institute of Labor Economics (IZA).
    13. Michael Fertig & Stefanie Schurer, 2007. "Earnings Assimilation of Immigrants in Germany: The Importance of Heterogeneity and Attrition Bias," SOEPpapers on Multidisciplinary Panel Data Research 30, DIW Berlin, The German Socio-Economic Panel (SOEP).
    14. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    15. ter Horst, Jenke R. & Nijman, Theo E. & Verbeek, Marno, 2001. "Eliminating look-ahead bias in evaluating persistence in mutual fund performance," Journal of Empirical Finance, Elsevier, vol. 8(4), pages 345-373, September.
    16. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    17. Joachim Inkmann, 2010. "Estimating Firm Size Elasticities of Product and Process R&D," Economica, London School of Economics and Political Science, vol. 77(306), pages 384-402, April.
    18. Fertig, Michael & Schurer, Stefanie, 2007. "Labour Market Outcomes of Immigrants in Germany: The Importance of Heterogeneity and Attrition Bias," IZA Discussion Papers 2915, Institute of Labor Economics (IZA).
    19. Juergen Jung, 2022. "Estimating transition probabilities between health states using US longitudinal survey data," Empirical Economics, Springer, vol. 63(2), pages 901-943, August.
    20. Kapteyn, Arie & Michaud, Pierre-Carl & Smith, James P. & van Soest, Arthur, 2006. "Effects of Attrition and Non-Response in the Health and Retirement Study," IZA Discussion Papers 2246, Institute for the Study of Labor (IZA).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:psi:resdis:16. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rob Lyons (email available below). General contact details of provider: https://edirc.repec.org/data/psiiiuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.