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The performance of sample selection estimators to control for attrition bias


  • Astrid Grasdal

    (Department of Economics, University of Bergen, Bergen, Norway)


Sample attrition is a potential source of selection bias in experimental, as well as non-experimental programme evaluation. For labour market outcomes, such as employment status and earnings, missing data problems caused by attrition can be circumvented by the collection of follow-up data from administrative registers. For most non-labour market outcomes, however, investigators must rely on participants' willingness to co-operate in keeping detailed follow-up records and statistical correction procedures to identify and adjust for attrition bias. This paper combines survey and register data from a Norwegian randomized field trial to evaluate the performance of parametric and semi-parametric sample selection estimators commonly used to correct for attrition bias. The considered estimators work well in terms of producing point estimates of treatment effects close to the experimental benchmark estimates. Results are sensitive to exclusion restrictions. The analysis also demonstrates an inherent paradox in the 'common support' approach, which prescribes exclusion from the analysis of observations outside of common support for the selection probability. The more important treatment status is as a determinant of attrition, the larger is the proportion of treated with support for the selection probability outside the range, for which comparison with untreated counterparts is possible. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • Astrid Grasdal, 2001. "The performance of sample selection estimators to control for attrition bias," Health Economics, John Wiley & Sons, Ltd., vol. 10(5), pages 385-398.
  • Handle: RePEc:wly:hlthec:v:10:y:2001:i:5:p:385-398 DOI: 10.1002/hec.628

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    References listed on IDEAS

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    Cited by:

    1. Reichert, Arndt & Tauchmann, Harald, 2014. "When outcome heterogeneously matters for selection: a generalized selection correction estimator," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 762-768.
    2. Duflo, Esther & Glennerster, Rachel & Kremer, Michael, 2008. "Using Randomization in Development Economics Research: A Toolkit," Handbook of Development Economics, Elsevier.
    3. Hernandez-Hernandez, Emilio & Sam, Abdoul G. & Gonzalez-Vega, Claudio & Chen, Joyce J., 2012. "Does the insurance effect of public and private transfers favor financial deepening? evidence from rural Nicaragua," MPRA Paper 38339, University Library of Munich, Germany.
    4. Chivers, David, 2017. "Success, survive or escape? Aspirations and poverty traps," Journal of Economic Behavior & Organization, Elsevier, pages 116-132.
    5. Alison Snow Jones & David W. Richmond, 2006. "Causal effects of alcoholism on earnings: estimates from the NLSY," Health Economics, John Wiley & Sons, Ltd., vol. 15(8), pages 849-871.
    6. Arndt Reichert & Harald Tauchmann, 2012. "When Outcome Heterogeneously Matters for Selection – A Generalized Selection Correction Estimator," Ruhr Economic Papers 0372, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    7. repec:zbw:rwirep:0372 is not listed on IDEAS

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure


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