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Please Call Again: Correcting Non-Response Bias in Treatment Effect Models

Author

Listed:
  • Behaghel, Luc

    () (Paris School of Economics)

  • Crépon, Bruno

    () (CREST)

  • Gurgand, Marc

    () (Paris School of Economics)

  • Le Barbanchon, Thomas

    () (Bocconi University)

Abstract

We propose a novel selectivity correction procedure to deal with survey attrition, at the crossroads of the "Heckit" and of the bounding approach of Lee (2009). As a substitute for the instrument needed in sample selectivity correction models, we use information on the number of attempts that were made to obtain response to the survey from each individual who responded. We obtain set identification, but if the number of attempts to reach each individual is high enough, we can come closer to point identification. We apply our sample selection correction in the context of a job-search experiment with low and unbalanced response rates.

Suggested Citation

  • Behaghel, Luc & Crépon, Bruno & Gurgand, Marc & Le Barbanchon, Thomas, 2012. "Please Call Again: Correcting Non-Response Bias in Treatment Effect Models," IZA Discussion Papers 6751, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp6751
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    References listed on IDEAS

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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. Michael Kremer & Edward Miguel & Rebecca Thornton, 2009. "Incentives to Learn," The Review of Economics and Statistics, MIT Press, vol. 91(3), pages 437-456, August.
    3. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
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    Cited by:

    1. Teresa Molina Millán & Karen Macours, 2017. "Attrition in randomized control trials: Using tracking information to correct bias," FEUNL Working Paper Series novaf:wp1702, Universidade Nova de Lisboa, Faculdade de Economia.
    2. de Chaisemartin, Clement, 2013. "Defying the LATE? Identification of local treatment effects when the instrument violates monotonicity," The Warwick Economics Research Paper Series (TWERPS) 1020, University of Warwick, Department of Economics.
    3. Teresa Molina Millan & Karen Macours, 2017. "Attrition in randomized control trials: Using tracking information to correct bias," NOVAFRICA Working Paper Series wp1702, Universidade Nova de Lisboa, Faculdade de Economia, NOVAFRICA.
    4. Kassenboehmer, Sonja C. & Schurer, Stefanie & Leung, Felix, 2015. "Testing the Validity of Item Non-Response as a Proxy for Cognitive and Non-Cognitive Skills," IZA Discussion Papers 8874, Institute for the Study of Labor (IZA).
    5. Fricke, Hans & Frölich, Markus & Huber, Martin & Lechner, Michael, 2015. "Endogeneity and Non-Response Bias in Treatment Evaluation: Nonparametric Identification of Causal Effects by Instruments," IZA Discussion Papers 9428, Institute for the Study of Labor (IZA).
    6. Heng Chen & Geoffrey R. Dunbar & Rallye Shen, 2017. "The Mode is the Message: Using Predata as Exclusion Restrictions to Evaluate Survey Design," Staff Working Papers 17-43, Bank of Canada.
    7. David McKenzie, 2017. "Identifying and Spurring High-Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition," American Economic Review, American Economic Association, vol. 107(8), pages 2278-2307, August.
    8. TENIKUE Michel & TEQUAME Miron, 2018. "Economic and Health Impacts of the 2011 Post-Electoral Crisis in Côte d?Ivoire: Evidence from Microdata," LISER Working Paper Series 2018-03, LISER.

    More about this item

    Keywords

    treatment effect model; sample selectivity; survey non response; randomized controlled trial;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • J6 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers

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