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Sample attrition bias in randomized experiments: A tale of two surveys

Author

Listed:
  • Luc Behaghel

    (PSE - Paris School of Economics, LEA - Laboratoire d'Economie Appliquée - INRA - Institut National de la Recherche Agronomique, CREST - Centre de Recherche en Économie et Statistique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, J-PAL Europe - J-PAL Europe)

  • Bruno Crépon

    () (CREST - Centre de Recherche en Économie et Statistique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, J-PAL Europe - J-PAL Europe)

  • Marc Gurgand

    (PSE - Paris School of Economics, CREST - Centre de Recherche en Économie et Statistique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, J-PAL Europe - J-PAL Europe, PJSE - Paris-Jourdan Sciences Economiques - ENS Paris - École normale supérieure - Paris - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

  • Thomas Le Barbanchon

    (CREST - Centre de Recherche en Économie et Statistique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, Unité de gestion de la direction de l'action régionale et des relations avec l'enseignement supérieur (DARES) - INRA - Institut National de la Recherche Agronomique)

Abstract

The randomized trial literature has helped to renew the field of microeconometric policy evaluation by emphasizing identification issues raised by endogenous program participation. Measurement and attrition issues have perhaps received less attention. This paper analyzes the dramatic impact of sample attrition in a large job search experiment. We take advantage of two independent surveys on the same initial sample of 8,000 persons. The first one is a long telephone survey that had a strikingly low and unbalanced response rate of about 50%. The second one is a combination of administrative data and a short telephone survey targeted at those leaving the unemployment registers; this enriched data source has a balanced and much higher response rate (about 80%). With naive estimates that neglect non responses, these two sources yield puzzlingly different results. Using the enriched administrative data as benchmark, we find evidence that estimates from the long telephone survey lack external and internal validity. We turn to existing methods to bound the effects in the presence of sample selection; we extend them to the context of randomization with imperfect compliance. The bounds obtained from the two surveys are compatible but those from the long telephone survey are somewhat uninformative. We conclude on the consequences for data collection strategies.

Suggested Citation

  • Luc Behaghel & Bruno Crépon & Marc Gurgand & Thomas Le Barbanchon, 2009. "Sample attrition bias in randomized experiments: A tale of two surveys," PSE Working Papers halshs-00566836, HAL.
  • Handle: RePEc:hal:psewpa:halshs-00566836
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00566836
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    References listed on IDEAS

    as
    1. David Card & Raj Chetty & Andrea Weber, 2007. "The Spike at Benefit Exhaustion: Leaving the Unemployment System or Starting a New Job?," American Economic Review, American Economic Association, vol. 97(2), pages 113-118, May.
    2. Duflo, Esther & Glennerster, Rachel & Kremer, Michael, 2008. "Using Randomization in Development Economics Research: A Toolkit," Handbook of Development Economics, Elsevier.
    3. Ashenfelter, Orley & Ashmore, David & Deschenes, Olivier, 2005. "Do unemployment insurance recipients actively seek work? Evidence from randomized trials in four U.S. States," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 53-75.
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    Cited by:

    1. Damon Jones & Aprajit Mahajan, 2015. "Time-Inconsistency and Saving: Experimental Evidence from Low-Income Tax Filers," NBER Working Papers 21272, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    attrition; counselling; chômage; recherche d'emploi; accompagnement; sélection de l'échantillon; unemployment; job search; sample selection;

    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
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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