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Endogeneity and Non-Response Bias in Treatment Evaluation: Nonparametric Identification of Causal Effects by Instruments

Listed author(s):
  • Fricke, Hans

    ()

    (University of St. Gallen)

  • Frölich, Markus

    ()

    (University of Mannheim)

  • Huber, Martin

    ()

    (University of Fribourg)

  • Lechner, Michael

    ()

    (University of St. Gallen)

This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non-response bias, using two instrumental variables. Making use of a discrete instrument for the treatment and a continuous instrument for non-response/attrition, we identify the average treatment effect on compliers as well as the total population and suggest non- and semiparametric estimators. We apply the latter to a randomized experiment at a Swiss University in order to estimate the effect of gym training on students' self-assessed health. The treatment (gym training) and attrition are instrumented by randomized cash incentives paid out conditional on gym visits and by a cash lottery for participating in the follow-up survey, respectively.

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File URL: http://ftp.iza.org/dp9428.pdf
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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 9428.

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Length: 36 pages
Date of creation: Oct 2015
Handle: RePEc:iza:izadps:dp9428
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  1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
  2. 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.
  3. Toru Kitagawa, 2015. "A Test for Instrument Validity," Econometrica, Econometric Society, vol. 83(5), pages 2043-2063, 09.
  4. Luc Behaghel & Bruno Crépon & Marc Gurgand & Thomas Le Barbanchon, 2015. "Please Call Again: Correcting Nonresponse Bias in Treatment Effect Models," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 1070-1080, December.
  5. Tan, Zhiqiang, 2006. "Regression and Weighting Methods for Causal Inference Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1607-1618, December.
  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. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.
  8. Lisa Farrell & Michael A. Shields, 2002. "Investigating the economic and demographic determinants of sporting participation in England," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 335-348.
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