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Missing Treatments

  • Molinari, Francesca

    (Cornell U)

The existing literature on treatment e¤ects assumes perfect observability of the treatments received by the population of interest. Even in cases of imperfect compliance, it is usually as- sumed that both the assigned and administered treatment are observed (or missing completely at random). This paper abandons such assumptions. Imperfect observability of the received treatment can arise as a result of survey nonresponse in observational studies, or noncompliance with randomly assigned treatments that are not directly monitored. I study the problem in the context of observational studies. I derive sharp worst case bounds without assuming anything about treatment selection, and I show that the bounds are a function of the available prior information on the distribution of the missing treatments. Under the maintained assumption of monotone treatment response, I show that no prior information on the distribution of missing treatments is necessary to get sharp informative bounds. I apply the methodologies recently proposed by Imbens and Manski (2004) and Chernozhukov, Hong, and Tamer (2004) to derive two types of confidence intervals for the partially identi.ed parameters. The results are illustrated with an empirical analysis of drug use and employment using data from the National Longitudinal Survey of Youth.

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Paper provided by Cornell University, Center for Analytic Economics in its series Working Papers with number 05-11.

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Date of creation: May 2005
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Handle: RePEc:ecl:corcae:05-11
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  1. Kaestner, Robert & Grossman, Michael, 1995. "Wages, Workers' Compensation Benefits, and Drug Use: Indirect Evidence of the Effect of Drugs on Workplace Accidents," American Economic Review, American Economic Association, vol. 85(2), pages 55-60, May.
  2. 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.
  3. Robert Kaestner, 1994. "The Effect of Illicit Drug Use on the Labor Supply of Young Adults," Journal of Human Resources, University of Wisconsin Press, vol. 29(1), pages 126-155.
  4. Charles A. Register & Donald R. Williams, 1992. "Labor market effects of marijuana and cocaine use among young men," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 45(3), pages 435-451, April.
  5. Mullahy, John & Sindelar, Jody, 1996. "Employment, unemployment, and problem drinking," Journal of Health Economics, Elsevier, vol. 15(4), pages 409-434, August.
  6. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
  7. Andrew M. Gill & Robert J. Michaels, 1992. "Does drug use lower wages?," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 45(3), pages 419-434, April.
  8. E. Tamer & V. Chernozhukov & H. Hong, 2004. "Parameter Set Inference in a Class of Econometric Models," Econometric Society 2004 North American Winter Meetings 382, Econometric Society.
  9. Pizer, William & Imbens, Guido, 2000. "The Analysis of Randomized Experiments with Missing Data," Discussion Papers dp-00-19, Resources For the Future.
  10. Kaestner, Robert, 1991. "The Effect of Illicit Drug Use on the Wages of Young Adults," Journal of Labor Economics, University of Chicago Press, vol. 9(4), pages 381-412, October.
  11. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
  12. Juan Carlos Chavez-Martin del Campo, 2004. "Partial Identification of Poverty Measures with Contaminated Data," Econometric Society 2004 Latin American Meetings 221, Econometric Society.
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