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Trimming for Bounds on Treatment Effects with Missing Outcomes

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  • David S. Lee
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    Abstract

    Empirical researchers routinely encounter sample selection bias whereby 1) the regressor of interest is assumed to be exogenous, 2) the dependent variable is missing in a potentially non-random manner, 3) the dependent variable is characterized by an unbounded (or very large) support, and 4) it is unknown which variables directly affect sample selection but not the outcome. This paper proposes a simple and intuitive bounding procedure that can be used in this context. The proposed trimming procedure yields the tightest bounds on average treatment effects consistent with the observed data. The key assumption is a monotonicity restriction on how the assignment to treatment effects selection -- a restriction that is implicitly assumed in standard formulations of the sample selection problem.

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    Bibliographic Info

    Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0277.

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    Date of creation: Jun 2002
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    Handle: RePEc:nbr:nberte:0277

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    References

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    1. Angrist, J.D., 1996. "Conditional Independance in Sample Selection Models," Working papers 96-27, Massachusetts Institute of Technology (MIT), Department of Economics.
    2. repec:att:wimass:8909 is not listed on IDEAS
    3. Horowitz, Joel & Manski, Charles, 1997. "Nonparametric Analysis of Randomized Experiments With Missing Covariate and Outcome Data," Working Papers 97-16, University of Iowa, Department of Economics.
    4. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
    5. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
    6. 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.
    7. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
    8. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May.
    9. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-94, July.
    10. Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," Review of Economic Studies, Wiley Blackwell, vol. 70(1), pages 33-58, January.
    11. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
    12. Andrews, Donald W K & Schafgans, Marcia M A, 1998. "Semiparametric Estimation of the Intercept of a Sample Selection Model," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 497-517, July.
    13. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-18, May.
    14. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
    15. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
    16. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
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    Cited by:
    1. repec:feb:natura:0005 is not listed on IDEAS
    2. Joshua Angrist & Eric Bettinger & Michael Kremer, 2004. "Long-term consequences of secondary school vouchers: Evidence from administrative records in colombia," Natural Field Experiments 00204, The Field Experiments Website.
    3. David S. Lee, 2005. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," NBER Working Papers 11721, National Bureau of Economic Research, Inc.
    4. Kremer, Michael R. & Miguel, Edward & Thornton, Rebecca, 2009. "Incentives to Learn," Scholarly Articles 3716457, Harvard University Department of Economics.
    5. Gustavo Bobonis & Edward Miguel & Charu Sharma, 2004. "Iron deficiency anemia and school participation," Natural Field Experiments 00219, The Field Experiments Website.
    6. Guido Imbens & Charles F. Manski, 2003. "Confidence intervals for partially identified parameters," CeMMAP working papers CWP09/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Rafael Lalive & Analía Schlosser & Andreas Steinhauer & Josef Zweimüller, 2011. "Parental Leave and Mothers' Careers: The Relative Importance of Job Protection and Cash Benefits," NRN working papers 2011-14, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    8. John DiNardo & David S. Lee, 2002. "The Impact of Unionization on Establishment Closure: A Regression Discontinuity Analysis of Representation Elections," NBER Working Papers 8993, National Bureau of Economic Research, Inc.
    9. Coelli, Michael B., 2011. "Parental job loss and the education enrollment of youth," Labour Economics, Elsevier, vol. 18(1), pages 25-35, January.
    10. Philip Oreopoulos, 2003. "The Long-Run Consequences Of Living In A Poor Neighborhood," The Quarterly Journal of Economics, MIT Press, vol. 118(4), pages 1533-1575, November.
    11. Gustavo J. Bobonis & Edward l Miguel & Charu Puri-Sharma, 2006. "Anaemia and School Participation," Working Papers id:337, eSocialSciences.

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