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Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models

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  • Heckman, James J.

    ()
    (University of Chicago)

  • Navarro, Salvador

    ()
    (University of Wisconsin-Madison)

Abstract

This paper investigates four topics. (1) It examines the different roles played by the propensity score (probability of selection) in matching, instrumental variable and control functions methods. (2) It contrasts the roles of exclusion restrictions in matching and selection models. (3) It characterizes the sensitivity of matching to the choice of conditioning variables and demonstrates the greater robustness of control function methods to misspecification of the conditioning variables. (4) It demonstrates the problem of choosing the conditioning variables in matching and the failure of conventional model selection criteria when candidate conditioning variables are not exogenous.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 768.

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Length: 49 pages
Date of creation: Apr 2003
Date of revision:
Publication status: published in: Review of Economics and Statistics, 2004, 86(1), 30-57
Handle: RePEc:iza:izadps:dp768

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Keywords: identification; discrete choice; instrumental variables; control functions; matching;

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References

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  1. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 109(4), pages 673-748, August.
  2. Carneiro, Pedro & Heckman, James J. & Vytlacil, Edward, 2010. "Estimating Marginal Returns to Education," IZA Discussion Papers 5275, Institute for the Study of Labor (IZA).
  3. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, Econometric Society, vol. 70(1), pages 331-341, January.
  4. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
  5. Petra E. Todd & Jeffrey A. Smith, 2001. "Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods," American Economic Review, American Economic Association, American Economic Association, vol. 91(2), pages 112-118, May.
  6. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, Elsevier, vol. 66(1), pages 33-39, January.
  7. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 112(482), pages 854-893, October.
  8. Carneiro, Pedro & Hansen, Karsten & Heckman, James, 2003. "Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice," Working Paper Series, IFAU - Institute for Evaluation of Labour Market and Education Policy 2003:9, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  9. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," Review of Economic Studies, Oxford University Press, vol. 72(1), pages 1-19.
  10. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 64(4), pages 605-54, October.
  11. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
  12. Vijverberg, Wim P. M., 1993. "Measuring the unidentified parameter of the extended Roy model of selectivity," Journal of Econometrics, Elsevier, Elsevier, vol. 57(1-3), pages 69-89.
  13. Aakvik, Arild & Heckman, James J. & Vytlacil, Edward J., 2005. "Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 15-51.
  14. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, Elsevier, vol. 30(1-2), pages 239-267.
  15. 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, Wiley Blackwell, vol. 65(3), pages 497-517, July.
  16. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, Southern Economic Association, vol. 68(2), pages 210-223, October.
  17. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2002. "Removing the Veil of Ignorance in Assessing the Distributional Impacts of Social Policies," IZA Discussion Papers 453, Institute for the Study of Labor (IZA).
  18. Hansen, Karsten T & Heckman, James J & Mullen, Kathleen J, 2003. "The effect of schooling and ability on achievement test scores," Working Paper Series, IFAU - Institute for Evaluation of Labour Market and Education Policy 2003:13, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  19. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  20. James J. Heckman, 1998. "Detecting Discrimination," Journal of Economic Perspectives, American Economic Association, American Economic Association, vol. 12(2), pages 101-116, Spring.
  21. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, Econometric Society, vol. 66(2), pages 315-332, March.
  22. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  23. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
  24. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  25. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, American Economic Association, vol. 76(4), pages 604-20, September.
  26. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, Econometric Society, vol. 64(6), pages 1263-97, November.
  27. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, American Economic Association, vol. 80(2), pages 313-18, May.
  28. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
  29. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center, Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
  30. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
  31. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
  32. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, Elsevier, vol. 58(1-2), pages 3-29, July.
  33. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 64(4), pages 487-535, October.
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