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A Note on Adapting Propensity Score Matching and Selection Models to Choice Based Samples

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Author Info

  • Heckman, James J.

    ()
    (University of Chicago)

  • Todd, Petra E.

    ()
    (University of Pennsylvania)

Abstract

The probability of selection into treatment plays an important role in matching and selection models. However, this probability can often not be consistently estimated, because of choice-based sampling designs with unknown sampling weights. This note establishes that the selection and matching procedures can be implemented using propensity scores fit on choice-based samples with misspecified weights, because the odds ratio of the propensity score fit on the choice-based sample is monotonically related to the odds ratio of the true propensity scores.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4304.

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Length: 12 pages
Date of creation: Jul 2009
Date of revision:
Publication status: published in: Econometric Journal, 2009, 12 (Supplement), S230-S234
Handle: RePEc:iza:izadps:dp4304

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Related research

Keywords: propensity scores; matching models; choice-based sampling; selection models;

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References

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  1. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2009. "Understanding Instrumental Variables in Models with Essential Heterogeneity," Working Papers 200941, Geary Institute, University College Dublin.
  2. 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 88-12, Chicago - Economics Research Center.
  3. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
  4. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  5. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  6. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-88, November.
  7. James J. Heckman & Salvador Navarro-Lozano, 2003. "Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models," NBER Working Papers 9497, National Bureau of Economic Research, Inc.
  8. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  9. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
  10. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
  11. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
  12. Manski, Charles F., 1986. "Semiparametric analysis of binary response from response-based samples," Journal of Econometrics, Elsevier, vol. 31(1), pages 31-40, February.
  13. 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.
  14. 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.
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Citations

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Cited by:
  1. Peter R. Mueser & Carolyn J. Heinrich & Kenneth R. Troske & Kyung-Seong Jeon & Daver C. Kahvecioglu, 2010. "New Estimates of Public Employment and Training Program Net Impacts: A Nonexperimental Evaluation of the Workforce Investment Act Program," Working Papers 1003, Department of Economics, University of Missouri.
  2. Nancy Nicosia & John M. MacDonald & Rosalie Liccardo Pacula, 2012. "Does Mandatory Diversion to Drug Treatment Eliminate Racial Disparities in the Incarceration of Drug Offenders? An Examination of California’s Proposition 36," NBER Working Papers 18518, National Bureau of Economic Research, Inc.
  3. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
  4. Okudaira, Hiroko & Ohtake, Fumio & Kume, Koichi & Tsuru, Kotaro, 2013. "What does a temporary help service job offer? Empirical suggestions from a Japanese survey," Journal of the Japanese and International Economies, Elsevier, vol. 28(C), pages 37-68.
  5. Richard Disney & Eleonora Fischera & Trudy Owens, . "Has the Introduction of Microfinance Crowded-out Informal Loans in Malawi?," Discussion Papers 10/08, University of Nottingham, CREDIT.
  6. Bernal, Raquel & Fernández, Camila, 2013. "Subsidized childcare and child development in Colombia: Effects of Hogares Comunitarios de Bienestar as a function of timing and length of exposure," Social Science & Medicine, Elsevier, vol. 97(C), pages 241-249.

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