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How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment

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  • Elizabeth Ty Wilde

    (Princeton University)

  • Robinson Hollister

    (Swarthmore College)

Abstract

In recent years, propensity score matching (PSM) has gained attention as a potential method for estimating the impact of public policy programs in the absence of experimental evaluations. In this study, we evaluate the usefulness of PSM for estimating the impact of a program change in an educational context (Tennessee's Student Teacher Achievement Ratio Project [Project STAR]). Because Tennessee's Project STAR experiment involved an effective random assignment procedure, the experimental results from this policy intervention can be used as a benchmark, to which we compare the impact estimates produced using propensity score matching methods. We use several different methods to assess these nonexperimental estimates of the impact of the program. We try to determine “how close is close enough,” putting greatest emphasis on the question: Would the nonexperimental estimate have led to the wrong decision when compared to the experimental estimate of the program? We find that propensity score methods perform poorly with respect to measuring the impact of a reduction in class size on achievement test scores. We conclude that further research is needed before policymakers rely on PSM as an evaluation tool. © 2007 by the Association for Public Policy Analysis and Management

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Policy Analysis and Management.

Volume (Year): 26 (2007)
Issue (Month): 3 ()
Pages: 455-477

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Handle: RePEc:wly:jpamgt:v:26:y:2007:i:3:p:455-477

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Web page: http://www3.interscience.wiley.com/journal/34787/home

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References

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  1. Alan B. Krueger, 1997. "Experimental Estimates of Education Production Functions," NBER Working Papers 6051, National Bureau of Economic Research, Inc.
  2. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, Econometric Society, vol. 76(6), pages 1537-1557, November.
  3. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 305-353.
  4. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
  5. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, Econometric Society, vol. 66(5), pages 1017-1098, September.
  6. 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.
  7. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, StataCorp LP, vol. 4(3), pages 290-311, September.
  8. Roberto Agodini & Mark Dynarski, 2004. "Are Experiments the Only Option? A Look at Dropout Prevention Programs," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 180-194, February.
  9. 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.
  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. Friedlander, Daniel & Robins, Philip K, 1995. "Evaluating Program Evaluations: New Evidence on Commonly Used Nonexperimental Methods," American Economic Review, American Economic Association, American Economic Association, vol. 85(4), pages 923-37, September.
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Cited by:
  1. Barnow, Burt S., 2010. "Setting up social experiments: the good, the bad, and the ugly," Zeitschrift für ArbeitsmarktForschung - Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 43(2), pages 91-105.
  2. Gary King & Emmanuela Gakidou & Nirmala Ravishankar & Ryan T. Moore & Jason Lakin & Manett Vargas & Martha Mar�a Téllez-Rojo & Juan Eugenio Hernández �vila & Mauricio Hernández �vila & Hécto, 2007. "A “politically robust” experimental design for public policy evaluation, with application to the Mexican Universal Health Insurance program," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 26(3), pages 479-506.
  3. Richard P. Nathan, 2008. "The role of random assignment in social policy research," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 27(2), pages 401-415.
  4. David A. Freedman, 2009. "Limits of Econometrics," International Econometric Review (IER), Econometric Research Association, Econometric Research Association, vol. 1(1), pages 5-17, April.
  5. Katz, Lawrence & Duncan, Greg J. & Kling, Jeffrey R. & Kessler, Ronald C. & Ludwig, Jens & Sanbonmatsu, Lisa & Liebman, Jeffrey B., 2008. "What Can We Learn about Neighborhood Effects from the Moving to Opportunity Experiment?," Scholarly Articles 2766959, Harvard University Department of Economics.
  6. Robert Bifulco, 2010. "Can Propensity Score Analysis Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within-Study Comparison," Center for Policy Research Working Papers, Center for Policy Research, Maxwell School, Syracuse University 124, Center for Policy Research, Maxwell School, Syracuse University.
  7. Gennetian, Lisa A. & Hill, Heather D. & London, Andrew S. & Lopoo, Leonard M., 2010. "Maternal employment and the health of low-income young children," Journal of Health Economics, Elsevier, Elsevier, vol. 29(3), pages 353-363, May.

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