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Matching vs Differencing when Estimating Treatment Effects with Panel Data: the Example of the Effect of Job Training Programs on Earnings

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  • Chabé-Ferret, Sylvain

Abstract

This paper compares matching and Difference-In-Difference matching (DID) when estimating the effect of a program on a dynamic outcome. I detail the sources of bias of each estimator in a model of entry into a Job Training Program (JTP) and earnings dynamics that I use as a working example. I show that there are plausible settings in which DID is consistent while matching on past outcomes is not. Unfortunately, the consistency of both estimators relies on conditions that are at odds with properties of earnings dynamics. Using calibration and Monte-Carlo simulations, I show that deviations from the most favorable conditions severely bias both estimators. The behavior of matching is nevertheless less erratic: its bias generally decreases when controlling for more past outcomes and it generally provides a lower bound on the true treatment effect. I finally point to previously unnoticed empirical results that confirm that DID does well, and generally better than matching on past outcomes, at replicating the results of an experimental benchmark.

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Paper provided by Toulouse School of Economics (TSE) in its series TSE Working Papers with number 12-356.

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Date of creation: Oct 2012
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Handle: RePEc:tse:wpaper:26567

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Keywords: Matching - Difference in Difference - Evaluation of Job training Programs.;

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  1. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
  2. Gobillon, Laurent & Magnac, Thierry & Selod, Harris, 2010. "Do unemployed workers benefit from enterprise zones? The French experience," TSE Working Papers 10-201, Toulouse School of Economics (TSE).
  3. Ziebarth N & Karlsson M, 2009. "A Natural Experiment on Sick Pay Cuts, Sickness Absence, and Labor Costs," Health, Econometrics and Data Group (HEDG) Working Papers 09/34, HEDG, c/o Department of Economics, University of York.
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  6. Puhani, Patrick A. & Sonderhof, Katja, 2010. "The effects of a sick pay reform on absence and on health-related outcomes," Journal of Health Economics, Elsevier, vol. 29(2), pages 285-302, March.
  7. Mayer,T. & Mayneris, F. & Py, L., 2013. "The impact of Urban Enterprise Zones on establishment location decisions: Evidence from French ZFUs," Working papers 458, Banque de France.
  8. 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.
  9. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
  10. Jay Bhattacharya & William B. Vogt, 2007. "Do Instrumental Variables Belong in Propensity Scores?," NBER Technical Working Papers 0343, National Bureau of Economic Research, Inc.
  11. Barry Arnold & Robert Beaver & Richard Groeneveld & William Meeker, 1993. "The nontruncated marginal of a truncated bivariate normal distribution," Psychometrika, Springer, vol. 58(3), pages 471-488, September.
  12. Markus Frölich, 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 77-90, February.
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