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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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Bibliographic Info

Paper provided by LERNA, University of Toulouse in its series LERNA Working Papers with number 12.24.381.

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Date of creation: Oct 2012
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Handle: RePEc:ler:wpaper:26551

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  1. Martin Browning & Mette Ejrnaes, 2006. "Modelling income processes with lots of heterogeneity," Economics Series Working Papers 285, University of Oxford, Department of Economics.
  2. Patrick A. Puhani & Katja Sonderhof, 2009. "The Effects of a Sick Pay Reform on Absence and on Health-Related Outcomes," SOEPpapers on Multidisciplinary Panel Data Research 248, DIW Berlin, The German Socio-Economic Panel (SOEP).
  3. Gobillon, Laurent & Magnac, Thierry & Selod, Harris, 2012. "Do Unemployed Workers Benefit from Enterprise Zones? The French Experience," IZA Discussion Papers 6357, Institute for the Study of Labor (IZA).
  4. 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.
  5. 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.
  6. Thierry MAYER & Florian MAYNERIS & Loriane PY, 2012. "The Impact of Urban Enterprise Zones on Establishments' Location Decisions: Evidence from French ZFUs," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2012019, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
  7. 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.
  8. Fatih Guvenen, 2006. "Learning your earning: are labor income shocks really very persistent?," Discussion Paper / Institute for Empirical Macroeconomics 145, Federal Reserve Bank of Minneapolis.
  9. 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.
  10. Ziebarth, Nicolas R. & Karlsson, Martin, 2010. "A natural experiment on sick pay cuts, sickness absence, and labor costs," Journal of Public Economics, Elsevier, vol. 94(11-12), pages 1108-1122, December.
  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. Jay Bhattacharya & William B. Vogt, 2007. "Do Instrumental Variables Belong in Propensity Scores?," NBER Technical Working Papers 0343, National Bureau of Economic Research, Inc.
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