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Evaluating Continuous Training Programs Using the Generalized Propensity Score

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Author Info

  • Kluve, Jochen

    ()
    (Humboldt University Berlin, RWI)

  • Schneider, Hilmar

    ()
    (CEPS/INSTEAD)

  • Uhlendorff, Arne

    ()
    (University of Mannheim)

  • Zhao, Zhong

    ()
    (Renmin University of China)

Abstract

This paper assesses the dynamics of treatment effects arising from variation in the duration of training. We use German administrative data that have the extraordinary feature that the amount of treatment varies continuously from 10 days to 395 days (i.e. 13 months). This feature allows us to estimate a continuous dose-response function that relates each value of the dose, i.e. days of training, to the individual post-treatment employment probability (the response). The dose-response function is estimated after adjusting for covariate imbalance using the generalized propensity score, a recently developed method for covariate adjustment under continuous treatment regimes. Our data have the advantage that we can consider both the actual and planned training durations as treatment variables: If only actual durations are observed, treatment effect estimates may be biased because of endogenous exits. Our results indicate an increasing dose-response function for treatments of up to 100 days, which then flattens out. That is, longer training programs do not seem to add an additional treatment effect.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3255.

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Length: 47 pages
Date of creation: Dec 2007
Date of revision:
Publication status: published in: Journal of the Royal Statistical Society, Series A (Statistics in Society), 2012, 175 (2), 587-617 [Details & Download]
Handle: RePEc:iza:izadps:dp3255

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Keywords: generalized propensity score; continuous treatment; program evaluation; training;

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References

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  1. Miquel, Ruth & Lechner, Michael & Wunsch, Conny, 2005. "Long-Run Effects of Public Sector Sponsored Training in West Germany," ZEW Discussion Papers 05-02, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  2. Jochen Kluve & Boris Augurzky, 2005. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers, Rheinisch-Westfälisches Institut für Wirtschaftsforschung 0021, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
  3. Flores-Lagunes, Alfonso & Gonzalez, Arturo & Neumann, Todd C., 2007. "Estimating the Effects of Length of Exposure to a Training Program: The Case of Job Corps," IZA Discussion Papers 2846, Institute for the Study of Labor (IZA).
  4. Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
  5. 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.
  6. Schneider, Hilmar & Uhlendorff, Arne, 2006. "Die Wirkung der Hartz-Reform im Bereich der beruflichen Weiterbildung," IZA Discussion Papers 2255, Institute for the Study of Labor (IZA).
  7. Lechner, Michael, 1999. "Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption," IZA Discussion Papers 91, Institute for the Study of Labor (IZA).
  8. Jere R. Behrman & Yingmei Cheng & Petra E. Todd, 2004. "Evaluating Preschool Programs When Length of Exposure to the Program Varies: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 108-132, February.
  9. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, Elsevier, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
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