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Evaluating Continuous Training Programs Using the Generalized Propensity Score

  • Jochen Kluve
  • Hilmar Schneider
  • Arne Uhlendorff
  • Zhong Zhao

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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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.77465.de/dp752.pdf
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Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 752.

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Length: 44 p.
Date of creation: 2007
Date of revision:
Handle: RePEc:diw:diwwpp:dp752
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  1. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  2. Lechner, Michael & Miquel, Ruth & Wunsch, Conny, 2004. "Long-Run Effects of Public Sector Sponsored Training in West Germany," IZA Discussion Papers 1443, Institute for the Study of Labor (IZA).
  3. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
  4. Alfonso Flores-Lagunes & Arturo Gonzalez & Todd C. Neumann, 2007. "Estimating the Effects of Length of Exposure to a Training Program: The Case of Job Corps," Working Papers 1042, Princeton University, Department of Economics, Industrial Relations Section..
  5. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
  6. 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.
  7. 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).
  8. 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).
  9. Jochen Kluve & Boris Augurzky, 2005. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 0021, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
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