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Evaluating continuous training programmes by using the generalized propensity score

Listed author(s):
  • Jochen Kluve
  • Hilmar Schneider
  • Arne Uhlendorff
  • Zhong Zhao

Summary: The paper assesses the heterogeneity 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 probability of employment (the response). The dose-response function is estimated after adjusting for covariate imbalance by using the generalized propensity score, which is a recently developed method for covariate adjustment under continuous treatment regimes. Our data have the advantage that we can consider both the actual and the planned durations of training 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 120 days, which then flattens out, i.e. longer training programmes do not seem to add an additional treatment effect.

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File URL: http://hdl.handle.net/10.1111/j.1467-985X.2011.01000.x
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Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

Volume (Year): 175 (2012)
Issue (Month): 2 (April)
Pages: 587-617

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Handle: RePEc:bla:jorssa:v:175:y:2012:i:2:p:587-617
DOI: j.1467-985X.2011.01000.x
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  1. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
  2. 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.
  3. Werner Eichhorst & Klaus F. Zimmermann, 2007. "And Then There Were Four...How Many (and Which) Measures of Active Labor Market Policy Do We Still Need?," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 53(3), pages 243-272.
  4. Blien, Uwe & Hirschenauer, Franziska & Arendt, Manfred & Braun, Hans Jürgen & Gunst, Dieter-Michael & Kilcioglu, Sibel & Kleinschmidt, Helmut & Musati, Martina & Roß, Hermann & Vollkommer, Dieter & We, 2004. "Typisierung von Bezirken der Agenturen für Arbeit (Classification of employment office areas)," Zeitschrift für ArbeitsmarktForschung - Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 37(2), pages 146-175.
  5. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
  6. Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 761-783, November.
  7. 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 88-12, Chicago - Economics Research Center.
  8. Ulf Rinne & Marc Schneider & Arne Uhlendorff, 2011. "Do the skilled and prime-aged unemployed benefit more from training? Effect heterogeneity of public training programmes in Germany," Applied Economics, Taylor & Francis Journals, vol. 43(25), pages 3465-3494.
  9. repec:iab:iabzaf:v:37:i:2:p:146-175 is not listed on IDEAS
  10. 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).
  11. Gerfin, Michael & Lechner, Michael, 2001. "A Microeconometric Evaluation of Active Labour Market Policy in Switzerland," CEPR Discussion Papers 2993, C.E.P.R. Discussion Papers.
  12. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
  13. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502.
  14. 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.
  15. repec:iab:iabzaf:v:40:i:1:p:45-64 is not listed on IDEAS
  16. Jacobi, Lena & Kluve, Jochen, 2007. "Before and after the Hartz reforms: The performance of active labour market policy in Germany," Zeitschrift für ArbeitsmarktForschung - Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 40(1), pages 45-64.
  17. Juan Jose Diaz & Sudhanshu Handa, 2006. "An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator: Evidence from Mexico’s PROGRESA Program," Journal of Human Resources, University of Wisconsin Press, vol. 41(2).
  18. Conny Wunsch, 2005. "Labour Market Policy in Germany: Institutions, Instruments and Reforms since Unification," University of St. Gallen Department of Economics working paper series 2005 2005-06, Department of Economics, University of St. Gallen.
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