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Subsidized Vocational Training: Stepping Stone or Trap? Assessing Empirical Effects Using Matching Techniques

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  • Eva Dettmann
  • Jutta Günther

Abstract

Using replacement matching on the basis of a statistical distance function we try to answer the question of whether subsidized vocational training is related to a negative image effect for the graduates. The results show that young people with equal qualifications acquired during subsidized vocational training are disadvantaged solely due to the kind of education they have received. The probability of finding adequate employment is lower than in the control group. Besides the 'general effect' of support we also find less favorable job opportunities for those who attended 'external' as compared to 'workplace-related' training.

Suggested Citation

  • Eva Dettmann & Jutta Günther, 2013. "Subsidized Vocational Training: Stepping Stone or Trap? Assessing Empirical Effects Using Matching Techniques," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 149(IV), pages 405-443, December.
    • Handle: RePEc:ses:arsjes:2013-iv-1
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    1. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    2. Marco Caliendo & Reinhard Hujer, 2006. "The microeconometric estimation of treatment effects—An overview," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 199-215, March.
    3. Fredriksson, Peter & Johansson, Per, 2002. "Program evaluation and random program starts," Working Paper Series 2003:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    4. 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.
    5. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
    6. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    7. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    8. Annette Bergemann & Bernd Fitzenberger & Stefan Speckesser, 2009. "Evaluating the dynamic employment effects of training programs in East Germany using conditional difference-in-differences," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 797-823.
    9. Woessmann, Ludger, 2004. "How Equal Are Educational Opportunities? Family Background and Student Achievement in Europe and the United States," IZA Discussion Papers 1284, Institute of Labor Economics (IZA).
    10. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
    11. 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.
    12. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    13. Eva Reinowski & Birgit Schultz & Jürgen Wiemers, 2005. "Evaluation of Further Training Programmes with an Optimal Matching Algorithm," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 141(IV), pages 585-616, December.
    14. Joshua Angrist & Jinyong Hahn, 2004. "When to Control for Covariates? Panel Asymptotics for Estimates of Treatment Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 58-72, February.
    15. Herbert S. Buscher & Eva Dettmann & Marco Sunder & Dirk Trocka, 2009. "Will There Be a Shortage of Skilled Labor? An East German Perspective to 2015," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 60(Supplemen), pages 55-82.
    16. 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.
    17. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
    18. 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.
    19. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    20. 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.
    21. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
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    More about this item

    Keywords

    microeconometric evaluation; matching; vocational education;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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