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Do government sponsored vocational training programmes help the unemployed find jobs? Evidence from Russia

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  • A. Nivorozhkin
  • E. Nivorozhkin

Abstract

The study estimates the employment effect of vocational training programmes for the unemployed in urban Russia. The results of propensity score matching indicate that training programmes had a non-negative overall effect on the programme participants relative to non-participants.

Suggested Citation

  • A. Nivorozhkin & E. Nivorozhkin, 2007. "Do government sponsored vocational training programmes help the unemployed find jobs? Evidence from Russia," Applied Economics Letters, Taylor & Francis Journals, vol. 14(1), pages 5-10.
    • Handle: RePEc:taf:apeclt:v:14:y:2007:i:1:p:5-10
    DOI: 10.1080/13504850500425295
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    References listed on IDEAS

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    1. 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.
    2. World Bank, 2003. "The Russian Labor Market : Moving from Crisis to Recovery," World Bank Publications - Books, The World Bank Group, number 15007, December.
    3. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
    4. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
    5. Christopher J. O'Leary & Alena Nesporova & Alexander Samorodov, 2001. "Manual on Evaluation of Labour Market Policies in Transition Economies," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number cjo2001, August.
    6. Jochen Kluve & Christoph M. Schmidt, 2002. "Can training and employment subsidies combat European unemployment? [‘The non-parametric identification of treatment effects in duration models’]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 17(35), pages 409-448.
    7. 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.
    8. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
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    Cited by:

    1. P. Azad & P. K. Sujathan, 2022. "Hazard Analysis of Unemployment Duration of Return Migrants: The Case of Indian State of Kerala," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(4), pages 881-901, December.
    2. Aysit Tansel & H. Mehmet Taşçı, 2010. "Hazard Analysis of Unemployment Duration by Gender in a Developing Country: The Case of Turkey," LABOUR, CEIS, vol. 24(4), pages 501-530, December.
    3. Tasnim Khan & Fatima Yousaf, 2013. "Unemployment Duration of First Time Job Seekers: A Case Study of Bahawalpur," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 1(1), pages 8-19, December.
    4. Lilas Demmou & Andreas Wörgötter, 2015. "Boosting Productivity in Russia: Skills, Education and Innovation," OECD Economics Department Working Papers 1189, OECD Publishing.
    5. Furmanov, Kirill & Shelkovnikova, Irina, 2014. "Unemployed and out of the labor force: What is the difference? Modeling the probability of finding a job," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 86-106.

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