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Impact evaluation of job training programmes: Selection bias in multilevel models

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  • R. Bellio
  • E. Gori

Abstract

This paper focuses on the evaluation of a job training programme composed of several different courses. The aim is to evaluate the impact of the programme for the participants with respect to non-participants, paying attention to possible differences in the effectiveness between the courses. The analysis is based on discrete data with a hierarchical structure. Multilevel modelling is the natural choice in this setting, but the results may be severely affected by selection bias. We propose a two-step procedure, which suits both the hierarchical structure and the observational nature of data. The method selects the appropriate control group, using standard results of the propensity score methodology. A suitable multilevel model is formulated, and the dependence of the results on the amount of non-random sample selection is analysed within a likelihood-based framework. As a result, rankings for comparative performances are obtained, adjusted for the amount of plausible selection bias. The procedure is illustrated with reference to a data set about a job training programme organized in Italy in the late 1990s.

Suggested Citation

  • R. Bellio & E. Gori, 2003. "Impact evaluation of job training programmes: Selection bias in multilevel models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(8), pages 893-907.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:893-907
    DOI: 10.1080/0266476032000075976
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    Cited by:

    1. Elena Ragazzi & Lisa Sella, 2013. "The effectiveness of vocational training policies: methods for an impact evaluation," CERIS Working Paper 201314, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    2. Hajime Seya & Junyi Zhang & Makoto Chikaraishi & Ying Jiang, 2020. "Decisions on truck parking place and time on expressways: an analysis using digital tachograph data," Transportation, Springer, vol. 47(2), pages 555-583, April.
    3. Greta Falavigna & Elena Ragazzi & Lisa Sella, 2014. "Gender inequalities and labour integration. An integrated approach to vocational training in Piedmont," CERIS Working Paper 201407, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    4. Elena Ragazzi & Lisa Sella, 2013. "Una valutazione di impatto delle politiche formative regionali: il caso piemontese," CERIS Working Paper 201315, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    5. Leonardo Grilli & Carla Rampichini, 2010. "Selection bias in linear mixed models," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 309-329.
    6. Falavigna Falavigna & Elena Ragazzi & Lisa Sella, "undated". "Vocational training and labour market: inclusion or segregation paths? An integrated approach on immigrant trainees in Piedmont," CERIS Working Paper 201425, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    7. Elena Ragazzi & Lisa Sella, 2013. "Migration and work: the cohesive role of vocational training policies," CERIS Working Paper 201316, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    8. Leonardo Grilli & Carla Rampichini, 2007. "A multilevel multinomial logit model for the analysis of graduates’ skills," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(3), pages 381-393, November.

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