Do the skilled and prime-aged unemployed benefit more from training? Effect heterogeneity of public training programmes in Germany

Abstract

This study analyses the treatment effects of public training programmes for the unemployed in Germany. Based on propensity score matching methods, we extend the picture that has been sketched in previous studies by estimating the treatment effects of medium-term programmes for different skill and age groups. Our results indicate that programme participation has a positive impact on employment probabilities and earnings for almost all sub-groups. We find little evidence for the presence of heterogeneous treatment effects, and the magnitude of the differences is quite small. Our results thus – at least in part – conflict with the strategy to provide training increasingly to individuals with better employment prospects.

Acknowledgements

The data used in this article originate from the evaluation of public training programmes as part of the evaluation of the proposals of the Hartz Commission. Schneider et al. (2007) contains details. We would like to thank Marco Caliendo, Marton Csillag, Michael Lechner, Peter Mueser, Hilmar Schneider, Zhong Zhao, two anonymous referees as well as participants at the ESPE Conference in Chicago, the IZA Conference on the Evaluation of Labor Market Programmes in Bonn, the EALE Conference in Oslo, the EEA Meeting in Budapest, the CAPE Conference in Nuremberg, the Conference of the German Statistical Society in Kiel, the VfS Conference in Munich and the Seminar at the George Washington University for the valuable discussions and helpful comments. Arne Uhlendorff also thanks DIW DC, where part of this research was pursued during his stay in fall 2007. All remaining errors are our own.

Notes

¹ LaLonde (2003) and Kluve (2010) summarize the international literature on the evaluation of ALMP. Recent examples for evaluation studies of other ALMP measures in Germany include Jaenichen and Stephan (2009) for wage subsidies, Baumgartner and Caliendo (2008) for start-up programmes and Caliendo et al. (2008b) for job creation schemes.

² Caliendo et al. (2008a) investigate a similar question for job creation schemes in Germany and present evidence for the presence of effect heterogeneity. Although previous results of negative average effects are confirmed in their study, some strata of the population benefit from participation in job creation schemes.

³ One could think of additional interesting dimensions of effect heterogeneity like nationality, health restrictions and family status. In this article, we focus on age and vocational education. The reasons for this are (a) that we have for many variables, like health restrictions and nationality, relatively small sample sizes, and (b) that age and vocational education are highly correlated with other dimensions like family status and previous earnings. However, the analysis of additional dimensions of effect heterogeneity could be an interesting topic for further research.

⁴ The IEB is in general not publicly available. Only a 2.2% random sample (the Integrated Employment Biographies Sample, IEBS) can be obtained for research purposes. See e.g. Jacobebbinghaus and Seth (2007) for details on the IEBS. The IEB consists of four different administrative data sources: the employees’ history (BeH), the benefit recipients’ history (LeH), the job seekers’ database (ASU/BewA), and the programme participants’ master data set (MTH). For a more detailed description see e.g. Schneider et al. (2007).

⁵ The number of participants entering a programme differs between the quarters analysed. We take this into account by applying corresponding weights when calculating the average treatment effects on the treated.

⁶ One could argue for stricter age restrictions, for example, because of early retirement regulations in Germany. However, if one is interested in the average effects of treatment on the treated and there are participants older than 55 or 60, there is no reason to exclude these individuals.

⁷ A detailed description of selected variables can be found in Table A1 of the Appendix.

⁸ The apprentices have previously finished secondary education, which ranges from 9 to 13 years of schooling.

⁹ See e.g. Winkelmann (1996) for a broader and more general overview about the three pillars of the German educational system: general schooling, vocational training and university education.

¹⁰ This means that we do not observe self-employment earnings, and remunerations are only reported up to the social security contribution ceiling.

¹¹ When there are many covariates, it is impractical to match directly on covariates because of the curse of dimensionality. See e.g. Zhao (2008) for some comments on this problem.

¹² If we drop the assumption of independence and allow for nonzero correlation between treatment effects, implications only change marginally.

¹³ The exact specifications are not reported here, but available upon request. Table A1 provides additional information about the variables used in the regressions.

¹⁴ The matching algorithm is implemented using the PSMATCH2 Stata ado-package by Leuven and Sianesi (2003).

¹⁵ For instance, Lechner and Wunsch (2008) require nonparticipation in the follow-up period after programme entry for comparison individuals. Applying the same definition of nonparticipation to our data lowers the estimated treatment effects (Section ‘Sensitivity analysis’ for details). Although we opted for the above-stated definition of nonparticipation and do not exclude future participants, the alternative approach clearly has the advantage of employing a very straightforward definition of nonparticipation.

¹⁶ Exceptions are the sub-samples of East German participants in type 2 without a vocational degree or 50 years and older, as well as female East German participants in type 3 without a vocational degree.

¹⁷ Lechner and Melly (2007) argue that realized earnings are only a ‘crude’ measure of the causal effect of training on productivity and propose to estimate bounds for the earnings effects. However, this approach goes beyond the scope of this article.

¹⁸ We thus follow the prevailing approach in the recent evaluation literature. A different approach concentrates on treatment effects only after the end of the programme. For advantages and disadvantages of both approaches see e.g. Caliendo and Kopeinig (2008).

¹⁹ However, these results are available from the authors upon request. Minor differences are very likely due to the exclusion of individuals who are 25 years or younger in our analysis of treatment effects across skill groups.

²⁰ Figures A1–A4 are included in the Appendix of this article.

²¹ The detailed results of Sections ‘Treatment effect: East versus West Germany’ and ‘Sensitivity analysis’ are not reported here but available from the authors upon request.