As the first, substantive contribution, this paper revisits the effectiveness of two widely used public sponsored training programs, the first one focusing on intensive occupational training and the second one on short-term activation and job entry. We use an exceptionally rich administrative data set for Germany to estimate their employment and earnings effects in the early 2000s. We employ a stratified propensity score matching approach to address dynamic selection into heterogeneous programs. As a second, methodological contribution, we carefully assess to what extent various aspects of our empirical strategy such as conditioning flexibly on employment and benefit histories, the availability of rich personal information, handling of later program participations, and further methodological and specification choices affect estimation results. Our results imply pronounced negative lock-in effects in the short run in general and positive medium-run effects on employment and earnings when job-seekers enroll after having been unemployed for some time.We find that data and specification issues can have a large effect on impact estimates.

Participation in intensive training programs for the unemployed in Germany is allocated by awarding training vouchers. Using rich administrative data for all vouchers and actual program participation, the authors provide first estimates of the short-run and long-run employment and earnings effects of receiving a training voucher award based on a selection-on-observables assumption. The results imply that, after the award, voucher recipients experience long periods of lower labor market success compared to had they not received training vouchers. Small positive employment effects and no gains in earnings were observed four to seven years after the receipt of the voucher award. In addition, the findings suggest stronger positive effects both for all low-skilled individuals who were awarded and redeemed a voucher and for low-skilled and medium-skilled individuals who chose to take degree courses than for higher-skilled recipients.

In this paper, I study the causal effects of part-time work on current and future wages. To estimate these effects, I use a random effects model with a wage equation capturing the employment history and a dynamic multinomial probit component for the choice of employment status. Exclusion restrictions from the institutional context are exploited to support identification. The results suggest that working part-time with few hours has a large causal effect on current wages, but more extensive part-time work does not reduce current wages. However, both types of part-time work lead to negative long-term wage effects.

As the first, substantive contribution, this paper revisits the effectiveness of two widely used public sponsored training programs, the first one focusing on intensive occupational training and the second one on short-term activation and job entry. We use an exceptionally rich administrative data set for Germany to estimate their employment and earnings effects in the early 2000s. We employ a stratified propensity score matching approach to address dynamic selection into heterogeneous programs. As a second, methodological contribution, we carefully assess to what extent various aspects of our empirical strategy such as conditioning flexibly on employment and benefit histories, the availability of rich personal information, handling of later program participations, and further methodological and specification choices affect estimation results. Our results imply pronounced negative lock-in effects in the short run in general and positive medium-run effects on employment and earnings when job-seekers enroll after having been unemployed for some time.We find that data and specification issues can have a large effect on impact estimates.

This paper estimates the impact of training incidence and duration on employment transitions accounting for the endogeneity of program participation and duration. We specify a very flexible bivariate random effects probit model for employment and training participation and we use Bayesian Markov Chain Monte Carlo (MCMC) techniques for estimation. We develop a simulation approach that uses the estimated coefficients and individual specific effects from the MCMC iterations to calculate the posterior distributions of different treatment effects of interest. Our estimation results imply positive effects of training on the employment probability of the treated, lying between 12 and 21 percentage points ten quarters after program start. The effects are higher for women than for men and higher in West Germany than in East Germany. Further, we find that the effect of training versus waiting underestimates the effect of training versus no training in the medium and long run by a third. Finally, our results show that longer planned enrolment lengths of three and four quarters as opposed to just two quarters lead to an increase in employment rates in the medium and long run by four to eleven percentage points.