The impact of participation in job creation schemes in turbulent times

Abstract

This paper analyzes the impact of participation in job creation schemes (JCSs) on job search outcomes in the context of the turbulent East German labor market in the aftermath of the German reunification. High job destruction characterized the economic environment. JCSs were heavily used in order to cushion this development. Using data from 1990-1999 and building upon the timing-of-events approach, we estimate multivariate discrete time duration models taking selection based on both observed and unobserved heterogeneity into account. Our results indicate that after initial negative effects during the typical program duration of twelve months, probably driven by reduced job search effort during participation resulting in a rearrangement of the job queue, the impact on the job finding probability becomes insignificantly positive. Additional results, however, suggest that female and highly skilled participants leave unemployment quicker than other groups, which results in highly skilled women benefiting from participation. In general, we find no significant impact on post-unemployment employment stability. Our results are robust to allowing for random treatment effects. Also taking into account endogenous participation in training programs, endogenous censoring, or multiple treatment effects do not change the results.

Reducing unemployment? Examining the interplay between active labour market policies

“…Empirically, they seemingly fail to do so as most individual‐level studies show negative correlations between employment and participation in public job creation schemes (Card et al, 2018; Kluve, 2010). With that said, there is some evidence from evaluations in Germany that job creation policies may be beneficial for particular groups, for example, older workers in relation to “one‐euro‐jobs” (Hohmeyer & Wolff, 2012), or women with higher education in job creation programs created in the wake of German reunification in the late 1990s (Bergemann, Pohlan, & Uhlendorff, 2017).…”

Empirical Monte Carlo Evidence on Estimation of Timing-of-Events Models

“…2 The key idea is to use actual data on treated units to simulate placebo treatments and then base the simulations on these 1 An early example is Abbring et al (2005) who study the effect of benefit sanctions on the reemployment rate, with unobserved factors such as personal motivation potentially affecting both the time to a benefit sanction (treatment) and time in unemployment (outcome). Recent examples include Crépon et al (2018), Richardson and van den Berg (2013), Caliendo et al (2016), Busk (2016), Lindeboom et al (2016), Holm et al (2017), Bergemann et al (2017) on labor market policies; Williams (2009, 2012), McVicar et al (2018) on cannabis use; van Ours et al (2013), van den Berg and Gupta (2015), Palali and van Ours (2017) on health settings; Bijwaard et al (2014) on migration; Jahn and Rosholm (2013) on temporary work; and Baert et al (2013) on overeducation.…”

Workers’ job mobility in response to severance pay generosity

“…This type of approach has been mainly used in the literature on dynamic treatment evaluation of job training programs(Richardson and van den Berg, 2013), unemployment benefits sanctions (van der Klaauw and van Ours, 2013), or job creation schemes(Bergemann, Pohlan, and Uhlendorff, 2017).13 For instance, firm-specific capital may represent another type of mobility cost. However, this and other types of mobility costs converge to zero as the layoff probability increases.…”