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Free, free, set them free? Are programmes effective that allow job centres considerable freedom to choose the exact design?

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  • Tamara Harrer
  • Andreas Moczall
  • Joachim Wolff

Abstract

Active labour market programmes are expected to be quite effective if job centres have a substantial degree of freedom to deliver tailor‐made individual services. For Germany, we studied the effectiveness of Schemes for Activation and Integration (SAI), which were introduced in 2009 to grant such freedoms to implement short training and private placement services. We estimated SAI participation effects on welfare recipients’ earnings and employment rate using propensity score matching and rich administrative data. We distinguished between participation in in‐firm training or training in other settings, and considered effect heterogeneity by gender, region and non‐employment duration. Participation substantially improved the participants’ earnings and employment rate, in‐firm training more so than training in other settings. Our employment effect estimates were not considerably larger than those previously found for comparable pre‐reform programmes. A lack of experience with SAI and a still inadequate client focus in the period studied might explain this.

Suggested Citation

  • Tamara Harrer & Andreas Moczall & Joachim Wolff, 2020. "Free, free, set them free? Are programmes effective that allow job centres considerable freedom to choose the exact design?," International Journal of Social Welfare, John Wiley & Sons, vol. 29(2), pages 154-167, April.
    • Handle: RePEc:wly:injsow:v:29:y:2020:i:2:p:154-167
    DOI: 10.1111/ijsw.12405
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    Cited by:

    1. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    2. Stefan Tübbicke, 2023. "How sensitive are matching estimates of active labor market policy effects to typically unobserved confounders?," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-16, December.
    3. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
    4. Wapler, Rüdiger & Wolf, Katja & Wolff, Joachim, 2022. "Do active labor market policies for welfare recipients in Germany raise their regional outflow into work?," Journal of Policy Modeling, Elsevier, vol. 44(3), pages 550-563.

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