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Active labour market policies for the long-term unemployed: New evidence from causal machine learning

Daniel Goller (), Tamara Harrer (), Michael Lechner and Joachim Wolff ()

No 2108, Economics Working Paper Series from University of St. Gallen, School of Economics and Political Science

Abstract: We investigate the effectiveness of three different job-search and training programmes for German long-term unemployed persons. On the basis of an extensive administrative data set, we evaluated the effects of those programmes on various levels of aggregation using Causal Machine Learning. We found participants to benefit from the investigated programmes with placement services to be most effective. Effects are realised quickly and are long-lasting for any programme. While the effects are rather homogenous for men, we found differential effects for women in various characteristics. Women benefit in particular when local labour market conditions improve. Regarding the allocation mechanism of the unemployed to the different programmes, we found the observed allocation to be as effective as a random allocation. Therefore, we propose data-driven rules for the allocation of the unemployed to the respective labour market programmes that would improve the status-quo.

Keywords: Policy evaluation; Modified Causal Forest (MCF); active labour market programmes; conditional average treatment effect (CATE) (search for similar items in EconPapers)
JEL-codes: J08 J68 (search for similar items in EconPapers)
Pages: 85 pages
Date: 2021-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eur and nep-lab
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Working Paper: Active labour market policies for the long-term unemployed: New evidence from causal machine learning (2021) Downloads
Working Paper: Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning (2021) Downloads
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