Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning.

Autor/inn/en	Goller, Daniel; Harrer, Tamara; Lechner, Michael; Wolff, Joachim
Institution	Forschungsinstitut zur Zukunft der Arbeit
Titel	Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning. Gefälligkeitsübersetzung: Aktive arbeitsmarktpolitische Maßnahmen für Langzeitarbeitslose: Neue Evidenz durch kausales maschinelles Lernen.
Quelle	Bonn (2021), 84 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	IZA discussion paper. 14486
Sprache	englisch
Dokumenttyp	online; Monographie
Schlagwörter	Künstliche Intelligenz; Erfolgskontrolle; Beschäftigungseffekt; Arbeitslosengeld; Arbeitsmarktpolitik; Arbeitsuche; Arbeitsvermittlung; Berufliche Integration; Weiterbildungsförderung; Teilnehmer; Anwendung; Auswahl; Auswirkung; Maßnahme; Trainingsmaßnahme; Wirkungsforschung; Langzeitarbeitsloser
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." (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2021/4