Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed.

Autor/inn/en	Goller, Daniel; Lechner, Michael; Moczall, Andreas; Wolff, Joachim
Titel	Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed.
Quelle	In: Labour economics, (2020) 65, 38 S. PDF als Volltext kostenfreie Datei (Preprint); PDF als Volltext kostenfreie Datei (Preprint); PDF als Volltext (3) Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	0927-5371
DOI	10.1016/j.labeco.2020.101855
Schlagwörter	Schätzung; Künstliche Intelligenz; Algorithmus; Künstliche Intelligenz; Übereinstimmung; Schätzung; Langzeitarbeitsloser; Algorithmus; Beschäftigungseffekt; Trainingsmaßnahme; Wirkungsforschung; Übereinstimmung; Langzeitarbeitsloser
Abstract	"Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a radius matching framework. Considering two popular methods, the results are ambiguous: We find that using LASSO based logit models to estimate the propensity score delivers more credible results than conventional methods in small and medium sized high dimensional datasets. However, the usage of Random Forests to estimate the propensity score may lead to a deterioration of the performance in situations with a low treatment share. The application reveals a positive effect of the training programme on days in employment for long-term unemployed. While the choice of the 'first stage' is highly relevant for settings with low number of observations and few treated, machine learning and conventional estimation becomes more similar in larger samples and higher treatment shares." (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2020/3