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
Institution	Forschungsinstitut zur Zukunft der Arbeit
Titel	Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed. Gefälligkeitsübersetzung: Verbessert die Schätzung des Propensity-Scores durch machinelles Lernen die Matching-Schätzung? Das Beispiel von Programmen für Langzeitarbeitslose in Deutschland.
Quelle	Bonn (2019), 39 S. PDF als Volltext kostenfreie Datei (Preprint) Link als defekt meldenVerfügbarkeit
Reihe	IZA discussion paper. 12526
Sprache	englisch
Dokumenttyp	online; Monographie
ISSN	2365-9793
Schlagwörter	Schätzung; Künstliche Intelligenz; Algorithmus; Beschäftigungseffekt; Arbeitsmarktpolitik; Trainingsmaßnahme; Wirkungsforschung
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	2020/1