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Titel
Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed / Daniel Goller (University of St.Gallen), Michael Lechner (University of St.Gallen, CEPR, CESIfo, IAB, IZA and RWI), Andreas Moczall (IAB), Joachim Wolff (IAB) ; IZA Institute of Labor Economics
VerfasserGoller, Daniel ; Lechner, Michael ; Moczall, Andreas ; Wolff, Joachim
KörperschaftForschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, August 2019
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Elektronische Ressource
Umfang1 Online-Ressource (39 Seiten) : Diagramme
SerieDiscussion paper ; no. 12526
URNurn:nbn:de:hbz:5:2-198529 
Zugriffsbeschränkung
 Das Dokument ist öffentlich zugänglich im Rahmen des deutschen Urheberrechts.
Volltexte
Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed [0.9 mb]
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Zusammenfassung

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.