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, Michael Lechner, Andreas Moczall and Joachim Wolff
Additional contact information
Andreas Moczall: Institute for Employment Research (IAB), Nuremberg, Germany
Joachim Wolff: Institute for Employment Research (IAB), Nuremberg, Germany

No 202005, IAB-Discussion Paper from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]

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) ((en))

Keywords: Bundesrepublik Deutschland; Beschäftigungseffekte; Propensity Score Matching; Algorithmus; künstliche Intelligenz; Langzeitarbeitslose; Schätzung; Trainingsmaßnahme; arbeitsmarktpolitische Maßnahme; Wirkungsforschung; 2009-2010 (search for similar items in EconPapers)
JEL-codes: C21 J68 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2020-02-12
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Citations: View citations in EconPapers (14)

Published in/as: Labour Economics (2020), online first, doi:10.1016/j.labeco.2020.101855

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Related works:
Journal Article: Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed (2020)
Working Paper: Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed (2019)
Working Paper: Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:iab:iabdpa:202005

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