Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed

42 Pages Posted: 4 Sep 2019

See all articles by Daniel Goller

Daniel Goller

University of St. Gallen

Michael Lechner

University of St. Gallen - Swiss Institute for Empirical Economic Research

Andreas Moczall

Institute for Employment Research (IAB)

Joachim Wolff

Institute for Employment Research (IAB)

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.

Keywords: programme evaluation, active labour market policy, causal machine learning, treatment effects, radius matching, propensity score

JEL Classification: J68, C21

Suggested Citation

Goller, Daniel and Lechner, Michael and Moczall, Andreas and Wolff, Joachim, Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed. IZA Discussion Paper No. 12526, Available at SSRN: https://ssrn.com/abstract=3445793

Daniel Goller (Contact Author)

University of St. Gallen

Bodanstrasse 8
SIAW-HSG
St.Gallen, 9000
Switzerland

Michael Lechner

University of St. Gallen - Swiss Institute for Empirical Economic Research ( email )

Varnbuelstrasse 14
St. Gallen, 9000
Switzerland
+41 71 224 2320 (Phone)

Andreas Moczall

Institute for Employment Research (IAB) ( email )

Regensburger Str. 104
Nuremberg, 90478
Germany

Joachim Wolff

Institute for Employment Research (IAB) ( email )

Regensburger Str. 104
Nuremberg, 90478
Germany

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