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 (),
Andreas Moczall () and
Joachim Wolff ()
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Andreas Moczall: Institute for Employment Research (IAB), Nuremberg
Joachim Wolff: Institute for Employment Research (IAB), Nuremberg
No 12526, IZA Discussion Papers from Institute of Labor Economics (IZA)
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 (search for similar items in EconPapers)
JEL-codes: J68 C21 (search for similar items in EconPapers)
Pages: 42 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-pay
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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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