Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables

By M. Lechner, Conny Wunsch. Social Science Research Network, 2013

154 citations recorded by the source corpus.

Based on new, exceptionally informative and large German linked employer-employee administrative data, we investigate the question whether the omission of important control variables in matching estimation leads to biased impact estimates of typical active labour market programs for the unemployed. Such biases would lead to false policy conclusions about the cost-effectiveness of these expensive policies. Using newly developed Empirical Monte Carlo Study methods, we find that besides standard personal characteristics, information on individual health and firm characteristics of the last employer are particularly important for selection correction. Moreover, it is important to account for past performance on the labour market in a very detailed and flexible way. Information on job search behaviour, timing of unemployment and program start, as well as detailed regional characteristics are also relevant.

Subfields: Economics and Econometrics, Political Science and International Relations, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ad765564cfffb7a1803831cfa639869e5f60add8

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Citations in the corpus

Youth unemployment and active labor market policies in Europe (2016, methodology): “The ALMP evaluation literature offers some guidelines regarding the most informative characteristics to be included to control for nonrandom selection (e.g., Lechner and Wunsch, 2013; Caliendo, Mahlstedt, and Mitnik, 2014), and we preselected studies adhering to these guidelines.”
Unobservable, but unimportant? The relevance of usually unobserved variables for the evaluation of labor market policies☆ (2017, background): “This specification provides a reference model including variables which are consistently found to be key drivers of selection into training (e.g. Dolton and Smith, 2011; Lechner and Wunsch, 2013).”
The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators (2018, background): “…number of simulation studies has investigated and compared the finite sample behavior of various point estimators, see Frölich (2004), Zhao (2004), Lunceford and Davidian (2004), Busso, DiNardo, and McCrary (2014), Huber, Lechner, and Wunsch (2013), and Frölich, Huber, and Wiesenfarth (2017).”
Practical procedures to deal with common support problems in matching estimation (2017, methodology): “It is comparable to many administrative data sets in Europe (see Lechner and Wunsch, 2013).”