Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/242441 
Year of Publication: 
2021
Series/Report no.: 
Beiträge zur Jahrestagung des Vereins für Socialpolitik 2021: Climate Economics
Publisher: 
ZBW - Leibniz Information Centre for Economics, Kiel, Hamburg
Abstract: 
Empirical evaluations of national minimum wages, such as in Germany or the UK, rely on bite measures that capture treatment variation; measured from the incidence (or intensity) of employees paid below the threshold before the minimum wage was introduced or raised. Bite-dependent estimations face the problem of dynamic selection, implying that even in the absence of the minimum wage the bite may have changed over time. We apply a machine learning method from the field of regularized regression to predict the contemporary bite of the German minimum wage, allowing us to address unobserved dynamic selection in an empirical evaluation of long run effects of the minimum wage. Our LASSO predicted bites show clear improvements over simple forward updating of the initial bite, allowing us to estimate contemporary effects of the German minimum wage from 2015 to 2017.
Subjects: 
minimum wage
bite
evaluation
dynamic selection
machine learning
LASSO
JEL: 
J31
J38
C49
C21
Document Type: 
Conference Paper

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