@techreport{Muehler2007Returns,
abstract = {The present paper examines the wage effects of continuous training programs using individual-level data from the German Socio Economic Panel (GSOEP). In order to account for selectivity in training participation we estimate average treatment effects (ATE and ATT) of general and firm-specific continuous training programs using several state-of-the-art propensity score matching (PSM) estimators. Additionally, we also apply a combined matching difference-indifferences (MDiD) estimator to account for unobserved individual characteristics (e.g. motivation, ability). While the estimated ATE and ATT for general training are significant ranging between about 4 and 7.5 %, the corresponding wage effects of firm-specific training are mostly insignificant. Using the more appropriate MDiD estimator, however, we find a more precise and highly significant wage effect of about 5 to 6 %, though only for general training and not for firm-specific training. These results are consistent with standard human capital theory insofar as general training is associated with larger wage increases than firm-specific training. Furthermore, we conclude that firms may intend to use specific training to adjust to new job requirements, while career-relevant changes may be conditioned to general training.},
address = {Mannheim},
author = {Grit Muehler and Michael Beckmann and Bernd Schauenberg},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {M53; C21; J31; J24; 330; Continuous training; wage effect; average treatment effect; selectivity bias; propensity score matching estimators; Weiterbildung; Bildungsertrag; Arbeitsmarktpolitik; Sch\"{a}tztheorie; Sch\"{a}tzung; Deutschland},
language = {eng},
number = {07-048},
publisher = {Zentrum f\"{u}r Europ\"{a}ische Wirtschaftsforschung (ZEW)},
title = {The Returns to Continuous Training in Germany: New Evidence from Propensity Score Matching Estimators},
type = {ZEW Discussion Papers},
url = {http://hdl.handle.net/10419/24615},
year = {2007}
}
