Evaluating continuous training programs using the generalized propensity score1

Arbeitspapier

This paper assesses the dynamics of treatment effects arising from variation in the duration of training. We use German administrative data that have the extraordinary feature that the amount of treatment varies continuously from 10 days to 395 days (i.e. 13 months). This feature allows us to estimate a continuous dose-response function that relates each value of the dose, i.e. days of training, to the individual post-treatment employment probability (the response). The dose-response function is estimated after adjusting for covariate imbalance using the generalized propensity score, a recently developed method for covariate adjustment under continuous treatment regimes. Our data have the advantage that we can consider both the actual and planned training durations as treatment variables: If only actual durations are observed, treatment effect estimates may be biased because of endogenous exits. Our results indicate an increasing dose-response function for treatments of up to 100 days, which then flattens out. That is, longer training programs do not seem to add an additional treatment effect.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Technical Report ; No. 2007,39

Klassifikation und Themen

• Klassifikation: Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Mobility, Unemployment, and Vacancies: Public Policy
• Thema: Training ; program evaluation ; continuous treatment ; generalized propensity score ; Weiterbildung ; Dauer ; Arbeitsmarktpolitik ; Wirtschaftspolitische Wirkungsanalyse ; Berufliche Integration ; Deutschland

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Kluve, Jochen ; Schneider, Hilmar ; Uhlendorff, Arne ; Zhao, Zhong
• Ereignis: Veröffentlichung
• (wer): Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
• (wo): Dortmund
• (wann): 2007

Weitere Informationen

• Handle: http://hdl.handle.net/10419/36609
• Letzte Aktualisierung: 10.03.2025, 11:41 MEZ