Long-run effects of dynamically assigned treatments: A new methodology and an evaluation of training effects on earnings.

Autor/inn/en	Berg, Gerard J. van den; Vikström, Johan
Institution	Forschungsinstitut zur Zukunft der Arbeit
Titel	Long-run effects of dynamically assigned treatments: A new methodology and an evaluation of training effects on earnings. Gefälligkeitsübersetzung: Die Langzeitwirkungen von dynamisch angepassten Maßnahmen: Neue Methoden und eine Evaluation von Trainingswirkungen auf das Einkommen.
Quelle	Bonn (2019), 73 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	IZA discussion paper. 12470
Sprache	englisch
Dokumenttyp	online; Monographie
ISSN	2365-9793
Schlagwörter	Bildungsertrag; Einkommenseffekt; Arbeitsloser; Arbeitsmarktchance; Qualifizierungsmaßnahme; Arbeitslosenberatung; Teilnehmer; Wirkungsforschung; Schweden; USA
Abstract	"We propose and implement a new method to estimate treatment effects in settings where individuals need to be in a certain state (e.g. unemployment) to be eligible for a treatment, treatments may commence at different points in time, and the outcome of interest is realized after the individual left the initial state. An example concerns the effect of training on earnings in subsequent employment. Any evaluation needs to take into account that some of those who are not trained at a certain time in unemployment will leave unemployment before training while others will be trained later. We are interested in effects of the treatment at a certain elapsed duration compared to 'no treatment at any subsequent duration'. We prove identification under unconfoundedness and propose inverse probability weighting estimators. A key feature is that weights given to outcome observations of non-treated depend on the remaining time in the initial state. We study earnings effects of WIA training in the US and long-run effects of a training program for unemployed workers in Sweden. Estimates are positive and sizeable, exceeding those obtained by using common static methods, and suggesting a reappraisal of training." (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2020/1