Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/279282 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
CESifo Working Paper No. 10532
Verlag: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Zusammenfassung: 
This paper develops a machine-learning method that allows researchers to estimate heterogeneous treatment effects with panel data in a setting with many covariates. Our method, which we name the dynamic causal forest (DCF) method, extends the causal-forest method of Wager and Athey (2018) by allowing for the estimation of dynamic treatment effects in a difference-in-difference setting. Regular causal forests require conditional independence to consistently estimate heterogeneous treatment effects. In contrast, DCFs provide a consistent estimate for heterogeneous treatment effects under the weaker assumption of parallel trends. DCFs can be used to create event-study plots which aid in the inspection of pre-trends and treatment effect dynamics. We provide an empirical application, where DCFs are applied to estimate the incidence of payroll tax on wages paid to employees. We consider treatment effect heterogeneity associated with personal- and firm-level variables. We find that on average the incidence of the tax is shifted onto workers through incidental payments, rather than contracted wages. Heterogeneity is mainly explained by firm-and workforce-level variables. Firms with a large and heterogeneous workforce are most effective in passing on the incidence of the tax to workers.
Schlagwörter: 
causal forest
treatment effect heterogeneity
payroll tax incidence
administrative data
JEL: 
C18
H22
J31
M54
Dokumentart: 
Working Paper
Erscheint in der Sammlung:

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.