Difference-in-Difference Causal Forests, with an Application to Payroll Tax Incidence in Norway

Evelina Gavrilova, Audun Langørgen, Floris T. Zoutman and Floris Zoutman

No 10532, CESifo Working Paper Series from CESifo

Abstract: This paper introduces the difference-in-difference causal forest (DiDCF) method, which extends Wager and Athey’s (2018) causal-forest technique for estimating heterogeneous treatment effects to settings with dynamic treatment effects. Regular causal forests require independence between treatment assignment and the outcome variable (after conditioning out observables). In contrast, DiDCFs provide consistent estimates with a parallel trend assumption. DiDCFs can be used to create event-study plots. The method is applied to estimate payroll tax incidence on wages. We find that heterogeneity in incidence is explained by firm- and workforce-level variables. Firms with a large and heterogeneous workforce are most effective in passing on the incidence of the payroll tax to workers.

Keywords: causal forest; parallel trends; treatment effect heterogeneity; payroll tax incidence; administrative data (search for similar items in EconPapers)
JEL-codes: C18 H22 J31 M54 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-eur, nep-inv, nep-lma and nep-pub
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_10532

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