Frida Kvamme
The presentation will be in two parts:
SSB report 2019/41
- Martin Eckhoff Andresen & Frida Kvamme
Abstract
This report evaluates the Norwegian thin capitalization rule implemented in 2014. Firms in a multinational enterprise structure can use internal debt to shift profit from high-tax to low-tax countries, thus eroding their tax base and facing a different effective tax rate than purely domestic companies.
Using rich Norwegian register data we use a generalized difference-in-difference approach to look at effects on the use of internal debt, tax revenue and profit shifting more generally. Results suggest that companies exposed to the rule reduce their use of internal debt increase their tax base significantly more than otherwise similar firms following the reform.
The response among companies with connections to low-tax jurisdictions seems to be somewhat smaller, indicating possible alternative profit shifting mechanisms. Nonetheless, we find increased tax revenue also from these companies, leading us to conclude that the introduction of thin capitalization rules to some extent limited the extent of international profit shifting from Norwegian companies altogether.
Master thesis, University of Oslo, spring 2020
- Frida Kvamme
ABSTRACT
(work in progress)
This thesis evaluates the effects of the Norwegian thin capitalization rule on profit shifting. Earlier research has suggested that multinational firms face opportunities of mitigating the effects of the rule, possibly through alternative channels of profit shifting.
Using extensive register data on firms in Norway, this thesis looks at differential firm reaction to implementation of the rule. Preliminary results do indeed suggest that the effects differ for firms based on several factors.
Most notably, firms with connections to low-tax jurisdictions reduce their internal interest costs, but their tax base does not increase correspondingly.
Future thesis work will involve extending and understanding this result through analysis of detailed accounting data, with an aim to identify the explaining factors.