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Cited by 310

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Ownership of intellectual property and corporate taxation

Open access
Griffith, Rachel, Miller, Helen, O'Connell, Martin·2014·Journal of Public Economics

Abstract

Intellectual property accounts for a growing share of firms' assets. It is more mobile than other forms of capital, and could be used by firms to shift income offshore and to reduce their corporate income tax liability. We consider how influential corporate income taxes are in determining where firms choose to legally own intellectual property. We estimate a mixed (or random coefficients) logit model that incorporates important observed and unobserved heterogeneity in firms' location choices. We obtain estimates of the full set of location specific tax elasticities and conduct ex ante analysis of how the location of ownership of intellectual property will respond to changes in tax policy. We find that recent reforms that give preferential tax treatment to income arising from patents are likely to have significant effects on the location of ownership of new intellectual property, and could lead to substantial reductions in tax revenue.

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Metadata

Authors
Griffith, R., Miller, H., & O'Connell, M.
Venue
Journal of Public Economics
Publisher
Elsevier BV
Published
2014-04
Volume
112
Pages
12-23
ISSN
0047-2727 (ISSN-L)
OpenAlex
W2058291667
Cited by
310
References
35
FWCI
126.73
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Ownership of intellectual property and corporate taxation. https://app.cochat.ai/papers/10.1016/j.jpubeco.2014.01.009
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Griffith, R., Miller, H., & O'Connell, M. (2014). Ownership of intellectual property and corporate taxation. Journal of Public Economics. https://doi.org/10.1016/j.jpubeco.2014.01.009

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.