Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 365

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

The Missing Profits of Nations

Tørsløv, Thomas, Wier, Ludvig, Zucman, Gabriel·2023·The Review of Economic Studies

Abstract

By exploiting new macroeconomic data known as foreign affiliates statistics, we show that affiliates of foreign multinational firms are an order of magnitude more profitable than local firms in a number of low-tax countries. Leveraging this differential profitability, we estimate that 36$\%$ of multinational profits are shifted to tax havens globally. US multinationals shift twice as much profit as other multinationals relative to the size of their foreign earnings. We analyse how the location of corporate profits would change if shifted profits were reallocated to their source countries. Domestic profits would increase by about 20$\%$ in high-tax European Union countries, 10$\%$ in the US, and 5$\%$ in developing countries, while they would fall by 55$\%$ in tax havens. We provide a new international database of GDP, trade balances, and factor shares corrected for profit shifting. In contrast to the picture painted by official statistics, our results suggest that the corporate capital share has increased not only in North America but also in high-tax European countries. Capital is making a comeback globally, but its rise is obscured by the tax avoidance strategies of multinational companies.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/restud/rdac049

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 35 references

Metadata

Authors
Tørsløv, T., Wier, L., & Zucman, G.
Venue
The Review of Economic Studies
Published
2022-07-26
Volume
90
Issue
3
Pages
1499-1534
ISSN
  • 0034-6527 (ISSN-L)
  • 1467-937X
OpenAlex
W3159393143
Cited by
365
References
35
FWCI
119.72
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Missing Profits of Nations. https://app.cochat.ai/papers/10.1093/restud/rdac049
The paper
Tørsløv, T., Wier, L., & Zucman, G. (2023). The Missing Profits of Nations. The Review of Economic Studies. https://doi.org/10.1093/restud/rdac049

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.