Tax Treaties Worldwide: Estimating Elasticities and Revenue Foregone
Bibliographic record
Abstract
Much of the foreign direct investment worldwide is affected by one of more than 3000 bilateral tax treaties. There is an agreement that dividend and interest payments respond to these tax treaties' provisions, but evidence is scarce as to the magnitude of this response. We aim to fill in this gap for as many countries as possible by estimating the elasticities of dividend and interest income with respect to withholding tax rates, and the associated revenue foregone, exploiting the best available cross-country datasets. We collect information on withholding tax rates from the International Bureau of Fiscal Documentation; this includes information on EU directives, which imply zero withholding rates among all the EU member states and Switzerland, in addition to standard bilateral tax treaties. We combine this detailed information on withholding tax rates with foreign direct investment data from the International Monetary Fund, which we use to approximate bilateral dividend and interest flows; this results in a large panel data set of around 65,000 annual country-pair observations. While also observing heterogeneity in elasticities across countries, we estimate dividend flows to be highly elastic in a cross-country regression: a 1% increase in the applicable withholding tax is associated with a 2.3% - 2.6% decrease in dividend flows. We apply the elasticities to estimate potential tax revenue foregone. We estimate the largest annual revenue foregone for the United States (2.3 - 2.9 billion USD) and Canada (1.4 - 3.2 billion USD), while the investor country behind the largest revenue foregone is the Netherlands (2.9 - 3.3 billion USD). We arrive at somewhat lower and less robust estimates for interest income. Although our headline revenue estimates are, as expected, lower than static estimates that do not reflect elasticities, we nevertheless show that the revenue foregone of tax treaties remain non-negligible for some countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".