Daňové elasticity odtoků přímých zahraničních investic a dopady smluv o zamezení dvojího zdanění na daňové příjmy
Bibliographic record
Abstract
This thesis provides a cross-country analysis of potential tax revenue losses due to the ways different countries tax over-border dividend and interest incomes of multinational enterprise. Withholding taxation of outgoing dividends and interest payments is regulated by domestic tax rules as well as bilateral double tax treaties. The signing of such a treaty might substantially reduce the tax rate levied by the source country on the outgoing passive income and thus decrease its tax revenue. We create a large panel dataset and estimate withholding tax rate elasticities of dividend and interest outflows for a large set of countries around the world. Subsequently, we use these elasticities to estimate potential tax revenue losses due to outgoing dividend and interest payments for the source countries in our dataset. The results show highly elastic dividend outflows, 2.3% - 2.58% decrease related to 1% increase in the applicable withholding tax. We also find substantial tax revenue losses due to dividend outflows for a number of source countries, the largest for Canada (1.35 - 3.19 billion USD) and the United States (2.27 - 2.94 billion USD). The investor country behind the largest part of potential losses shows up to be the Netherlands. JEL Classification F21, F23, H25, H26 Keywords double tax treaty;...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".