Profit shifting to tax havens: Withholding tax impact on passive flows from Poland
Bibliographic record
Abstract
This paper aims to identify determinants and channels of tax avoidance through passive flows to tax havens using harmful tax competition, considering the effect of changes in withholding tax regulations. For the empirical analysis, we exploit the data of 110,907 nonresidents from 134 countries that gain passive flows paid by Polish taxpayers. We utilise data from IFT-2R returns on withholding tax (WHT) for 2012-2019. We use a two-stage Arellano-Bond estimator of the Generalised Method of Moments (GMM) with instrumental variables for dynamic panel data and the difference-in-differences method. We show that the amendment to the withholding tax legislation, preceded by a no-avoidance clause in 2017, reduced passive flows overall and separately from the manufacturing and service sectors, primarily profit-shifting through interest, royalties and intangible services payments. Moreover, our results confirm that the tax system tightening against aggressive tax competition has reduced passive income transfers from service companies, not manufacturing companies. Instead, the latter make higher transfers to tax havens included in both Polish and the E.U. list of countries applying harmful tax competition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".