Implementing partial tax harmonization in an asymmetric tax competition game with repeated interaction
Bibliographic record
Abstract
Abstract This paper investigates the conditions under which partial harmonization for capital taxation is sustained in a repeated interactions model of tax competition when there are three countries with heterogenous capital endowments. We show that regardless of the structure of the coalition (i.e., full or partial tax coordination), whether partial tax harmonization is sustainable or not crucially depends on the extent to which the capital endowment of the medium‐sized country is similar to that of the large or small country. The most noteworthy finding is that the closer the capital endowment of the median country is to the average one, the less likely the tax harmonization including the median country is to prevail and the more likely the partial tax harmonization excluding the median country is to prevail. We also show that partial tax harmonization makes the member countries of the tax union better off and non‐member countries worse off, which stands in sharpe contrast with previous studies, such as Konrad and Schjelderup (1999) and Bucovetsky (2009).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".