Why Do Most Countries Set High Tax Rates on Capital? ⋆ Nicolas Marceau Université duQuébec àMontréal and CIRPÉE
Bibliographic record
Abstract
of mobility, modeled as mobile and immobile capital. An agreement among countries not to give preferential treatment to mobile capital results in an equilibrium where mobile capital is nevertheless taxed relatively lightly. In particular, one or two of the smallest countries, measured by their stocks of immobile capital, choose relatively low tax rates, thereby attracting mobile capital away from the other countries, which are then left to set revenue maximizing taxes on their immobile capital. This conclusion holds regardless of whether countries choose their tax policies sequentially or simultaneously. In contrast, unrestricted competition for mobile capital results in the preferential treatment of mobile capital by all countries, without cross-country differences in the taxation of mobile capital. Nevertheless our main result is that the non-preferential regime generates larger global tax revenue, despite the sizable revenue loss from the emergence of low-tax countries. By extending the analysis to include crosscountry differences in productivities, we are able to resurrect a case for preferential regimes, but only if the productivity differences are sufficiently large.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".