Relativity, inequality and optimal taxation of internationally mobile top incomes
Bibliographic record
Abstract
Abstract We use the Mirrlees income tax model with migration between two countries to investigate the optimal labour income taxation of top earners, where individuals vary in skill levels and migration costs. We consider three scenarios of relative consumption concerns: comparisons with average consumption in the population, upward comparisons and comparisons with median‐skill individuals. The tax formulas for the optimal marginal tax rate imposed on top skills are derived in closed form. Ceteris paribus, the optimal tax rate decreases with the migration elasticity of top earners but increases with the degree of inequality or the intensity of relative concerns. To explore the joint impact of these factors on the optimal tax rate, we find that even when governments prioritize the most redistributive social objective (maxi‐min), an increase in the migration elasticity of top earners weakens the demand for reducing inequality, leading to a situation where the retention and attraction of top talent take precedence over inequality mitigation. Conversely, an increase in the migration elasticity of these individuals enhances the demand for correcting positional externalities. The quantitative significance of these theoretical findings is supported by numerical examples based on parameter estimates from empirical studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".