Efficient Migration and Income Tax Competition
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the consequence of the brain drain for the income tax systems of the source and destination countries for the migration, if the two countries’ policies are set noncooperatively by self–interested voters. It is assumed that the brain drain does increase the value of world output: workers with the highest income–earning ability are assumed to be more productive in one country than in another. There are costs to migration of these high–ability workers, costs that are less than the gain in the value of their production. However, for lower–ability workers, the gains in production in moving from the low–productivity country to the high–productivity country are assumed to be less than the migration costs. Voters in the high–productivity country want to capture rents from migrants. These voters are aware of the influence their tax policy has on people's migration decisions. Voters in the low–productivity country also behave strategically. I solve for the Nash equilibrium income tax rates. Increased mobility of highly skilled workers cannot decrease, and may increase, progressivity in the income tax system of the destination country, if migration actually occurs. Finally, the effects of transfers between countries on their income tax systems are examined. Redistribution between countries tends to lead to less redistribution within countries. If transfers between countries are set by a vote of all residents of both countries, then the transfer chosen will be the one that leads to the least progressive tax possible being chosen in each country.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it