Returns to Skill, Tax Policy, and North American Migration by Skill Level Canada and the United States 1995-2001
Bibliographic record
Abstract
Higher after-tax returns to skill in U.S. states compared to Canadian provinces have raised the issue that higher skilled Canadian workers especially will find migration to the U.S. economically attractive, and especially so after the North American Free Trade Agreement (NAFTA), provisions of which facilitate such cross-country migration through special visas. In this study we develop, estimate, and simulate a nested logit model of migration among 59 Canadian and U.S. sub-national areas using over 70, 000 microdata observations on workers across all deciles of the skill distribution obtained from the U.S. and Canadian censuses of 2000/2001 Combining microdata on individual workers with area data, including estimates of after-tax returns by skill decile based on standardized wage distributions and large scale microsimulation tax models for Canadian provinces and U.S. states, we are able to consider the effects of tax policy differences across countries on worker migration. Our ability to identify highly skilled individuals using these data enables us to simulate the effects of changes to taxes (under balanced budget conditions) on the migration propensities of individuals as well as the magnitude of the aggregate migration streams. Simulations suggest that increasing Canadian after-tax returns to skill and implementing fiscal equalization (reducing the average Canadian tax rate to the average U.S. level with offsetting expenditure reductions to maintain budget neutrality) would effectively reduce southward migration and especially so amongst highly skilled workers. The required reductions in tax rates and public expenditures are relatively large however and therefore would be expected to raise other substantial public policy concerns.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".