Fiscal Policy, Human Capital, and Canada-US Labor Market Integration
Bibliographic record
Abstract
Abstract: This paper analyzes some of the implications of North American labor market integration for fiscal policy. The economies of Canada and the US are both characterized by highly integrated internal markets for goods and services as well as for labor and capital, and subnational governments in both economies play an important role in the financing and provision of public goods and services, including higher education. Despite theoretical insights from traditional trade theory that suggest that “trade and migration are substitutes, ” labor markets in both the US and Canada exhibit substantial and persistent interregional migration, with gross migration rates that greatly exceed net migration rates, especially for highly-educated workers. High gross migration rates are consistent with the hypothesis that education contributes to skill-specialization and worker heterogeneity, and that mobility provides a form of insurance for investment in risky human capital. Mobility also constrains the ability of competitive governments to engage in redistributive financing of human capital investment, and recent trends in both the US and Canada reveal a diminishing level of financial support for public-sector institutions by subnational governments. The implications of labor market integration for the efficiency of resource allocation, for income determination, and for fiscal competition are important for evaluations of tax and education policies both at the subnational and at the international levels. An earlier version of this paper was presented at a conference on “Social and Labour Market
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".