Do Local Residents Value Federal Transfers?
Bibliographic record
Abstract
A fundamental governance challenge for federal nations is benefiting from decentralization, while addressing potential negative side effects, including vertical and horizontal imbalances. Inefficient migration due to differential net fiscal benefits in subnational units is one potential negative side effect. To avoid this type of migration, federal payments to disadvantaged subnational units, a place-based policy, are often advocated. In this article, we assess federal equalization transfer payments in Canada as an example of such a policy. Equalization is appraised in terms of its marginal influence on interprovincial migration, after accounting for the persistent relative attractiveness (unattractiveness) of provinces as migration destinations/origins. We then compare equalization to an alternative policy that directly subsidizes workers. Compared to a ``people-based'' policy of wage subsidies, our findings suggest that at the margin, these federal transfers have virtually no impact on net migration.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".