Fiscal Integration with Internal Trade: Quantifying the Effects of Equalizing Transfers
Bibliographic record
Abstract
Fiscal transfers between regions exist within many countries. Explicit transfers, such as Canada's equalization program, redistribute funds directly. Countless federal revenue and spending programs do so indirectly. Like capital flows between countries, such transfers interact with trade and affect the distribution of economic activity within and between subnational jurisdictions. Previous research has largely abstracted from trade considerations; we fill this gap. With the aid of a rich quantitative model and detailed data on within-country trade and financial flows, we uncover important effects of fiscal transfers on provincial income, migration, and national GDP in Canada. The effects are large. Transfers lower Alberta's real income by over 8 per cent and its population by over 12 per cent, and increase PEI's real income by 30 per cent and its population by 50 per cent. As employment shifts to lower productivity regions, we find transfers shrink Canada's real GDP by 0.8 per cent and income-sensitive transfers do so by as much as 1.2 per cent — equal to $19-28 billion today. Finally, fiscal transfers affect the size and distribution of gains from internal trade liberalization and spread gains across all regions, even if policy (like the New West Partnership) liberalizes trade only among some.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".