Econometric Insights into Fiscal Federalism : Assessing risk-sharing in the United States, Canada and the European Union
Bibliographic record
Abstract
This study investigates the redistributive and stabilizing effects of fiscal transfers within the United States, Canada, and the European Union, focusing on the period from 2000 to 2022. Asymmetric shocks pose challenges to the stability of currency unions, potentially incentivizing regions to exit the union, while the need for social cohesion within a currency union fosters a demand for redistributive flows between regions. With monetary policy constrained within a currency union, the fiscal system could play a key role in stabilizing income in response to asymmetric shocks. Using a quantitative approach with panel data, the study explores long-term income redistribution and short-term stabilization. The findings reveal that Canada exhibits the highest level of redistribution, with an estimated 25 percent of income disparities reduced through taxes and transfers, followed by the United States at 16 percent. In contrast, the European Union shows minimal redistribution, with little to no significant income equalization across member states. On the contrary, there is evidence that the fiscal flows at the European level move from poorer to wealthier member states, rather than the opposite, suggesting that these flows serve purposes beyond the direct welfare of citizens. In terms of stabilization, the results are inconclusive, although estimates were obtained. Stabilization was divided into two effects, where the delayed effect of stabilization was estimated using data that was lagged once, and the instantaneous effect, using non-lagged data. The delayed effect is estimated at 81 percent for the United States, 55 percent for Canada, and 52 percent for the EU. The instantaneous effect of stabilization is estimated at 53 percent for the US, 11 percent for Canada, and 0.4 percent for the EU. However, these estimations are subject to concerns of stationarity and autocorrelation issues in the time series. As such, no definitive conclusions on stabilization could be drawn from this study.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".