Dynamics and Heterogeneity of Inter‐ and Intranational Risk Sharing
Bibliographic record
Abstract
This study extends the empirical model of incomplete risk sharing developed by Crucini ( ) by allowing unequal income pooling and explores the implications of using an alternative measure for aggregate risk. Based on samples from Canadian provinces, G‐7, and OECD countries spanning the years 1961–2008, we show that the empirical procedure used by Crucini tends to overstate the average degree of risk sharing and understate the dispersion of risk sharing, when compared to our unequal income pooling model. The empirical results from our unequal pooling model show that (i) the degree and dispersion of risk sharing across Canadian provinces, G‐7 countries, and OECD countries remain stable over time; (ii) the degree of risk sharing across Canadian provinces is higher than that across the G‐7 and OECD countries; and (iii) the degree of risk sharing seems positively related to equity and trade diversification.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".