Risk sharing in a federation with population mobility and long horizons
Bibliographic record
Abstract
In this paper risk sharing among individuals within and across regions in a federation with population mobility and infinite horizons is considered. It is shown that the regional authorities will not fully exploit gains from interregional risk sharing when population mobility is imperfect. In the Nash equilibrium there is complete risk sharing among the individuals within each region, however, which corresponds to the policies of the central authority. Regional authorities who care about their reputation may be able to commit to an efficient allocation. It is possible that improvements in the degree of mobility will make such commitments less likely. JEL Classification: H77, E61, and F36. Le partage du risque dans une fédération où la population est mobile et l'horizon temporel long. Ce mémoire examine le partage du risque entre personnes à l'intérieur des régions et entre régions dans une fédération où il y a mobilité de la population et horizon temporel infini. On montre que les autorités régionales n'exploiteront pas pleinement les gains en provenance d'un partage inter‐régional du risque quand la mobilité de la population est imparfaite. Cependant, dans un équilibre à la Nash, il y a partage complet du risque entre les individus de chaque région, ce qui correspond aux politiques des autorités centrales. Les autorités régionales qui tiennent à leur réputation peuvent être capables de s'engager à une allocation efficace. Il est possible que les améliorations dans le degré de mobilité rendent ces engagements moins probables.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".