Consommation, partage de risque et assurance informelle : développements théoriques et tests empiriques récents
Bibliographic record
Abstract
L’étude du partage optimal des risques dans une économie, soit au niveau agrégé, soit au niveau d’un village, a été profondément renouvelée par les résultats des articles empiriques rejetant pour la plupart les théories existantes. Le rejet de l’hypothèse de revenu permanent et de l’assurance complète a conduit à modéliser les imperfections des marchés afin d’élaborer des théories compatibles avec les profils de consommation observés et le degré de partage de risque obtenu. Dans cette revue de littérature, nous exposons ces théories économiques de partage de risque en consommation et les mécanismes « informels » d’assurance en fonction de la complétude des marchés. Les diverses sources d’imperfections peuvent provenir de problèmes d’asymétries d’information ou de limites à l’engagement. Nous présentons aussi les différentes méthodes employées pour tester ces diverses théories parmi les études empiriques récentes les plus significatives.
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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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".