Notice bibliographique
Résumé
Abstract If insurance providers cannot determine the risk levels of their various clients, but individuals do know this information, then adverse selection arises. We present the canonical models of adverse selection in insurance markets that shed some light on the economic consequences of this phenomenon. Under symmetric information, companies observe all relevant risk characteristics. By charging risk‐type specific prices, all risk types purchase full coverage. The result is an efficient allocation of resources with no risk bearing costs incurred by individuals. Under asymmetric information (adverse selection), if contracting is exclusive (i.e. each insured can contract with one and only one insurer), high risks end up with full insurance, but low‐risk types end up with partial coverage. Under nonexclusive contracting (i.e. each insured may purchase contracts from more than one insurance provider), higher risk individuals end up with too much insurance, while low risk individuals end up with too little. In either case, the allocation of resources is inefficient. Key Concepts Under symmetric information, each risk type purchases full insurance at the risk type specific actuarially fair odds rate and this allocation is pareto efficient. However, if these contracts were offered under asymmetric information, one expects high risk types to report being low risk in order to receive a more favorable contract If insurance companies do not have access to information about immutable characteristics of their potential customers where these characteristics affect the risk level of some activity, then this creates a situation of asymmetric information and a problem of adverse selection (also called antiselection). As individuals who are ‘bad risks’ (i.e. low risk) will desire more insurance than ‘good risks’ (i.e. high risk) if the price is the same for all, then insurers would end up selling more insurance to the bad risks and so the overall price of insurance would reflect this. If a pooling contract is offered and purchased, there is always a profitable deviation that is only attractive to low risk types but not high risk types. However, if insurance companies have sufficient foresight, they would not offer such a contract in the first place as such a deviation will make losses once other firms react to it. Adverse selection may also result in screening strategies whereby insurers offer higher coverage at a higher unit price in order to attract high‐risk types with lower risk types ending up with less insurance coverage.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».