Annuities Markets Around the World: Money’s Worth and Risk Intermediation
Notice bibliographique
Résumé
Annuities markets around the world are small. However, they have been growing in recent years and are likely to grow further as a result of reforms in public social security systems and private pension plans, that partially replace defined benefit plans with funded defined contribution plans. When people retire they may choose, and are sometimes required, to annuitize these defined contribution savings. Therefore, it is important to learn whether or not annuities markets exist, how they operate and what kinds of market failure can be anticipated. Several papers have already analyzed US annuitie smarkets. This paper extends that analysis by examining annuities markets in other countries. We present evidence from Canada, the UK, Switzerland, Australia, Israel, Chile and Singapore—a variety of high and middle-income countries—and replicate the results from the US. This paper focuses on analyses of the expected present discounted value (EPDV) of cash flows from the annuity, and the money’s worth ratio (MWR), which is the EPDV divided by the initial premium cost. We find that, when discounting at the risk-free rate, MWR’s for annuitants are surprisingly high--greater than 95% in most countries and sometimes greater than 100%. MWR’s for the average population member are lower but still exceed 90% in most cases. We show that differential interest rate structures largely explain differential monthly payouts across countries, while differential mortality rates, especially projected improvement factors, help explain differences in measured MWR’s, given these monthly payouts and interest rates. The high MWR’s raise the question: How do insurance companies cover their costs despite these high MWR’s? We hypothesize that for each annuity sale, insurance companies get a large sum of money up-front that they invest in a portfolio of corporate bonds, mortgages, and some equities, earning a rate of return that exceeds the risk-free rate by 1.3% or more per year. They turn this “risky” portfolio into a safer annuity by a variety of risk-intermediation, term-intermediation techniques. This allows them to sell a product that is nearly risk-free, while earning a “spread” that covers their costs. We present data on cost and investment returns that are consistent with this hypothesis. The limited opportunity to earn this spread may help explain why price indexed annuities in the UK charge higher loads to cover their costs and risks. For consumers who would prefer to accept this investment risk and capture this spread themselves, the appropriate discount rate is higher and the MWR is lower, helping to explain the low demand for annuities in voluntary markets.
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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 | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».