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Record W3125512052

Annuities Markets Around the World: Money’s Worth and Risk Intermediation

2001· preprint· en· W3125512052 on OpenAlexaboutno aff
Estelle James, Xue Song

Bibliographic record

VenueRePEc: Research Papers in Economics · 2001
Typepreprint
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDiscountingPresent valueLife annuityPopulationPensionDifferential (mechanical device)Interest rateCash flowActuarial scienceBusinessFinance
DOInot available

Abstract

fetched live from OpenAlex

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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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.331
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2001
Admission routes1
Has abstractyes

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