Annuities Markets Around the World: Money’s Worth and Risk Intermediation
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".