Portfolio choice and longevity risk in the late seventeenth century: a re-examination of the first English tontine
Bibliographic record
Abstract
Tontines and life annuities both insure against longevity risk by guaranteeing (pension) income for life. The optimal choice between these two mortality-contingent claims depends on personal preferences for consumption and risk. And, while pure tontines are unavailable in the twenty-first century, the first longevity-contingent claim (and debt) issued by the English government in the late seventeenth century offered a choice between the two. This article analyzes financial and economic aspects of King William's 1693 tontine that have not received attention in the literature. In particular, it compares the stochastic present value (SPV) of the tontine vs the life annuity and discusses characteristics of investors who selected one versus the other. Finally, the article examines the issue of whether high reported tontine survival rates should be attributed to anti-selection or fraud. In sum, this article is an empirical examination of annuitization decisions made by actual investors in the late seventeenth century.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".