Adam Smith's reversionary annuity: money's worth, default options and auto-enrollment
Bibliographic record
Abstract
When Adam Smith – author of Wealth of Nations (1776) and Theory of Moral Sentiments (1759) – was elected a professor at the University of Glasgow in 1751, he also joined an annuity ‘scheme’ that was unique for its time. The Scottish Ministers’ Widows’ Fund, as it was known, offered members of the Presbyterian Church as well as the university a choice of levels at which to contribute investment savings, ranging from 2 to 10 percent of their wages. The life-contingent benefits were in the form of a reversionary annuity to a spouse and/or lump sum death benefit to children. This article (i) describes the scheme in financial and actuarial terms, (ii) values Smith's reversionary annuity and (iii) examines the choices made by individual participants. The specific research contribution is to compile the archival data to measure the extent of insurance anti-selection and to demonstrate that debates around choice architecture, default options and auto-enrollment, which infuse the literature in the twenty-first century, were prevalent in the mid eighteenth. For the record, Adam Smith actively contributed at the highest allowed rate, but it wasn't a ‘good’ investment for him, either ex ante or ex post. As for why, one must read the article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".