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Record W4387412067 · doi:10.1017/s0968565023000070

Adam Smith's reversionary annuity: money's worth, default options and auto-enrollment

2023· article· en· W4387412067 on OpenAlexaff
Moshe A. Milevsky

Bibliographic record

VenueFinancial History Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsAnnuityLife annuityActuarial scienceEconomicsLump sumAdam smithInvestment (military)FinancePaymentPensionLawPolitical scienceClassical economics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2023
Admission routes1
Has abstractyes

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