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Record W2103605813

RISK POOLING AND THE MARKET CRASH: LESSONS FROM CANADA'S PENSION PLAN

2009· article· en· W2103605813 on OpenAlexaboutno aff
Ashby Monk, Steven A. Sass

Bibliographic record

VenueIssues in Brief · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsActuaryPensionSassPlan (archaeology)CrashPoolingManagementActuarial scienceEconomicsFinanceComputer scienceHistory
DOInot available

Abstract

fetched live from OpenAlex

Defined contribution plans are now the nation’s primary private retirement income program and repository of retirement savings. About two thirds of the assets held in such plans are invested in equities, as is the case in the defined benefit plans they largely replaced. Equities can dramatically reduce the cost of providing retirement incomes, given their high expected returns. But, as illustrated by the recent market crash, equities are also risky. The resulting losses (and gains) in retirement income are also distributed very unevenly in the nation’s 401(k)-IRA system. The crash hardly affected the retirement prospects of the young: the bulk of the retirement income they will draw from 401(k)s and IRAs will come from future contributions and future returns. Those at the cusp of retirement, by contrast, are heavily exposed: retirement savings are then at their peak and there is little time to adjust work, saving, and retirement plans in response to the market crash. This concentration of risk is highly troubling, as the 401(k)-IRA system has become the nation’s primary private retirement income program, and has led to calls to reform. The challenge is to capture the higher expected returns equities offer in a way that provides reasonably secure and reliable incomes in retirement. One approach would make individual retirement accounts more secure and reliable through the use of mandates, defaults, guarantees, or risk-sharing arrangements. This brief offers a different approach, examining the Canada Pension Plan (CPP) and how it manages the risk that comes with investing retirement savings in equities...

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.084
GPT teacher head0.384
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2009
Admission routes1
Has abstractyes

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