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Record W1988230560 · doi:10.1017/s1474747206002654

Private pensions and government guarantees: clues from Canada

2007· article· en· W1988230560 on OpenAlexaffabout
Norma Nielson, DAVID K. W. CHAN

Bibliographic record

VenueJournal of Pensions Economics and Finance · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPensionPrivate pensionGovernment (linguistics)WelfareMoral hazardSample (material)Pension fundUnemploymentLabour economicsBusinessEconomicsActuarial sciencePublic economicsDemographic economicsFinanceIncentiveEconomic growth

Abstract

fetched live from OpenAlex

The Pension Benefits Guarantee Fund (PBGF) was established in the province of Ontario in 1980, thus creating in Canada a rare opportunity for intranational empirical research on the impacts of governmental protection on private plans and their participants. This paper examines Canadian data on pension plans for effects attributable to Ontario's government guarantees for some plans. We find that significant variables related to an increase in the number of pension plans in Canada are higher interest rates, a larger labour market, and, consistent with the deferred compensation theory from labour economics, lower real disposable income of workers. The number of members in pension plans is related significantly to the same variables and also to tax rates and unemployment. The analyses show that the Ontario environment for pension plans is significantly different from the rest of Canada. Those plans covered by the Pension Benefit Guarantee Fund exhibit a lower degree of funding per participant than do the remainder of the plans in the sample, supporting the argument that a government guarantee is related to a moral hazard problem in Ontario pension financing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.063
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2007
Admission routes2
Has abstractyes

Explore more

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