MétaCan
Menu
Back to cohort
Record W4280600752 · doi:10.1177/08863687221097412

The Effect of Social Security Coverage on U.S. State and Local Government Public Pension Plan Funding

2022· article· en· W4280600752 on OpenAlexaboutno aff
Bruce J. Perlman, Branco Ponomariov, Christopher G. Reddick

Bibliographic record

VenueCompensation & Benefits Review · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPensionSocial securityDe factoBusinessGovernment (linguistics)Plan (archaeology)State (computer science)Quarter (Canadian coin)Public economicsFinanceActuarial sciencePublic administrationEconomicsPolitical science

Abstract

fetched live from OpenAlex

Approximately a quarter of U.S. state and local workers’ pension plans are not covered by Social Security. This is problematic and sometimes surprising for many public sector workers, who may receive limited or no Social Security benefits upon retirement due to insufficient or no contributions paid into the system throughout their careers. While non-covered public pension plans are legally obliged to provide a comparable degree of coverage, the lack of a de-facto partial federal guarantee on pension funding has interesting implications explored in this paper. We find that pension plans not covered by Social Security have lower funded ratios and are more volatile due to greater dependence on potentially more aggressive plan investments, assumptions, and performance. We also find that the funded ratio deteriorates over time for both types of plans, and this deterioration might be faster for Social Security covered plans.

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)0.003
metaresearch head score (Gemma)0.022
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 score0.088
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.236
Teacher spread0.214 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCompensation & Benefits ReviewSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207