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Record W3123834116

The Displacement Effect of Public Pensions on the Accumulation of Financial Assets

2009· preprint· en· W3123834116 on OpenAlexaff
Michael D. Hurd, Pierre‐Carl Michaud, Susann Rohwedder

Bibliographic record

VenueDeep Blue (University of Michigan) · 2009
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversité du Québec à Montréal
FundersAustralian GovernmentU.S. Social Security Administration
KeywordsGenerosityPensionLiberian dollarEarningsEconomicsIncentiveLabour economicsFinance
DOInot available

Abstract

fetched live from OpenAlex

The generosity of public pensions may depress private savings and provide incentives to retire early. While there is plenty of evidence supporting the latter effect, there remains considerable controversy as whether or not public pensions crowd out private savings. This paper uses international micro-datasets collected over recent years to investigate whether public pensions displace private savings. The identification strategy relies on differences in the progressivity or non-linearity of pension formulas across countries. We also make use of large heterogeneity in earnings across education group and country. The evidence we present is consistent with previous studies using cross-sectional and time-series variation in savings and pensions. We estimate that an extra dollar of pension wealth depresses accumulated financial assets at the time of retirement by 23 to 44 cents and that an extra ten thousand dollars in pension wealth reduces the average retirement age by roughly 1 month.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.032
GPT teacher head0.243
Teacher spread0.211 · 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
Published2009
Admission routes1
Has abstractyes

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