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Record W653591541

Impacts of Cyclical Downturns on the Third Pillar of the RIS and Policy Responses

2013· preprint· en· W653591541 on OpenAlexaffabout
James Davies, Xiaoyu Yu

Bibliographic record

VenueRePEc: Research Papers in Economics · 2013
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsRecessionUnemploymentEconomicsPensionLabour economicsPillarStock (firearms)Order (exchange)Great recessionDemographic economicsFinanceMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper explores impacts of recessions on private pensions and retirement savings in Canada. We estimate that the 2008-09 recession saw declines in average family wealth and retirement assets of 11% and 14% respectively. Average wealth recovered by the end of 2010, but retirement assets remained 2% lower than before the recession. Losses were higher for those more exposed to the stock market, such as older workers and retirees with DC pension plans or large RRSPs. Without the recovery the recession would have reduced expected retirement income of future retirees by averages of 3.4% and 11.0% for DB and DC plans respectively. In order to analyze unemployment and early retirement effects, the paper examines a hypothetical economy with a recession once a decade. For DB plans, unemployment caused by recessions can reduce pensions by up to 25% if it strikes late and reduces final average pay. Early retirement may reduce DB pensions up to 50%. Overall, effects tend to be smaller with DC plans, but early career unemployment or early retirement can have substantial impacts. Enhancing CPP/QPP is compared with wide adoption of Pooled RPPs (PRPPs). Expected retirement income is higher with PRPPs but so is risk.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.024
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.028
GPT teacher head0.296
Teacher spread0.269 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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