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Record W3123826938

Worse Than It Looks: The True Burden and Risks of Federal Employee Pension Plans

2016· article· en· W3123826938 on OpenAlexaboutno aff
William B. P. Robson, Alex Laurin

Bibliographic record

VenueC.D. Howe Institute Commentary · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPensionLiabilityNotional amountDebtValue (mathematics)Government (linguistics)Actuarial scienceObligationBusinessInflation (cosmology)EconomicsPresent valueFinancePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Federal government employees enjoy pure defined-benefit pensions that promise relatively generous benefits to a large current and former workforce. Being largely unfunded, these plans impose on taxpayers obligations running into the hundreds of billions of dollars. What is worse, misleading accounting understates the true burden and risks these plans create for Canadian taxpayers. This Commentary provides more economically meaningful estimates of the value of federal employee pensions to their participants, and the cost to taxpayers. Its goal is twofold: to alert Canadians to the fiscal burdens and risks created by these plans; and to prompt discussion of reforms that could produce more durable and affordable pensions for federal employees. Official figures on the current cost of these plans and their accumulated obligation use notional interest rates to calculate their value. Because their pension promises are guaranteed by taxpayers and indexed to inflation, the appropriate discount rate is the yield on federal-government real-return bonds (RRBs), which for years has been much lower than the assumed rate in official figures. Correcting this distortion produces a fair-value estimate for Ottawa’s unfunded pension liability of $269.3 billion at the end of 2014/15 – around $30, 000 per family of four, and $117.9 billion higher than the reported number. Because the unfunded pension liability is part of Ottawa’s debt, the fair-value adjustment also raises the net public debt by $117.9 billion: from the $612.3 billion reported at the end 2014/15 to an adjusted $730.2 billion. The authors note that recent changes will raise the share of these plans’ costs that their participants must fund, but object that the reported current costs of these plans – and therefore the total contribution rates that determine employer and employee shares – are too low. With RRB yields at recent levels, even the higher employee contributions anticipated by the reforms would leave the taxpayers’ true share far above 50 percent. A fair-value approach to the current service cost can ensure that participants and taxpayers equally share the cost of accruing benefits. The authors further note, however, that even 50:50 sharing of the true current service cost of federal pensions would leave taxpayers exposed. This exposure would apply not only to fluctuations in the annual costs as interest rates, experience, and plan provisions changed but – far more important– to fluctuations in the value of previously earned benefits as well. Ottawa could protect taxpayers from this risk by capping employer contributions at a fixed share of pensionable pay. To relieve taxpayers of their current sole responsibility for risks in the federal plan, Ottawa would need to switch to a shared-risk, target-benefit model already common in much of the provincial public sector, which calculates benefits with reference not only to salary and years of service but also to the plans’ funded status.

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)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.931
Threshold uncertainty score0.502

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0100.007
Scholarly communication0.0080.004
Open science0.0020.001
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.072
GPT teacher head0.332
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2016
Admission routes1
Has abstractyes

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