Ottawa's Pension Abyss: The Rapid Hidden Growth of Federal-Employee Retirement Liabilities
Bibliographic record
Abstract
As Canadians saving for retirement are becoming painfully aware, rates of return on investment are much lower than they used to be. As a result, providing a given income in retirement now requires much more saving. Low returns are depressing incomes from RRSPs and defined-contribution pension plans, and causing target-benefit pension plans to reduce their promises. But defined-benefit pension plans cannot adjust their promises, and are showing large deficits. The largest and richest defined-benefit pensions in the country are those of federal government employees, and their situation is especially daunting. Despite recent high-profile changes to the pension plans of federal public servants, uniformed personnel and MPs, a critical flaw remains: the contributions to these plans, even after the changes, come nowhere close to covering the rocketing cost of their promises. Official figures on the current cost of these plans and their accumulated obligation use notional interest rates. 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, which is much lower than the assumed rate in official figures. A fair-value calculation shows that the values of different federal employee pension entitlements grow at rates from near 50 percent to more than 70 percent of pay annually. Even after the recent reforms, taxpayers will bear by far the greatest part of these costs. More startling yet is the accumulated unfunded liability of the plans, which at fair value stood at $267 billion at the end of March 2012, almost $118 billion worse than shown in the Public Accounts. The reforms did nothing to reduce the burden of this liability on taxpayers – who will have to fund most of these pensions as they become payable, even as they must save more to fund their own, less comfortable, retirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".