Ottawa’s Hidden Deficit: The Widening Gap between Federal Government Pension Liabilities and Assets
Bibliographic record
Abstract
Defined-benefit (DB) pension plans have been in trouble in recent years, largely because their sponsors have tended to underestimate their liabilities. As Canadians saving for retirement in registered retirement saving plans and defined-contribution pension plans have begun to realize, low yields on low-risk assets require more saving to achieve a given future income. Many DB plan sponsors, however, use inflated assumptions about returns – effectively presuming that risky investments will unfailingly pay off – to obscure the need for higher contributions. In Canada, the largest DB plans are those for federal government employees. Canadian taxpayers underwrite a promise that assumes a rate of return well in excess of current interest rates on federal government bonds – even on assets that do not exist. Although Ottawa has taken steps to rein in the growing cost of pension plans for public servants, uniformed personnel and MPs – arguably the largest and richest defined-benefit pensions in the country – these plans’ unfunded liabilities are massive, much larger than reported, and still growing. The contributions to these plans, even after the most recent reforms, come nowhere close to covering the rocketing cost of their taxpayer-guaranteed promises. An economically meaningful fair market value estimate of the unfunded liability of federal government employees’ pension plans puts it at $272 billion as at March 31st, 2013 – some $120 billion higher than reported. The same approach to determining the annual cost of benefits accruing in these plans shows it to be between 45 and 60 percent of pensionable pay – more than twice as high as reported, and a far higher rate of tax-deferred wealth accumulation than is available to other Canadians. The federal government should incorporate these numbers in the official measures of its net debt and annual budget balance. This would be a key first step toward reforms that would alleviate a burden that few taxpayers know they bear, and that would protect taxpayers from risks few know they run
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".