Time and Money: Tracking the Fiscal Impact of Demographic Change in Canada
Bibliographic record
Abstract
Putting something aside for old age is common sense. Individuals should save during their working years to provide for their children and their own retirement. Likewise, aging countries should anticipate how their future age structure will affect their public finances. What does demographic change imply for age-sensitive public programs in Canada, and how well do current patterns of spending prepare us for those changes? This e-brief quantifies the impact of demographic change on major public programs in Canada. It compares the share of gross domestic product (GDP) that will be required to service our programs for health, education, the elderly and children in the future with the share required in the recent past. Discounted at 5 percent over 50 years, these programs create a net liability for governments of more than $810 billion. But there are winners and losers. Overall, Ottawa comes out ahead, with a small net asset: prospective declines in spending on children outweigh increases in its pension obligations. Provinces, however, face sizeable increases in healthcare spending only partially offset by falling education budgets, with the outlook generally worsening as one moves from west to east across the country. Maintaining the current age distribution of public spending in these programs will require future taxpayers to pay more for their lifetime package of programs than did their predecessors. These calculations highlight the need for budget surpluses, for greater fiscal capacity at the provincial level, and for productivity growth to support Canada’s social programs in the future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".