Bibliographic record
Abstract
While large government deficits and debt raise concerns regarding intergenerational fairness, their longterm intergenerational impacts can significantly differ, depending on demographic shifts and future economic policy. In particular, population aging in Canada has accelerated during the past decade due to declining fertility and improving life expectancy. This demographic transition poses new fiscal challenges since it dampens growth in government revenue while putting pressure on government spending, particularly in healthcare and public pensions. Generational accounting is a powerful tool for assessing the lifetime fiscal burden on current and future generations, given demographic and economic projections. The method requires estimating the present value of government’s current and future net revenues to cover all current and future spending plus net debt. A large imbalance between the net tax burden faced by current and future generations over their lifetimes, in favor of current generations, would mean that existing fiscal policies are unfair and unsustainable. Using generational accounting, this Commentary shows that the projected lifetime fiscal burdens of the youngest generation (born since 2005) and future generations are very high: higher than those of any other generations, especially those born from the mid-1950s to the 1990s. Generally speaking, babyboomers and their children fare well in this scenario, but the grandkids of babyboomers do not. Looking to the future, we also specifically compare the prospective net tax burden faced by today’s newborns with those that will be faced by future generations. Here, the results are less troubling. We find future generations of Canadians are expected to face a slightly lower lifetime tax burden than newborns, implying relative intergenerational balance looking out into the future. However, small changes to the baseline scenario can make that balance tip unfavourably for future generations. For example, both higher-than-expected interest rates and lower-than-expected population growth would lead to generational imbalance by imposing higher net tax burdens on future generations. Also, failing to restrain the growth of healthcare spending below its recent experience (1996 to 2010 average) could shift the tax burden to future, unborn generations, and lead to a large and likely untenable imbalance. To ensure future intergenerational fairness and sustainability, policies that improve labour market outcomes of youth, women and immigrants, and that encourage a longer working life, should be supported. Restraining the growth of healthcare spending at a sustainable level is also a must.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".