Paying for the Boomers: Long-Term Care and Intergenerational Equity
Bibliographic record
Abstract
The aging of Canada’s babyboomers is going to put significant pressure on the way in which we pay for and organize long-term care (LTC) services. The demand for LTC services remains relatively small for the first decade of life after age 65, but rises sharply around the time people turn 80. Looking closely at the demographic projections, once the first boomer cohort enters into the 80 and older group – roughly around 2030 – the demand for LTC will sharply increase. Under current systems of delivering and paying for long-term care, we estimate that the cost of long-term care services will roughly triple over the next 40 years, growing from around $69 billion in 2014 to around $188 billion in 2050, in inflation-adjusted dollars. Public LTC costs are estimated to grow from around $24 billion in 2014 to around $71 billion in 2050, and the private burden is anticipated to be even higher, growing from around $44 billion to about $116 billion over the same period of time. Policymakers must therefore act soon to improve the way we finance long-term care. The apparently simple solution of expanding Canada’s public health system to cover all LTC costs should be rejected due to the additional stress that the expected growth in costs would put on future budgets and taxpayers of working age. The number of seniors relative to the working-age population is rapidly increasing and the economic growth rate appears to be falling, meaning today’s working-age generations likely will not have incomes grow fast enough to offset the programs’ rising public costs. Intergenerational equity concerns should factor into decisions to expand the public share of LTC costs.A multi-pronged solution to better target means-tested public subsidies and allow growth of private insurance and savings should be pursued instead. Policymakers could do so in a manner that assures LTC access for those who need it but can’t afford it. And because many Canadians today believe, somewhat falsely, that governments will pay for their future LTC costs, reforms must encourage individuals to take on a greater responsibility to pay for their own future LTC. It’s important to strike the right balance between the costs to government or taxpayers and those that can be reasonably borne by individuals. Provincial governments should proactively formulate a consistent set of means tests to determine what patients will have to pay and appropriate subsidies if and when they no longer have the means to do so. Clear and widely publicized rules of this kind would go a long way to help boost personal savings for LTC and increase the demand for insurance from individuals who want to secure their assets for future generations. Policymakers, meanwhile, face many urgent issues with respect to guaranteeing LTC access for those who cannot pay for it themselves, including waiting lists and the imbalance between institutional and home-based care, which should be another priority in the coming years.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".