Private financing of long-term care: income, savings and reverse mortgages
Bibliographic record
Abstract
To what extent would older Europeans be able to pay for their long-term care needs, out of their income and assets, if they had no access to informal care or public insurance? To answer this question, we build a microsimulation model and estimate the disability trajectories of those currently aged 65 or older in nine European countries using the Survey of Health, Ageing and Retirement in Europe. We focus on the potential role of reverse mortgages in home equity liquidation. According to the simulations, 57% of people 65 and over will experience disability. Conditional on need, care will be required for 4.4 years on average. Of those with no partner, 6% of dependent individuals could pay for their long-term care out of their income alone, 22% if they used all their savings except their home. The proportion would double to 49% if they took out reverse mortgages on their main residence. However, one-quarter would be able to finance less than 10% of their long-term care expenses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".