Savings Needed to Fund Health Insurance and Health Care Expenses in Retirement: Findings from a Simulation Model.” EBRI Issue Brief, no. 317 (Employee Benefit Research Institute
Bibliographic record
Abstract
Modeling retiree health costs: This Issue Brief examines the uncertainty of health care expenses in retirement by using a Monte Carlo simulation model to estimate the amount of savings needed to cover health insurance premiums and out-of-pocket health care expenses. This type of simulation is able to account for the uncertainty related to individual mortality and rates of return, and computes the present value of the savings needed to cover health insurance premiums and out-of-pocket expenses in retirement. These observations were used to determine asset targets for having adequate savings to cover retiree health costs 50, 75, and 90 percent of the time. Not enough savings: Many individuals will need more money than the amounts reported in this Issue Brief because this analysis does not factor in the savings needed to cover long-term care expenses, nor does it take into account the fact that many individuals retire prior to becoming eligible for Medicare. However, some workers will need to save less than what is reported if they keep working in retirement and receive health benefits as active workers. Who has retiree health benefits beyond Medicare? About 12 percent of private-sector employers report offering any Medicare supplemental health insurance. This increases to about 40 percent among large employers. Overall, nearly 22 percent of retirees age 65 and older had retiree health benefits in 2005 to supplement Medicare coverage. As recently as 2006, 53 percent of retirees age 65 and older were covered by Medicare Part D, 24 percent had outpatient prescription drug coverage through an employment-based plan. Only 10 percent had no prescription
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".