Publicly Funded Medical Savings Accounts: Expenditures and Distributional Impacts
Bibliographic record
Abstract
This paper presents the findings from simulations of the introduction of publicly funded Medical Savings Accounts in the province of Ontario, Canada. The analysis exploits a unique data set linking population-based health survey information with individual-level information on all physician services and hospital services utilization over a four year period. The analysis provides greater detail than have previous analyses regarding: the distributional impacts of publicly funded MSAs across individuals of differing health statuses, incomes, ages and current expenditures; the impact of differing degrees of risk-adjustment for MSA contributions; and the impact of MSA funding over multiple years, incorporating year-to-year variation in spending at the individual level. In addition, it analyses designs for publicly funded MSAs than existing studies. Government uses information available from period t-1 to allocate its budget for year t between MSA contributions and catastrophic insurance in a manner that is actuarially fair for the public sector: the government first withholds funds equal to expected catastrophic insurance payments under the MSA plan, and then allocates only the balance to individual MSA accounts. The government captures the savings associated with reduced health care utilization under MSAs and we examine deductibles that vary by income rather than current health care expenditures. The impacts on public expenditures under these designs are more modest than existing studies and under plausible assumptions MSAs are predicted to decrease public expenditures. MSAs, however, are predicted to have unavoidable negative distributional consequences with respect to both public expenditures and out-of-pocket spending.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".