Does Public Health Care Redistribute from Me to You, or Just to Myself When I’m Old?
Bibliographic record
Abstract
Impressions of the degree of income inequality can be substantially altered when publicly financed in- kind benefits like health care are included (e.g. Smeeding et al (1993); Verbist et al., 2012). The vast majority of these analyses have been cross-sectional. But age is then a major confounder, since the elderly tend to have both lower incomes and higher health care utilization, while the middle aged are both healthier and have higher incomes. As a result, from a lifetime perspective, the redistributive impact of publicly financed health care is likely overstated compared to typical cross-sectional estimates. In this analysis, we provide both cross-sectional and lifetime estimates of the distribution of Canada’s publicly funded health care. This analysis is complicated by Canada’s fiscal federalism, where most health care is provided at the provincial level, while provincial revenues come not only from provinces ’ own taxes, but also from federal to provincial fiscal transfers. Further, life expectancy increases with income, which may be important when taking a life course perspective. These various elements have been woven together into a microsimulation model that synthesizes period birth cohorts for men and women, disaggregated by income group. The result is estimates of the lifetime as well as cross-sectional distributional impacts of Canada’s publicly funded health care. While the extent of
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".