Health care expenditure and income in Canada: Evidence from panel data
Bibliographic record
Abstract
This paper investigates the long-run relationship between health care expenditures (HCE) and income using Canadian provincial data spanning a period of 40 years from 1981 to 2020. We study the non-stationary and cointegration properties of HCE and income and estimate the long-run income elasticities of HCE. Using heterogeneous panel models that incorporate cross-section dependence via unobserved common correlated factors to capture global shocks, we estimate long-run income elasticities that lie in the 0.11-0.16 range. Our results indicate that health care is a necessity good for Canada. These elasticity estimates are much smaller than those estimated in other studies for Canada. We find that HCE and income in Canada are cointegrated and that short-run changes in federal transfers significantly and positively affect HCE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".