Income inequality and life expectancy in Canada: New evidence from province-level panel regression, 1996–2019
Bibliographic record
Abstract
OBJECTIVES: Previous research on the association between income inequality and population health measures has yielded mixed results. This reflects, in part, the level of income inequality and surrounding political economic context of the setting in question. Previous research in Canada has not consistently identified an association between income inequality and population health measures. Those studies, however, largely focused on time periods prior to the manifestations of neoliberal policy reforms, which led to high levels of income inequality characterized by rising income at the top of the distribution. Our objective was to investigate the population-level association between income inequality and life expectancy in Canada during the years 1996-2019, a period of high after-tax income inequality in Canada. METHODS: We used ordinary least squares panel multivariate regression analysis of publicly available aggregate data on after-tax income inequality and life expectancy for the 10 Canadian provinces during the period 1996-2019. We used an inequality variable that is sensitive to the disproportionate growth in income at the top of the income distribution (share of income held by top 5%); we took into account the proportion of the First Nations, Métis, and Inuit populations across provinces and over time; and we separately analyzed female, male, and total populations. RESULTS: We found a robust, negative and statistically significant association where higher population-level after-tax income inequality was associated with lower average life expectancy in Canada. CONCLUSION: Our findings speak to the far-reaching consequences of neoliberalism, and to the need for public policy that will reduce income inequality in the interest of the public's health.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".