The Effect of College Education on Health and Mortality: Evidence from Canada
Bibliographic record
Abstract
We investigate the returns to college attendance in Canada in terms of health and mortality reduction. To do so, we first use a dynamic health microsimulation model to document how interventions which incentivize college attendance among high school graduates may impact their health trajectory, health care consumption and life expectancy. We find large returns both in terms of longevity (4.2 years additional years at age 50), reduction in the prevalence of various health conditions (10-15 percentage points reduction in diabetes and 5 percentage points for stroke) and health care consumption (28.4% reduction in lifetime hospital stays, 19.8 for specialists). We find that education impacts mortality mostly by delaying the incidence of health conditions as well as providing a survival advantage conditional on having diseases. Second, we provide quasi-experimental evidence on the impact of college attendance on long-term health outcomes by exploiting the Canadian Veteran’s Rehabilitation Act, a program targeted towards returning WW-II veterans and which incentivized college attendance. The impact on mortality are found to be larger than those estimated from the health microsimulation model (hazard ratio of 0.216 compared to 0.6 in the simulation model) which suggest substantial returns to college education in terms of healthy life extension which we estimate around one million canadian dollars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".