The Long-Term Impact of Education on Mortality and Health: Evidence from Sweden
Bibliographic record
Abstract
There is a well-documented large positive correlation between education and health and yet it remains unclear as to whether this is a causal relationship. Potential reasons for this lack of clarity include estimation using different methods, analysis of different populations and school reforms that are different in design. In this paper we assess whether the type of school reform, the instrument and therefore subgroup identified and the modelling strategy impact the estimated health returns to education. To this end we use both Regression Discontinuity and Difference in Differences applied to two Swedish school reforms that are different in design but were implemented across overlapping cohorts born between 1938 and 1954 and follow them up until 2013. We find small and insignificant impacts on overall mortality and its common causes and the results are robust to regression method, identification strategy and type of school reform. Extending the analysis to hospitalisations or self-reported health and health behaviours, we find no clear evidence of health improvements due to increased education. Based on the results we find no support for a positive causal effect of education on health.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".