The effect of education on health: evidence from national compulsory schooling reforms
Bibliographic record
Abstract
This paper sheds light on the causal relationship between education and health outcomes. We combine three surveys (SHARE, HRS and ELSA) that include nationally representative samples of people aged 50 and over from fourteen OECD countries. We use variation in the timing of educational reforms across these countries as an instrument for education. Using IV-probit models, we find causal evidence that more years of education lead to better health. One additional year of schooling is associated with 6.85 percentage points (pp) reduction in reporting poor health and 3.8 pp and 4.6 pp reduction in having self-reported difficulties with activities of daily living (ADLs) and instrumental ADLs, respectively. The marginal effect of education on the probability of having a chronic illness is a 4.4 pp reduction. This ranges from a reduction of 3.4 pp for heart disease to a 7 pp reduction for arthritis. The effects are larger than those from a probit model that does not control for the endogeneity of education. However, we do not find conclusive evidence that education reduces the risk of cancer, stroke and psychiatric illness.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".