Econometric analysis of the long-run relationship between preventive care spending and mortality: evidence from OECD countries, 1970–2019
Bibliographic record
Abstract
While the share of health spending from public sources dedicated to preventive care has increased, the extent to which this preventive care spending can reduce mortality is still uncertain, mainly since effects may occur only in the long run. This paper takes advantage of a recent econometric method to empirically examine the long-run relationship between mortality and public preventive care spending in 37 OECD countries from 1970 to 2019. We construct an unbalanced longitudinal dataset on all-cause mortality and public preventive spending from publicly available OECD datasets. We detect cointegration and cross-sectional dependence in our data. This leads us to use the dynamic common correlated effects (DCCE) panel error correction model from Chudik and Pesaran (2015) to address these issues and account for heterogeneity across OECD countries. Our results indicate a long-run preventive care spending elasticity of $$-$$ - 0.10 in the OECD, and Granger non-causality tests suggest this may be a causal effect of spending on mortality. We also find that the long-run preventive care spending elasticity is of +0.04 for life expectancy at age 65. To better understand mechanisms, we explore the subcategories of preventive care spending and find that early disease detection programs and immunization programs drive the mortality reduction. To compare with other government health expenditures, we run our models using inpatient and outpatient healthcare expenditures as predictors and find the long-run association with mortality is less consistent. Overall, our findings indicate that higher preventive care spending may help reduce mortality in the long run in OECD countries, but this relationship is likely small.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".