The macroeconomic implications of healthcare
Bibliographic record
Abstract
Health-care systems play a crucial role in supporting human health. They also have major macroeconomic implications, an aspect that is not always properly acknowledged. Countries spend very different amounts on healthcare, with spending in North America (Canada and the United States) more than twice as much per capita as in the European Union on average, and there are significant differences between EU countries too. Various explanatory factors such as income levels, population age structures and epidemiological profiles cannot explain the differences between countries. Decisions on the optimal level of spending should also consider various others factor, including the macroeconomic implications of health-care systems. Whatever amount is spent on health care, it should be spent efficiently, in order not to waste resources and to improve the macroeconomic impacts. We demonstrate that there are threshold effects whereby certain quantitative indicators of health tend to improve with increased spending only up to certain amount of spending, but not further. Using a standard method to measure efficiency, data envelopment analysis (DEA), we find significant differences between countries, suggesting that not all countries use existing technologies and best practices to their full potential. This finding calls for policy responses. Health-care systems matter for the macroeconomy because of their large size in output, employment and research. They also have direct fiscal implications in terms of the long-term sustainability of public finances, while health-care spending decisions influence short-term economic development through the fiscal multiplier effect, which is substantial. Most southern European countries cut health-care spending aggressively in recent years, likely amplifying the depth of their recessions and possibly causing hysteresis effects from long-term unemployment and reduced productivity. Fiscal consolidation strategies should aim to preserve spending items that have large fiscal multipliers, including health-care expenditures. Health-care systems also influence labour force participation, productivity and human capital formation through various channels, and thereby have an influence on overall macroeconomic outcomes. They also play an important role in inequality, and we find that inequality of access to health care is particularly high in about one-third of EU countries, which calls for policy responses. It is essential that discussions of health systems consider both the opportunity cost and the economic value of investing in health. Such an approach can help policymakers resist the temptation to default to the potentially inefficient status quo.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".