The Impact of Public Health Expenditure on Economic Development – Evidence from Prefecture-Level Panel Data of Shandong Province
Bibliographic record
Abstract
Public health expenditure is an indispensable part of social economy. The public has always paid close attention to public health expenditure. In order to study the quantitative relation between public health expenditure and social economic development, this paper investigates prefecture-level cities in Shandong Province, due to the unique characteristics of Shandong Province. Making theoretical and empirical contributions, this paper augments the Cobb-Douglas production function with public health expenditure and empirically analyzes economic development of prefecture- level cities in Shandong Province. A panel data set is established, followed by multivariate regression analysis. Empirical results find that public health expenditure per capita and coverage of medical insurance can significantly promote social economic development. However, the expansion and growth of the number of health institutions does not necessarily promote economic development. Instead, it may even hold back economic development by causing personnel redundancy and waste of resources. If the government transfers its investment focus from the scale and the speed of development of medical services to their fairness and efficiency, public health expenditure may vastly improve both public health and economic development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".