Factors Affecting Household Health Expenditures: Evidence from Iran
Bibliographic record
Abstract
Abstract Background Productivity plays a crucial role in driving economic growth, and its primary determinant is human capital. Human capital, in turn, is influenced by health and education investments, leading both households and governments to allocate substantial resources toward these sectors. Understanding the factors influencing health expenses is vital for designing effective policies. Methods This study investigates the factors affecting health costs by analyzing data from various provinces in the Islamic Republic of Iran, spanning from 2006 to 2019. The data, sourced from the Statistics Center of Iran, was analyzed using the panel data approach to uncover the relationships between health expenditure and several key variables. Results The analysis revealed significant disparities in health expenditure among provinces. Sistan & Baluchestan had the lowest health expenditure per household, while Tehran reported the highest. Inflation emerged as a significant factor, having a negative impact on health expenses due to its influence on purchasing power. Conversely, the education and salary of household heads had a positive impact on health expenditure. Notably, economic growth did not exhibit a significant relationship with health expenses. Conclusions The study highlights the importance of education and income levels in shaping health expenditures, while inflation can curtail spending on health due to reduced purchasing power. Policymakers should consider these factors to ensure equitable and effective allocation of health resources. Key messages • Improving health and well-being, essential for comprehensive progress and human development, relies significantly on household health expenditure. • The efforts to enhance human capital should focus on reducing inflation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".