Determinants of capital structure: a case of hospitals in China
Bibliographic record
Abstract
BACKGROUND: Hospitals possess distinct characteristics that influence their capital structures, particularly in China, where the hospital industry is predominantly public but includes private not-for-profit and private for-profit hospitals. There is currently limited empirical evidence on how operational and financial factors influence the debt-to-asset decisions of hospitals in China. METHODS: This study analyzed data from 909 hospitals in China collected in 2013 and 2014 through the China National Health Statistical Information Report. Using a two-part model, we examined the effects of ownership, hospital type, revenue stream diversification, market share, and other characteristics on short-term, long-term, and total debt-to-asset ratios. RESULTS: The analysis revealed that private for-profit (FP) hospitals and specialized hospitals did not significantly reduce the probability of assuming short-term debt. However, private FP hospitals with less diversified revenue streams were more likely to rely on short-term debt to meet operational needs. Private ownership also reduced the probability of assuming long-term debt. Higher returns on assets (ROA) were significantly associated with a lower probability of assuming any debt or short-term debt but had no significant effect on the likelihood of assuming long-term debt. Most hospital characteristics were not significant predictors of short-term or long-term debt-to-asset ratios, with the exception that being a general hospital was linked to higher long-term debt-to-asset ratios. Notably, private FP hospitals with higher ROA were associated with increased long-term debt-to-asset ratios, indicating that profitability plays a key role in their long-term borrowing decisions. CONCLUSION: This study highlights the influence of financial and operational factors on hospital capital structures in China. The results emphasize the need for policies to support private hospitals, particularly in obtaining long-term loans and diversifying financing options, to promote equitable access to funding and improve healthcare delivery. These insights contribute to the understanding of hospital debt dynamics and provide implications for healthcare policy and management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".