Competition, Debt Maturity, and Adjustment Speed in China: A Dynamic Fractional Estimation Approach
Bibliographic record
Abstract
The purpose of this study was to investigate the capital structure adjustment rate in different levels of product market competitions. We classified Chinese non-financial listed firms into highly, moderately, and less competitive firms and applied an unbiased dynamic panel fractional estimator on unbalanced panel data of 10,941 firm-year observations during the period of 1998 to 2015. We find that the adjustment rate of highly and less competitive firms towards long-term target capital structure is higher (28.2–29.1%) as compared to the adjustment rate towards short-term target capital structure (18.8–18.9%). On the other hand, the adjustment rate of moderately competitive firms towards long-term target capital structure is slower (22.3%) as compared to the adjustment rate towards short-term target capital structure (25.3%). Further, the adjustment rate of highly and less competitive firms differs significantly between long-term and short-term target capital structure, while the adjustment rate of moderately competitive firms remains steady. Highly competitive large firms follow the limited liability model to adjust their target capital structure and support trade-off theory, while both small and large firms follow the limited liability and predation models in moderately and less competitive environments, respectively.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".