Bibliographic record
Abstract
This study provides fresh evidence on how sensitive long and short-term debts are to their financial determinants, looking at firms with different degrees of foreign ownership. Specifically, the study divides ownership into three groups, Purely Domestic, Joint Ventures and Wholly Foreign Firms and compares how their respective long-term debt and short-term debts respond to changes in their financial determinants. Wholly Foreign Firms and Joint Ventures are presumed to be more efficient and have a larger pool of cash flow compared to Purely Domestic Firms. In addition, firms with some degree of foreign ownership are presumed to be more endowed with tangible assets than firms without any degree of foreign ownership. Differences in these endowments are expected to reflect in the debt maturity choices of the different types of firms. Using ORIANA Dataset covering the period 2000-2008 on over 20,000 Chinese firms and the GMM technique, this study finds that while wholly-foreign firms and joint ventures’ short-term debt respond less to changes in cash flow compared to domestic firms’, there is no difference between how wholly-foreign firms and joint ventures long-term debt respond to changes in cash flow. Contrary to our expectations, we find that an increase in collateral will allow foreign firms to increase their long-term debt much more than Joint Ventures and Purely Domestic Firms. The implication of these results is that China’s attempt to improve its financial system has not fully gained grounds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".