Do Financial Frictions Explain Chinese Firms’ Saving and Misallocation?
Bibliographic record
Abstract
We use firm-level data to identify financial frictions in China and explore the extent to which they can explain firms' saving and capital misallocation.We first document the features of the data in terms of firm dynamics and debt financing.State-owned firms have higher leverage and pay much lower interest rates than non-SOEs.Among privately owned firms, smaller firms have lower leverage, face higher interest rates, and operate with a higher marginal product of capital.We then develop a heterogeneous-firm model with two types of financial frictions, default risk, and a fixed cost of issuing loans.Our model generates endogenous borrowing constraints as banks consider the firm's productivity, asset, and debt when providing a loan.Using evidence on the firm size distribution and financing patterns, we estimate the model and find it can explain aggregate firms' saving and investment and around 50 percent of the dispersion in the marginal product of capital within private firms, which translates into a TFP loss as high as 12%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".