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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".