Exporting and Frictions in Input Markets: Evidence from Chinese Data
Bibliographic record
Abstract
This paper investigates the impact of international trade on input market distortions. We focus on a specific friction, binding borrowing constraints in capital markets. We propose a theoretical model where a firm's demand for capital is constrained by an initial asset allocation and past sales. While the initial distribution of assets induces misallocation if the asset endowment at more productive firms does not fully cover their demand for capital, the dependence of the borrowing constraint from past sales proxies for cross-firm differences in the cost of default, which is empirically higher at larger firms. Overtime, an increase in sales relaxes the borrowing constraint; similarly, shocks to market access--such as opening to trade--contribute to easing the financial constraints, thus accelerating the convergence toward the frictionless allocation. To analyze the empirical relationship between market access and credit frictions, we draw on the annual surveys conducted by the Chinese National Bureau of Statistics (NBS) for 1998 to 2007, and we construct firm-level measures of distortions that control for firm heterogeneity. We find smaller labor and capital distortions across exporting firms; such distortions are even smaller in sectors where firms face lower tariffs or are more dependent on external financing, a proxy for the presence of binding financial constraints. Our empirical analysis also shows that export shocks significantly reduce the dispersion across input returns over time, with the effect mostly occurring at constrained firms. Our findings point to within-sector input reallocation as an important channel to overcome misallocation in open economies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".