Risk sharing and export performance with firm heterogeneity
Bibliographic record
Abstract
Abstract We investigate how market uncertainty affects the export performance of a firm through financial frictions. We first extend Melitz's (2003) heterogeneous firm trade model by incorporating demand shocks, linking the demand uncertainties to the financing costs of firms. In this extension, the default probability is endogenously determined by a firm's productivity and demand uncertainty. Hence, firms with higher productivity or lower market uncertainty are offered lower interest rates and thus show better export performance. As an application, we also show that a risk‐sharing mechanism, that pools default risk for a certain group of firms, lowers the default risk. This mechanism allows banks to charge lower interest rates to the member firms and therefore ultimately improves their export performance in both extensive and intensive margins. We find a real‐world example of such a mechanism from business groups in Korea. Using Korean firm‐level data, we show that the more diversified the business group, the greater the likelihood that its member firms export and the bigger their export revenues. We also show that our results are robust to alternative explanations for Korean business groups’ export competitiveness.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".