Trade cost and export diversification: Evidence from Chinese firms
Bibliographic record
Abstract
We investigate the relationship between the number of varieties a firm decides to export (its export scope) and the characteristics of the destination country. Using Chinese firm-level customs data for 2001 and 2006, we document that Chinese exporters adjust their export scope to different characteristics of destination countries. We show that firms export fewer varieties to countries that display higher exchange rate volatility, that are farther away from China, or that impose higher import-tariff rate. Also, we find that the response to the tariff reduction process due to China’s entry into the WTO in 2001 is heterogeneous across firms: high productivity firms (the total factor productivity is measured through the Olley-Pakes method) expanded their export scope, while low productivity firms reduced it. With this evidence at hand, we develop a flexible and tractable theoretical model to rationalize our empirical findings. Our framework considers heterogeneous firms’ optimization decisions involving both production and export varieties and their interplay with the exchange rate volatility, the distance to the destination country, and the tariff rate. Our model predicts that the export scope decreases in the level of exchange rate volatility, distance, and tariff rate of a destination country: firms can reduce the export scope if the destination countries suffer negative demand shocks, but cannot expand the export scope if positive shocks occur, due to insufficient pre-investment in production capacity. Also, our model predicts that high-productivity firms have an advantage in producing higher quality products, and in response to a tariff reduction the demand for high-quality products increases more than that for low-quality products: thus, high productivity firms react by expanding their export scope, but low productivity firms may reduce their export scope due to the increase in market competition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".