Productivity and Firms’ Sales Destination: <scp>C</scp>hinese Characteristics
Bibliographic record
Abstract
Abstract In the trade literature, it is often assumed that there is little or no trade cost within a country's borders, but large trade costs across a country's borders. Thus, productive firms self‐select into exporters and the less productive firms can only serve domestic consumers. This paper presents a similar but different case in China, whose domestic markets are segmented by provincial borders mainly owing to the various (hidden) protective measures favoring local firms. These discriminative measures are de facto trade barriers. It applies the heterogeneous trade theory to examine the effects of firms’ productivity on their sales choices in both the international and domestic markets, in the presence of intra‐national and international trade costs. We find that productive firms not only self‐select into exporters, but also into sales in other provincial markets. This pattern is sensitive to firms’ locations and ownerships. For foreign direct investment (FDI)‐controlled firms, increases in productivity are associated with a higher probability of selling into other provincial markets, rather than into international ones. Productivity increases for firms operating in the inland area exhibit different patterns than those in the Eastern area.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".