Export and Productivity of China's Textile Enterprises:An Empirical Analysis on Listed Companies 2001～2010
Bibliographic record
Abstract
Based on the data from China's listed textile companies during the period from 2001 to 2010,we empirically test the two hypotheses of new-new trade theory.The results indicate that(1) exporters have higher productivity than non-exporters and those with higher export intensity are more productive than those with lower intensity;(2) the self-selection effect does exist,i.e.there is a causal relationship between productivity and export,and that the effect is more significant among exporters with higher export intensity;(3) for the sample companies as a whole,learning-by-exporting does not exist;but,for the companies with high export intensity,that effect does exist.We conclude that the empirical test based on China's firm level data during the past 10～15 years should further take firm-specific export barriers into account,besides such special factors as processing trade mode and ownership.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".