Chinese Firm and Industry Reactions to Antidumping Initiations and Measures
Bibliographic record
Abstract
Because of large and rapid growing export volumes and its formal status as a non-market economy; China has been the subject of large numbers of both antidumping initiations and measures.Current estimates are that around 40% of such actions are against China; India, in turn, is the largest source of initiation against China by number of actions.Here we explore the reactions of Chinese firms and industries to these actions.No other papers to our knowledge explore these reactions empirically.We use industrial panel data on all Chinese firms in the industry, foreign firms operating within China and state owned enterprises (SOE) for aggregated firms group between 1997 and 2007.This provides information on sales, profits, firm numbers, labor productivity, and employment.We are able to link this data with a World Bank dataset on antidumping actions by industry by country (both by and against) for the same period.We then use a dynamic system GMM estimator to explore the importance of different forms of Chinese firms' overall response to both initiations and measures.We also separately analyze antidumping actions against China from developed and developing countries, US and EU to compare their different effects.We find that antidumping actions by developed and developing countries negatively impact industrial profits and employee and firm numbers and also exports.Output impacts are the smallest.Labor productivity is improved by antidumping actions.We also find that different kinds of firms show different responses.All firms together in an industry react to antidumping the most, and foreign and SOE firms show a much smaller response.Also, developed countries' antidumping actions have more negative impact than developing countries' actions for all firms and SOEs, but foreign firms' impacts are the opposite.Chinese industry reactions to antidumping actions by the US and EU are the same as for other developed countries, but the effects of US actions are larger.US antidumping actions have more impact than EU's on firm numbers, employees and exports, and EU antidumping has more influence than US on output, profit and labor productivity.Finally, comparing Chinese, foreign, and SOE firm's reactions to US and EU antidumping actions, our results show foreign firms to be hurt more by antidumping from EU.We discuss policy implications in a concluding section.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".