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Record W1500251059 · doi:10.1080/00036846.2015.1008772

Chinese firm and industry reactions to antidumping initiations and measures

2015· article· en· W1500251059 on OpenAlex
Chunding Li, John Whalley

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Economics · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsCentre for International Governance InnovationWestern University
Fundersnot available
KeywordsChinaBusinessPanel dataProductivityDeveloping countryEconomicsInternational tradeEconometricsMacroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.108
GPT teacher head0.243
Teacher spread0.135 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it