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Record W2000849807 · doi:10.1108/15253831111149771

China's exports in a world of increasing oil prices

2011· article· en· W2000849807 on OpenAlexaff
Byron Gangnes, C. Alyson, Ari Van Assche

Bibliographic record

VenueMultinational Business Review · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsEconomicsChinaSpeculationGravity model of tradeInternational tradeValue (mathematics)International economicsOriginalityBilateral tradeMacroeconomicsGeography

Abstract

fetched live from OpenAlex

Purpose This paper aims to examine the impact of oil prices on trade's sensitivity to distance. Furthermore, it seeks to investigate if the nature of trade and the type of goods have a mediating role on the oil prices' impact on trade. Design/methodology/approach A set of gravity models are estimated on a unique panel dataset from China Customs Statistics that reports trade by customs regime (processing trade vs non‐processing trade) and by transportation mode (air vs sea) for the years 1988‐2008. Findings Higher oil prices increase the sensitivity of China's exports to distance. This effect is especially pronounced for processing exports, where goods cross borders multiple times. On the other hand, it is smaller for exports shipped by air. While these results are statistically significant, their economic effects are relatively small. This paper estimates that the quadrupling of oil prices between 2002 and 2008 has increased the elasticity of Chinese exports with respect to distance by a mere 5‐7 per cent. Social implications The surge of oil prices in recent years has led to speculation that rising transportation costs could end the period of dramatic world trade growth – in the words of Rubin, “[…] Your world is going to get a whole lot smaller.” This paper suggests that this concern is overstated. Originality/value This is the first paper that estimates the mediating role that the nature of trade and the type of goods play on trade's sensitivity to distance.

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.

How this classification was reachedexpand

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.239
Teacher spread0.134 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2011
Admission routes1
Has abstractyes

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