Competitiveness in global trade: The case of the automobile industry
Bibliographic record
Abstract
Numerous studies handle analyses of revealed comparative advantages of global trade (especially in agriculture sector) using Balassa index, but the selected automobile industry represents new potentials to study. This study focuses on the competitiveness of automobile industry, which is a key sector due to its high value-added activities, a competitive market, with increasing technology requirements and high employment characteristics. The aim of our paper is to analyse the revealed comparative advantages of global automobile trade as well as the duration and stability of Balassa indices by applying Markov transition probability matrices and Kaplan-Meier survival function. The source of data is global automobile exports at HS6 level for 1997-2016. The paper has reached numerous conclusions. First, by analysing characteristics of global automobile trade, it turned out that China, USA, Japan and Germany were the biggest producers of cars, however the top exporters were Germany, Japan and Canada in the period analysed, together giving 40% of all products exported - the top10 countries, however, gave 71% of concentration. Second, our analysis has made it clear that the most traded/exported automobile product is vehicle with only sparkling ignition internal combustion (1500-300cm3) (870323) globally, giving more than 40% of all vehicle exports between 1997 and 2016. Third, the calculation of Balassa indices showed that Spain and Japan had highest comparative advantages in all periods analysed among the most important automobile exporters in the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".