Horizontal and vertical intra-industry trade of Nafta and Mercosur: The case of the automobile industry
Bibliographic record
Abstract
The NAFTA and MERCOSUR agreements seem to have accelerated the regional integration process respectively within North and South of America. In the South in particular, MERCOSUR has led to trade liberalisation and deregulation, which has resulted in significant growth of its regional trade. In this article, we study the pattern of that trade growth in the automobile industry. Our results highlight an increase of intra-industry trade in the corresponding industry since the beginning of the 1990s. Firstly, we use the Grubel and Lloyd indicator (1975). Secondly, following Abd-el-Rahman (1991), Greenaway et al. (1995), Fontagné and Freudenberg (1997), we distinguish horizontally differentiated goods from vertically differentiated goods using a comparison of the unit values. With the increase of intra-industry trade, it appears that MERCOSUR has favoured in particular the development of trade in vertically differentiated goods. In NAFTA, intra-industry trade exists in most sectors and in two bilateral relations (US-Canada and US-Mexico). In\nMERCOSUR, the automobile industry has experienced the highest rate of growth in intraindustry trade, which accounts for 66% of total trade and 90% of all intra-regional trade. Thirdly, we analyse the nature of that increase and more precisely, the determinants of intra-industry trade. In order to explain the pattern of trade for the automobile industry, we use a gravity-type model taking into account some country-specific variables.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".