Trade Protectionism and Intra-industry Trade: A USA - EU Comparison
Bibliographic record
Abstract
The aim of this work is to find patterns for products included in the customs tariffs of the USA and the EU (composed of over 5000 products disaggregated at the 6 digit-level) which share similarities, defined by a set of international trade variables, namely the index of revealed comparative advantages (RCA), the Grubel-Lloyd index, and other indicators of international trade. There is a strand in the literature advancing a theory that links the degree of intra-industry trade with the level of protectionism. In order to test this theory we use cluster analysis as a method of data analysis and the Grubel-Lloyd index as a classification variable between groups. For each of the analyzed regions we obtain four different groups. Thereafter each of these four clusters are further characterized with the help of the other international trade indicators and the tariffs. Finally, we establish a comparison between the two regions by examining possible differences and similarities. The results show a significant difference in the tariffs applied between the USA and the EU, with the USA presenting a lower level of protectionism. Additionally, the results for the USA show a positive relationship between the degree of intra-industry trade and a lower level of protectionism, while for the EU the results are not conclusive.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".