Vertical Specialization of Production: Critical Review and Empirical Evidence for the Mexican Manufacturing Industries 1994-2014
Bibliographic record
Abstract
This paper surveys Vertical Specialization (VS) from different empirical approaches, including the various conceptualizations of VS, the problems with its quantification, and a case study. The empirical estimation of this paper refers to the Mexican manufacturing industry and the in-bond industry (also known as maquiladora). The purpose of this paper is to compare VS, through the application of the Vertical Intra-Industry Trade (VIIT) indexes, for maquiladora and non-maquiladora industries and to contrast the degree to which the maquiladora industry is integrated with the global value chain relative to the non-maquiladora activities for the 1994-2006 period. Furthermore, this paper quantifies VIIT for the post-maquiladora period (2007-2014) in order to discuss if there have been changes in the VIIT since the conclusion of the maquiladora program in 2006. In particular, this paper tests if the quality ladder hypothesis applies to Mexico for its bilateral trade with the United States and Canada. The empirical estimations show the remarkable differences between maquiladora and non-maquiladora VS and the impacts that changes in the NAFTA tariff schedule for the automotive industry have had on the bilateral trade pattern.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".