NAFTA and the Evolution of Mexico's Competitive Advantages in the US Market: A Value-Added Approach
Bibliographic record
Abstract
Understanding your market and the value creation though your supply chain is a relatively young discipline. Thanks to new statistical indicators, it is now possible to conduct sectoral market analysis in value-added. This paper is an application to Mexico and the NAFTA. The objective of this conference paper is to map and characterize the various NAFTA Regional Value Chains, their evolution since the inception of NAFTA and identify the industries that are the most vulnerable to a disruption of the regional supply chain providing them with competitive inputs. To measure the vulnerability of Mexican industries to a disruption of the regional supply chain (the ultimate objective of the exercise), the paper estimates the impact on production costs on key manufacturing sectors that would be expected from a reversion of NAFTA under various hypothesis. When looking at the performance of Mexico in the US manufacturing market, the paper points at some substitution between Canada and Mexico as US supplier of inputs. It shows that, by supplying competitive inputs to the US manufacture exporters, Mexico and China helped the efforts of the US industry to remain competitive on the world market. This is in particular the case for the US automobile industry. The analysis of the sectoral origin of the value added embodied in Mexico’s exports of manufacture to the US reveals also the low content of embodied business services in Mexican products. Considering that manufacture and business services are the main driver of competitiveness, this result is worrying. (This paper is the conference version of an article ultimately published in Spanish.)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".