Causes of the salary levels in the Mexican automotive industry three years after the USMCA
Bibliographic record
Abstract
The United States, Mexico, and Canada (USMCA) seek to promote fair wages and adequate working conditions, especially in Mexico, by strengthening labor rights and freedom of association. The objective of this research is to determine the factors that influence salary levels in the Mexican Automotive Industry (MAI), through a causality analysis in the Granger sense, to generate a panorama that allows a decision-making process in the Mexican salary policy. With data from the National Institute of Statistics and Geography, the Bank of Mexico and Statista, autoregressive vector models were estimated to determine causalities in the Granger sense. It was proven that minimum wage, employed personnel, production, total sales, and exports are some causes of remuneration in the sector, with the minimum wage being the most significant. The above suggests that the salary increase involves several actors, such as the government (minimum wage), the organization (production, sales and exports) and the market (employed personnel), therefore, the design of appropriate labor policies will contribute to the dignification of salaries inside the MAI.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".