Trade agreements and decent work in Mexico: the case of the automotive and textile industries
Bibliographic record
Abstract
The study examines the effects of trade liberalization on employment and the labour market in Mexico's manufacturing industry. The analysis places special emphasis on assessing the extent to which the industry's distinct trade performance is accompanied by an improvement in labour conditions with the objective of ensuring decent work. For this purpose, the study applies the framework of decent work indicators developed by the International Labour Organization (ILO), in combination with input-output analysis, to explore selected links between international trade and certain indicators of decent work in two industries of Mexico's manufacturing sector: automotive and textile. We chose these two industries because of the key differences in their organizational structures, their roles in global value chains (GVC) and their dynamism in recent decades. With the policy shift towards trade liberalization in recent decades, the automotive industry has come to be regarded as the jewel of Mexico's export market. The textile industry, in contrast, suffered a severe shock as trade liberalization brought about increased competition in Mexico's domestic market, despite the industry increasing its participation in GVCs. A key contribution of the study was to construct a set of relevant time series indicators of decent work for these two industrial activities in Mexico, based on ILO guidelines and official data. Taking into account this set of indicators, as well as Mexico's labour market regulatory reforms and their links to trade agreements, including the Agreement between the United States of America, the United Mexican States and Canada (USMCA), the study finds important differences in these two industries' advance towards decent work, which can be partly explained by their distinct performances in international trade. Based on these results, the study offers some policy recommendations to help achieve a more robust pace of progress towards decent work.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".