Trade Liberalization and Industrial Pollution in Mexico: Lessons for the FTAA"
Bibliographic record
Abstract
As the barriers to hemispheric trade and integration are lowered, it will be asked whether we will we hear the "giant sucking sound" of poorer nations luring U.S. and Canadian firms south to take advantage of low wages and lax environmental regulations? Or, will Latin American nations passively accept this problematical specialization in doing the world's cheap and dirty work? Mexico is the ideal laboratory for such research. Though NAFTA took effect in 1994, trade liberalization in Mexico began long before that. From 1982 to 1996 Mexico transformed itself from one of the most closed to one of the most open economies in the world. As a first step in such efforts, this paper looks at the relationship between industrial pollution and economic activity in Mexico, compares those results to the United States, and draws out implications for the FTAA. The study finds that many of the industries deemed the dirtiest in the world economy are actually cleaner in Mexico than in the US, and the industries labeled the cleanest are dirtier in Mexico. To generalize, this exhibits that trade liberalization can have both positive and negative environmental effects in developing economies. Sectors where plant vintage determines pollution levels can benefit from their ability to take advantage of newer technologies after liberalizing trade, as is the case with the Mexican steel industry. However, if pollution is a function of end of pipe technology, as in the paper industry, pollution levels are determined by levels of regulation, enforcement and compliance, which are lower in Mexico.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".