Mexico's 20 years of North American Free Trade Agreement: Socio-Environmental Trends and Unequal Exchange
Bibliographic record
Abstract
The North American Free Trade Agreement (NAFTA) is the most influential trade agreement, signed by the governments of USA, Canada and Mexico in 1992. It came into effect the 1st of January of 1994 promising economic growth and better employment opportunities to reduce Mexican emigration. The Zapatista Army of National Liberation (EZLN), a civil resistance movement against capitalist neo-liberalism, protested the agreement, warning that it would feed social inequalities and threaten indigenous rights, autonomy, land access and use of natural resources. The Zapatistas feared the NAFTA would reinforce a master-servant relationship where Mexican human and natural resources are displaced, undermined or employed for the benefit of USA-CAN. In this paper we use Multiregional Input-Output Analysis based on the EORA model to examine changes in the carbon, land material, water and employment footprints in Mexico derived from the NAFTA agreement. We pay particular attention to the fairness of the resource exchange between USA and Mexico. We find all the consumption-based footprints grew between the period of 1990-2015. The carbon footprint increased by 50%, land by 32%, material by 46% and water by 566%. Territorial based employment rose by 7% and consumption based employment by 14%. Consumption of land and water considerably sped up after NAFTA. Remarkably, the land footprint doubled between 1994 and 2003, whereas GDP only increased by 20%. After that peak, the changes in land footprints retracted and stabilized at a 32% yearly increase until 2015, which corresponded to a 65% increase in GDP. Carbon intensity per unit of GDP has noticeably decreased after the NAFTA, nevertheless rising consumption heavily drives carbon emissions, eating-up efficiency gains. We confirm that the unequal trade has increased after the NAFTA, with surpluses for carbon, materials and more heavily for labour -meaning Mexico has become a net source for these resources. Not so for land and water, where Mexico remains a net consumer (2) We confirm that a large portion of the increases in Mexico’s carbon, material and water are destined to satisfy USA-CAN consumption. (3) We confirm a master-servant dynamic where the employment embodied in trade leaves Mexico with a 73% surplus -a net supplier of labor among NAFTA partners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".