Migration from Mexico to the US: The Impacts of NAFTA on Mexico and the United States and What to Do Going Forward
Bibliographic record
Abstract
Research indicates four main causes for migration from Mexico to the United States: Incredibly high crime rates, unemployment, poverty rates, and natural disasters. The first two are especially important in regards to trade between the two border sharing countries. Since agreeing to virtually total free trade, the United States has been able to take advantage of Mexico in such a way that has created further deterioration of the state. If the government of Mexico cannot resurrect the thousands of personal business that were effected do to NAFTA, the U.S. cannot expect for migration from Mexico to deteriorate or halt. By displacing Mexico’s small business owner’s, Mexico has effectively made their citizens weak to the inevitable increase in poverty, and the Cartels that have bought out swaths of land and human lives. In this paper, I reveal the direct correlations between agreements within the NAFTA and the millions of displaced agricultural workers in Mexico that caused an increase of immigration from Mexico to the United States. On January 1, 1994, The North American Free Trade Agreement (NAFTA), which includes Canada, Mexico, and the United States, was officially formed. By 2008, virtual free trade in almost all goods and services was established amongst all three countries, with the exception of a limited number of agricultural products traded specifically with Canada. The trade agreements of NAFTA were coupled with a surge of Mexican migrants to the U.S. The question under investigation is, why did so many Mexicans move to the United States after the NAFTA was signed? Contrary to the theoretical benefits of free trade, many citizens of the United States and Mexico have developed deep contention towards the agreement between their countries. In the United States, those whose’ lives revolve around the manufacturing industry argue that increased trade with low-wage countries, such as Mexico, threatens their employment due to industrial re-location. Mexican’s argue that the U.S. is dumping agricultural products and manufacturing industries that destroy local business and decrease the standard of living. More than 35 million Americans have Mexican roots, and Mexico is the United States’ third-largest trading partner, next to China. Despite the positive correlation that the economic gravity theory presents, that both countries are at an advantage to trade with one-another, I’m afraid it’s information is flawed. As the United States and Mexico attempt to find a way to grow their economies and decrease migration, from Mexico to the U.S., it is imperative for them to understand that free-trade, under the current NAFTA, has actually increased immigration from Mexico to the U.S. because of minimal protectionisms for Mexico’s workers- especially in agriculture. This paper is written with the intent to inform people of the relationship between trade and immigration, specifically, between the U.S. and 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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".