Bibliographic record
Abstract
It has been 15 years since North American Free Trade Agreement took effect in January 1994. When President Clinton gave remarks at signing of NAFTA side agreements, he promised that NAFTA will create 200,000 jobs within the U.S. in the first two years of its effect, as well as generating another 1 million jobs three years later. Moreover, the research released by United States International Trade Commission in 1992 indicates that NAFTA will help both the U.S. real Gross Domestic Product and employment rate rise 5 percent per year and 0.1 to 2.5 percent. However, 15 years has passed, but the only question still exists. “How is the benefit of NAFTA to the U.S.?” According to the huge growth of trade and Foreign Direct Investment among the three nations, NAFTA seems like a success. Nevertheless, instead of looking at trade and FDI, this research aims to figure out NAFTA’s effect to the U.S. by applying regression analysis to analyze unemployment rate, personal income, and Gross State Product. These data are organized into a panel data and analyzed by Ordinary Least Square and Random Effect Model. The fundamental theories that back this thesis up can be divided into two categories, the classical theory of international trade and gravity model. The classical theory of international trade including division of labor, the theory of absolute advantage, the theory of comparative advantage, and theory of factor endowment explain that how do countries become better of after they trade with each other, as well as how do NAFTA integrate successfully in such a great economic disparity. Furthermore, in the sense of gravity model, the geographical distance and the difference of economic masses of two different countries can really influence the trade between them. To sum up, the main purposes of this research are, firstly, are the benefits of NAFTA to the fifteen border states much more significant than non-border states? Secondly, did those senators really concerns about the interest of their own states before they cast the vote? Finally, according to the result of the empirical work, border states to Canada has barely benefited from NAFTA. On the contrary, border states to Mexico has fairly performed on their economic growth since NAFTA took effect in 1994. The change of unemployment rate has a very significant drop, and both personal income and GSP have a rather small but positive growth. Beside, the result based on the states of vote for and vote against NAFTA does certainly provide very strong evidence that most of the senators did concern about their state interest before they cast the vote.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".