Institutional Arrangements that Affect Free Trade Agreements: Economic Rationality Versus Interest Groups
Bibliographic record
Abstract
textabstractThis dissertation presents a time motion study of what actually happens at the busiest U.S-Mexican border crossing at Laredo. The North American Free Trade Agreement (NAFTA) assumes seamless border crossings without detailing however how this would be achieved particularly in the case of trucking, the most important cargo transport mode. This dissertation presents evidence that NAFTA has not led to an efficient border crossing: a border that could be crossed in 15 minutes with a single truck and driver takes several days, drivers, and pieces of equipment. In fact it takes longer to cross the Rio Grande than go from Chicago to Laredo by truck. This is contrasted with a mini-time motion study of an efficient border crossing at Ambassador bridge between the United States and Canada. This bridge has more traffic than all Laredo bridges combined, yet it has only 4 lanes versus the 22 crossing lanes available at Laredo. Unnecessary river bridges (none costing less than one hundred million dollars) and access roads are being built, instead of attempting to solve an institutional problem involving inefficient procedures. The latter, among others, comprise federal and state inspections of cargo and motor vehicles; limited hours of bridge operation; and limitations on the operations of trucks in each country. The time-motion study establishes which practices or regulations cause which inefficiencies and what are the consequences in terms of time, money, and equipment. This analysis shows the way in which interest groups profit from inefficiency, and it also reveals the economic forces at work on the local and national level in both countries. Such inefficiencies not only cost importers and exporters time and money- they also cause welfare losses to the entire economy because of the distortions they introduce to consumption and sourcing decisions. In order to measure the macro-economic impact of these non-tariff barriers, the dissertation uses the General Trade Analysis Project- GTAP model- to simulate the removal of iceberg trade costs. The results of the analysis indicate that the removal of such barriers would benefit the Mexican economy by $1.8 billion per year, while the U.S. economy would see a welfare increase of about $1.4 billion per year. Trade flows between Mexico and the United States would likewise increase, with southbound trade expanding by about $6 billion and northbound trade growing about $1 billion per year. This work is relevant for business and government people pressing the case for well intended free trade agreements and promoting the technology that can expedite greater volumes of trade. Further research along the lines of this work may provide a refined theoretical and methodological basis for cross-border policy.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.001 |
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".