Measuring Border Crossing Costs and their Impact on Trade Flows: The United States-Mexican Trucking Case
Bibliographic record
Abstract
This article presents the economic implications of the costs and times of crossing the border between the United States and Mexico. We measure the microeconomic impact of the inefficiencies of crossing the U.S.-Mexican border on shippers. We identify and explain the institutional factors and vested interests that permit cross-border inefficiencies to appear and endure and estimate the costs of these inefficiencies associated with cross-border movements between the U.S. and Mexico. Inefficiencies here are defined as money paid by shippers for charges for non-essential bordercrossing services. These inefficiencies not only cost exporters and importers 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 both the primary and secondary impacts of these nontariff barriers, we use the General Trade Analysis Project-GTAP- model to simulate the removal of iceberg trade costs equal in magnitude to the measured nontariff barriers at the U.S.- Mexican border. The measures of inefficiency at the U.S.-Mexican border come from detailed border surveys and data analysis performed by Haralambides and Londoño-Kent (“Impediments to Free Trade: The Case of Trucking and NAFTA in the U.S.-Mexican Border”, mimeo, 2002). These measures of distortion are then used to calibrate an iceberg tariff within the GTAP model. We aggregate the GTAP version 5 database to 5 regions (U.S., Mexico, Canada, EU, Rest of World) and to 11 sectors. The removal of iceberg tariffs is simulated by shocking the values of the variable AMS, augmenting technical change for the relevant sectors and trade flows. We estimate that 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 by about $1 billion per year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".