Bibliographic record
Abstract
Using consumer price indexes from cities in the U.S., Canada and Mexico, we estimate the "border effect" on U.S.-Mexican relative prices and find that it is nearly an order of magnitude larger than for U.S.-Canadian prices. However, during a very stable sub-period in Mexico (May 1988 to November 1994), the "width" of the U.S.-Mexican border falls dramatically and becomes approximately equal to the U.S.-Canadian border. We then show that when consideration is limited to cities lying geographically very close to the U.S.-Mexican border--San Diego, Los Angeles, Houston, Dallas, Tijuana, Mexicali, Juarez, and Matamoros--the border width falls compared to that estimated with the full sample of U.S. and Mexican cities, but falls only very slightly. We also present evidence that the border effect in U.S.-Mexican prices is not primarily due to the border effect in U.S.-Mexican wages. Finally, using the prices of 276 highly dis-aggregated goods and services, we estimate the variability of relative prices of different items within Mexican cities. This measure of relative price variability declines during the stable peso sub-period, but by less than the decline in nominal and real (i.e., CPI-based) exchange rate variability. Our results are strong evidence of a "nominal border effect" in relative prices within NAFTA, but also indicate that real side influences are important.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".