Estimating the Border Effect: Some New Evidence
Bibliographic record
Abstract
To what extent do national borders and national currencies impose costs that segment markets across countries?To answer this question we use a dataset with product level retail prices and wholesale costs for a large grocery chain with stores in the U.S. and Canada.We develop a model of pricing by location and employ a regression discontinuity approach to estimate and interpret the border effect.We report three main facts: 1) The median absolute retail price and whole-sale cost discontinuity between adjacent stores on either side of the U.S.-Canada border is as high as 21%.In contrast, within-country border discontinuity is close to 0%; 2) The variation in the retail price gap at the border is almost entirely driven by variation in wholesale costs, not by variation in markups; 3) The border gap in prices and costs co-move almost one to one with changes in the U.S.-Canada nominal exchange rate.We show these facts suggest that the price gaps we estimate provide only a lower bound on border costs.
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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 | 0.028 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".