Cross-border prices, costs, and mark-ups
Bibliographic record
Abstract
Very Preliminary: Do not quote/circulate We use data on retail prices and wholesale costs for detailed products at the barcode level from 325 stores of a large grocery chain to measure the effect of the US-Canadian border on market segmentation. Theoretically, we use a model of pricing and location on the circle to document possible patterns of cross-border prices. Empirically, we find clear evidence of international market segmentation. Cross-border price gaps are significantly higher than within country price gaps. Using the precise geographic location of each store, we find that UPC level prices and wholesale costs are discontinuous at the border. Our findings indicate that most differences in cross border prices arise from differences in an apparently tradable component of costs and not from systematic mark-up differences. We thank Michal Fabinger, Robert Johnson, Lorenz Küng, Nick Li, Gloria Sheu and Kelly Shue for excellent To what extent do national borders and national currencies impose costs that segment markets across countries? Some of the central questions in international economics, ranging from the transmission
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".