Borders, varieties and distribution costs: Evidence from a US–Canada retail chain
Bibliographic record
Abstract
Abstract Using data from a large retailer operating in Canada and the United States, I examine how market size and retailer size at the local and regional levels shape the variety of products available to consumers at a given store. The average Canadian store carries many fewer varieties, and I show that this has a potentially large (negative) effect on consumer welfare for Canadian (vs. American) shoppers. I propose a novel method to quantify the international difference in retail distribution costs and find evidence that they are much higher in Canada. Exploiting intra‐national variation in market and retailer characteristics, I find that up to a quarter of the international variety gap can be explained by observed local and regional market characteristics that lead to larger retail scale in the United States. I also show that retailer scale has independent effects on product variety, conditional on market characteristics, and that manufacturer size and retailer size are substitutes in distribution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".