Virtual Borders: Online Nominal Rigidities and International Market Segmentation
Bibliographic record
Abstract
Do prices respond to macro shocks?Does the mere presence of international frontiers hinder trade?We revisit these questions by studying a dataset of online book prices for a number of US and Canadian retailers.We believe our dataset is well suited to this task for a number of reasons: (1) data for multiple retailers are available; (2) the products sold are identical across retailers; (3) the sample spans a period of large fluctuations in the bilateral exchange rate; (4) the nature of the industry is such that physical distance is irrelevant beyond shipping costs which are observable; (5) nominal frictions in the form of menu costs are arguably minimal; and (6) proxies for sales are available for most retailers.Given the unique nature of our dataset, the first objective of the paper is to document the degree of price rigidity and price dispersion.Our main findings are: online book prices display significant stickiness; there is a large degree of heterogeneity across retailers in terms of price rigidity and pricing strategy; price dispersion is high both within and across borders.Also, price levels do not appear to respond to exchange rate fluctuations.Building on the predictions from a simple two-country, multi-firm model and by exploiting information contained both in prices and quantities, we show that market segmentation is probably behind this disconnect .
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".