INTERNATIONAL PRICE COMPETITION ON THE INTERNET: A CLINICAL STUDY OF THE ONLINE BOOK INDUSTRY
Bibliographic record
Abstract
How borderless is the new economy? This paper addresses this question by examining the nature of price competition in books between a world leader in internet retailing, Amazon.com and the largest online retailer in Canada, Chapters.ca. The internet allows Canadian consumers to circumvent protectionist barriers by purchasing directly online from US booksellers. However Canadian online booksellers still enjoy considerable protection in the form of significant shipping cost advantages in selling to Canadian customers. The paper constructs a large panel dataset on prices, delivery schedule and popularity rankings of more than 5,000 books over 21 weeks, all collected from the internet. Using this data, the paper demonstrates that in spite of the existence of this shipping cost differential, the Canadian company sets prices that are on average remarkably close to the US prices, adjusted for the exchange rate. The paper also analyzes how the price differential varies over time and across different product characteristics as well as the dynamics of price setting in the two stores. While there is significant variation of the price differential across the different segments, this differential is always significantly smaller than the shipping cost advantage. This suggests that Chapters is partially foregoing its available rent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".