Sticker Shock: The Causes of the Canada-US Price Differential
Bibliographic record
Abstract
Whether they are buying dinnerware, diapers, or dairy products, Canadian consumers are becoming frustrated by the higher prices of similar goods in Canadian stores relative to US stores. The federal government has promised to introduce legislation to address alleged price discrimination in Canada relative to the US. But before the government acts, it should understand why Canadian prices are often higher and how much its own policies are the cause of relatively higher prices for Canadian consumers. The Canada-US price gaps at the retail and wholesale levels seem to closely track the Canada-US exchange rate. Using detailed data from 2004 through 2007 from a major grocery retailer operating in Canada and the US, I find that the Canada-US gaps in wholesale prices play a much greater role than gaps in retail margins in generating the price gaps consumers observe. There is less competition among manufacturers in Canada, as reflected in the fact that the average product category carried by the retailer has one-third fewer major brands in Canada than in the US. While more competition is associated with smaller increases in price gaps, there is little evidence to conclusively demonstrate what fraction of the wholesale price gap is due to a legitimately higher cost of operating in Canada. What can Canadian governments, especially the federal government, do to reduce the price gap between Canada and the US? The easiest thing Canadian governments can do if they want to reduce the Canada-US wholesale price gap is eliminate existing tariffs and supply-management policies that are responsible for the largest price gaps. The government should also consider building on recent actions that allow consumers to pay US prices for goods by increasing duty-free exemptions for travellers and postal shipments. As well, it should consider the role of certain taxes and regulations that could be raising the cost of doing business in Canada for manufacturers, distributors and retailers. If the federal government is serious about reducing prices for Canadians, it might want to first look at some of its own policies before tasking the Competition Bureau with investigating companies charging higher prices in Canada relative to the US.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".