Bibliographic record
Abstract
This study models the article entitled, “Cross-border restaurant price and exchange rate interactions” by Thomas M. Fullerton Jr., Karen P. Fierro, and Emmanuel Villalobos, Department of Economics & Finance, University of Texas at El Paso, TX 79968-0543, USA. The Law of One Price, in essence, states that a good must sell for the same price in all locations. In efficient markets, when two products are identical, it is intuitive that the two products sell for the same price. However, when these product prices are mismatched, there is an opportunity for the buyer/consumer to arbitrage the mismatching; that is, to take advantage and buy an identical product from another market at a below-equilibrium price. This concept can be traced back to 1760-1770 France, when economists began to apply this “law” to international markets.1 But does the concept actually hold true across political borders? According to the Purchasing Power Parity (PPP), the exchange rate between two currencies should adjust so that, when expressed in the same currency, an identical good in two different countries has the same price.2 For example, if the PPP holds, a product that sells for $1.50 CAN should sell for $1.00 USD, if the exchange rate between the countries is $1.50USD/CAN. This study provides a snapshot analysis into the pricing activity of five international restaurant franchises on both sides of the United States/Canada border in Port Huron, Michigan and Sarnia, Ontario. Through this analysis, I seek to identify opportunities for consumer cross-border arbitrage.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".