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The Law of One Price: A Canada/U.S. exploration

2004· article· en· W2143530230 on OpenAlexaffabout
John R. Baldwin, Beiling Yan

Bibliographic record

VenueReview of Income and Wealth · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsEconomicsLaw of one priceExchange rateArbitrageCommodityCurrencyMonetary economicsMarket integrationLiberalizationPrice levelInternational economicsMid priceMacroeconomicsFinancial economicsMarket economy

Abstract

fetched live from OpenAlex

The paper examines whether arbitrage tends to equalize commodity prices for internationally traded homogenous products. It also investigates whether the increasing integration of North American markets has reduced price differences over time, and tests the validity of the so‐called Law of One Price. We find that price differences for homogenous tradables between Canada and the U.S. are smaller than those for differentiated tradables and non‐tradables, and are statistically insignificant over the period 1985 to 1999. We find no support for the notion that the increasing integration of North American markets due to trade liberalization has reduced price differences between Canada and the United States. Instead, the shifts in the price differences (expressed in the same currency) generally reflected fluctuations in the exchange rate. Canadian prices adapt with a lag to U.S. price changes that are brought about by changes in the exchange rate.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0040.004
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.056
GPT teacher head0.244
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2004
Admission routes2
Has abstractyes

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