Revisiting the border: an assessment of the law of one price using very disaggregated consumer price data
Bibliographic record
Abstract
On the other hand, short-term deviations from the law of one price \nacross national borders might reflect nominal exchange rate misalignment. \nThat is, in each country nominal goods prices might be set in the local currency. Nominal exchange rates reflect not only current market conditions \nbut also expectations of the future. As the nominal exchange rate fluctuates \nbut goods prices adjust only slowly, there arise deviations of prices \n(expressed in a common currency) across borders. That is, let PyS$ be the \nU.S. dollar price of good i sold in the U.S., and P(,A$ the Canadian dollar \nprice of the same good sold in Canada. Both of these prices might adjust \nsluggishly to changes in demand or supply. As SUS$/CA$ the U.S. dollar per Canadian dollar exchange rate, fluctuates as the market learns news of \nfuture economic conditions, there will be deviations from the law of one \nprice condition, PyS$ = SUS$ICA$PFA $. Devereux and Engel (2003) have \nargued that under these circumstances, there are gains to stabilizing \nnominal exchange rates. When there is local-currency pricing, changes in \nthe nominal exchange rate do not change relative prices faced by consumers. Prices of foreign-produced and domestically-produced goods are \nboth sticky in the local currency. There is no "expenditure switching" effect \nof exchange rate changes, so a flexible exchange rate does not help facilitate goods market adjustment. On the contrary, because short-term fluctuations in the nominal exchange rate induce price wedges between countries, \nthey lead to inefficient allocation of resources. Exchange-rate stability can \nminimize these distorting deviations from the law of one price.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".