By Gita Gopinath, Pierre-Olivier Gourinchas,
Bibliographic record
Abstract
A well-established fact in international economics is that relative prices at the retail level across countries, expressed in a common currency, comove closely with the nominal exchange rate. Understanding why is central to answering some of the classic questions in international economics, ranging from the gains from market integra-tion to the transmission of shocks across borders. Two ingredients are necessary to generate this pattern in the data. First, some economic forces must cause retail prices to differ across countries. Standard explanations emphasize the importance of local nontraded retailing costs or pricing to market at the retail level (Ariel Burstein, Martin Eichenbaum, and Sergio Rebelo 2005; Pinelopi K. Goldberg and Frank Verboven 2005). Second, cross-border transaction costs must be large enough to prevent arbi-trage. Indeed, a large body of literature interprets the price gap of similar goods across borders as a measure of these transaction costs (Charles Engel and John Rogers 1996). In this paper, we bring new data and a new approach to these questions. We use weekly data at the barcode level on retail prices and wholesale costs for 250 US stores (in 19 states) and 75 Canadian stores (in 5 provinces) of a single retail chain between January 2004 and June 2007 on over 4,000 products. At short horizons
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.044 |
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".