Revisiting the border: an assessment of the law of one price using very disaggregated consumer price data
Bibliographic record
Abstract
We reexamine the evidence for border effects in deviations from the law of one price, using data for consumer prices from Canadian and U.S. cities. The study parallels Engel and Rogers (1996), except that this study uses actual price data rather than price index data. We find evidence of border effects both in the levels of prices and the percentage change in prices. Even accounting for distance between cities and relative population sizes, we find that the absolute difference between prices in the U.S. and Canada in our data (annual from 1990 to 2002) is greater than seven percent. This difference exists among tradables and nontradables, though for some categories of tradables (clothing and durables) the difference is smaller. The findings are similar for annual changes, though the magnitude is smaller: the border accounts for a difference in 1.5 percent in annual (log) price changes. Relative population sizes and distance are helpful in explaining price level differences (between Canadian and U.S. cities) for traded goods, but are less helpful in explaining price level differences for nontraded goods or for accounting for differences in price changes for either traded or nontraded goods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".