Geographic Barriers to Commodity Price Integration: Evidence from US Cities and Swedish Towns, 1732-1860
Bibliographic record
Abstract
We study the role of distance and time in statistically explaining price dispersion for 14 commodities from 1732 to 1860. The prices are reported for US cities and Swedish market towns, so we can compare international and intranational dispersion. Distance and commodity-specific fixed effects explain a large share -roughly 60% -of the variability in a panel of more than 230,000 relative prices over these 128 years. There was a negative "ocean effect": international dispersion was less than would be predicted using distance, narrowing the effective ocean by more than 3000 km. Price dispersion declined over time beginning in the 18th century. This process of convergence was broad-based, across commodities and locations (both national and international). But there was a major interruption in convergence in the late 18th and early 19th centuries, at the time of the Napoleonic Wars, stopping the process by two or three decades on average.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".