Income and Exchange Rate Sensitivities of Cross-Border Freight Flows: Evidence from U.S.-Canada Exports and Imports
Bibliographic record
Abstract
by Junwook ChiThis paper aims to improve understanding of the long-run impacts of the gross domestic product (GDP), real exchange rate, and the producer price index (PPI) on U.S.-Canada bilateral freight flows in a dynamic framework. Special attention is given to cross-border exports and imports by truck, rail, pipeline, and air. Using the fully modified ordinary least squares (FM-OLS) approach, the paper finds that the GDP of the importing country is a pronounced factor influencing U.S.-Canada cross-border trade, suggesting that economic growth of the country is a powerful driver in the relative intensity of bilateral freight flows. The real exchange rate tends to be positively associated with U.S. imports, but negatively associated with U.S. exports, indicating that the U.S. dollar depreciation against the Canadian dollar increases demand for U.S. commodities in Canada, but weakens demand for Canadian commodities in the United States. The long-run effects of the selected economic variables on cross-border exports and imports are found to vary by mode of transportation. The Canadian GDP has a positive and significant effect on U.S. freight exports by all transportation modes, but U.S. exports by pipeline are more sensitive to a change in Canadian GDP than U.S. exports by truck and rail. The findings in this paper provide important policy and managerial implications for cross-border transportation planning in the United States and Canada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".