Exchange Rate Pass-Through to Provincial Consumer Prices: A Geographical-Goods Level Analysis
Bibliographic record
Abstract
This study investigates exchange rate pass-through (ERPT) to provincial consumer prices in Canada from a geographical and goods-specific perspective. ERPT refers to the extent in which changes in the exchange rate influence domestic price movements. For example, a depreciation of the Canadian dollar relative to the U.S. dollar may increase the price of imports into Canada from the U.S., thereby raising consumer prices in Canada. Using extensive datasets from 2000 to 2023, this research quantifies the magnitude and speed of ERPT across various provinces and consumer price index (CPI) categories. The methodology employs a regression model framework, building upon the approach of Savoie-Chabot and Khan (2015) to estimate the impact of short-run and long-run exchange rate fluctuations on consumer prices. The findings highlight significant regional and categorical variation in ERPT. For example, Alberta exhibits the highest short-run ERPT for energy consumer prices where a 1% increase in the exchange rate leads to a 0.60% increase in prices. In contrast, Quebec exhibits the lowest short-run ERPT for energy consumer prices where a 1% increase in the exchange rate leads to only a 0.18% increase in prices. Price responses in each of these provinces differs from the short-run ERPT of Canada which is 0.32%. Furthermore, energy prices exhibit the highest sensitivity to exchange rate fluctuations, while most often categories excluding energy show minimal impact. These results emphasize the importance of region-specific economic policies to address distinct provincial price responses to exchange rate fluctuations. This study offers valuable insights for policymakers to develop strategies that mitigate any adverse economic effects of exchange rate changes and promote regional economic stability and growth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".