A New Measure of the Canadian Effective Exchange Rate
Bibliographic record
Abstract
Canada’s international competitiveness has received increasing attention in recent years as exports have fallen short of expectations and Canada has lost market share. This paper asks whether the Bank of Canada’s current effective exchange rate measure, the CERI, is still an accurate measure of Canada’s international competitiveness. Overall, while the CERI represented an improvement over previous measures when it was introduced, we find that it has several drawbacks that make it less well suited to address current competitiveness issues. To address these deficiencies, we develop a new Canadian effective exchange rate (CEER) index using a methodology based on current international best practices. The new index includes a broader set of countries and uses annually updated competition-based weights. These weights account for both Canada’s bilateral trade with another country and the competition Canada faces from that country on a product-by-product basis in third markets. We find that the CEER has depreciated less than the CERI in recent years, reflecting the greater importance of third-market competition from emerging-market economies in the CEER. This could help explain why Canada’s share of the U.S. import market has continued to decline despite the recent large depreciation of the Canadian dollar against the currencies of a number of advanced economies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".