Potential output in Canada: 2024 assessment
Bibliographic record
Abstract
This note contains the Bank of Canada’s 2024 staff assessment of potential output in Canada. Between 2023 and 2027, potential output growth is expected to average around 2% annually. Relative to the April 2023 assessment (Champagne, Hajzler et al. 2023), the level of potential output is revised up in the near term but is roughly unchanged by 2026 (Table 1). This upward revision mostly reflects higher-than-expected population growth, underpinned by a surge in newcomers to Canada since the second half of 2022. Potential output growth in 2023 is relatively unchanged because the higher-than-anticipated contribution from population growth was offset by lower trend labour productivity. Over 2024–26, investment and trend total factor productivity (TFP) are expected to be lower. Population growth is also expected to be lower in 2025 and 2026, reflecting the federal government’s recently announced plans to reduce arrivals of non-permanent residents. We consider both upside and downside risk scenarios and construct a range around our benchmark estimates, with a focus on the uncertainty around population growth and business investment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".