Assessing global potential output growth: April 2024
Bibliographic record
Abstract
This note presents the annual update of Bank of Canada staff estimates for growth in global potential output. These estimates serve as key inputs to the analysis supporting the April 2024 Monetary Policy Report. Global potential output growth is assessed to have mostly recovered from its COVID-19 pandemic low, rising from 2.1% in 2020 to an estimated 3.0% in 2024.1 This increase is largely driven by a recovery in oil-importing emerging-market economies (EMEs), which are experiencing a gradual easing of pandemic-related downward pressures on capital accumulation and total factor productivity (TFP) growth (Chart 1 and Chart 2). Potential output growth has mostly returned to pre-pandemic average levels in all regions except China, where it is estimated to have steadily declined. Looking ahead, we expect global potential output growth to edge down to 2.9% in 2027 (Table 1). This modest decrease mainly reflects slowing growth in trend labour input (TLI) amid the rapid aging of the global population. Aging is also expected to weigh on labour productivity, although in some countries, shifts in the age composition of the workforce toward more productive cohorts may help dampen the effects (Guénette and Shao, forthcoming).2 Compared with last year’s assessment, global potential output growth has been revised up by 0.2 percentage points (pps), on average, over 2023–26. This mostly reflects stronger-than-expected capital accumulation in oil-exporting economies in addition to positive revisions to trend labour force participation in the United States and oil-importing EMEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".