Low Inflation in Advanced Economies: Facts and Drivers
Bibliographic record
Abstract
Since the global financial crisis, core inflation has been persistently below target in most advanced economies. Recently, it has weakened further in several advanced economies despite gradually diminishing slack. This note reviews recent developments in core inflation across advanced economies and identifies distinctive patterns across regions. In many advanced economies outside of the United States, the decrease in core inflation started in 2016 and was driven primarily by goods prices—most likely because of lower export prices from China in 2016 and past exchange rate appreciations. In the United States, however, the decline in core inflation began in 2017 and has been driven mostly by service prices rather than goods prices. We estimate that roughly two-thirds of the softening in US core inflation may be due to idiosyncratic factors that are unlikely to persist, while the remaining one-third may be attributable to more persistent factors. We then examine the extent to which the persistent weakness in inflation after the global financial crisis can be explained by common factors across advanced economies. Our results suggest that global factors do not play much of a role in influencing domestic core inflation dynamics but that they remain important determinants of total inflation, acting mainly through the goods inflation channel—particularly through energy prices. Finally, we briefly investigate the relationship between economic slack and inflation. We find that the relationship between the output gap and core inflation remains positive, albeit modest, in advanced economies and that a significant share of the post-crisis weakness in inflation has been driven by domestic economic slack. In addition, we find that the relationship between the output gap and inflation is more evident with core services inflation than core goods inflation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".