Unveiling inflation: Oil shocks, supply chain pressures, and expectations
Bibliographic record
Abstract
After decades of low and stable inflation, advanced economies experienced a sharp and persistent surge in inflation following the COVID-19 pandemic. While many studies have examined the sources of this inflation, less attention has been paid to how domestic inflation expectations amplify global shocks. This paper makes a novel contribution by quantifying that amplification mechanism across six advanced, inflation-targeting economies: the United States, Canada, New Zealand, the Euro Area, the United Kingdom, and Norway. Using a structural Bayesian vector autoregression model, we jointly identify global demand and supply shocks, including various oil market shocks and global supply chain disruptions, as well as domestic shocks to inflation and inflation expectations. We show that these global shocks were key drivers of the post-pandemic inflation surge in all countries studied. Importantly, our counterfactual analysis reveals that inflation expectations have significantly amplified the transmission of global shocks, particularly in Canada, New Zealand, and the US. These findings demonstrate that the interaction between global forces and country-specific expectations is central to understanding inflation dynamics, and underscore the importance of managing inflation expectations as a tool to mitigate persistent 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".