What do central bankers talk about when they talk about inflation? The rise and fall of inflation narratives
Bibliographic record
Abstract
The 2021 debate over the causes of inflation was dominated by contrasting narratives around the drivers of, and solutions to, rising prices. But how these ideas did or did not penetrate central banks, the politically independent institutions responsible for keeping prices stable, remains unclear. In this paper we investigate how the Bank of England, European Central Bank, and Federal Reserve discussed and deployed specific inflation narratives over time in their attempts to diagnose and treat the inflation of the period. We focus on four narratives that identify the main drivers of inflation in (1) excessive public spending, (2) higher wages in the labour market than warranted by productivity, (3) supply side disruptions to critical markets such as energy, and (4) corporate profit margin expansion. We use a large language model to tag central banks’ speeches with relevant narratives at sentence level, which allows us to quantify how much each central bank discussed each narrative. The results shed new light on how these three central banks interfaced with the recent debate around 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".