Monetary Policy and Financial Stability: Cross-Country Evidence
Bibliographic record
Abstract
Central banks may face challenges in achieving their price stability goals when financial stability risks are present. There is, however, considerable heterogeneity among central banks with respect to how they manage these potential trade-offs. In this paper, we review the institutional and operational policy frameworks of ten central banks in major advanced economies and then assess the effect of financial stability risks on their monetary policy decisions according to these frameworks. To do so, we construct a time-varying financial stability orientation (FSO) index that quantifies a central bank’s policy orientation with respect to financial stability that spans the major viewpoints of the literature: “leaning against the wind” versus “cleaning up after the crash.” The index encompasses three dimensions: (i) the nature of the statutory frameworks, (ii) the extent of the regulatory tool kit, and (iii) the prominence of financial stability references in central bank monetary policy statements. We then include our FSO index in a modified Taylor rule, which is estimated using a cross-country panel of up to ten central banks for the period from 2000Q1 to 2014Q4. We find that in episodes of high financial stability risks, measured by a strongly positive credit to GDP gap, “leaning-type” central banks, i.e., those with a high FSO index value, appear to account for financial stability considerations in their monetary policy rate decisions. For “cleaning-type” central banks, we do not find this to be the case. Our baseline specification suggests that a representative leaning-type central bank’s policy rate is about 0.3 percentage points higher when financial stability risks are present than the policy rate of a representative cleaning-type central bank. We also find that the strength of this response increases in the additional presence of a house price boom but not so for the simultaneous occurrence of an equity price boom.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".