QUANTITATIVE EASING AND CREDIT SUPPLY: 13 DEVELOPED ECONOMIES SIMULATED
Bibliographic record
Abstract
Purpose: This paper investigates how quantitative easing (QE) affects credit supply in advanced economies and whether the strength and direction of this impact vary across countries. It aims to identify the extent of heterogeneity in the credit channel of monetary transmission and determine under which structural and institutional conditions QE effectively stimulates credit creation. Design/methodology/approach: The study uses quarterly BIS data for 13 advanced economies from 2008–2024. It applies the Local Projections method (Jordà, 2005) to estimate impulse response functions of credit to QE shocks, measured by changes in central bank balance sheets. The model includes country fixed effects and lagged endogenous variables to capture heterogeneity and dynamic adjustment. The analysis focuses on cross-country differences in the transmission of unconventional monetary policy. Findings: Results reveal three clusters of credit responses to QE: (i) strong positive effects in Australia, the Euro area, New Zealand, the UK, and the US; (ii) weak but positive effects in Canada, Korea, Norway, and Poland; and (iii) weak and negative effects in Denmark, Japan, Sweden, and Switzerland. These outcomes indicate that QE supports credit growth only when supported by favorable financial and institutional environments. The findings confirm that QE’s impact is heterogeneous and context-dependent rather than uniform across economies. Research limitations/implications: A key limitation relates to lag selection in dynamic models, which may influence impulse response precision. Future studies should apply formal information criteria (AIC, BIC, HQC) and consider bank-level datasets or alternative transmission channels such as asset prices or exchange rates. Practical implications: Liquidity injections alone are insufficient to ensure credit expansion. Policymakers should complement QE with measures that enhance bank profitability, reduce risk constraints, and stimulate loan demand. In flexible financial systems, QE can boost lending and investment, while in more rigid systems its effects remain limited. Social implications: By clarifying how QE shapes credit creation, the study helps explain how monetary policy contributes to recovery, employment, and financial stability. Weak credit responses may restrict the broader social benefits of expansionary policies. Originality/value: This is among the first comparative studies of QE’s credit effects across 13 economies within a unified empirical framework. It documents substantial cross-country heterogeneity and offers methodological guidance for future research on unconventional monetary policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".