The Impact of Macroeconomic Factors on Financial Stability: Evidence from OECD Countries
Bibliographic record
Abstract
Financial stability is an essential element for economic stability, especially in OECD member countries, which face ongoing macroeconomic challenges.This study aims to analyze the impact of macroeconomic factors on the financial stability of 27 OECD countries during the period 2008-2023.The data were obtained from the World Bank and include annual figures for indicators such as GDP, inflation, interest rates, exchange rates, unemployment, and nonperforming loans (NPLs).Financial stability in this paper is measured through the banks' Zscore, while the econometric analyses applied include the Fixed Effects Model (FEM), Random Effects Model (REM), and Panel Corrected Standard Errors (PCSE) -with the latter identified as the most suitable for interpreting the results.The results reveal that Gross Domestic Product (GDP) has a positive effect on financial stability (β = 0.256); however, this relationship is not statistically significant (p > 0.05).The exchange rate demonstrates a positive and statistically significant effect (β = 0.031, p < 0.05), indicating that currency fluctuations are associated with improved financial stability.In contrast, inflation (β = -0.073,p < 0.01) and unemployment (β = -0.242,p < 0.05) exert negative and statistically significant effects, implying that worsening macroeconomic conditions substantially weaken financial stability.The non-performing loans (NPL) ratio also displays a negative effect (β = -0.008),though this result is not statistically significant (p = 0.914, p > 0.05).Lastly, the interest rate shows a positive and statistically significant coefficient (β = 0.110, p < 0.05), suggesting a counterintuitive relationship that deviates from theoretical expectations.The findings underscore the critical role of macroeconomic stability in ensuring financial stability.This study contributes to the existing literature by demonstrating that sound and prudent economic policies can substantially enhance financial stability in developed economies, particularly those within the OECD.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".