Identifying the nexus between financial stability and economic growth: the role of stability indicators
Bibliographic record
Abstract
Purpose This study aims to examine the interrelationship between financial stability and economic growth with a comprehensive analysis. Design/methodology/approach The panel Granger causality testing approach is carried out to the panels of the Fragile Five (F5) and the Group of Seven (G7) countries for the period 1998–2020. To capture the different aspects of financial stability the authors use eight different indicators. Findings The findings reveal some important implications: the relationship between financial stability and economic growth is sensitive to the financial stability indicators for both the F5 and G7 countries. The stability indicators related to the credit market contain much more causality relationship with economic growth than the indicators related to the stock market. Z-score and provisions to nonperforming loans (NPLs) are among the two variables with the highest causality relationship with economic growth. The least number of causality link is found for the Regulatory Capital Ratio and Stock Price Volatility in F5 countries and Credit Ratio, NPLs and Stock Price Volatility in G7 countries. Economic growth affects financial stability through credit market stability indicators and mostly for the F5 countries. No causal relationship is found for any of the financial stability indicators of Canada, the UK and the USA from economic growth to financial stability. Originality/value Since the linkages between financial stability and economic growth may vary due to country/group specific differences, apart from the previous studies, the authors select two different groups of countries in terms of financial stability and economic size.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".