Unveiling economic resilience: exploring the impact of financial vulnerabilities on economic volatility through the economic vulnerability index
Bibliographic record
Abstract
Abstract While earlier studies have explored the relationship between economic vulnerability and economic resilience, they have repeatedly overlooked the significance of financial vulnerabilities within the economic vulnerability index, and the effects of financial and economic vulnerabilities on economic volatility. An attempt is made in this study to close this research gap by conducting a detailed analysis of the relationship between financial vulnerabilities, economic vulnerability and economic volatility, stressing the significance of tweaking prevailing vulnerability indices to fully encapsulate its multidimensional nature in developing countries. Employing panel data for 142 countries over the 2002 to 2022 period and a robust econometric approach like the Driscoll and Kraay fixed effect method, the study reveals that financial vulnerabilities yield significant coefficients to influence economic volatility, thereby accentuating their significance in the Economic Vulnerability Index. Sub-group analyses reinforce the need for incorporating financial variables in vulnerability investigations. Moreover, the causality tests reveal that all the variables and indices meant to capture the economic and financial vulnerabilities Granger causes economic volatility across the sample. In essence, this study fills a critical gap in existing research by demonstrating, that financial vulnerabilities significantly influence economic volatility, underscoring the imperative of integrating financial variables into vulnerability assessments for policymakers and scholars focusing on sustainable development. This study contributes to a broader understanding of economic vulnerability by highlighting the crucial role of financial vulnerabilities in driving economic volatility, suggesting a fundamental reconsideration of existing vulnerability assessment frameworks for policymakers and researchers focused on sustainable development frameworks. By uncovering the causal relationship between financial vulnerabilities and economic volatility across a diverse set of countries, the findings underscore the imperative of integrating financial factors into vulnerability investigations to enhance resilience and stability in developing economies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".