Economic Freedom and Banking Performance: Capital Buffers as the Key to Profitability and Stability in Liberalized Markets
Bibliographic record
Abstract
This study examines the moderating effect of bank capitalization on the relationship between economic freedom and banking performance, offering comparative evidence from both advanced and emerging economies. Using an unbalanced panel of 213 countries from 1993 to 2018, this study applies a two-step System Generalized Method of Moments approach to address dynamic effects, endogeneity, and unobserved heterogeneity. The results show that economic freedom exerts a negative and significant impact on bank profitability (ROA and ROE), particularly in emerging markets with weaker institutional safeguards. Strong internal capital buffers, on the other hand, mitigate these adverse effects and enhance resilience, supporting stable profitability under liberalized conditions. Regulatory capital shows a less consistent and sometimes restrictive role. Disaggregated results indicate that equity buffers most effectively cushion the risks of financial and investment freedom, whereas trade freedom is less sensitive to capital levels. The findings emphasize that successful liberalization depends on institutional capacity and capitalization strength, highlighting the importance of tailored prudential frameworks. The study contributes to debates on financial liberalization, Basel III, macroprudential regulation, and bank risk management, underscoring that a “one-size-fits-all” liberalization strategy may undermine stability and efficiency unless supported by robust capital buffers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".