Corruption Control as a Catalyst for Financial Development: A Global Comparative Study
Bibliographic record
Abstract
This study investigates the impact of anti-corruption efforts on financial development across different economies, using G7 and E7 countries as comparative groups. Recognizing corruption as a barrier to economic growth, the research examines how effective corruption control can enhance the efficiency of the financial sector, foreign direct investment (FDI), and capital market development. The methodology includes panel cointegration tests—namely Pedroni, Kao, and Westerlund tests—alongside fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimations to assess the long-term relationships between corruption control and financial development. The findings reveal a statistically significant cointegration relationship, suggesting that anti-corruption measures positively influence financial development in both G7 and E7 countries, albeit more strongly in E7 economies. Specifically, the Westerlund test results, which take cross-sectional dependencies into account, reinforce the robustness of the findings. The study underscores the importance of tailoring anti-corruption policies to each country’s unique economic framework, highlighting that while G7 countries benefit from advanced institutional structures, E7 countries experience more pronounced effects of corruption control on financial development and FDI. These insights contribute to the policy discourse on sustainable economic development by emphasizing the role of governance quality in fostering robust financial systems and attracting international investment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".