Money Laundering in Global Economies: How Economic Openness and Governance Affect Money Laundering in the EU, G20, BRICS, and CIVETS
Bibliographic record
Abstract
Purpose—This study examines the interaction of economic openness, governance, and money laundering. The paper’s main objective is to analyze how trade openness, foreign direct investment, and anti-corruption measures influence the risk of money laundering in specific economic blocs. Design/methodology/approach—This study analyzes these economic blocs (EU, G20, BRICS, and CIVETS) using annual data from the Basel Institute on Governance and World Bank statistics for 2012–2021. A panel-corrected standard errors (PCSE) estimator is employed to examine the relationships among the variables, accounting for cross-sectional dependence and ensuring robust parameter estimation. The corruption control index is a proxy for governance effectiveness, though it does not directly measure regulatory strength. Future research should incorporate more specific variables to evaluate the regulatory impact. Findings—This study reveals significant variations in money laundering risks by a country’s income category and economic bloc influenced by economic openness and governance structures. Economic growth and foreign direct investment (FDI) inflows exhibit contrasting effects on money-laundering risks; they tend to exacerbate risks in middle-income countries, while high-income nations demonstrated a lower risk of money laundering, likely due to more robust governance structures. Trade openness and anti-corruption measures generally reduced risks in wealthier countries, highlighting the importance of strong governance frameworks. These insights suggest that anti-money-laundering policies should be tailored to fit different regions’ unique economic and institutional contexts for enhanced effectiveness. Originality—This study employs a structured approach to analyzing a decade of panel data from key economic blocs, providing insights into the intricate relationships between governance, economic openness, and money laundering risks. Bridging the gap between theoretical research and practical, actionable strategies serves as a valuable resource for improving the effectiveness of anti-money-laundering (AML) measures on a global scale.
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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 | 0.005 |
| 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 | 0.001 |
| 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".