Financial Inclusion and Economic Growth Across the Globe: The Role of Anti-money Laundering Regulations
Bibliographic record
Abstract
This study examines the moderating effect of anti-money laundering (AML) regulations on the relationship between financial inclusion and economic growth, and investigates whether this impact is threshold-specific. Utilising a panel dataset from 213 countries (2012–2019), we employ a two-step system, Generalised Method of Moments (GMM) and the Seo et al. (2019) dynamic panel threshold regression model. Our findings confirm that financial inclusion generally stimulates economic growth. Crucially, we demonstrate that the impact of financial inclusion on economic growth is contingent on the intensity of AML regulations. Specifically, financial inclusion promotes growth below a certain threshold of AML regulation, but surprisingly, it inhibits growth when AML regulations exceed this threshold. This threshold effect is particularly pronounced in developing and African economies compared to developed countries. Theoretically, this study extends the understanding of financial inclusion and economic growth by introducing a critical non-linear moderating role for AML regulations, suggesting that an optimal level of regulation exists beyond which the intended benefits may be reversed. Policy implications underscore the need for regulators to consider these threshold effects when designing and implementing AML frameworks, ensuring that financial inclusion continues to drive economic growth effectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".