Digital governance as a tool against money laundering: Cross-country evidence for financial crime reduction
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DOIhttp://dx.doi.org/10.21511/pmf.15(1).2026.06
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Article InfoVolume 15 2026, Issue #1, pp. 68-86
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Type of the article: Research Article
Abstract
Money laundering threatens global financial integrity, while digital governance is increasingly seen as a tool to enhance transparency and regulatory capacity. This study operationalized digital governance through the United Nations E-Government Development Index, which captures the scope and quality of online public services, telecommunications infrastructure, and human capital. The paper aims to examine whether improvements in e-government development are associated with measurable reductions in systemic money-laundering vulnerabilities at the country level. The study uses an unbalanced panel of 171 countries for 2012–2024 (982 observations). Fixed- and random-effects models with Box–Cox transformations were estimated, with the Hausman test guiding model selection and cluster-robust and Driscoll–Kraay standard errors ensuring reliable inference. The results demonstrate a statistically significant and economically meaningful inverse relationship between e-government development and money-laundering risk, measured by the Basel AML Index. In the preferred fixed-effects specification, the coefficient on the transformed EGDI is –1.56 (p < 0.001), indicating that within-country improvements in digital governance capacity are associated with substantial reductions in AML vulnerability over time. This effect remains robust across alternative error structures, with 95% confidence intervals of [–1.96, –1.17] under cluster-robust estimation and [–1.75, –1.38] under Driscoll–Kraay correction. The inclusion of country-specific fixed effects reveals considerable structural heterogeneity in baseline AML risk (approximately 1.15–3.90), while time effects display limited variation over the sample period (approximately 2.11–2.19), confirming that the risk-reducing role of digital governance is not driven by specific countries or particular years.
Acknowledgment
This article was prepared based on the results of a study funded by the Ministry of Education and Science of Ukraine, “GovTech for Ukraine: A Digital, Secure, Transparent, and Equitable State in Times of War and Post-War Reconstruction” (registration number: 0126U000544).
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JEL Classification (Paper profile tab)G28, H83, K42, O38
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References65
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Tables7
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Figures2
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- Figure 1. Country-specific fixed effects of AML risk
- Figure 2. Evolution of global AML risk: Time fixed effects from the panel model (2012–2024)
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- Table 1. Descriptive statistics with all the variables (Country, Year, EGDI, Basel AML Index)
- Table 2. Panel regression results: Effect of e-government development on AML risk
- Table 3. Fixed effects estimates with robust standard errors
- Table 4. Countries with the highest and lowest fixed effects (Baseline AML risk)
- Table 5. Time fixed effects model estimation results
- Table 6. Estimated time fixed effects
- Table B1. Country-specific fixed effects
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