Money laundering and terrorist financing risks and democratic governance: a global correlational analysis

Abstract

Purpose

This paper aims to examine the association between money laundering (ML)/terrorist financing (TF) risks (hereafter, money laundering risks) and democratic governance across 117 countries.

Design/methodology/approach

A cross-sectional design was used to examine the association between ML risks and democratic governance by a quantitative approach. The findings are based on annual ratings of 117 countries on ML/TF risks and democracy while controlling for criminality and peace. The data was compiled from the Basel Anti-Money Laundering/Countering Financing Terrorism Risks Index, the Economic Intelligence Unit (Democracy Index), the Global Initiative against Transnational Organized Crimes (Criminality Index) and the Institute for Economics and Peace Index for 2020.

Findings

A multiple linear regression model found a statistically significant negative association between democratic governance and ML risks (B = −0.354, t = −7.454, p = <0.001) and a significant positive association between criminality and ML risks (B = 0.242, t = 2.692, p = 0.008).

Research limitations/implications

A cross-sectional design cannot determine causal inferences and generalization (Levin, 2006). The study only used a year to examine the hypothesis of a negative correlation between ML risks and democratic governance, thus making generalization difficult.

Originality/value

Extant literature examined ML, terrorism and AML diversely. There was a need to estimate the association between ML risks and democratic governance, especially globally, during a global crisis like COVID-19, when democratic principles, such as the rule of law, transparency and accountability, are challenged. Many personnel were laid off, thus limiting supervision for ML and TF. This study presents evidence of this association.

Citation

Kalokoh, A. (2024), "Money laundering and terrorist financing risks and democratic governance: a global correlational analysis", Journal of Money Laundering Control, Vol. 27 No. 5, pp. 825-844. https://doi.org/10.1108/JMLC-09-2023-0151