The intricacies surrounding the measurement and modelling of money laundering (ML), corruption (COR), and black-market prevalence (BMP), as well as their effects on socio-economic development (SD), introduce significant challenges to accurately capturing and understanding these phenomena. There is a plethora of theories on individual measurements of each concept, but regrettably, they are not immune to criticism, and no such explicit approach is available to study the nexus between these complex concepts. The current study employs the multiple indicator approach to measure ML, COR, and SD, and utilizes the PLS-SEM approach to explore the relationships between these complex concepts, with a focus on the mediating role of the BMP. The per capita investment (PCI) expenditures, modelled through the multiple indicator approach, have been used as the control variables. The study has adopted a data-driven approach to conduct pre- and post-estimation analysis for the constructs and validate the results for a cross-section of 198 countries in 2022. The results indicate that the impact of corruption on socio-economic development is negative and statistically significant. The black market has a direct, negative, and significant impact on socio-economic development; similarly, the BMP has a positive and significant effect on ML. In addition to the direct impact on socio-economic development, BMP also indirectly affects SED through the ML. The direct effects of ML on SED are adverse, while it has an indirect positive impact on SED through its significant multiplier effect on per capita investment. These findings have implications for anti-money laundering and anti-corruption policies worldwide.
Ahmad et al. (Mon,) studied this question.