Determinants of money laundering: evidence from Italian regions

Abstract

Purpose The main purpose of this paper is to analyze the determinants of money laundering in Italy. Given the high heterogeneity in terms of economic, social and institutional characteristics, Italy is a compelling case study. Design/methodology/approach By using annual data over the period 2008 to 2013, the authors estimate a balanced panel data linear model using feasible generalized least squares. Following the main literature on the economics of crime, the authors regress the crime rate in each region-year against a set of determinants that include socio-economic, enforcement and crime-specific factors. Findings The authors’ findings reveal that, in most Italian regions, enforcement activities do exert significant deterrence on criminal behaviors: and a negative relationship between enforcement and money laundering can be identified only when there are high levels of enforcement efforts. Moreover, the authors find that the major determinants influencing the rate of money laundering differ between northern, central and southern regions, confirming the existence of regional dualism. In particular, the crime rate in the northern and central area is positively related to the level of corruption and the incidence of mafia-type crimes and negatively related to educational attainment, whereas in the southern regions, money laundering is positively related to the size of the gaming and gambling sector. Originality/value The present paper contributes to the extant literature on the economics of crime in several ways. First, it explicitly analyzes a specific type of financial crime, which presents the higher degree of sanctioning regime in the Italian legislation. Second, Italy offers an important country study because of the forceful presence of mafia clans and organized crime systems operating in the illegal market.

Information and communication technologies, artificial intelligence, and the fight against money laundering in Africa

“…Thus, anti‐corruption measures reduce the illicit outflow of capital and contribute positively to the fight against corruption. This suggests a positive relationship between crime and ML and corroborates existing work (Reganati & et Oliva, 2018) and international organizations' perception (FATF, UNODC, GIABA). Financial development's negative effect on money laundering highlights the most successful economic systems' ability to effectively combat ML and the vulnerability of embryonic financial systems to ML.…”

“…The spread of information technologies based on computer and communication equipment has blurred borders and interconnected national economies. A hyper‐connected business ecosystem that frequently straddles national jurisdictions can facilitate new forms of crime, including cybercrime and cyber‐ML (Bortner, 1996; Lilley, 2000; Reganati & et Oliva, 2018). Some specific features of electronic payment systems may induce potential ML risks.…”

“…According to Idowu and et Obasan (2012), ML has negative consequences on the economy, including loss of government revenue, increased crime rates in society, and threatening a nation's political stability and internal security. Similarly, by using shell companies to hide ill-gotten gains, money launderers could drive out legitimate businesses that suffer a competitive disadvantage compared to businesses that do not have traditional financing for their activities (Reganati & et Oliva, 2018). Thus, the fight against ML (AML) has been vigorously introduced and developed into a global enforcement regime.…”

“…On the one hand, higher payments may increase employment chances in the legal sector and reduce the crime rate (Rose-Ackerman, Søreide, 2013). On the other hand, increased income opportunities improve people's situation and potentially increase income from crime (Reganati & et Oliva, 2018).…”

“…However, the financial sector's digitalization is not without adverse effects. While enabling financial inclusion, ICTs could also facilitate criminals' intrusion and expose the financial sector to cybercrime, especially money laundering (ML) and terrorist financing (Bortner, 1996; Lilley, 2000; Reganati & et Oliva, 2018). Highly organized national and even global mafia networks that pursue self‐serving and selfish objectives engage in various illegal activities, including drug trafficking, smuggling, fraud, extortion, and corruption.…”