Relationship of Anti-Money Laundering Index with GDP, financial market development, and Human Development Index

Abstract

Money laundering has a direct impact, among other things, on the economic development of a country. The aim of this research is to determine the correlation between money laundering and economic development expressed through GDP, as well as between financial market development (FDI) and the Human Development Index (HDI). The results of the research show that there was a significant relationship between the observed variables, i.e. that there is a relation of the Anti-Money Laundering Index (AMLI) on GDP, financial market development and the HDI. Namely, given that medium-strong links between the observed variables have been established, it can be claimed that there is reason to believe that "copying the behaviour" of a certain country in the fight against money laundering can further develop the financial market, influence human development or an increase in GDP per capita. In particular, a decrease in the AMLI was expected to increase the FDI (R2 = 0.2601). A decrease in the AMLI was expected to increase the HDI (R2 = 0.5747). In that way, financial institutions are directly affected, which negatively relates to economic and political stability.

Efficiency of Anti-Money Laundering: The Case of Northern European Countries

“…When analysing money laundering, the expected 8% correlation between the volume of GDP and the amount of money laundering in a country could be observed, which shows that when a country adopts an anti-money laundering model, there is an increase in the volume of GDP (Šikman & Grujić, 2021). This may be seen in the gradually rising GDP of the Nordic countries of Denmark, Norway, Sweden and Iceland.…”

“…It is imperative that any financial institution or country would take appropriate actions to prevent future cases of money laundering. The countries' ability to adapt to current turmoil or other political or financial difficulties makes it possible to alter and improve the financial market, which results in better and more targeted antimoney laundering techniques being applied in countries, which in part could also boost a country's overall consumption and GDP (Šikman & Grujić, 2021).…”

“…It is calculated according to the countries' money laundering preventative measures and other factors and it can show what is the current ranking of the country worldwide relating to the anti-money laundering measures taken. Besides, financial market development indicators in a country may rise as the AMLI decreases (Šikman & Grujić, 2021).…”

The 'Bad Behavior Index': A Composite Measure of the Development Hindering Behavior of Individuals and Institutions

“…To elaborate, proxies are selected to reflect prominent theories in academic literature, within the context of development, such as Alesina et al (1996) seminal study on political instability, endogenous growth theory (Romer, 1994), resource curse theory (Auty, 1995) et al Moreover, proxies were also selected based on evidence from academic literature such as Scully's (1992) views on economic freedoms, and how they lead to countries that are highly efficient at inputs into outputs; N'Zue's (2018) position on reducing pollution, and how it can support sustainable growth as well as improving societal welfare; and Wahyudi et al's (2021) who posit that lower levels of corruption lead to higher levels of development and quality of life. The theories and supporting literature which justify the selection of the BBI proxies due to their impact on the economic and social well-being of countries are presented as follows: 1) Economic Freedoms (Scully, 1992;Doucouliagos & Ulubasoglu, 2006;Williamson & Mathers, 2011;Piątek et al, 2013;Hussain & Haque, 2016;Brkić et al, 2020;Gezer, 2020); 2) Monopolistic Markets (Bae et al, 2021); 3) Resource Curse Thesis (Auty, 1995;Sachs & Warner, 2001); 4) Unemployment (Kukaj, 2018;Priambodo, 2021); 5) Savings (Krieckhaus, 2002;Misztal, 2011); 6) Inflation (Akinsola & Odhiambo, 2017;Yolanda, 2017); 7) Infrastructure (Kusharjanto & Kim, 2011;Palei, 2015;Mohanty et al, 2016;Apurv & Uzma, 2021); 8) Money Laundry (Argentiero et al, 2008;Kumar, 2012;Hetemi et al, 2018;Šikman & Grujić, 2021); 9) Corruption (Mo, 2001;Akçay, 2006;…”