Following Achim and Borlea (2020), Achim et al (2021a, 2021b) an economic and financial crime index –FinCrime index, is built starting from the main important categories of economic and financial crimes (corruption, shadow economy, money laundering and cybercrime), as follows:

(a) The level of corruption from a certain country is determined using the Corruption Perceptions Index (CPI) provided by Transparency International. Subjectively perceived levels of corruption in the public sector of worldwide countries are reflected through this index, on a range from 0 (the most corrupt) to 100 (no corruption).

(b) The level of shadow economy is determined using the database elaborated by Medina and Schneider (2019), in which the size of the shadow economy is calculated as a percentage of the official GDP.

(c) The level of money laundering is measured using Basel AML (i.e., Basel Anti-Money Laundering Index), which measures the risk of money laundering and terrorist financing around the world countries. This score ranges between 0, which is associated with the lowest risk, and 10, which is associated to the largest risk of money laundering. The Basel AML score is calculated for the world countries from the year 2012 on. We work here with data available for the 2012-2020 period.

(d) The level of cybercrime is determined with the help of the Cybersecurity index. For this purpose, we use the Global Cybersecurity Index (GCI), belonging to the International Telecommunication Union. This index ranges between 0, meaning the most vulnerable countries in terms of cyber risks (highest level of cybercrime), and 1 for the less vulnerable ones (lowest level of cybercrime). The larger the level of the GCI is, the more reduced the level of cybercrime is. Here we work with data available for the 2015-2020 time period (where 2020 comprises the most recent available data).

According to the methodology of Achim and Borlea (2020), Achim et al (2021a, 2021b) the standardized values of corruption, shadow economy, money laundering and cybercrime are used in order to obtain homogenous data. The aggregated resulting FinCrime index ranges between 0 and 1 and it reflects the minimum and maximum levels of economic and financial crimes, respectively.

Basically, this score can be used to measure the level of economic and financial crimes that characterizes a particular country, allowing comparative analyses between countries: the closer the values of national FinCrime indexes are to 0, the lower the levels of economic and financial crimes are in those particular countries, while values of FinCrime indexes closer to 1 reveal a more pronounced development of economic and financial crime phenomena in those countries.