Hanna Yarovenko

Computer Science and Engineering Department,

University Carlos III of Madrid, Madrid, Spain;

Economic Cybernetics Department, Sumy State University,

Sumy, Ukraine;

hyaroven@inf.uc3m.es

ORCID 0000-0002-8760-6835

Illicit practices: Experience of developed countries

Tetyana Vasilyeva

Department of Financial Technologies and Entrepreneurship,

Sumy State University, Sumy, Ukraine

t.vasylieva@biem.sumdu.edu.ua

ORCID 0000-0003-0635-7978

Leonas Ustinovichius

Faculty of Engineering Management,

Bialystok University of Technology, Białystok, Poland 

l.uscinowicz@pb.edu.pl

ORCID 0000-0002-0027-5501

Sandor Remsei

Faculty of Economics,

Széchenyi István University,

Gyor, Hungary, remsei.sandor@sze.hu 

ORCID 0000-0001-8862-4544 

Abstract. The article is devoted to finding the answer to two research questions. What illegal practices are most significant for clusters of developed countries formed by similarities in trends in corruption, shadow economy, money laundering, and crime rates? What social, economic, regulatory, and digital factors most influence them in each group? The pair correlation coefficients for illicit practices indicators confirm the presence of tight and statistically significant relationships in their trends for 36 developed countries. The agglomerative clustering and canonical analysis results identified that tackling the shadow economy is crucial for Estonia, Slovenia, and Lithuania; corruption for Portugal, Hungary, Cyprus, etc.; the shadow sector and crime levels for Denmark, Norway, Finland, Sweden, and New Zealand; corruption, money laundering, and crime for Canada, Germany, the USA, etc.; four illegal practices for Italy, Greece, Turkey, Croatia, Bulgaria, and Romania. The canonical analysis revealed that social and regulatory factors influence the trends of illicit practices in developed countries more than economic and digital ones. Network analysis showed their single moderate influence in most cases. Edge evidence probability analysis confirmed a high probability of a relationship between some pairs of social, economic, regulatory, digital and illegal indicators. However, Bayesian network analysis showed a low likelihood of mutual influence of single factors, confirming the importance of the group influence.

Received: May, 2023

1st Revision: March, 2024

Accepted: May, 2024

DOI: 10.14254/2071-8330.2024/17-2/8

JEL Classification: E26, K42, O17

Keywords: illicit practice, corruption, crime, money laundering, shadow economy, developed countries