Economic and Financial Crime: Corruption, shadow economy, and money laundering [1st ed.] 9783030517793, 9783030517809

Citation preview

Studies of Organized Crime 20

Monica Violeta Achim Sorin Nicolae Borlea

Economic and Financial Crime Corruption, shadow economy, and money laundering

Studies of Organized Crime

Series Editor Dina Siegel Willem Pompe Institute, Utrecht University,  Utrecht, The Netherlands

This series will publish theoretically significant books in two primary areas. One is the political economy of organized crime and criminality whether at the transnational, national, regional or local levels (focus on financial crime, political corruption, environmental crime and the expropriation of resources from developing nations). The other is human rights violations particularly in Third World countries. Manuscripts that cover either historical or contemporary issues of the above, utilizing qualitative methodologies, are equally welcome. In addition, we are particularly interested in publishing the work of sophisticated junior scholars. More information about this series at http://www.springer.com/series/6564

Monica Violeta Achim • Sorin Nicolae Borlea

Economic and Financial Crime Corruption, shadow economy, and money laundering

Monica Violeta Achim Faculty of Economics and Business Administration Babes-Bolyai University Cluj-Napoca, Romania

Sorin Nicolae Borlea Faculty of Economics University of Oradea Oradea, Romania Faculty of Economics, Informatics and Engineering ‘Vasile Goldis’ Western University of Arad Arad, Romania

ISSN 1571-5493 Studies of Organized Crime ISBN 978-3-030-51779-3    ISBN 978-3-030-51780-9 (eBook) https://doi.org/10.1007/978-3-030-51780-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The present book deals with economic and financial crime issues regarding its most spread forms of our days  – corruption, shadow economy and money laundering. The approaches throughout the book focus on both the theoretical and practical sides of the investigation of causes leading to the committing of such blameable acts and to the identification of their effects on the economic, social and political life as well as of the measures to prevent, fight against and investigate them. Unlike other researches in the field, this book brings a significant added value by means of a structured presentation of the main important economic and financial crimes (corruption, shadow economy and money laundering) in the light of causes and effects produced, with knowledge of the state of the art, but also with empirical studies carried out by the authors within some of their own researches. Also, the causal approaches from the perspective of economic, political, social and cultural determinants represent an important added value brought to the present literature researches. The comparisons, discussions and critical analysis of economic and financial types of crime committed at the international level increase the additional value of this book. This book is addressed to large categories of users represented by researchers and business experts, but is also addressed to decision-making authorities at national and international level, who, in this way, will know the causes and effects of these economic and financial crimes and will finally adopt the best solutions to fight against such blameable acts. This book gathers the vast existing economic and financial crime literature within a condensed work, providing an extremely useful database and research themes to develop advanced research studies destined to researchers in the field of economic and financial crime. Thus, the core audience of this book are most likely academic scholars, students and researchers in law and social sciences (economics, sociology, criminology, political science and economic psychology), and experts working in political institutions (ministries of finance, international organizations, e.g. OECD).

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Preface

We are aware that the theme approached through the present book is very complex and generous; therefore, we fully assume the limitations of the current research work. With gratitude in advance, we remain open to any opinion, comment or ­criticism from the readers regarding improvement of the judgements used as well as of the book as a whole. Cluj-Napoca, Romania  Monica Violeta Achim  Arad, Romania  Sorin Nicolae Borlea A valuable, accessible and up-to-date resource for academic scholars in economics, political science and criminology, sociology and economic psychology and for experts in political institutions dealing with financial crime, corruption, money laundering, shadow economy, and tax avoidance. – Prof. Dr Erich Kirchler, University of Vienna Financial and economic crimes especially in periods of crises are dangerous challenges of democratic systems. The authors address important issues of a modern economic system in their book. This book offers a broad variety of analysis from a scientific and practical standpoint. – Maximilian Edelbacher, University of Vienna

Acknowledgements

Warm and special thanks to Professor Ilie Parpucea, PhD, for providing useful suggestions in the area of statistics and economics during our hard work over many years in the field of economic and financial crime research. Special thanks to PhD Candidate Delia Dragomir, PhD Candidate Camelia Catana, PhD Candidate Narcisa Bodescu and authorized person Alexandra Pescariu for supervising the translation of our book.

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About This Book

The book titled Economic and financial crime: Corruption, Shadow Economy and Money Laundering. Causes, effects and solutions. Theoretical and practical approaches” is structured on four chapters as follows: The Chap. 1 entitled “Economic and Financial Crime: Theoretical and Methodological Approaches” presents historical issues, concepts, forms of economic and financial crime and their showing up areas as well as the highlighting of their characteristic features (volume, intensity, direction and frequency). Then, this first chapter reveals the concepts of corruption, shadow economy, tax avoidance and money laundering and presents the measuring instruments of corruption, shadow economy and money laundering as well as the some case studies. The Chap. 2 entitled “Determining Economic and Political Factors of Economic Crime” presents the main economic and political causes of such acts, resulting in the reviewing of the specialized literature. Some of these important causes we identified are: the level of the economic development, the tax pressure, the pubic governance, the corporate governance and soundness of the banking system. Based on the specialized literature review, for each of these causes, the conceptual issues including the practical ways of measurement as well as the influence of these factors on the level of corruption, shadow economy and money laundering have been highlighted. Also we have documented with our own empirical studies the influences of these variables on economic and financial crime, adding a significant value to this work. The Chap. 3 entitled “Determining Behaviour Factors of the Economic and Financial Crime” is focused to behaviour factors which determine and influence the economic and financial crimes. Among these factors, particular attention is paid to cultural factors, religion, tax morale, trust (focussing on the trust in the state) and happiness condition (subjective well-being) of individuals. For each of these factors, there are theoretical approaches including definitions of concepts as well as practical ways of measuring these variables, indicating some possible databases

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About This Book

available worldwide. Additionally, the chapter brings documentation from the specialized literature about the existence of some causality relations between ­behavioural factors and economic and financial crimes as well as our own empirical studies. The final chapter entitled “Effects of Economic and Financial Crimes: Ways of Fighting Against” emphasizes the effects of such economic and financial crimes at economic, financial social and political levels. The presentations summarize the main types of effects of the economic and financial crimes based on a large review of the specialized literature. In order to fight against these crimes, this chapter also reveal the international bodies and initiatives to prevent, fight against and investigate the economic and financial crime. Finally, short allegations are made about using judicial expertise as specific technical method used to investigate economic and financial crime.

Contents

1 Economic and Financial Crime: Theoretical and Methodological Approaches ����������������������������������������������������������������������������������������������    1 1.1 Theoretical Approaches��������������������������������������������������������������������    1 1.1.1 Theoretical Approaches Regarding the Economic and Financial Crime��������������������������������������������������������������    1 1.1.2 Theoretical Approaches Regarding Corruption��������������������    7 1.1.3 Theoretical Approaches Regarding Shadow Economy��������    9 1.1.4 Theoretical Approaches Regarding the Tax Avoidance��������   13 1.1.5 Theoretical Approaches of Money Laundering��������������������   20 1.1.6 Relation Between Corruption, Shadow Economy, and Money Laundering. Theoretical Approaches ����������������   26 1.2 Measuring Instruments����������������������������������������������������������������������   30 1.2.1 Corruption Measurement������������������������������������������������������   31 1.2.2 Shadow Economy Measurement������������������������������������������   32 1.2.3 Money Laundering Measurement ����������������������������������������   35 1.2.4 Assessing an Economic and Financial Crime Index������������   38 1.3 Practical Approaches������������������������������������������������������������������������   41 1.3.1 Corruption in the European Union countries������������������������   41 1.3.2 Shadow Economy in the European Union Countries ����������   42 1.3.3 Money Laundering in the European Union Countries����������   43 1.3.4 Relation Between Corruption, Shadow Economy, and Money Laundering. Empirical Approaches ������������������   44 1.4 Determinants of the Economic and Financial Crime������������������������   51 References��������������������������������������������������������������������������������������������������   63 2 Economic and Political Determinants of Economic and Financial Crime��������������������������������������������������������������������������������   73 2.1 Economic Development��������������������������������������������������������������������   73 2.1.1 Concept of Economic Development and Measuring Tools��   73 2.1.2 Economic Development and Corruption������������������������������   74 2.1.3 Economic Development and Shadow Economy ������������������   77 xi

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2.1.4 Economic Development and Money Laundering ����������������   80 2.2 Tax Pressure��������������������������������������������������������������������������������������   82 2.2.1 General Approaches Regarding the Tax Pressure ����������������   82 2.2.2 Tax Pressure and Corruption������������������������������������������������   88 2.2.3 Tax Pressure and Shadow Economy ������������������������������������   96 2.2.4 Tax Pressure and Money Laundering������������������������������������   98 2.3 Public Governance����������������������������������������������������������������������������  101 2.3.1 General Approaches Regarding the Public Governance ������  101 2.3.2 Public Governance and Corruption��������������������������������������  106 2.3.3 Public Governance and Shadow Economy ��������������������������  109 2.3.4 Public Governance and Money Laundering��������������������������  113 2.4 Corporate Governance����������������������������������������������������������������������  115 2.4.1 Concept of Corporate Governance����������������������������������������  115 2.4.2 Corporate Governance, Data Manipulation, and Fraud��������  118 2.4.3 Measuring Instruments of the Corporate Governance����������  129 2.4.4 Corporate Governance and Corruption ��������������������������������  140 2.4.5 Corporate Governance and Shadow Economy ��������������������  143 2.4.6 Corporate Governance and Money Laundering��������������������  147 2.5 Banking System Soundness��������������������������������������������������������������  152 2.5.1 General Approaches��������������������������������������������������������������  152 2.5.2 Soundness of Banks and Corruption������������������������������������  157 2.5.3 Soundness of Banks and Shadow Economy ������������������������  160 2.5.4 Soundness of Banks and Money Laundering������������������������  162 References��������������������������������������������������������������������������������������������������  169 3 Behavioural Determinants of Economic and Financial Crime������������  177 3.1 Culture����������������������������������������������������������������������������������������������  177 3.1.1 General Approaches Regarding Culture ������������������������������  179 3.1.2 Culture and Corruption ��������������������������������������������������������  183 3.1.3 Culture and Shadow Economy����������������������������������������������  195 3.2 Religion��������������������������������������������������������������������������������������������  209 3.2.1 Conceptual Approaches Regarding Religion������������������������  209 3.2.2 Religion and Corruption ������������������������������������������������������  210 3.2.3 Religion and Shadow Economy��������������������������������������������  214 3.3 Tax Morale����������������������������������������������������������������������������������������  218 3.3.1 Conceptual Approaches on Tax Morale��������������������������������  218 3.3.2 Tax Morale and Corruption��������������������������������������������������  219 3.3.3 Tax Morale and Shadow Economy ��������������������������������������  220 3.4 Trust in the State ������������������������������������������������������������������������������  221 3.4.1 General Approach on Trust in the State��������������������������������  221 3.4.2 Trust and Corruption������������������������������������������������������������  223 3.4.3 Trust and Shadow Economy ������������������������������������������������  224 3.5 Happiness������������������������������������������������������������������������������������������  225 3.5.1 Conceptual Approaches on Happiness����������������������������������  226

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3.5.2 Assessment of Happiness in the European Union Countries��������������������������������������������������������������������  227 3.5.3 Happiness and Corruption����������������������������������������������������  228 3.5.4 Happiness and Shadow Economy ����������������������������������������  232 References��������������������������������������������������������������������������������������������������  237 4 Effects of Economic and Financial Crimes. Ways of Fighting Against����������������������������������������������������������������������������������  245 4.1 Effects of Economic and Financial Crime����������������������������������������  245 4.1.1 Economic and Financial Effects ������������������������������������������  245 4.1.2 Social Effects������������������������������������������������������������������������  250 4.1.3 Political Effects��������������������������������������������������������������������  255 4.2 Bodies and Initiatives to Prevent, Control, and Investigate the Economic and Financial Crime��������������������������������������������������  255 4.2.1 Bodies for Preventing and Fighting Against Economic and Financial Crime��������������������������������������������������������������  255 4.2.2 Measures to Prevent and Fight Against Economic and Financial Crime��������������������������������������������������������������  257 4.2.3 Judicial Expertise: Specific Technical Procedure Used in the Process of Economic and Financial Crime Investigation��������������������������������������������������������������������������  263 References��������������������������������������������������������������������������������������������������  268 Index������������������������������������������������������������������������������������������������������������������  273

About the Authors

Monica  Violeta  Achim  is affiliated to Babeş-Bolyai University, Faculty of Economics and Business Administration, Department of Finance, Cluj-Napoca, Romania. She obtained her PhD in 2004 from Babes-Bolyai University, Romania. Since 2017, she has been a Professor in the Department of Finance, Faculty of Economics and Business Administration, Babes-Bolyai University, Romania. In the same year she became PhD supervisor in the field of Finance within Doctoral School of Economics, in the same faculty. She teaches the discipline “Economic and financial crime” at the Doctoral School of Finance, Faculty of Economics and Business Administration, Babeş-Bolyai University, Romania. Sorin  Nicolae  Borlea  is affiliated to two Romanian universities as follows: University of Oradea, Faculty of Economics, Doctoral Scool of Economics, Oradea, Romania, and “Vasile Goldis” Western University of Arad, Faculty of Economics, Informatics and Engineering, Department of Economics, Arad, Romania. He obtained his PhD in Finance in 2008 from Babes-Bolyai University, Romania. Since 2015, he has been a Professor at “Vasile Goldiș” Western University of Arad, Faculty of Economics, Informatics and Engineering, Department of Economics. Since 2018 he has been a PhD supervisor at Oradea University, Faculty of Economics, Doctoral School of Economics, Romania, where he teaches the discipline “Economic and financial crime.”

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List of Figures

Fig. 1.1 Money laundering circuit. (Source: own processing) ������������������������  25 Fig. 1.2 Relation between shadow economy, tax avoidance, and money laundering. (Source: own processing) ������������������������������������  32 Fig. 2.1 Laffer curve. (Source: Laffer (2004), The Laffer Curve: Past, Present, and Future, The Heritage Foundation)�������������������������  88

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List of Graphs

Graph 1.1   Corruption in European Union countries, 2005–2015.   (Source: own processing)��������������������������������������������������������������������44 Graph 1.2   Corruption trend in the European Union countries,  2005–2015���������������������������������������������������������������������������������������� 45 Graph 1.3   Corruption in the European Union countries by regions,   2005–2015. (Source: own processing)���������������������������������������������� 45 Graph 1.4   Shadow economy (% in GDP) in European   Union countries, 2005–2015. (Source: own processing)������������������ 47 Graph 1.5   Shadow economy in the European Union countries   by regions, 2005–2015. (Source: own processing) �������������������������� 47 Graph 1.6   Evolution of the shadow economy (% in GDP) in the   European Union countries, 2005–2015.  (Source: own processing)������������������������������������������������������������������ 48 Graph 1.7   Money laundering in the European Union countries,   2012–2017. (Source: own processing)�������������������������������������������� 49 Graph 1.8   Evolution of money laundering in the European   Union countries, 2012–2017. (Source: own processing)������������������ 50 Graph 1.9   Money laundering in the European Union countries by   regions, 2012–2017. (Source: own processing)�������������������������������� 50 Graph 1.10  Correlation between corruption and shadow    economy. (Source: own processing)����������������������������������������������� 53 Graph 1.11  Correlation between corruption and money laundering������������������ 54 Graph 1.12  Correlation between shadow economy and    money laundering. (Source: own processing) �������������������������������� 55 Graph 2.1   Graph 2.2   Graph 2.3   Graph 2.4    

Correlation between GDP/capita and corruption������������������������������ 76 Correlation between GDP/capita and shadow economy������������������ 79 Correlation between GDP/capita and money laundering������������������ 83 The fiscal freedom in the European Union countries 2005–2018. (Source: own processing)���������������������������������������������� 90

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List of Graphs

Graph 2.5    The fiscal freedom by geographical zones in the European    Union countries at average, 2005–2018.    (Source: own processing)���������������������������������������������������������������� 90 Graph 2.6    Evolution of fiscal freedom in the European Union    countries, in average, 2005–2018. (Source: own processing)�������� 91 Graph 2.7    Correlation between corruption and fiscal freedom������������������������ 94 Graph 2.8    Correlation between the shadow economy and fiscal freedom ���� 100 Graph 2.9    Correlation between money laundering and fiscal freedom���������� 103 Graph 2.10    Governance effectiveness in the European Union countries,    2005–2018. (Source: own processing)������������������������������������������ 107 Graph 2.11  Regulatory quality in the European Union countries,    2005–2018. (Source: own processing)������������������������������������������ 108 Graph 2.12  Rule of law in the European Union countries,   2005–2018. (Source: own processing)������������������������������������������ 108 Graph 2.13  Correlation between corruption and public governance���������������� 111 Graph 2.14  Correlation between shadow economy and public governance ���� 116 Graph 2.15  Correlation between public governance and money laundering���� 119 Graph 2.16  Correlation between efficacy of corporate board and    strength audit and reports. (Source: own processing) ������������������ 135 Graph 2.17  Efficiency of corporate board in European Union    countries, 2006–2016�������������������������������������������������������������������� 145 Graph 2.18  Efficiency of corporate board by geographical areas in    European Union countries, 2006–2016���������������������������������������� 145 Graph 2.19  Strength audit and reports in European Union countries,   2006–2016������������������������������������������������������������������������������������� 146 Graph 2.20  Strength audit and reports by geographical area, in the     countries of the European Union, 2006–2016, (Source: own    processings)������������������������������������������������������������������������������������ 146 Graph 2.21  Correlation between corruption and efficacy of corporate board�� 149 Graph 2.22  Correlation between corruption and strength audit and reports���� 150 Graph 2.23  Correlation between shadow economy and efficacy    of corporate board������������������������������������������������������������������������ 154 Graph 2.24  Correlation between shadow economy and strength   audit and reports���������������������������������������������������������������������������� 155 Graph 2.25  Correlation between money laundering and efficacy   of corporate board ������������������������������������������������������������������������ 158 Graph 2.26  Correlation between money laundering and strength    audit and reports�������������������������������������������������������������������������� 159 Graph 2.27  Soundness of bank in European Union countries, 2006–2016������ 164 Graph 2.28  Soundness of bank by geographical regions of the    European Union, 2006–2016. (Source: own processing)�������������� 165 Graph 2.29  Correlation between corruption and soundness of banks�������������� 165 Graph 2.30  Correlation between shadow economy and soundness of banks���� 166 Graph 2.31  Correlation between money laundering and soundness of banks�� 168

List of Graphs

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Graph 3.1   Power distance (PD) in European Union countries.  (Source: own processing)���������������������������������������������������������������� 182 Graph 3.2   Individualism versus collectivism (IDV) in European   Union countries. (Source: own processing)������������������������������������ 183 Graph 3.3   Masculinism versus feminism (MAS) in European   Union countries. (Source: own processing)������������������������������������ 183 Graph 3.4   Uncertainty avoidance (UAI) in European Union countries.  (Source: own processing)���������������������������������������������������������������� 184 Graph 3.5   Long-term orientation (LTO) versus short-term orientation   in European Union countries. (Source: own processing)���������������� 184 Graph 3.6   Indulgence versus restraint (IND) in European   Union countries. (Source: own processing)������������������������������������ 185 Graph 3.7   Corruption and power distance (PD) by geographical   areas of the European Union���������������������������������������������������������� 189 Graph 3.8   Corruption and individualism versus collectivism (IDV)   by geographical areas of the European Union. (Source:   own processing)������������������������������������������������������������������������������ 189 Graph 3.9   Corruption and masculinism versus feminism (MAS) by   geographical areas of the European Union������������������������������������ 189 Graph 3.10  Corruption and uncertainty avoidance (UAI) by    geographical areas of the European Union. (Source:    own processing)���������������������������������������������������������������������������� 190 Graph 3.11  Corruption and long-term orientation (LTO) by geographical    areas of the European Union. (Source: own processing)�������������� 191 Graph 3.12  Corruption and indulgence versus restraint (IND)    by geographical areas of the European Union. (Source:    own processing)���������������������������������������������������������������������������� 191 Graph 3.13  Correlation between shadow economy and power    distance (PD) in European Union������������������������������������������������ 199 Graph 3.14  Shadow economy and power distance (PD) by   European Union regions. (Source: own processing)���������������������� 200 Graph 3.15  Correlation between shadow economy and individualism   versus collectivism (IDV) in the European Union������������������������ 200 Graph 3.16  Shadow economy and individualism versus   collectivism (IDV) by European Union regions.   (Source: own processing)�������������������������������������������������������������� 201 Graph 3.17  Correlation between shadow economy and masculinism   versus feminism (MAS) in the European Union���������������������������� 202 Graph 3.18  Shadow economy and masculinism versus   feminism (MAS) by European Union regions.  (Source: own processing)���������������������������������������������������������������� 202 Graph 3.19  Correlation between shadow economy and   uncertainty avoidance (UAI) in the European Union�������������������� 203 Graph 3.20  Shadow economy and uncertainty avoidance (UAI)    by European Union regions. (Source: own processing)���������������� 203

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Graph 3.21  Correlation between shadow economy and   long-term orientation (LTO) in the European Union�������������������� 204 Graph 3.22  Shadow economy and long-term orientation (LTO)   by European Union regions. (Source: own processing)���������������� 204 Graph 3.23  Correlation between shadow economy and indulgence   versus restraint (IND) in the European Union������������������������������ 205 Graph 3.24  Shadow economy and indulgence versus restraint (IND)    by European Union regions. (Source: own processing)���������������� 205 Graph 3.25  Shadow economy and trust. (Source: D’Hernoncourt   and Méon (2012))�������������������������������������������������������������������������� 225 Graph 3.26  Happiness (well-being) in European Union countries ������������������ 228 Graph 3.27  Happiness (happy life years) in European Union   countries. (Source: own processing) �������������������������������������������� 228 Graph 3.28  Correlation between corruption and subjective well-being������������ 231 Graph 3.29  Correlation between corruption and feeling of   happiness. (Source: own processing)�������������������������������������������� 231 Graph 3.30  Correlation between shadow economy and subjective  well-being���������������������������������������������������������������������������������������� 236 Graph 3.31  Correlation between shadow economy and happy life   years. (Source: own processing)���������������������������������������������������� 236 Graph 4.1   Plot of corruption against health variables. (Source:   Achim, Borlea and Văidean (2019). Corruption and   health outcomes within an economic and cultural framework,   European Journal of Health Economics, 1–13)���������������������������� 254

List of Tables

Table 1.1 Terms used to designate “another” economy than the official one������������������������������������������������������������������������������������ 10 Table 1.2 Taxonomy of shadow economy activity types������������������������������������ 11 Table 1.3 Delimitation of “observed” economy from the “unobserved” one ������������������������������������������������������������������������������ 13 Table 1.4 Classification of EU countries by regions������������������������������������������ 43 Table 1.5 Correlation coefficients of corruption, shadow economy, and money laundering������������������������������������������������������������������������ 52 Table 1.6 Regression of shadow economy depending on corruption���������������� 53 Table 1.7 Regression of money laundering depending on corruption���������������� 54 Table 1.8 Regression of shadow economy depending on the money laundering������������������������������������������������������������������������������������������ 55 Table 2.1 Correlation coefficients between corruption and GDP/capita������������ 77 Table 2.2 Regression of corruption depending on the GDP/capita�������������������� 77 Table 2.3 Correlation coefficients of the shadow economy with the GDP/capita �������������������������������������������������������������������������� 80 Table 2.4 Regression of shadow economy depending on the GDP/capita �������� 81 Table 2.5 Correlation coefficients of money laundering and GDP/capita���������� 83 Table 2.6 Regression of money laundering depending on the GDP/capita�������� 84 Table 2.7 Correlation coefficients between corruption and fiscal freedom�������� 94 Table 2.8 Regression of corruption depending on the fiscal freedom���������������� 94 Table 2.9 Correlation coefficients between shadow economy and fiscal freedom���������������������������������������������������������������������������� 101 Table 2.10 Regression of the shadow economy depending on the Fiscal freedom ���������������������������������������������������������������������� 101 Table 2.11 Correlation coefficients between money laundering and fiscal freedom���������������������������������������������������������������������������� 103 Table 2.12 Regression of money laundering depending on the Fiscal freedom ���������������������������������������������������������������������� 104

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Table 2.13 Correlation coefficients between corruption and public governance���������������������������������������������������������������������������������������� 112 Table 2.14 Corruption regression depending on the public governance������������ 113 Table 2.15 Correlation coefficients between shadow economy and public governance���������������������������������������������������������������������������������������� 116 Table 2.16 Regression of shadow economy depending on the public governance���������������������������������������������������������������������������������������� 116 Table 2.17 Correlation coefficients between public governance and money laundering���������������������������������������������������������������������������������������� 119 Table 2.18 Regression of the money laundering depending on the public governance���������������������������������������������������������������������������� 119 Table 2.19 Similarities and differences between creative accountancy and fraud������������������������������������������������������������������������������������������ 126 Table 2.20 Coefficients of correlation between corruption and efficacy of corporate board���������������������������������������������������������������������������� 149 Table 2.21 Coefficients of correlation between corruption and strength audit and reports���������������������������������������������������������� 150 Table 2.22 Regression of corruption depending on efficacy of corporate board���������������������������������������������������������������������������� 151 Table 2.23 Regression of corruption depending on strength audit and report�������������������������������������������������������������������������������� 151 Table 2.24 Correlation coefficients between shadow economy and efficacy of corporate board�������������������������������������������������������� 154 Table 2.25 Correlation coefficients between shadow economy and strength audit and reports���������������������������������������������������������� 155 Table 2.26 Regression of shadow economy depending on efficacy of corporate board���������������������������������������������������������������������������� 156 Table 2.27 Regression of shadow economy depending on strength audit and reports������������������������������������������������������������������������������ 156 Table 2.28 Correlation coefficients between money laundering and efficacy of corporate board�������������������������������������������������������� 158 Table 2.29 Regression of money laundering depending on efficacy of corporate board���������������������������������������������������������������������������� 159 Table 2.30 Correlation coefficients between money laundering and strength audit and reports���������������������������������������������������������� 160 Table 2.31 Regression of money laundering depending on strength audit and reports������������������������������������������������������������������������������ 161 Table 2.32 Correlation coefficients between corruption and soundness of banks�������������������������������������������������������������������������������������������� 166 Table 2.33 Regression of corruption depending on the soundness of banks�������������������������������������������������������������������������������������������� 166 Table 2.34 Correlation coefficients between shadow economy and soundness of banks�������������������������������������������������������������������� 167 Table 2.35 Regression of shadow economy depending on soundness of banks�������������������������������������������������������������������������������������������� 167

List of Tables

xxv

Table 2.36 Correlation coefficients between money laundering and soundness of banks�������������������������������������������������������������������� 167 Table 2.37 Regression of money laundering depending on the soundness of banks�������������������������������������������������������������������������������������������� 167 Table 3.1 Descriptive statistics and corruption modelling ������������������������������ 213 Table 4.1 Sanctions on fraud in the Member States of the European Union������������������������������������������������������������������������������������������������ 261 Table 4.2 Differences between financial control and judicial accounting expertise ������������������������������������������������������������������������ 265

Chapter 1

Economic and Financial Crime: Theoretical and Methodological Approaches

1.1  Theoretical Approaches 1.1.1  T  heoretical Approaches Regarding the Economic and Financial Crime 1.1.1.1  History of the Economic and Financial Crime To better understand the concept of economic and financial crime and to try a definition of, it is necessary to make a short incursion regarding the history of this fact. The economic crime is a notion which “timidly” occurred at the beginning of the twentieth century. By the middle of the same century, it got “strengthened” and became a fact which was very present by the end of the twentieth century and particularly at the beginning of the millennium (twenty-first century). Bonger (1905) was among the first researchers who made the distinction between “street” crime and “economic” crime, including in the second category the crimes committed by merchants and entrepreneurs in relation with the properties as a result of the maximization of the speculative logic and capitalization. Later on, Sutherland (1940) defined the “white collar criminality” as being closely related to the upper classes showing there really existed another crime (from the upper classes) punished by the criminal law, and until that time, the criminology had not paid any scientific attention. In other words, we distinguish the differences regarding the approaches of the two authors mentioned above. While Bonger (1905) considered the economic crime as crime on the “property” resulting from the maximization of the speculative logic and capitalization, Sutherland (1940) considered this crime as a crime of white collars. The phrase “white collar criminality”, as economic and financial crime, was acknowledged by Sutherland in his famous work entitled “White-Collar Criminality”, issued in 1940. It is to be noted that the Sutherland’s theory is e­ mpirical © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 M. V. Achim, S. N. Borlea, Economic and Financial Crime, Studies of Organized Crime 20, https://doi.org/10.1007/978-3-030-51780-9_1

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and based on the investigation of the criminal activities of 70 companies belonging to the largest 200 companies from the United States. In his work Criminalité économique et criminalité organisée, Queloz (2002) summarized most of Sutherland’s learnings in three steps: (a) Sutherland showed that it is committed by upper classes, namely, by honourable people with a high social status, in relation with their business and culture and their professional environment. (b) Sutherland underlined that the white-collar crime reflect crimes forbidden and punished by the criminal law (fraud, breach of trust and abuse of position, fraudulent management, corruption, etc.) which are harmful from social point of view and causes significant economic damage. (c) Finally, Sutherland criticized the fact that criminology had not paid previously any scientific attention to this crime committed by the white collar individuals, but made all the efforts for the street crime and consequently, against those who are most convicted by criminal court and sent to prison. The criminals belonging to the “white collars” are “people who hold an upper position in the society, hold managing positions (...), posing as philanthropic persons as they are involved in various activities for community benefit” (Leția 2014, p. 14). The authors Bonger and particularly Sutherland wanted to demonstrate that, besides the so-called “classic” crime or “common” crime considered as “poor people’s crime”, there exist the crime committed by the “rich,” but the criminology paid no special attention to it. Despite the existence of some empirical and doctrinaire proofs related to such more special criminality, it will not raise any deep interest except for the situations when it started to become more and more intense and spread within several countries and even worldwide (end of the twentieth century, early twenty-first). With the time, Sutherland’s study proved to be only partly true because the economic and social growth created other categories of economic criminals who are not necessarily owners of properties or individuals from upper classes. Thus, a study performed by KPMG on 69 countries, investigating more than 348 economic crime files, concluded that profile of a business criminal is represented by a man of 36–45  years old; he works in financial domain; if employed, he commits frauds against his own employer; hold a high management position; has been employee of the same companies for more than 10 years; commits the crimes to satisfy his greed and not to achieve the professional target; and takes advantage of the functional weakness of the control structures (Leția 2014, p. 15). On the other hand, the studies of the specialized literature (Queloz 2002; Niță 2008, p. 26; Leția 2014, p. 14; Aniței and Lazăr 2016, p. 16) identified another characteristic of the crimes of the economic criminality and, namely, that they assume high-professional knowledge and competence and respectively a specialization of the knowledge by those who commit such crime. Under such circumstances, the economic and financial crime is closely related to the economic and social changes and development of the society, and it can occur as innovations made by individuals

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so they adjust to the society changes (Merton 1968). In an ever-changing society, the adjustment to the new economic and social conditions can be carried out in a different way by individuals of the society. Thus, businessmen can invent different forms of the white-collar crime as tax evasion and money laundering, while poor people can develop illegal activities, such as prostitution, gambling, and drug sales (Aniței and Lazăr 2016, pp. 17–18). According to Durkheim (1974), one of the greatest sociologists of the time, “criminality is normally part of the society like birth and death, and a society without crime would be pathologically over controlled”, so that from theoretical point of view, “crime could completely disappear only if all the members of the society had the same values, but such a standardization is neither possible nor wanted” (quoted by Amza (2002, p. 420)). Consequently, the economic and financial crime can be approached from at least two perspectives: As legal fact From a legal point of view, the criminality includes “the ensemble of human behaviour considered as crimes punished and convicted as such, under certain circumstances, within a criminal law system (sub-system) specifically determined from geo-historical perspective” (Moldoveanu 1999, p. 13). As social fact As for the social point of view, the criminality constitutes a mass social fact occurring on regular basis, which has existed and will always exist. According to the specialists, all that can be done is to adopt measures to reduce it. And it is “a utopia to think that criminality will be completely rooted out” (Niță 2008, p. 23). 1.1.1.2  Concept of the Economic and Financial Crime At international level, there is no common definition given to all the states, regarding the economic and financial crime (Leția 2014, p. 13), but in practice, this concept is associated with numerous deeds such as corruption, theft, cheating, embezzlement, data distortion, electronic fraud, forgery, counterfeiting, data and document cover up and destruction, money laundering, tax evasion, crimes regarding the accounting books, faked offers at public acquisitions, tender etc. The fact there is neither legally nor didactically is any unitar definition explained in the light of the continuous evolution of the technical means which result in extremely different ways of manifestation of such activities. However, different authors have tried to define the concept of economic and financial crime in various forms as follows: • The economic crime represents “the illegal acts committed by an individual or a group of individuals to obtain a financial or professional advantage. In such crimes, the offender’s principal motive is economic gain. Cyber crimes, tax

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

e­ vasion, robbery, selling of controlled substances, and abuses of economic aid are all examples of economic crimes” (US legal 2020). The economic and financial crime consists of the “asset misappropriation, bribery and corruption, accounting and tax fraud, cybercrime, procurement fraud”, and “it represents a persistent threat to business and business process” (PricewaterhouseCoopers 2016, p. 4). According to the Cambridge Dictionary (2020), the concept of “criminality” means illegal activities or behaviour. The economic and financial crime includes “the crimes provided by the special laws with dispositions belonging to the business criminal law, refers to the competition, commercial societies intellectual property, money laundering, banking regime, securities, tax evasion, accountancy regime, customs procedure, public authority, lands etc.” (Pantea 2010). The economic and financial crime represents “all those forms of non-violent crime causing a financial loss” (Leția 2014, p. 14). The economic and financial crime represents a breach of trust exploiting the good faith of participants at the economic life, the apparent credibility and stability of the financial, commercial, banking circuit of documents, etc. (Leția 2014, p. 14). The economic and financial crime, called also “business crime”, is defined as the total unlawful acts and deeds committed by individuals, associations, societies, or organizations in relation with the progress of some businesses or financial, banking, customs, commercial transactions using the cheating, fraud, breach of trust, forgery of turnovers, money laundering, fraudulent bankruptcy, tax evasion, insurance policies, etc. (Aniței and Lazăr 2016, p. 15). The economic and financial crime is defined by the identification of the features common to deeds circumscribed in this notion, respectively: (i) WHERE? – their committing under the economic, business and financial life, either private or public (ii) HOW? – using the means and methods which do not call on force or psychiatric violence – cheating, forgery, counterfeiting, corruption, exploitation of commercial secrets, of personal data (iii) WHO? – by persons who have knowledge in the economic, commercial, or financial field (iv) GOAL? – with the goal of accumulating profit, economic domination, saving of economic entities in difficult condition (processing based on Queloz 2002 quoted by Leția 2014, p. 14)

In accordance with the specialized literature (Pantea 2010), the economic and financial crime can be approached at two levels (macro and micro) as follows: • The economic and financial macro-criminality comprises the segment of economic and financial crime which prejudices the state and national security. These crimes are committed by groups of criminals highly specialized and resulting in the triggering of significant prejudices or the creation of some severe dangerous

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conditions, assume corruption deeds at a higher level, crimes of money laundering, and customs crimes, which have a trans-border character. • The economic and financial micro-criminality is the segment of economic and financial crime defined by a minimum breach of social values defended by the criminal laws, prejudicing life quality and could be a severe danger in future. This form of economic and financial crime occurs as fraud crime, tax avoidance, and corruption and causes small and repeated prejudices to the state or citizens. Starting from the definitions identified in the specialized literature and practice, the economic and financial crime could be defined as an ensemble of illegal deeds (crimes) committed by individuals or organizations in order to produce or intermediate the production of economic and financial benefits. The economic and financial crime is specific for the businesses and could occur as business crimes such as corruption, fraud, tax evasion, money laundering, etc. Also, the illegal business such as prostitution, gambling, smuggling drugs or human beings trade, etc. produce (or intermediate) the obtaining of economic and financial benefits for certain stakeholders. Consequently, we appreciate that we should include in the definition of the economic and financial crime concept such crimes, too. 1.1.1.3  C  haracteristic Features of the Economic and Financial Crime: Volume, Intensity, Direction, and Frequency The analytic approach of the economic and financial crime reveals a series of characteristic features: (a) Volume or extent, providing the number of crimes related to a population fraction (usually 100,000 inhabitants) for a given period of time, for instance, 1 year. The ratio between the number of economic-financial crimes and the population taken into account is called crime rate. It shows several forms as follows: • Real crime represents the total crimes concretely committed in a given period of time at the level of a number of inhabitants. This is the largest dimension of the crime rate, its limits being impossible to know. • Registered crime represents all the crimes tracked and identified. • Crime brought to justice represent all the crimes submitted to the law court for settlement. • Judged crime represents all the judged crimes against which the entitled bodies decided, in relation with a fraction of population. • These four forms represent the revealed, apparent or lawful crime. • Blacklisted or hidden crime represents all the crimes that were not registered and consequently not detected and nor judged. These are crimes which have not been submitted to the entitled authorities. Their number is much bigger than that of the crimes registered, detected, and judged.

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

(b) Intensity expresses the ratio between the total highly socially dangerous antisocial deeds and the socially little dangerous deeds committed. (c) Crime direction represents a characteristic feature indicating the concrete objects towards which the antisocial deed is oriented. This may be the national security, the individual, the public and private property, morals, and manners. The weight of a certain category of crimes occurring within a certain period of time, all being categorized as criminal deeds, actually indicates the prevailing direction of the crimes within the respective period. (d) Crime frequency indicates the number of crimes of a certain type related to a time unit. The number of thefts or robberies committed by year, month, week, day, hour, or second can be calculated for instance at national, zonal, and county level. 1.1.1.4  Forms of the Economic and Financial Crime The economic and financial crime is usually considered as covering the following crimes: • • • • • • • • • •

Corruption Tax evasion Fraud Electronic criminality Money laundering Cybercrime Terrorism funding Market abuse and utilization of confidential information Information security Gambling, prostitution, smuggling, drugs trade, etc.

In the following, the main fields and forms of crime occurrence are presented, although in real life they merge in a very complex way: • In the financial and banking field: illegal lending operations, accepting too high risks as well as there are not ensured the necessary guarantee conditions of reimbursement and recovery of the debt on due dates, card fraud, embezzlement operations and illicit fund transfer (in fact, money laundering through banking circuit); illegal and inefficient capital placement, utilization of some false payment means, crimes regarding the commercial effect procedure-bill of exchange, promissory note, cheques, crimes associated with the securities, crimes regarding the capital market; illegal foreign currency take out of the country; money counterfeiting; crimes associated with derived instruments, crimes in relation with virtual currencies (BITCOINS, ETHEREUM, etc.), including money laundering using such currencies (European Commission 2017) • In the commercial field: trade and smuggling of cigarettes, alcohol, coffee, electronic devices, energetic raw materials, and primary processed products of p­ etrol,

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wood, metal etc.; utilization of dummy companies particularly in case of commercial companies developing import-export operations; illicit sales of raw materials and strategic products or products making the object of export special control procedure established at international level; seizing of markets and enforcement of excessive prices • In companies: company procedure crimes consisting of the introduction of untrue data in the documents submitted to the public or associates, utilization of goods, or credit of the company for a purpose which is in contradiction with the company interests; crimes related to illegal share capital issue; bankruptcy crimes including fraudulent bankruptcy consisting of the insolvency and creditor defrauding, transfer or cover up of some parts of assets, etc. (see the work Bodu and Bodu (2016) for a detailed presentation of the crimes associated with the company procedures and bankruptcy crimes) • In the economic and social field: trade of drugs, gambling; prostitution and prostitution promotion, illegal sales of art objects, “work under table”, irregular migration; use of non-profit organizations (foundations) to offer a lawful appearance to the money obtained from dirty business (money laundering) • In the informatics field: the development of digital technologies and internet created a new category of individuals passionate by cybernetics attacks, the so-­ called hackers group. A study carried out by PricewaterhouseCoopers (2018) indicated that the cybercrime is the third most important type of fraud reported at the level of industries from worldwide (representing about 34% at average of the total frauds), behind the frauds associated with the asset embezzlement (about 44%), and frauds against the consumers (about 36%). The same study reflects an enhanced concern of the managers regarding the cybercrime (41% of the managers interviewed said that they spent two times more time at least, to investigate the cybercrime). The results of the study also identified that the cyber-­ attacks mainly aimed at the disturbances of the business process (30%), asset embezzlements (24%), security leaks (21%), and intellectual thefts (12%). Crimes committed in this way have no frontier given that both the aggressors and the victims are found all over the world and it makes difficult to identify them. Moreover, in most of cybercrime cases, the criminals use false identities or personal data obtained also through this system. In the following chapters, we will conduct a detailed analysis of the main forms of economic and financial crime, namely, corruption, shadow economy (including tax evasion), and money laundering.

1.1.2  Theoretical Approaches Regarding Corruption From etymology point of view, the word corruption is derived from the Latin word “rumpere” and means “to tear/ break”, and here the meaning is to break the law.

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According to the definition given by the World Bank “corruption is the illegal utilization of the public resources in order to obtain a personal gain”. Likewise, Transparency International (2020) defines corruption as being the “abuse of power given for obtaining private benefits.” The corruption disrupts the democratic governing and the rule of law and influences in a negative way the economic development as it is an obstacle for the increase of investments and economic growth (Mauro 1995). The corruption is a deed which had occurred since the Ancient Times being considered as the most severe and most spread form of behaviour meant to pervert the public business management (Conseil de l’ Europe 1996, p. 78). In our days, corruption is an extremely complex fact, and it can be approached from different perspectives economic, legal, sociological, philosophical ethical, etc. (Cârjaliu 2009). Most of the literature about corruption associates this term with bribery for obtaining private benefits. This private benefit may be attracted by entrepreneurs for avoiding the taxation or regulations or to win public contracts. Different studies indicated that the corruption negatively influences the business and economic growth (Mauro 1995; Djankov et al. 2002; Dreher and Schneider 2010; Sahakyan and Stiegert 2014). Johnson (2018) considers that corruption can take many forms such as bribery, embezzlement, money laundering, or tax evasion. In other words, the corruption is closely related to other economic and financial crimes approached in this work. From the point of view of the corruption deed, we identify small-, big-, and medium-scale corruption. In order to classify corruption in a size category, the corruption laws and regulations in Romania (Law no. 78, 2000; Emergency Ordinance no. 43, 2002; Emergency Government Ordinance no. 63, 2013) provide three criteria, as follow: (a) Value of the bribery or of the unfair benefits is more than 10,000 Euro. (b) Prejudices caused is more than 200,000 Euro. (c) Regardless of the value of the prejudice, the corruption crimes are committed by individuals holding important positions in the state (state secretaries, members of the government deputies, senators, judges, prosecutors, etc.) As for the volume of such fact, a study carried out by the European Commission (2015) within the Eurobarometer Flash 2015 about the attitude of companies against corruption in EU provides: • 40% of the companies from EU declare that corruption represents a real concern for them during the activity development. • 71% of the companies declare that corruption is widely spread in their countries. • 44% of the respondents declare that the only method to succeed in business is to have political connections. • 34% of the companies which have participated at public tenders or public acquisitions procedures during the last 3 years considered corruption an obstacle in winning a contract.

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• 68% of the companies agreed that favouritism and corruption hindered the competition in business in their country. • 4% of the enterprises declare that they have been asked or have been expected to pay bribery to receive certain public service supply contracts or authorizations during the last 12 months. More recently, the study drawn up by Transparency International (2017) within the report entitled Global Barometer referring to Corruption indicated that 25% individuals from the whole world declared that they were forced to pay bribery to access the public services during the last 12 months. The study also found out that policemen and elected officials from government represent the most corrupt groups.

1.1.3  Theoretical Approaches Regarding Shadow Economy Corruption and shadow economy are two destructive activities which often work together undermining the democratic governing and the rule of law and negatively influencing the economic growth. Compared to corruption, the shadow economy seems to be a much more complex fact. From conceptual point of view, we notice that the specialized literature present extremely different opinions about the definition of the shadow economy. Certainly, the term suggest the existence of another economy, an economy alternative to the usual or known one and which designate the activities which escape to legal norms and statistics. To this aim, the study drawn up by Roubard and Seruzier (1991) presenting the terms used in the specialized literature when the shadow economy concept is discussed is remarkable. Moreover, these terms are classified depending on three characteristics and namely: neutrality of the term (code 1); practices deliberately occult (code 2); and positive judgement (code 3) (Table 1.1). The first category of the characteristics called neutrality of term (code 1) adopts a point of view considered neutral which does not comprise any value judgement on the activity itself and neither does it present any motivation of the subjects engaged in this type of activities. The second category of the characteristics called practices deliberately occult (code 2) designate an ensemble of practices found at the law borderline, and the adjectives used have a prevailing negative nuance. The third category of the characteristics called positive judgement (code 3) reflects behaviour of the subjects which aim at the alternative economic space also comprising other issues such as the social and cultural ones related to the social organization, solidarity networks, and cultural origins and combine the traditional forms of organization with the basis of “another development”. In the present work, we preferred to use the term of “shadow economy” when we refer to the “other” economy.

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Table 1.1  Terms used to designate “another” economy than the official one 1; 2 1 1; 2 2 2 2; 3 3 3 2 3 1; 2 2 3

Unofficial economy Unstructured economy Undeclared economy Dissimulated economy Underground economy Parallel economy Alternative economy Autonomous economy Grey economy Marginal economy Invisible economy Illegal economy Counter economy

1 1 2 2 2 3 3 2 2 2 3 2; 3 2

Unregistered economy Unobserved economy Undisclosed economy Submarine economy Illicit economy Secondary economy Dual economy Occult economy Black economy Irregular economy Peripheral economy Informal economy Shadow economy

Source: Roubard and Seruzier (1991)

Once we clarified the approaches from semantic and conceptual point of view, we shall proceed with the definition of this term from the perspective of actions to which it refers. As for the definition of shadow economy, the opinions of different authors are much varying. The shadow economy is defined in different ways depending on the scope of its components as follows: • “All the economic activities commonly unregistered and which, if were observed, would contribute to the official calculation of the Gross Domestic Product calculated (or observed)” (Feige 1989, 1994; Schneider et al. 2010, 2015a, b; Frey and Pommerehne 1984). • “The production of goods and services from the market, either legal or illegal, which escapes from detection in the GDP official estimations” (Smith 1994, p. 18). • “Those economic activities and the derived incomes which bypass the regulating rules, taxation or government observation” (Thomas 1999; Feige 1989; Dell’Anno 2003; Dell’Anno and Schneider 2004); • “Drugs, commercial vice and prostitution, loan sharking, gambling (except where permitted by law), barter, illegal production of trademarked goods, employment of illegal aliens, do-it-yourself projects, skimming of business revenue, tax evasion” (Shelak 1997). • A larger definition of the underground economy is provided by Lippert and Walker (1997), who carry out a differentiated presentation of the taxonomy of the underground economy types as shown in the Table 1.2: As shown in Table 1.2, the shadow economy does not include only illegal activities but also undisclosed incomes obtained from the production of lawful goods and services, either from monetary or non-monetary transactions.

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Table 1.2  Taxonomy of shadow economy activity types Activity type Illegal activities

Legal activities

Monetary transactions Trade of stolen goods, production and trade of drugs, prostitution, gambling, smuggling, fraud and trade of human beings, drugs, and weapons Tax fraud Lawful tax avoidance Employees’ Undisclosed incomes discounts and obtained by the free lancers, incomes, salaries collateral benefits and assets resulted from unregistered work aiming at obtaining lawful goods and services

Non-monetary transactions Trade with drugs, stolen goods, smuggling, etc.; growth of plants to obtain rugs for own use, theft for own use Tax fraud Lawful tax avoidance All activities Barter with carried out on lawful goods own account and and services neighbours’ mutual aid

Source: Lippert and Walker (1997)

• According to the OECD (2002, 2008) and Eurostat (2014), the “unobserved” economy refers to all the production activities which cannot be comprised in the source of basic data used for the elaboration of the national accounts. It includes the following activities: 1. “The underground (shadow- our note) activities, define those lawful and productive activities, but which are deliberately undisclosed by the public authorities in order to avoid:

(a) payment of incomes, value added tax or other taxes; (b) payment of contributions to social security; (c) the requirements regarding certain lawful standards of labour market such as the minimum wage, maximum work hours, standards of safety or health etc.; (d) compliance with certain administrative procedures such as the filling in of statistic questionnaires or of other administrative forms.”

2. Illegal activities define those productive activities at production borderline comprised in the national account system which:

(a) Generate goods and services forbidden by the law (for instance, the production and distribution of illegal drugs) (b) Are illegal when are carried out by unauthorized producers (for instance, unlicensed practices for medicines)

3. Production of goods in own households for final own use representing those productive activities which result consists of the goods or service consumption in the household where they were produced, for instance:

(a) Production of crops and animal breeding (b) Production of other goods for own use and use

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(c) Construction of own houses and other own real estate properties (d) The rent required by the dwelling owners and payment for domestic services to the persons who supply such services

4. Informal unobserved services in the unofficial sector covering also the observed activities informally undertaken. In general, the informal activities are “those productive activities performed within the households, which are unregistered and/or smaller business than those which size is specified regarding the labour force occupation and which obtain a certain market production (...)”. • In different studies, Schneider and his collaborators proposed to periodically measure the shadow economy level in different states of the world, and their initiatives are remarkable (Alm et  al. 2004; Schneider and Klinglmair 2004; Schneider et  al. 2010, 2015a; Schneider 2011, 2013; Medina and Schneider 2018). In numerous studies conducted by Schneider and his collaborators (Torgler and Schneider 2009; Schneider 2011, 2013), for measuring the level of the shadow economy, the authors used the following definition given to the shadow economy: • “The shadow economy includes those productive and lawful activities, but which are deliberately undisclosed in order to avoid: (a) payment of incomes, value added tax or other taxes; (b) payment of contributions to social security; (c) requirements to meet certain lawful standards of the labour market such as the minimum wage, maximum number of work hours, safety or health standards etc.; (d) compliance with certain administrative procedures like for instance, filling in the statistic questionnaire or other administrative form.” Considering the vast definition of the “unobserved” economy proposed by OECD (2002, 2008) and Eurostat (2014) (see Table 1.3), we notice that Schneider adopts a more restricted version of defining the shadow economy maintaining only the first category of activities within the area of underground economy and, namely, the underground activities. As a result, to measure the shadow economy, Schneider has not included the illegal activities (for instance, trade of drugs, smuggling, money laundering, and embezzlement), the production carried out in households for own use or any informal unobserved activity which are part of the formal sector. All these activities are difficult to measure, and this is why Schneider does not take them into account in his work to estimate the shadow economy level. Within this more restricted definition, the shadow economy presents two main components (Schneider 2013). The first component representing a higher weight (about two thirds) is the undeclared work which refers to the salaries which the employees and employers do not declare to avoid taxation or the regulation of the labour force market. The second component (about a third) is represented by unreported incomes from business, to avoid a part of the tax burden.

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1.1  Theoretical Approaches Table 1.3  Delimitation of “observed” economy from the “unobserved” one

Non-observed economy

Observed economy

Underground (shadow-our note) activities

Reported/registered activities

Illegal activities Informal sector Non-observed

Observed

Activities undertaken by households for their own Deficiencies in the basic data collection programme

Source: Eurostat (2014), Essential SNA: Building the basics. 2014 Edition. Luxembourg: Publications Office of the European Union, p. 122. Source: Eurostat (2014, p. 122)

Within this work, the terms, definitions, and evaluation of the shadow economy are used in accordance with Schneider’s point of view. Consequently, when we refer to the shadow economy, we refer only to the lawful activities but which are not disclosed to the public authorities, and we do not consider the illegal, domestic, or informal activities. Also, we use the evaluation of the shadow economy as presented by Schneider (2013, 2015; Medina and Schneider 2018), where the shadow economy is calculated as a percentage of the official GDP. The databases elaborated by Schneider and his collaborators are largely used by different authors in their studies about the shadow economy (Nastav and Bojnec 2008, 2014; Torgler 2002, 2007; Torgler and Schneider 2009; AT Kearney 2013) but also by the European Commission (2014).

1.1.4  Theoretical Approaches Regarding the Tax Avoidance According to Schneider (2013, 2015; Medina and Schneider 2018) regarding the definition and measurement of the shadow economy, the tax avoidance represents an important component of shadow economy when the goal of the shadow economy is to avoid the lawful regulations for paying less taxes and fees to the state budget. There are also other partial opinions (Ciupitu and Tudorache 2015) or even contrary opinions (Dinga 2008) about the existence of a very close connection between shadow economy and tax avoidance. Based on the idea that the tax avoidance can be

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identified (observed) and non-identified (non-observed), the study conducted by Ciupitu and Tudorache (2015) appreciate that the tax avoidance is born both from the official economy and particularly from shadow economy. In other words, the two facts are partly overlapping. On the other hand, the study carried out by Dinga (2008) considers that “although the tax avoidance has connotations which belong to the semantic area of the shadow economy, this is not a component of the shadow economy but rather this is at the interference of the shadow economy with the official one”. Given the differentiated approaches found in the specialized literature regarding the relation between shadow economy and tax avoidance, in this section we shall review the definitions of the tax avoidance identified in the specialized literature in order to clarify the concepts. Thus, the definition of the tax avoidance has various approaches in the specialized literature: • Tax avoidance represents “A term that is difficult to define but which is generally used to describe the arrangement of a taxpayer’s affairs that is intended to reduce his tax liability and that although the arrangement could be strictly legal it is usually in contradiction with the intent of the law it purports to follow” (OECD 2020- Glossary of tax terms). • Tax avoidance represents “ways of paying only the smallest amount of tax that you legally have to” (Oxford Dictionary 2020). • Tax avoidance reflects “an intended distortion of the material facts carried out by the tax payer in order to specifically escape from the tax payment” (Internal Revenue Service IRS, USA 2017). • In Romania, the tax Law 241/2005 about the prevention and fight against the tax avoidance at Art. 9 stipulates the followings: 1. the following deeds committed in order to escape the fulfilment of the fiscal obligations constitute crimes of tax avoidance and they are punished with imprisonment of 2 years up to 8 years and the interdiction of some rights:

(a) Concealment of the good or taxable or billable source (b) Omission (total or partial) of highlighting in the accounting books or other lawful documents, the commercial operations performed or the incomes obtained (c) Registering in the accounting books or in other lawful documents the expenses which are not based on real operations or the highlighting of other fictitious operations (d) Modification, destruction, or concealment of accounting books, memories of taximeters, or fiscal electronic cash registers or of other devices for data storage (e) Execution of double entry bookkeeping using documents or other devices for data storage

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(f) Escaping from the financial, fiscal, or customs controls by failure to declare, fictitious declaration, or incorrect declaration of the head-offices or secondary establishments of the checked entities (g) Substitution, deterioration, or alienation by the debtor or by thirds of the seized goods in accordance with the provisions of the Fiscal Procedure Code and Criminal Procedure Code.

2. If, as a result of the deeds provided at paragraph (1), a prejudice of more than 100,000 EUR equivalent in national currency was caused, the minimum sentence provided by the law and its maximum limit will be increased by 2 years. 3. If the prejudice resulting further to the commitment of the deeds provided at paragraph (1) is more than 500,000 EUR in equivalent of national currency, the minimum limit of the punishment provided by the law and its maximum limit will be increased by 3 years. • The tax avoidance is a deliberate and intended practice of the tax payers to not fully disclose the taxable income, so that they pay a smaller tax and a fiscal law breaching by means of which a taxable entity neglect to pay the due tax or reduces the fiscal obligation through fraudulent actions or false actions regarding the form of the income tax. (Olabisi 2010). Based on the definitions of the tax avoidance and of the shadow economy, we join Schneider’s opinion considering the tax avoidance as a component of the shadow economy when the main goal is to avoid the taxes to the state. Tax avoidance could be realized legally or illegally. The legal tax avoidance is also called as tax optimization. The illegal tax avoidance is called tax evasion. A. Legal tax avoidance (tax optimization) If tax avoidance represents the avoidance of tax payment to the state by breaking the law provisions, then tax avoidance represents “that form of interpretation of the fiscal laws which, without being fraudulent, leads to the diminution of the taxable base and thus, to the payment of smaller taxes” (Bodu and Bodu 2016, p.  196). This method to reduce the taxable base is also called legal (lawful) tax avoidance if it is considered in relation with the illegal tax avoidance which, under this context is called tax fraud (Bodu and Bodu 2016, p. 196). The tax optimization or the legal tax avoidance represents the tax payer action to bypass the law when it presents certain gaps or glitches, totally or partly evading the tax payments because of this legislation deficiency. This type of tax avoidance also occurs when new regulations are adopted in the field of tax avoidance, when new taxes are adopted, or when the legislation in force is frequently amended. Considering these changes, the tax payers try to find the main methods and exploitation means of the law. Thus, the factors which encourage the lawful tax avoidance are related to the granting of some tax concessions, temporary exemptions, income taxation based on income standards, law gaps, and lack of regulation of some expenses.

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The existence of “tax-free” zones encouraged the increase of the legal tax avoidance at international level. These are also called tax haven zones, and they represent territorial enclaves where the national legislation is not applied. In relation with these tax haven zones, there were also developed tax optimization processes carried out through the offshore companies. The offshore companies represent those companies which are incorporated in jurisdictions enjoying a differentiated taxation rate depending on the place where the commercial business is developed, and in most offshore jurisdictions, the profit tax and tax on dividends is 0%. In accordance with Tax Justice Network (2018), in 2018, among the ten biggest tax havens of the world are Switzerland, the United States, Cayman Islands, Hong Kong, Singapore Luxemburg, Germany, Taiwan, United Arabian Emirates, etc. This ranking is determined using the score of the financial secret of each country calculated depending on the fiscal laws, transparency of the tax system and company structures, as well as the confidentiality regulations of the banking field. The tax optimization implies the identification of the means through which a company can benefit of tax concessions or beneficial provisions of the legislation regarding its business. The purpose of the tax optimization is to benefit of the existing tax concessions and the favourable provisions of the legislation, and it results in the lawful diminution of the fiscal impact. In other words, tax optimization can be considered a component of the lawful tax avoidance carried out to obtain a maximum fiscal gain, and it often implies the relocation in different jurisdictions where the income taxation is low. Thus, there exists a competition between countries from taxation point of view. The multinational companies play a very important role for the economy of a country so that many countries try to facilitate their presence on the own territory to benefit by the potential contributions to their economy. The easiest way to attract or convince the multinational companies to stay in the host country consists in offering them a taxation system with low rates. This is the reason which actually triggered a fight in the whole world including in Europe. The average of taxation rate applied to the companies in European Union decreased from 18.2% in 1995 to 16.9% in 2010 (European Commission 2012). This way, multinational enterprises exploit gaps and mismatches in the international tax rules to artificially shift profits to low or no tax jurisdictions and avoid paying their fair share of tax. Although these tax avoidance strategies were in most cases legal, it has begun to be considered a major issue for the OECD because of avoiding paying the fair share of taxes. As a result, the fight of combating international tax avoidance materialized in an international collaboration to end tax avoidance under the Inclusive framework of Base erosion and profit shifting (BEPS). Under the OECD/ G20 Inclusive Framework on BEPS, over 135 countries are collaborating to put an end to tax avoidance strategies that exploit gaps and mismatches in tax rules to avoid paying tax. An international account of tax legislation and procedure is very welcome in order to understand the procedure and tax rules in an international and comparative environment (Costaș 2018).

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B. Illegal tax avoidance (tax evasion/tax fraud) On the other hand, the illegal or illicit tax avoidance consists of the taxable object covering up, sub-evaluation of the basic taxable amount or the use of other methods to avoid the payment of the due tax. It can be defined as a conscious action of the tax payer who is violating the law provision to not pay the due fiscal obligations. This type of avoidance through breaking the law is also called as tax evasion or tax fraud. According with Oxford Dictionary (2020), tax evasion is “the crime of deliberately not paying all the taxes that you should pay”. This type of evasion occurs when the tax payers breach in a fraudulent way the tax law, and it materializes in various economic documents such as: • False accounting entries (fictitious transactions, fictitious stocks, overrated assets or creation of fictitious investments, under evaluated debts, covering up the increase of outstanding claims, artificial revenue growth because of some manipulated estimations, etc.) • Manipulations between the affiliated companies • Manipulation of promotional discounts • Illegal expenses record • Falsification of financial and accounting documents in order to diminish the tax base • Destruction of financial-accounting documents • Organization of double bookkeeping • Failure to declare or flawed declaration of commercial activities or revenues, etc. The methods of tax evasion expression are closely related to the impacted tax type or fee. In case of the profit tax, the tax fraud refers to the following situations (Amarița 2017): • Reduction of the tax base by including expenses that are not supported by justifying documents or lawful base • Recording of oversized expenses or over the limit allowed by the laws • Deduction of associates’ personal expenses or interests to loans granted by the company managers • Failure to entirely register the revenues received or recording in the document’s delivery prices lower than those actually practised • The transfer of the taxable revenues to newly established companies within the same group, which are in the period of exemption from the profit tax payment, simultaneously with the registration of losses by the mother company • Determination of the profit tax by unsuitable application of the law provisions particularly regarding the diminution of the tax corresponding to the reinvested profit • Uncalculatingly the tax corresponding to the revenues received from economic activities, by some non-profit organization • The differences established through the control papers or even the obligations associated to the due profit tax are not registered in the accounting records.

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The fraud techniques used for the value added tax are found among the following ones (Amarița 2017): • Wrong application of the procedure for the value-added tax deductions based on documents where the tax is not registered or deductions which are not based on documents or are based on illegal documents, VAT deductions as a result of the repeated registration of invoices in the purchase journals, deductions based on documents belonging to other companies, deductions of the value added tax corresponding to the operations exempted of the deduction right • Operations of the value added tax area which are not comprised in the tax calculation base • Failure to record and to pay the value-added tax corresponding to the advance payments from the clients • Failure to record as subject to VAT when the minimum threshold rate is exceeded • Avoiding the payment of the value-added tax corresponding to the import of goods by submitting fictitious papers of donation from foreign partners. As for the tax on wages, the most frequent tax fraud methods are (Amarița 2017): • Non-taxation of all the sources paid to the employees as wages. • Incorrect application of the taxation tables comprising the remuneration rights. • Failure to withhold and transfer the tax on wages due for the personnel employed based on agreements or for day workers. • No cumulating of all salary incomes for taxation purpose • Failure to record the payment obligations regarding the tax on wages. • Non-compliance with the laws referring to the determination of the tax base. • Failure to include in the tax base all the achieved incomes particularly those incomes for which the taxation is based on the declaration of the taxable subject. • Identification of methods for generating fictitious expenses incurred by the wage pay of some individuals. • Establishing some incentives for the employees which would not be included in the category that imposes the global income tax so that a smaller tax is paid to the state budget. • The employees obtain a certain status which gives them the right to the exempted from the payment of the global income tax such as the certificates of revolutionary or certificates of disability. Referring to the excise duties, the fraud methods include the following activities (Amarița 2017): • Failure to comprise all the taxable amounts in the tax base • Tax base reduction by underevaluation of the imported products at customs using double documents

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• Failure to calculate the excise duties corresponding to the modifications of the alcoholic concentrations • Failure to include the excise duties in the selling price of the products for which excise duties are due • Failure to highlight in the accounting books the excise duty payment obligation • Avoiding the payment of excise duties by changing the name of the products subject to excise duty payment and their transfer to the category of products non subject to excise duties payment or to the category of products subject to reduced excise duties. Such approach of the two forms of the tax avoidance (legal and illegal is also agreed by other redoubtable specialist (Tulai 2007, p. 172). The definition and understanding of the differences between the legal tax avoidance and the illegal one is very important because they distinguish the lawful actions made by an entity from the illegal ones. This threshold is easily and frequently exceeded, the tax payer passing easily and progressively from the abstention from breaching the law to the ability of doing it, and from abuse against the law to qualified tax fraud (Trif 2015). Moreover, the understanding of the two concepts is made difficult by the legal frame regarding the tax avoidance in Romania (Law no. 241/2005 as subsequently amended) which, according to the law experts (Puț 2017, 2018), is not rigorous from the conceptual point of view, considering that “the tax authority does not distinguish between tax avoidance and tax fraud” although there exist clear doctrinaire differences between the two terms. Thus, the “tax avoidance” essentially represents the ensemble of the lawful means by which the tax payers evade the incomes from taxation, while the “tax fraud” represents the ensemble of the actions and deeds which by omission, evade the incomes achieved from taxation. Now, the breaching of the tax judicial norms meets all the constitutive elements of a crime which according to the Romanian positive law has an unsuitable name –crime of tax avoidance (Puț 2017, 2018). In conclusion, the Romanian legal norms use the term of tax avoidance with a penal connotation, namely, tax fraud which is in contradiction with the international doctrinaire regulations. In Romania, the main cause leading to the increase of the number of the tax fraud cases consists of the unclear, dense regulations and which leave room for interpretation. In Romania, the laws are not clear regarding the provisions and instructions for implementation. Thus, the taxed entity but also the tax authorities have many opportunities to evade the tax laws and the payment of financial obligations. All these result in the exploitation of these loopholes by those who intend to evade the obligations of tax and fees payment (Gyuricza et al. 2017).

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1.1.5  Theoretical Approaches of Money Laundering In a synthetic definition, the money laundering represents the make-up process of illegally procreated sources by criminal procedures so that they get a legal appearance. To understand the money laundering action, a short historical presentation is required. The money laundering concept is associated with Al Capone in 1931 during the prohibition period, in the United States. He developed real businesses with the “launderers” in order to hide the origin of the amounts cashed from the business with alcohol. While Al Capone was judged for tax avoidance, Meyer Lanski transferred, in Switzerland, illegal funds through a complex system of phantom c­ ompanies, holdings, and off-shore accounts. After 1934, Switzerland established the bank secrecy principles. In 1970, the United States opened the doors to the legislation in the field of money laundering through the Bank Secrecy Act, according to which the banks are required to report to Internal Revenue Service the suspect transactions higher than 10,000 USD, and the fund transfer and their hidden placement in financial institution is sanctioned as crime but without naming it money laundering. Also, the failure to report the accounts from foreign banks is sanctioned in the same way. The term of money laundering is officially used for the first time in 1972, in Watergate Scandal when the Committee for Nixon’s re-election as President transferred money illegally obtained for the election campaign from Mexico, then transferred them back through a company of Miami. Concretely, the history of the money laundering regulation started in the middle of 1980 with the criminalization of the cash from illegal crimes with drugs in the United States, Great Britain, and Australia. The money laundering crime was introduced and sanctioned as such in USA through the Money Laundering Control Act of 1986. In 1989, there was established an intra-government body called Financial Action Task Force on Money Laundering (FATF) meant to establish policies for fighting against money laundering and terrorism funding. In Europe, it was only in 2005 that the Directive 2005/60/EC of the European Parliament and of the Council of 26 October 2005 on the prevention of the use of the financial system for the purpose of money laundering and terrorist financing was adopted. Despite the fight against this scourge of the world states and international organizations, the money laundering continues to represent an ever-growing fact. The United Nation Organization for Fight Against Drugs and Terrorism (UNODC) estimates the amount of money laundered in a year at global level around 2–5% of the global GDP or 800 billion dollars–2 trillion USD (United Nation Office on Drugs and Crime 2020). In relation with the trial to define the money laundering, the following approaches in the specialized literature are identified: • The money laundering consists of “bringing the money from illegal activity in a lawful business so that their source seems to be legal” (OECD 2017).

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• The money laundering represents the conversion or transfer of property, knowing that such property is derived from any [drug trafficking] offence or offences or from an act of participation in such offence or offences, for the purpose of concealing or disguising the illicit origin of the property or of assisting any person who is involved in the commission of such an offence or offences to evade the legal consequences of his actions (United Nations Convention Against Transnational Organized Crime (2000) (Palermo Convention). • Money launderers “send illicit funds through legal channels in order to conceal their criminal origins”.(..) “When money is laundered, criminals profit from their actions; they are rewarded by concealing the criminal act that generates the illicit proceeds and by disguising the origins of what appears to be legitimate proceeds” (Schott 2006). • The money laundering is the process through which the incomes obtained from crimes are “laundered” through lawful channels (using bank transactions), and then, they are reinvested in lawful activities (Ardizzi et al. 2014). • The money laundering consists of the “illegal transfer of goods to the lawful economic system by using labour and operations meant to hide the criminal source of the goods submitted to laundering” (Jurj-Tudoran and Șaguna 2016, p. 9). • The money laundering represents a “complex of transactions which include international transfers, dispersions in small amounts and transfer on the name of other persons benefiting of bank experts, brokers, accountants, notaries or lawyers. In case of money laundering there is created a perception of action legitimacy so that the goods (money) become available again for the criminals” (Jurj-Tudoran and Șaguna 2016, p. 4). • The crime of money laundering is a result crime which is always accompanied by another result crime and other means crimes which belong to the economic crime domain: tax avoidance, corruption, bank crime, cheating (Leția 2014, p. 40). • Directive 2005/60/EC of the European Parliament and of the Council of 26 October 2005 on the prevention of the use of the financial system for the purpose of money laundering and terrorist financing (European Parliament 2005) provides that the Member States have to ensure that the money laundering and terrorism funding are forbidden (art 1). In the meaning of the present directive, at art. 2 “the following behaviours are considered money laundering when committed intentionally: (a) the conversion or transfer of property, knowing that such property is derived from criminal activity or from an act of participation in such activity, for the purpose of concealing or disguising the illicit origin of the property or of assisting any person who is involved in the commission of such activity to evade the legal consequences of his action; (b) the concealment or disguise of the true nature, source, location, disposition, movement, rights with respect to, or ownership of property, knowing that

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such property is derived from criminal activity or from an act of participation in such activity; (c) the acquisition, possession or use of property, knowing, at the time of receipt, that such property was derived from criminal activity or from an act of participation in such activity; (d) participation in, association to commit, attempts to commit and aiding, abetting, facilitating and counselling the commission of any of the actions mentioned in the foregoing points.” • In Romania the definition of money laundering is in line with the European Directives in the field. To that end, the Article no. 29 of the Law 656 from 7 December 2002 republished, about the prevention and sanctioning of money laundering as well as for the implementation of measures for the prevention and fight against the terrorism funding, provides the following issues: •   “It is considered as money laundering crime and is punished with imprisonment from 3 to 10 years: (a) The exchange or transfer of goods knowing they are the result of crime committing in order to cover up or dissimulate their illicit origin or in order to help the person who committed the crime to escape prosecution, judgement or sentence execution; (b) Hiding or dissimulation of the real source of goods, of their location, of the disposition, circulation or ownership or of the ownership rights on them knowing that the goods are the result of a crime commission; (c) Acquisition, possession or utilization of goods, knowing that they resulted at a crime commission.” Based on the definitions presented above, we conclude that the money laundering represents a complex process by means of which an apparent legality regarding an amount of money from illegal business is acquired. Stages of money laundering Although numerous methods are involved in the money laundering process, the scheme of money laundering can be reduced in general, to three big stages (National Office for Prevention and Control of Money Laundering 2002): 1. PLACEMENT is the first stage when the amounts of money obtained from illicit activities are put into circulation and are effectively placed in the financial system. Before money placement, the PIECEMEAL process takes place, respectively, the money split in smaller amounts which are fewer suspects (below 10,000 EUR). Then, the placement of these amounts of money is carried out by constituting bank deposits or by buying a number of financial instruments (cheques, promissory notes, etc.) which are cashed later on. 2. The second stage, namely, the LAYERING or STRATIFICATION assumes the separation of illegal funds from their source by creating complex layers to hide the source and to ensure the anonymity. This can be done using the electronic

1.1  Theoretical Approaches

23

transfer of money in the accounts opened at banks from all over the world (it especially aims at those jurisdictions which do not cooperate in the investigations regarding the fight against money laundering). The stratification can be also done by establishing front companies. The one making money laundering drafts, for instance, fictitious import-export papers, on which basis the money is transferred from the initial placement location as payment for service supply or fictitious export operations, to another bank. Under certain circumstances, the money launderers can conceal the transfers as payment for goods or services, and thus, they are apparently legal. 3. INTEGRATION, the third stage assumes the legitimization of the funds obtained from crime commission by their reintroduction in the lawful circuit. The integration of the amounts of money in the lawful circuit can be carried out through investments on the real estate market, luxury goods market, or the funding of own business, and thus, these funds seem to be normal and “clean”, as providing from commercial activities. The three stages by means of which the money laundering can be done can be separated and distinct phases, or they may also occur simultaneously or they overlap. The utilization of the basic steps depends on the available mechanisms of money laundering and the requirements of the criminal organizations. Figure 1.1 shows a description of the money laundering circuit: Methods of money laundering The money laundering represents an extremely complex process, and in practice, there has been found out numerous methods to carry out this process. Below, there are presented, in a limitative manner, several such methods identified more frequently (National Office for Prevention and Fight Against the Money Laundering and Terrorism Funding 2002, 2004): 1. Money laundering through bank accounts: (a) Utilization of accounts which do not reflect the normal banking or commercial activities, but they are used only for money deposition or withdrawal. (b) Non-operational/inactive accounts which suddenly become active involving transactions with high amounts of cash. (c) Large cash amount withdrawal from a previously inactive account or from an account where there has been unexpectedly transferred a significant amount from other account opened in the country or abroad. (d) The cash withdrawal operations which are carried out throughout the same day from different subsidiaries of the same banking institution. (e) Transfer of funds and real estate values between accounts which do not seem to be controlled in common. (f) Accounts of a company where predominantly the depositions or withdrawals are carried out in cash (and not using the cheques). (g) The movements of the amounts through the companies’ accounts which cannot be clearly identified as being supported by economic justification

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are not complying with the company strategy and do not seem to be related to a lawful commercial contract. (h) Payments or cashing of high amounts on a client’s name without any certain motivation or plausible explanation. (i) Significant increase, without an apparent reason, of a client’s turnover reflected by the activity of its accounts. (j) A large number of accounts is opened by the client at the same banks or at different banks, and there are operated repeated transfers of large amounts of money between these accounts. (k) Depositions of small amounts of cash in the account of a client followed by the immediate transfer in an account opened at another bank. (l) Crediting an account by means of cheques issued by thirds with large amounts of money at the client’s favour. (m) Suspect movements of the funds from a bank to another bank and back again to the first bank. For instance, the following scheme has been noticed: (1) procurement of cheques from a bank; (2) opening an account in another bank; (3) deposition of the cheques in the second account; and then (4) electronic transfer of the funds from the second account to the account of the first bank which initially issued the cheques. (n) Periodical transfers from personal account to countries with a high-risk rate. 2. False loans/loans returned: (a) Companies applying for credits although, according to the financial statements, it results that the credit is not necessary. (b) Clients who reimburse surprisingly quickly the loans with funds from unknown sources. (c) Fictitious loans granted by shell companies (just a cover). Such a company registered in a tax haven is controlled by a company from the country, and the shell company funds are, in fact, funds of the company from the country which are recycled. The scheme is as follows: recycled money, found in the shell company accounts, are lent to the company from the country ­(actually, it is about self-loans) for which the latter pays interests and eventually delayed payment penalties which later on, by means of different methods, are cashed, by the real owner of shell company. Thus, the respective amounts get in the legal circuit. (d) The loans of cash and their reimbursement by means of bank instruments. 3. Utilization of offshore destinations (tax havens). These represent one of the most common and usual procedures for fraud and tax avoidance at international level. 4. The shell company (existing only on paper, they do not have any office, employees, independent assets, or own commercial operations, and they are used by their owners as a vehicle for business transactions or for controlling other companies).

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1.1  Theoretical Approaches Movement of cash from its source

Dirty Money

Placement Banks/ Casinos/ Bureau de change

Clean money Purchase real estates, luxury assets/funding own business Integration Integrating illicit money back into the economy

Cash converted into monetary Instruments

Layering

The separation of illegal funds from their source by creating complex layers to hide the source and to ensure the anonymity .

Off shore bank Material assets bought with cash then sold Source: Own processing

Fig. 1.1  Money laundering circuit. (Source: own processing)

5. Utilization of informal systems regarding the fund transfer, more precisely, the use of transfer systems parallel to the financial institutions, such as Western Union and Money Gram, aiming at the compensation between several physical or legal entities, but there is no real movement of these amounts of money. 6. Utilization of external operations particularly in relation with the tax havens. 7. Money laundering through transactions related to investments/Transactions on the stock market: (a) Unusual sale of high-value securities in exchange for cash which is withdrawn later on. (b) Transactions with securities to obtain cash which movement is not conducted through the clients’ accounts. (c) Acquisition of securities through the bank when this acquisition is not complying with the usual activity of the client. (a) Clients’ applications for benefiting of the investment administration when their source is not clear or when it is not complying with the client’s activity. (b) Acquisition of securities which are to be safely kept by banks when it is not complying with the client’s activity. (c) Acquisition of shares of companies which are established in tax havens followed by their sale to other similar companies.

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(d) Sale of shares for a price much higher than the market price to a company where the sole shareholder is just the owner of the respective shares. (e) Purchase of real estates by companies from tax havens followed by their sale to other similar companies. (f) Sales of equity interests for a price much higher than their market value to a company which sole associate if just the owner of the equity interests. 8. Utilization of non-profit organizations The non-profit organizations collect significant amounts of money from donors and, then, distribute these amounts to their beneficiaries, after covering the administrative costs. The amounts transferred then to the beneficiaries as well as the administrative expenses can be over evaluated and their utility is difficult to estimate. The non-profit organizations are often used in bad faith, being a widely spread method of money laundering. 9. Gambling (casinos, horse racing) The casinos and other gambling entities are agreed by the money launderers because they offer the possibility of intensive use of cash, being often used by the money launderers as simple exchange office where dirty money turns in clean money. In such cases, the criminal buys winning tickets and then the organizer’s ticket, and thus, the source of money is justified. Based on statistics, the electronic games annually increase by about 15%, incurring enormous amounts of money which may reach 10 billion EUR (Leția 2014, p. 41). 10. Other methods for money laundering, such as: (a) Evading the submission of declaration obligations regarding the amounts transferred abroad (b) Applying for the VAT reimbursement for fictitious operations, submission of unreal/fictitious financial statements (c) Making advance payments but no goods delivery/service supply takes place (d) Implication of financial institution officers who can be identified by their life standard which exceeds by far their wage rate, luxury holidays (e) Use of freelancers

1.1.6  R  elation Between Corruption, Shadow Economy, and Money Laundering. Theoretical Approaches All the three important forms of the economic and financial crime mentioned by us in this book, respectively, corruption, shadow economy, and money laundering indicate, among their common components, the avoidance of the regulations regarding the calculation and payment of taxes, and, consequently, they lead to the diminution of tax incomes and increase of public expenses and slow down the economic growth.

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27

As for the empirical relations established between the three forms of the economic and financial crime, based on the review of the specialized literature, we found out that they are not very clearly delimited. Thus, we notice first that the investigated studies of the specialized literature referring to the relations identified between corruption and shadow economy indicate the presence of both direct and indirect relations (Johnson et al. 1997; Fjeldstad 1996, 2003; Dreher and Schneider 2010; Buehn and Schneider 2009; Simonovic and Boskovic 2016, p. 117; Virta 2007; Borlea, Achim and Miron 2017). A first important group of studies (Fjeldstad 1996, 2003; Kaufman 2010; Ivanyna et al. 2010; Ghosh and Neanidis 2011; Borlea et al. 2017) identify direct relations between corruption and shadow economy. These studies offer empirical issues about the destructive role of corruption and officers’ bribery to allow further acting in shadow. In this context, Fjeldstad (1996, 2003) uses just the term of “fiscal ­corruption” underlining thus the fiscal role of corruption. Thus, as corruption enhances, the shadow activities extend accordingly, so that a positive relation between corruption and shadow activity is expected. Other authors (Johnson et al. 1997) created a complex model of corruption in relation with the official and unofficial economy, reaching similar results. Thus, they found out that the corruption fact functions as a fee on the activity of the companies from official economy, directing them to unofficial economy. Considering this point of view, Friedman et al. (2000) empirically demonstrate that the countries showing a high corruption rate have also a higher rate of shadow economy. Later on, Buehn and Schneider (2009) also identified a positive relation between corruption and shadow economy. In case of Serbia, too, the study performed by Simonovic and Boskovic (2016, p. 117) shows the existence of a positive relation direct) between corruption and shadow economy. Simonovic and Boskovic find that corruption of public officials is identified as often being a method to mask the shadow economy or as implementation means. Whether the corruption comes from or precedes the shadow economy, the relationship between these two facts is directly corroborated. The higher the shadow economy rate is, the higher the corruption rate is and vice versa. The institutions eroded by corruption represent weak obstacles on the way of the criminal structure development. Further on, the authors explain that there exists a direct connection between corruption and shadow economy when the public officials ask for bribe using the blackmail and conditioning the economic entities in various ways. Similar results were obtained by Borlea et al. (2017) in their study based on a sample from the European Union over the analysed period 2005–2014. The empirical findings of this study also confirm the existence of a strong positive relation between corruption and shadow economy; so, a higher rate of corruption involves a higher rate of shadow economy. A second group of studies, but more limited than the first, (Dreher and Schneider 2010; Virta 2007) gathers documentary evidence of the indirect results between corruption and shadow economy. Thus, the study developed by Dreher and Schneider (2010), for the countries with high incomes, found out that the high rates of corruption correlate with low rates of shadow economy. Similar results are obtained by

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Virta (2007), who investigated the relation between corruption and shadow economy in different geographical regions of the world, based on the observation that the different types of bribe may have different consequences regarding the shadow economy. Virta (2007) theorized that such corrupted practices are different within the world regions, being very frequently found in some regions. Further on, Virta underlined that the bribery made to obtain public contracts has different consequences regarding the size of the shadow economy in comparison with the bribery practised for the tax diminution. A negative relation between corruption and shadow economy was found in the countries from the tropical zone because within this region the public officials’ bribery is usually practised to work in the official sector. As for the differences identified between the two groups of studies regarding the sign of the relation established between corruption and shadow economy, several studies offer explanations. (a) Some of the studies (Weber 2005; Mocan 2008; Bătrâncea et al. 2017) explained such differences in the light of the different methods of measuring the facts or of the utilization of different control variables. For instance, the study conducted by Bătrâncea et al. (2017) investigated the force of the relation between shadow economy and corruption using data collected in 193 countries and territories. They used as control variables (or moderating) judiciary independence, police service reliability, human development (reflected by education level, suitable medical assistance, and life standards), and business freedom. Their results suggest that a small shadow economy is associated with a low corruption level when the following conditions are met for the nations: they enjoy of jurisdictions free of any political influence; they enforce law efficiently while protecting social interests; they make significant progress regarding the human development (e.g. improvement of education, healthcare and living standards); or they adopt efficient regulations for business environment in order to reduce bureaucracy. In other words, a positive relation between corruption and shadow economy exists when the above control variables are considered. (b) Another category of studies (Choi and Thum 2005; Dreher and Schneider 2010; Virta 2007) found out that the relation between corruption and shadow economy depends on the economic or regional development level of a country. Dreher and Schneider (2010) noticed that in the countries where the incomes are low, the public goods provided by the official are less efficient than in countries with high incomes, and this is the reason why numerous entrepreneurs (such as the owners of restaurants, bars, or even bigger production companies) chose to pay a bribe for operating within the unofficial sectorial. Therefore, it was found out that in such countries with low incomes, the relation between corruption and shadow economy is positive as a result of the fact that as corruption increases, the shadow economy is also increasing. On the other hand, in countries with high incomes, the public goods are more efficient, and here, only the small companies show their option to give bribe to stay in the unofficial sector. In exchange, the big companies choose to bribe the public officers to obtain a contract concluded in the public sector (for instance in construction sector).

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Further on, this contract is developed in the official sector and not in the unofficial. For these reasons, in countries with high incomes, the relation between corruption and shadow economy was identified as negative, respectively; the high rates of corruption is correlated with the low rates of shadow economy. The relation between corruption and shadow economy seems to be controversial in the specialized literature and from the perspective of the causality direction, not only from the sign perspective (direct or indirect). Intuitively, it is obvious that corruption influences the shadow economy through a simple logic mechanism, and, namely, that for surviving in a shadow zone, the corrupted representatives of the power must be paid, so that they allow the uninterrupted shadow business of the entities. On the other hand, other studies (Buehn and Schneider 2009, p. 27) reveal that the influence of the shadow economy on corruption occurrence is considered more intense than reverse influence. They indicated that in practice, corruption is the most profitable business of the quasi-democratic forces, the shadow economy is their strongest social programme, and the racketeering1 is the most encouraged taxation method (Tomaš 2010). The tax payers must use a significant part of the avoided taxes for the corruption of the power exponents as specific forms of racketeering, which essentially means that, in order to survive in a shadow economy, they have to pay grey taxes. Referring to the relation between shadow economy and money laundering, we identified a much more limited number of studies (Alm and Prinz 2013; Unger 2013; Cunder 2015; Pedneault 2009), and it is attributed to the fact that the regulations about the money laundering at international level are relatively recent (after 1989). Thus, Alm and Prinz (2013) considers that the tax avoidance is related to the shadow economy. On the other hand, Alm and Prinz reveal that both facts are related to the money laundering, although this type of influence was less investigated in the literature. Alm and Prinz (2013) explain the connections existing between tax avoidance, shadow economy, and money laundering by the fact that “all the money earned in a way or another in the unofficial economy without paying taxes (the so called “dark money” or “illicit money”) must be brought back in the official economy for buying goods and services”. As a result, the authors mentioned above identified the existence of a close connection between shadow economy, tax avoidance, and money laundering. On the other hand, Cunder (2015) after reviewing the shadow economy and money laundering found out that elements of the shadow economy (trade of illegal weapons, illicit trade of drugs, prostitution and trade of human beings, piracy as well as the incomes generated by the less organized crime and fraud such as classic criminality, corruption, tax avoidance, incomes generated by gambling if gambling

1  Racketeeringis often associated to organized crime, consisting of the action of offering a dishonest service (“racket”) to work out a problem which otherwise would not exist if the entity which offers the service had not caused this problem. A usual example of racket would be when a group of people cut the tyres of a car in a certain street, and then, the same group offer “protection” to the car owners in exchange for a certain price.

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is illegal, smuggling) represent sources of money which the criminals must launder so that to be able to use them in the legal circuit to improve their living standard, for reinvestments etc. (Pedneault 2009). A similar approach is conducted by Unger (2013) who considers that, besides tax avoidance, money laundering, and the tax avoidance can be considered components of the shadow economy. To this aim, Unger (2013) refers to the definitions of the terms to clarify the concepts but also to overcome the problems associated with the measurement of such events. Thus, referring to the definition of money laundering in the United States (definition which includes the illegal employment of workers as a typical example of money laundering) Unger states that it is overlapping with the definition of the shadow economy. At the same time, money laundering is defined as the effort to hide from authorities the sources illegally obtained, and tax avoidance represents the effort to hide from authorities the sources legally obtained. In this context, the two facts (tax avoidance and money laundering) can be considered components of the shadow economy. An almost complete overlap of the two facts (respectively, the shadow economy and money laundering) is carried out by the study developed by Rădulescu (2010). Thus, the author mentioned that the shadow economy consists of certain activities which persisted in time, particularly the illegal employment, tax fraud, illegal obtaining of goods, drug trade, smuggling, money laundering, etc. Then, the author referring to the relation between shadow economy and money laundering considers that “in general, all the activities belonging to the shadow economy result in the cover up of the gains obtained from this process through different laundering methods”. In other words, Rădulescu evaluated a very close connection between the two facts: shadow economy and money laundering. However, the study developed by Unger (2013) assessed a differentiation of the two concepts required by the imposed by the geographical approach. Thus, the author underlines that, while the shadow economy is defined (Schneider 2005) at a country level (as part of the cross-border crime), money laundering, especially that resulted from organized crime, is associated to trans-border crime (Unger 2013). The relation between shadow economy, tax avoidance, and money laundering could be seen according with Fig. 1.2.

1.2  Measuring Instruments Peter Drucker stated that “if you can’t measure it, you can’t improve it”, referring to any fact related to management domain. The fight against economic and financial crime requires the knowledge of the way in which it can be measured as well as of the instruments used to this aim. Further on, we shall try to review the most used tools identified in the specialized literature concerning the measurement of corruption, shadow economy, and money laundering facts. Moreover, starting with the determination of some individual measures, we shall try to elaborate aggregate

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31

measures for the evaluation of the economic and financial crime size at the level of any country of the world.

1.2.1  Corruption Measurement An important source regarding the corruption measurement is the one offered by Transparency International. This international organization calculates and reports two important indicators referring to corruption in the world countries, namely: • Corruption perception index (CPI) aggregates the data of different investigations regarding the corruption perception by the public sector in 180 countries of the world. This index scores countries on how corrupt their governments are believed to be. It is elaborated on a scale from 0 (meaning highly corrupted) to 100 (meaning very clean). • Global corruption barometer (GCB) represents a public opinion poll measuring the corruption level in different sectors of individuals’ everyday life by assessing the general public attitude against corruption. Unlike the corruption perception index which represents an aggregated and more extended indicator, the global corruption barometer allows the measurement of corruption in different sectors. The global corruption barometer is established on a scale from 1 (meaning not corrupted at all) to 5 (indicating completely corrupted). The indicator is calculated since 2003 for more than 100 countries of the world. • The indicators CPI and GCB are not perfectly interchangeable, but the studies certify a correlation which varies between 0.44 and 0.76 (Carden and Veron 2010). Among other measures of the corruption elaborated by different international bodies, the following measures are presented: • Control of corruption, calculated as perception of population about the measure where the public power is exercised to obtain private gains. The indicator is calculated by the World Bank within the World Governance Indicators  – WGI (World Bank 2020). Control of corruption is calculated and reported on a scale from – 2.5 (weak) to 2.5 (strong) about the level of the perceived corruption. The world governance indicators among which there is control of corruption indicator, too, are calculated for more than 200 countries since 1996. • Irregular payments and bribery, diversion of public funds, favoritism in decisions of government officials. All these indicators are calculated by the World Economic Forum while undertaking the steps for elaborating the global scores which characterize the global competitiveness of world economies. The indicators range between level 1 (the worst) and 7 (the best) associated with the ­existing corruption level. The indicators have been calculated since 2006 for 137 countries.

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Fig. 1.2  Relation between shadow economy, tax avoidance, and money laundering. (Source: own processing)

Shadow economy

Tax avoidance

Money laundering

Source : authors’ s processing

1.2.2  Shadow Economy Measurement The measurement of the shadow economy size represents a difficult and challenging task. This happens because it is difficult to measure something which is actually unknown (Kirchler 2007). However, many authors answered such challenges. In this sense, the initiatives developed by Schneider and his collaborators are remarkable, and they carried out periodical measurements of the shadow economy level in different world states (Alm et al. 2004; Schneider and Klinglmair 2004; Schneider 2011, 2013, 2015). Following the review of the specialized literature in the field, three main categories of methods used to evaluate the shadow economy can be concluded, respectively, direct method, indirect methods, and model-based methods. (a) Direct methods are based on volunteer answers within the questioning technics which require the subjects to make declarations regarding their economic activities. Also, the direct methods may consist of the controls performed by tax authorities. Both types of direct methods (using the questionnaire or controls) are followed by extrapolation regarding tax avoidance in national economy. Kirchler (2007) considers that the direct methods should be considered estimations of a lower limit because it is unlike that the direct evaluation method identify all the shadow activities. Concerning the use of direct methods for the shadow economy, Schneider and Buehn (2016) have similar opinions. They refer to the defects inherent to all investigations as the main disadvantage of direct methods. The results depend at a great extent on the respondent wish to cooperate and most of the interviewed subjects hesitate to declare the fraudulent behaviour. Thus, the answers provide an uncertain reliability which makes difficult the calculation of a real estimation (in monetary terms) of the undeclared employment dimension.

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In conclusion, the main disadvantage of these direct methods (either using the polls or the tax controls) consists of their estimative nature. These methods comprise only partly the shadow economy activities and may represent only estimations regarding the determination of a lower limit of the shadow economy size. These methods are liable to underestimate the level of the shadow economy because it is very likely that the individuals do not declare during the polls what they intend to hide from authorities. (b) Indirect methods According to Schneider and Buehn (2016) opinions, there are five indicators which give the possibility to evaluate the shadow economy, such as: 1. The discrepancy between national expenditure and income statistics This method is based on the discrepancy between incomes and expenses. In national accountancy, the value of the incomes from GDP should be equal with the expenses value. The difference between the income indicators and the expenses ones can be used as indicator of the shadow economy measurement. 2. The discrepancy between the official and actual labour force This method is based on the difference between the official labour force and the real one. If assuming that the total participation of the labour force remains constant, then a decreasing official rate of participation would indicate that the individuals migrate to shadow economy activities. The method could show, as a weakness, that the differences of rate of participation may have other causes. For instance, the individuals can clandestinely work, and at the same time, they work in parallel in the official sector (Schneider and Buehn 2016). 3. Transactions approach This method is developed by Feige (1994). The method assumes the existence of a constant relation over time between the volume of transactions and official GNP. The discrepancy between the official GNP and nominal GNP (which is based on the total value of the transactions known from national economy) may indicate the dimension of the shadow economy. Thus, the GNP of the shadow economy can be calculated by subtracting official GNP from total nominal GNP. To obtain reliable estimations regarding the shadow economy, it should be an accurate value of the total volume of transactions. This could be difficult to apply for cash transactions because they may be also dependent on the banknotes durability from the point of view of quality of the paper they are printed on (Schneider and Buehn 2016). Even if such approach is attractive, the necessary empirical requirements to obtain reliable estimations are difficult to meet, and consequently, the application of this method can provide doubtful results (Schneider and Buehn 2016). 4. The currency demand approach This method assumes that the shadow activities involve cash transactions because the shadow transactions (or hidden ones) are carried out as cash payments so that

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they do not leave any noticeable traces for the authorities. Thus, the increase of monetary demand is considered an indicator of the shadow activity enhancement. The approach of the monetary demand shows some disadvantages, one of them consisting of the fact that not all the transactions of the shadow economy are settled in cash but also many other disadvantages (see Schneider and Buehn (2016) approaches to get a complete view). Despite these disadvantages, this method seems to be one of the most used methods in many countries to evaluate the shadow economy size. 5. The physical input (electricity consumption) method This method correlates the electric power consumption with the GDP value. The method was developed by Kaufmann and Kaliberda (1996) who considered the electric power consumption as being the “best physical indicator of the global economic activity (official plus unofficial)”. It is supposed that the increase of the electric power consumption is correlated with the increase of the GDP (official and unofficial). The difference between this proxy measurement for the global economy and the official GDP estimations shows an estimation of the unofficial GDP. This method seems to be very easy and attractive. In spite of these advantages related to the simplicity of calculations, the specialists much criticized it. One of these disadvantages would be the fact that not all the shadow economy activities require a significant amount of electric power (for instance, personal services). Also, many other power sources can be also used (gas, oil, coal, etc.). Another criticized issue regarding this method consists of the fact that there exist important differences or changes related to the ratio between power elasticity and GDP among the countries, as well as in time (Johnson et al. 1997). (c) The model approach This method is developed by Frey and Weck-Hanneman (1984) and takes into consideration the numerous causes of the existence and increase of the shadow economy resulting in multiple effects. The present approach uses the application of MIMIC techniques (i.e. estimations based on multiple causes and indicators). The method uses models of structural equations to estimate the unnoticeable activities starting from causes and indicators. The causes can be reflected by the tax burden, state regulation burden, the attitude against taxes, or tax morale (Kirchler 2007). It is expected that the shadow economy activities are bigger as the real and perceived tax burden is higher, the rate of economic activity regulation is higher, and the tax morale is lower. The indicators of the shadow economy activities can be reflected by the progress of monetary transaction (in cash) and, respectively, the decrease of the participation of the labour force in the formal sector. Following the approaches of Schneider (2015) and Medina and Schneider (2018) throughout this book, when we discuss the shadow economy, we refer only to the legal activities, but which are hidden from the public authorities, and thus, our calculations will not include the illegal activities, those regarding the own use and the informal ones. For the evaluation of the shadow economy level, we use the most

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35

recent database elaborated by Medina and Schneider (2018), where the shadow economy is calculated as percentage of the official GDP for 158 countries during the period 1991–2015. If we strictly refer to the measurement of the tax avoidance, component of the shadow economy, the specialized literature (Hanlon and Heitzman 2010; Winnie 2016; Hasan et  al. 2017; Gebhart 2017) consider the following indicators as the most used ones for tax avoidance measurement: (a) Effective tax rate (ETR). According to General Acceptable Accounting Principles (US GAAP), ETR is defined as a ratio between total expenses incurred by the taxes (both current and deferred tax expenses) related to the pretax incomes. (b) Cash effective tax rate (CETR). The CETR is calculated as a ratio between the cash taxes paid and pre-tax incomes. According to Dyreng, Hanlon, and Maydew (2010), the ETR reflects the fiscal practices which reduce the tax expenses for the financial reporting purpose, and the CETR reflects the fiscal practices which reduce the effective taxes paid in cash. Based on the comparison of the values obtained for the ETR and CETR indicators with the applicable tax rate, there are obtained the indicators regarding the tax avoidance manifestation. Thus, if the ETR and CETR values are below the statutory tax rate, this could signal the avoidance of tax payment, respectively, the tax avoidance.

1.2.3  Money Laundering Measurement As for the steps undertaken to measure the money laundering level, these steps are in an early stage (Unger 2013) and are considered a very difficult task (Ardizzi et al. 2014), at least in the light of the following reasons: • The money laundering crimes are difficult to notice so that the resulting gains pumped in the legal financial system can be only estimated (Unger 2013; Ardizzi et al. 2014). • The secret nature of these illegal activities (Vaithilingam and Nair 2009; Vaithilingam et al. 2015). • The regulations concerning the definition of the money laundering are different in various countries so that these measures are incomparable by definition (Unger 2013). For instance, the undeclared work is considered money laundering crime in the United States, while in Germany and the Netherlands, this is not valid. The light drugs such as hashish, marijuana, as well as prostitution are legal in the Netherlands while in many other countries, they are illegal. As a result, the comparison of money laundering volume between the countries is difficult to make because of the measuring basis which is much different (Unger 2013).

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The specialized literature identifies relatively few methods for money laundering measuring, among which we mention the followings: A. As estimated percentage of money laundering reflected in the gross domestic product. In this sense, Camdessus (1998) quoted by Unger (2013) estimate that the volume of money laundering is estimated at a percentage of 2–5% of the GDP.  Referring to the money laundering measurement as percentage of the GDP, the literature considers that this method is extremely empirical as long as it is not scientifically substantiated (Walker and Unger 2009). A detailed presentation of the estimated volume of money laundering at the level of the world countries, calculated as percentage of the GDP for the year 2009 can be referred to in the ECOLEF project of European Commission (2013). B. Based on the estimation of gains from cranes at world level. For instance, it is often estimated that a percentage of 70–80% of the incomes resulted from drugs need to be laundered, while the rest is re-used in criminal activities. The advantage of using the incomes obtained from the data concerning the drug crimes is the fact that it is based on well-enough developed measurements of the drug production (Unger 2013). C. Using certain models taken over from the most known models for the evaluation of the shadow economy (method based on currency demand, method based on national statistics regarding the expenses and incomes (Tanzi 1999; Schneider and Windischbauer 2008; Unger 2013). For instance, the method based on the national statistics regarding the expenses and incomes is used by the World Bank. Thus, the difference between expenses and incomes reflect an estimation of the amounts obtained in an illicit manner, not only of the amounts obtained from money laundering. Even so, such a method offers extremely fluctuating results (Unger 2013), which put in question the credibility of such a method. D. Using certain economic models (Walker and Unger 2009; Zdanowicz 2009; Baker 2005; Schneider and Buehn 2016; Medina and Schneider 2018; Bagella et al. 2009). Applying these models for the evaluation of the money laundering fact, there are obtained conflicting results (certain models show a fluctuating evolution, others indicate an increase and others a decrease) (Unger 2013). E. Using a score which measure the risk of money laundering (Brettl and Usov 2010; Walker and Unger 2009; Dawe 2013; European Commission, ECOLEF 2013; Savona and Riccardi 2017; Basel Institute on Governance 2020). Among the most important studies which focused on the elaboration of an indicator of the risk of money laundering, there can be mentioned Brettl and Usov (2010) and Walker (2011). Brettl and Usov (2010) calculate the so-called threat indicator of money laundering, representing the threat rate for a country to become a target for money laundering in comparison with the other countries. Consequently, the threat is not represented here, by the volume of dark money which could be engaged in laundering operations in a certain country, but by the dimension of the threat indicator also calculated in relation with the other countries. The threat indicator is determined as a weighed arithmetical mean of the identified variables (TSk)

1.2  Measuring Instruments

37

contributing to the threat and the relative importance granted to them (Wk), using the formula: n



Threat indicator = ∑ ( TSk ∗ Wk ) k =1



The Brettl and Usov’s indicators (Brettl and Usov 2010) used a number of 35 threat variables (n = 35), grouped in six domains as follows: • Economic (GDP per capita, economic stability, trade with services, financial sector development, population and economic globalization, etc.) • Government condition (government corruption and attitude) • Application of the law and lawful environment (rule of law, banking secrecy, exchange rate control, etc.) • Social and technological changes (social globalization) • Criminal environment (global peace indicator, terrorism, thefts, etc.) • Special components and access (number of banks, cash utilization, casinos/gambling, gift cards, language, culture etc.) The main advantage of Brettl and Usov (2010) indicator consists of the fact that it takes into account different types of money laundering (from white collar crimes or drug crimes). Also, the Brettl-Usov method is based on a much more simple calculation, and it does not require a set of data available at world level (like in Walker approach) to obtain results (European Commission 2013, p. 57). In this section referring to the measurement of the money laundering as risk of money laundering, there have to be also mentioned the steps undertaken by The Financial Integrity Group of the International Monetary Fund (IMF) for the elaboration of a methodology by means of which the tendency rate of a country to become a target for the money laundering is determined. The methodology elaborated by The Financial Integrity Group consists of the utilization of a risk function between threats, vulnerabilities, and consequences (European Commission 2013, p.  55). Unfortunately, they have not obtained yet relevant results regarding the measurement of the money laundering fact in the world countries (Dawe 2013). Referring to the measurement of the money laundering as a risk of money laundering, we welcome the initiative of Basel Institute on Governance to elaborate such an indicator. The argument for this choice consists of the fact that there exist reliable quantitative data available in relation with the money laundering. For these reasons, such an indicator is meant not to measure the fact itself, but rather the risk of money laundering. More exactly, such an indicator does not measure the real existence of money laundering activity or of illicit amounts of money from a country, but it measures the risk rate, namely, the existing vulnerability regarding the engagement in illegal activities of money laundering and terrorism funding (Basel Institute on Governance 2020). The Basel Anti-Money Laundering (AML) indicator measures the risk of money laundering and terrorism funding in more than 129 countries of the whole world.

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This indicator is based on 14 aggregated indicators collected from sources available for the public, and they are grouped in five categories as follows: • Quality of the regulatory framework regarding the money laundering and terrorism funding (65%) • The risk regarding corruption (10%) • Transparency and reporting standards (15%) • Transparency in the public sector and responsibility (5%) • Political and judicial risk (5%) The risk of money laundering ranges between 0 (low risk) and 10 (high risk) for money laundering and terrorism funding. The Basel AML indicator regarding the risk of money laundering is relatively recent, and it was first published in 2012 by Basel Institute on Governance. Advantages of Basel AML indicator As it is a relatively recent method, this method of evaluation of money laundering fact has not been yet much known in the specialized literature. But, considering it is based on a relatively large number of indicators calculated and reported by international bodies recognized worldwide, we estimate that the calculated values of such indicators should reflect a volume of money laundering as real as possible. Another advantage of this indicator consists of the fact that it submits free of charge public reports, on annual basis, accessible to all the stakeholders.

1.2.4  Assessing an Economic and Financial Crime Index We propose further on a courageous and innovative endeavour to measure the volume of the economic and financial crime at a country level starting with the three components of the economic and financial crime which we take into account, namely, corruption, shadow economy and money laundering. To this aim, we shall use the available data utilized to measure these facts presented in the previous chapters, as follows: 1. Referring to corruption measurement (C), we use the corruption perception indicator (CPI), aggregating the data from different investigations regarding the corruption perception of the public sector in different countries in the world. The indicator is elaborated on annual basis on a scale from 0 (meaning very corrupted) to 100 (very clean), for 180 countries since 1995. 2. As for the measurement of the shadow economy (S), we use the database elaborated by Medina and Schneider (2018), where the shadow economy is calculated as percentage of the official GDP for 159 countries during the period 1991–2015; 3. For measuring the money laundering (L), we use the Basel AML indicator (Basel Anti-Money Laundering indicator) which measures the risk of money launder-

1.2  Measuring Instruments

39

ing and terrorism funding in more than 129 countries from worldwide since 2012. Combining these three components, we build an integrated index as the economic and financial crime index (CSL). All the three components have been normalized by using global minimum and maximum in the entire period and added giving equal weights. We assume that higher the value of the index, the higher would be the economic and financial crime exposure. We use the normalization data of corruption, shadow economy, and money laundering indicators, in order to obtain comparable values. Normalization is used to scale the data between 0 and 1. It is defined as

Yi = Xi _ n = [ Xi − Xmin ] / [ Xmax − Xmin ] ,



where Xi is the original value of variable X for the country “i”, Xmin represents the minimum value of X, and Xmax represents the maximum value of X. So, the original Xi values converts to the new Yi values which represents the normalized data of Xi (Xi_n) ranging between 0 and 1 (Han et al. 2011). Therefore, the CSL index is based on the following formula:

CSLi = ( Ci _ n + Si _ n + Li _ n ) / 3,



where: –– CSLi is the economic and financial crime index for the country “i” –– Ci_n is the normalized level of corruption for the country “i” (between 0 and 1) –– Si_n is the normalized level of shadow economy for the country “i” (between 0 and 1) –– Li_n is the normalized level of money laundering for the country “i”(between 0 and 1) The normalized levels of corruption, shadow economy, and money laundering of each country from the sample (for which all the data are available) are calculated taking into account their maximization direction. To this aim, the corruption perception index (CPI) is an indicator indirectly influencing the final index of the economic and financial crime (the higher values of CPI index reflecting a lower level of corruption). The other two indicators regarding the measurement of the shadow economy and the risk of money laundering represent the indicators with direct influences (the high values of these indicators reflect increasing values of the economic and financial crime). The determination of the normalized levels (between 0 and 1) for the corruption, shadow economy, and money laundering is based on the adapted formulas from below: (a) Ci_n is the normalized level of corruption for the country “i” which is determined as follows:

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

Ci _ n = ( Cmax − Ci ) / ( Cmax − Cmin ) ,



where: –– Ci reflects the corruption level (CPI levels) for the country “i” –– Cmin reflects the minimum level of CPI for the sample countries –– Cmax represents the maximum level of CPI for the sample countries Thus, the normalized level of corruption (Ci_n) ranges between 0, that is the lowest level of corruption and 1 representing the highest level of corruption. (b) Si_n is the normalized level of shadow economy for the country “i” which is based on the formula:

Si _ n = ( Si − Smin ) / ( Smax − Smin )



–– Si represents the level of shadow economy (as percentage of the official GDP) for the country “i”. –– Smin represents the minimum level of shadow economy (as percentage of the official GDP) for the sample countries. –– Smax represents the maximum level of the shadow economy (as percentage of the official GDP) for the sample countries. The normalized level of shadow economy (Si_n) ranges between 0, that is, the lowest level of shadow economy and 1 representing the highest level of the shadow economy. (c) Li_n is normalized level of money laundering for the country “i” following the formula:

Li _ n = ( Li − Lmin ) / ( Lmax − Lmin )



–– Li represents the level of Basel AML indicator for the country “i”. –– Lmin represents the minimum level of Basel AML indicator for the countries of the sample. –– Lmax represents the maximum level of Basel AML indicator for the countries of the sample. The normalized level of money laundering (Li_n) ranges between 0 that is the lowest level of Basel AML indicator and 1 representing the highest level of Basel AML indicator. The economic and financial crime index (CSL) will range between 0 that is the lowest level of the economic and financial crime and 1, representing the highest level of the economic and financial crime. We shall use this indicator in the following chapters to measure the economic and financial crime across different countries.

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41

1.3  Practical Approaches 1.3.1  Corruption in the European Union countries Further on, we propose to present some descriptive statistics regarding the level of corruption for the European Union countries during the period 2005–2015. Methodology To calculate the corruption level, we shall use the data offered by Transparency International regarding the Corruption Perception Index (CPI) about the corruption perception. In our study, the level of corruption is calculated as top position occupied by a country of the total 180 countries taken into account within the sample. The higher the ranking is, the higher the level of corruption, and the lower the ranking is, the lower the level of corruption, respectively. Also, we intend to investigate a space approach of the corruption level analysis among the European Union countries. To this aim, we shall use the classification of the countries belonging to the European Union (28 countries) by the four regions of Europe (Table  1.4) in accordance with the classification by regions provided by (2020). Results The Graph 1.1 reveals the top of the EU countries highlighting their ranking within the world top. Further to the review of the data from Graph 1.1, it is noticed that Romania, for the reviewed period, faces the highest level of corruption in the public sector among the European Union countries ranking the 71st place of the 180 countries considered in the study. Very high levels of corruption are found in Bulgaria, Greece, and Croatia. Opposed to them, registering the lowest level of corruption in European Union, there are found the Northern countries, namely, Denmark, Finland, Sweden, and the Netherlands. As for the dynamics for the period 2005–2015, the average level of corruption in the European Union shows insignificant movements (Graph 1.2). Between 2005 and 2011, we notice a trend of corruption level increase, a maximum value being reached in 2011, and after that the measures adopted to reduce this fact proved a higher efficiency so that the average level of corruption within the European Union countries indicated a descending trend. Referring to Romania, a continuous trend of corruption diminution is noticed so that for the period analysed, Romania dropped 27 positions in the top of the countries classified based on corruption level (from position 85 in the top of the countries classified based on corruption level, in 2005 to the 58 position reflecting a lower level of corruption in 2015). However, the corruption level registered in Romania exceeds by far the average value of the European Union countries throughout the reviewed period. The Graph 1.3 indicates an extremely obvious variation of the corruption level by the four geograph quadrants of the European Union. The highest level of corruption is found, in average in the Central and East Europe countries (51), followed by

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

the South Europe countries (43). The countries of the Northern Europe indicate the lowest level of corruption (3).

1.3.2  Shadow Economy in the European Union Countries We propose further on to analyse the levels of shadow economy among the European Union countries during the period 2005–2015. Methodology The level of the shadow economy is expressed in percentage as weight of the shadow economy in the GDP, as provided by the database calculated by Medina and Schneider (2018). To present the levels of shadow economy in the European Union countries, we used the descriptive methods, analysis, and synthesis. Also, as we performed in the previous chapter, we adopt a space approach of the level of the shadow economy within the European Union countries, too. To this aim, we grouped the 28 countries of the European Union in four geograph zones, respectively countries from Central and Eastern Europe (CEE), countries of Northern Europe (North), countries of Southern Europe (South), and Western Europe (West) (Table 1.4). Results and discussions The results obtained reflect that the level of the shadow economy within the European Union countries is 18% at average (as percentage of the GDP), which shows that at average about one fifth of the European Union GDP is lost because of the shadow economy, for the period analysed. The highest levels of shadow economy are found in Cyprus (32%), Malta (29%), and Romania and Greece (26%). Opposed to them, there are Austria and the Netherlands (9%), Germany, Great Britain, and Luxembourg (10%) (Graph 1.4). The Graph 1.5 indicates the existence of a significant variation of the level of the shadow economy by the four geograph quadrants of the European Union. The ­highest level of the shadow economy is found in the countries from the Southern Europe (about 26%), followed by the countries from Central and Eastern Europe (about 20%). The countries from Western Europe indicate the lowest level of the shadow economy (about 12%), then followed by the countries from Northern Europe (about 14%). The differences found in the four regions regarding the level of the shadow economy raised questions about the specific causes occurred at the countries level, economic, political, legal causes (rate of economic growth, institutional quality, regulation quality, tax pressure), but also the social and cultural ones (culture, tax morale, religion, etc.). The Graph 1.6 reveals the evolution of the shadow economy level at average, within the European Union over the time span 2005–2015. At the European Union level, the volume of the shadow economy, at average, (expressed as percent in GDP) indicates a general decreasing trend from a maximum value of about 20% found in the year 2005 to a minimum value of about 17% in the year 2015. Romania revealed

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43

Table 1.4  Classification of EU countries by regions No. 1 2 3 4

EU regions North of Europe South of Europe West of Europe

Countries of the European Union (28) Denmark, Finland, Sweden (3 countries) Cyprus, Greece, Italy, Portugal, Spain, Malta (6 countries) Austria, Belgium, France, Germany, Ireland, Luxembourg, Netherlands, Great Britain (8 țări)) Central and East Bulgaria, Croatia, Czech Republic, Estonia, Latvia, Lithuania, Poland, Europe Romania, Slovakia, Slovenia (11countries)

Source: EuroVoc (2020)

levels of the shadow economy much higher than those of the European Union average value throughout the period of analysis. Despite it, in Romania, the general level of the GDP lost in the shadow activities is reduced with the time, from about 30% in the year 2005 to about 23% in the year 2015.

1.3.3  Money Laundering in the European Union Countries Further on, we propose to analyse the money laundering fact within the European Union countries. Methodology We shall measure the money laundering fact using the AML (Basel Anti-Money Laundering) index which evaluates the risk of money laundering and terrorism funding. The Basel AML index has been calculated since 2012. There are available data in this sense, for the period 2012–2017, and this is why we took into account this period for our analyse. For presenting the risk of money laundering in the European Union countries, we used the descriptive methods, analysis, and synthesis. Also, like we did in the previous chapter, we want to adopt also an approach by European Union four geographical areas (see Table 1.4). Results and discussions From the Graph 1.7, it is noticed that the highest risks of money laundering occurrence are found in Greece, Luxembourg, Germany, and Austria, while the lowest risks of money laundering are found in Finland, Estonia, Slovenia, Lithuania, and Bulgaria. Romania occupies a middle position in the top of the countries classified based on the risk of money laundering (position 14/28). Our results are in line with those of the study developed by the European Commission (2013) within the ECOLEF Project (2013, p. 13) which also finds out that in Luxembourg, Great Britain, and other Western countries of Europe there are found the highest money laundering activities (expressed in absolute sizes of money laundering). Among the reasons mentioned in this sense, the high rate of the financial market sophistication, the economic growth reflected as GDP/capita, but also the cultural influences could be mentioned.

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

The Graph 1.8 shows the average value of the evolution of the risk of money laundering in the European Union countries between 2012 and 2017. At the European Union level, based on the adoption of measures for fighting against the money laundering, the risk of money laundering indicated a decreasing trend, from a maximum value of 4.73 registered in the year 2012 to a value of 4.52 in the year 2017. Referring to the position occupied by Romania, we have noticed that only in 2014 and 2015 the risk of money laundering was higher than the values existing in the European Union; in all the other years this level of this risk was below that registered in the member countries. The Graph 1.9 presents the low-enough variations of the risk of money laundering on the four geograph quadrants of the European Union. The highest risk of money laundering is found in the countries of the Southern Europe (5 points) followed by the countries of the Western Europe (4.89 points). The countries of the Northern Europe indicate at average, the lowest level of the risk of money laundering (3.76 points).

1.3.4  R  elation Between Corruption, Shadow Economy, and Money Laundering. Empirical Approaches We propose, further on, to investigate the relation between corruption, shadow economy, and money laundering based on the data available worldwide for the period 2005–2015.

2 3 4

18 18 14 14 17 8 11

22

32 32 27 30 31

37

51 55 46 46 48 49

59 63

68 70 71

34

Denmark Finland Sweden Netherlands Luxembourg Germany United Kingdom Austria Ireland Belgium France Estonia Spain Portugal Cyprus Slovenia Malta Hungary Lithuania Poland Czech Republic Latvia Slovakia Italy Croatia Greece Bulgaria Romania Average

100 90 80 70 60 50 40 30 20 10 0

Source: own processing Graph 1.1  Corruption in European Union countries, 2005–2015. (Source: own processing)

1.3  Practical Approaches 90

85

80

45

84

70

71

70

69

75

69

69

66

60

69 58

50 40 32

30

32

35

33

32

37

35

37

36

34

30

20 10 0

2005

2006

2007

2008

2009

EU Average

2010

2011

2012

2013

2014

2015

Romania Average

Graph 1.2  Corruption trend in the European Union countries, 2005–2015

60 51 50

43

40 30 20

15

10 0

3 CEE

North

South

West

Source: own processing Graph 1.3  Corruption in the European Union countries by regions, 2005–2015. (Source: own processing)

Methodology The level of the shadow economy is expressed in percentage as ratio of the level of the shadow economy in the GDP according to the database provided by Medina and Schneider (2018). To calculate the level of corruption, we shall use the data offered by Transparency International regarding the corruption perception index (CPI). In our study, the level of corruption is calculated as position occupied in the top by a country of the

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

total 180 countries taken into account of the sample. The higher the position occupied, the higher the corruption level is and, respectively, the lower the position in the top, the lower the corruption level. The level of the money laundering is determined using the Basel AML (Basel Anti-Money Laundering index) which measures the risk of money laundering and terrorism financing. In order to study the relation between corruption, shadow economy and money laundering, we have used the descriptive methods, the correlation coefficients, and the regression analysis, and we have carried out the statistics tests necessary to ensure results with high accuracy. The statistics processing are performed using the SPSS statistic software. Based on the specialized literature review, it can be concluded that rather the corruption deeds are influencing the level of the shadow economy. Intuitively, this is correct because first, the bribery of the public officers occurs in order to avoid the official economy (which requires the tax payments and, then, to ensure the unofficial functioning). Here is the formulation of the working hypothesis: Hypothesis 1: An increase of the corruption level results in an increase of the shadow economy. Further on, an increasing level of the corruption is expected to result in an increase of the risk of money laundering in the light of the fact that a poor control of corruption in the state institutions, including at the level of the banking authorities of surveillance leads to a high risk of not identifying the suspect transactions, and thus, the risk of money laundering increases. The proposed working hypothesis for analysis is: Hypothesis 2: An increase of corruption results in an increase of the risk of money laundering. At the same time, the shadow activities generate large amounts of money which requires a “laundering” stage so that to be able to introduce them within the legal financial circuit (Cunder 2015; Pedneault 2009; Unger 2013). In conclusion, it is expected that an increase of the shadow economy would result in an increase of the risk of money laundering. Thus, we formulate the following working hypothesis: Hypothesis 3: An increase of the money laundering rate leads to the increase of the shadow economy. Results and discussions To test the three working hypotheses for the beginning, we calculate the correlation coefficients of corruption, shadow economy, and money laundering. From Table 1.5, we find out that there is a medium to strong significantly statistically relation (between 0.4 and 0.7), considering a significance threshold of 1%, between the three facts. In other words, our results reveal that corruption, shadow economy, and risk of money laundering represent facts strongly correlated in-between.

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35%

32%

30% 25%

22%

20% 15% 10%

8% 9%

10%10%

12%

13%

14%14%14%

15%

16%17%

18%

23%

24%25%25%

26%26%

27%

29%29%30%30%

20%

19%19%

5% 0%

Source: own processing Graph 1.4  Shadow economy (% in GDP) in European Union countries, 2005–2015. (Source: own processing)

30%

26%

25% 20%

20%

13%

15%

11%

10% 5% 0%

CEE

North

South

West

Source: own processing Graph 1.5  Shadow economy in the European Union countries by regions, 2005–2015. (Source: own processing)

Testing hypothesis 1: An increase of the corruption level results in the increase of the shadow economy. The Graph 1.10 shows a correlated arrangement of the corruption and shadow economy at an R squared = 0.456. In other words, a percentage of 45.6% of the

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35 30

30

29

27

25 20

20

18

17

15

28

25

27

19

17

19

25

25

18

18

24 18

23

23

17

17

10 5 0

2005

2006

2007

2008

2009

EU Average

2010

2011

2012

2013

2014

2015

Romania Average

Source: own processing Graph 1.6  Evolution of the shadow economy (% in GDP) in the European Union countries, 2005–2015. (Source: own processing)

shadow economy variation can be explained by the average corruption level of the sample countries for the investigated period. The regression coefficient of the shadow economy in relation with corruption is positive (a value of 0.174) and significant at a significance threshold of 1%. Thus, for an increase by one point of the corruption level, there is at average an increase of the shadow economy by 0.174 points (Table 1.6). A close correlation between the two variables (at a correlation coefficient of c = 0.675) can be also noticed. Considering these results, we can conclude that the hypothesis 1 is accepted at the level of our sample so that we can empirically document that an increase of corruption level results in an increase of the shadow economy. Testing hypothesis 2: An increase of corruption level results in the increase of the risk of money laundering. Referring to the hypothesis 2 testing, the Graph 1.11 and Table 1.7 show a direct significant influence of corruption on the risk of money laundering, at an R squared = 0.431. Thus, a percentage of 43.1% of the variation of the risk of money laundering can be explained by the corruption level, at average, in the sample countries for the investigated period. The regression coefficient of the risk of money laundering in relation with the corruption is positive (value of 0.016) and significant at a significance threshold of 1%. Also, the two variables are strongly correlated (at a correlation coefficient c = 0.657). In conclusion, we consider that the research hypothesis 2 is accepted (at a significance threshold of 1%), respectively, at the level of the analysed sample. We found out that the increase of the corruption level leads to the increase of the risk of money laundering.

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Finland Estonia Slovenia Lithuania Bulgaria Sweden Malta Denmark Portugal Hungary Belgium Poland Ireland France Romania Czech Republic Croatia United Kingdom Slovakia Latvia Cyprus Netherlands Spain Italy Austria Germany Luxembourg Greece average

10 9 8 7 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 5 4 4 4 4 3 4 3 3 2 1 0

Source: own processing Graph 1.7  Money laundering in the European Union countries, 2012–2017. (Source: own processing)

Testing hypothesis 3: An increase of the level of money laundering leads to the increase of the shadow economy. The Graph 1.12 shows a correlated arrangement (at an average correlation coefficient c  =  0.446) of the risk of money laundering and shadow economy at an R squared = 0.199. The regression coefficient of the risk of money laundering is positive (4.676) and statistically significant at a significance threshold of 1% (Table 1.8). As a result, the research hypothesis 3 is accepted, which is an increase of the risk of money laundering leads to an increase of the shadow economy. In conclusion, we consider that investigation hypothesis 3 is accepted (at a significance threshold of 1%), respectively, at the level of the analysed sample, and we documented that an increase of the risk of money laundering leads to an increase of the level of the shadow economy. Conclusions, limitations, and investigation directions The processing outlined in the present study clearly reveal the existence of a strong positive relationship between corruption, shadow economy, and risk of money laundering. All the three working hypotheses were accepted at a significance threshold of 1%. In other words, our study reflects that at an increase of the shadow economy level, an increase of corruption level lead to the increase of the risk of money laundering, and, respectively, an increase of the level of money laundering leads to an increase of the shadow economy. We consider that the present study regarding the identifying some relations between corruption and the three facts is only indicative and can be eventually used as starting point for more detailed studies. We certainly invoke some limitations of the study presented, among which such a limitation would be the usage of the variable means. Therefore, a panel analysis (which takes into account the series of data

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4.80 4.75

4.73

4.70

4.69

4.68

4.65

4.68

4.61

4.60 4.55 4.50

4.49

4.45

4.58

4.58

4.47

4.46

4.55 4.50

4.40 4.35 4.30

2012

2013

2014

EU Average

2015

2016

2017

Romania Average

Source: own processing Graph 1.8  Evolution of money laundering in the European Union countries, 2012–2017. (Source: own processing) 10 9 8 7 6 5 4 3 2 1 0

4.29

3.76

CEE

North

5.00

4.89

South

West

Source: own processing Graph 1.9  Money laundering in the European Union countries by regions, 2012–2017. (Source: own processing)

followed in time and space) would much better catch these influences. Another important limitation is represented by the fact that for the regression used, we did not use control variables. Thus, to get a much higher accuracy of the results, it is necessary that the relations between the three phenomena were analysed surprising the moderating effects of more variables such as level of economic development, tax pressure, public governance, etc.

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1.4  Determinants of the Economic and Financial Crime As seen on the previous chapters, the economic and financial crime is part of the society being in a close connection with its evolution and growth. Regarding corruption, the study of Transparency International (2020) reveals that a quarter of the African’ s population pays bribe for public services such as health care and education. In this view, the chief of International Monetary Fund cited by Greenhalgh (2016) says that 2% of global gross domestic product is annually paid in bribes meaning about 1.5–2 trillion USD per year around the world. As for the size of shadow economy, the gross domestic product of European countries is underreported by 19% because of unrecorded shadow economy activities (Achim et  al. 2019). According to estimates from Global Financial Integrity (2019), the illicit financial flow over the 2006–2015 time periods gets to over 20% of developing countries’ trade with advanced economies, on average. Thus, despite all the efforts made to combat the level of economic and financial crime, it remains a long-standing problem. Under these circumstances, the political decision-makers need to aware the causes they may create incentives for engaging in economic and financial crime activities, in order to adopt an effective fight. Therefore, the economic and financial growth, the economic crisis, globalization, the technical and scientific revolutions, the government policies, the law system, as well as the social and cultural imprint contributed to the occurrence and growth of the economic and financial crime throughout the time. The issues concerning the economic and financial crimes as well as the explanatory factors are largely discussed in the specialized literature. The approaches may be general (Durkheim 1974; Aniței and Lazăr 2016; Leția 2014), generally referring to the causes of the economic and financial crime occurrence, or they may be specific (Tanzi 1998; Melé 2014; Duțulescu and Nișulescu-Ashrafzadeh 2016; Bucur 2011; Schneider and Williams 2013; Feld and Schneider 2010; Schneider and Buehn 2016; Chong and López-de-Silanes 2015; Schwarz 2011), dealing separately with the specific causes of certain forms of the economic and financial crime (corruption, shadow economy, money laundering, etc.). As for the causes invoked by the general approaches regarding the economic and financial crime identified in the specialized literature, we present further on, a synthesis of some such causes: • The study conducted by Aniței and Lazăr (2016, p. 16) reveals that the economic growth and economic crisis are causing high social changes, stimulating the criminals through the tide effect (created by suspending legislation and the time elapsed until the promulgation of a new legislation). In this sense, a study conducted by PricewaterhouseCoopers (2018) identified the existence of a close relation between the economic development level of a country and the fraud occurrence. It highlighted that in the developing countries, a percentage of 15% of the investigated financial societies expect a significant increase of the sources allocated to investments in order to find out the fraud during the following 2 years, compared to only 9% noticed in the developed countries.

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• In a direct relationship with economic development, a higher level of technology leads to a smaller size of economic and financial crime. Thus, the findings of Bird and Zolt (2008), Elgin and Oyvat (2013), Immordino and Russo (2018), Goel et al. (2012), Okunogbe and Pouliquen (2018), Slemrod (1990), and Suh et al. (2018) also find a negative relationship between technologies and various types of economic and financial crime. For instance, Slemrod (1990) and Bird and Zolt (2008) find that higher technologies conduct to higher efficiencies in tax administration systems further reducing the level of tax evasion. For European countries, Immordino and Russo (2018) find that VAT evasion reduces as the payments with debit and credit cards increase and with an increase of cash withdrawals. Regarding tax collection, Okunogbe and Pouliquen (2018) find that adopting high technology under the form of e-filing conducts towards collecting double values of tax payments, and therefore the size of tax evasion decreases. Moreover, Elgin and Oyvat (2013) find that investments in internet usage reduce the size of the shadow economy. Regarding corruption as another form of financial crime, Slemrod (1990), Bird and Zolt (2008) and Goel et al. (2012) also find that technologies reduce the level of corruption by reducing the face-to-face interaction between taxpayers and taxing authorities. Regarding the prevention of money laundering, various authors (Amoore and de Goede 2005; De Goede 2008; Levi and Wall 2004; Sadgali et al. 2019; Zoldi 2015) find that investments in high technologies under the form of data mining, artificial intelligence, machine learning and risk profiling tools are used to track the flow of illicit funds in domains such as money laundering and terrorist financing, thus the money laundering is reduced. In addition, Suh et al. (2018) find that investing in ethical culture leads towards a reduction of occupation frauds in banks. However, there are another studies who find that the use of modern technologies does not only bring positive effects to the company but it has also attracted fraudsters and Table 1.5  Correlation coefficients of corruption, shadow economy, and money laundering

Corruption

Shadow economy

Risk of money laundering

Pearson correlation Sig. (2-tailed) N Pearson correlation Sig. (2-tailed) N Pearson correlation Sig. (2-tailed) N

Shadow Corruption economy 1 .675a

Risk of money laundering .657a

185 .675a

.000 158 1

.000 164 .446a

.000 158 .657a

158 .446a

.000 143 1

.000 164

.000 143

164

Source: own processing a The correlation is significant at a significance threshold of 1%

53

1.3  Practical Approaches R2Linear = 0.456

shadow economy

60.00

40.00

20.00

.00 00

50.00

100.00 Corruption

150.00

200.00

Source: own processing Graph 1.10  Correlation between corruption and shadow economy. (Source: own processing) Table 1.6  Regression of shadow economy depending on corruptiona

Model 1 (Constant) Corruption

Unstandardized coefficients B Std. Error 14.287 1.462 .174 .015

Standardized coefficients Beta .675

t 9.769 11.433

Sig. .000 .000

Source: own processing Dependent variable: Shadow economy

a

c­ riminals to misuse the technology for financial benefits under the form of cybercrime (Ali et al. 2019;Gogolin 2010; McAfee 2018; Ryman-Tubb et al. 2018). Thus, emerging high IT technologies greatly empowers criminals to misuse the technology for financial frauds. This way, cybercriminals can use a number of ways to commit crimes at a high distance, in another jurisdiction, hiding their identities and beyond the reach of any prosecutor (Ali et al. 2019). Furthermore, the cost of global cybercrime has increased from $445 billion in 2014 to $608 billion in 2017 (McAfee 2018). Moreover, credit card fraud causes significant financial losses to merchants and banks. According to Robertson (2016), the worldwide card fraud losses rose from $7.6 billion in 2010 to $21.81 billion in 2015, or 300% over 5 years. By 2020, global card fraud losses are expected to reach $31.67 billion. The proceeds of this fraud are known to finance terrorism, arms and drug crime (Ryman-Tubb et al. 2018).

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1  Economic and Financial Crime. Theoretical and Methodological Approaches 2

R Linear = 0.431

9.00

Risk of money laundering

8.00

7.00

6.00

5.00

4.00

3.00 00

50.00

100.00

150.00

200.00

Corruption

Graph 1.11  Correlation between corruption and money laundering Table 1.7  Regression of money laundering depending on corruptiona

Model 1 (Constant) Corruption

Unstandardized coefficients B Std. Error 4.623 .136 .016 .001

Standardized coefficients Beta .657

t 33.999 11.080

Sig. .000 .000

Source: own processing a Dependent variable: Risk of money laundering

• The globalization represents a factor encouraging the economic and financial growth, as well (Leția 2014, p. 16). Under this context, it is highlighted the role of the multinational as subjects of the economic-financial crime. The multinational societies follow the maximization of their profits at any cost, so that the differences of the legislation among the countries are used as “legal door to illegalities”. • The quality of regulations and the rule of laws (legislative system) are identified in the investigations conducted by Bucur (2011, p. 12) as being responsible for the growth of the economic and financial crime. The quality of regulations refers to the ability of the government to formulate and implement solid policies and regulatory acts allowing and promoting the private sector growth. On the other hand, the rule of laws reflect the supremacy of the law which should result in the balancing of the relations between it and the state (Puț 2015) or the extent to which the population shows confidence and comply with the society rules, own-

55

1.4  Determinants of the Economic and Financial Crime 2

R Linear = 0.199

shadow economy

60.00

40.00

20.00

00 3.00

4.00

5.00

6.00

7.00

8.00

9.00

Risk of money laundering

Source: own processing Graph 1.12 Correlation between shadow economy and money laundering. (Source: own processing) Table 1.8  Regression of shadow economy depending on the money launderinga Unstandardized coefficients Standardized coefficients Model B Std. error Beta t Sig. (Constant) .136 4.738 .029 .977 Risk of money laundering 4.676 .789 .446 5.925 .000 Source: own processing a Dependent variable: Shadow economy

ership rights, police and courts, as well as the probability of crime and violence. • The legislative system determines the engagement in committing economic-­ financial crimes under the following circumstances: “existence of a judicial system which is either incomplete or overwhelmed being constantly behind the business environment; exploitation of some legislative gaps which could encourage the crimes committing; abundance of continuously changing regulatory acts, making impossible the development of a correct economic activity even when good faith is present; inconsistency of the international business criminal law which is unclear and incomplete, stimulating the fraud committing; we refer here to the differences between the legal and political regimes of the countries, too, doubled by the existence of some contradictory regulations in banking, tax, commercial fields as well as the legal treatment applied” (Bucur 2011, p. 12).

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

• The political factors may also play an important role to enhance the economic and financial crime (Dumitriu 2017). For instance, individuals that developed their activity at law edge or even by breaching it obtaining high profits created then immunity and got power by “buying” political positions. Such politicians initiated and supported unhealthy legislative initiatives which offered them protection, but severely impacted the business environment weakening the ­ national economic power. Another example of economic and financial crimes generated by the political factors is represented by the criminal actions of illegal funding of the political parties, and most of the reasons aim at crimes of corruption or associated to other criminal activities (tax avoidance and money laundering). The individuals holding political positions take advantage of their influence and authority to obtain undue benefits for the party of which they are members. Such undue benefits may occur as supply of funded goods or services as follows: opinion poll, promotional stuff distributed during the election campaigns, food products (sugar, oil, meat, etc.) or even other type (medicines, cell phones, etc.) distributed to the voters as electoral bribery, artistic shows where singers or bands are performing, ensuring transport means for the party members or voters transport, etc. (Dumitriu 2017). • The same study conducted by PricewaterhouseCoopers (2018) revealed the role of the internal (managing) factors for the occurrence of economic crime. The study identified that a percentage of 59% of the financial companies investigated in the developing countries reported that the economic crimes are caused by internal factors while this percentage is more reduced, namely, 39% for the developed countries. • According to some authors (Leția 2014), the banking and financial institution soundness are in the front line of the fight against crimes in business, and this is due to the fact that the amounts of money obtained from crime committing transit through the accounts opened at the said institutions. In this sense, a significant attention has to be paid to the transparency of the banking and financial sector, to the monitoring of the transactions developed through the clients’ accounts, and to banking surveillance. • However, the psychology and sociology researchers from the judicial domain (Durkheim 1974, Merton 1968, etc.) revealed the major role played by the cultural factors in the fight against economic and financial crime. Thus, the sociologist Durkheim (1974) invoked regarding the occurrence of the crime fact, the social and cultural factors in direct relation with the economic growth. Durkheim introduced, for the first time, the term of anomy, as a consequence of the social division. In his opinion, the anomy represents the dislocation and deterioration of the collective conscience, morality diminution, and regulatory disorder. Thus, during the periods of quick social changes, the basic norms suspend their functionality, and the anomy condition leads to the increase of the criminal behaviour. Based on the same idea, Merton (1968) explains the anomy condition as the difference between the social structure and the cultural one, explaining that the society proposed to its members certain goals without providing them with the necessary means to achieve them. The individuals being unable to achieve the

1.4  Determinants of the Economic and Financial Crime

57

goals to which they aspired and which the society evaluates resort to abnormal actions and illicit means. • With the publication of Lynn and Vanhanen (2002) on IQ data for a large number of countries in the world, researchers started to pay attention to the role of intelligence in complying the law thus leading to a high institutional quality ­(Rindermann 2008; Potrafke 2012). In relation to this, several authors have tried to investigate the relationship between some components of economic and financial crime such as corruption (Potrafke 2012; and Lv 2017) or shadow economy (Čiutienė et al. 2015; and Salahodjaev 2015). They find that a high IQ population associates to less corruption and less shadow economy in those countries. For instance, Salahodjaev (2015) provides empirical evidence for the claim that intelligence is negatively associated with shadow economy. This is because intelligence offers prerequisites for understanding and accepting the government implements policies which is designed to reduce shadow economy. In the same view, Čiutienė et al. (2015) find a dependence between human capital and the power of the shadow economy for the case of Lithuania. Regarding other crime such as money laundering, to our knowledge, there is only a descriptive study of Lowe (2017) which argues for the need of predictive intelligence in the anti-­ money laundering fight. Based on the specialized literature review, a general presentation of the main causes of corruption is provided below: • Tanzi (1998) in his study conducted for the International Monetary Fund, he started with the argument of Gary Beckel, laureate of Nobel prize for economy, who stated that “if we abolish the state, we abolish corruption”, which in other words means that corruption would be directly related to the magnitude of the public sector. Tanzi (1998) is in contradiction with Beckel’s arguments with obvious realities reflected by the list of countries as, for instance, Canada, Denmark, the Netherlands, or Sweden where, at the same time, the level of corruption is low, but there is a high rate of the public sector (measured as percentage of the tax incomes or public expenses in the GDP). Tanzi considers that the way in which the state operates to finalize its tasks and offer goods and public services is much more important than the magnitude of the public sector. Thus, Tanzi (1998) presents several direct and indirect causes of corruption, both being related to the state functioning. • Among the direct causes, mention should be made on: –– Regulatory documents and authorizations (the way in which different authorizations are obtained considering that they require much time and numerous documents to be drafted and these issues are much diminished further to the favours directly granted by the officials after they accepted the reception of a compensation as bribery) –– Issues associated to taxation (when the law is unwieldly and difficult to understand by the tax payers, it could be interpreted in different ways so that the tax payers often need consultancy, the tax inspectors salaries are low, the

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

corruption actions of the tax authorities are ignored, or when they are found out the penalties are low etc.) –– Decisions regarding the public expenses (referring to the allocation of the investment projects to performers, the way in which the goods and services are often purchased at prices lower than the market price, etc.); –– Financing of the political parties from public funds. –– Among the indirect causes, the following should be mentioned: quality of bureaucracy, level of public sector salaries, institutional controls, regulations and law transparency, penalization methods, etc. • Domènec Melé, founder of Yese Business School University, made a classification of the ten determining factors of corruption which he considers applicable to a larger or smaller extent, to different cultural and geograph environments. These factors can be grouped in four categories as follows (Melé 2014): –– Personal factors (greed; degradation of personal ethic sensitivity, either because of poor education level or of a negative experience of learning; lack of civic feelings of the employees of either the public or private institutions (for instance the politicians who get involved with the political life to achieve their own goals and not because they have civic feelings); low level of consciousness or lack of courage to report corrupted behaviour and the situations leading to corruption) –– Cultural factors (culture environment where corruption is excused by defensive behaviours or even admiration shown to the criminals by statements like, for instance “one should be clever enough to avoid tax payments” or by rationalizing some no moral false arguments of the type “everybody does it” “take advantage of it as long as you can”, “life is short”; lack of transparency particularly at institutional level, but in other types of organizations, too) –– Institutional factors (inefficient rules and controls; slow court trials) –– Organizational factors (lack of moral criteria when somebody is promoted merely for his/her loyalty shown to the person who holds control or holds a responsibility position etc.; diminution or poor reaction against allegations of corruption creating a favourable environment for corruption perpetuation) • As for Romania, the study carried out by Duțulescu and Nișulescu-Ashrafzadeh (2016) identified among the main causes of corruption, the low life standard (compared to that of citizens from Western Europe), as well as the general conception of people, which proves to be permissive enough referring to this fact. Referring to the causes of shadow economy, some of them have been taken from the specialized literature: • A bibliometric approach regarding the determining factors of the shadow economy is carried out in the study conducted by Medina and Schneider (2018). Based on the literature review, the authors identified 13 factors determining the shadow economy, namely, 1. Tax burden; 2. Quality of institutions and corruption; 3. Quality of regulatory acts; 4. Quality of public services; 5. Tax morale;

1.4  Determinants of the Economic and Financial Crime

• •

•

• • •

•

•

59

6. Discouragement measures; 7. Growth rate of the official economy; 8. Entrepreneurial level; 9. Unemployment rate; 10. Volume of the agriculture sector; 11. Utilization of cash in economy; 12. Rate of the labour force; and 13. Economy growth (reflected by the GDP/capita or the economic growth rate). Tax burden, state regulatory act burden, attitude against the taxes or tax morale (Kirchler 2007). Quality of regulatory acts and rule of law (legislative system); The studies conducted by Johnson et al. (1997) and Enste and Schneider (2002) revealed results similar to those previously mentioned. So, they identified that, besides the tax pressure and tax morale, the level of economy regulation lead to higher levels of the shadow economy. On the other hand, a high regulation level of labour market or of goods market restricts the individual and entrepreneur’s freedom to act in the formal economy. Quality of institutions and bureaucracy; Different studies revealed the importance of ensuring a high confidence level in the governing institution to ensure a good state functioning (Kirchler 2007; Torgler and Schneider 2009; Torgler 2007; Park and Blenkinsopp 2011; Fritzen et al. 2014). The ineffective rate of ensuring public goods generates a low confidence in the authorities and determines the individuals and entrepreneurs to undertake shadow activities in order to obtain more benefits more quickly. Public governance, level of economic growth and happiness of a people (Achim et al. 2018). Burden of taxes and social security contributions, quality of institutions, regulations, services offered by public sector, official economy growth, independent activities (the freelancers) (Schneider and Buehn 2016) In a study carried out in Serbia (a country where the shadow economy and corruption levels are high), on a representative sample constituted of 1250 companies and entrepreneurs the following main causes of shadow economy were identified: unsuitable tax policy particularly in the labour field, ineffective and selective labour force, high administrative burden in conducting businesses, poor quality of regulations, high level of corruption and low level of tax morale, low confidence in the state and public institutions, and existence of a high weight of cash payments of transactions by total payments (Simonovic and Boskovic 2016). Among these causes, corruption is indicated as the fourth most important cause of the shadow economy in Serbia (Simonovic and Boskovic 2016). It is also in Serbia that a comprehensive classification of the shadow economy causes is carried out by Marinkovic (2005) quoted by (Simonovic and Boskovic 2016, p. 116) including: causes generated by the political and economic system, causes generated by the local and regional specific features, causes generated by traditions, culture, life style, historical heritage, (lack of social representations, generalization of theft from state fortune), causes generated by the personal characteristics of a people and the individual value system (for instance, inclination towards risk). Çule and Fulton (2009) noticed that the diminution of shadow economy and corruption is a complex fact implying the business culture, social expectations, and

60

1  Economic and Financial Crime. Theoretical and Methodological Approaches

political considerations, so it is not only about the legal issues for the amendment of the tax regulations. • Greed, as universal human motivation is considered an important component to clear up the behaviour of laws compliance (Bucur 2011, p. 50). • The study conducted by Jiménez et al. (2015) reveals the essential role of education materialized in the stages when the entrepreneurs are trained and a suitable mentality against entrepreneurialism is created. More precisely, their study indicates that both secondary education and tertiary education have a much different effect on the engagement in formal or informal entrepreneurial activity. Thus, the formal entrepreneurialism is positively associated with the secondary and tertiary education while the informal entrepreneurialism is negatively influenced only by the tertiary education. Referring to education, certain studies (Chan et al. 2000; Kasipillai et al. 2003) found out that the decisions of the American respondents to comply with the tax laws were firstly determined by their age and education. Likewise, the study conducted by Kasipillai et al. (2003) evaluates the influence of the education on the tax observance among the students of Malaysia. The statistics findings confirm the prevalence of a relation between education and tax compliance. This relation is generally consistent, particularly regarding the issues related to the general avoidance and personal avoidance. An improvement of the personal compliance rate among the students was noticed particularly among women after a semester of attending preliminary course of taxation. However, certain studies (McGee 2008) have not identified the level of education as playing a part in the tax attitude among the countries of the sample. • Several studies (Schneider and Williams 2013; Feld and Schneider 2010; Schneider and Buehn 2016) certify a positive relation between independent economic activities (physical entities authorized to develop economic activities) and the level of the shadow economy. The higher the independent activity rate, the higher the shadow economy activities. Theoretically, we expect that the independent activities offered more freedom to entrepreneurs regarding the level and structure of the declared economic activities. However, we noticed this factor as one of the factors which is poorly enough proved in the specialized literature. • Discouragement measures; The discouragement measures corresponding to the financial crime consist of the policies for the prevention and fight against these facts. The existence of an inefficient institutional and organization framework which is not adapted to the current conditions and is unable to ensure a firm and efficient response (for instance, because of the presence of parallelism, of a reduced technical and information system, lack of strict specialization or low level of human resource qualification) can generate the inefficiency of the adoption of discouragement measures. Despite the strong focus on the discouragement of the financial criminal activities through policies of fight against corruption and shadow economy, there exist few studies about the effects of discouragement. This is due to the data regarding the legal issues and the frequency of controls which are not internationally available; including for the OECD

1.4  Determinants of the Economic and Financial Crime

61

countries as such data are difficult to collect. Schneider and Buehn (2016) consider that there are few empirical proofs demonstrating that the fines and penalties do not exercise a negative impact on the shadow economy, but the risk of detecting, subjectively perceived by the individuals, exercises a positive impact. However, the results are often inconsistent, and the Granger causality tests indicate that rather the dimension of the shadow economy can affect the discouragement, instead that the discouragement reduced the shadow economy (Feld and Schneider 2010; Schneider and Buehn 2016). • The technical and scientific revolutions and online transaction growth led to the occurrence of a new category of shadow economy that is the digital shadow economy (Gaspareniene et al. 2016; Remeikiene et al. 2017). We consider the study elaborated by Remeikiene et  al. (2017) is interesting in the light of the elaboration of a definition of the digital shadow economy as being represented as illegal activities consisting of the supply of goods and digital services resulted at the operation exceptionally performed in the digital space breaching the existing regulations. Referring to the causes identified in the literature regarding the engagement in money laundering crimes, we present below some of such results: • A study conducted by PricewaterhouseCoopers (2018) identified the existence of a close relation between the economic growth level of a country and the money laundering. Thus, in the developing countries, a percentage of 58% of the financial companies analysed (financial institutions, mutual funds, insurance companies, dealers, etc.) experienced the fight against the money laundering during the last 2 years, compared to a lower percentage of 48% found the developing countries. • The tax pressure is identified in the specialized literature (Chong and López-de-­ Silanes 2015; Schwarz 2011) as playing a determining role in the engagement of money laundering crimes taking into account that the money laundering is often carried out through tax havens. • The quality of regulations regarding the money laundering crimes (Chong and López-de-Silanes 2015; Schwarz 2011) together with the efficiency of the law system reflected by the rule of law (Chong and López-de-Silanes 2015; Vaithilingam and Nair 2009; Ardizzi et al. 2014) are having an important role on the diminution of the money laundering fact. • The probability of detecting the abnormal and suspect transactions is rendered difficult by the level of business sophistication (Chong and López-de-Silanes 2015; McKenna 2017). Thus, the more sophisticated the business, the lower is the probability of detecting the suspect transactions, and it leads to an increase of the rate of engaging in money laundering operations. • The money laundering is expected to decrease while adopting some stronger audit and reporting standards (Vaithilingam and Nair 2009). It was found out that by adopting some strong audit and reporting standards the risk of not detecting the suspect transactions and consequently, the probability of getting engaged

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1  Economic and Financial Crime. Theoretical and Methodological Approaches

in illegal activities like money laundering is reduced (Drezewski et  al. 2012; Vaithilingam and Nair 2009; Nikoloska and Simonovski 2012). • A high level of the banking system soundness reflected by high transparency, monitoring of the financial transactions and of the bank accounts, financial surveillance represents the factors identified in the specialized literature as ­determining factors of the money laundering diminution (Leția 2014, p.  113, p. 131, p. 171; Vaithilingam and Nair 2009; Nikoloska and Simonovski 2012). Thus, a poor banking surveillance could be an easy target that the money is laundered without any suspicion. For instance, in the United States both the banks and the non-banking financial institutions must report the financial transactions exceeding 10,000 dollars a day as well as any suspect criminal activities. In Romania, according to the regulations harmonized with the European Directives, it is compulsory for the entities to report any cash deposition/withdrawal operations corresponding to the external transfers exceeding the equivalent of 10,000 EUR (National Office for Prevention and Control of Money Laundering 2004). In conclusion, a better quality of the banking surveillance means a good soundness of the banks, and thus, the channels of the money laundering operations are reduced. • Education is also mentioned as an important factor for the diminution of money laundering (Favarel-Garrigues et  al. 2007; McKenna 2017; Nikoloska and Simonovski 2012; Isa et al. 2015; Lowe 2017). Thus, the financial and business sophistication rate makes more difficult the detecting of suspect transactions from the financial-banking system employees (McKenna 2017). To this aim, the banks should elaborate criteria able to identify the suspect transactions related to the money laundering (Favarel-Garrigues et al. 2007). In relation with this issue, Nikoloska and Simonovski (2012) demonstrated the role of the bank employee education to apply suitable criteria for identifying the suspect transactions and for the money laundering prevention. Isa et al. (2015) followed the same idea and concluded that human expertise is required to deal with a false alarm and to truly evaluates whether the cases mentioned by the system really reflect a threat regarding the risk of money laundering. Considering these issues, Lowe (2017) dedicated a vast descriptive study to underline the necessity of a predictive intelligence to support the programs for fighting against money laundering in the financial sector. Based on the specialized literature reviewing, we conclude that the determining factors of the economic-financial crime can be classified taking into account their action area (macro and micro factors) and their nature (economic, political, social, and cultural factors) as follows: (i) Determining factors at macro level: • Economic factors: economic development, tax pressure, financial and banking system development, technical and scientific revolutions, technology, digital economy

1.4  Determinants of the Economic and Financial Crime

63

• Political and legal factors: public governance (reflected by the efficiency of institutions, quality of regulations, rule of law, etc.) • Social and cultural factors: attitude regarding the taxes or tax morale, culture, religion, education including intelligence, confidence; (ii) Determining factors at micro level: corporate governance including also the quality of financial audit and reporting. Referring to the identification of the most important determining factors of the economic-financial factors, based on the investigation of the results existing in the literature and specialized practise we can draw a conclusion regarding the most representative factors, namely, level of economic development, technology, tax pressure, quality of public governance, banking system soundness, corporate governance, and social and cultural factors (attitude against taxes or tax morale, culture, religion, confidence, intelligence). For these reasons we pay a special attention to these factors regarding the definition of concepts, measuring instruments, empirical statistics, and evidence of their influence on the economic and financial crime.

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Vaithilingam, V., Nair, M., & Thiyagarajan, T. (2015). Managing money laundering in a digital economy. Journal of Asia-Pacific Business, 16(1), 44–65. Virta, H. (2007). Corruption and shadow economy: Differences in the relationship between countries. Discussion papers, Helsinki Center of Economic Research, 171. Walker, C. (2011). Terrorism and the law. Oxford: Oxford University Press. Walker, J., & Unger, B. (2009). Measuring global money laundering: “The Walker Gravity Model”. Review of Law & Economics, 5(2). https://doi.org/10.2202/1555-5879.1418. Weber, A. (2005). How far perceptions go. Transparency Brazil Working Paper. The World Bank: Washington, DC. Winnie, V. A. T. (2016). The effect of good corporate governance on tax avoidance: An empirical study on manufacturing companies listed in IDX period 2010–2013. Asian Journal of Accounting Research, 1, 28–38. World Bank. (2020). World Bank indicators. Available at https://data.worldbank.org/indicator; Available at http://www.worldbank.org/. Accessed on 15 Mar 2020. Zdanowicz, J. (2009). Trade-based money laundering and terrorist financing. Review of Law and Economics, 5, 855–878. Zoldi, S. (2015). Using anti-fraud technology to improve the customer experience. Computer Fraud & Security, 7, 18–20.

Links http://www.fatf-gafi.org/about/. Financial Action Task Force on Money Laundering (FATF). https://www.worldbank.org/. World Bank. https://www.weforum.org/. World Economic Forum.

Chapter 2

Economic and Political Determinants of Economic and Financial Crime

2.1  Economic Development As we have noticed at the investigation of the determining factors of the economic and financial crime (in the previous Sect. 1.4), the level of the economic development of a country represents one of the most important factors identified in the literature and practice. A high living standard can result in a better compliance with the law, and, consequently, the incentives for paying bribery and also the shadow and money laundering activities are much reduced. Different studies (Husted 1999; Treisman 2000; Paldam 2001, 2002; Kirchler 2007; De Rosa et al. 2010; Achim et al. 2018a) demonstrate that the highest rates of illegal economic activities and the highest rates of corruption are found in the developing and transition countries, while in the countries with high incomes there are found lower rates of corruption and shadow economy.

2.1.1  Concept of Economic Development and Measuring Tools The individuals’ living standard can be indicated in simple terms, by the economic growth of a country. This can be expressed in different ways, the most used being: gross domestic product (GDP) and gross national product (GNP) calculated by total population or per capita. The gross domestic product represents the market value of all the goods and final services produced within a certain period of time in an economy. At present, the GDP is used to a large extent worldwide as an indicator expressing the development level and prosperity of a nation. A higher economic growth is correlated with a higher capacity of paying and collecting taxes as well as with a © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 M. V. Achim, S. N. Borlea, Economic and Financial Crime, Studies of Organized Crime 20, https://doi.org/10.1007/978-3-030-51780-9_2

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relatively higher demand of public goods and services (Chelliah 1971). In accordance with the approaches provided by Torgler and Schneider (2009), we shall use the GDP indicator per capita as estimation of the economic growth of a country.

2.1.2  Economic Development and Corruption 2.1.2.1  Theoretical Approaches A consistent category of studies (Husted 1999; Mauro 1995; Treisman 2000; Paldam 2001, 2002; Gundlach and Paldam 2009; De Rosa et al. 2010; Achim et al. 2018a) shows that the countries with low incomes and, respectively, a low level of the economic development face the highest levels of corruption. A high level of economic development can lead to a better compliance with the laws, while a low level of it may create the opportunity for corruption occurrence, as an expression of the population disagreement regarding the supply of public goods and the state ability to ensure the welfare. Husted (1999) stated that “because the level of economic development is related to the general level of resource wealth, it is expected that corruption is much more frequent in less developed economies”. Additionally, Husted noticed that financial satisfaction, the tax payment, and corruption are closely correlated. In a similar way, Torgler (2004) conclude that “if the financial situation of a household is bad, the tax payments could be considered as a hard restriction of their possibility set, which could result in the diminution of the tax honesty”. As a result, for this reason, the bribery paid to avoid taxes is an expected fact. In the same line, the studies conducted by Treisman (2000) and Paldam (2001, 2002) found out that the corruption is a defect determined by poverty (“poverty disease”) which disappears when the country becomes richer. Goel and Ram (2013) substantiated these findings and demonstrated that the economies in transition stage show a higher level of corruption than the developed countries. Based on the analysis of the bilateral causality between income and corruption, an empirical study carried out by Gundlach and Paldam (2009) concluded that the long-term causality relation shows itself completely from income to corruption and underlined that the cross-country characteristics of the corruption from different countries can be entirely explained by the cross-country income characteristics from different countries. Thus, De Rosa et al. (2010) found a strong correlation of 0.81 between GDP and corruption level. More recently, the panel study conducted by Achim, Borlea, and Anghelina (2018b) for 185 countries over the period 2005–2014 identified a negative influence of the economic development on the corruption level. This explains why the countries where the incomes are high are facing a low corruption level. The authors revealed that the intensity of corruption diminution due to the increase of the GDP per capita is more obvious in the developing countries than in the developed ones. Some similar findings belong to Treisman (2000) and Paldam (2001, 2002) who found that GDP coefficient values, from the models of the corruption evaluation, are

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higher for the developing countries compared to the developed countries, ones concluding that the poverty eradication could reduce the corruption. However, there exist a second group of more restricted studies (Caselli and Michaels 2013) who found a positive association between the economic growth rate and corruption explaining that a high level of wealth could result in the increase of the possibilities to get benefits increasing thus the corruption level. A third group of studies (Huang 2016) did not find the existence of a significant causality relation between corruption and the economic growth for most Asia-­ Pacific countries. In the end, the authors concluded that, for these countries, the anti-corruption policies used by the political makers to promote the economic growth of a country could be inefficient. 2.1.2.2  Practical Approaches Taking into account the inconsistent results found in the specialized literature regarding the relation between the level of the economic growth and corruption, we propose further on the investigation of such a relation within an empirical study. Methodology Based on the theory and specialized practice, we expect that the population of the poorer countries is more inclined to bribe the public officers in order to obtain immediate benefits. In conclusion we propose to test the following working hypothesis: Hypothesis 1: An increase of the level of the economic growth leads to the decrease of the corruption level. The level of the economic growth of a country is measured using the GDP/capita (expressed in USD/capita). The data provide from the databases of the World Bank Group (2020a). Corruption is measured as the place occupied by the sample countries depending on the value of the Corruption Perception Index (CPI) provided by Transparency International (2020a). A high position held in the classification by countries reflects a high corruption rate existing in the respective country. The sample is represented by a number of 183 countries for which all the data regarding two variables are available, and the period of analysis is comprised between 2005 and 2018. To this aim, we use the descriptive methods, the correlation coefficients, and the regression analysis, and we carry out statistical tests necessary to ensure a high accuracy of the results. The statistics processing is carried out using the SPSS statistic software. Results and discussions Graph 2.1 shows a correlated arrangement of the two variables, for an R squared = 0.476. This means that a percentage of 47.6% of the corruption variation can be explained by the medium level of the financial satisfaction of the populations from the sample countries for the period comprised between 2005 and 2018. The

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correlation coefficient shown in Table 2.1 is negative, its value is −0.69 which indicates a close indirect correlation between the two variables. In other words, a high level of economic development is correlated with a low corruption level. Table 2.2 indicates that the value of the regression coefficient of the GDP/capita variable in relation with the corruption variable is negative (−0.002) and it is significant at a significance threshold of 1%. It indicates that at an increase by a unit of the GDP/capita there is obtained at average a diminution of the corruption level by 0.001 units. Conclusions, limitations, and investigation directions The study no doubt reveals the existence of a significant influence of the level of the economic development on the corruption level. The above study presents as limitations, the non-use of some control variables. Thus, for obtaining a much higher accuracy of the results, the relation between economic development and corruption requires to be analysed caching the moderating effects of some control variables such as tax pressure or institutional quality.

R2Linear = 0.476

200.00

Corruption

150.00

100.00

50.00

.00 00

20000.00

40000.00 60000.00 80000.00 100000.00 120000.00

GDP/capita

Graph 2.1  Correlation between GDP/capita and corruption

2.1 Economic Development

77

Table 2.1  Correlation coefficients between corruption and GDP/capita Corruption

GDP/capita

Corruption 1

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

185 −.690** .000 183

GDP/capita −.690** .000 183 1 183

Source: own processing **The correlation is significant at a significance threshold of 0.01 (two-tailed) Table 2.2  Regression of corruption depending on the GDP/capita

Model Constant GDP/capita

Non-standardized coefficients B Std. error 109.077 3.173 −.002 .000

Standardized coefficients Beta −.690

t 34.379 −12.830

Sig. .000 .000

Dependent variable: corruption Source: own processing

a

2.1.3  Economic Development and Shadow Economy 2.1.3.1  Theoretical Approaches Referring to the shadow economy, several studies confirm that a higher level of the economic growth of a country generates a better capacity of tax payment and collection and a higher demand of public goods and services (Chelliah 1971; Torgler 2004; Torgler and Schneider 2009). For instance, the study conducted by Torgler (2004) indicates a strong relation between the financial satisfaction and tax payments. Torgler states that “if the financial situation of a household is bad, the tax payments could be considered as a hard restriction of their possibility set, which could result in the diminution of the tax honesty”. Therefore, there exist tendencies to migrate towards the shadow activities. Further on, based on the investigation of the results obtained by Schneider and his collaborators in different studies (Alm et al. 2004; Medina and Schneider 2018; Schneider and Klingmair 2004; Schneider 2015), the highest levels of the illegal economic activities are found in the developing countries and countries in transition stage. In Africa and South America, 41% of the economic activities are clandestine. In Europe, in case of the economies under transition stage, the shadow economy is estimated at 38%. The countries indicating the lowest level of the shadow economy are Switzerland, the United States, and Austria, while Bolivia and Georgia are on top position reaching a percentage of more than 66% (Kirchler 2007). These findings are supported by the study conducted by Orviska and Hudson (2003), who noticed that in developed countries, the tax fraud is estimated at 20% of the total

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income, and in the developing countries the percentage is even higher. Similar results were obtained even at the level of a country with different levels of growth by regions. Thus, Brosio et al. (2002) investigate the tax frauds in different regions of Italy and found out that in the poorer regions from the South of Italy the tax fraud is significantly higher than in the richer northern regions. The authors explained that the shadow economy and the tax non-compliance are possible expressions of the population disagreement regarding the supply of public goods and failure of the state to ensure the welfare. In a study conducted by Achim, Borlea, Găban, and Cuceu (2018a) for the European Union countries over the period between 2007 and 2013, the authors validate the hypothesis according to which the richer a country is, the lower is the tendency of the its citizens to get involved with shadow activities. The results have been differentiated among the old member countries (EU 15) and the new ones from the European Union (EU 13). The authors also found a higher impact of economic development on the shadow economy in the old EU countries than in the new EU countries. More exactly, a percent of 38% from the variation of shadow economies of the old EU countries is explained by economic development of these countries, while this percent is only 17% for the new EU countries. These results suggest that the new EU countries have many more problems that cause them to slip into underground activities other than poverty (institutional quality/regulation quality/rule of law, etc.) 2.1.3.2  Practical Approaches Further on, we propose the investigation of the relation between the level of the economic development and that of the shadow economy. Methodology Further to the review of the specialized literature, it is expected that the population of the poorer countries to be more inclined to obtain benefits from shadow activities hidden from the authorities. In conclusion, we propose to test the following working hypothesis Hypothesis 1: An increase of the level of the economic development leads to the diminution of the level of the shadow economy. The level of economic development is measured using the GDP/capita indicator (expressed as mean of the USD /capita). The data are taken over from the databases of the World Bank Group (2020a, b). The level of the shadow economy is measured as weight of the shadow economy in the GDP using the database determined by Medina and Scheinder (2018). The sample is represented by a number of 155 countries for which all the data are available for the period of analysis comprised between 2005 and 2015. As analysis method we shall use the descriptive methods, the correlation coefficients, and the regression analysis, and we carry out the statistical tests necessary to

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ensure a high accuracy of the results. The statistics processing is carried out using the SPSS software. Results and discussions Graph 2.2 shows a correlated arrangement of the two variables, at an R squared = 0.388. It means that, at average, a percentage of 38.8% of the shadow economy variation can be explained by the level of the economic growth of the sample countries between 2005 and 2015. The correlation coefficient indicated in Table 2.3 is negative, with a value of −0.657, reflecting an indirect high relation between the two variables at a significance threshold of 1%. The value of the regression coefficient of the level of economic development variable and its high significance (Table 2.4) also provides clear signs of the existence of a significant negative influence of the level of the economic development on the level of the shadow economy. Conclusions, limitations, and investigation directions The study presented is meant to represent only a start point for more advanced studies regarding the investigation of the influence of the level of the economic development of a country on the level of the shadow economy. Thus, in order to obtain a better accuracy of the results, the relation between the level of the economic development of a country and the level of the shadow economy must be analysed catching 2 R Linear = 0.388

shadow economy

60.00

40.00

20.00

.00 .00

20000.00

40000.00

60000.00 80000.00 100000.00 12000.00 GDP/capita

Graph 2.2  Correlation between GDP/capita and shadow economy

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Table 2.3  Correlation coefficients of the shadow economy with the GDP/capita Shadow economy

GDP/capita

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Shadow economy 1 156 −.657** .000 155

GDP/capita −.657** .000 155 1 183

**The correlation is significant at a significance threshold of 0.01 (two-tailed)

the moderating effects of some control variables such as the tax pressure or institutional quality. All these together with the utilization of advanced techniques of data analysis (panel type eventually) will result in a higher soundness of the results obtained.

2.1.4  Economic Development and Money Laundering 2.1.4.1  Theoretical Approaches The developing countries are much more vulnerable against money laundering crimes because of the existing legislative gaps of the developing process of the financial sector, of privatization, or of the creation of the securities market (see Kroll (1994) regarding the discussions about money laundering in Bulgaria and the particular concerns related to the asset misappropriation of the private enterprises during the transformation of the Bulgarian economy from a state-controlled economy to a market economy). The economic changes which took place in the former communist countries from Eastern Europe created opportunities for money laundering by the unscrupulous individuals based on a slow regulatory system referring to the instruments for detecting, investigating, and tracking the money laundering operations (Schroeder 2001). Indeed, based on the engagement of most of the emerging markets in the privatization process, the committing of money laundering crimes dramatically enhanced (Schroeder 2001). Also, in the emerging countries, there exist records regarding the increase of the transborder transfers of cash to market with free agreements regarding the detection and placement of cash as well as the increase of the investments of the groups of organized crime in the field of real estate properties and businesses. Thus, considering these changes the emerging countries underwent to make the transition to the market economy, the negative effects of the money laundering tend to be much enhanced than that of the developed countries (McDowell 2001).

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Table 2.4  Regression of shadow economy depending on the GDP/capitaa

Model Constant GDP/capita

Non-standardized coefficients B Std. error 34.801 .903 .000 .000

Standardized coefficients Beta −.657

t 38.551 −10.789

Sig. .000 .000

Source: own processing a Dependent variable: shadow economy

2.1.4.2  Practical Approaches We propose, further on, to investigate the causality relation between the level of the economic growth and the rate of engagement in money laundering crimes. Methodology Based on the results identified in the specialized literature, we expect the population of the poorer countries to be more inclined to get involved in money laundering crimes to obtain immediate benefits. In conclusion, we propose to investigate the following working hypothesis: Hypothesis 1: An increase of the level of the economic development leads to the diminution of the engagement rate in money laundering crimes. The level of the economic development is measured using the GDP/capita indicator (expressed as mean USD/capita). The data have been taken over from the databases of the World Bank Group (2020a, b). The level of the money laundering crime is determined using the Basel AML indicator (Basel Anti-Money Laundering Index) which measures the risk of money laundering and terrorism funding. The sample is represented by a number of 162 countries, and the analysed period is comprised between 2012 and 2017 for which all the data are available. The analysis methods include the descriptive methods, correlation coefficients, and regression analysis, and we carry out statistical tests necessary to ensure a high accuracy of the results. The statistical processing is carried out using the statistical SPSS software. Results and discussions Graph 2.3 shows a correlated arrangement between the level of the GDP/capita and the risk of money laundering at an R squared = 0.235. In other words, we notice that at average, a percentage of 23.5% of the variation of the risk of money laundering can be explained by the level of the economic growth. The correlation coefficient indicated in Table 2.5 is negative, with a value of −0.485, which reflect an indirect medium intense relation between the level of the economic development and the risk of engagement in money laundering crimes. The value of the regression coefficient of the economy development variable (Table 2.6) also reveals the existence of a negative and significant influence of the level of the economic development on the risk of money laundering.

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2.2  Tax Pressure The tax pressure also called tax burden is found among the most frequently mentioned causes of the economic and financial crimes. The specialized literature reveals the role of the tax pressure on the economic and financial crime by the fact that a high taxation rate may result in corruption actions of the public officers to avoid the tax payment or by getting involved in shadow activities (Devereux and De Mooij 2009; Dreher and Siemers 2009; Dreher and Schneider 2010; McGee 2012; Schneider and Klinglmair 2004; Torgler and Schneider 2009). It is assumed that the tax burden is related to the corruption taking into account that the officer bribery is made by entrepreneurs to obtain some private gains such as the avoidance of taxation and regulations or the winning of public contracts (Fjeldstad 1996, 2003; Kaufman, 2010). As for the shadow activities, a significant number of studies (Tanzi 1999; Schneider and Buehn 2012a, b; Schneider 2005; Schneider and Buehn 2016) certify that the shadow economic activities enhance as the real and charged tax burden increases. The increase of the total cost of the labour force can stimulate the diminution of the income tax by means of migration to the shadow economy.

2.2.1  General Approaches Regarding the Tax Pressure 2.2.1.1  Concept of Tax Pressure and Measuring Instruments The tax pressure and the tax burden are concepts closely related to taxation. The specialized literature identifies numerous definitions of the tax pressure such as: • “Tax pressure or tax burden represents the total value of the tax paid by a certain group of individuals, industry etc., particularly compared to what pay other groups, industries”(Cambridge Dictionary 2020). • “Tax pressure measures the answer to general economic and social questions about the effect of the tax policy on the distribution of the incomes and wealth” (Atrostic and Nunns 1991). • “The tax pressure represents how overwhelming the taxes are, or in other words, how big is the tax burden on the tax payers’ shoulders” (Tulai 2003, p. 287). • The tax pressure represents “the intensity with which income is taken from physical and legal entities at the level of the whole society by means of taxation” (Mara 2010, p. 148). • “The tax pressure is an indicator of the measurement of the income share gained from production which transit the budget through a compulsory and public impairment process instead of letting them free and available for the private initiative” (Manolescu 1997, p. 69).

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2.2 Tax Pressure Table 2.5  Correlation coefficients of money laundering and GDP/capita Risk of money laundering

GDP/capita

**

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Risk of money laundering 1 164 −.485** .000 162

GDP/capita −.485** .000 162 1 183

The correlation is significant at a significance threshold of 0.01 (two-tailed)

In conclusion we consider that the tax pressure reflects the volume of taxes and fees paid to the state budget and the way in which this volume is felt by the tax payers from the point of view of their wealth. Referring to the measurement of the tax pressure, the specialized literature (Corduneanu 1998) highlights two general approaches: (a) Tax pressure in terms of flow, which represents the monetary amount of the tax obligation which is charged in the income at individual, sectorial, or global level.

2

R Linear = 0.235

9.00

Risk of money laundering

8.00

7.00

6.00

5.00

4.00

3.00 .00

20000.00

40000.00 60000.00

80000.00 100000.00 120000.00

GDP/capita

Graph 2.3  Correlation between GDP/capita and money laundering

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Table 2.6  Regression of money laundering depending on the GDP/capitaa Model Constant GDP/capita

Non-standardized coefficients B Std. error 6.324 .098 −2.986E-005 .000

Standardized coefficients Beta −.485

t 64.394 −7.017

Sig. .000 .000

Source: own processing a Dependent variable: risk of money laundering

(b) Tax pressure in terms of indicators, which reflects the ratios between the tax flows and economic flows creating the income. In this case, the indicators show the share of the national product taken over by the state and borne by the tax payers. In the specialized literature, for measuring the tax pressure, the tax pressure calculation in terms of indicators, more exactly as taxation rate, is commonly used (Tulai 2003, p. 287).The taxation rate can be at global level (national), at the level of the economic entities, and at the level of the individual (physical entity tax payer). According to Schneider et al. (2010), the measuring of the tax burden is not easy to define because the taxation and social security systems are different among the countries. According to Schneider et al. (2010), the tax burden can be estimated in several ways: (a) As tax incomes (% of the GDP). ( b) As total taxation rate (% of the commercial profits or of the turnover). (c) As a global indicator of the tax freedom, sub-component of the economic freedom indicator, calculated by Heritage Foundation. The measurement of the tax pressure starting with the tax freedom indicator is used by different authors (Torgler 2002, 2004, 2007; Dreher and Schneider 2010; Torgler and Schneider 2009; Achim et al. 2018b), being considered a more complete method of tax pressure assessment. In conclusion, based on the specialized literature investigated and on the available data sources, the level of the tax pressure from a country can be measured by means of the following indicators: (a) Tax revenue (as % of GDP), where tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. The data are provided from the World Bank (2020a). (b) Profit tax (as % of commercial profits) where profit tax is the amount of taxes on profits paid by the business, using the databases of the World Bank (2020a). (c) Labour tax and contributions (as % of commercial profits), where labour tax and contributions is the amount of taxes and mandatory contributions on labour paid by the business. The data are provided from the World Bank (2020a, b). (d) Total tax and contribution rate (the taxes and contributions as percentage of the commercial profit), which measures the amount of taxes and mandatory contributions payable by businesses after accounting for allowable deductions and

2.2 Tax Pressure

85

exemptions as a share of commercial profits. Taxes withheld (such as personal income tax) or collected and remitted to tax authorities (such as value-added taxes, sales taxes, or goods and service taxes) are excluded. The data are provided from the World Bank (2020a). (e) Tax wage (as % of the total labour cost), which measures that part of labour costs which is taken in tax and social security contributions net of cash benefits. The tax wage is defined as the ratio between the value of the taxes and social security contributions, at average, by an employee without children, and the corresponding total labour cost of employer. The indicator reflects the extent to which the labour taxation discourages the labour force employment. This indicator is provided by OECD (2020). (f) Fiscal freedom variable using the database of Heritage Foundation (2020), as calculation basis of the economic freedom indicator. It ranges from 0 to 100, where 0 is the least fiscal freedom and 100 is the maximum degree of fiscal freedom. A higher fiscal burden implies a lower degree of fiscal freedom. So, the fiscal freedom consists of three quantitative factors, namely, (a) the top marginal tax rate on individual income, (b) the top marginal tax rate on corporate income, and (c) the total tax burden as percentage of GDP (Heritage Foundation 2020). In scoring fiscal freedom, each of these numerical variables is weighted equally as one third of the component. Thus, the fiscal freedom indicator is calculated as follows (Heritage Foundation 2020): Fiscal freedom ij = 100 − α ( Factorij ) , where 2





• Fiscal freedom represents the fiscal freedom1 of the country for each of the factors j. • Factor ij represents the value of the factors j previously presented for the country i (on a scale from 0 to 100). • α is the coefficient established at the level of 0,03. In our opinion, the measurement of the tax pressure as tax freedom indicator is the most complete method of reflecting the tax pressure. Moreover, because this indicator has minimum and maximum limits clearly defined (from 0 to 100), it provides a high comparability of the results obtained in different countries. In addition, the fiscal freedom indicator has been used by various authors in their research (Achim et al. 2018b; Dreher and Schneider 2010; Torgler and Schneider 2009) in order to express the level of fiscal burden of a country. 2.2.1.2  Laffer Curve and Tax Avoidance Even since 1776, Adam Smith stated that the high rates of taxes would destroy the taxation base. Later on, the relation between tax pressure rate and incomes cashed at the state budget was revealed by Laffer (2004) as Laffer curve. The theory developed by Laffer shows that the modifications of the taxation rates could have two

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effects on the incomes: arithmetical effect and economic effect (Laffer 2004). The arithmetical effect refers to the fact that when the tax rate decreases, also the tax incomes (expressed by taxable income unit) will decrease. Otherwise, when the taxation rate increases, the arithmetical effect will lead to an increase of the tax incomes collected by taxable income unit. But, the economic effect recognizes the existence of a positive impact of the taxation rate decrease on labour and production and, thus, on the taxation base. On the other hand, the increase of the taxation will have a reverse economic effect of penalization of the participation at the taxable activities. Therefore, the arithmetical effect will always act the other way against the economic effect. Consequently, when the economic and arithmetical effects of the taxation rate modifications are combined, the consequences of the taxation rate modification on the total tax incomes are no longer obvious enough. In other words, the Laffer curve reflect that in case of a tax pressure increase, the tax incomes show an increase up to a maximal point (M), after which the tax incomes begin to decrease up to null values, if the tax pressure rate would reach the 100% level (actually at a taxation of 100% of the incomes, any taxable activity would disappear (see Fig. 2.1)). The tax pressure assumes certain limitations of tolerance from the tax payers’ psychological and political limits (Mara 2010, p. 149). These limits are imposed by the tax payers’ reactions as they can strongly oppose the increase of the taxation rate and could react by tax avoidance, fraud, production activity diminution, or even rebellion (Hoanță 2000, p. 165). The Laffer curve represents a theoretical approach of the facts presented because the exact definition of this M taxation threshold where the tax pressure is considered excessive is difficult to provide. This threshold is varying depending on the territorial and economic circumstances so that the tax system of a country will be placed either to the left or to the right side of the M point (Mara 2010, p. 150). The Laffer curve can be divided in two zones (Mara 2010, p. 150): • Zone 1: the zone of the left side of M point (normal or admissible zone) where the increase of the taxation share ensures the increase of tax cashing. • Zone II: The zone of the right side of M point (inadmissible zone) where any increase of the taxation rate leads to the diminution of tax cashing; this correlation between the two ones becomes indirect. As for the relation between the tax pressure and tax incomes as a Laffer curve, the study conducted by Busato and Chiarini (2013) comes to strengthen these results. Thus the authors mentioned analysed the relation between the tax policy, tax avoidance, and shadow economy in a dynamic model of general equilibrium, and their results provided solid arguments for a certain Laffer type parameter curve. Referring to the analysis of the Laffer curve in some of the countries, certain studies (Trabandt and Uhlig 2006; Trandafir and Brezeanu 2010) were directed to such analysis. Trabandt and Uhlig (2006, pp. 1–69) analysed, by means of comparison, the Laffer curve for the United States as well as for the EU 15 countries. They revealed that in the United States and EU 15 zone, the labour and capital are on the left side of the Laffer curve, but the EU 15 economy is situated much closer to the prohibited slopes than the United States. Also, they found out in the EU 15 econ-

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omy, the slope of the Laffer curve is much flatter than in United States reasoned by a much higher distortion in EU 15 zone. For Romania, Trandafir and Brezeanu (2010) were preoccupied to build the Laffer curve based on the tax incomes obtained in the period between 2000 and 2010. The results of the analysis indicate that the Laffer curve slope, throughout both the whole period and by each year, placed the Romanian economy within the inadmissible or prohibited zone. It means that an ever more important share of the tax payers’ incomes is taken over by the state and the tax payers from the economy restrain their taxable activities and, thus, the taxable base is reduced. It is also interesting to know the result obtained by the authors regarding the identification of the optimal taxable threshold during the investigated period, threshold which is carried out at a taxation of 10.96% that corresponds to the maximum level of the real tax incomes obtained. 2.2.1.3  Tax Pressure in European Union Countries Further on, we propose to analyse the level of the tax pressure in the European Union member states to identify certain indicators regarding the existence of a relation referring, on one hand, to the tax pressure and to corruption, shadow economy, and money laundering, on the other hand. Methodology To highlight the tax pressure level within the European Union countries, we shall use the available data about the tax pressure indicator calculated by Heritage Foundation (2020). The tax pressure indicator varies from 0 to 100 where 0 reflects the lowest level of the tax freedom and 100 is the maximum level of tax freedom. The analysed period is 2005–2018 and aims at the European Union countries (28). As for the methods, we used descriptive methods, comparison analysis, and synthesis. Results and discussions Graph 2.4 shows that, at average, the fiscal freedom in the European countries is much varying from a minimum of 37 points (Denmark) to 87 points (Bulgaria). Based on Graph 16 review, it can be concluded that for the analysed period, the European countries showing the lowest level of the tax freedom are Denmark, Sweden, Belgium, France, Austria, and the Netherlands with an indicator ranging between 37 and 52 points. In opposition, the European countries with the lowest level of the tax pressure are (in increasing order of the classification) Bulgaria, Lithuania, Romania, Latvia, Slovakia, and Estonia where the tax pressure indicator range is between 81 and 87 points. What we easily see analysing Graph 2.4, it is the fact that the countries situated at the end part of the classification are from Central and Eastern regions of Europe giving us a reason to further analyse the varying presence of the four quadrants as the European Union countries were distributed (see Table 1.4 presenting the way in which this classification was done).

2  Economic and Political Determinants of Economic and Financial Crime

Fig. 2.1  Laffer curve. (Source: Laffer (2004), The Laffer Curve: Past, Present, and Future, The Heritage Foundation)

M Total tax incomes

88

T

0

a

b

c

%

Tax pressure rate

Thus, Graph 2.5 indicates that the countries from Central and East Europe (CEE) (former communist countries) show the highest values of the tax freedom (the tax freedom indicator is about 78 points). They are followed by the countries from Southern Europe where there is obtained an average tax freedom indicator of about 64 points. The countries having the lowest level of the tax freedom are those from Northern Europe (47 points), followed by the countries from Western Europe (57 points). Referring to the evolution of the fiscal freedom in the European Union countries, it is noticed in Graph 2.6 that the average of tax freedom mean at the level of the member states shows a slight increase until the year 2011 and, after this year, the evolution is descending but, as a whole, we can see a general slightly ascending trend. Romania shows levels of the tax freedom above the average value of the European Union throughout the analysed period. Also, we notice that, during the analysed period, the tax freedom systematically increased in Romania (from an indicator of 70 points in 2005 to an indicator of 87 points in the period 2016–2018).

2.2.2  Tax Pressure and Corruption It is assumed that the tax pressure is associated to corruption considering that the entrepreneurs give bribery to the public officers in order to obtain some private gains such as tax and regulations avoidance or assignment of public contracts (Fjeldstad 1996, 2003; Kaufman 2010). However, the specialized literature provides few and inconclusive results regarding the tax pressure influence on the corruption level (Dreher and Siemers 2009; Dreher and Schneider 2010; McGee 2012). 2.2.2.1  Theoretical Approaches In the specialized literature, we identified few studies (Dreher and Siemers 2009; Dreher and Schneider 2010; McGee 2008, 2012; Achim et al. 2018b) dealing with the investigation of the tax pressure influence on the corruption level, and the results

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are inconsistent. For instance, Dreher and Siemers (2009) analysed the relation between the restrictions regarding the access to capital using panel data for 112 countries during the period 1984 and 1999. They found out that higher restrictions regarding the access to the capital involved a higher corruption level in the years before 1993, but they reduced corruption during the following years. Later on, Dreher and Schneider (2010) continued their study conducted on two big data sample: a sample of cross data from 120 countries and a panel data sample from 70 countries, both conducted over the 1994–2002 period. The authors obtained results demonstrating that the corruption fact is much more emphasized in countries with a lower tax burden contradicting, thus, the expectations. Analysing the perception of the tax avoidance and of corruption in Denmark, McGee (2008) showed that the Danish population considers the tax avoidance to be extremely unfavourable even if the taxation rate is one of the highest in the whole world, and, thus, this can explain the low level of corruption existing in Denmark. In a similar way McGee (2012) explained the situation in Armenia where it was noticed that, although the tax burden is significantly lower than in most of the countries, the Armenian people do not oppose so much the tax avoidance which results in a higher corruption level. Another similar paradox could be identified in China. Here, different authors (Jiang and Nie 2014; Huang 2016) documented in empirical studies the existence of the miracle of China, a country where the GDP continue to increase on behalf of a prevalence of the government corruption. Other authors (Kumar 2011) also noticed the abnormality of the emerging China which is characterized by a low economy freedom and a high economic growth rate. Another group of research studies (Graeff and Mehlkop 2003; Achim et  al. 2018b) identifies a differentiated behaviour of the different economic variables related to corruption, depending on the economic growth rate. Thus, Graeff and Mehlkop (2003) found out that some aspects of the economic freedom discourage corruption while some others don’t. However, the authors identified a strong relation between economic freedom and corruption, and this relation shows a different intensity in the poor and, respectively, rich countries. A similar study conducted by Achim, Borlea, and Anghelina (2018b) attempts to complete the previous literature and to clarify whether the tax policy plays a role regarding the level of corruption of a country. The present work investigates whether the increase of the tax pressure leads to a higher level of corruption and whether they are different in the developed countries compared to the developing countries. To this aim, the authors used an analysis of panel data on a sample consisting of 185 countries in the period 2005–2014. The authors found different results regarding the influence of the tax policy on the level of the corruption in the developed countries and in the developing countries. As for the developed countries, they found out that, based on a high institutional quality, a low level of the tax pressure leads to a low level of corruption, which correspond to expectations. On the other hand, in the developing countries which present a low institutional quality, a low level of tax pressure enhances the corruption fact because of the low efficiency of the governing and the people may easily avoid the law.

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Graph 2.4  The fiscal freedom in the European Union countries 2005–2018. (Source: own processing) 90 80

78 64

70

57

60

47

50 40 30 20 10 0

CEE

South

West

North

Graph 2.5  The fiscal freedom by geographical zones in the European Union countries at average, 2005–2018. (Source: own processing)

Concluding, the research findings suggest that governments and policy-makers need to acknowledge that the anti-corruption fight requires not only the right fiscal policies but also the right way of implementing these policies, recognizing the role of quality institutions, which need to prevail in any country.

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100 80 60

70 62

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2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 EU average

Romania

Graph 2.6  Evolution of fiscal freedom in the European Union countries, in average, 2005–2018. (Source: own processing)

2.2.2.2  Practical Approaches Starting with these contradictory findings, the question arises whether a higher tax burden creates opportunities for the officer bribery to avoid the taxes or to be assigned public contracts. Intuitively, the bribery is paid to avoid tax payments or the law regulations. In other words, the question arises whether the increase of the tax pressure is associated or not to an increase of the corruption level. Also considering the results of the previous investigations, a research question arises whether the relation between tax pressure and corruption is different among the countries depending on their economic growth. Study 1 Methodology Based on the results found in the specialized literature, one may assume that a higher level of tax pressure leads to public officer bribery to obtain certain tax benefits. In conclusion, we propose to test the following working hypothesis: Hypothesis 1: An increase of the tax pressure leads to an increase of the corruption level. We shall measure the tax pressure using the fiscal freedom, provided by the database of Heritage Foundation (2020). Fiscal freedom indicator ranges between 0 and 100 where 0 reflects the lowest level of the tax freedom and 100 is the maximum tax freedom rate. The level of corruption is measured as the place occupied by the sample countries depending on the score obtained by the Corruption Perception Index (CPI) provided by Transparency International (2020b). An upper position in the top occupied by a country regarding corruption represents a high level of corruption and vice versa.

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The sample is represented by 181 countries for which there are data available for the two variables, and the analysis period is 2005–2018. In order to investigate the relation between tax pressure and corruption, we used the descriptive methods, the correlation coefficients, and the regression analysis, and we carry out tests to ensure a high accuracy of the results. The statistical processing is performed using the statistical SPSS software. Results and discussions Graph 2.7 shows a correlated arrangement of the two variables, at an R squared = 0.028. We see that only a small percentage of 2.8% can be explained at average, by the variation of the tax freedom level assigned to the sample countries for the period 2005–2015. The correlation coefficient reflected by Table 2.7 is positive, with a value of 0.167 indicating a positive reduced statistically significant correlation between the two variables studied, at a significance threshold of 5%. Table 2.8 shows that the value of the regression coefficient of the tax freedom variable in relation with the corruption variable is positive (0.644) and significant at a significance threshold of 5%. This indicate that when there is an increase of the tax freedom by a point, it is found at average an increase of the corruption level by 0.644 points. Conclusions, limitations, and research directions The study reveals a positive weak enough relation between tax freedom and corruption. In other words, when there is an increase of the tax freedom (decreasing the tax pressure), there is also an increase of the corruption level. The result is surprising because it contradicts our expectations. Naturally, we would expect that the corruption level increased as a result of the tax pressure increase. Despite all these, similar results are obtained in other studies such as Dreher and Siemers (2009), Dreher and Schneider (2010), and McGee (2008, 2012). These studies also revealed a negative influence of the tax pressure on the corruption level. The above study limitations consist of the non-use of some control variables in the analysis model of corruption depending on the tax pressure level. Thus, to obtain a higher soundness of the results, it is required that the relation between the tax pressure and the corruption level is analysed catching the moderating effects of more control variables as, for instance, the economic growth rate, the public governance, etc. Study 2 Methodology The relation between the tax pressure and corruption level is further investigated using two main control variables, for instance, the level of the economic growth and the institutional quality. Consequently, it can be assumed that a higher tax pressure could enhance the corruption facts. Thus, we formulated the following working hypothesis and research questions: Hypothesis 1.The increase of the tax pressure is associated to an increase of the corruption level.

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Research question 1. In what way does the relation between tax pressure and corruption differ between countries depending on the economic growth? The measurement of corruption and tax pressure is carried out as we established in the previous study. Selected control variables • The level of the economic development is reflected by the GDP/capita, using the World Bank database (World Bank 2020a). • The institutional quality is measured with the government efficiency variable. This indicator measures the perceptions of the quality of the public services and their ability to produce and implement good policies and to provide public goods. The indicator reflects the public governing performance on a scale ranging between −2.5 (weak) and 2.5 (strong). The data source is collected from the Worldwide Governance Indicators (World Bank 2020b). Sample The current study was conducted further to classify the developed countries and the developing ones. This classification is based on the data provided by the World Bank (2015) in the report referring to the “Country and Lending Groups” 2015, where the countries are classified as countries with high incomes, countries with average superior incomes, countries with average inferior incomes, and countries with low incomes. Consequently, the World Bank (2015) classified the economies with low and average incomes as “developing economies” and the countries with high incomes as “developed countries”. Finally, the current study sample is formed of 185 countries (49 developed countries and 136 developing countries) for which all the required data are available. To achieve the goal of this study, we substantiated the following multiple regression of the corruption level depending on the causal factors as follows:

Corruption = β 0 + β1 Tax pressure + β 2 Controls + ε where,

(2.1)

We use a panel-type analysis on a sample consisting of 85 countries applied on data corresponding to the period 2005–2014. Results and discussions A first result is obtained after analysing the whole sample of countries. Thus, we found out a negative influence of the tax pressure on the corruption level which is in contradiction with our expectations. However, similar results were obtained by other studies too, for instance, Dreher and Siemers (2009), Dreher and Schneider (2010), and McGee (2008, 2012). A second result is the identification of differentiated results when the influence of the tax pressure on the corruption level is controlled by the economic growth rate and the institutional quality. In this way, we noticed that, with the high institutional quality characteristic for the developed countries, a higher tax pressure leads to a higher level of corruption which corresponds to our expectations.

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2

R Linear = 0.028

200.00

Corruption

150.00

100.00

50.00

.00 .00

20.00

40.00

60.00

80.00

100.00

Fiscal freedom

Graph 2.7  Correlation between corruption and fiscal freedom Table 2.7  Correlation coefficients between corruption and fiscal freedom Corruption

Fiscal freedom

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Corruption 1 185 .167* .024 181

Fiscal freedom .167* .024 181 1 181

Source: data own processing **The correlation is significant at a significance threshold of 0,05 (two-tailed) Table 2.8  Regression of corruption depending on the fiscal freedoma

Model Constant Fiscal freedom

Non-standardized coefficients B Std. error 36.559 21.850 .644 .284

Source: data own processing a Dependent variable: corruption

Standardized coefficients Beta 0.167

t 1.673 2.271

Sig. .096 .024

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For the developing countries, we identified a strong and negative influence of the tax pressure on the corruption level if this relation is controlled by a low institutional quality. Thus, in the developing countries, the influence of a low institutional quality enhances the negative role of the tax pressure on corruption. In these countries, a low tax pressure enhances corruption because it is rather a matter of low governing efficiency where the subjects can easily avoid the laws. In conclusion, it can be said that the relation between corruption and the tax pressure must be analysed in a multi-dimensional manner revealing with priority the moderating role of the economic growth and of the institutional quality of a country. A third result consists of the identification of some negative influences of the economic growth rate of a country and of the institutional quality on the corruption level. This can explain why the countries with high incomes and which are characterized by a high quality of the state institutions show at the same time a low level of corruption. Our results are supported by numerous studies referring to the influence of the economic growth rate of a country (of the incomes per capita) on corruption level (Husted 1999; Treisman 2000; Paldam 2001, 2002; Gundlach and Paldam 2009; and De Rosa et al. 2010) or referring to the influence of the institutional quality on corruption (Kirchler 2007; Torgler and Schneider 2009; Park and Blenkinsopp 2011; Forson et al. 2017). In conclusion, the influence of the tax pressure on the corruption level must be analysed in correlation with other factors determining the corruption level such as the level of the economic growth and the institutional quality. Our research reveals the identification of some differentiated results of the tax policy influence on the corruption level in the developing countries compared to the developing ones. For the developed countries, we found out that, with high quality of the institutions, a low tax pressure leads to a lower level of corruption which corresponds to our expectations. In the developing countries which are facing on the contrary, a low level of the institutional quality, a low tax pressure enhanced corruption because it is rather a low governing efficiency and thus people can avoid the laws. Our findings suggest that some tax policies can function in some countries but not in the others and thus can explain the low efficiency of the different anti-­ corruption policies which is particularly found in the countries with low incomes. Thus the current investigation could have significant implications for the governments and the political decision-makers directing to the adoption of the best decisions in the fight against corruption. The government bodies of the whole world must become aware of the necessity to adopt differentiated tax policies depending on the country development level characterized by different levels of the institutional quality (bureaucracy, public service quality, their capacity to collect taxes, to produce and implement efficient public policies, to provide public goods). A low tax pressure can reduce corruption in the developed countries and can enhance it in the developing countries depending on the existence of some different levels of governing efficiency. Thus, the governments and the decision-makers must know that the fight against corruption requires not only correct tax policies, but there must be paid a special attention to the method of these policies implementation and to the institu-

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tional quality of each country. A detailed presentation of the study is found in the work of Achim, Borlea, and Anghelina (2018b).

2.2.3  Tax Pressure and Shadow Economy 2.2.3.1  Theoretical Approaches Most of the specialized literature works which dealt with the analysis of the taxation system effects on the corporate behaviour reveal that the shadow economic activities are enhanced as the real and perceived tax pressure increases (Devereux and De Mooij 2009; Frey and Weck-Hannemann 1984; Schneider and Klinglmair 2004; Torgler and Schneider 2009). The study of OECD (2009) also supports the hypothesis according to which the high taxation rates lead to the identification of discrete locations of the profitable investments made by the multinational companies. Sweden is a good example of a state with high tax pressure and undesirable taxation effects. Between the years 1970–1980, the Swedish government adopted the most severe progressive taxation system of the incomes among the industrialized countries, and the marginal taxation rates for the largest category of employees were ranging between 80% and 90%. The excessive tax pressure generated high levels of the tax avoidance (Agnell and Persson 2000). The same findings are supported by Putniņš and Sauka (2015), in their study conducted for the Baltic countries (Latvia, Estonia, and Lithuania). They noticed that the discontent against the tax system and government provide a plausible explanation of the shadow economy dimension of these countries. The high taxation rates determine the migration of the investors to other countries which are “tax oasis” or “tax havens” to get a more favourable tax treatment, and this issue is well known, too. According to the OECD criteria (OECD 2009) a tax haven is characterized by the absence or existence of a reduced number of taxes on the incomes, the lack of transparency, the lack of an effective exchange of information, and lack of substantial economic activities. Additionally, every country that fulfils the above-mentioned criteria must commit to put in application the principles of transparency and exchange of information for tax purposes. The economic analysts declared that about 70% of the monetary mass of the world is run through the tax havens (Buziernescu and Antonescu 2007). Despite all these, there are studies which contradict the existence of a causality relation between the tax pressure and the volume of the shadow economy (Friedman et al. 2000; Kawano and Slemrod 2016; Achim et al. 2018a). Thus, Friedman et al. (2000) evaluated the determining factors of the shadow activities in 169 countries using data of the year 1990 and found out that the companies function unofficially, not for avoiding the taxation but for reducing the regulatory and corruption burden. The corruption, bureaucracy, and a weak judicial system are systematically associated to a higher level of the informal sector. Moreover, Dreher and Schneider (2010)

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found out that the higher taxation rates even involve more restrained shadow activities. On the other side, Torgler and Schneider (2009) conclude on some “mixed empirical proofs” identified in the specialized literature regarding the correlation between tax burden and shadow economy. Likewise, the authors Torgler and Schneider (2009), in their study conducted for 55 countries over the period 1990–1999, did not identify a significant statistical correlation between the tax burden and the shadow economy. Also, Kawano and Slemrod (2016) identified a weak relation between business taxation rates and the tax incomes collected further to business, at least on short term, but it does not exclude a stronger relation on long term. Similarly, the study conducted by de Achim, Borlea, Găban, and Cuceu (2018a) for the European Union countries referring to the period 2007–2013 did not identify the tax pressure as a determining factor for the shadow economy. To obtain as complete as possible estimations of the tax pressure, it was measured using three indicators, respectively: (a) as total tax rate (as the percent of commercial profits); (b) as tax revenue (as the percentage of GDP-TAX2); and (c) as fiscal freedom variable, a subcomponent of the Heritage Foundation’s economic freedom index). The levels of the shadow economy for the European Union member states are determined as percentage of the GDP as they are determined by Schneider (2013) in the period 2007–2013. Other independent variables such as the wealth of a country, public governance, or the individuals’ happiness are also included in the econometric model of the shadow economy evaluation. Thus, similar with some previous studies (Friedman et  al. 2000; Dreher and Schneider 2010; Torgler and Schneider 2009; Kawano and Slemrod 2016) the work of Achim, Borlea, Găban, and Cuceu (2018a) did not find the expectation that an increase of tax pressure leads to an increase of shadow economy, but rather opposite results. The explanation regarding these results against the expectations could be found using different methods of measuring the tax pressure for each country. Referring to this issue, Williams and Schneider (2016) noticed that the tax and social security systems are vastly different among countries and due to this reason the values could not be fully comparable. In conclusion, we consider that there does not exist any unity of the results obtained by the specialized studies regarding the existence of an influence of the tax pressure on the shadow economy, and this relation must be analysed within a national-specific framework using as much as possible variables to moderate the effects. 2.2.3.2  Practical Approaches We propose further on to investigate the relation between tax pressure and shadow economy. Methodology Based on the results identified in the specialized literature, we expect that a higher tax pressure level leads to higher stimulations for avoiding the tax legislation and,

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consequently, a higher rate of engagement in shadow activities. In conclusion, we propose the following working hypothesis: Hypothesis 1: An increase of the public tax pressure leads to an increase of the shadow economy. We shall measure the tax pressure using the indicator represented by the fiscal freedom. The higher the tax pressure is, the lower the tax freedom is. The source of the data is represented by Heritage Foundation (2020). The level of the shadow economy is determined as percentage of the GDP as presented in the source offered by Medina and Shneider (2018). We shall analyse the relation between the two variables using a sample consisting of 157 countries for which all the data are available for the period 2005–2018. To this aim, we use the descriptive methods, correlation coefficients, and regression analysis, and we carry out statistical tests necessary to ensure a high accuracy of the results. The statistical processing is carried out using the statistical SPSS software. Graph 2.8 and Table 2.9 present a reduced correlation between the shadow economy and Fiscal freedom. The correlation coefficient is positive, with a value of 0.183, reflecting a poor correlation of the two variables, and the variation of the shadow economy is explained by the fiscal freedom only at a percentage of 3%. From Table 2.10 we find out that the value of the regression coefficient of the fiscal freedom variable in relation with the shadow economy is positive (0.198) and significant at a significance threshold of 5%. This reflects that an increase by one point of the level of the tax freedom there is at average an increase of the shadow economy level by 0.198 points. Conclusions, limitations, and research directions The calculations mentioned above offer clear indications on the existence of a relation between tax freedom and shadow economy. With an increase of the fiscal freedom rate, there is an increase of the shadow economy. It means that at a decrease of the tax pressure, the level of the shadow economy increases which contradicts the expectations. Naturally, we would expect that the level of the shadow economy increases as a result of the tax pressure increase. However, similar results are obtained in other studies, too, for instance, Friedman et  al. 2000, Kawano and Slemrod (2016), and Achim, Borlea, Găban, and Cuceu (2018a).These findings do not reveal a positive influence of the tax pressure on the level of the shadow economy, either it does not reveal any relation.

2.2.4  Tax Pressure and Money Laundering 2.2.4.1  Theoretical Approaches The tax havens are considered the main way by means of which the companies elaborate the tax optimization strategies to reduce the tax expenses. Taking into considerations these issues, the companies constantly attempt to obtain tax benefits

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and opportunities to protect their assets in offshore jurisdictions (Afrăsinei and Carp 2018). The studies conducted by Fuest and Riedel (2012), Janský and Kokeš (2015), and Afrăsinei and Carp (2018) reveal that the entities which have connections with the tax havens show higher profitability and pay lower taxes in comparison with the other companies. Considering that the money laundering is often carried out through tax havens to avoid tax payments, the tax pressure is considered a main activity to fuel money laundering (Chong and López-de-Silanes 2015; Schwarz 2011; Schlenther 2013). 2.2.4.2  Practical Approaches We propose further on to analyse an eventual relation between tax pressure and money laundering. Methodology Based on the investigations of the results provided by the specialized literature, we expect that a higher level of the tax pressure from a country leads to higher stimulation of the tax legislation avoidance, by getting engaged in money laundering operations (for instance, by means of tax havens). In conclusion, we propose the following working hypothesis: Hypothesis 1: An increase of the tax pressure leads to the increase of money laundering volume. We shall measure the tax pressure using the indicator represented by the fiscal freedom. The higher the fiscal pressure is, the lower the fiscal freedom is. The source of the data is represented by Heritage Foundation (2020). The level of the money laundering is determined using the Basel AML indicator (Basel Anti-Money Laundering Index) which measures the risk of money laundering and terrorism funding. The sample is represented by a number of 161 countries, and the analysed period is 2012–2017; all the data are available for it. To this aim, we use the descriptive methods, correlation coefficient, and regression analysis, and we carry out the statistical tests required to ensure a high accuracy of the results. The statistical processing is carried out using the SPSS statistical software. Results and discussions Graph 2.9 and Table 2.11 show a reduced correlation between the risk of money laundering and the tax freedom. A percentage of 7% of the variation of the risk of money laundering is explained by the level of the tax pressure corresponding to the data of the sample. The correlation coefficient is positive having the value of 0.266 which reflects a poor level of correlation between the two variables. Table 2.12 shows that the value of the regression coefficient of the tax freedom variable in relation with the risk of money laundering variable is positive (0.027)

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and significant at a significance threshold of 1%.This indicates that an increase by one point of the level of fiscal freedom results in, at average, an increase of the risk of money laundering by 0.027 points. Conclusions, limitations, and research directions The above calculations reveal a statistically significant influence of the fiscal pressure on the risk of money laundering (at a threshold of 1%) explaining the variation of the risk of money laundering in a percentage of 7%. The coefficient sign is a positive one, unexpected from the perspective of our expectations. In other words, our results indicate that with an increase of the fiscal freedom rate (respectively, the decrease of the tax pressure), the increase of the risk of money laundering occurs. Naturally, we would expect that the risk of money laundering increases as a result of the tax pressure increase. Our study is meant to be only a starting point for more detailed investigations regarding the identification of the explanatory factors about the money laundering fact. Consequently, we are aware that there exist numerous limitations of our investigations which if known can open the way for obtaining much more reliable results. One of the limitations of our investigation consists of the utilization of a data processing methodology which is relatively simple, but it can be improved by using panel-type studies. Another important limitation is the failure to use some important

2

R Linear = 0.034

Shadow economy

60.00

40.00

20.00

.00 40.00

60.00

80.00

Fiscal freedom

Graph 2.8  Correlation between the shadow economy and fiscal freedom

100.00

101

2.3 Public Governance Table 2.9  Correlation coefficients between shadow economy and fiscal freedom Shadow economy

Fiscal freedom

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Shadow economy 1 158 .183* .022 157

Fiscal freedom .183* .022 157 1 181

Source: data own processing **The correlation is significant at a significance threshold of 0,05 (two-tailed)

control variables when it investigated the influence of the tax pressure on some facts belonging to the economic and financial crime domain, for instance, the level of the economic growth, the institutional quality, etc. Similar studies (Achim et al. 2018b), for instance, found out differentiated influences of the tax pressure on corruption in the developed countries in comparison with those from the developing countries. Being aware of these limitations, they could constitute in future a base for the elaboration of some more detailed studies.

2.3  Public Governance The public governance is identified as one of the most important causes of the economic and financial crimes.

2.3.1  General Approaches Regarding the Public Governance We shall try further on to define the public governance based on the investigations of the concepts existing in the specialized literature, and we shall provide a presentation of the measuring instruments used worldwide. Further on, the presentation of some statistics of the public governance at the level of the European Union coun-

Table 2.10  Regression of the shadow economy depending on the Fiscal freedoma

Model Constant Fiscal freedom

Non-standardized coefficients B Std. error 13.619 6.579 .198 .086

Source: data own processing a Variable dependent: shadow economy

Standardized coefficients Beta .183

t 2.070 2.311

Sig. .040 .022

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tries is relevant for the investigation step and then for the causality relation between public governance and economic and financial crime. 2.3.1.1  Concept of Public Governance and the Measuring Instruments The adhering of the European countries to the European Union raised the question regarding the importance of the “best practices” in public governance as key elements for explaining the rate of compliance of the European Union member states with the provisions required by the adhering process. For the beginning, the studies were dedicated to issues such as the Europeanization, and further on they focused on the public governing structures able to accelerate the Europeanization process (Dimitrova 2002; Tosun 2014). Numerous projects launched by the European Commission focused on the development of the public governance of the members states and on the adoption of “good practices” even before the adhering (for the community acquis adoption and implementation), as a priority to create the suitable framework for putting into application the cohesion and convergence policies. Goetz (2001) raised the question regarding the necessity of a massive restructuring of the administration of the post-communist countries from Central and East Europe. Lippert et al. (2001) considers that the key objective of “good governance” is not to build a modern bureaucracy for the future member states but to allow these countries to act as efficient actors in the governance system on several levels. A year later, Dimitrova (2002) underlined that the higher the administrative or government capacity is, the better the European Union laws are implemented by the member states. The lacks of some unitary norms of the European Union and the preferential adoption of some administrative reforms lead to an enhanced variation of the success regarding the consolidation of the administrative institutions. In accordance with the recommendations of the European Commission and the best available practices of the European Union member states, the golden rule is that the lower the number of the institutions involved at different governance levels is (sectoral and regional), the higher the governance efficiency is. Numerous actors consider the “governance” concept as being very difficult to define and quantify by means of a single indicator or of a combination of indicators which could show all the governing dimensions (Andrews 2008; Kaufmann et al. 2010). However, the attempts of measuring the governance at public level shown by the World Bank together with its experts in the field, namely, Kaufmann, Kraay, and Masstruzii (2010), are remarkable. They tried to develop an indicator regarding the public governance called within the World Governance Indicators (WGI). Such an indicator is also used by numerous researchers who investigated the determining causes of the shadow economy of different countries (Torgler and Schneider 2009; Thießen 2010). The World Governance Indicators (WGI) provided by the World Bank (2020b) summarize the opinion regarding the quality of governance provided by a large number of enterprises and citizens within some aggregated and individual governance indicators calculated for 215 economies since 1996 until now referring to six dimensions of the governing as follows (World Bank 2020b):

2.3 Public Governance

103 2

R Linear = 0.071

9.00

Rosk of money laundering

8.00

7.00

6.00

5.00

4.00

3.00 40.00

60.00

80.00

100.00

Fiscal freedom

Graph 2.9  Correlation between money laundering and fiscal freedom

(a) Voice and responsibility (VA) which capture the freedom of expression, freedom of association, and freedom of press. (b) Political stability and absence of violence (PV) evaluates the perception measured of the probability to destabilize or turn out the government. (c) Governance efficiency (GE) captures the perceptions regarding the public services to produce and implement the good policies and to supply public goods. (d) The regulatory quality (RQ) captures the perceptions regarding the capacity of the government to formulate and implement solid policies and regulations which allow and promote the development of the private sector (also includes the perception of tasks imposed by the excessive regulation). Table 2.11  Correlation coefficients between money laundering and fiscal freedom Risk of money laundering

Fiscal freedom

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Risk of money laundering 1 164 .266** .001 161

Source: own processing **The correlation is significant at a significance threshold of 0,01 (two-tailed)

Fiscal freedom .266** .001 161 1 181

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Table 2.12  Regression of money laundering depending on the Fiscal freedoma

Model Constant Fiscal freedom

Non-standardized coefficients B Std. error 3.825 .604 .027 .008

Standardized coefficients Beta .266

t 6.338 3.483

Sig. .000 .001

Source: own processing a Dependent variable: risk of money laundering

(e) Rule of law (RL) reflects the perception on the measure where the agents have confidence and comply with the society laws, ownership rights, police and law courts, and the crime and violence probability rate. (f) Control of corruption (CC) reveals the perceptions on the measure where the public power is exercised to obtain private gains. Each component is calculated and reported on a scale from −2.5 (weak) to 2.5 (strong) related to the governance performance. Various studies have showed the importance of a good functioning of the public governance or of the state apparatus regarding the corruption and shadow economy facts. The issues associated to the bureaucracy regulatory framework, law and order observance, confidence rate, discouraging measures are stimulating the individuals’ involvement with corruption and shadow activities. 2.3.1.2  Public Governance in European Union Countries Further on, we propose to investigate the quality of the public governance in the European Union countries for the period 2005–2015. The present study will provide us with clues about the links between the quality of governance and the phenomena of economic and financial crime. Methodology To measure the public governance quality we shall appeal to its three dimensions, as they are determined by the World Bank (2020b) within the World Governance Indicators (WGI), namely, governance effectiveness, regulatory quality, and rule of law. For each dimension the World Bank calculate scores ranging on a scale from −2.5 (weak) to 2,5 (strong) referring to the governance performance. The governance effectiveness indicator as it is reflected by the World Bank (2020b) shows the perception of the population regarding the public service quality and the independence rate against the political pressures formulated by the government as well as the credibility of the commitment assumed by it regarding the adoption of these policies. Regulatory quality indicator catches the perception on the government capacity to formulate and implement solid policies and regulations promoting and stimulating the private sector development (it also includes perception of the tax pressure imposed by the excessive regulation).

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105

Rule of law indicator as presented by the World Bank reflects the extent to which the agents have confidence and observe the society rules and particularly the quality of contract execution, ownership rights, police and law courts, and the probability of crimes and violence. The data sample consists of 28 European Union member states, and the analysis period is 2005–2018. The methods used include descriptive methods, comparison, analysis, and synthesis. Results and discussions Graph 2.10 shows that for the analysed period, the lowest public governance effectiveness was found in Romania (the only country of the European Union with a negative score). This is followed by Bulgaria, Italy, and Greece. By contrast, the most effective public governance is again found in the Northern countries (Finland, Denmark, Sweden, and the Netherlands). Graph 2.11 reflects the regulatory quality in the European Union countries. One can be seen that among the European Union countries, Croatia seems to have the lowest regulatory quality. It is closely followed by Romania and Bulgaria. By contrast, the highest regulatory quality is found in Denmark, Finland, the Netherlands, and the United Kingdom. Graph 2.12 shows that among the European Union countries with the lowest confidence in the rule of law, there are Bulgaria, Romania, and Croatia. On the opposite pole there are Finland, Denmark, Sweden, Austria, and the Netherlands. There can be seen that the European Union countries belonging to the former communist block shows very low levels of the public governance effectiveness and low confidence in the rule of law. The explanations indicate the burden of the communism heritage from these countries characterized by public governance which still has much to learn until the achievement of the real values of democracy, and this is also reflected by the low living standard of the population from these countries. Within the public bodies, the mechanisms of control play a significant role both for the prevention and for the detection of corruption cases. Also, the member countries must promote the increase of the public confidence in justice and administration and, last but least, the implication of the civil society in decision-making processes. The declaration of the public officers’ fortune, the elaboration of norms referring to the conflicts of interest, the enhancement of the role of the court of audit for encouraging the anti-corruption reforms, the clear identification of the elected officers for corruption deeds, the establishment of a clear harmonized definition at the level of the European Union of the “public officer”, and the establishment of a legal framework and well-regulated and transparent system of funding of the ­political parties are only some of the anti-corruption measures identified by the European Commission (2014). Based on the analysis of the previous chapters, we also found out that the post-­ communist countries present at the same time also the highest levels of the economic and financial crime. Further on, we shall directly approach the relation between the public governance and corruption, shadow economy, and money laundering facts reflecting the reviewing of the specialized literature regarding this issue but also of the conduct of some empirical studies.

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2.3.2  Public Governance and Corruption 2.3.2.1  Theoretical Approaches Different studies revealed the importance of ensuring a high rate of confidence in the governing institutions to guarantee a good functioning of the state (Kirchler 2007; Torgler and Schneider 2009; Park and Blenkinsopp 2011; Fritzen et al. 2014). Corruption and confidence are two important determining factors for the public governance quality (Fritzen et  al. 2014). A higher institutional quality of a state determines that its citizens have a higher confidence in the state, and, consequently, these will be less interested in cheating. The government has a strong discretionary power regarding the allocation of resources, and the bribery is paid to avoid the payment of taxes or to avoid the compliance with the lawful regulations (Torgler and Schneider 2009). Consequently, the societies showing a high rate of confidence in the state indicate at the same time a more reduced level of corruption. The confidence in the government or in the public services reflect the subjective judgements of the citizens by means of which they consider that the government is competent, reliable and honest and at the same time satisfies their needs (Park and Blenkinsopp 2011). A low institutional quality determines a low confidence in the government leading to search for methods to avoid the law (Kirchler 2007). One of these methods is the bribery of the public officers to avoid the payment of taxes, and, thus, corruption is enhanced. In this regard, the study carried out by Dreher et  al. (2009) for a sample consisting of 18 countries of OECD revealed that the improvement of the institutional quality reduce corruption and shadow economy in these countries. Referring to the regulation of the business start-up in the study conducted by Djankov et al. (2002) on a sample of 85 countries, they showed that the value of the official costs of business start-up are very high in most countries. This obstacle can be bypassed by the enterprises enhancing, thus, the corruption level. Djankov et al. (2002) found out that in the countries where the regulations for the business start-up are strong, the corruption level and the level of the shadow economy are higher than in the countries where the regulations referring to the business start-up are weaker. On the other hand, the studies conducted by Kirchler (2007), Torgler and Schneider (2009), and Park and Blenkinsopp (2011) identified that a high institutional quality generate low interest regarding the law avoidance and thus the corruption level is reduced. Moreover, the recent study conducted by Forson et al. (2017) for a sample of more than 22 countries from sub-Saharan Africa for the period 1996–2013 found out that the efficiency of the governing and the quality of the regulations are determining factors for corruption level. They concluded about the existence of some complementary sources of corruption from the perspective of the institutional inefficiency. Additionally, the study conducted by Forson et al. (2017) identifies that the influence of the institutional quality on corruption is much higher in the developing countries than in the developed ones. The excessive bureaucracy,

2.3 Public Governance

107

the lack of transparency, and the ambiguous and confusing legislation particularly stir up a poor person who becomes more and more preoccupied by the officers corruption to obtain immediate benefits. 2.3.2.2  Practical Approaches The role of the public governance regarding corruption constituted the object of our researchers. Methodology Further to the investigation of the specialized literature and practice, we expect that a low quality of the public governance consisting of an excessive bureaucracy, low transparency, a dense and inconsistent regulatory system generate discontent among the population who will appeal to the public officers bribery or obtain immediate benefits. In conclusion, we propose to test the following working hypothesis: Hypothesis 1: An increase of the public governance quality leads to a diminution of corruption. Corruption is measured as the place occupied by a country from the total countries included in the analysis (185), depending on the score obtained by the Corruption Perception Index (CPI), provided by Transparency International (2020b). A top position occupied by a country regarding the corruption level reflects a high corruption level and vice versa. The public governance will be measured as mean of the scores obtained by the six governing dimensions as provided by the World Bank (2020b): voice and 2.5 2 1.5 1 0.5

-0.5

Romania Bulgaria Italy Greece Croatia Poland Hungary Latvia Lithuania Slovakia Czech Republic Slovenia Portugal Estonia Spain Malta Cyprus France Ireland Belgium Germany United Kingdom Austria Luxembourg Netherlands Sweden Denmark Finland

0

Graph 2.10  Governance effectiveness in the European Union countries, 2005–2018. (Source: own processing)

2.00 1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00

2  Economic and Political Determinants of Economic and Financial Crime

Croatia Romania Bulgaria Greece Slovenia Italy Poland Portugal Slovakia Latvia Hungary Spain Lithuania Czech Republic France Malta Cyprus Belgium Estonia Austria Germany Ireland Luxembourg Sweden United Kingdom Netherlands Finland Denmark

108

Graph 2.11  Regulatory quality in the European Union countries, 2005–2018. (Source: own processing)

responsibility, political stability and absence of violence, governance efficiency, rule of law, and control of corruption. The global score of the public governance is situated on a scale from −2.5 (weak) to 2.5 (strong) regarding the public governance performance. The sample is represented by a number of 185 countries, and the period of analysis is for which all the data are available. To this aim we use the descriptive methods, correlation coefficients, and regression analysis, and we carry out the statistical tests necessary to ensure a high accuracy of the results. The statistical data processing is carried out using the statistical SPSS software.

2.5 2 1.5 1 0.5

-0.5

Bulgaria Romania Croaa Italy Slovakia Greece Poland Hungary Latvia Lithuania Czech Republic Slovenia Portugal Cyprus Spain Estonia Belgium Malta France Germany United Kingdom Ireland Luxembourg Netherlands Austria Sweden Denmark Finland

0

Graph 2.12  Rule of law in the European Union countries, 2005–2018. (Source: own processing)

2.3 Public Governance

109

Results and discussions Graph 2.13 and Table 2.13 show an extremely close correlation between corruption and public governance. Almost 87% of the corruption variation at the level of a country is explained, at average, by the quality of the public governance of that country. The correlation coefficient (c = −0.931) reflects an indirect and extremely close relation between the two variables. Table 2.14 shows that an increase by one point of the quality of the public governance leads to a decrease of the corruption level by 50 units (respectively about 50 positions of the ranking top countries). Conclusions, limitations, and research directions The results of the study confirm the investigation hypothesis previously established, and we found out that an increase of the quality of the public governance leads to a significant diminution of the corruption level. Similar results are found in the specialized literature (Kirchler 2007; Dreher et al. 2009; Torgler and Schneider 2009; Park and Blenkinsopp 2011; Fritzen et al. 2014) confirming thus an extremely closed relation between the two variables. A low institutional quality determines a low confidence in governance leading to the identification of methods to avoid the laws, for instance, by public officers’ bribery to avoid the tax payment, and, thus, corruption is enhanced (Kirchler 2007). The study conducted by Dreher et al. (2009) demonstrates that the improvement of the institutional quality results in the diminution of the corruption level. The above study, although provides results which are in line with those presented in the specialized literature, presents some limitations regarding the techniques used and the failure to include in the analysis some control variables (for instance, the economic growth level). In this view, the study conducted by Forson et al. (2017) is suggestive; it indicates that the influence of the institutional quality on corruption is much stronger in the developing countries than in the developed ones. In conclusion, a differentiated approach of the analysis by the two categories of countries (developed and developing), the use of a more complex methodology of data processing (for instance of panel type), as well as the utilization of some well-­ substantiated control variables can determine a higher degree of soundness of the results of this relation investigation.

2.3.3  Public Governance and Shadow Economy 2.3.3.1  Theoretical Approaches The studies conducted by Torgler and Schneider (2009) demonstrate the importance of the quality of the public governance on the level of the shadow economy. Also, Torgler (2004) found out that the direct democratic rights and the local autonomy have a significant positive effect on the dimensions of the shadow economy. In a study carried out on a number of cross data available for 120 countries and panel data for 70 countries for the period 1994–2002, Dreher and Schneider (2010) found

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that an increase of the corruption indicator by one point results in the increase of the level of the shadow economy (expressed as percentage of the GDP) by 1.5–3.5 points. Reviewing different studies, Kirchler (2007) concluded that the shadow activities increase as the confidence in the public governance decreases, the tax morale is deteriorated, and the legal regulations regarding the economic activities multiply. The study conducted by Richardson (2006) shows that the confidence is negatively correlated with the tax avoidance; consequently, a low level of confidence in the tax authorities is correlated with the high levels of the tax avoidance. Also, the study carried out by Kogler et al. (2013) confirms the role of the confidence and power as important determining elements of the tax compliance rate and concluded that the highest level of tax compliance and the lowest level of the tax avoidance are obtained under the circumstances provided by high confidence and power in governance activity. Thus, during the last years, the confidence in the government authorities as well as the tax morale and the motivational issues have been investigated to identify whether they influence on tax avoidance (Kogler et  al. 2013; Prinz et  al. 2014; Antoci et al. 2014). In the study elaborated by Achim, Borlea, Găban, and Cuceu (2018a) for the European Union countries over the period 2007–2013, the quality of the public governance is associated with a lower trend of engagement in shadow activities of the EU member countries. The quality of the public governance was estimated using the World Governance Indicators (WGI) provided by the World Bank (2020b), and the magnitude of the shadow economy for the European Union member countries is determined as percentage of the GDP as it was determined by Schneider (2013), for the period 2007–2013. Referring to the influences of different components of the public governance on the shadow economy (as they are reflected by the WGI governance indicator elaborated by the World Bank), the study conducted by Achim, Borlea, Găban, and Cuceu (2018a) showed that each of these components of public governance negatively and significantly influences the shadow economy at the level of the sample countries. This study shows that, regarding the new members of the European Union, which adhered after the May 1, 2004 (countries represented by EU 13), the influence of the governance components on the shadow economy is a little bit weaker. Thus, the impact of the components voice and responsibility (VA), political stability and absence of violence (PS), governance effectiveness (GE), regulatory quality (RQ), and rule of law (RL) on the shadow economy is negative but significant only at a significance threshold of 5% and 10%. The influence of the control of corruption (CC) on the shadow economy was found to be negative but statistically ­insignificant. As a result, this factor seems to not be determining for the shadow economy in these countries. However, the results obtained by Achim, Borlea, Găban, and Cuceu (2018a) are somehow in line with the results obtained by Dreher and Schneider (2010). The latter found proofs showing that this relation can be different among the countries with high incomes and those with low incomes rate, and it is explained by the different mechanisms which are prevailing in each category of country. By connecting to the results obtained by Achim, Borlea, Găban, and Cuceu (2018a), because the new EU members present a GDP per capita three times smaller than the

111

2.3 Public Governance

old EU members (EU 15), the different mechanisms which characterize the countries with high incomes and those with low incomes mentioned by Dreher and Schneider (2010) could be used as explanations of the different results obtained regarding the impact of the corruption control on the shadow economy within the two groups of countries (EU 15 and EU 13). 2.3.3.2  Theoretical Approaches Further on, we propose to investigate a potential relation of causality existing between the public governance and the shadow economy. Methodology Based on the results identified in the specialized literature, we expect that a low quality of the state institution effectiveness, the lack of discouraging measures, and the excessive bureaucracy generate the stimulation of the population to prefer to act within the unofficial economy (shadow economy) rather than in the official one, as the latter is perceived as being cumbersome and pressing. In conclusion, we propose to test the following working hypothesis:

2 R Linear = 0.867

200.00

Corruption

150.00

100.00

50.00

.00 -3.00

-2.00

-1.00

.00

Public governance Graph 2.13  Correlation between corruption and public governance

1.00

2.00

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2  Economic and Political Determinants of Economic and Financial Crime

Table 2.13  Correlation coefficients between corruption and public governance Corruption

Public governance

**

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Corruption 1 185 −.931** .000 185

Public governance −.931** .000 185 1 185

The correlation is significant at a significance threshold of 0,01 (two-tailed)

Hypothesis 1: The increase of the public governance quality leads to a diminution of the level of the shadow economy. The public governance will be measured as mean of the scores obtained by the six dimensions of the governance as provided by the World Bank (2020b): voice and responsibility, political stability and absence of violence, governance effectiveness, rule of law, and corruption control. The global score of the public governance is situated on a scale from −2.5 (weak) to 2.5 (strong) regarding the public governance performances. The shadow economy is measured as percentage of the GDP using as data source the information provided by Medina and Schneider (2018). The sample is represented by 185 countries, and the analysis period is 2005–2015 for which all the data are available. To achieve this purpose, we use the descriptive methods, correlation coefficients, and regression analysis, and we carry out statistical tests necessary to ensure a high accuracy of the results. The statistical processing is carried out using the statistical SPSS software. Results and discussions Graph 2.14 and Table 2.15 show the existence of an indirect strong enough correlation between the quality of the public governance and the shadow economy. About 48% of the variation of the shadow economy level can be explained by the quality of the public governance (R squared = 0.484). Table 2.16 shows that the coefficient of the public governance variable is statistically negative and significant at a significance threshold of 1%. We found out that the increase by a point of the quality of the public governance leads to the decrease of the shadow economy, at average by 9.295 units (respectively, 9.295% of the GDP). Conclusions, limitations, and research directions The results of our study confirm our working hypothesis concluding that the increase of the quality of the public governance leads to the diminution of the level of the shadow economy. Thus, a high regulatory level, a low confidence in the rule of law, police, and justice, and a low rate of public goods and service generation represent an important impulse for the involvement in shadow economy.

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The results of the above-mentioned study are in line with the studies conducted by Torgler (2005), Torgler and Schneider (2009), Dreher and Schneider (2010), Kirchler (2007), and Achim, Borlea, Găban, and Cuceu (2018a) who similarly identified a significant negative influence of the quality of the public governance on the level of the shadow economy. The present study proposes to represent only a starting point for the investigation of the relation between public governance and shadow economy. To obtain a high accuracy of the results, it is required to use some control variables in the analysis model of the shadow economy, for instance, the economic growth level, the tax pressure, social and cultural variables, etc. Also, the utilization of more advanced methodologies of analysis of the panel type of the data can ensure the result soundness.

2.3.4  Public Governance and Money Laundering 2.3.4.1  Theoretical Approaches The Organization for Security and Cooperation in Europe (OSCE) (2012) in the declaration about the consolidation of good governance and fight against corruption, money laundering, and terrorism funding states that “the poor public governance is among the factors that contribute to the spreading of money laundering fact and terrorism funding”. According to this declaration, it is once again confirmed the OSCE commitment to “fight against the money laundering, terrorism funding, and associated crimes, turning them into political priorities supported by suitable judicial instruments, suitable financial, human, and institutional resources and, if required, the utilization of some suitable instruments for their practical and efficient implementation”. Also, the specialized studies identified an important role of the legal, financial infrastructure and law enforcement authorities (Peterson 2001, p.15) to fight against money laundering acts. The provision of a suitable regulatory framework of the money laundering (Chong and López-de-Silanes 2015; and Schwarz 2011) together with the efficiency of the judicial system (Vaithilingam and Nair 2009, Ardizzi et al. 2014) are the factors with a significant impact on the diminution of the money laundering crime. Table 2.14  Corruption regression depending on the public governancea

Model Constant Public governance

Non-standardized coefficients B Std. error 81.706 1.319 −50.689 1.465

Source: own processing a Dependent variable: corruption

Standardized coefficients Beta −.931

t 61.967 −34.612

Sig. .000 .000

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2.3.4.2  Practical Approaches Further on, we propose to investigate an eventual causality relation between the public governance and money laundering. Methodology Based on the results found in the specialized literature, we expect that the low quality of the state institution effectiveness, the lack of measures able to detect the suspect transactions or of the discouraging measures, generated the stimulation of the involvement in money laundering activities. In conclusion, we propose to test the following working hypothesis: Hypothesis 1: The increase of the quality of the public governance leads to the diminution of the money laundering volume. The public governance will be measured as mean of the scores obtained by the six dimensions of the governance as provided by the World Bank (2020b): voice and responsibility, political stability and absence of violence, governance effectiveness, rule of law, and control of corruption. The global indicator of the public governance is situated on a scale from −2.5 (weak) to 2.5 (strong) regarding the public governance performances. The level of money laundering is determined using the Basel AML (Basel Anti-­ Money Laundering Index) indicator measuring the risk of money laundering and terrorism funding, as it is provided by Basel Institute on Governance (2020). The sample is represented by a number of 164 countries for which all the data corresponding to the period 2015–2018 are available. To this aim, we use the descriptive methods, correlation coefficients, and regression analysis, and we carry out tests to ensure a high accuracy of the results. The statistical processing is performed using the statistical SPSS software. Results and discussions Graph 2.15 shows a reverse correlation between the quality of the public governance and the risk of money laundering. A percentage of 41.6% of the variation of the risk of money laundering is explained by the quality of the public governance (R square = 0.416). At a correlation coefficient of −0.645, the relation between the two variables appears as being indirect and of medium to high intensity (Table 2.17).The regression coefficient of the public governance variable is statistically significant at a significance threshold of 1%. We find out that the increase of the quality of the public governance leads to the diminution of the risk of money laundering by 0.845 points (Table 2.18). Conclusions, limitations, and research directions The results of our study confirm the investigation hypothesis established, concluding that the increase of the quality of the public governance leads to a diminution of the risk of involvement in the money laundering crimes. The results of the investigation mentioned above, are in line with the specialized literature (Chong and López-­ de-­Silanes 2015; Schwarz 2011; Vaithilingam and Nair 2009; Ardizzi et al. 2014)

2.4 Corporate Governance

115

about the documentation of a direct significant influence of the public governance on the risk of money laundering.

2.4  Corporate Governance 2.4.1  Concept of Corporate Governance As it is a highly topical issue, the theme of the corporate governance was largely discussed in the specialized literature, particularly in the German and Anglo-Saxon literature, and this is the reason for the diversity of the definitions: • In the etymological point of view, the concept of governance comes from Old Greek word “Kybernaien”, then from the Latin language “governance”, to designate the guiding of a boat on the sea. • The Oxford Dictionary (2020) defines “governance” as action, manner or leading function, administration, management. • The term “good governance” was mentioned for the first time in the year 1932 by Berle and Means (1932), within the theory which they developed, theory which constitutes, even today, the basis of the management systems. • Tricker (1984), called by Cadbury as “father of corporate governance”, considers that the essential elements of a good governance are company strategy, executive management, responsibility, and surveillance. • A very well-known definition is the one given by Shleifer and Vishny (1997) according to which the corporate governance refers to the way in which the suppliers of funds of a company ensure that they will receive the due benefits resulting further to the investment made. • Strictly accounting approach of the concept of corporate governance belongs to Law on Control and Transparency in Business (KonTraG 1998), which defines the corporate governance as the regulatory action regarding the control and transparency of the annual reports. The author considers that the administrator is obliged to ensure the maintenance of suitable systems for the administration of risk and internal control monitoring. Also, KonTraG (1998) insists on the obligation of the board of directors to report to the surveillance board the issues related to funding, investments, and employment planning. • An extended definition of the corporate governance is given by Ethical Investment Research Services body which considers the corporate governance as being a set of relations between the company management, executive directors, shareholders, and other stakeholders. For the shareholders this managing process can lead to the increase of the confidence regarding the obtaining of fair profitability of their investments. For the other parties, it can provide the certainty that the company activity is developed in a responsible manner in its relation with the society and the environment (Maier 2005, p. 5).

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R2 Linear = 0.484

Shadow economy

60.00

40.00

20.00

.00 –2.00

–1.00

1.00 .00 Public governance

2.00

Graph 2.14  Correlation between shadow economy and public governance Table 2.15  Correlation coefficients between shadow economy and public governance Shadow economy

Public governance

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Shadow economy 1 156 −.695** .000 156

Public governance −.695** .000 156 1 185

Source: own processing **The correlation is significant at a significance threshold of 0.01 (two-tailed) Table 2.16  Regression of shadow economy depending on the public governancea

Model (Constant) Public governance

Non-standardize coefficients B Std. error 28.794 .696 −9.295 .774

Source: own processing a Dependent variable shadow economy

Standardize coefficients Beta −.695

t 41.347 −12.008

Sig. .000 .000

2.4 Corporate Governance

117

• According to the Organization for Cooperation and Economic Development (OECD 2015), the corporate governance represents the system by means of which the companies are directed and controlled. In details, it refers to the way in which the rights and responsibilities are shared among the categories of participants to the company activity like the board of directors, the managers, shareholders, and other groups of stakeholders specifying, at the same time, the way in which the decisions referring to the company activity are made, the way in which the strategic objectives are defined which are the means to achieve the objectives and the way of monitoring the financial performances. Thus, the concept is considered as having two sides: the behavioural (referring to the way in which the managers of a corporation, the shareholders, the employees, creditors, and the suppliers the state and other group of stakeholders interact within the general strategy of the company) and the normative one (referring to the set of regulations corresponding to these relations and the behaviour described above – commercial law of companies, law of securities and capital markets, law of bankruptcy, competition law, requirements of the listing rule, etc.). • The World Bank (2005) states that the corporate governance encompasses both the determination of value-added by firms and the allocation of it among stakeholders that have relationships with the firm. Under this definition, the objective of a good corporate governance framework is to maximize the contribution of the firm to the overall economy. In this case, corporate governance would include the relationship between shareholders, creditors, and corporations; between financial markets, institutions, and corporations; and between employees and corporations. Under this definition, corporate governance could also encompass corporate social responsibility pertaining to such issues as charitable contributions or environmental concerns. • International Federation of Accountants (IFAC 2003) defines the corporate governance as ensemble of practices of the board of directors and of the executive management to ensure the strategic action directions, the achievement of the proposed objectives, administration of risks, and responsible utilization of financial resources. • The activity of the board of directors is generally supported by the technical structures (committees) represented by the risk committee, the remuneration committee, the nomination committee, and the audit committee. The risk committee is especially found within the credit institutions and is meant to support the Board of Directors to evaluate to which extent the institution complies with the coordinates of the level of the tolerance to risks established through the banking strategy. The control of significant risks to which the entity is exposed must draw the attention on the risk assumption, permanently monitoring the risk of the activity and the existing capital volume. • Referring to the relation between corporate governance and corporative social responsibility, the study conducted by Kolk and Pinkse (2010) on a sample of 250 companies concluded that more than half of the companies analysed allocated a special section of corporate governance in their reports regarding the corporative social responsibility. Finally, the authors conclude about the very

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close relation between the concept of corporate governance and the corporative social responsibility. • In Romania, the concept of corporative governance is relatively recent; preoccupations of the authors for this issue are found only in the works after 2008. Thus, according to Morariu et al. (2008, p.182), the corporate governance represents the system by means of which the entities are governed and controlled. A similar idea was issued by the authors Feleagă et al. (2011) who state that “the corporate governance represents an ensemble of rules of the game” by means of which the companies are internally administrated and supervised by the board of directors to protect the interests of all the participants”. –– Based on the above-mentioned definitions, we conclude that the corporate governance is the system by means of which the companies are governed and controlled to achieve the objectives of an economic entity.

2.4.2  Corporate Governance, Data Manipulation, and Fraud The directors together with the financial manager are directly responsible for the failure to observe the accounting regulations. In this sense, the existence of an audit committee within the company is meant to assist the functioning of the board of directors to achieve the objectives. Thus, the audit committee is meant to ensure that the risks faced by the entity are identified by the management and the latter took sufficient and solid enough measures to guarantee that the risks will not be materialized. In other words, the corporate governance as a system by which the economic entities are governed and controlled is directly responsible of the true and fair value of the financial state reflected in the accountancy. 2.4.2.1  E  arning Management or Creative Accounting – Instrument of “Embellishment” of the Accounting Data Because of the freedom which is assigned to the professional accountant, the data included in the financial statements can be easily manipulated by the unscrupulous practitioners of the accountancy profession in the so-called “creative accounting” or “earning management”. Thus, observing the letter, but not the spirit of law, by means of the creative accountancy techniques, the values of different categories of asset, liability, equity, income, and expense can be distorted to present favourable indicators of the financial performance. More exactly, assets, liabilities, equity, incomes, or expenses can be manipulated in order to provide a favourable picture of the entity to the thirds. Referring to the definition of creative accounting or earning management, there are many approaches:

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R2Linear = 0.416

9.00

Risk of money laundering

8.00

7.00

6.00

5.00

4.00

3.00 -3.00

-2.00

-1.00 .00 Public governance

1.00

2.00

Graph 2.15  Correlation between public governance and money laundering Table 2.17  Correlation coefficients between public governance and money laundering Risk of money laundering Pearson correlation Sig. (two-tailed) N Public governance Pearson correlation Sig. (two-tailed) N

Risk of money laundering Public governance a 1 −.645** .000 164 164 −.645** 1 .000 164 185

**The correlation is significant at a significance threshold of 0.01 (two-tailed) Table 2.18  Regression of the money laundering depending on the public governancea

Model (Constant) Public governance

Standardized coefficients B Std. error 5.921 .069 −.845 .079

Source: data own processing a Dependent variable: risk of money laundering

Standardized coefficients Beta −.645

t 85.240 −10.745

Sig. .000 .000

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• Griffith, American journalist, considers that: “All the enterprises hide their benefits. The financial reportings published are based on records which have been “adjusted” in a delicate manner or even substantially amended. The numbers presented to the investors have been wholly modified to protect the guilty individuals. It represents the biggest scam since the Trojan Horse up to now. In fact, this scam is legitimate and bears the name of creative accountancy” (quoted from Feleagă and Malciu 2002, p.389). • The practitioner position seems to be less incisive regarding this practice. In quality of practitioner of the accounting profession, Jameson (1988) considers that the accountancy process in its essence requires an operation with different motivations and ideas. Based on this diversity, there occur manipulations, scams, and falsification in case of some less scrupulous accountants. Jameson stated that these creative accounting practices do not breach the accounting laws or standards, and, consequently, they comply with the letter of the law but not with its spirit. Jameson thus declares the negative character of the creative accountancy which distorts the financial results of the enterprise misguiding the users of the accounting information. • A very complete definition is provided by Naser (1993, p.59) who considers the creative accountancy as a process by means of which: • “The accounting numbers are manipulated and taking advantage of the flexibility there are selected those practices of measurement and information that allow the synthesis document transformation from what they should be in what the managers wish; and • The transactions are structured in such a manner that they allow the production of the accounting result wanted”. • The creative accounting can be defined as an ensemble of methods aiming at either the modification of the financial results, in order to maximize or minimize it, or the presentation of the financial statements but the two do not exclude each other. The accounting options have always existed available for the accountant, and they do not involve creativity in the strict negative meaning of the word (Stolowy 2009). • A more complex view is reflected by the study conducted by Groșanu (2013) who stated that “the creative accounting is the result of the flexibility existing within the accounting regulations and which, if applied in good faith, allow ensuring a fair and true picture of the financial position and of the performances of the economic entity. Every user of the accounting information follows certain interests, and the flexibility given by the accounting regulations is often used to meet some private interests at the expense of the public interest.” • On the other hand, Vladu et al. (2017) added that no scientific method would allow anybody to find or to observe the absolute accounting truth. • Earnings management occurs when managers use judgement in financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the underlying economic performance of the company or to influence contractual outcomes that depend on reported accounting numbers (Healy and Wahlen (1999, p. 368)).

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The creative accounting techniques refer at the following issues, at least (Achim 2008): • Selection of methods, accounting policies, and options and their modification • Utilization of estimations and accounting provisions • Completion of artificial transactions to manipulate the values of the balance sheet or to smoothen the result • The time of selecting some transactions in a normal way leads to the modification of the picture through the accounts • Utilization of alternatives to recognize the elements of the financial position and performances The managers represent the most important category of stakeholders who would like an increase of the company performances and, consequently, they would be stimulated in the manipulation of the financial results with the aim of the appearance of as high as possible profits. More particularly when the managers’ remunerations are determined by the magnitude of the accounting result (as incentives) then, they would be tempted to become opportunist. So, they will try to find the best solutions to increase the profits, and it is rarely when they appeal to the utilization of the flexibility and gaps of the accounting regulations in order to present the financial statement of the company in a different manner from that which would result to the usual application of the existing norms. Another category of stakeholders interested in the application of creative accountancy technique is represented by the investors who want to obtain as high as possible profits in order to get a high volume of dividends. 2.4.2.2  Relation Between Creative Accounting and Fraud From the point of view of those who are involved in the establishment, verification, and control of the statements, the delimitation of the creative accountancy concept from that of fraud is required as well as the clarification of the two concepts. In accordance with the Cambridge Dictionary (2020), the word “fraud” is defined as “the  crime  of getting  money  by  deceiving  people” or “the crime of obtaining money or property by deceiving people”. The International Standards on Auditing (ISA), through ISA 240, define the financial fraud as “an intended action committed by one or more individuals from the management, from the group of individuals who were assigned the governance task, from employees or third parties which implies the use of scam to obtain an illegal or unfair benefit” (International Federation of Accountants (IFAC) 2009). The definition of the word “fraud” is also different from a country to another. In general, the American definition includes in the creative accountancy the word “fraud” placing it within the illegality area, while in the United Kingdom the creative accounting is not seen as an illegal issue but rather using flexibility in accounting; therefore it excludes the fraud. The flexibility allowed in the accounting is used

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to the extent to which the company offers to the users a fair picture of the financial statements so that they can make correct economic decisions such as the decision to buy, to sell, or to keep the shares. However, this flexibility allowed by the accounting rules offer the managers the possibility to use the creative accounting. The use of such techniques is carried out observing the law, but the problem is that they get away from the basic objective of the accounting – namely, that of offering to the users a fair and true value (picture) of the accounting statements. Both the creative accounting and the fraud mainly occur during the times of financial difficulties of the company and are meant to amend the truth. The creative accountancy assumes the “embellishment” of the accounting data, process which is not considered as being illegal, but it is certainly one which breaches the ethical standards. Many times, the creative accounting and fraud are considered as being synonyms, but there exist numerous differences. Fraud is made in bad faith by means of which the law is breached – it has a negative character – while the creative accountancy complies with law and not its spirit. The creative accounting is legal, maybe even it could be a factor of achieving the true and fair value in accounting, when it is applied with good faith (Groșanu 2013, p. 33). On the other hand, the creative accounting could prove to be closer to the fraud “if the loopholes of the regulations are used to obtain advantages of some categories of users of the accounting information, to the detriment of others” (Groșanu 2013, p. 33). In general, the specialized literature identifies two opinion regarding the creative accounting, one by means of which it a priori has a negative character and another through which the creative accounting is not necessarily a negative thing; on the contrary it can contribute to the promotion of a fair picture. The common denominator of the different approaches regarding the creative accounting refers to present a distorted picture of the company, as being prosperous, more attractive thus misguiding the investors and shareholders. The similarities and differences between the creative accounting and fraud will be schematically presented in Table 2.19. As seen in the table above, the creative accounting is situated at the border between legal and illegal matters, respectively, at the border between “legality and moral fraud”, and “the step to fraud is small and many times invisible” (Groșanu 2013, p. 35). 2.4.2.3  R  ole of Audit in Detecting the Risk of Fraud Through the Financial Statements The big financial scandals that broke out worldwide were based on creative accounting practices combined with fraud and complicity with audit firms in order to cover the problems faced by these entities. In certain situations, the large audit companies were either accomplice or unable to detect the frauds and were considered guilty for the financial losses caused to the companies subject to fraud and were punished to pay compensations (Ball 2009, p. 277–300).

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The International Standards on Auditing presents the main objectives of the financial auditor as well as its responsibilities regarding the identification of the financial frauds. However, ISA 240 specifies that throughout its mission, the auditor must ensure that the risk of fraud will not influence significantly its opinion and implicitly the quality of its mission (International Federation of Accountants (IFAC) 2009). ISA 240 presents the risk of fraud as a probability of fraudulent deeds at the elaboration of the financial statements or at the level of the audited company patrimony. The same ISA 240 defines the financial fraud as an intended deed involving the use of scams to obtain an unfair or illegal benefit from one or several individuals from the company management, from the persons responsible with the governance, from the employees, or from thirds (International Federation of Accountants (IFAC) 2009, p.166). Thus, we are able to consider that the financial auditors have an extremely important role in discouraging the creative accounting. According to the Institute of Internal Auditors, internal auditing is an independent, objective assurance and consulting activity designed to add value and improve an organization’s operations. It helps an organization accomplish its objectives by bringing a systematic, disciplined approach to evaluate and improve the effectiveness of risk management, control, and governance processes (Coetzee and Bruyn 2001). The specialized literature confirmed the important role of the audit for the engagement in manipulation techniques of the financial results. Thus, the study carried out by Davidson and quoted by Vladu and Matiș (2010) concluded that the “board of directors and the audit committees dominated by the non-executive members are significantly associated with a lower possibility of administrating the salary gains”. Last but not least, a more enhanced activity of the board of directors and of the audit committee translated in a larger number of meetings can lead to a lower rate of the creative accounting (Groșanu 2013). Actually, the creative accounting practices occur when managers use their financial knowledge either to mislead certain stakeholders about the economic performance of the company or to influence the contractual results that depend on the reported accounting figures. In addition, when the remuneration of managers depends on the financial performance of the company, it is in their interest to create the illusion of high performance by resorting to creative accounting. There are many companies that offer managers both direct compensation – represented by attractive salaries and bonuses – and indirect, represented by prestige or promotions, which are based on the financial performance of the company. In order to find solutions to the remuneration of managers’ problem, the federal agency, which supervises the application of the federal laws by the listed companies and regulates the real estate transactions in the United States (US Securities and Exchange Commission-SEC), made constant efforts in the field of regulating control in corporate governance and strengthening the internal control and audit function as palliative to this problem (Böckli 2005). The OECD regulations (2004) about corporate governance make the following reference to management remuneration: An entity must present the remuneration policy of the Board members and

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managers as well as information about the Board members, including their qualifications, the selection process, other company directorships and whether they are regarded as independent by the board. Consequently, the corporate governance codes of countries introduced serious parts meant to regulate the remuneration and other bonuses for managers (Achim and Borlea 2013). Cadbury report (1992) underlines the role of the audit committee as additional mechanism for the protection of the shareholders’ interests to improve the responsibility and transparency of the financial information and the internal control function (Hsu and Wu 2014). According to the OECD principles of corporate governance (2004), the corporate governance framework must ensure a disclosure accurate and in due time about the financial statements, financial performance, company ownership, and performance. To do it, the board has to establish an independent audit committee which is able to ensure the integrity of the financial reporting and of the internal control system including of the internal and external audit procedures. The audit committee should include exclusively the non-executive directors and should consist of a sufficient number of independent directors. The company must organize internal audits to independently, periodically evaluate the reliability and efficiency of the system of the risk management, internal control, and the practices of corporate governance. The companies will include a statement of corporate governance titled “Comply or Explain Statement” in the annual report in a distinct section. This statement will comprise a self-evaluation about the way in which the “provisions that have to be observed” are fulfilled as well as the measures adopted in order to observe the provisions that have not been completely fulfilled. For the case of Romania, the sections B and C of the Corporate Governance Code provided by Bucharest Stock Exchange refer exactly to the issues related to system of the risk administration and internal control and remuneration issues, respectively. 2.4.2.4  Methods to Detect the Manipulation of the Financial Statements The employees, the internal audit, the financial audit, and the managing staff are the first who can identify the existence of a fraud besides the experts in the field. They can identify the existence of the fraud by the simple fact that the reporting documents are submitted late, the behaviour of the individual committing the fraud, lack of some goods, or the enrichment of some of the employees “overnight”. Besides the methods of direct observation of the manipulation of the financial statements, the specialized literature comprises statistical and mathematical methods which can reveal the risk of the results manipulation through the financial statements. The existence of the risk of fraud can be seen by means of a series of specific indicators called in literature “red flags” (Robu and Robu 2013). Based on the red flags indicators, the auditor can apply analytical procedures by means of which to obtain proofs of the existence of fraud through the financial statements.

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The fraud can always be disclosed because of the interconnections between the components of the financial statements (income statement, cash flow statement, and balance sheet). The investigation of fraud through the financial statements can be done using some indicators for detecting accounting manipulations, indicators proposed by Beneish (1999). Taking into account the analysis of certain financial ratios, the specialized studies (Talab et al. 2017, p 289; Robu and Robu 2013; Hasan et al. 2017; Vladu et al. 2017) consider that a suitable tool to support the auditors in identifying the accounting fraud is M Score model elaborated by Beneish (1999). The M score is a reliable tool to detect the fraud being built to support the auditors in the process of detecting the risk of fraud in the financial statements. A comprehensive study of Talab et al. (2017) concludes that the M score elaborated by Beneish and used to identify the possibility of accounting fraud is a reliable one. It was demonstrated that the M score of Beneish is a popular and powerful instrument for detecting the manipulation. The Beneish model is also known for its popularity, simplicity, and reliability in detecting the fraud. Despite its popularity, the most common techniques for measuring the manipulation degree of the financial statements have not significantly changed during the last 30 years (proposed by Dechow et al. 1995, Vladu et al. 2017). Presentation of M-Beneish model Among the most well-known models of the evaluation of the manipulation degree of the financial statements is the model elaborated by Professor Beneish (1999). The M-Beneish model is a statistical model using the financial indicators calculated with the accounting data of a specific company to verify the probability of manipulating the results reported through the financial statements. The M-Beneish score is a probabilistic model and as a result it cannot detect with an accuracy of 100% the companies which are manipulating their results. At the elaboration of the M score, Beneish excluded from the sample the financial institutions. It means that the M score for the fraud detection cannot be applied for the financial institutions (banks, insurance companies). The model uses comparisons between the current and precedent year (Beneish 1999). At the elaboration of the M score, Beneish (1999) proposed a series of indicators which can be used for the application of the analytical procedures to identify the frauds from the financial statements, as follows: 1. Days Sales in Receivables Index (DSRI) represents the ratio between the period of collecting receivables from one financial year to the previous one. As long as there are no extreme changes of the crediting policy, it is expected that this ­indicator has a linear structure. An increase in period of collecting receivables may be a red flag for the manipulation of financial data.

DSRI = ( Net Receivables t / Salest ) / ( Net Receivables t −1 / Salest −1 )



2. Gross Margin Index (GMI) represents the ratio between the gross margin rate registered by a year before the fraud notifying and the gross margin rate regis-

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Table 2.19  Similarities and differences between creative accountancy and fraud Creative accounting Similarities 1. Intended actions 2. Distorts the truth 3. Occurs during times of financial difficulties 4. Creates advantages Differences 1. Complying with the letter of the law

Fraud

1. No complying with the letter of the law 2. No complying with the spirit of the law 2. No complying with the spirit of the law 3. Can be conducted in good faith (when the flexibility result 3. It is conducted contributes to the achievement of a fair and true value) or with with bad faith bad faith (some of the users of the information are disadvantaged)

Source: own processing

tered in the financial year when the fraud was notified. The reduction of Gross margin ratio in the current year compared with the previous year represents a negative signal for the future perspectives and reflects the fact that such companies are more engaged in result manipulation. Therefore, a GMI score greater than 1 indicates that the gross margin ratios deteriorated, motivating the management team to manipulate the numbers to look better than they might be otherwise. A GMI score greater than 1 is an important red flag for any auditors and accountants to show the degree of manipulation financial data.

GMI = ( Salest −1 − COGSt −1 ) / Salest −1 ] /[ ( Salest − COGSt ) / Salest  ,



where COGS is cost of goods sold (COGS) and it refers to the direct costs of producing the goods sold by a company. 3. Asset Quality Index (AQI) is used to identify the eventual frauds at the company level regarding the evaluation of the assets. It shows the modification of the weight of other immobilized assets, except for the tangible ones, within the total assets compared to the previous period. The higher the AQI value is, the higher the possibility of manipulating the results is. AQI =

1 − ( Current Assetst + PP & E t + Securitiest ) / Total Assetst  , 1 − ( ( Current Assetst −1 + PP & E t −1 + Securitiest −1 ) / Total Assetst −1 )   



where PP&E represents property plant and equipment (PPE) 4. Sales Growth Index (SGI) represents the ratio between the sales levels registered throughout two consecutive periods of reporting. The increase of the sales is not

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an indicator of the data manipulation; however, the companies which register increase in sales are much more likely to engage in manipulations of results. SGI = Salest / Salest −1



5. Depreciation Index (DEPI) represents the ratio of the depreciation rate in year t-1 compared with the year t. The higher the value of the depreciation index indicates that the rate at which assets are depreciated has slowed down, possible due to increasing revision of the estimated lives of the tangible assets which finally would lead to the increase of incomes. DEPI =

( Depreciation / ( PP & E ( Depreciation / ( PP & E t −1

t −1

t



t

+ Depreciation t −1 ) )

+ Depreciation t ) )



6. Sales General and Administrative Expenses Index (SGAI) measures the variation of this type of expenses in relation with the sales level. The overheads may include a series of incentives or bonuses for the managers. The existence of a correlation between SGAI and sales is expected. A disproportionate increase of this ratio reflects a negative signal about firms’ future prospects.

SGAI = ( SG & A Expense t / Salest ) / ( SG & A Expense t −1 / Sales t −1 )



7. Leverage Index (LVGI) measures the ratio between the total debt of an enterprise and the total assets. The higher the ratio value is, the higher is the debt in relation with the total assets. This variable is introduced to catch the incentives in the debt contracts to manipulate the gains. LVGI =

( Current Liabilitiest + Total Long Term Debt t ) / Total Assetst  ( Current Liabilitiest −1 + Total Long Term Debt t −1 ) / Total Assetst −1 



8. Total Accruals to Total Assets (TATA) describes the relation which is established between the level of the total accruals (the change in working capital other than cash and depreciation) and the level of the total assets. Accruals reflect the extent to which managers make discretionary accounting choices to alter earnings. Thus, a higher level of accruals associates with a higher likelihood of profit manipulation. For the companies with risk of fraud, there can be noticed the increase with the time of the percentage of the accruals as a result of the fraud on the financial statements, which probably are determined by a series of fictitious sales (Robu and Robu 2013). TATA =

( Income from Continuing Operationst − Cash Flows from Operationst ) Total Assetst



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In 1999, Professor Messod Beneish published the original variant of the score function (M) which used eight financial indicators to detect the manipulation of the results presented by the financial statements. The equation is the following:



M = –4.84 + 0.92 ∗ DSRI + 0.528 ∗ GMI + 0.404 ∗ AQI + 0.892 ∗ SGI + 0.115 ∗ DEPI – 0.172 ∗ SGAI + 4.679 ∗ TATA – 0.327 ∗ LVGI

Beneish further on modified the M score function eliminating some of the less significant variables (SGAI, DEPI, and LVGI), obtaining a more simple equation with five variables:



M = −6.065 + 0.823 ∗ DSRI + 0.906 ∗ GMI +0.593 ∗ AQI + 0.717 ∗ SGI + 0.107 ∗ DEP P

Values of the M score higher than −2.22 indicate a higher probability of manipulating the gains. An example of M-Beneish model for the Romanian economy is carried out in the study conducted by Robu and Robu (2013), with the purpose to analyse and evaluate the risk of fraud determined by the accounting manipulations starting from the indicators proposed by Beneish. To this aim, the authors substantiated a function of discrimination which explains 100% of the total variation of the risk of fraud shown with the Beneish indicators. The function of discrimination elaborated by the authors presents the following formula:



M − RiskFraud − Beneish = − 0.383 ∗ DSRI + 0.039 ∗ GMI − 0.325 ∗ AQI +0.448 ∗ SGI + 0.273 ∗ DEPI +0.915 ∗ SGAI + 0.478 ∗ LVGI − 0.153 ∗ TATA

For the function of discrimination M-Risk Fraud-Beneish elaborated for Romania by Robu and Robu (2013), there are obtained three intervals of classification of the companies by groups of risk: 1 . The interval [−2.841; −0.355] – zone free of risk of financial fraud – safe zone 2. The interval (−0.355; 0.313)  – zone of uncertainty (grey zone) regarding the occurrence of the risk of fraud and this is a case which assumes the application of additional audit procedures 3. The interval [0.313; 2.453] – zone with risk of financial fraud where there exist the use of tricks to misrepresent the company image or to reduce the transparency of the financial reports

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2.4.3  Measuring Instruments of the Corporate Governance 2.4.3.1  International Approaches Research centres and international rating agencies (Standards & Poor’s, Credit Lyonnais Securities Asia (CLSA)) have developed a series of systems for measuring corporate governance at the level of company, markets, or even countries, by granting ratings/scores. For the elaboration of the corporate governance score, we consider as edifying the methodology adopted by the Standards & Poor’s Rating Agency. This selection is based on various specialized studies using the Standards & Poor’s methodology as a basis for developing transparency scores for different markets (Doidge et al. 2007; Kusneciovs and Pal 2011; Desoky and Mousa 2012). In order to assess corporate governance, Standards & Poor’s methodology follows two components: • The country score is achieved by analysing the efficiency of the legal infrastructure for regulating and informing the market, on the quality of the corporate governance of the company, in relation to the way in which external environment acts, respectively, at the macroeconomic level. • The score of the company is granted according to the effectiveness of the interaction between managers, shareholders, and other stakeholders, at microeconomic level. For the most accurate assessment of the quality of corporate governance, both microeconomic and macroeconomic components are important. According to Standard & Poor’s, the score given to companies reflects the degree to which the company adheres to internationally recognized corporate governance standards, respectively, its codes and principles of good practice. In this regard, Standard & Poor’s methodology developed in 2012 uses the system with a number of 98 criteria regarding the attributes of corporate governance. The establishment of the transparency score by Standard & Poor’s (T&D ranking) is based on the investigation of 98 attributes of corporate governance, detailed in three broad categories: • Property structure, in relation to which, the following are analysed: transparency of property structure; concentration and influence of the property structure on the company and the relations with the shareholders when the analysis concerns regularity of/access to information on the general meeting of shareholders; the voting process and the way of meeting the shareholders; and property rights (28 attributes). • Financial transparency and dissemination of information: quality and content of information considered public; programming and access to information dissemination; and the independence and position of the company’s auditors (35 attributes);

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• Governance structure and process in relation to which the following are analysed: governance structure and efficiency; the role and composition of management; and the role and level of independence of the executive directors (35 attributes) (Standard and Poor’s 2012). The results of the study conducted by Standard & Poor’s (2012) highlight the high level of transparency of the markets in the United Kingdom and the United States. Although these results do not specifically distinguish the Australian market from the Asia-Pacific markets, the results of the Australian market reflect a high level of transparency comparable to that of the United Kingdom and the United States. Transparency scores recorded at the level of Europe and the developed countries of Asia are quite low and close to those recorded by the USA provided only by the annual reports. Emerging markets in Asia and Latin America reflect a low level of transparency of corporate governance, especially regarding the transparency of directors and executive structures. In the context of the financial crisis broke out in 2008, which has undermined the confidence in corporate governance of companies, there are increasing concerns about the development of these scores in an attempt to highlight the quality of the corporate governance system as accurately as possible. Thus, the Standard & Poor’s rating agency has developed the governance rating system by adding to it distinct areas of interest, such as (Standard and Poor’s 2012): • The managerial culture emphasizes whether the system of governance ensures the interests of the stakeholders in a balanced way. In this sense, in the managerial culture, excessive governance is considered an indicator of governance deficiency. Alternatively, a governance that dominates the board of directors through the control exercised by the general manager (CEO) is an indication of lack of governance. • Deviations/offenses of a legal/fiscal nature; it is pursued whether the company is consistently in conflict with the law, and, in this regard, the number of fines or the number of actions in court is highlighted. • Communication, in relation to which the mode of communication with different stakeholders is analysed and it is highlighted whether there are consistent conflicts of communication with them. • The internal control that is assessed according to the number of deviations from the standards/norms registered at different activity levels. Finally, the corporate governance score relates to a four-tier scale, thus strong, satisfactory, fair, and weak. In the process of assessing the quality of corporate governance at the level of countries around the world, the score of efficacy of corporate board calculated by the World Economic Forum within the Global Competitiveness Index (GCI) is extremely useful. This score is determined and reported annually as a global tool for measuring national competitiveness for economies around the world and is an integral part of the Global Competitiveness Report. The score range between 1 (weak)

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to 7 (good) thus reflecting the efficiency of corporate governance within national economies. As we can see from the previous chapter, the audit plays an extremely important role in ensuring a real and transparent reporting of financial information, that is to say, increasing the efficiency of the audit carried out in a company contributes significantly to increasing the quality of corporate governance. To demonstrate this relationship empirically, we calculated and plotted the correlation between the two variables efficacy of corporate board and strength audit and reports, using the scores reported by the World Economic Forum worldwide, for the period 2006–2016. Graph 2.16 shows an extremely close correlation (c = 0.887) between the efficacy of corporate board and strength audit and reports. Moreover, approximately 78% of the variation in the quality of corporate governance is explained by the ­quality of the audit carried out in the respective company (R2 = 0.787). A strong and positive relationship between accounting standards and the quality of corporate governance is also identified in the literature (Boța-Avram 2013; Wijayati et  al. 2016). For example, Wijayati et al. (2016) explain this close correlation by the fact that, in practice, strong accounting standards force companies to disclose information in ways that generate transparent, accurate, and comparable financial information, which leads to an increase in the quality of corporate governance by increasing the transparency of information provided by the company to third parties. We conducted the analyses with the purpose of substantiating the conclusion that the quality of the audit represents, indeed, a very good estimator of the quality of corporate governance. As a result, the scores calculated for strength audit and reports by the World Economic Forum can also be successfully used as a proper tool for assessing the quality of successful corporate governance. 2.4.3.2  National Approaches: Case of Romania Once the beneficial practices of corporate governance have been understood and assimilated in developed countries, the emerging countries, in their turn, feel the need to adopt the “best practices” of corporate governance against a background of changes required by the transition to a market economy. The Bucharest Stock Exchange (BSE) carries out the first transactions only starting with 1995. Thus, for the admission to the Stock Exchange, BSE created the Plus Category (“transparency plus”) and adopted the first Corporate Governance Code only in 2001. Listed companies would promote to the Plus Category only after they had fully taken over the provisions of the Corporate Governance Code in their constituent acts. This approach has not had the expected success, only one company requesting promotion to the Plus Category. In the following years, BSE created the Institute of Corporate Governance, which is committed to educating listed issuers on promoting appropriate corporate governance standards and has been an active participant in discovering the best corporate

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governance practices, contributing to the adoption of the White Charter on Corporate Governance in the countries of South and East Europe. In 2008, the Bucharest Stock Exchange revises the Corporate Governance Code, and, in 2015, it is completely replaced by a new governance code that responds to the changes in the legal framework in Europe and Romania as well as to the new aspirations of the society and of the stakeholders regarding the obligations and conduct of companies. The code has entered into force since the financial year 2016 and is applied voluntarily by the companies traded on the regulated market operated by BSE. Companies that decided to adopt all or part of it have the obligation to submit annually to BSE a declaration of compliance or non-compliance with the provisions of the corporate governance code (the “Comply or Explain Statement”), specifying recommendations that have been effectively implemented as well as the method of implementation. The objective of the Corporate Governance Code of the Bucharest Stock Exchange is to increase the confidence in the listed companies, by promoting improved corporate governance standards in these companies. Good corporate governance is a powerful instrument for strengthening market competitiveness. The Bucharest Stock Exchange maintains a mechanism based on the “comply or explain” principle, through which clear, accurate, and up-to-date information is transmitted on the market regarding the compliance of the corporate governance rules by the listed companies. The companies shall include a corporate governance statement in the annual report in a separate section, which shall include a self-assessment on how “the provisions to be respected” are met, as well as the measures taken to comply with the provisions that are not fully fulfilled. The Corporate Governance Code of the Bucharest Stock Exchange is similar to those adopted by other European Union member states and provides new compliance recommendations, important for the executives and the boards of directors that run the Romanian companies. The Bucharest Stock Exchange considers the provisions of the code as having supplementary character to other normative acts in Romania, applicable to companies traded on the regulated market (e.g. the law of commercial companies, the law of accounting, the law on the capital market, etc.). Elaboration of a corporate governance score for Romanian companies (Achim and Borlea 2013) In developing the corporate governance score, we rely on the Standard and Poor’s methodology (which underpins corporate governance scores). For this purpose, we will use as information base, the information contained in the “Comply or Explain” statement, which the companies voluntarily report to the Bucharest Stock Exchange. These statements are posted publicly on the company’s website. According with Bucharest Stock Exchange Corporate Governance Code (2015) within the “Comply or Explain Statement”, a number of 40 questions are presented, structured in four sections, as follows:

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Section A: Responsibilities This section assesses the extent to which the role of the board of directors in a unitary system and the role of the supervisory board/executive board in a dual system are clearly defined and documented in the articles of incorporation, internal regulations, and/or other similar documents. In this regard, there must be a clear delimitation between the powers and duties of the general meeting of shareholders, the board, and the executive management. The board shall meet with sufficient regularity to ensure fulfilment of its tasks effectively. This section evaluates the extent to which the composition of the Board and its committees has an appropriate balance in terms of competence, experience, gender diversity, knowledge, and independence of members, enabling them to perform effectively their duties and responsibilities. It is recommended that the majority of non-executive members of the Board or of the Supervisory Board to be independent. Section A.  Responsibilities includes 11 questions, synthetically presented as follows: A.1. All companies must have a board internal regulation that includes the terms of reference/responsibilities of the board and the key management functions of the company. A.2. Provisions for conflict of interest management should be included in the board regulation. A.3. Board of directors or the supervisory board shall be composed of at least five members. A.4. Most members of the board of directors must have no executive function. At least one member of the board of directors or the supervisory board must be independent in the case of standard category companies. A.5. Other relatively permanent professional commitments and obligations of a member of the board, including executive and non-executive positions on the board of non-profit companies and institutions, must be disclosed to potential shareholders and investors before nomination and during his/her term of office. A.6. Any member of the board must submit to the board information on any relationship with a shareholder who directly or indirectly holds shares representing more than 5% of all voting rights. This obligation refers to any kind of relation that may affect the member’s position on matters decided by the board. A.7. Company must appoint a secretary of the board responsible for supporting the activity of the board A.8. Corporate governance statement shall inform whether a board evaluation has been conducted under the leadership of the Chairman or of the nominating committee and, if so, shall summarize the key measures and changes resulting from it. The company must have a policy/guide regarding the evaluation of the board, including the purpose, criteria, and frequency of the evaluation process. A.9. The corporate governance statement must contain information on the number of meetings of the board and committees during the past year, the participation of the directors (in person and in absentia), and a report of the board and committees on their activities.

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A.10. The corporate governance statement must contain information on the exact number of independent members of the board of directors or the supervisory board. A.11. The board of the premium tier companies must establish a nominating committee made up of non-executive members, which shall lead the procedure for appointing new members to the board and make recommendations to the board. Most members of the nominating committee must be independent. Section B: Risk management system and internal control This section evaluates the efficiency of the risk management system and internal control. The board must establish the principles and modalities of approaching the risk management system and internal control at the company level. The company must organize internal audits in order to independently, periodically evaluate the safety and efficiency of the internal risk management and control system and corporate governance practices. The board of directors or the supervisory board, as the case may be, must set up an independent audit committee that can ensure the integrity of the financial reporting and internal control system, including internal and external audit procedures. Section B: Risk management system and internal control includes 12 questions, synthetically presented as follows: B.1 Board must establish an audit committee in which at least one member must be an independent non-executive director. Most members, including the chairman, must have proven to have adequate qualification relevant to the functions and responsibilities of the committee. At least one member of the audit committee must have appropriate audit or accounting experience that can be proven. B.2. Chairman of the audit committee must be an independent non-executive member. B.3. Within its responsibilities, the audit committee must carry out an annual evaluation of the internal control system. B.4. Evaluation should take into account the effectiveness and comprehensiveness of the internal audit function, the degree of adequacy of the risk management and internal control reports presented by the audit committee of the board, the promptness and effectiveness with which the executive management solves the deficiencies or weaknesses identified as a result of internal control, and the submission of relevant reports to the attention of the board. B.5. Audit committee should evaluate conflicts of interest in relation to the transactions of the company and its subsidiaries with related parties. B.6. Audit committee must evaluate the efficiency of the internal control system and the risk management system. B.7. Audit committee should monitor the application of legal standards and generally accepted internal audit standards. The audit committee must receive and evaluate the reports of the internal audit team. B.8. Whenever the code mentions reports or analyses initiated by the audit committee, they must be followed by periodic (at least annually) or ad hoc reports that are to be submitted to the council afterwards;

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B.9. No shareholder may be granted preferential treatment over other shareholders in connection with transactions and agreements concluded by the company with shareholders and their affiliates, B.10. The board must adopt a policy to ensure that any transaction of the company with any of the companies with which it has close relationships, whose value is equal to or greater than 5% of the company’s net assets (according to the latest financial report) is approved by the Board following a mandatory opinion of the Board ‘s audit committee and correctly disclosed to the shareholders and potential investors, to the extent to which these transactions fall within the category of events that are subject to reporting requirements; B.11. Internal audits must be performed by a structurally separate division (internal audit department) within the company or by hiring an independent third-party entity. B.12. In order to ensure the fulfilment of the main functions of the internal audit department, it must report functionally to the council through the audit committee. For administrative purposes and within the management’s obligations to monitor and reduce risks, it must report directly to the general manager. Section C: Just reward and motivation This section refers to the remuneration policy for the non-executive and executive members. The level of remuneration must be sufficient to attract, retain, and motivate competent and experienced persons within the board of directors and executive 6.00

Efficacy of corporate board

5.00

4.00

R2Linear = 0.787

3.00

2.00

1.00

00 .00

2.00

4.00

6.00

Strenght audit and and reports

Graph 2.16  Correlation between efficacy of corporate board and strength audit and reports. (Source: own processing)

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management. The board must ensure transparency regarding remuneration. Shareholders must receive relevant information in order to understand the principles applied by the company regarding the remuneration policy, which is based on the fair reward and motivation for the members of the board and the general manager (CEO) or the members of the executive board. A company must have a remuneration policy and rules that define that policy. It should establish the form, structure, and level of remuneration of the members of the board of directors, the general manager, and, where appropriate, the members of the executive board. Section C. Just reward and motivation comprises a single question formulated as: C.1. Company must publish the remuneration policy on its website and include in the annual report a statement regarding the implementation of the remuneration policy during the annual period that is the subject of the analysis. The remuneration policy of the management members should make reference to the argumentation of the decisions regarding the remuneration detailing the components of the remuneration of the executive management (such as salaries, annual bonuses, long-term incentives related to the value of the shares, benefits in kind, pensions, and others) and describe the purpose, principles, and assumptions underlying each component (including general performance criteria for any form of variable remuneration). Any essential change in the remuneration policy must be published in a timely manner on the company’s website. Section D: Adding value through investor relations This section evaluates the extent to which the company communicates with investors. Thus, the extent to which the most important information in Romanian and English reaches Romanian and foreign investors is evaluated. Also, companies must make every effort to allow non-executive shareholders to attend general meetings, including by using electronic means of communication, by encouraging the electronic voting system. Section D. Adding value through investor relations comprises 16 questions, synthetically presented as follows: D.1. The company must organize an investor relations service – indicating to the general public the person/persons responsible or the organizational unit. In addition to the information required by the legal provisions, the company must include on its website a section dedicated to investor relations, in Romanian and English, with all relevant information of interest to investors: D.1.1. The main corporate regulations: the articles of incorporation and the procedures regarding the general meetings of the shareholders. D.1.2. The professional CVs of the members of its governing bodies, other professional commitments of the members of the board, including executive and non-executive positions within the boards of directors of companies or non-­ profit institutions.

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D.1.3. Current reports and periodic reports (quarterly, half yearly and annual reports)  – at least those referred to in paragraph D.8  – including current reports with detailed information regarding non-compliance with this code. D.1.4. Information regarding the general meetings of the shareholders: the agenda and the information materials; the procedure for electing the members of the board; the arguments supporting the candidates’ proposals for election to the board, together with their professional CVs; shareholder questions regarding the items on the agenda and the company’s answers, including the decisions adopted. D.1.5. Information on corporate events, such as the payment of dividends and other distributions to shareholders, or other events leading to the acquisition or limitation of the rights of a shareholder, including the deadlines and principles applied to these transactions. The respective information shall be published within a time frame that shall allow investors to make investment decisions. D.1.6. Name and contact details of a person who shall be able to provide, upon request, relevant information. D.1.7. Company presentations (e.g. investor presentations, quarterly results presentations, etc.), financial statements (quarterly, half-yearly, yearly), audit reports, and annual reports. D.2. The company shall have a policy regarding the annual distribution of dividends or other benefits to shareholders, proposed by the general manager or the executive board and adopted by the board, in the form of a set of guidelines that the company intends to follow regarding the distribution of the net profit. The principles of the annual distribution policy to shareholders shall be published on the company’s website. D.3. The company shall adopt a policy regarding forecasts, whether they are made public or not. The forecasts refer to quantified conclusions of some studies aiming at establishing the global impact of a number of factors over a future period (so-called hypotheses): by its nature, this projection has a high level of uncertainty, the actual results being able to differ significantly from the forecasts originally presented. The forecasting policy shall determine the frequency, the period considered, and the content of the forecasts. If published, forecasts can only be included in annual, half-yearly, or quarterly reports. The forecasting policy shall be published on the company’s website. D.4. The rules of the general meetings of the shareholders should not restrict the participation of the shareholders in the general meetings and the exercise of their rights. Amendments to the rules shall enter into force at the earliest, starting with the next shareholders meeting. D.5. External auditors shall be present at the general meeting of shareholders when their reports are presented at these meetings. D.6. The board shall present to the annual general meeting of the shareholders a brief assessment on the internal control systems and significant risk manage-

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ment, as well as opinions on issues subject to the decision of the general meeting. D.7. Any specialist, consultant, expert, or financial analyst may attend the shareholders’ meeting on the basis of a prior invitation from the board. Accredited journalists may also attend the general meeting of shareholders, unless the chairman of the board decides otherwise. D.8. Quarterly and half-yearly financial reports shall include information in both Romanian and English on key factors influencing changes in sales, operating profit, net profit, and other relevant financial indicators, both from quarter to quarter and from year to year. D.9. A company shall organize at least two meetings/teleconferences with analysts and investors each year. The information presented on these occasions shall be published in the investor relations section of the company website on the date of the meetings/teleconferences. D.10. If a company supports different forms of artistic and cultural expression, sports activities, and educational or scientific activities and considers that their impact on the innovative character and competitiveness of the company is part of its development mission and strategy, it shall publish the policy regarding its activity in this area. To each of the above questions, the companies must answer with YES/NO. If the answer is NO, then they must EXPLAIN.  In our attempt to develop a corporate governance score, we shall award 1 point for each YES answer and 0 points for answer NO. The mathematical model of the corporate governance score is presented as a summation of the scores accumulated by a company, for each of the four sections investigated, as follows:



11

12

16

j =1

j =1

j =1

CG = ∑ Re sj + ∑Riskj + Re m + ∑Investj, where



• CG represents the value of the corporate governance score obtained by a company. • Resj represents the score obtained on the questions “j” related to Section A. Responsibilities (0 or 1); Res represents the total score obtained by a company in this field (between 0 and 11). • Riskj represents the score obtained on the questions “j” of Section B. Risk management system and internal control (0 or 1); Risc represents the total score obtained by a company in this field (between 0 and 12). • Rem represents the score obtained by a company on Section C. Just reward and motivation (between 0 and 1); Rem represents the total score obtained by a company in this field (between 0 and 1). • Investj represents the score obtained on the questions “j” of Section D. Adding value through investor relations (0 or 1); Investj represents the total score obtained by a company in this field (between 0 and 16).

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The minimum value of the governance score obtained by a company is 0 and the maximum value is 40. Using such an individual score, it can then be determined an average score of corporate governance at the level of the Romanian companies, according to the formula: N



CG = ∑ i =1

CGi , where N

• CG represents the average value of the corporate governance score registered for the Romanian companies. • CGi represents the governance score achieved by each of the “i” companies analysed. • N represents the number of companies analysed (selected in the sample). Using a score calculated according to a methodology similar to the one presented above, the study by Achim and Borlea (2013) for the companies listed on the Bucharest Stock Exchange highlighted the existence of an average degree of adoption of the principles of good corporate practices in the percentage of about 60%, related to the reports made at the end of 2012. At that time, with all the progress made in this regard, many of the best practices of corporate governance of the Romanian companies were well below the European average or even below the average recorded for other emerging countries. The biggest problems were detected in the following aspects: • Transparency of the property structure, especially the transparency regarding the Internal Regulation on the Functioning of the Company; only 38% of the analysed companies posted this document on their company website. • Independence of the members of the board of directors; only 58% of the companies analysed have a sufficient number of independent members. • Existence of advisory committees: –– In just over half of the companies analysed, the board of directors uses the support of advisory committees/commissions to examine specific topics. Only a quarter of the companies analysed constitute a nominating committee within the company. In the other cases, the nomination is made by the members in office of the board of directors or by the shareholders. –– Less than a half of the number of companies analysed have an audit committee. –– Regarding remuneration policy, only 38% of the total Romanian companies have a remuneration committee made up exclusively of non-executive directors and only 36% of the companies analysed present the remuneration policy of the company in the Corporate Governance Statute/Regulation on 2012. Along with the adoption of the new Bucharest Stock Exchange Governance Code, which entered into force on January 1, 2016, the concerns of companies

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towards the adoption of best governance practices have become increasingly accentuated, so now we estimate that the percentage of adoption of best practices is much higher, compared to the 60% threshold calculated for 2012. 2.4.3.3  Corporate Governance in the European Union Countries Next, we aim to investigate the quality of corporate governance in the countries of the European Union for the period 2006–2016. The present study will provide us with clues to the existence of links regarding the quality of corporate governance and economic-financial crime. Methodology In order to be able to measure the quality of corporate governance in different countries, we will use two important indicators: (a) Efficacy of corporate board (b) Strength audit and reports Both indicators are calculated and reported in the Global Competitiveness Indicator (GCI), determined as a global tool for measuring national competitiveness for economies around the world. This score is provided annually by the World Economic Forum in the Global Competitiveness Report. Both indicators are between level 1 (the weakest) and 7 (the best), thus reflecting the efficiency of corporate governance within national economies. The analysis period is 2006–2016 and covers the countries of the European Union (28). The methods we use are descriptive methods, comparison, analysis, and synthesis. Results and discussions Graphs 2.17, 2.18, 2.19, and 2.20 show that the lowest levels of corporate governance including the lowest levels of audit efficiency are registered in the countries of Central, Southern, and Eastern Europe. At the opposite end, the highest level of corporate governance efficiency is located in the countries of Western and Northern Europe. Romania ranks 23rd out of the 28 countries of the European Union analysed in terms of corporate governance quality and on the penultimate place, before Italy, in terms of audit efficiency and reporting fidelity.

2.4.4  Corporate Governance and Corruption 2.4.4.1  Theoretical Approaches Corporate governance is the system by which companies are run and controlled. It basically refers to how the board of directors performs and how it determines the company’s values. Effective corporate governance requires the adoption of relation-

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ships in which board members (directors) are honest and open with each other. It is also about the relationship between the directors, shareholders, managers, and auditors whom the shareholders designate. According to Transparency International (2020a), corruption is the abuse of entrusted power for private gain. Thus, if the relations within the governance structures are not well founded by codes of good corporate practice and respected, there is a possibility that they will give in to immediate favours, with major costs and risks for the company and all those involved. In this context, the occurrence of corruption is closely linked to the theory of the agency (Achim and Borlea 2013). According to this theory, the shareholders who are also the owners of the company or the principals delegate authority, in whole or in part, to a mandatory (agent) for managing their own interests. For example, at the company level, an agent relationship is established between owners (shareholders) and managers, the former entrusting the management of their assets to the latter (Clarke 2004). Similarly, creditors (bankers), as principals, entrust their capital (they lend it), to shareholders and managers, as agents of the management of these capitals. According to the theory of the agency, the shareholders (the principals) expect their mandatories (agents) to act and make decisions in their interest, of those who have mandated. On the other hand, the agent cannot take only those decisions that pursue only the interests of the principal (Padilla 2002). Such a conflict of interest between owners and managers was first pointed out by Berle and Means (1932), then by Adam Smith (1976), and then extensively developed by Jensen and Meckling (1976). Specifically, the conflict of interest between the two parties lies in the control-­property separation, as highlighted by Davis et al. (1997). Here, the theory of asymmetric information and moral hazard (Achim and Borlea 2013) comes into play. Managers (insiders) have greater access to company information than shareholders (from outside). A large number of studies have shown that managers, in pursuit of their own personal interests, tend to hide relevant information to shareholders (Arnold and de Lange 2004). For example, managers can increase reported profits to get higher bonuses (Shuto 2007). However, what the management shows is a distorted financial situation that incorporates manipulative actions. Such actions may be directly related to the bribery of the persons requested to be involved in the manipulation of financial data. An example in this regard is the fact that in order to win a procurement contract, management and insiders may be tempted to pay bribes to obtain facilitation in this direction. The problem of asymmetric information makes it difficult to access real information by outside shareholders (outsiders). Because, by its nature, the bribe is secret, managers can hide such transactions by deceiving shareholders (from outside) (Wijayati et al. 2016). In the short run, both the managers and the shareholders of a corporation could reap benefits from corruption. However, this cannot be maintained for a long term (Wijayati et al. 2016). In this regard, Wu (2005) argues that bribery involves hidden costs, which can potentially turn into future risks, such as legal costs, fines, and reputational damage.

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At the empirical level, we have identified a limited attention given to the investigation of the relationship between corporate governance and corruption, in the literature (Wijayati et al. 2016; Wu 2005; Rasheed and Yazdanifard 2013). Thus, Wu (2005) identified a significant impact of corporate governance quality on reducing the level of corruption at a country level. To measure the quality of corporate governance, Wu (2005) referred to the role of the board of directors and the quality of accounting regulations. On the other hand, the study conducted by Wijayati et al. (2016) in emerging countries in Southeast Asia (Indonesia, Malaysia, and Thailand) investigates four elements of corporate governance that could reduce opportunities for corruption, namely, shareholders’ rights, board of directors, accounting and auditing standards, and transparency. The study concludes that corporate governance mechanisms can reduce opportunities for corruption. In conclusion, effective corporate governance that ensures greater efficiency of governance structures including high protection of minority investors will make the company stronger, thus reducing the risks of corruption as well as the major costs involved. 2.4.4.2  Practical Approaches Below we will conduct an empirical study in order to investigate any possible influence of the quality of corporate governance on the level of corruption. In this sense, we underlie the following research hypothesis: Hypothesis: A high quality of corporate governance leads to a decrease in the level of corruption. Methodology For our purpose we measure the quality of corporate governance using two indicators, namely, efficacy of corporate board and strength audit and reports. Both indicators are calculated and reported in the Global Competitiveness Indicator (GCI), determined as a global instrument for measuring national ­competitiveness for 185 worldwide economies. This score is annually provided by the World Economic Forum (2020) in the Global Competitiveness Report. Both indicators are between level 1 (the weakest) and 7 (the best), thus reflecting the level of corporate governance efficiency existing in national economies and the efficiency level of audit efficiency and reporting fidelity, respectively. Corruption is measured as the rank occupied by the analysed countries according to the score obtained by the Corruption Perception Index, provided by Transparency International (2020b). A high position held by a country reflects a high level of corruption and vice versa. The sample is represented by 152 countries, and the analysis period is 2006–2016, for which data are available for all variables analysed.

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For this purpose, we use the descriptive methods, the correlation coefficients, and the regression analysis, and we carry out the statistical tests necessary to ensure a high accuracy of the results. Results and discussions Graph 2.21 and Table  2.20 show a mean, indirect connection of corruption with corporate governance (c  =  −0.58; R2  =  0.337). The correlation is even closer (c  =  −0.693; R2  =  0.48) when the quality of corporate governance is estimated through the strength audit and reports (Graph 2.22 and Table 2.21). Table 2.22 shows that the value of the regression coefficient of the efficacy of corporate board variable in relation to the corruption variable is negative (−41.094) and significant at a significance threshold of 1%. This reflects the fact that, at a one point increase in the quality of corporate governance, a decrease in the level of corruption is achieved on average with 41.094 units (reflecting a decrease of approximately 41 positions in the hierarchy of countries affected by corruption). A similar significant influence on corruption is also identified for the strength audit and report variable (Table 2.23). Conclusions and limits of the research In conclusion, the test results of our hypothesis confirm the existence of a negative and significant influence of the efficacy of corporate board on the level of corruption. These results are even more robust if we use the strength audit and report as an estimator for the efficiency of corporate governance. The lack of use of control variables as well as the non-use of more complex statistical methods for processing data (e.g. the panel type) could be invoked as limits of this research and object for improvements in other future studies.

2.4.5  Corporate Governance and Shadow Economy 2.4.5.1  Theoretical Approaches Problems deriving from the agent theory regarding the conflict of interests between the two parties (owners, on the one hand, and managers or agents, on the other hand) can lead to information hiding, which is not in favour of certain interested parties, which may slip even in carrying out shadow economic activities (undeclared work, tax avoidance, etc.) In the specialized literature, we have identified few studies that investigated the relationship between governance mechanisms and shadow economy. Instead, we identified numerous studies that investigated governance mechanisms in relation to tax avoidance (Hanlon and Heitzman 2010; Kourdoumpalou 2016; Winnie 2016; Van de Pilos 2017; Mappadang et  al. 2018). Because, according to Schneider’s approaches (Schneider 2013, 2015; Medina and Schneider 2018) regarding the definition and measurement of the shadow economy, tax avoidance is an important

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component of the shadow economy, we can consider that such studies can be relied upon, as in direct relation with the shadow economy. In this regard, the study of Lanis and Richardson (2011), after conducting a cross-section of 401 corporations, finds a relation between director composition and tax aggressiveness. Similarly, the study conducted by Winnie (2016) on a number of 120 companies in the manufacturing industry listed on the Indonesian Stock Exchange during 2010–2013 is very relevant. Thus, the authors find that the way of compensation of managers, the audit committee, and the quality of the audit has an influence on the amplitude of tax avoidance. Tax avoidance is determined using the effective tax rate (ETR), which reflects the percentage in which taxes can be avoided by the company compared to the applicable tax rate (Hanlon and Heitzman 2010). More specifically, the conclusions of the study conducted by Winnie (2016) are summarized as follows: • Higher compensation of managers leads to higher level of tax avoidance. • As regards the audit committee, the authors find that a higher number of members within the audit committee is indirectly correlated with the level of tax avoidance. Thus, the higher the number of members of the audit committee, the more difficult it is for the company to do tax avoidance. • The audit quality is found to have a negative effect on tax avoidance. The audit quality is measured according to the size of the company performing the audit; it is considered high if the audit is performed by a Big Four company, or medium-­ low, if the audit is performed by a non-Big Four company. Entity benefits of a greater confidence from the fiscal and public authorities if it ensures a high integrity of the information presented in the financial statements. When the company is audited by a reputable auditing company (of the Big Four), tax avoidance is difficult to do. Such auditing companies have a high reputation and maintain the confidence of stakeholders. Also for Indonesia, Mappadang et  al. (2018) identifies a positive relationship between the number of the board members and tax avoidance. Thus, they find that the higher the number of board members, the more they will allow managers to maximize profits, including by avoiding taxation. In practice, the supervisory board represents the interests of the shareholders, so they will be oriented towards obtaining the highest profits for the shareholders, even if this will be achieved at the cost of tax avoidance. On the other hand, the authors find a negative relationship between the percentage of institutional investors and the volume of tax avoidance, in other words the existence of institutional investors does not allow management to avoid taxation. For Greece, the study conducted by Kourdoumpalou (2016) shows that tax avoidance is lower when the chairman of the board is also the owner of the company. A strong negative relationship is found between tax avoidance and equity participation percentage of the owner and his family, on one hand, and between tax avoidance and equity participation percentage held by the members of the board of directors, on the other hand. The authors also find that remunerating the members of the board of directors by profit distribution significantly reduces tax avoidance,

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7 6 5 4

4.0 4.1 4.2 4.2 4.2

4.3 4.3 4.4

4.8 4.9 5.0 4.5 4.5 4.6 4.7 4.7

5.1 5.2 5.3

5.4 5.4 5.4 5.5 5.5 5.5 5.6

5.8 5.8

3 1

Italy Greece Bulgaria Croaa Cyprus Romania Slovenia Poland Hungary Portugal Spain Latvia Malta Slovakia Czech Republic Lithuania Estonia Ireland France Belgium Austria Germany Luxembourg Denmark United Kingdom Netherlands Finland Sweden

2

Graph 2.17  Efficiency of corporate board in European Union countries, 2006–2016 7 6

5.7

5.41

5

4.57

4.37

4 3 2 1

West

South

North

CEE

Graph 2.18  Efficiency of corporate board by geographical areas in European Union countries, 2006–2016

while tax avoidance is higher when the members of the board of directors are at the same time employees of the company. Regarding board independence but also other features, Khaola and Moez (2019), after they analysed 105 European firms during the period 2005–2012, find that board independence, board diversity, and CEO’s dual functions have a significant and negative effect on the relationship between tax planning and firm value. However, after analysing S&P 500 companies, Van de Pilos (2017) cannot statistically prove that more independent directors on the board influence corporate tax avoidance. Based on those investigated above, we can conclude that corporate governance mechanisms can exercise incentives for engaging in shadow economic activities.

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7 6 5

4.20

4.50 4.60 4.30 4.40

5.00 5.00 5.00 4.80 4.90 4.90 4.90 4.90

5.20 5.20

5.40

5.80 5.60 5.70 5.70 5.70

6.10 5.90 5.90 5.90 6.00 6.00

6.30

4 3

1

Italy Romania Bulgaria Croa a Greece Slovenia Spain Latvia Poland Slovakia Portugal Czech Republic Lithuania Ireland Hungary Cyprus Estonia Belgium France Denmark Germany Austria United Kingdom Malta Luxembourg Netherlands Sweden Finland

2

Graph 2.19  Strength audit and reports in European Union countries, 2006–2016 7 6

6.03

5.76 5.00

5

4.86

4 3 2 1

West

South

North

CEE

Graph 2.20  Strength audit and reports by geographical area, in the countries of the European Union, 2006–2016, (Source: own processings)

2.4.5.2  Practical Approaches Next, we will conduct an empirical study in order to investigate a possible influence of the quality of corporate governance on the shadow economy level, starting from the following research hypothesis: Hypothesis: A high quality of corporate governance leads to a decrease in the shadow economy level.

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Methodology The quality of corporate governance is measured with the help of the indicators efficacy of corporate board and strength audit and reports which we referred to in the previous chapter. The level of the shadow economy is determined as a percentage in GDP, as presented in the source provided by Medina and Shneider (2018). We analyse the relationship between the two variables using a sample of 138 countries for which all data are available at the level of the common period 2006–2015. As a methodology, we use the descriptive methods, the correlation coefficients, and the regression analysis, and we carry out the statistical tests necessary to ensure a high accuracy of the results. Results and discussions Graph 2.23 and Table 2.24 show a mean, indirect connection of shadow economy with corporate governance (c = −0.51; R2 = 0.260). The correlation is even closer (c  =  −0.631; R2  =  0.398) when the quality of corporate governance is estimated through strength audit and reports (Graph 2.24 and Table 2.25). In other words, a percentage of 26% to 39.8% of the variation of the shadow economy is explained by the variation of the quality of efficacy of corporate board and strength audit and reports, respectively. Table 2.26 shows that the value of the regression coefficient of the shadow economy in relation to the variable efficacy of corporate board is negative (−10.567) and significant, at a significance threshold of 1%. This reflects the fact that, at an increase of one point in the quality of efficacy of corporate board, there is a decrease of the shadow economy with 10.567 units on average (reflecting a decline of the shadow economy by 10.567% on average in total GDP). A significant influence on the shadow economy is also identified for the variable strength audit and reports (Table 2.27). Conclusions and limits of the research In conclusion, our hypothesis testing results confirm the existence of a negative and significant influence of the quality of corporate governance on the level of the shadow economy. Hypothesis testing results are even more robust if we use audit quality as an estimator for corporate governance efficiency (the values of the correlation coefficients and of R2 are slightly higher).

2.4.6  Corporate Governance and Money Laundering 2.4.6.1  Theoretical Approaches Deficiencies of the governance system consisting of the existence of conflicts of interest (e.g. when a credit institution has to decide the form under which it provides financial services to a company whose officials are represented in the bank’s board of directors), a deficient internal control system unable to detect the risks to which

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the company is exposed, and the suspicious transactions in which it is involved are often invoked as factors conducive to economic and financial crimes. Even though there were no direct causes, among the factors that generated bankruptcy and financial crisis in the early 2000s, many authors (cited by Achim et al. 2010) also mention factors related to accounting practices, such as the inability of the accounting model to cope with these innovations, which allowed the occurrence of certain off-balance sheet financial assets; issues regarding the recognition of assets, determining the value of entities included in the scope of consolidation; and the complexity of certain hybrid instruments that made it difficult to be properly evaluated. The research of Vaithilingam and Nair (2009) points out that the volume of money laundering crimes would be reduced systematically if the country adopted stronger audit and reporting standards. A strong audit and reporting standard would reduce the risk of undetected suspicious transactions and therefore lead to a lower possibility of engaging in illegal money laundering activities (Drezewski et  al. 2012; Vaithilingam and Nair 2009; Nikoloska and Simonovski 2012). The board of directors would usually receive aggregate reports from the management regarding the entity’s transactions. Thus, the directors, who are not executive managers at the same time, would not be able to discover or prevent on their own any illegal action. Even the audit committee does not conduct audit investigations alone, but it is related to external auditors. Identifying money laundering crimes in companies (including banks) can be done through efficient mechanisms of corporate governance structures such as internal controls (including those regarding identity verification) and compliance with regulatory reporting requirements (such as transaction thresholds). Also, the internal audit function can be extremely useful in identifying money laundering crimes, and therefore the reporting line of internal auditors should be taken into account (they should report, for example, to the audit committee and not to the financial manager). Money laundering through front companies, shell companies, and other company structures often uses both national and foreign companies. Foreign entities are usually in jurisdictions with strong protection of secrecy, which makes it difficult to identify such operations. In conclusion, there is sufficient evidence in the literature and practice that deficiencies in the governance system facilitate the involvement in money laundering crimes. 2.4.6.2  Practical Approaches Identifying an influence of the quality of corporate governance on engaging in money laundering crimes has also been the subject of our empirical research. In this sense, we underlie the following research hypothesis: Hypothesis: High quality of corporate governance leads to a decrease in the volume of money laundering crimes.

2.4 Corporate Governance

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2

R Linear = 0.337

200.00

Corruption

150.00

100.00

50.00

00 .00

1.00

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3.00

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Efficacy of corporate board

Graph 2.21  Correlation between corruption and efficacy of corporate board Table 2.20  Coefficients of correlation between corruption and efficacy of corporate board Corruption

Efficacy of corporate board

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Corruption 1 185 −.580** .000 152

Efficacy of corporate board −.580** .000 152 1 152

**The correlation is significant at a significance threshold of 0,01 (two-tailed)

Methodology Quality of corporate governance is measured with the help of the following indicators: efficacy of corporate board and strength audit and reports, which we referred to in the previous chapter. The volume of money laundering crimes is measured using a risk of money laundering under Anti-Money Laundering Risk Index (AML index).

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R2Linear = 0.480

200.00

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150.00

100.00

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.00 .00

2.00

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Strenght audit and report

Graph 2.22  Correlation between corruption and strength audit and reports Table 2.21  Coefficients of correlation between corruption and strength audit and reports Corruption

Strength audit and reports

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Corruption 1 185 −.693** .000 152

Strength audit and reports −.693** .000 152 1 152

Source: own processing **The correlation is significant at a significance threshold of 0,01 (two-tailed)

The sample of countries is represented by a number of 144 countries for which all necessary data were available. For this purpose, we use the descriptive methods, the correlation coefficients, and the regression analysis, and we carry out the statistical tests necessary to ensure a high accuracy of the results. Results and discussions Graph 2.25 shows a correlated arrangement of the money laundering risk and efficacy of corporate board, at a R2 = 0.258. In other words, a percentage of 25.8% of

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Table 2.22  Regression of corruption depending on efficacy of corporate boarda

Model (Constant) Efficacy of corporate board

Non-standardized coefficients Std. B error 269.609 21.683 −41.094 4.708

Standardized coefficients Beta −.580

t Sig. 12.434 .000 −8.728 .000

Dependent variable: corruption

a

Table 2.23  Regression of corruption depending on strength audit and reporta Non-standardized coefficients Standardized coefficients Model B Std. error Beta t Sig. (Constant) 244.332 14.038 17.404 .000 Strength audit and report −35.390 3.006 −.693 −11.774 .000 Source: own processing Dependent variable: corruption

a

the variation in money laundering risk can be explained by the efficacy of corporate board in the assessed countries, for the period 2006–2016. The correlation coefficient reflected in Table 2.28 is negative, in the amount of −0.508, which reflects an average degree of correlation of the two variables. Table 2.29 shows that the value of the regression coefficient of the variable efficacy of corporate board in relation to the money laundering risk is negative (of −0.883) and significant at a significance threshold of 1%. This reflects the fact that, at an increase of one point in the quality of efficacy of corporate board, an average decrease in the risk of money laundering by 0.883 points is achieved. In conclusion, the results of our hypothesis testing confirm a negative and significant influence of the efficacy of corporate board on the risk of money laundering. Hypothesis testing results are even more robust if we use audit quality as an estimator for quality governance. Graph 2.26 shows that an even higher percentage, of 41%, of the variation of the money laundering risk can be explained by the quality of the audit, compared with a percentage of 25.8% determined in relation to the corporate governance in general. And the correlation coefficient reflects a stronger link between the money laundering risk and the audit quality (c  =  −0.64) than between the risk of money laundering and the quality of corporate governance expressed (c = −0.508), which reflects an average degree of correlation of the two variables (Table  2.30). Regarding regression results, Table  2.31 shows that at an increase of one point in the efficiency of the strength audit and the reports, an average decrease in the level of money laundering risk is realized by 0.807 points (the result is statistically significant at 1% significance threshold).

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Conclusions and limits of the research In conclusion, the research hypothesis established above is accepted, which means that an increase in the quality of corporate governance at the microeconomic level leads to a decrease in the volume of money laundering crimes committed in the economy of the respective country.

2.5  Banking System Soundness 2.5.1  General Approaches 2.5.1.1  T  he Concept of Banking System Soundness and Measuring Instruments Because the banking system is the one that mediates the economic and financial transactions carried out in economic activities, the specialized literature and practice record the importance of the banking system development in the prevention and detection of economic and financial crimes. In this regard, for example, the investigation of the bank failures that generated the global financial crisis at the beginning of 2008, against the background of the American crisis of high risk mortgages, leads to the identification of accounting frauds and trading with privileged information, considered as important threats to the US economy (Bucur 2011, p. 353). Among the investigated entities are the Swiss bank U.B.S. and the major US financial groups Morgan Stanley, Merrill Lynch, Bear Stearns, and Citigroup (Bucur 2011, p. 353). The biggest fraud that has ever taken place on the global financial market was also committed in the same period. Through pyramid financial schemes as the Caritas model, the American tycoon Bernard Madoff committed a fraud of 50 billion dollars. Regarding the commitment of such large-scale fraud, the former managing director of the International Monetary Fund – Dominique Strauss-Kahn – stated that “if there was a stronger control by the US authorities, such schemes would be impossible to put into practice”. Regarding the bankruptcy of the Societe Generale Bank, a bank fraud amounting to 4.9 billion Euros was identified, considered to be the biggest fraud in the history of the banking industry (Bucur 2011, p. 353). This was committed by employees of the bank who, benefiting from deficiencies in the risk control systems of the bank, engaged in unauthorized transactions, fraud offenses, and unauthorized manipulation of the bank’s computer system and abuse of trust (a detailed description of the fraud system of the Societe Generale Bank of France is presented in the Bucur 2011 study, pp. 353–356). Moises Naim, “Foreign Policy” magazine analyst, appreciated on February 20, 2008, the fact that “one of the paradoxes of the global financial crisis is the lack of

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transparency in the sense that information has rarely been as incomplete and difficult to interpret as it is now, especially due to the overuse and widespread use of very sophisticated financial instruments, such as derivatives, which are difficult to evaluate and their risk degree is difficult to estimate” (Bucur 2011, p. 353). In Romania, the major bank failures (Bancorex, Dacia Felix, Banca Agricolă, Bankoop, Columna, Credit Bank, etc.) have raised big problems regarding the corporate governance, transparency, the existence of conflicts of interest, and the bad management of a too large volume of non-performing loans, in other words, an extremely low bank soundness. All these issues highlight the importance of the banking system soundness in reducing economic and financial crimes. As regards the measuring of the banking system soundness, the specialized literature highlights numerous concerns in this regard. • Bose et al. (2012) following the methodology proposed by Calomiris and Beim (2001) evaluate the development of banking system through both indicators: the depth and the efficiency of the banking sector. The depth of the banking sector is determined on the basis of two indicators, namely, liquid liabilities and total domestic credit provided by depository banks, both as percentages of GDP. These indicators measure the lending volume of the banking system, and, thus, they are considered suitable for capturing the depth of banking sectors (Levine and Zervos 1998). Then, the efficiency of the banking system is measured by Bose et  al. (2012) using four indicators: bank overhead costs, the net interest margin, the lending-­ deposit rate spread, and the level of bank concentration. Finally, the overall quality of a banking system is given by a composite indicator of the banking system development obtained by aggregating the two scores presented above (assigned for both depth and efficiency). • On the other hand, Berdiev and Saunoris (2016) use three different measures for measuring financial development, namely, demand for currency, credits granted by the financial sector to the private sector, and credits granted by the financial sector to the private sector and central administration. All three variables are measured as a percentage of GDP. • In national and international banking practice, banking rating systems are calculated and used for general assessment of global risk, such as the CAMEL model (United States), the CAAMPL model (Romania), PERLAS (World Council of Credit Unions WOCCU), SRUIF, etc. (Derviz and Podpiera 2008; Borlea 2009; Rostami 2015). For example, the CAAMPL system developed by the National Bank of Romania and applied to the Romanian banks for assessing their soundness is based on the evaluation of six components that uniformly and comprehensively reflect the performance of a bank, in accordance with the banking legislation and regulations in force, as follows: capital adequacy (C), shareholding quality (A), asset quality (A), management (M), profitability (P), and liquidity (L).

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R2Linear = 0.260

Shadow conomy

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.00 3.00

3.50

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5.50

6.00

Graph 2.23  Correlation between shadow economy and efficacy of corporate board

• In order to assess the performance and vulnerability of the financial system, we note the steps taken by the International Monetary Fund, which has taken care to establish and calculate the so-called Financial Soundness Indicators. In this respect, a number of 52 indicators are calculated, which characterize and evaluate different aspects related to the performance of the financial-banking system, including capital adequacy, asset quality, liquidity, profitability, and risks related to the financial-banking system (International Monetary Fund 2020). The downside of these assessments is that they do not offer a comprehensive measure of Table 2.24  Correlation coefficients between shadow economy and efficacy of corporate board Shadow economy Efficacy of corporate board Shadow economy Pearson correlation 1 −.510** Sig. (two-tailed) .000 N 158 138 Efficacy of corporate board Pearson correlation −.510** 1 Sig. (two-tailed) .000 N 138 152 **

The correlation is significant at a significance threshold of 0.01 (two-tailed)

2.5 Banking System Soundness

155 2 R Linear = 0.398

Shadow economy

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00 2.00

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Strenght audit and reports

Graph 2.24  Correlation between shadow economy and strength audit and reports

assessing the soundness of the banking financial system, but they only provide analytical measures for evaluating certain specific components. • Attention was also given to the elaboration of aggregate indicators by the World Economic Forum (2020), in their efforts to develop Global Competitiveness Indicators to characterize the world’s economies. To this end, in addition to many other indicators, they also calculate an indicator called soundness of bank in different countries, in order to evaluate the soundness of the banking system. The indicator is between level 1 (the weakest) and 7 (the best) thus reflecting the soundness of the banking system within an economy. The advantage of such an Table 2.25  Correlation coefficients between shadow economy and strength audit and reports Shadow economy

Pearson correlation Sig. (two-tailed) N Strength audit and reports Pearson correlation Sig. (two-tailed) N

Shadow economy Strength audit and reports 1 −.631** .000 158 138 −.631** 1 .000 138 152

Source: own processing **The correlation is significant at a significance threshold of 0,01 (two-tailed)

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Table 2.26  Regression of shadow economy depending on efficacy of corporate boarda

Model (Constant) Efficacy of corporate board

Non-standardized coefficients Std. B error 76.573 7.056 −10.567 1.529

Standardized coefficients Beta −.510

t Sig. 10.852 .000 −6.912 .000

Dependent variable: shadow economy

a

Table 2.27  Regression of shadow economy depending on strength audit and reportsa Non-standardized coefficients Standardized coefficients Model B Std. error Beta t Sig. (Constant) 67.591 4.226 15.992 .000 Strength audit and reports −8.602 .906 −.631 −9.491 .000 Source: own processing Dependent variable: shadow economy

a

aggregate measure is clearly superior to the other measures, because it offers an overview of the soundness of the banking system and also the database is public and generous (available for 137 countries) from 2006 to the present. 2.5.1.2  Banking System Soundness in European Union Countries Next, we intend to investigate the soundness of the banking system in the countries of the European Union for the period 2006–2016. The present study will provide us with clues to the existence of links regarding the soundness of the banking system and economic-financial crimes. Methodology The soundness of the banking system in different countries will be determined using the scores calculated by the World Economic Forum (2020) in the Report on Global Competitiveness, with regard to the soundness of bank indicator. The indicator ranges between level 1 (the weakest) and 7 (the best), thus reflecting the various levels of soundness of banking systems within an economy. The analysis period is 2006–2016 and covers the countries of the European Union (28). As methods, we use descriptive methods, comparison, analysis, and synthesis. Results and discussions Graph 2.27 shows that the weak soundness of banking systems are registered in Ireland, Slovenia, Greece, Romania, and Cyprus and the most solid ones are found in Finland, Malta, Luxembourg, Sweden, and Slovakia. Romania ranks 25th out of the 28 countries of the European Union analysed in terms of the banking system

2.5 Banking System Soundness

157

soundness. According to the analysis by geographical areas (Graph 2.28), the countries of Central, South, and East of Europe have the weakest banking system (with a score of about 5.18–5.19 points). At the opposite end, the most robust banking systems are found in the countries of Northern Europe (score of 6.06 points) and Western Europe (5.34 points).

2.5.2  Soundness of Banks and Corruption 2.5.2.1  Theoretical Approaches The literature (Park 2012; Chen et  al. 2015; Barry et  al. 2016) highlights a very close relationship between corruption and the performance of the banking system. For example, the study conducted by Park (2012) in 76 countries highlights a significant direct influence of corruption on problems arising in relation to non-­ performing loans. Corruption distorts the correct allocation of banking sources from good projects to bad projects, which leads to a decrease in the quality of private investments and a decrease in the economic growth, respectively. Corruption in the banking system can occur for several reasons, such as companies can bribe politicians (e.g. to obtain loans bypassing the loan evaluation and analysis stages), banks can bribe politicians (e.g. to get regular tolerance), and so on (see Munshi (1999) and Park (2012) for examples of the relationship between corruption and bank performance). Most likely, the final result will materialize in the misdirection of financial sources from normal projects to inefficient projects, leading to an increase in the volume of non-performing loans. The study conducted by Chen et  al. (2015) analyses how corruption affects banks’ risk-taking behaviour, in direct relation to the financial crises of the last 30 years. The analysis is performed for a number of 1200 banks from 35 emerging economies for the period 2000–2012. The authors find clear evidence that high levels of corruption increase risk-taking behaviour in banks. The study also provides evidence for a better understanding of why crises have occurred more often in countries with higher levels of corruption (Laeven and Valencia 2013). An interesting result is obtained by Barry et al. (2016), who investigated a possible relationship between corruption in the lending process and the ownership structure of banks. The authors find that the corruption involved in the lending process is higher when state-owned or family-owned banks offer a high proportion of credits in the economy. A stronger regulatory environment, either through a stronger supervisory regime or a higher quality of external audits, helps reduce corruption in the case of bank lending, if it is induced by family-controlled property, but not if it is induced by state-controlled property. Another interesting result of the authors is that when banks are controlled by other banks, the level of corruption in lending is reduced.

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R2Linear = 0.258

9.00

Money laundering risk

8.00

7.00

6.00

5.00

4.00

3.00 .00

1.00

2.00 3.00 4.00 Efficacy of corporate board

5.00

6.00

Graph 2.25  Correlation between money laundering and efficacy of corporate board Table 2.28  Correlation coefficients between money laundering and efficacy of corporate board

Money laundering risk

Efficacy of corporate board

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Money laundering risk 1

Efficacy of corporate board −.508**

164 −.508**

.000 144 1

.000 144

152

Source: own processing **The correlation is significant at a significance threshold of 0,01 (two-tailed)

2.5.2.2  Practical Approaches Next, we propose to investigate the existence of a link between the banking system soundness and the involvement in corruption offenses, based on an empirical study, starting from the following research hypothesis:

159

2.5 Banking System Soundness Table 2.29  Regression of money laundering depending on efficacy of corporate boarda

Non-standardized coefficients Standardized coefficients Model B Std. error Beta t Sig. (Constant) 9.899 .580 17.065 .000 Efficacy of corporate board −.883 .126 −.508 −7.032 .000 Source: own processing a Dependent variable: money laundering risk R2Linear = 0.410

9.00

Money laundering risk

8.00

7.00

6.00

5.00

4.00

3.00 .00

2.00

4.00

6.00

Strenght audit and reports Graph 2.26  Correlation between money laundering and strength audit and reports

Hypothesis: A high quality of the soundness of the banking system leads to a decrease in the level of corruption. Methodology We will determine the soundness of the banking system in different countries using the scores calculated for the soundness of banks indicator, by the World Economic Forum (2020) in the Global Competitiveness Report. The indicator ranges between levels 1 (the weakest) and 7 (the best), thus reflecting the banking systems soundness within an economy. Corruption is measured as the rank occupied by the analysed countries according to the score obtained by the Corruption Perception Index, provided by Transparency

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Table 2.30  Correlation coefficients between money laundering and strength audit and reports

Money laundering risk

Strength audit and reports

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Money laundering risk 1

Strength audit and reports −.640**

164 −.640**

.000 144 1

.000 144

152

**The correlation is significant at a significance threshold of 0,01 (two-tailed)

International (2020b). A high position occupied by a country in the top referring to the level of corruption reflects a high level of corruption and vice versa. The sample is represented by 140 countries, for which data are available for the two variables, and the analysis period is 2006–2016. Results and discussions Graph 2.29 shows a correlated arrangement of the variables corruption and soundness of banks, at a R2 = 0.251. In other words, a quarter of the variation in the volume of corruption can be explained by the soundness of the banking system. The correlation coefficient reflected in Table 2.32 is negative, in the value of −0.501, which reflects an inverse correlation of the two variables, at a medium intensity. Table 2.33 shows that an increase with one point of the banking system quality leads to a decrease of the level of corruption by 23.882 points, on average (a decrease with approx. 24 positions in the hierarchy of the countries classified by the level of corruption). Conclusions and limits of the research Our hypothesis testing results confirm that a high quality of the banking system soundness leads to a decrease in the level of corruption. The robustness of the above results can be achieved by using control variables as well as more complex data processing methodologies (e.g. panel type).

2.5.3  Soundness of Banks and Shadow Economy 2.5.3.1  Theoretical Approaches A number of studies analyses the relationship between the soundness of the banking system and the shadow economy (Blackburn et al. 2010; Bose et al. 2012; Berdiev and Saunoris 2016). Thus, in the study by Blackburn et al. (2010) the authors find that the lower the stage of financial development, the higher the incidence of tax

2.5 Banking System Soundness

161

Table 2.31  Regression of money laundering depending on strength audit and reportsa Non-standardized coefficients Standardized coefficients Model B Std. error Beta t Sig. (Constantă) 9.585 .382 25.065 .000 Strength audit and reports −.807 .081 −.640 −9.925 .000 Source: own processing a Dependent variable: money laundering risk

avoidance and the size of the shadow economy, respectively. Similarly, Bose et al. (2012) investigates the relationship between banking development and the level of shadow economy on a sample of 137 countries from 1995–2007. Their results show that an improvement in the banking sector development is associated with a lower level of the shadow economy. In addition, the authors consider that both the depth and the efficiency of the banking sector matter in reducing the size of shadow economies. Similar results are obtained by Berdiev and Saunoris (2016), who analyse the dynamic relationship between financial development and shadow economy, using data for 161 countries from 1960–2009. The authors find that financial development reduces the size of the shadow economy. In addition to the previous results, they find some evidence of inverse causality between these variables; namely, a shock to the shadow economy hinders financial development. The degree of banking development can also be reflected by the percentage of cash in circulation, in total money engaged in economy. In this regard, Birch (2015), in his study, makes clear arguments that less money in circulation actually represents a higher level of the shadow economy. Thus, the author cites the chief cashier of the Bank of England, who estimates that only about a quarter of the cash put into circulation is used to buy and sell things. The difference is either shipped abroad, outside the banking system (treasured), or used to support the shadow economy. The United Nations Convention against Corruption (2004) provides measures regarding the prevention and detection of transfers of proceeds of crime and of the goods acquired illegally, respectively. In this regard, the regulation stipulates that “each state party shall apply the appropriate and effective measures to prevent, with the help of its regulatory and control bodies, the establishment of banks that have no physical presence and are not affiliated with a regulated financial group”. In addition, it is stipulated that “states parties may require their financial institutions to refuse to establish or pursue appropriate banking relationships with such institutions and to avoid establishing relationships with foreign financial institutions that allow the use of their accounts by banks that have no physical presence and which are not affiliated with a regulated financial group”.

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2.5.3.2  Practical Approaches Below, we propose to investigate the existence of a connection between the soundness of the banking system and the shadow economy, starting from the following research hypothesis: Hypothesis: A high quality of the banking system soundness leads to a decrease in the level of the shadow economy. Methodology We will determine the soundness of the banking system in different countries using the scores calculated for the soundness of banks indicator, by the World Economic Forum in the Global Competitiveness Report. The indicator ranges between levels 1 (the weakest) and 7 (the best), thus reflecting the soundness of the banking systems within an economy. The level of the shadow economy is determined as a percentage in GDP, as presented in the source provided by Medina and Shneider (2018). We will analyse the relationship between the two variables using a sample of 135 countries for which all data are available, for the common period 2006–2015. Results and discussions Graph 2.30 shows a correlated arrangement of the variables shadow economy and soundness of banks, at a R2 = 0.141. In other words, a percentage of 14.7% of the variation in the level of shadow economy can be explained by the soundness of the banking system. The correlation coefficient reflected in Table 2.34 is negative, in the value of −0.384, which reflects a medium degree of correlation of the two variables. Table 2.35 shows that at a one point increase in the quality of the banking system, the average level of the shadow economy decreases by 4.995 points (which means a decrease of about 5% of the level of the shadow economy in the volume of GDP). Conclusions and limits of the research The results of our research confirm the hypothesis that a high quality of the banking system soundness leads to a decrease of the level of the shadow economy. The robustness of the above results can be emphasized by the use of control variables as well as more complex data processing methodologies (e.g. panel type).

2.5.4  Soundness of Banks and Money Laundering 2.5.4.1  Theoretical Approaches In the stages carried out in the process of money laundering, banking institutions represent the first point of contact of the criminals (Isa et al. 2015). Banking institutions are the most commonly used money laundering instruments due to several factors, including multiple services provided by financial institutions, such as deposits, loans, and currency exchange (Idowu and Obasan 2012). With the help of

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163

banking institutions, criminals transfer illegally sourced money to bank accounts in the country or abroad, to receive a legal appearance. Therefore, the first step in the money laundering process consists in placing the amounts of money provided by illicit activities in the financial-banking system (by making deposits or purchasing financial instruments that are subsequently collected). In this context, a high level of transparency in the banking and financial sector, a high degree of monitoring of financial transactions and bank accounts, and an adequate financial supervision are factors identified in the specialized literature as having a decisive role in reducing money laundering crimes (Leția 2014, p 113, p131. p.  171; Vaithilingam and Nair 2009; Nikoloska and Simonovski 2012). Thus, a weak banking supervision could be an easy target for money to be washed without suspicion. In the United States, for example, both banks and non-bank financial institutions must report financial transactions in excess of $ 10,000 per day, as well as any suspected criminal activity. In conclusion, a higher quality of banking supervision means a good soundness of banks and diminishes the channels by which money laundering crimes are committed. At European Union level, the European Directives against money laundering (Directive (EU) 2015/849, Directive 91/308 / EEC, Directive 91/308 / EEC, Directive 2006/70 / EC) have taken over the 40 recommendations of the Financial Action Task Force FATF-GAFI on information exchange, as well as between banking institutions and public authorities. In order to increase vigilance and mitigate the risks arising from cash payments, persons trading goods should be covered by this Directive to the extent that they make or receive cash payments of at least EUR 10,000. In Romania, the National Bank of Romania regulates, for credit institutions and non-banking financial institutions, the minimum standards for their elaboration of norms for knowing their clients, as an essential part of managing the risk of money laundering and financing of terrorism.1 The norms for knowing the clients must include at least the following elements (Art. 5, paragraph (2), National Bank of Romania, Regulation no. 9/2008): (a) A policy of customer acceptance, establishing at least the categories of clientele that the institution aims to attract, the gradual acceptance procedures and the hierarchical level of approval of customer acceptance depending on the degree of risk associated with the category in which they are classified, and the types of products and services that can be provided to each category of clientele (b) Procedures for the identification and permanent monitoring of the clients for their classification in the corresponding clientele category, respectively, for the transition from one clientele category to another (c) The content of standard measures, simplified measures, and additional measures, for knowing the client, for each of the categories of clientele and of products or transactions subject to these measures 1  Regulation No. 9 of July 3, 2008, Official Monitor, Part I 527 July 14, 2008, regarding the client’s knowledge in order to prevent money laundering and terrorist financing

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7 6 5 4

4 4

4

6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

3 2 0

Ireland Slovenia Greece Romania Cyprus Bulgaria Latvia United Kingdom Hungary Lithuania Portugal Italy Poland Belgium Croaa Spain Germany Denmark Netherlands Austria Estonia Czech Republic France Slovakia Sweden Luxembourg Malta Finland

1

Graph 2.27  Soundness of bank in European Union countries, 2006–2016

(d) Procedures for permanent monitoring of customer operations in order to detect unusual and suspicious transactions (e) Ways to approach transactions and clients in and/or from jurisdictions that do not require the application of procedures for acquainting the client and keeping records regarding it, equivalent to those provided in Law no. 656 (2002), with the subsequent amendments and completions, and in the Government Decision no. 594/2008, and in which their application is not supervised in a manner equivalent to that regulated by the specified legislation (f) Ways of drawing up and keeping the appropriate records, as well as establishing access to them (g) Procedures and measures to verify the implementation of the elaborated norms and to evaluate their efficiency, including through external audit (h) The standards for hiring and training programmes for personnel in the field of knowing clients (i) Internal reporting procedures and to the competent authorities Institutions must ensure the continuous training of the staff, so that the persons with responsibilities in the field of knowing clients are adequately trained in order to prevent money laundering and terrorist financing. Also, the supervision of bank accounts and customer transactions is an integral part of the banking supervision process exercised by the banking supervisory authorities of each country.2

2  In Romania, the Emergency Ordinance No. 99/2006 on credit institutions and capital adequacy regulates the conditions of access to banking activity and its activity in the territory of Romania, the prudential supervision of credit institutions and financial investment services companies, and the supervision of payment systems and settlement systems for transactions with financial instruments.

2.5 Banking System Soundness

165

7 6.06 6

5.18

5.19

5.34

CEE

Souh

West

5 4 3 2 1

North

Graph 2.28  Soundness of bank by geographical regions of the European Union, 2006–2016. (Source: own processing)

On the other hand, the United Nations Convention Against Corruption (2020) states that each state shall establish a complete internal regime of regulation and control of banks and non-banking financial institutions, with the purpose to discourage and detect all forms of money laundering. The regulation focuses on the requirements regarding the customer identification, the registration of operations, and the declaration of suspicious operations.

Graph 2.29 Correlation between corruption and soundness of banks

2

R Linear = 0.251

200.00

Corruption

150.00

100.00

50.00

00 2.00

3.00

4.00 5.00 6.00 Soundness of bank

7.00

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2  Economic and Political Determinants of Economic and Financial Crime

Table 2.32  Correlation coefficients between corruption and soundness of banks Corruption

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Soundness of banks

Corruption 1

Soundness of banks −.501** .000 140 1

185 −.501** .000 140

140

Source: own processing **The correlation is significant at a significance threshold of 0,01 (two-tailed) Table 2.33  Regression of corruption depending on the soundness of banksa

Model (Constant) Soundness of banks

Non-standardized coefficients B Std. error 193.569 17.402 −23.882 3.516

Standardized coefficients Beta −.501

t 11.123 −6.792

Source: own processing a Dependent variable: corruption

2

R Linear = 0.147

Shadow economy

60.00

40.00

20.00

00 2.00

3.00

4.00 5.00 6.00 Soundness of bank

Graph 2.30  Correlation between shadow economy and soundness of banks

7.00

Sig. .000 .000

2.5 Banking System Soundness

167

Table 2.34  Correlation coefficients between shadow economy and soundness of banks Shadow economy

Soundness of banks

Shadow economy 1

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

158 −.384** .000 135

Soundness of banks −.384** .000 135 1 140

Source: own processing **The correlation is significant at a significance threshold of 0,01 (two-tailed) Table 2.35  Regression of shadow economy depending on soundness of banksa Non-standardized coefficients Standardized coefficients Model B Std. error Beta t Sig. (Constant) 52.349 5.116 10.233 52.349 Soundness of banks −4.955 1.034 −.384 −4.793 −4.955 Source: own processing Dependent variable: shadow economy

a

Table 2.36  Correlation coefficients between money laundering and soundness of banks Money laundering risk

Soundness of banks

Pearson correlation Sig. (two-tailed) N Pearson correlation Sig. (two-tailed) N

Money laundering risk 1 164 −.366** .000 131

Soundness of banks −.366** .000 131 1 140

Source: own processing **The correlation is significant at a significance threshold of 0,01 (two-tailed) Table 2.37  Regression of money laundering depending on the soundness of banksa

Model (Constant) Soundness of banks

Non-standardized coefficients B Std. error 7.951 .494 −.442 .099

Source: own processing Dependent variable: money laundering risk

a

Standardized coefficients Beta −.366

t 16.104 −4.474

Sig. .000 .000

168

2  Economic and Political Determinants of Economic and Financial Crime

Graph 2.31 Correlation between money laundering and soundness of banks

2

R Linear = 0.134

9.00

Risk of money laundering

8.00

7.00

6.00

5.00

4.00

3.00 2.00

3.00

4.00 5.00 6.00 Soundness of bank

7.00

2.5.4.2  Practical Approaches The identification of an influence of the banking system soundness on the participation in money laundering crimes was also the object of our empirical research. In this sense, we underlie the following research hypothesis: Hypothesis: High quality of the banking system soundness leads to a decrease in the volume of money laundering crimes. Methodology The volume of money laundering crimes is measured using the indicator Anti-­ Money Laundering Risk Index (AML index). The banking system soundness in different countries will be determined using the scores calculated for the soundness of banks indicator, by the World Economic Forum (2020) in the Global Competitiveness Report. The indicator is between level 1 (the weakest) and 7 (the best), thus reflecting the banking system soundness within an economy. The sample of countries is represented by a number of 131 countries and the analysis period is 2012–2016, for which all data were available. For this purpose, we use the descriptive methods, the correlation coefficients, and the regression analysis, and we carry out the statistical tests necessary to ensure a high accuracy of the results. Results and discussions Graph 2.31 shows a correlated arrangement of the variables risk of money laundering and soundness of banks, at a R2 = 0.134. In other words, a percentage of 13.4% of the variation in risk of money laundering can be explained by the soundness of the banking system. The correlation coefficient reflected in the table is negative, in

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the amount of −0.3668, which reflects a medium degree of correlation of the two variables (Table 2.36). Table 2.37 shows that the value of the regression coefficient of the variable soundness of banks in relation to the variable money laundering risk is negative (−0.442) and significant at a significance threshold of 1%. This reflects the fact that, with a one point increase in banking soundness, there is a decrease in the level of money laundering risk by 0.442 points, on average. Conclusions and limits of the research Our hypothesis testing results confirm that a high quality of the banking system soundness leads to a decrease in the volume of money laundering crimes. The robustness of the above results can be emphasized by the use of control variables as well as more complex data processing methodologies (e.g. panel type).

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Transparency International. (2020b). Corruption perception index. Available at: /www.transparency.org/research/cpi/. Accessed on 5 Feb 2020. Treisman, D. (2000). The causes of corruption: A cross-national study. Journal of Public Economics, 76, 399–457. Tricker, B. (1984). Corporate governance: Practices, procedures and powers in British companies and their boards of directors. Aldershot: Gower Publishing. Tulai, C. I. (2003). Finanţele publice şi fiscalitatea (public finance and fiscality). Cluj-Napoca: Casa Cărții de Știință Publishing House. United Nations Convention against Corruption. (2004). United Nation, New York. United Nations Convention against Corruption. (2020). Available at https://www.unodc.org/unodc, Accessed at 10 May 220. Vaithilingam, V., & Nair, M. (2009). Mapping global money laundering trends: Lessons from the pace setters. Research in International Business and Finance, 23, 18–30. Van de Pilos, N. (2017). Tax Avoidance and Corporate Governance Does the board of directors influence tax avoidance? The Erasmus School of Economics, U.K). available at https://thesis. eur.nl/pub/38909/Pilos_387123–2-.pdf. Accessed on 15 Dec 2019. Vladu, A. B., & Matiș, D. (2010). Corporate governance and creative accounting: Two concepts strongly connected? Some interesting insights highlighted by constructing the internal history of a literature. Annales Universitatis Apulensis Series Oeconomica, 12(1), 332–346. Vladu, A. B., Amat, O., & Cuzdriorean, D. D. (2017). Truthfulness in accounting: How to discriminate accounting manipulators from non-manipulators. Journal of Business Ethics, 140((4), 633–648. Wijayati, N., Hermes, N., & Holzhacker, R. (2016). Corporate governance and corruption: A comparative study of Southeast Asia. In R.  Holzhacker, R.  Wittek, & J.  Woltjer (Eds.), Decentralization and governance in Indonesia. Development and Governance (Vol. 2). Springer International Publishing, Switzerland. Williams, C. C., & Schneider, F. (2016). Measuring the global shadow economy. The prevalence of informal work and labor. Northampton: Edward Edgar Publishing Limited. Winnie, V. A. T. (2016). The effect of good corporate governance on tax avoidance: An empirical study on manufacturing companies listed in IDX period 2010-2013. Asian Journal of Accounting Research, 1, 28–38. World Bank. (2005). Developing corporate governance codes of best practice (Vol. 1 Rationale). World Bank (2015). Country and lending groups. World Bank, Washington. World Bank. (2020a). World Bank indicators, available at https://data.worldbank.org/indicator available at http://www.worldbank.org/. Accessed on 15 March 2020. World Bank. (2020b). The worldwide governance indicators. Available at http://info.worldbank. org/governance/wgi/#home. Accessed on 13 Jan 2020. World Economic Forum. (2020). The global competitiveness reports. Wu, X. (2005). Corporate governance and corruption: A cross-country analysis. Governance: An International Journal of Policy, Administration, and Institution, 18(2), 151–170.

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Chapter 3

Behavioural Determinants of Economic and Financial Crime

3.1  Culture Numerous recent studies are beginning to go beyond the explanations of a strictly economic nature regarding the identification of the causes of economic phenomena, paying more and more attention to the analysis of the impact of social values, norms, and attitudes (Frey and Stutzer 2012; Halla 2010; Schneider and Klinglmair 2004; Torgler and Schneider 2009; Pickhardt and Prinz 2014). Thus, the term behavioural economics has been developing more and more in the economic sciences, lately. According with authors (Lin 2012), behavioural economics study “the effects of psychological, cognitive, emotional, cultural and social factors on the economic decisions of individuals and institutions and how those decisions vary from those implied by classical theory”. Various authors among non-economists believe that attitude and opportunity are the main motivations for committing economic crimes. Such authors (Bucur 2011, p. 49) consider that the employment of illicit activities of individuals is determined by several main reasons, such as: • Have a need or a purpose to reach. • Have the necessary skills to commit crimes. • Their values and attitudes allow them to realize that they will commit illicit acts including criminal acts. • Have the opportunity to commit crime. Schmölders (1960, p. 38) considers that the starting point of any investigation of the discipline exercised by citizens on their fiscal behaviour must be the way the state is reflected in the minds of citizens. Awareness of the role of the state leads to the civic and fiscal feelings of the citizens and to a fundamental attitude regarding the issues of their condition. In this regard Kirchler (2007) offers broad explanations regarding the economic psychology of taxpayers, how fiscal behaviour is reflected © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 M. V. Achim, S. N. Borlea, Economic and Financial Crime, Studies of Organized Crime 20, https://doi.org/10.1007/978-3-030-51780-9_3

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through social representations of tax obligations and their connection with individual, social, and societal attitudes and norms. The concept of social representations used by Kirchler (2007, p. 49) when referring to taxpayer behaviour refers to “the whole integration framework of the multitude of variables presented in literature as tax compliance determinants”. At the societal level, these determinants refer to ethics, values, social norms, and tax morale. Of course, the cultural impact of these determinants is obviously realized. At the individual level, subjective knowledge, respectively, perceptions regarding fiscal obligations and fiscal non-compliance, are part of social representations, such as attitudes and behavioural intentions (Kirchler 2007, p. 49). Equity perceptions are also an important variable regarding fiscal non-compliance. Continuing the same idea, studies by Ajzen (1991) and Fishbein and Ajzen (2010) analyse the attitudes, norms of constraint, and control exercised over individual behaviour regarding the choice of compliance and non-compliance options through the theory of thought action and the theory of planned behaviour. Kirchler (2007) uses the concept of social representations directly related to the concept of tax morale, introduced by Schmölders (1960). For some researchers (Bucur 2011, p. 50), greed, as a universal human motivation, is an important component in deciphering the behaviour of legal compliance. Lewis (1978) considers that people’s attitudes, judgments, and behavioural intentions are more affected by what they believe than what they are in reality. Regarding the concept of “tax” and how this word is reflected in the minds of individuals, Schmölders (1960) conducted a study on German contributors to which he asked the following question: “What do you think of when you hear the word ‘taxes’?” The results of the study reflect the fact that about 33% of the associations were concerned with technical notions such as tax legislation, tax, etc., in 29% of cases there were negative associations, and 10% of the respondents did not associate taxes with anything. The more negative associations were found among self-­ employed (41%) and the fewest among civil servants (23%). Thus, it can be seen that the representations of taxation differ by groups of taxpayers, depending on the form of payment, the level of income, or age. For example, employees who receive their monthly salary in cash and net form (and who only know scripturally the gross amount of the salary and the value of the related contributions) are less aware of the payment of tax obligations, considering them own money losses. These employees perceive taxes as an exchange between individuals and government. On the other hand, the rich perceive the ratio of contributions and benefits to be unequal or unfavourable to them, while poorer individuals perceive this ratio as equal or favourable to them. In the study conducted by Schmölders (1960), he asked the participants to indicate to what extent they agree with the state’s decisions, noting, among other things, that the young subjects offered answers with more negative connotations than the older subjects did. Another form of social representation of fiscal behaviour refers to social norms. According with Wenzel (2005) the social norms represent “the perceived frequency or acceptance of tax fraud within a reference group”. If a taxpayer considers that the non-compliance is widespread and constitutes an accepted social behaviour, then

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that taxpayer will most likely not comply. A study conducted in Canada (cited by Bucur 2011, p. 50) indicates that although 58% of the citizens considered that the tax system was correct, 21% of them avoided paying taxes; the justification offered by one of these participants is very interesting, namely: “Everyone else does. GST (tax act) is stupid and if a law is stupid, I don’t see why it should not be violated. Besides, I pay taxes that are too high”. Numerous studies identify socio-cultural factors among the underlying causes of corruption and shadow economy. For example, we mention the studies carried out by Husted (1999), Davis and Ruhe (2003), Fisman and Miguel (2007), Barr and Serra (2010), Halkos and Tzeremes (2011), Tong (2014), and Achim (2016) which document the existence of a significant influence between culture and corruption. Austwick and Berga (2016) investigate the causes of shadow economy in Latvia and identified the cultural factor as one of the main causes of the high level of shadow economy in this country. They concluded that because Latvia is a relatively young country, its citizens are not yet accustomed to the culture of paying taxes. Regarding the individual norms and the attitudes of the citizens, greed can be invoked as an important component in deciphering the behaviour of legal compliance (Bucur 2011, p. 50). Aspects of religion and how it influences the values of a nation may also be responsible for the spread of corruption (Faleye 2013; North et al. 2013). Next we will investigate the definition of the concept of “culture” as well as the concrete possibilities of evaluation, continuing with a presentation of the cultural models existing at the level of the countries belonging to the European Union. Finally, we will investigate the relationship between culture and corruption, and shadow economy, respectively, as a review of the specialized literature, but also by presenting case studies.

3.1.1  General Approaches Regarding Culture 3.1.1.1  Concept of Culture and Measuring Instruments According to Hofstede (2011), culture is defined as “the collective mental programming of the human mind that distinguishes one group of people from another”. Aspects such as honesty, trust in authorities, trust in people, pride, relationship with nature and the world, relationship with others, ability to do things properly, ways to avoid uncertainty, orientation in time and space, and also the long-term and short-­ term orientation are elements that characterize the culture of a nation and can determine the behaviour of individuals who carry out economic activities. Trompenaars (1993) identifies among the cultural characteristics the following: universalism and particularism, individualism and communitarianism, specific and diffuse, neutral and affective, and achievement and attribution. For example, British culture is universally oriented. The actions of the British are based on norms rather than relations. At the opposite end, the Bulgarian culture is particularly oriented,

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because the focus is on relationships, rather than norms, and personal contact is vital to the success of a business. Like most people with a particularistic culture, they believe that agreements can be modified at any time, depending on the situation. In the Universalist culture, a contract is a law and must therefore be respected. On the other hand, the British culture is a diffuse one, compared to the Bulgarian one, which is a specific one. The differences are in the level and purpose of the involvement of citizens in relation to other people. It is well known that the British are very direct; they go directly to the subject. They have an active communication with a new person, regardless of the prior information they have about that person. On the opposite side, Bulgarian culture is a specific one; Bulgarians need to know as much as possible about a person before an effective communication takes place. In this context, for example, Elenkov and Fileva (2006) explain the bankruptcy of the British company Rover in Bulgaria, due to the socio-cultural environment of the host country, which differs significantly from that of the British. Investigating the causes of this bankruptcy, among the determining factors are those related to the socio-cultural differences, the British company not considering a priority to know and assimilate the values and culture of the population of Bulgaria, which differs essentially from those in the United Kingdom, in terms of universalism and particularism, individualism and communitarianism, specific and diffuse, neutral and affective, and achievement and attribution. The studies carried out by Hofstede are extremely useful for understanding the cultural dynamics of nations (Javidan et al. 2006). Hofstede’s cultural model consists of six dimensions (Hofstede Center 2020): 1. Attitude towards social inequality or power distance (with the acronym PD). This dimension expresses the degree to which the less powerful members of a society accept and expect this power to be unevenly distributed. The fundamental issue here is how a society accepts inequalities between people. Individuals belonging to societies with a high degree of power distance accept a hierarchical order in which everyone has a place and no other justification is needed. In societies with short power distance, individuals strive to equalize the distribution of power and require justifications for inequalities in power. 2. Attitude towards the community or individualism versus collectivism (with the acronym IDV). The high score of such a dimension reflects the individualism that can be defined as a preference for a weak social framework, in which individuals are expected to care only for themselves and their immediate families. Its opposite, collectivism, is a preference for a social framework closely linked to society, where individuals can expect their relatives or members of a particular group to take care of them in exchange for unquestionable loyalty. The position of a society is reflected on this dimension if the self-image of the people is defined in terms of “me” or “us”. 3. Attitude towards success or masculinity versus femininity (with the acronym MAS). The masculinity part of this dimension represents a preference in society for achievement, heroism, assertiveness, and material rewards for success. Such a society is generally more competitive. Its opposite, femininity, means the

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p­ reference for cooperation, modesty, care for the poor, and the quality of life, and a female society is generally more consensus-oriented. 4. Attitude towards avoiding the unknown or uncertainty (acronym UAI). This dimension expresses the degree to which members of a society feel uncomfortable with uncertainty and ambiguity. The fundamental issue here is how a society perceives that the future can never be known: future must be controlled or let it happen. Countries with strong UAI maintain rigid codes of belief and behaviour and are intolerant of unorthodox behaviours and ideas. Companies with a low level of UAI maintain a more relaxed attitude in which practice matters more than principles. 5 . Attitude towards the passing of time or long-term orientation (with the acronym LTO). Each society must maintain links with its own past, while facing the challenges of the present and the future. Societies give priority to these two existential goals differently. Societies that have low scores of this dimension, for example, prefer to maintain secular traditions and norms and look at social change with suspicion. Societies with high scores on this dimension of culture have a more pragmatic approach: they encourage hard work and efforts in modern education as a way to prepare for the future. 6 . Attitude towards controlling one’s own desires or indulgence and restraint (with the acronym IND). Indulgence characterizes a society that allows the relatively free expression of basic and natural human movements regarding the joy of life and fun. Restraint characterizes a society that suppresses the satisfaction of needs and regulates it through strict social norms. Each dimension places a nation’s culture on a scale of 0–100. At the time of this study, Hofstede’s model had been for a large number of countries (Hofstede Center 2020). 3.1.1.2  Culture in European Union Countries Next, we intend to analyse the cultural characteristics at the level of the European Union countries, in order to identify certain indications on the existence of a connection between culture, on one hand, and corruption phenomena and shadow economy, on the other. Methodology The cultural characteristics are highlighted using the Hofstede model with the six cultural dimensions: power distance (PD), individualism versus collectivism (IDV), masculinity versus femininity (MAS), uncertainty avoidance (UAI), long-term orientation (LTO), and indulgence and restraint (IND). The sample of countries is represented by the countries of the European Union (28). As methods, we use descriptive methods, comparison, analysis, and synthesis.

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Results and discussions Graph 3.1 shows that the societies with the shortest power distance are among the countries of Northern and Western Europe, namely, Austria (11), Denmark (18), Ireland (28), Sweden (31), and Finland (33). Societies with the highest power distance are among the former communist countries in Central and Eastern Europe, Slovakia (100), Romania (90), Croatia (73), and Slovenia (71). The countries with the highest levels of individualism are the United Kingdom (89), Hungary and the Netherlands (80), Italy (76), and Belgium (75), and the most collectivist societies are among the countries of Southern and Eastern Europe, namely, Portugal (27), Slovenia (21), Bulgaria and Romania (30), and Croatia (33) (Graph 3.2). The most masculine societies in the European space are found in Slovakia (100), Hungary (88), Austria (79), and Italy, and the most feminine are identified in Sweden (5), Latvia, the Netherlands (14), and Denmark (16) (Graph 3.3). The societies with the highest inclination to avoid the uncertainties, namely, those that take the least risks are found in Greece (100), Portugal (99), Belgium (94), and Poland (93), and those with the lowest risk aversion are found in Northern Europe, that is, Denmark (23), Sweden (29), Great Britain (35), and Ireland (35) (Graph 3.4). The societies with a long-term orientation horizon are located in Germany (83), Belgium, Lithuania, and Estonia (82), and those with a shorter horizon are Ireland (24), Portugal (28), Denmark (35), and Finland (38) (Graph 3.5). The most indulgent societies are found among the countries of Northern Europe, namely, Sweden (78), Denmark (70), Great Britain (69), and Holland (68), and the most detained are among the Baltic countries, namely, Latvia (13), Estonia (16), and Lithuania (16) but also Bulgaria (16) and Romania (20) (Graph 3.6).

90

44 46 40 40 42 35 35 38 33 28 31 11

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Austria Denmark Ireland Sweden Finland Germany United Kingdom Netherlands Luxembourg Estonia Lithuania Latvia Hungary Italy Spain Czech Republic Greece Portugal Belgium France Poland Bulgaria Slovenia Croatia Romania Slovakia

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Graph 3.1  Power distance (PD) in European Union countries. (Source: own processing)

3.1 Culture

80 80 74 75 76 67 70 70 71 71 63 58 60 60 60 60 51 52 55

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33 35 27 27 30 30

Portugal Slovenia Bulgaria Romania Croatia Greece Spain Slovakia Austria Czech Republic Estonia Lithuania Luxembourg Poland Finland Germany Ireland Latvia France Sweden Denmark Belgium Italy Hungary Netherlands United Kingdom Cyprus Malta

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Graph 3.2  Individualism versus collectivism (IDV) in European Union countries. (Source: own processing)

5 9

19 19 14 16

31 26 30

40 40 42 42 43

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79

88

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Sweden Latvia Netherlands Denmark Lithuania Slovenia Finland Estonia Portugal Bulgaria Croatia Spain Romania France Luxembourg Belgium Greece Czech Republic Poland Germany United Kingdom Ireland Italy Austria Hungary Slovakia Cyprus Malta

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Graph 3.3  Masculinism versus feminism (MAS) in European Union countries. (Source: own processing)

3.1.2  Culture and Corruption 3.1.2.1  Theoretical Approaches The most frequent representative factor to show the moral dimension of economic behaviour is culture. Numerous studies document the existence of a significant influence between culture and corruption (Husted 1999; Davis and Ruhe 2003; Fisman and Miguel 2007; Barr and Serra 2010; Halkos and Tzeremes 2011; Tong 2014; Achim 2016). Husted (1999) found that the phenomenon of corruption is significantly associated with the cultural phenomenon. He identified a cultural profile of a corrupt

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51 53 23

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65 65 59 60 63

75 70 70 74

99 100 90 93 94 85 86 86 88 80 82

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Denmark Sweden Ireland United Kingdom Slovakia Netherlands Finland Estonia Latvia Germany Lithuania Austria Luxembourg Czech Republic Italy Croatia Hungary Bulgaria France Spain Slovenia Romania Poland Belgium Portugal Greece Cyprus Malta

100 90 80 70 60 50 40 30 20 10 0

3  Behavioural Determinants of Economic and Financial Crime

Graph 3.4  Uncertainty avoidance (UAI) in European Union countries. (Source: own processing)

24

28

35 38 38

53 49 51 52 45 48

67 69 69 70 63 64 58 58 60 61

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82 82 82 83

Ireland Portugal Denmark Finland Poland Greece Spain Slovenia United Kingdom Romania Sweden Croatia Hungary Austria Italy France Luxembourg Netherlands Bulgaria Latvia Czech Republic Slovakia Belgium Estonia Lithuania Germany Cyprus Malta

100 90 80 70 60 50 40 30 20 10 0

Graph 3.5  Long-term orientation versus short-term orientation (LTO). (Source: own processing)

country as being represented by the existence of a high power distance, a high masculinity, and a high degree of uncertainty avoidance. Fisman and Miguel (2007) investigated the relationship between culture and corruption in a study on violations of parking regulations in New York by diplomats from over 149 countries. They found out that diplomats from highly corrupt countries are more likely to break the parking law than diplomats from less corrupt countries. They concluded that the corruption phenomenon is partly a cultural phenomenon. Davis and Ruhe (2003) also find that cultural dimensions of power distance and masculinity versus femininity explain much of the variation in the level of corruption using a cross-sectional analysis conducted on a sample of 50 countries. In addi-

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Graph 3.6  Indulgence versus restraint (IND) in European Union countries. (Source: own processing)

tion, the authors identify individualism as a determining factor for the level of corruption of countries. Similar results are obtained by Baughn et al. (2010) who investigate the propensity of firms from 30 different countries to engage in international bribery. They find that high power distance countries showed a somewhat greater propensity for providing bribes in transactions with less-developed nations. In addition, the study of Halkos and Tzeremes (2011) using a sample of 77 countries found that a high power distance and higher collectivist values of a society are associated with a higher level of corruption, but, in terms of masculinity, the results indicate a “U” shape relationship that is not statistically significant. Tong (2014) explored the relationship between corruption and cultural psychology by examining the Chinese people. He identified that the negative experiences of the individual in childhood (e.g. poverty, hunger) along with a collectivist agrarian tradition are associated with a greater inclination towards corruption. Relevant to the culture-corruption relationship is the study conducted by Barr and Serra (2010). They conducted two experiments on bribery, in 2005 and 2007, with the participation of students from Oxford University, belonging to two groups of countries, one represented by the most strongly corrupt countries (a number of 33 countries) and the other group represented by the least corrupt countries (a number of 22 countries) selected from all over the world. Both experiments have shown that among students, culture significantly influences corruption, but not the same in the case of graduates. Thus, the values and beliefs regarding the phenomenon of ­corruption may be closely linked to the country of origin, but for immigrants, these values and beliefs could be modified as a result of changing the context of action. According to Hofstede Center (2020), the power distance (PD) refers to “the degree to which less powerful members of a society accept and expect power to be unevenly distributed”. A high level of power distance means a hierarchical order in which everyone has a place and no justification is needed. In a culture with a high

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power distance, the superiors offer favours to subordinates in exchange for their loyalty, and corruption may arise as a result of nepotism and favouritism (Husted 1999). Different studies demonstrate a positive relationship between power distance and corruption (Husted 1999; Davis and Ruhe 2003; Murdoch 2009; Halkos and Tzeremes 2011; McLaughlin 2013; Tong 2014). The cultural dimension individualism versus collectivism (IDV) refers to the extent to which the decision of a person’s life is taken in the opinion of an individual or a group (family or relatives). This refers to the extent to which people’s self-­ image is defined in terms of me or us (Hofstede Center 2020). A high score of this dimension indicates a strongly individualistic society in which the law is respected. In countries such as the United States, the United Kingdom, and Australia, individual initiative, competition, and democracy are highly valued (Davis and Ruhe 2003). It is to be expected that in a collectivist society, people will be inclined to break the law in order to support their own groups, based on unquestionable loyalty. Therefore, corruption can increase. Various studies show that as a society is less individualistic (more collectivist, respectively), the level of corruption is higher (Davis and Ruhe 2003; Murdoch 2009; Halkos and Tzeremes 2011; Tong 2014). The cultural dimension, masculinism versus feminism (MAS), refers to a society’s concern for achievements, heroism, assertiveness, and material rewards for success (masculinity) or for cooperation, modesty, care of the weak, and quality of life (femininity) (Hofstede 1980; Hofstede Center 2020). In a study on 42 countries, Davis and Ruhe (2003) empirically identified a significant positive relationship between masculinity and corruption. They concluded that in countries with the highest score of masculinity, people prefer to receive money, titles, or other materialistic rewards or social positions, so that the level of corruption increases. In Venezuela, Gonzales-Fabre (1996, p. 60) found that a high level of corruption is motivated by “personal accumulation of wealth”. Husted (1999) empirically identified a significant relationship between corruption and masculinity and associated high corruption with high earnings, recognition, advancement, and workplace challenges. After investigating the explanations for the different levels of corruption in countries such as Scandinavia and Africa, McLaughlin (2013) shows that some cultural variables such as masculinity play a role in determining the level of corruption. Thus, we can conclude that the preference for material rewards creates the appropriate framework for extending corruption practices. The cultural dimension, namely, uncertainty avoidance (UAI) expresses “the degree to which the members of a society feel uncomfortable with the uncertainties and the ambiguity” (Hofstede Center 2020). In a society with a high level of uncertainty avoidance, corruption can be viewed as a mechanism for reducing uncertainty, in order to achieve more certain and immediate results (Husted 1999). Carrying out a comprehensive meta-analysis of the literature, Tong (2014) concludes that “in countries with low UAI, such as China, the ambiguity and adaptability of laws and regulations to meld on various situations make corruption more likely”. Therefore, we expect that the higher the level of uncertainty avoidance, the higher the level of corruption.

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The cultural dimension long-term orientation versus short-term orientation (LTO) refers to “how each society must maintain some links with its own past, while facing the challenges of the present and future” (Hofstede Center 2020). This dimension is called by Hofstede as “Confucian dynamism” to show that it refers to a choice, coming from Confucius’ ideas of choosing two opposing poles: long-term orientation versus short-term orientation. The first pole is a positive one and reflects a dynamic and future-oriented mentality, while the second pole is a negative one and presents a more static and tradition-oriented mentality (Fang 2003). Hofstede (1997) characterized the long-term orientation by “persistence, ordering relations by status, saving, and sense of shame” and the short-term orientation through “personal stability, face protection, respect for tradition, and reciprocity of greetings, favours, and gifts”. Under these circumstances, we can expect that the short-term orientation will increase the need to ask for gifts and favours in order to reap immediate benefits. Therefore, we expect that as the orientation of individuals is aimed at a shorter horizon, the expected level of corruption is higher. The cultural dimension indulgence versus restraint (IND) refers to the preference of a society to allow relatively free satisfaction of basic and natural human impulses in terms of the joy of life, as opposed to suppressing the satisfaction of needs and regulating it through strict social norms (Hofstede Center 2020). A high score of indulgence would mean a society that manifests its desire to realize its impulses and desires in terms of joy of life and fun. This society really appreciates free time and spends a lot of money in this direction. Individuals belonging to a restrained company are restricted by social norms and break these norms for incentives to demand and offer, even illicit private payments. In this context, we expect that the more a society is restrained, the higher the level of corruption, and the more indulgent a society is, the lower the level of corruption. 3.1.2.2  Practical Approaches Corruption and Culture in European Union Countries Next, we aim to analyse at a descriptive level the relation between the level of corruption and the cultural characteristics in the countries of the European Union, in order to identify certain indications about the existence of a connection on culture and corruption. Methodology Corruption is measured by the Corruption Perception Index (CPI) provided by Transparency International (2020). The cultural characteristics are highlighted using the Hofstede model with the six cultural dimensions: power distance (PD), individualism versus collectivism (IDV), masculinity versus femininity (MAS), uncertainty avoidance (UAI), long-­ term orientation (LTO), and indulgence and restraint (IND). The sample of coun-

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tries is represented by the countries of the European Union (28). As methods, we use descriptive methods, comparison, analysis, and synthesis. The analysis period is 2005–2015 and covers the countries of the European Union (28). As methods, we use descriptive methods, comparison, analysis, and synthesis. We also intend to investigate a spatial approach to the analysis of the level of corruption for the member countries of the European Union, also considering one of the aims of this study, to investigate the influences of behavioural factors (culture, religion, tax morale, happiness, etc.) on the phenomena of corruption and shadow economy. For this, we will use the classification of the countries belonging to the European Union (28 countries) into the four regions of Europe, as provided by EuroVoc (2020) (Table 1.4). Results and discussions Graph 50 highlights at a descriptive level certain similarities between the level of corruption and the power distance (PD) by geographical areas of the European Union. Thus, the countries of Northern Europe have the lowest power distance score (27.33, on average) and at the same time the lowest level of corruption (3.27, on average). On the opposite side are the countries of Central and Eastern Europe that have the highest levels of power distance (63.73, on average) and the highest levels of corruption (52.86, on average). After the countries of Central and Eastern Europe, the countries of Southern Europe record quite high levels of power distance (57.5, on average) as well as high levels of corruption (42.86, on average). It can be seen that the level of corruption and the level of power distance have a growing evolution from North to South of Europe, as well as from West to East of Europe (Graph 3.7). Graph 3.8 reflects descriptive evidence regarding the levels of corruption compared with the levels of individualism versus collectivism by geographical areas of the European Union. It can be seen that the countries of Central and Eastern Europe as well as the countries of Southern Europe have low levels of individualism (high levels of collectivism, respectively), (50.91 and 47.25, respectively) and at the same time the highest levels of corruption (52.88 and 42.88, respectively). On the other side are the countries of Northern and Western Europe, with the highest levels of individualism (69.33 and 70.88, respectively) and the lowest levels of corruption (3.27 and 11.06, respectively). Similarly, as in the case of the power distance, we also find for the dimension of individualism versus collectivism that the level of corruption and the level of cultural dimension individualism versus collectivism show differentiated developments from the North to the South of Europe, as well as from the West to the East of Europe. Graph 3.9 reflects descriptive evidence on the levels of corruption compared to the levels of masculinity versus femininity by geographical areas of the European Union. Societies belonging to the countries of Northern Europe have the lowest level of corruption (3.27) and at the same time have the lowest level of masculinity (15.67) compared to the countries belonging to the other European geographical regions. High levels of masculinity are also found in countries of Central and Eastern

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70.00 60.00 50.00

63.73

57.50

52.88

51.27 40.00

42.86

40.00

34.53

27.33

30.00 20.00

11.06

10.00

3.27

0.00 CEE

North

South

West

Corruption

Average EU

PD

Graph 3.7  Corruption and power distance (PD) by geographical areas of the European Union 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00

70.88

69.33 52.88

58.62

50.91

47.25 42.86

11.06

3.27

CEE

34.53

North

South Corruption

West

Average EU

IDV

Graph 3.8  Corruption and individualism versus collectivism (IDV) by geographical areas of the European Union. (Source: own processing)

60.00 50.00

52.88 46.18

55.00

50.00

45.96

42.86

40.00

34.53

30.00 15.67

20.00 10.00 0.00

11.06

3.27 CEE

North

South Corruption

West

Average EU

MAS

Graph 3.9  Corruption and masculinism versus feminism (MAS) by geographical areas of the European Union

190 100.00 90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00

3  Behavioural Determinants of Economic and Financial Crime

90.00 74.60

69.62

52.88 37.00

52.67

42.86

34.53 11.06

3.27 CEE

North

South Coruption

West

Eu Average

UAI

Graph 3.10  Corruption and uncertainty avoidance (UAI) by geographical areas of the European Union. (Source: own processing)

Europe as well as those of Southern Europe (46.18 and 50, respectively), in parallel with high levels of corruption (52.88 and 42.86). Results contrary to those found in other European regions are found in the countries of Western Europe, where we find that there are quite high levels of masculinity (55), against the background of a low level of corruption (11.06). Graph 3.10 presents descriptive records regarding the levels of corruption compared to the levels of uncertainty avoidance by geographical areas of the European Union. Graph 3.10 shows that the lowest levels of corruption are found in the countries of the Northern and Western regions of Europe (3.27 and 11.06, respectively), which are populated by societies with the lowest levels of uncertainty avoidance (with scores of 37 and 52.67, respectively). Thus, as can be noted, corruption level and uncertainty avoidance level also have growing evolutions from North to South of Europe, as well as from West to East of Europe. The descriptive evidences regarding the levels of corruption compared with the levels of long-term orientation by geographical areas of the European Union are reflected in Graph 3.11. The highest levels of corruption are found in the countries of Central and Eastern Europe (with an average score of 52.88), companies that present at the same time the highest scores of the long-term orientation, that is, a predominant orientation on the long term. On the opposite side are the countries of Northern Europe that present the lowest average level of corruption (3.27) against the background of the lowest average score of the long-term orientation (42), i.e. a predominantly short-term average orientation. Graph 3.12 presents the graphical analysis of the average levels of corruption and average scores on indulgence versus restraint for the European Union regions. Similar results are noticed in this case too; more precisely the countries of the North and West of Europe have significant differences compared to the countries of Central, East, and South of Europe. Thus, the lowest levels of corruption are found in the countries of Northern and Western Europe (3.27 and 11.06, respectively) in parallel with very high indulgence scores that characterize the societies living in these countries (68.33 and 64.33, respectively). On the other side are the countries

191

3.1 Culture 70.00 60.00

63.50

60.67

52.88

50.00

42.00

40.00

42.86

58.92

45.50 34.53

30.00 20.00 10.00 0.00

11.06

3.27 CEE

North

South Corruption

West

EU Average

LTO

Graph 3.11  Corruption and long-term orientation (LTO) by geographical areas of the European Union. (Source: own processing) 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00

68.33 52.88

64.33 42.86

39.25

34.53

26.30 11.06

3.27 CEE

42.58

North Corruption

South

West

Average EU

IND

Graph 3.12  Corruption and indulgence versus restraint (IND) by geographical areas of the European Union. (Source: own processing)

of Central and Eastern Europe, and those of Southern Europe, respectively, which have the highest levels of corruption (52 and 42.86) and the lowest level of indulgence that characterizes the societies living in these regions (26.3 and 39.25, respectively). In this case, too, it is noticed a similar development of corruption and indulgence versus restraint levels extending from the North to the South of Europe and from the West to the East of Europe. In conclusion, for most of the cultural components, there are similar evolutions of corruption levels in the same directions and, at the same time, of the cultural characteristics. More precisely, corruption levels are increasing from North to South of Europe and from West to Central and East of Europe based on the cultural characteristics determined by the six cultural dimensions of the Hofstede model.

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Empirical Studies This section aims to provide, in addition to the descriptive evidences that attest the existence of a link between the level of corruption and the cultural dimensions of a society, empirical evidence, in the form of empirical case studies. To this end, it is exemplified the empirical study conducted by Achim (2016), which investigates a possible role played by cultural factors in the level of corruption. Methodology For this purpose, the cultural dimensions of Hofstede’s model were used, and the level of corruption worldwide was evaluated using the Corruption Perception Index (CPI) as it is provided by Transparency International (2020). The study was conducted for a sample of 98 countries, using the method of ordinary least squares (OLS). Based on the review of the literature presented above, Achim (2016) formulated the following working hypotheses: Hypothesis 1. The cultural factors characterizing a society influence the level of corruption: Hypothesis 1.1. The greater the power distance, the higher the level of corruption. Hypothesis 1.2. The less individualistic (more collectivist) a society is, the higher the level of corruption. Hypothesis 1.3. The higher the masculinity of a society, the higher the level of corruption. Hypothesis 1.4. The higher the level of uncertainty avoidance, the higher the level of corruption. Hypothesis 1.5. The shorter the short-term orientation of a society, the higher the level of corruption. Hypothesis 1.6. The less indulgent a society is, the higher the level of corruption. Results and discussions In summary, the results of the study conducted by Achim (2016) are the following: 1. Firstly, Achim (2016) identifies three of the six main components of Hofstede’s model as having a significant influence on corruption, namely, power distance (PD), individualism-collectivism (IDV), and long-term orientation (LTO). Statistical influences of cultural dimensions on masculinism-feminism (MAS), uncertainty avoidance (IND), and indulgence versus restraint (IND) were rejected by statistical tests. 2. Secondly, the results of this study show that the phenomenon of corruption is explained by the power distance in proportion of 33.5%. A culture characterized by a high level of power distance implies a higher level of corruption. This is a hierarchical society, where the employees recognize the legitimate power of the leader. In order to maintain the loyalty of subordinates, superiors may request bribes as a prerequisite for asserting their position. The countries with the

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193

­greatest power distances are Malaysia (100), Saudi Arabia, Iraq, and Guatemala (95), Russia (93), Albania, and Kuwait (90). The countries with the least power distance are Austria (11), Denmark (18), New Zealand (22), and Norway (31). Therefore, hypothesis 1.1 is accepted. Our results are supported by the findings of Husted (1999), Davis and Ruhe (2003), Halkos and Tzeremes (2011), McLaughlin (2013), and Tong (2014) who found a positive correlation between the power distance and corruption. 3. Thirdly, individualism versus collectivism explains the variation of corruption in proportion of 37%. The more collectivist a society is, the higher the level of corruption, and the more individualistic a society, the lower the level of corruption. In a collectivist society, the network of friends and family creates lasting relationships that can stimulate corrupt behaviours. The countries with the highest individualism score are the United States (91), Australia (90), the United Kingdom (89), the Netherlands (80), New Zealand (79), and Denmark (74), while the countries with the lowest score are Guatemala (6), Ecuador (8), Panama (11), Venezuela (12), and Colombia (13). The results showed that hypothesis 1.2 is accepted, indicating that a less individualistic (more collectivist) society has a higher level of corruption. These results are in line with those of Davis and Ruhe (2003), Halkos and Tzeremes (2011), and Tong (2014), who underlined the main role of collectivist society and social network in promoting corruption. 4. Another conclusion of the study identified that the orientation horizon of a society (long-term orientation versus short-term orientation) may explain the level of corruption in that country. But, this time, the proportion is slightly lower, 10.8%. Also, it was found that the corruption phenomenon can increase in the case of a shorter-term-oriented society, which supports hypothesis 1.5, and the tests have confirmed the statistical significance of the results. This can be explained by the fact that a shorter-term-oriented society has the need to make requests for favours and gifts to get immediate benefits. A high score of long-term orientation versus short-term orientation (LTO) indicates a longer-term orientation of this type of society. A high score means that societies value tradition and long-term commitments, while a low score reflects a willingness to accept change, which is not hindered by tradition (Hofstede 1997). According to Hofstede’s cultural model, countries with long-term orientations are South Korea (100), China (87), Japan (88), Germany (83), Belgium, Lithuania, and Estonia (82), while Honduras (8), Nigeria (13), and Ghana (4) are the most short-term-oriented countries. The results of the study reflect a negative correlation between LTO and corruption (c = −0.344), which is statistically significant (p