This book explores the intricate realm of tax avoidance, synthesizing existing empirical literature in the field. The work starts by exploring the theoretical underpinnings of tax avoidance, dissecting its unique features compared to tax evasion. It delves into measurement methodologies and dissects the determinants contributing to its prevalence. Moreover, it analyzes the economic consequences of tax avoidance, emphasizing its impact on critical accounting issues, including financial reporting transparency, cost of capital, and firm value. Next, the book offers a foundational understanding of graph theory, unveiling the core elements of networks, such as nodes and edges. The book covers the theoretical fundamentals and addresses the practical side of constructing networks based on real-world relational systems. It emphasizes the importance of effective data collection and representation methods and underscores the importance of optimizing network layouts for enhanced visual representation. Using network analysis, the book further offers a deep dive into empirical studies on tax avoidance over the past two decades, revealing insights into the collaborative nature of this stream of research. Finally, the book summarizes the key insights of the network analysis on tax avoidance. It underscores the dynamic nature of individual authors' roles and affiliations, shedding light on the collaborative dynamics within institutions.

Presents a comprehensive analysis of tax avoidance research Introduces network analysis of key tax avoidance collaborations Discusses avenues for future research

Autorentext

Antonio De Vito joined the University of Bologna (Italy) in 2022. Before returning to Italy, he was an Assistant Professor of Accounting at the IE Business School, IE University in Madrid, Spain. As Assistant Professor, he taught several accounting and taxation courses, both at the undergraduate and graduate levels, and won several teaching awards. His research has been published in top academic journals (e.g., Journal of Financial and Quantitative Analysis, Journal of Accounting and Public Policy, Accounting and Business Research), presented at international conferences, and featured in various news outlets. Since 2021, he has been a European Accounting Association Annual Conference Scientific Committee member. In addition, he has acted as a reviewer for leading academic journals such as the British Journal of Management, European Accounting Review, Accounting and Business Research, Journal of Accounting and Public Policy, Corporate Governance: An International Review, and Financial Accountability & Management. He is currently a member of the Editorial Boards of the Journal of International Accounting, Auditing, and Taxation, and Accounting in Europe. Francesco Grossetti is an Assistant Professor of Accounting Analytics and Data Science at Bocconi University in Milan, Italy. He graduated in Astrophysics and Space Physics from Università Degli Studi di Milano Bicocca and obtained a Ph.D. in Applied and Computational Statistics from Politecnico di Milano, Italy. He employs computational and statistical approaches to gain insights into the behavior of companies and investors' perceptions. Specifically, he uses and enhances Natural Language Processing techniques and language models to examine the impact of financial and non-financial disclosures on key financial performance. Additionally, he leverages Deep Learning-based Computer Vision methods to extract visual information from financial disclosures. Furthermore, he investigates the economic implications of Blockchain and other Distributed Ledger Systems, exploring their potential to foster novel research avenues and revolutionize accounting practices. He has been serving as a reviewer for the Journal of Accounting and Public Policy, Accounting Forum, Finance Research Letters, and the Journal of Business, Finance, and Accounting.

Inhalt

Introduction.- Exploring Tax Avoidance: A Synthesis of the Literature.- Network Analysis: A Mathematical Framework.- Networks of Tax Avoidance Research.- Conclusion.