Global Shell Games

Global Shell Games : Experiments in Transnational Relations, Crime, and Terrorism

4.29 (14 ratings by Goodreads)
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Every year a staggering number of unidentified shell corporations succeed in hiding perpetrators of terrorist financing, corruption and illegal arms trades, but the degree to which firms flout global identification standards remains unknown. Adopting a unique, experimental methodology, Global Shell Games attempts to unveil the sordid world of anonymous shell corporations. Posing as twenty-one different international consultants, the authors approached nearly 4,000 services in over 180 countries to discover just how easy it is to form an untraceable company. Combining rigorous quantitative analysis, qualitative investigation of responses and lurid news reports, this book makes a significant research contribution to compliance with international law and international crime and terrorism whilst offering a novel, new approach to the field of political science research. Global Shell Games is an invaluable resource for scholars of international relations, and a fascinating, accessible read for anyone interested in learning about worldwide criminal practice in corporate finance.
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Product details

  • Paperback | 274 pages
  • 152 x 229 x 15mm | 410g
  • Cambridge, United Kingdom
  • English
  • 20 Tables, black and white; 16 Line drawings, unspecified
  • 1107638836
  • 9781107638839
  • 756,788

Table of contents

Preface; 1. Introduction; 2. Explaining the global shell game; 3. Overall compliance, tax havens, OECD and developing countries; 4. Corruption and terrorism; 5. Laws and standards; 6. Penalties, norms, and US origin; 7. Conclusion; References; Appendices.
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About Michael G. Findley

Professor Michael Findley's research and teaching address civil wars, terrorism and development. He uses field experiments, statistical and computational models and some interviews, and conducts ongoing fieldwork in Uganda, South Africa and Malawi. Findley's publications have appeared in the American Journal of Political Science, the Journal of Politics, International Studies Quarterly, the British Journal of Political Science, the Journal of Conflict Resolution, the Journal of Peace Research, Public Choice, Complexity, the Minnesota Law Review, and World Development, among others. Findley also works extensively with international development organizations. In particular, his work geocoding foreign aid has been adopted by, or developed with, the World Bank, USAID, the African Development Bank, the International Aid Transparency Initiative, and many aid recipient countries. Daniel Nielson is Director of the Political Economy and Development Lab at Brigham Young University, where he is also Associate Professor of Political Science. He is a founder and principal investigator of AidData. He received his PhD in international affairs from the University of California, San Diego in 1997. He has been a visiting scholar at Duke University, the College of William and Mary, and the Centro de Investigacion y Docencia Economicas (CIDE) in Mexico. He has been a principal investigator on major grants from the US National Science Foundation, the William and Flora Hewlett Foundation, and the Bill and Melinda Gates Foundation. He has also served as a consultant for the World Bank and UNICEF. Jason Sharman is Professor and Deputy Director of the Centre for Governance and Public Policy at Griffith University, Australia. His research focuses on the global regulation of corruption and money laundering and tax havens, as well as empires and sovereignty. Sharman's earlier books include The Money Laundry: Regulating Criminal Finance in the Global Economy (2011), Corruption and Money Laundering: A Symbiotic Relationship (with David Chaikin, 2009) and Havens in a Storm: The Struggle For Global Tax Regulation (2006). He has worked as a consultant for the Financial Action Task Force on money laundering and the World Bank/United Nations Stolen Assets Recovery initiative.
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Rating details

14 ratings
4.29 out of 5 stars
5 57% (8)
4 29% (4)
3 7% (1)
2 0% (0)
1 7% (1)
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