When Data Comes Home: Next Steps in International Taxation’s Information Revolution

Over the last decade, there has been a revolution in cross-border tax information exchange and reporting. While this dramatic shift was the product of multiple forces and events, a fundamental reality is that politics, technology, and law intersected to drive the shift to the point where nation-states will now transmit and receive from each other significant ongoing flows of taxpayer information. States can now expect to accumulate large stashes of data on cross-border income, assets, and activities on a scale and level of comprehensiveness unmatched by previous information exchange regimes. This article examines the pressing follow-up question of how this data will be used and what issues nation-states will confront when data comes home. Although concerns about data protection and use have been raised in critiquing the new cross-border information exchange regimes, a systematic examination of how governments might use or fail to use data and when those uses will pose unacceptable risks has yet to be undertaken. This article analyzes how the forces that drove the revolution—politics, technology, and law—are likely to interact and affect tax enforcement and data usage at the nation-state level going forward. We argue that despite the dominant focus on global developments, structures, and capacities, domestic politics and technological constraints will likely play an equally if not more significant role in data use and protection as countries receive data and decide what to do with it. The mere fact that collective political will on a global level produced the information revolution does not prevent domestic political forces from either derailing the revolution in practice or from redirecting data to other uses. We map the potential risks, examine the extent to which domestic legal regimes will have the capacity or inclination to protect taxpayer privacy and constrain distributional outcomes in a politics-driven world of ubiquitously available data, and predict the likely outcomes, responses and solutions