Data flows are the lifeblood of our modern social and economic interactions. However, concerns related to privacy and data protection, national security, cybersecurity, digital protectionism and regulatory reach, among others, have led to a surge in regulation conditioning (or prohibiting) its flow or mandating that data be stored or processed domestically.
The implications of these measures are not well understood, especially where it relates to finding a balance between enabling flows while also ensuring that data receives the desired safeguards when transferred abroad, a concept that has also been referred to as data free flows with trust (DFFT).
This report aims to identify the potential economic implications and opportunity costs associated with different data flow and data localisation regulations. It draws on information from a business questionnaire, econometric analysis and mathematical modelling exercises to provide an empirical grounding to enable policy makers to weigh the opportunity costs and benefits involved in their regulatory choices. It is novel in that it incorporates both the potential costs that might be associated with data flow regulation, but also the potential benefits that arise from growing trust in economic transactions afforded by data protection frameworks. While subject to some limitations and caveats, the results provide insights into the main channels of impacts from data regulations.
For regulations affecting the movement of data, the results suggest that:
Cross-border data flows are a key element of the global economy. Data autarky, or what might otherwise be considered as ‘full fragmentation, where all economies fully restrict their data flows, would lead to global GDP losses of 4.5% and reductions in exports of 8.5%.
The absence of data flow regulation is also associated with negative economic outcomes. Indeed, if all economies removed their data flow regulation trade costs would fall, but so too would trust. Overall, global GDP would fall by nearly 1% and global exports by just over 2%. The impacts would be largest for high-income economies which could see their GDP fall by over 2%.
Open regimes that include safeguards balance the trade costs associated with data regulation with the trust benefits of data safeguards. Indeed, if such approaches were adopted by all economies, global exports would grow by 3.6% and global GDP by 1.77%. Benefits would be highest for low and lower-middle income economies which could see their GDP rise by over 4%.
The economic costs of geoeconomic fragmentation of data flow regimes are potentially sizeable (more than 1% real global GDP loss), but much smaller than those associated with full fragmentation reflecting an already fragmented regulatory landscape.
Overall, more global solutions that balance free-flows with trust are likely to deliver better economic outcomes for countries at all levels of development.
For data localisation measures (those explicitly mandating local storage or processing), the findings suggest that:
Removing existing data localisation measures would deliver small but positive impacts. Exports would rise by 0.26% and GDP by 0.18%. Gains are, however, potentially large for low-income economies which could see their GDP rise by over 1%.
Data storage requirements without flow prohibitions lead to relatively small economic costs. If such requirements were adopted by all economies, global GDP would fall by less than 0.1%. That said, low-income economies are projected to see strong increases in GDP from moving to less restrictive forms of data localisation.
When storage conditions are combined with flow prohibitions, even if only for a limited set of sectors (financial, telecommunications, and ICT services), impacts can rise quickly. If all economies adopt these, GDP would fall by 0.5% and exports by nearly 1%. Losses would be highest for high-income economies.
At the extreme, a strict data localisation measure is the same as a complete prohibition on transferring data (see numbers associated with full fragmentation). Storage conditions combined with flow prohibitions, when applied across all sectors of the economy, would deliver impacts that are nearly nine times larger than under more targeted sectoral prohibitions.
Overall, the impact of data localisation depends strongly on the type of measure implemented. Developing countries will benefit most from removing data localisation measures.
The empirical evidence presented suggests that getting data regulation right matters for economies at all levels of development. It underscores the need to find more global or convergent solutions to issues related to data regulation.
