OMC - Consecuencias económicas de la reglamentación de los datos

Economic Implications of/uni00A0Data Regulation BALANCING OPENNESS AND/uni00A0TRUSTEconomic Implications of Data Regulation BALANCING OPENNESS AND TRUSTThis work is published under the responsibility of the Secretary-General of the OECD and the Secretariat of the World Trade Organization (WTO). The opinions expressed and arguments employed herein do not necessarily reflect the official views of the Member countries of the OECD or the Members of the WTO. The names and representation of countries and territories used in this joint publication follow the practice of the OECD. This document, as well as any data and map(s) included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Specific territorial disclaimers applicable to the OECD: Note by the Republic of Türkiye The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Türkiye recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Türkiye shall preserve its position concerning the “Cyprus issue”. Note by all the European Union Member States of the OECD and the European Union The Republic of Cyprus is recognised by all members of the United Nations with the exception of Türkiye. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.

Please cite this publication as: OECD/WTO (2025), Economic Implications of Data Regulation: Balancing Openness and Trust, OECD Publishing, Paris,

https://doi.org/10.1787/aa285504-en. ISBN 978-92-64-72409-9 (print)

ISBN 978-92-64-62894-6 (PDF)

ISBN 978-92-64-90904-5 (HTML)

WTO ISBN 978-92-870-7225-2 (print)

ISBN 978-92-870-7224-5 (PDF)

Photo credits: Cover © Natro/Shutterstock.com.

Corrigenda to OECD publications may be found at: https://www.oecd.org/en/publications/support/corrigenda.html.

© OECD/WTO 2025

Attribution 4.0 International (CC BY 4.0) This work is made available under the Creative Commons Attribution 4.0 International licence. By using this work, you accept to be bound by the terms of this licence (https://creativecommons.org/licenses/by/4.0/). Attribution – you must cite the work. Translations – you must cite the original work, identify changes to the original and add the following text: In the event of any discrepancy between the original work and the translation, only the text of original work should be considered valid. Adaptations – you must cite the original work and add the following text: This is an adaptation of an original work by the OECD. The opinions expressed and arguments employed in this adaptation should not be reported as representing the official views of the OECD or of its Member countries. Third-party material – the licence does not apply to third-party material in the work. If using such material, you are responsible for obtaining permission from the third party and for any claims of infringement. You must not use the OECD logo, visual identity or cover image without express permission or suggest the OECD endorses your use of the work. Any dispute arising under this licence shall be settled by arbitration in accordance with the Permanent Court of Arbitration (PCA) Arbitration Rules 2012. The seat of arbitration shall be Paris (France). The number of arbitrators shall be one. 3

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Foreword Today, our social and economic activities are underpinned by the movement of data across international borders. They help us connect with family and friends; they support research addressing global challenges;

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Foreword Today, our social and economic activities are underpinned by the movement of data across international borders. They help us connect with family and friends; they support research addressing global challenges; they enable the co -ordination of production along supply chains; and allow firms, notably smaller ones, and people to access global markets. In sum, cross-border data flows have become the lifeblood of modern day social and economic activities. However, as more and more data crosses international borders, concerns across a range of policy areas have amplified. For instance, for pri vacy and data protection, there are concerns that, when data is transferred abroad, it might not receive the same, or the desired and expected, degree of protection. Crossborder data flows also raise issues in the context of national security, intellectual property protection, digital industrial policy and regulatory reach. These concerns have led to a growing adoption of regulation which conditions (or prohibits) the transfer of data across borders, and/or data localisation measures which mandate that data be stored or processed domestically. To make the most out of the evolving digital environment, policy makers increasingly need to balance the trade costs of regulating data policies with the trust benefits of data safeguards. This has come to be known as data free flows with trust . This joint OECD -WTO report provides empirical evidence on these issues with a view to helping policy makers weigh the potential opportunity costs and benefits involved in their regulatory choices. This report does not aim to offer a precise measurement of the economic impact of the current regulatory landscape. Nor does it aim to pit approaches against each other. Rather it is an effort to identify how different data-related measures might affect economic activity and to provide a baseline for assessing the relative magnitude of potential effects through a set of hypothetical scenarios.4 

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Acknowledgements This report was approved by Marion Jansen (Director, Trade and Agriculture Directorate, OECD) and Ralph Ossa (Director, Economi c Research and Statistics Division, WTO). The authors of the report are

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Acknowledgements This report was approved by Marion Jansen (Director, Trade and Agriculture Directorate, OECD) and Ralph Ossa (Director, Economi c Research and Statistics Division, WTO). The authors of the report are Andrea Andrenelli (OECD), Eddy Bekkers (WTO), Javier López González (OECD), Gabrielle Marceau (WTO) and Roger So (WTO). We are grateful for the useful comments received from Julia Niel son, Susan Stone, Clarisse Girot , and Frank Van Tongeren from the OECD, Johanna Hill, Bright Okogu, Ralph Ossa, Marc Bacchetta and Antonia Carzaniga from the WTO, as well as Anabel Gonzalez (IADB) and Robert Teh. We also acknowledge the useful discussions at the: G7 Scientific Roundtable on Digital Policy and Data Governance in the age of AI held in June 2024 , and the comments received during the BIICL 2023 Annual WTO Conference on Trade and Technology: Challenges and Opportunities and the 27th GTAP Conference in Bordeaux in 2023. Michèle Patterson (OECD) and Anthony Martin (WTO) managed the production of this report. 5

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Table of contents Foreword 3 Acknowledgements 4 Executive Summary 7 1 Introduction 9 2 Data flows and the evolving regulatory environment 11 2.1. What is different about data? 12 2.2. Why and how is data flow regulation emerging? 14 2.3. What issues does data regulation raise? 18 3 Identifying the potential economic impact of different broad approaches to data regulation 19 3.1. Insights from a business questionnaire 19 3.2. Modelling the economic implications of data regulation 23 3.3. Identifying the size of the associated costs and benefits 26 4 Outcomes of the simulations 30

regulation 19 3.1. Insights from a business questionnaire 19 3.2. Modelling the economic implications of data regulation 23 3.3. Identifying the size of the associated costs and benefits 26 4 Outcomes of the simulations 30 4.1 Changes in data-flow regulation can have sizeable impacts 30 4.2. Impacts of data localisation tend to be smaller, but depend strongly on the measure applied 35 5 Policy implications 39 References 41 Notes 44 Annex A. Further insights from the OECD-WTO Business Questionnaire 48 Note 51 Annex B. Econometric analysis 52 Note 53 Annex C. Additional data flow regulation simulations 54 C.1. Introducing the data flow shocks one by one 54 C.2. Impact on trade patterns from changes in data flow regulation 556 

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Annex D. Additional data localisation regulation simulations 57 D.1. Introducing the data localisation shocks one by one 57 D.2. Impact on trade patterns from changes in data localisation policies 58 Annex E. Sector and region mapping (CGE simulations) 60

Tables Table 3.1. Breakdown of CGE policy shocks related to data flow regulation (maintaining data localisation policies) 24 Table 3.2. Breakdown of CGE policy shocks related to data localisation (maintaining data flow policies) 25

Figures Figure 1.1. Measures that affect the location or movement of data are on the rise 9 Figure 2.1. International Internet bandwidth is growing 11 Figure 2.2. There is no wisdom without knowledge, no knowledge without information, and no information without data 13 Figure 2.3. Broad approaches to cross-border data flow regulation 16 Figure 2.4. A typology of data localisation measures and requirements for data flow 17

Figure 2.2. There is no wisdom without knowledge, no knowledge without information, and no information without data 13 Figure 2.3. Broad approaches to cross-border data flow regulation 16 Figure 2.4. A typology of data localisation measures and requirements for data flow 17 Figure 3.1. How hard would it be for your business to identify/separate your data by type and geographical origin? 20 Figure 3.2. How much of the volume of data that your business sends or receives across international jurisdictions is likely to be personal data? 20 Figure 3.3. Perceived impact of conditions for cross-border data transfers 21 Figure 3.4. Perceived impact of data localisation measures: By how much do you think regulation can increase your data management costs? 22 Figure 3.5. Data localisation and public policy objective 23 Figure 3.6. Trade cost increase arising from an increase in the DSTRI 26 Figure 3.7. Cost increase arising from a change in data management costs 27 Figure 3.8. Projected changes in ad valorem trade costs, trust and data management costs under different scenarios 28 Figure 4.1. Projected change in global exports and GDP as a result of different data flow regulation scenarios 30 Figure 4.2 Projected change in real exports and real GDP by income group from changes in data flow regulation 32 Figure 4.3. Projected change in real exports and real GDP by aggregate region from changes in data flow regulation 33 Figure 4.4. Projected change in global exports and GDP as a result of data localisation measures 36 Figure 4.5. Projected change in real exports and real GDP by income group from changes in data localisation measures 37 Figure 4.6. Projected change in real exports and real GDP by aggregate region from changes in data localisation measures 38

Box

Figure 4.5. Projected change in real exports and real GDP by income group from changes in data localisation measures 37 Figure 4.6. Projected change in real exports and real GDP by aggregate region from changes in data localisation measures 38

Box Box 2.1. How does data flow through the Internet? 13 7

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Executive Summary Data flows are the lifeblood of our modern social and economic interactions. However, concerns related to privacy and data protection, national security, cybersecurit y, 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,

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.8 

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

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.

• 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. 9

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Cross-border data flows underpin today’s economic and social interactions. They help people connect with family and friends located in different geographical loca tions; they support research address ing global challenges (as was the case during the COVID-19 pandemic); they enable the co-ordination of production along global supply chains; and they allow firms, notably smaller ones, and people to access global markets. In sum, cross-border data flows have become the lifeblood of modern day social and economic activities. As more and more data crosses international borders, concerns across a range of policy areas have amplified. For instance, for privacy and data prote ction, there are concerns that, when data is transferred

markets. In sum, cross-border data flows have become the lifeblood of modern day social and economic activities. As more and more data crosses international borders, concerns across a range of policy areas have amplified. For instance, for privacy and data prote ction, there are concerns that, when data is transferred abroad, it might not receive the same, or the desired and expected , degree of protection. Cross-border data flows also raise issues in the context of national security, intellectual property protection , digital industrial policy and regulatory reach. These concerns have led to a growing adoption of regulation which conditions (or prohibits) th e transfer of data across borders, and/or data locali sation measures which mandate that data be stored or processed domestically (Flaig et al., 2016[1]; World Bank, 2016 [2]; Cory, 2017[3]; Cory and Dascoli, 2021 [4]; Casalini and López González, 2019 [5]; López González, Casalini and Porras, 2022[6]; WEF, 2020[7]) (Figure 1.1). Figure 1.1. Measures that affect the location or movement of data are on the rise

Note: “Data regulation” includes different types of regulation relating to data transfers and local storage requirements. Numbers are affected by the way in which regulations are structured, as this varies by economy; some economies may have a single regulation covering a wide range of measures; others will hav e several different regulations covering, for example, restrictions on data flows for different types of data, and local storage requirements. Evidence suggests that the number of measures have continued to increase up to 2024, including through important rises in data localisation measures (Del Giovane, López González and Ferencz, 2023[8])..

Source: Casalini and López González (2019[9]).

0 2 4 6 8 10 12 14 0 50 100 150 200 250 Modifications (right axis) Number of data regulations (left axis) 1 Introduction10 

Source: Casalini and López González (2019[9]).

0 2 4 6 8 10 12 14 0 50 100 150 200 250 Modifications (right axis) Number of data regulations (left axis) 1 Introduction10 

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Although t he Internet is global and to some extent borderless, regulations are not . There have been mounting worries that the emerging regulatory landscape is becoming increasingly complex and fragmented [see Evenett and Fritz (2022[10])]. The economic implications of this growing digital fragmentation and of rising data-flow regulation are, however, not well understood. Against this backdrop, the aim of this report is to begin filling some of the existing evidence gaps with a view to identify ing some of the potential economic implications and opportunity costs associated with different approaches to data flow regulation . This work builds on a small literature that uses computable general equilibrium (CGE) models for the analysis of data flow regulation (Bauer et al., 2014[11]; Flaig et al., 2016[1]). It is novel in that it extends the analysis to incorporate both the potential costs that might be associated with data flow regulation and the potential benefits that might arise from growing trust between countries. In this sense, it also provides a better understanding of the implications of what has come to be known as data free flows with trust. The work does not aim to provide a precise measurement of the economic impact of the current regulatory landscape. Nor does it aim to pit approaches against each other. Rather it is an effort to identify how the impact of data-related measures might be transmitted through the economy and to provide a baseline for assessing the relative magnitude of potential effects through a set of hypothetical scenarios. While subject to some limitations and caveats, it is hoped that this illu strative and hypothetical modelling exercise can provide policy makers with needed empirical grounding to enable them to weigh the opportunity costs and benefits involved in their regulatory choices.

assessing the relative magnitude of potential effects through a set of hypothetical scenarios. While subject to some limitations and caveats, it is hoped that this illu strative and hypothetical modelling exercise can provide policy makers with needed empirical grounding to enable them to weigh the opportunity costs and benefits involved in their regulatory choices. To this end, this report is structured as follows. The following section provides an overview of the evolving policy landscape, identifying the why, what and how of emerging data flow regulation. Section 3 discusses results from a business questionnaire which motivates the methodological choices behind the mode lling exercise, described in Section 4. Section 5 provides an analysis of the main results and concludes with some policy observations. 11

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Global traffic from data centres is estimated to have increased fourfold since 2015 – from 5 zettabytes in 2015 to around 20 in 2021. 1 To put that into perspective, a zettabyte is 1 000 000 000 000 000 000 000 bytes (21 zeros), that is, a thousand exabytes, a billion terabytes, or a trillion gigabytes. There are 20 times more bytes of traffic from data centres than there are stars in the expanding universe.2 The pace of change shows no signs of slowing ; quite the opposite : global IP traffic is expected to continue growing at an accelerating pace (CISCO, 2020 [12]), including after faster growth in bandwidth de mand during the first wave of the COVID-19 pandemic (OECD, 2020[13]).3 In terms of the cross -border elements of this traffic, data from Telegeography show a near 800 -fold increase in international internet bandwidth capacity (a measure of international information -carrying capacity) in the period 2002 -2021 (Figure 2.1). International internet bandwidth is especially high acros s OECD countries, but it is also important in emerging economies like the People’s Republic of China

increase in international internet bandwidth capacity (a measure of international information -carrying capacity) in the period 2002 -2021 (Figure 2.1). International internet bandwidth is especially high acros s OECD countries, but it is also important in emerging economies like the People’s Republic of China (hereafter “China”), India, the Russian Federation (hereafter “Russia), and Brazil ( Figure 2.1), which are important exchange points in the global internet architecture. As data becomes increasingly critical to economic activity and trade, understanding what it is, how it flows and how value can be derived from its use becomes ever more pressing. Figure 2.1. International Internet bandwidth is growing

A. International Internet bandwidth (Tbps)

0 100 200 300 400 500 600 700 800 900 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2 Data flows and the evolving regulatory environment12 

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Note: Top panel shows cumulative international bandwidth in terrabytes per second. Bo ttom panel shows distribution across the globe. Darker shades identify greater international bandwidth.

Source: Own calculations based on Telegeography data. 2.1. What is different about data?

The economic activity that growing data traffic supports is not easy to identify and measure. How bits and bytes translate into dollars and cents is hard to establish. This is because, from an economics perspective, data is different (Aaronson, 2018[14]). It is unlike other resources, factors of production, or inputs. • Data is valued at use, not at volume . For instance, a spreadsheet with 100 personal shopping entries may occupy the same memory space as one with 100 personal health records but its underlying value will be different. A retailer will value the shopping entries more than a health

• Data is valued at use, not at volume . For instance, a spreadsheet with 100 personal shopping entries may occupy the same memory space as one with 100 personal health records but its underlying value will be different. A retailer will value the shopping entries more than a health service provider (which will value the personal health records more). • The value of data can increase when merged with other data to become greater than the sum of its parts . For instance, the shopping entries linked to the health records can help target advertisements towards the health-conscious shopper. • Data has both inherent and potential value . Data not used today can become valuable tomorrow with changing busine ss dynamics or when combined with other data yet to become available. 4

Information in the health records may become more useful over time to analyse new conditions. • Data can be used by different actors simultaneously (data is non-rivalrous) and can be copied and shared at virtually no cost . This means that its use can serve many different purposes at once. 5 The 100 personal health records may be used both by one health service provider to research cures for cancer and by another to provide remote health services without being depleted. These characteristics imply that although data remains excludable (that is, it can be reserved for only certain users or uses), there are significant economic and societal benefits from sharing and re-using data (OECD, 2019[15]; 2021[16]), including across borders. Data can be thought of as a factor of production, an intangible asset, an intermediate input into production and even capital with increasing returns. This versatility m akes data sui generis and is why characterisations of data as the “new oil” (The Economist, 2017[17]) can be misleading (Mandel, 2017[18]).

B. International Internet bandwidth (Tbps) 13

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Although, like oil, data is an essential input into the economy, data is not scarce, is neither consumed nor depleted when used, and can be copied and transferred at virtually no cost.6

ECONOMIC IMPLICATIONS OF DATA REGULATION © OECD/WTO 2025

Although, like oil, data is an essential input into the economy, data is not scarce, is neither consumed nor depleted when used, and can be copied and transferred at virtually no cost.6 Ultimately, data are vast and unordered or unprocessed records that carry little meanin g; they become information when analysed to identify relationships between data points. 7 Knowledge is generated by analysts who recognise the importance of the information, and wisdom is generated by the decisions that make the most of the streams of anal ysed data. In this data -information-knowledge-wisdom (DIKW) hierarchy (Figure 2.2), each stage is dependent on those that come before it. There is no w isdom without knowledge, no knowledge without information, and no information without data. 8 Figure 2.2. There is no wisdom without knowledge, no knowledge without information, and no information without data

Source: Adapted from Rowley (2007[19]).

Advances in AI, including the rise of generative AI models which now permeate all sectors of the economy, are leading to important changes for international trade (Ferencz, López -González and Oliván -García, 2022[20]; WTO, 2024[21]) and also raising new challenges for data governance (OECD, 2024[22]). In the age of GenAI, data availability, access and variance, coupled with processing power, are posed to deliver new opportunities and challenges (Agrawal, Gans and Goldfarb, 2019[23]). Box 2.1. How does data flow through the Internet? The way data travels through the Internet is not straightforward. When a file is sent from a computer in Economy A to a recipient in Economy B it is first broken down into different “packets”. These are like little parcels of information marked with the IP address of the sender, that of the recipient, and a code identifying the sequence in which the packets are to be reassembled at destination. Once the packets

Economy A to a recipient in Economy B it is first