Economic Implications of Data Regulation

1. Introduction

Cross-border data flows underpin today’s economic and social interactions. They help people connect with family and friends located in different geographical locations; they support research addressing 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 protection, 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) the transfer of data across borders, and/or data localisation 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 have 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]).

Although the 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 identifying 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 illustrative 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 modelling exercise, described in Section 4. Section 5 provides an analysis of the main results and concludes with some policy observations.