The value of cross-border data flows to Europe: Risks and opportunities
Frontier Economics was commissioned by DIGITALEUROPE to estimate the economic impact of cross-border data flows on the economy of the European Union (EU), and the potential impact of changes to existing restrictions on these flows. This report provides new, robust estimates of the economic cost to the EU of additional restrictions, and the potential economic gain that could result from liberalisation. Our estimates are relatively conservative. We focus on modelling the role of cross-border data flows in facilitating international trade (imports and exports), as this allows us to use an established robust methodology to quantify the economic impact of these flows. However, we do not quantify other benefits of cross-border data flows. In particular, our results do not take into account the role of cross-border data flows in supporting the delivery of public services, and they largely exclude the economic impact of cross-border activities within companies, which typically are not recorded in international trade data. The key messages from our analysis are summarised below.
Cross-border data flows are central to the EU economy, and especially its trade and investment. Many sectors, including high-value manufacturing, IT and information services, media, and cultural sectors, are highly reliant on data. This suggests that data localisation internationally and in the EU carries serious risks for sectors that are part of both the EU’s industrial strategy and its wider social and economic agenda.
The future direction of global policy on data flows therefore matters. The difference between a path that is moderately liberalising and one that is moderately restrictive is economically significant: worth a little over 1.5% in EU GDP per year. This is equivalent to approximately one year of GDP growth for the EU according to the IMF’s long-run forecasts. Over a ten-year period to 2030, the difference between a moderately liberalising path and a moderately restrictive path would amount to €2 trillion, in today’s money.
A moderately restrictive scenario is one in which the EU is unable to rely on GDPR transfer mechanisms, and in which trade partners increase their overall levels of restrictions on cross-border data flows. Our modelling suggests that such a scenario leads to a reduction in EU exports of around 4%, and in 1% of GDP per year. Cumulatively to 2030, losses amount to €1.3 trillion. The loss in output corresponds to around 1.3 million jobs in the impacted sectors.
The majority of the pain is self-inflicted: a majority of the EU’s export losses in restrictive scenarios (around 58%) come from an increase in its own restrictions than from partner actions. Domestic measures that increase data localisation act as a tax on a country’s exports.
By contrast, if the EU and major trade partners adopted measures to facilitate cross-border data transfers, EU exports as a whole would grow by a little over 2% per year, adding 0.6% to GDP per year, which is around 0.7 million jobs in the impacted sectors. Cumulative effects to 2030 are worth around €720 billion.
The downside risks from the moderate restrictiveness scenario outweigh the upside risks from the more liberalising scenario. The priority for the EU and its partners should in the first instance be to avoid increased restrictiveness by locking in existing levels of liberalisation.
The estimated GDP effects are conservative. This is because the primary channel of impact that is modelled is via effects on international trade. Other channels of impact, such as costs to innovation are also important. The analysis is also based on trade and national account data that records transactions between firms, rather than internal flows of goods and services. The significant impacts of data restrictions on internal flows are therefore not captured. Finally, we do not model other policy measures (such as the restrictions on operations of non-national businesses) that might accompany data localisation.
Data localisation requirements could also hurt sectors that do not participate heavily in international trade, such as healthcare. Indeed, research activities increasingly feature collaboration across borders: for example, it is estimated that in 2019 more than 5,000 collaborative projects between EEA countries and the US National Institutes of Health alone (NIH, 2019). Moreover, up to one-fourth of inputs into the provision of healthcare consists of data-intensive products and services.
The impacts are likely to affect both large and small businesses. In EU manufacturing, small and medium enterprises account for 23% of exports. Therefore, a relatively crude apportioning of the results shown in section 4.3 suggests that exports by data-reliant manufacturing SMEs in the EU are worth around €280bn. In the challenge scenario, exports from EU SMEs would fall by €14bn, and in the growth scenario they would increase by €8bn.
Assessing the impact of data flows on SMEs in service sectors is challenging due to data limitations. However, we know that SMEs account for 61% of the turnover of EU data-reliant services sectors. Therefore, it is likely that exports of data-reliant services from EU SMEs account for a significant proportion of the value of cross-border data flows to the EU economy.
Our modelling also provides conservative estimates of the economic contribution of cross-border data flows to the EU. A loss of cross-border data flows on exports from data reliant sectors would lead to an annual reduction in EU GDP worth at least €330bn, or around 2.5% of total EU GDP.
Table of content
Terms of reference
The role of cross-border data flow in the EU economy
Uses of data in modern economy
Case studies of how different sectors rely on cross-border data flows
Approach to modelling the impact of data localisation
Overview of approach
Identification of data-reliant sectors
Measuring restrictions on cross-border data flows affecting these sectors
Modelling of two scenarios
The impact of data localisation on trade and economic growth
Trade impacts of policy scenarios
Impact on GDP and employment
Further detail on modelling results
Assessing the overall role of data flow in facilitating trade