This paper examines how policies regulating the cross-border movement and domestic use of electronic data on the internet impact the productivity of firms in sectors relying on electronic data. In doing so, we collect regulatory information on a group of developed economies and create an index that measures the regulatory restrictiveness of each country’s data policies. The index is based on observable policy measures that explicitly inhibit the cross-border movement and domestic use of data. Using cross-country firm-level and industry-level data, we analyse econometrically the extent to which these data regulations over time impact the productivity performance of downstream firms and industries respectively. We show that stricter data policies have a negative and significant impact on the performance of downstream firms in sectors reliant on electronic data. This adverse effect is stronger for countries with strong technology networks, for servicified firms, and holds for several robustness checks.
Corresponding author: firstname.lastname@example.org, Senior Economist at ECIPE & Université Libre de Bruxelles (ULB) and ECARES, Avenue des Arts 40, 1000, Brussels; Martina Francesca Ferracane, email@example.com, PhD candidate at Hamburg University, Policy Leader Fellow at the European University Institute (EUI) and Research Associate at ECIPE; Janez Kren, firstname.lastname@example.org, PhD candidate at the University of Leuven. We thank Giorgio Garbasso, Nicolas Botton, Valentin Moreau, Cristina Rujan for their excellent research assistance. We would also like to thank Stela Rubinova, Julian Nyarko, Sébastien Miroudot, Ben Shepherd and Hosuk Lee-Makiyama as well as participants of the 5th PEPA/SIEL Conference at Tilburg University, the 34th Annual Conference of the European Association of Law and Economics (EALE), the PEP Digital Information Policy Scholars Conference at George Mason University, and the Services-Led Development Conference at the ADBI in Tokyo for their useful comments on earlier drafts.
2. Related Literature
This paper closely relates to the previous literature on the effect of restrictive services policies on downstream firm productivity such as Arnold et al. (2015; 2011). In line with their work on services, the identification strategy in this paper weighs an index on restrictive data policies by the share of input use of data for each downstream sector. This value is then regressed on firm-level TFP. The reason for using a similar methodology is that policy restrictions on data relate closely to services regulation as many digital services depend on the use and transfer of data for their business. For instance, Opresnik and Taisch (2015) show that data is generated through the use of services in the production processes of firms and that this data is exploited in later stages of the production process for more innovative activities and new services for consumers. This allows for an increased value extraction using big data and, as a result, data-related services become increasingly an essential factor to improve the firms’ productivity.
This paper takes a similar line. More restrictive data policies are expected to have an adverse effect on downstream firms in sectors that depend on data in their production process. Today many firms in data-intense sectors rely heavily on data and therefore policies that restrict the use and cross-border transfer of data are expected to reduce their efficiency and eventually productivity. Yet, data policies have only come under the spotlight in recent years as a consequence of the widespread adoption of cloud computing services and the increased cross-border provision of services over the internet.
The empirical research on data policies and firms’ productivity is relatively scarce. To the best of our knowledge, van der Marel et al. (2016) is the only study that explores how regulatory policies related to electronic data affect TFP, albeit at an industry-level. The authors make a first attempt at analysing this linkage econometrically by setting up a data regulatory index using existing indices of services regulation. They calculate the costs of data policies for domestic firms by establishing a link between regulation in data services and TFP at the industry-level in downstream sectors across a small set of countries. They find that stricter data policies tend to have a stronger negative impact on the downstream performance of industries that are more data-intense. They also employ their econometric results in a general equilibrium analysis using the Global Trade Analysis Project (GTAP) to estimate the wider macroeconomic impact.
Other studies have looked specifically at one policy framework regarding data, namely the EU General Data Protection Regulation (GDPR). Christensen et al. (2013) uses calibration techniques to evaluate the impact of the GDPR proposal on small- and medium-sized enterprises (SMEs) and concludes that SMEs that use data rather intensively are likely to incur substantial costs in complying with these new rules. The authors compute this result using a simulated dynamic stochastic general equilibrium model and show that up to 100,000 jobs could disappear in the short-run and more than 300,000 in the long-run. Another study by Bauer et al. (2013) uses a computable general equilibrium GTAP model to estimate the economic impact of the GDPR and finds that this law could lead to losses up to 1.3 percent of the EU’s GDP as a result of a reduction of trade between the EU and the rest of the world.
Our study builds on these aforementioned works by bringing new contributions. First of all, we contribute to the general literature on services regulation by focusing on one particular policy area, namely restrictions related to the domestic use and cross-border movement of data. Currently, many data flow disciplines are being discussed as part of various negotiations at the World Trade Organization (WTO) and regional trade agreements. Yet, to date no thorough empirical study has undertaken an effort to find a significant effect of these measures on productivity and trade. Second, we construct a regulatory index measuring the restrictiveness of data policies. The data policy index considers a set of policies that impose a substantial cost on the use and cross-border movement of data and are therefore expected to increase the costs for the provision of downstream goods and services. In turn, this would have an impact on the productivity of the firm, which we measure with firm-level data.
Building on this approach, this paper follows Iootty et al. (2016) which uses cross-country productivity data of firms covering a wide set of developed economies using ORBIS to come up with several firm-level TFP measures of productivity. Gal and Hijzen (2016), among others, also use cross-country firm-level data of productivity sourced from the same ORBIS database to measure the economic performance of firms. However, in their paper, the authors use a broader measure of output performance whereas we specifically employ TFP. Moreover, both Iootty et al. (2016) and Gal and Hijzen (2016) analyse the productivity impact of a wider set of policy measures of overall product market reform or in services and not of data policies in particular.
In short, our study combines all aforementioned works by using an identification strategy similar to Arnold et al. (2015; 2011) but applied to data policies, for a wider set of countries and by developing specific cross-country TFP performance at the firm level.
 Other previous works that employ similar identification strategy with firm-level productivity data in a services context are Fernades and Paunov (2012) and Duggan et al. (2013) with each using a different TFP proxy.
 Recent work by Goldfarb and Trefler (2018) discus the potential theoretical implications of data policies such as data localisation and privacy regulations on trade although this is put in a broader context of Artificial Intelligence (AI). Nonetheless, the authors do make clear that an expanded AI industry in which data flows are an important factor would have clear implications for trade in services. Similarly, Goldfarb and Tucker (2012) point out that privacy regulations may harm innovative activities by presenting the results of previous studies undertaken with respect to two services sectors, namely in health services and online advertising. Both studies show that there are strong linkages between the effective sourcing and use of data, services sectors and services trade.