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: email@example.com, Senior Economist at ECIPE & Université Libre de Bruxelles (ULB) and ECARES, Avenue des Arts 40, 1000, Brussels; Martina Francesca Ferracane, firstname.lastname@example.org, PhD candidate at Hamburg University, Policy Leader Fellow at the European University Institute (EUI) and Research Associate at ECIPE; Janez Kren, email@example.com, 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.
Between 2000 and 2015, global traffic of data over the internet rose by a factor of 863. This represented an annual compound growth rate of 62.1 percent (Figure 1). For many firms the amplified use of data has become an essential element of the production processes in the current digital era, aiming to increase their economic performance. At the same time, many governments have started to regulate the use and transfer of data over the internet. These policies are likely to have an impact on the productivity performance of firms.
This paper investigates whether measures regulating electronic data have an impact on firms’ productivity. We do so by employing a cross-country analysis over time of policy measures on the use and transfer of data for a group of developed economies. To our understanding, this paper makes a unique contribution to the literature by showing how regulatory policies on data have an impact on the firm’s productivity performance. In particular, we assess how stricter data policies affect the firm’s productivity in downstream sectors relying on data. Our policy frameworks on data across countries cover both how the flow of data across borders and the domestic use of data are regulated.
We define data policies as those regulatory measures that restrict the commercial use of electronic data. We limit our analysis to policy measures which are implemented at the national or supranational level (such as the EU). Although there is a great number of data policies implemented by local public entities, these are not the policies on which we focus on this paper. We identify two main categories of data policies. The first category covers those policies that impact the cross-border transfer of data; the second category covers policies that apply to the use of data domestically. The former category deals with all measures that raise the cost of conducting business across borders by either mandating companies to keep data within a certain border or by imposing additional requirements for data to be transferred abroad. The latter category refers to all measures that impose certain requirements for firms to access, store, process or more generally make any commercial use of data within a certain jurisdiction.
Investigating the relationship between the regulatory approaches countries apply on the domestic use and cross-border transfer of data and the performance of downstream firms requires three novel datasets that we have uniquely developed. These are (a) information on how restrictive countries are regarding the domestic use and cross-border transfer of electronic data, (b) a measure of cross-country performance of firms and finally (c) an indicator measuring the extent to which sectors use data as part of their production process.
Regarding the first set of information, we have created a quantifiable and detailed set of policy information on the regulatory framework of 64 economies towards the use and cross-border transfer of data as developed in Ferracane et al. (2018). This comprehensive dataset contains extensive information on the state and history of data policies. This information on data policies has been condensed into a composite (weighted) time-varying policy index for each country covered. The data policy index takes on values ranging between 0 (completely open) to 1 (virtually closed) with intermediate scores reflecting varying degrees of applied policy restrictions on the use and cross-border transfer of data. The creation of this database together with its corresponding index represents in itself a major contribution to the existing literature, which can be used for future research in this area.
For our second set of information on the performance of firms, we use consistent firm-level data over a group of developed economies from the ORBIS database. In particular, we exploit the TFP estimate recently developed by Ackerberg et al. (2015) which has been applied in various studies such as Arnold et al. (2015) and Fernades and Paunov (2012). The productivity literature has put forward several empirical methodologies for constructing a credible TFP indicator with estimation strategies from Olley and Pakes (1996) and Levinsohn and Petrin (2003) as the most commonly used ones. The TFP measure by Ackerberg et al. (2015) improves on the previous two approaches by addressing their collinearity problem. In this paper, we use this Ackerberg TFP estimate throughout all our regressions, but also perform robustness checks with the alternative TFP proxies to compare the results, including Hsieh and Klenow’s (2014; 2009) TFPR and TFPQ measures.
Finally, our third set of information is an indicator measuring the extent to which different sectors use data as part of their production process. This indicator links up the cross-country TFP estimates of firms and the index on countries’ data policies with input shares that measure the reliance on data for each sector. This identification strategy weights each country’s state of data policies with each sectors’ dependence on data as an input. The use of data for each sector is computed in an exogenous manner by taking detailed input-output coefficients from a country not part of our analysis, namely the US. Employing this methodology assumes that sectors which employ comparatively more data in their production process are more affected by the changes in data policies.
We perform our analysis in a cross-country panel setting. The results show that stricter more restrictive data policies do indeed have a significant negative impact on the productivity performance of firms in downstream data-intense sectors. In addition, we find that this negative impact is stronger for countries with a better digital-enabling environment and for manufacturing firms that also produce services. Moreover, the results are robust when correcting for other regulatory policies in services sectors following Arnold et al. (2015; 2011). In the analysis, we apply the appropriate fixed effects and control variables, and take account of the potential reverse causality by applying a lag between the time of implementation of the data policies and the measurement of firms’ productivity. In addition, we also split out our main index of data policies into different types of policies, namely policies that affect the domestic use of data and the ones that affect the cross-border movement of data to see whether the two individual sub-indexes have a different impact on firm productivity.
Our work contributes to the existing literature in three ways. First, to our knowledge, we are the first to create a dataset in which the regulatory framework of countries regarding data has been quantified from a descriptive into a measurable index. Although existing works have undertaken a similar exercise with respect to other regulatory policies on services (Arnold et al., 2015) or more generally on non-tariff barriers (Kee et al., 2009), to date no work has made a similar effort for data policies. Second, we relate our policy index to micro-level data on the productivity performance of firms across a group of countries. This departs from much of the previous research that is based on a single country and allows us to exploit cross-country differences as an additional source of variation. It also allows us to use industry-year fixed effects to control for possible changes. Furthermore, having a group of countries makes it possible to extrapolate policy conclusions across countries. Third, we provide robust evidence on the way in which these data-related policies affect the productivity of firms that are more dependent on data.
The rest of this paper is organised as follows. The next section discusses the previous literature regarding the use and cross-border transfer of data and their related economic effects. Section 3 elaborates on the three sets of data used in this paper. It also provides some descriptive analysis on how the use of data in different sectors relates to productivity. Section 4 presents the estimation strategy and Section 5 reviews the estimation results. Finally, the last section concludes by putting the results in a wider context.