Do data policy restrictions impact the productivity performance of firms and industries?

• Published date: 01 August 2020
• Author: Martina Francesca Ferracane,Erik Marel,Janez Kren
• DOI: http://doi.org/10.1111/roie.12467
• Date: 01 August 2020

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wileyonlinelibrary.com/journal/roie Rev Int Econ. 2020;28:674–722.

© 2020 John Wiley & Sons Ltd

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INTRODUCTION

Between 2000 and 2015, global traffic of data over the internet rose by a factor of 863. This repre-

sented an annual compound growth rate of 62.1% (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 pro-

ductivity performance of firms.

Received: 27 February 2019

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Revised: 6 November 2019

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Accepted: 8 January 2020

DOI: 10.1111/roie.12467

ORIGINAL ARTICLE

Do data policy restrictions impact the productivity

performance of firms and industries?

Martina FrancescaFerracane1

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JanezKren2

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Erikvan der Marel3

1EUI, ECIPE, Hamburg University,

Hamburg, Germany

2Faculty of Economics and Business,

University of Leuven, Leuven, Belgium

3ECIPE, Université Libre de Bruxelles,

Brussels, Belgium

Correspondence

Erik van der Marel, ECIPE, Université

Libre de Bruxelles (ULB) and ECARES,

Avenue des Arts 40, Brussels 1050,

Belgium.

Email: erik.vandermarel@ecipe.org

Abstract

This paper examines how policies regulating the cross-bor-

der movement and domestic use of electronic data on the in-

ternet impact the productivity of firms in sectors relying on

electronic data. In doing so, we collect regulatory informa-

tion 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 meas-

ures 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 productiv-

ity performance of downstream firms and industries, respec-

tively. 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.

JEL CLASSIFICATION

D22; D24; F14; L86

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FERRACANE Et Al.

This paper investigates whether measures regulating electronic data have an impact on firms' pro-

ductivity. 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.

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

data sets 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, and (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 infor-

mation on the regulatory framework of 64 economies toward the use and cross-border transfer of data

as developed in Ferracane, Lee-Makiyama, and Marel (2018). This comprehensive data set 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) and 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 es-

timate recently developed by Ackerberg, Caves, and Frazer (2015) which has been applied in various

FIGURE 1 Global data traffic. Source: Cisco (Visual Networking Index); IP stands for Internet Protocol, BP

stands for petabyte which is a multiple of the unit byte for digital information, that is, 1,0005 bytes [Colour figure can

be viewed at wileyonlinelibrary.com]

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FERRACANE Et Al.

studies such as Arnold, Javorcik, Lipscomb, and Mattoo (2015), and Fernandes and Paunov (2012).

The productivity literature has put forward several empirical methodologies for constructing a cred-

ible 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 re-

strictive 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), Arnold, Javorcik, and Mattoo (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-bor-

der 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 data set in which the regulatory framework of countries regarding data has been quan-

tified 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 nontariff barriers (Kee, Nicita, & Olarreaga, 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 organized 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 elab-

orates 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.