Néstor Duch-Brown and Bertin Martens

2015

Institute for Prospective Technological Studies Digital Economy Working Paper (2015-07)

Barriers to Cross-border eCommerce in the EU Digital Single Market

European Commission

Joint Research Centre

Institute for Prospective Technological Studies

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This publication is a Working Paper by the Joint Research Centre of the European Commission. It results from the Digital

Economy Research Programme at the JRC Institute for Prospective Technological Studies, which carries out economic

research on information society and EU Digital Agenda policy issues, with a focus on growth, jobs and innovation in the

Single Market. The Digital Economy Research Programme is co-financed by the Directorate General Communications

Networks, Content and Technology.

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JRC96872

ISSN 1831-9408 (online)

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Reproduction is authorised provided the source is acknowledged.

Abstract

This report presents empirical evidence about the internationalisation of European online firms. After describing some

characteristics that define the internationalisation of firms engaged in online activities, it mainly focuses on the obstacles

that these firms face when trying to sell their products and/or services to other EU Member States. It relies on data from a

firm survey carried out in January-February 2015 in four different sectors (manufacturing, wholesale and retail,

accommodation and food and information and communication) and is complemented with additional sources of

information. We find that a limited number of barriers really matter for online trade. These include settling the costs of

cross-border disputes, suppliers’ restrictions to selling cross-border, delivery costs, taxation rules, and knowledge of the

rules abroad. In line with the offline trade literature, the data confirm that they matter mostly for small firms, which find

it harder to overcome the fixed trade costs associated with these barriers.

Acknowledgments

The authors1 would like to thank their colleagues Lucilla Sciolli, Martin Ulbrich, Gianluca Papa, Andrea

Martens, Ivan Breskovic, Claire Whitaker, Konstantinos Zisis, and Dan Dionisie from DGs CNECT, GROW

and JUST for their comments on earlier versions of this report and contributions to the debate.

1 Both authors are researchers at the JRC/IPTS in Seville (Spain). The views and opinions expressed in this

report are the authors’ and do not necessarily reflect those of the JRC or the European Commission. This research was co-financed by DG CNECT.

4

Summary

This report presents empirical evidence about the internationalisation of European online firms. After

describing some characteristics that define the internationalisation of firms engaged in online activities,

it mainly focuses on the obstacles that these firms face when trying to sell their products and/or

services to other EU Member States. It relies on data from a firm survey carried out in January-February

2015 in four different sectors (manufacturing, wholesale and retail, accommodation and food and

information and communication) and is complemented with additional sources of information. We find

that a limited number of barriers really matter for online trade. These include settling the costs of cross-

border disputes, suppliers’ restrictions to selling cross-border, delivery costs, taxation rules, and

knowledge of the rules abroad. In line with the offline trade literature, the data confirm that they matter

mostly for small firms, which find it harder to overcome the fixed trade costs associated with these

barriers.

5

Table of Contents

Acknowledgments ............................................................................................................................................ 3

Summary ............................................................................................................................................................... 4

1. Introduction .................................................................................................................................................... 6

2. Data and methodology ............................................................................................................................ 8

2.1 Data structure and potential sample bias ................................................................... 8

2.2 Firm characteristics ................................................................................................................... 9

2.3 Firms' online activities .......................................................................................................... 10

2.4 Methodology ............................................................................................................................... 13

3. Online internationalisation: a small numbers phenomenon ............................................ 14

4. The structure of target markets in online trade..................................................................... 17

5. E-commerce premia ............................................................................................................................... 20

6. Productivity and specialisation ......................................................................................................... 26

7. Barriers to cross-border e-commerce .......................................................................................... 30

7.1 Barriers to engage in cross-border e-commerce (extensive margin) ........ 32

7.2 Barriers that limit the volume of cross-border e-commerce (intensive margin) ................................................................................................................................ 34

8. Conclusions .................................................................................................................................................. 36

References ......................................................................................................................................................... 38

Annex .................................................................................................................................................................... 39

6

1. Introduction

E-commerce plays an important role in the EU economy. It has been growing at impressive rates during

the past 15 years. In 2014, it represented on average 7% of total retail sales in the EU-28. However,

growth is concentrated mainly in domestic markets while cross-border e-commerce seems to be lagging

behind. The Digital Agenda Scoreboard (2014) reports that more than 50% of all consumers buy online

but only 15% buy online across the border. On the other hand, in 2013, 15% of all firms sold online and

nearly half of them (7%) sold across the border2. This looks substantial when compared to the share of

exporting firms in offline trade (Berthou et al., 2015) and might indicate that the most important

barriers are on the consumer side. Still, the rapid rise of the Internet in the last two decades has

nurtured the idea of the "death of distance" (Cairncross, 1997) where all online services, both domestic

and cross-border, would only be a click away. We know today that the concept was largely overstated

and geographical distance and national borders remain important factors in online trade in goods and

services (Blum & Goldfarb, 2006; Lendle et al, 2012; Gomez et al, 2014; Alaveras & Martens, 2015).

Gomez et al (2014) report that in 2011 only 18% of all B2C e-commerce spending in the EU was cross-

border spending between Member States (MS). These studies seem to confirm the importance of

barriers on the consumer side, such as consumer preference for home markets (home bias) and cultural

and language differences, as the main sources of cross-border online trade costs. However, there may

still be a margin for regulatory and supply-side barriers in holding back online trade flows.

While policy makers cannot change geographical distance or language, they may be in a position to

reduce the regulatory trade costs associated with crossing national borders. Indeed, this has been the

main preoccupation of traditional offline trade policy. Since many offline sources of trade costs continue

to apply online, the reduction or elimination of regulatory barriers that induce trade costs remains

relevant online as well. Moreover, new barriers may appear in online trade. Many potential online

barriers were identified years ago (Coppel, 2000) and they have become more relevant now that e-

commerce has emerged as an important distribution channel. For instance, diverse tax regimes,

complications with payments systems, heterogeneous consumer protection rules, cross country legal

and regulatory barriers or vertical restrictions to selling online, among others, may stand in the way of a

fully-integrated digital single market in the EU. The EU’s Digital Single Market policy package targets

many barriers that are believed to hinder cross-border e-commerce. The removal of these barriers is

crucial in boosting economic integration in both the digital economy and the more traditional offline

Single Market. Both consumers and firms would greatly benefit from the reduction of these trade costs.

The main objective of this study is to identify the barriers to cross-border e-commerce in the EU that

really matter from a supply-side or firm perspective. For this purpose, the European Commission

2 Data from Eurostat's survey on the usage of information and communication technologies in enterprises,

which refers to companies with at least 10 employees and does not consider the financial sector.

7

launched a Eurobarometer firm-level survey in early 2015 with a specific focus on the barriers to cross-

border e-commerce. The survey combines evidence on firms’ perceptions of barriers to cross-border e-

commerce with information on actual cross-border e-commerce activity of these firms. The

Eurobarometer report (Flash Eurobarometer 413, 2015) shows that several barriers are frequently

mentioned as relevant by firms of all sizes, sectors and distribution channels. In this study we go a step

further and compare the perceived barriers with observed cross-border activity in order to check

consistency while controlling for a number of factors that might affect the relationship between these

two. In line with standard trade models, we split the analysis of cross-border e-commerce between an

extensive margin (whether a firm engages in cross-border e-commerce or not) and an intensive margin

(cross-border e-commerce as a share of total e-commerce).

We find that several perceived barriers in the survey have a statistically significant negative impact on

cross-border e-commerce, especially for small firms. These include the trade costs associated with

cross-border complaints and disputes, suppliers’ restrictions to selling abroad, high delivery costs,

dealing with foreign tax regulation and the lack of knowledge of the rules in other countries. These

findings provide empirical evidence for some of the barriers targeted in the Digital Single Market

Strategy paper and mainly for small firms. The findings are in line with offline cross-border trade

research where smaller firms find it harder to overcome the fixed costs associated with cross-border

trade.

The next section describes the survey data, the characteristics of firms in the survey sample and

explains the methodology used. Section 3 describes the online internationalisation status of EU firms

that derives from the survey. Section 4 analyses the markets targeted by firms selling and buying online.

Section 5 addresses the issue of both e-commerce premia and cross-border e-commerce premia.

Section 6 describes the productivity and specialisation patterns related to online activities. Section 7

discusses the results related to the barriers to cross-border e-commerce in the EU, both in terms of

engagement (extensive margin) and sales (intensive margin). In addition, a distinction is made in terms

of firms selling online and firms buying online. The last section offers conclusions and some policy

recommendations.

8

2. Data and methodology

2.1 Data structure and potential sample bias

The data used in this report were collected by TNS on the basis of a questionnaire applied to a sample

of 8,705 firms in 26 Member States3 in early 2015. The data are unique: there is no comparable data

source on perceived barriers to cross-border e-commerce by firms in the EU. The data were first reported

in the Flash Eurobarometer 413 (2015). Survey data are inevitably subject to difficulties. The sample

has to be representative of the population. Bias can come from difficulties in defining the appropriate

population to be sampled; from under-coverage, when parts of the population are inadequately

represented; from non-response, when the survey is full of missing entries; or also from measurement

error due to the presence of leading questions or the respondents' behaviour.

There is no inventory of the population of online firms in the EU. Hence, sampling had to start from the

universe of EU firms. TNS used the Orbis database on EU firms to construct the sample for all the

countries except the UK and Ireland. Since the actual sampling procedure applied by TNS is not known,

we can only verify the outcome of the sampling procedure by comparing sample composition with other

sources of information, in this case Eurostat's statistical surveys on firms, business registers or from

various administrative source (as compiled in the Structural Business Statistics –SBS- section of

Eurostat's database).

The sample includes 400 firms for the larger Member States, 300 firms for Croatia and Slovenia, 200

firms for Latvia, Hungary, Bulgaria and 100 firms in the case of Luxemburg, Estonia and Slovakia. The

data covers four sectors: manufacturing, wholesale and retail trade, accommodation and food and

information and communication. The data can be appropriately weighted to better represent the

universe of European firms4. We compare the weighted distribution of firms by country with firm survey

data from Eurostat (SBS). We find that Spain, Greece, the Netherlands, Sweden and Germany are

slightly overrepresented; while Italy, Poland, Romania, Portugal and Bulgaria are somewhat

underrepresented. This sample bias may affect the analysis since these countries differ in their

proportions of firms doing e-commerce and cross-border e-commerce and hence these differences may

not be captured by the survey data.

Additionally, we checked for potential sample bias at the sector level. Again, comparing the weighted

distribution of firms in the sample with that from Eurostat, we noticed that manufacturing, wholesale

and retail trade and accommodation and food sectors are under-represented whereas the information

3 The survey does not include data for Cyprus and Malta. 4 Sample bias can also be introduced by differences in firms' size distribution, the proportions of the types

of firms or the channels they use to sell/buy online. However, checking for sample bias in these cases is much more difficult because of the lack of appropriate data.

9

and communication sector is over-represented in the sample. Accommodation and food has one of the

highest proportions of firms engaged in e-commerce according to Eurostat.

The structure of the database is represented in Figure A1 in the Annex. The figure shows strong attrition

in the number of replies as the questionnaire progresses towards the barrier questions. This may present

a difficulty in adequately identifying the effects of these barriers. Another potential source of attrition

bias comes from segmentation. A simple sample truncation by firm size and sector for firms selling

online leaves us with a small number of observations in some of the partition cells. Moreover, these few

firms often share the same attributes and hence drop out of the estimations because they are

considered constants. For instance, this is a problem with large firms since they only represent 6% of the

total number of firms (see below). As a result, a partition by firm size may create problems. However,

despite the database’s minor limitations, it does help us to understand the behaviour of European firms

in terms of their online and cross-border activities.

2.2 Firm characteristics

Some basic features of the sample of firms are presented in Table 1. Apart from country and sector,

firm characteristics include age, size, type, activity and sales trend. Firm size is defined in terms of

employment. We have defined four categories: micro firms with 1 to 9 employees (56%)5; small firms

with 10 to 49 employees (25%); medium-sized companies employing between 50 and 249 individuals

(13%) and large firms with 250 or more employees (6%). We also split the sample into two age groups,

young firms created after 2009 (15%) and old firms already in operation before that date (85%). Firms

can also be characterised by type: independent (82%), part of a national group (8%) or part of an

international corporation (10%). The dynamics of firm performance is captured by sales trends. Firms

were asked about the trend of their turnover from the moment of the interview back to January of

2012: sales growing by more than 25% (12%); between 5 and 25% (32%); remained roughly the same

(35%); decreased between 5 and 25% (16%); and decreased by more than 25% (5%). Finally, firms are

classified by the nature of the online markets in which they operate. Firms can operate in several

markets simultaneously, hence the sum of the shares does not add up to 100%: firms selling goods to

consumers (62%) or other firms (75%); selling digital services online to consumers (9%) or other firms

(13%); or providing traditional services offline to consumers (29%) or other firms (39%). More firms are

apparently involved in Business-to-Business (B2B) than in Business-to-consumer (B2C) trade.

Table 1 shows that firms are more likely to buy online than to sell online. However, firms selling online

are more likely to do so cross-border than firms purchasing online. The larger the firm, the more likely it

is that it sells or buys online across the border. A firm is more likely to do cross-border e-commerce if it

5 The figure in parenthesis is the un-weighted share of firms in each category.

10

has expanded in the last two years –looking for new markets- and also if it has experienced difficulties

with a declining turnover, in which case exports may be seen as a new source of revenues.

Table 1: Characteristics of firms doing e-commerce

Selling Buying

Total Cross-border* Total Cross-border*

Age

Old 41.6% 46.7% 85.6% 41.0%

Young 38.9% 53.2% 88.1% 47.6%

Size

Micro 39.4% 47.2% 87.1% 41.1%

Small 44.9% 49.0% 82.5% 47.7%

Medium 56.1% 50.8% 77.3% 48.7%

Large 76.4% 60.3% 80.0% 59.8%

Type

Independent 40.1% 47.5% 86.2% 42.0%

National group 47.7% 47.5% 85.9% 41.3%

International group 50.8% 52.8% 84.6% 51.2%

Activity

Goods to consumers 48.5% 45.7% 83.9% 39.8%

Goods to firms 42.0% 46.3% 87.2% 45.1%

Digital services to consumers 67.7% 55.4% 83.1% 50.0%

Digital services to firms 53.5% 53.2% 87.9% 56.4%

Services to consumers 48.1% 53.1% 86.9% 42.3%

Services to firms 39.3% 53.2% 90.8% 47.2%

Sales trend

Fall by more than 25% 45.8% 42.5% 88.2% 39.4%

Fall between 5% and 25% 43.0% 44.0% 83.7% 39.2%

Remained the same 36.5% 39.5% 87.0% 38.9%

Rise between 5% and 25% 42.4% 55.7% 85.5% 44.9%

Rise by more than 25% 44.2% 58.4% 89.8% 54.7% * Cross-border figures are calculated with respect to the number of firms selling or buying online. Note: Figures are computed using weights. Source: own calculations with data from Eurobarometer 413.

2.3 Firms' online activities

The questionnaire tackled two main blocks of online activities by firms: sales and purchases. A

schematic representation of the data is shown in Figure A1 in the Annex. Questions about online sales

included the channel used (own website, small or large third party platforms, EDI-type transactions) and

whether the firm sells domestically, across the border to other EU countries or to third countries (US,

China, Japan, etc.). Firms engaged in e-commerce also responded to questions addressing the

importance of some pre-defined barriers to cross-border sales. Questions about online purchases

followed a similar structure, asking for the main channels, the geographic origin and the barriers faced.

11

In the sample, 3,945 firms (45%) declared they use e-commerce to sell their products and/or services.

Within this subset of firms, the most frequent channel used is the firm's own website (79%), followed by

small platforms, large platforms and EDI-type transactions, used by 28%, 26% and 23% of firms selling

online, respectively. Multi-channel strategies are used by 40% of the firms, and the remaining 60% only

use one channel for their e-commerce sales.

The average share of online sales over total turnover (excluding firms with null share) is 25%, but the

median is 10%. However, 7% of firms selling online declare their turnover from online sales to be zero.

In contrast, 5% of firms (165) are pure players, with their online sales representing 100% of their total

turnover. Firms are also asked about the geographic destination of their online sales. While 98% of

firms selling online do so domestically, 50% sell their products online across the border to other EU

Member States and 26% also to third countries. The breakdown of the turnover from e-commerce is as

follows: on average, 81% comes from online sales in the domestic market, 14% from sales to other EU

MS and the remaining 5% from third countries. In this last group, the US, Switzerland, Norway and

Iceland are the most frequent destinations for online sales made by EU firms. In the case of cross-

border e-commerce with other EU Member States, the average firm sells online to 4 different countries,

being Germany, the UK and Italy the most frequent destination markets..

Looking at online purchases, 7,156 firms (83%) answered that they purchase online. In this group, firms

basically use the provider's website to buy online (68%). While the proportion of firms that uses small

platforms (39%) and large platforms (37%) are quite similar, the proportion of firms using EDI-type

electronic transactions is rather low (20%). The proportion of firms using just one channel to buy online

is 57% -similar to that for selling, meaning that the multi-channel strategy is used by the remaining

43% of firms.

On average, the share of purchases online (excluding observations with zero value) is 23%, and the

median is again 10%. In this case, 4% of firms, which state that they buy online, indicate that their

share of online purchases is 0%, while some 3% of firms declare that their share of online purchases is

100%. Similarly, we know whether the firms purchase online from their domestic providers or go across

the border to procure items. On average, the share of purchases coming from the domestic market

accounts for 77% of purchases online, while 18% comes from other EU Member States and 5% from

third countries. In the case of online purchases, 93% of firms purchase online domestically, 49%

purchase across the border from other EU Member States and 21% buy from third countries.

The data shows that there are some significant differences between selling and purchasing online. For

all the Member States, firms are more likely to purchase online than sell online (Figure 1). As a matter of

fact, in the majority of countries the proportion of firms purchasing online is above 80%. However, if we

12

look at the cross-border dimension, this difference is no longer valid. First, for some countries the

proportion of firms selling online across the border is higher than the proportion of firms buying online

across the border. In addition, for the remaining countries the differences are much less pronounced

than in the previous case (Figure 2). This indicates that even if purchasing online is a more frequent

activity domestically, purchasing cross-border is as likely to face barriers as selling cross-border.

Figure 1: Firms selling and buying through e-commerce, by country

Source: own calculations with data from Eurobarometer 413

Figure 2: Firms selling and buying through E-commerce across the border, by country

Source: own calculations with data from Eurobarometer 413

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13

Table 2: Cross-border e-commerce, by sector

Engagement (%) Intensity (share)

Selling Buying Selling Buying

Total 47.8% 42.2% 10.3 12.2

Manufacturing 45.6% 40.7% 9.1 11.6

Wholesale and retail trade 36.7% 38.9% 5.9 12.9

Accommodation and food 76.7% 31.3% 23.9 7.2

Information and communication 57.0% 56.0% 12.4 13.7 Note: Figures are computed using weights. Source: own calculations with data from Eurobarometer 413.

The analysis of the incidence of barriers to cross-border e-commerce needs to take into account both

the decision to engage in sales and/or purchases across the border and the intensity with which the

firms do it. Table 2 shows some statistics by sector, weighted to better represent the overall EU picture.

The accommodation and food sector has the highest engagement rate for sales, since 77% of online

firms sell across the border. The equivalent for purchases is the information and communication sector,

where 56% of firms purchase electronically across the border. The picture is the same if we look at the

intensity of the cross-border activity: the accommodation and food sector has the highest share of e-

commerce turnover from sales to other EU countries, while information and communication firms show

the highest share of electronic purchases across the border over total electronic purchases. To sum up,

this is a unique and rich dataset that provides useful evidence on the barriers to cross-border e-

commerce sales and purchases in the EU.

2.4 Methodology

To assess the real impact of many of these barriers, we move beyond the descriptive statistics

presented and discussed in the previous section6. In the following sections, we will rely on two broad

categories of methodologies. First, we compute indicators to address specific topics related to the

process of online internationalisation of European companies and compare with their corresponding

offline values or across countries or sectors. Second, we rely on econometric analysis to find robust

evidence of the process of online internationalisation of European companies. This approach allows us

to make a direct comparison of the results of this report with previous studies, both in terms of firm-

level evidence on the (offline) internationalisation process and evidence on cross-border e-commerce.

Given the nature of the survey data and the absence of relevant counterfactuals, the regression results

presented in the following sections should be seen as correlations rather than causal effects. Still, the

results should point to some factors that hinder firms’ cross-border sales and purchases. Due to the

6 Additional descriptions of the data are provided by Flash Eurobarometer 413 (2015).

14

analytical nature of this report, the results and interpretations may differ from the findings published in

the Eurobarometer (2015) report.

3. Online internationalisation: a small numbers phenomenon

This section uses micro-data to show that online internationalised firms are few and, among these few,

only a handful of firms account for the bulk of aggregate online exports.

If we focus on online trade, we can rank each country's firms in terms of their individual online exports

to other EU Member States and then we can analyse this distribution. Table 3 shows the contribution of

different segments of the ranking to aggregate exports for the total EU economy and for each sector

included in the data. In particular, the table shows the contribution to total exports of the top 1%, 5%

and 10% of cross-border exporters. With the exception of information and communication activities, the

top 1% of online exporters account for around 90% of aggregate online exports; the top 5% for more

than 97% of online exports; and the top 10% almost reaches 100%. The results for the information and

communication sector are somewhat less extreme. Overall, Table 3 shows even more concentration in

online exports than the existing evidence on traditional offline exports. For instance, the top 1% of

offline exporters in Germany carry out 59% of exports, while the top 5 carry out 81%. In Italy, the

corresponding figures are 32% and 59%, respectively (Meyer and Ottaviano, 2007).

Table 3: Share of EU cross-border exports for top online exporters

Top 1% Top 5% Top 10%

Total 89 97 98

Manufacturing 91 98 99

Wholesale and retail 94 99 99

Accommodation and food 93 98 99

Information and communication 35 90 97 Source: own calculations with data from Eurobarometer 413

Next, we look at different country cases where we detect various degrees of concentration. Figure 3

shows four selected cases, while the full picture with all the countries in the sample can be found in the

Annex (Figure A2). For comparisons, a line representing the case in which all firms export the same value

would be a diagonal connecting the left-bottom and right-upper vertex. Hence, the further away the

curve is from this imaginary diagonal, the more concentrated the aggregate exports are in the hands of

a few firms. The figure shows a very high concentration of online exports for most countries. A

particularly striking feature is that as we move from Greece to Slovakia, the initial point also moves up,

meaning that the top online exporter represents a higher share of aggregate online exports.

15

Figure 3: Online exports concentration, selected countries

Source: own calculations with data from Eurobarometer 413

An interesting alternative is to look at the online export intensity. Table 4 does precisely that, and also

compares online exports with offline exports. The table indicates that almost 2% of firms in Belgium

that are selling online across the border have a share of online exports over turnover at least equal to

75%. In the case of offline trade, that share is 1.4%. The countries with the highest online export

intensity are Luxembourg (9.7%), Slovenia (7.4%) and Austria (6.2%). These countries are also among

those with the highest offline export intensity, to which we should add Slovakia (5.2%). At the other end

of the distribution, the countries with the lowest online export intensity are Estonia, Finland and Czech

Republic. In the offline case, the countries with the lowest export intensity are the UK, France and

Germany. These results show that, even though only a small subset of firms exports a major share of

their turnover, they still account for a large fraction of total exports. In addition, this happens in both

online and offline exports.

What Table 4 shows is that only a handful of firms export a large fraction of their turnover, both online

and offline. On aggregate, only around 2% of firms export more than 75% of their turnover online.

Similarly, around 3% of firms export more than 75% of their turnover offline. Comparing these

percentages with the ones reported in Table 3 reveals that the fraction of firms with top export intensity

is larger than the fraction of top exporters. Hence, top online exporters do not necessarily exhibit top

export intensity.

In summary, aggregate online exports are determined by a few top online exporters that are relatively

big. This points to the existence of a process, similar to what has already been found offline (Meyer and

Ottaviano, 2007) through which only firms that are large enough and have other attributes that make

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16

them different from the average firm can survive international competition. We will explore some of

these characteristics in Section 5. Before that, in Section 4, we will have a look at the different markets

(domestic, EU, or Rest of the World –RoW-) online firms' target.

In Section 2.2, we showed that the average EU firm sells on average to 4 export markets. The larger the

number of markets a firm serves, the larger their average distance from the firm's country of origin.

Hence, distance affects aggregate trade flows mostly by reducing the number of exporters rather than

by reducing the average exports per firm. Unfortunately, the available data do not allow us to compare

these effects. Hopefully, future research will target these very relevant issues.

Table 4: Distribution of the sample of online exporters by cross-border export intensity

Share of cross-border sales over total turnover greater than

Online Offline

75% 50% 25% 75% 50% 25%

Luxemburg 9.7 13.1 16.4 15.5 20.1 24.2

Slovenia 7.4 8.1 11.0 3.7 8.2 13.2

Austria 6.2 14.5 17.8 6.3 11.1 17.6

Ireland 3.7 9.9 16.7 2.6 4.5 12.8

Portugal 3.0 5.9 10.4 2.4 5.8 10.5

Lithuania 2.9 5.9 13.3 2.1 5.2 9.4

Belgium 1.9 4.5 6.5 1.4 3.7 12.7

Greece 1.8 4.2 9.0 2.1 3.3 7.2

Slovakia 1.8 8.4 10.4 5.2 10.1 16.2

Latvia 1.5 3.0 7.5 2.9 7.6 14.5

Denmark 1.4 2.5 4.9 3.9 9.5 15.8

Spain 1.2 2.1 5.9 2.3 4.2 9.2

Sweden 1.2 1.4 3.4 2.2 5.5 10.3

Germany 1.1 2.2 6.1 0.7 3.1 7.9

Romania 0.9 2.5 3.5 3.4 5.9 7.6

Croatia 0.8 4.7 7.9 2.4 5.7 10.4

Bulgaria 0.7 2.2 3.9 2.3 3.6 5.7

Poland 0.5 0.9 1.9 3.6 5.6 9.2

Italy 0.5 5.0 6.9 1.3 4.5 6.9

France 0.4 2.3 5.5 0.4 2.9 7.8

Hungary 0.2 1.2 3.6 1.8 6.6 11.8

UK 0.1 1.5 2.0 0.2 2.4 5.5

Netherlands 0.1 1.0 4.2 1.1 5.7 8.9

Czech Republic 0.1 3.6 7.6 1.6 4.5 8.6

Finland 0.0 1.6 3.2 1.5 2.7 6.0

Estonia 0.0 2.5 8.2 3.1 7.9 17.1

EU average 1.9 4.4 7.6 2.9 6.2 11.0 Source: own calculations with data from Eurobarometer 413

17

4 The structure of target markets in online trade

This section looks at the distribution of online sales and purchases in different target markets for firms.

For this purpose, we define three target markets: domestic, cross-border EU and rest of the world. It also

compares online sales with offline sales.

The survey supplies information about the markets that firms target. Figures A4a and A4b show the

target markets for online sales by firm size and sector, respectively. In order to be able to compare this

distribution with the one that corresponds to offline sales, Figures A5a and A5b show the target markets

for offline sales, by firm size and sector, respectively. Finally, we also look at the distribution of target

markets for firms buying online. In this case, Figures A6a and A6b show the corresponding plots for firm

size and sector. All these tables are in the statistical Annex.

Each figure portrays two types of information. First, it gives an account of the number of firms in each

segment (number of circles in each triangle). Additionally, each triangle also gives the location of each

one of these firms in the target market space. The figures show that most points are concentrated in or

around the top vertex, meaning that the majority of firms are mostly targeting their domestic markets.

This result is pervasive: it happens within the different segmentations by size and by sector and also

occurs for the different activities that firms perform - online sales (Figures A4a and A4b), offline sales

(Figures A5a and A5b) and online purchases (Figures A6a and A6b).

In order to compute a summary statistic that allow us to obtain more information about the importance

of the different target markets for the firms in the sample, we compute the distances with respect to an

hypothetical centroid defined as the top vertex. Then, we computed the average distance of each firm

from this hypothetical centroid. The results are shown in Table 5 by firm size and by sector. This variable

measures how far on average firms in each segment are from this point, where all firms would be

concentrating exclusively on the domestic market. This distance has been normalized and lies between

0, when the firm is located exactly in the hypothetical centroid, and 100 when it is located at the

maximum possible distance (i.e. in another vertex).

18

Table 5: Average distance to hypothesised Gravitational vertex, by size and sector

Online Offline

By size: Micro 10.9 7.5

Small 12.2 12.8

Medium 15.1 19.2

Large 12.7 20.8

By sector: Manufacturing 12.8 16.9

Wholesale and retail 7.5 7.3

Accommodation and food 25.5 13.0

Information and communication 11.7 9.8 Note: distance has been normalised and lies between 0 and 100. In this case, 0 means average location at the gravitational vertex, defined as 100% sales to the domestic market and 0% sales to other EU and RoW markets. A value of 100 would indicate average location at the maximum possible distance (i.e. in one of the alternative vertex).

The results from Table 5 are interesting. First, we should note that the average distances from the

hypothetical centroid are similar for online and offline sales in the two cases portrayed, i.e. by firm size

and by sector. Second, note that these distances are small and close to zero in the majority of cases.

This means that the mass of firms selling online and offline is located quite close to the top vertex. In

other words, the propensity to sell in the domestic market is very high whereas the propensity to export

–to other EU markets or to the RoW markets- is very low.

Looking at these results in more detail, we detect several interesting issues. First, looking at the values

of this indicator by firm size, we see that average distance is higher in online sales than offline sales for

micro firms, while it tends to be equal for small firms. However, it tends to be lower for medium-sized

and large firms. This indicates that micro firms rely more on online sales to be more active in

international markets whereas large firms tend to be more internationalised offline than online. When

looking at the results by sector, we see that manufacturing tends to be more open to international

markets offline than online, but that the reverse is true for both the accommodation and food and

information and communication sectors.

Finally, we have plotted the distribution of these distances comparing between online and offline. Figure

4a shows the plots by firm size and Figure 4b by sector. As can be seen from the different panels of the

figures, the majority of firms are concentrated in low values of the distance index, meaning that

domestic markets are more important than foreign markets. The proportion of firms lying far away from

the hypothetical centroid as defined previously is very small, corroborating the previous results. Finally,

19

the figures also show that the differences between the online and offline distributions are modest. With

a very few exceptions given by specific ups and downs, both distributions generally have the same

shape.

The decisions to target the domestic market instead of foreign markets, or to export online to other EU

or RoW countries instead of selling on the domestic market are not independent. A firm will decide its

strategy based on the expected profits to be obtained by each decision. To model these choices

simultaneously, a multivariate discrete choice model is needed. So, in order to account for possible

systematic correlations between decisions to sell online in different target markets, we estimate a

trivariate probit model with binary equations for each outcome (Cappellari and Jenkins, 2006). The

estimate for the cross equation correlation –presented in Table A6 in the Annex- is positive, indicating

complementarities between the three decisions (bottom lines of the table). The results from the

estimation indicate that a small number of factors affect the probability of selling on foreign markets.

Hence, most channels for selling online –with the exception of EDI-type transactions- significantly

increase the likelihood of selling online in all three target markets. Firms in the Accommodation and

food sector are more likely to be targeting all three markets. Being in the wholesale and retail sector has

a negative effect on the probability of targeting foreign markets, either other EU or RoW. Finally, the fact

that firms sell digital services to consumers also impacts positively on targeting the domestic and RoW

markets, although it has no impact on cross-border online trade with other EU Member States.

Figure 4a: Agglomeration in target market space, by size

Source: own elaboration with data from Eurobarometer 413.

0.1

.20

.1.2

0 20 40 60 80 0 20 40 60 80

Micro Small

Medium Large

Online Offline

x

Graphs by size_cat

20

Figure 4b: Agglomeration in target market space, by sector

Source: own elaboration with data from Eurobarometer 413.

5. E-commerce premia

This section shows that firms doing e-commerce score better than offline firms in various performance

measures. However, this distinction is less clear between online and offline exporters.

One important dimension of the growing use of e-commerce is that it is a form of technological change

which raises the firm's value added per unit of input, i.e. its productivity. Business-to-Business (B2B) e-

commerce can improve productivity, for instance, by permitting better inventory control, by allowing the

procurement of more suitable and cheaper inputs, and lowering transaction and information costs,

among many other reasons. Similarly, Business-to-Consumers (B2C) e-commerce can also boost

productivity by reducing time and costs in making transactions, by reducing handling costs and

facilitating better customer relationships. Hence, in this section, we look at some of the differences that

these transformations may have made in firms doing e-commerce. We also look at the productivity

differences for firms doing online trade.

Table 6a reports employment, turnover, wage and value added7 e-commerce premia8, defined as ratios

of firms doing e-commerce over firms not doing e-commerce. The message from this table is that in the

great majority of cases, e-commerce firms perform better than non-e-commerce firms. These

7 These variables were not originally included in the survey. The inclusion of wages and value added was

made possible by matching the survey with data from the Orbis database (Bureau van Dijk, 2015). For this reason, the number of firms in these cases is lower than the total number of available firms.

8 Strictly speaking, employment and turnover cannot be considered performance indicators. We deal with this issue below.

0.1

.20

.1.2

0 20 40 60 80 0 20 40 60 80

Manufacturing W holesale and retail

Accommodation and food Information and communication

Online Offline

x

Graphs by nace

21

differences are particularly acute in the case of employment and wages, for which the EU average

premia are the highest. However, as expected, there is huge cross-country variation in all items.

The overall e-commerce premia are particularly high for Bulgaria, Germany, Denmark and Croatia while

they are quite disadvantageous for Greece, Latvia, Lithuania and Slovakia. Looking at particular cases,

the employment premium is quite high in Austria while unfavourable in Greece; the turnover premium is

highest in Bulgaria and lowest in Slovakia; and the wage premium is also most unfavourable for e-

commerce firms in Slovakia while it turns out to be quite high for e-commerce companies in Poland.

Finally, an important variable is the value added premium, quite high in Hungary, Germany and Bulgaria,

and quite unfavourable in Greece, Ireland and Luxemburg. In addition, Table 6b shows that there is a

strong (and statistically significant) correlation between these variables, especially those more closely

related to performance (added value and wages).

Table 6a: Comparative performance: e-commerce

Employment Turnover Wage Added value

Austria 5.4 5.1 1.5 1.7

Ireland 5.4 1.1 2.3 0.5

Denmark 5.3 2.8 5.0

Netherlands 3.4 0.8 2.1

Croatia 3.3 6.5 3.2

Hungary 3.2 2.0 3.3 4.6

Germany 3.2 4.4 4.3 5.6

Sweden 2.5 2.1 2.0 2.1

Belgium 2.3 2.7 2.3 1.1

Spain 2.3 1.2 1.7 1.7

France 2.2 3.0 1.9 1.9

Bulgaria 2.0 9.1 2.6 5.8

Romania 2.0 6.5 3.1 3.0

Portugal 1.9 1.0 2.3 1.9

Poland 1.8 0.6 5.9 3.5

Italy 1.6 0.9 1.5 1.5

Finland 1.5 3.0 1.5 1.0

Slovenia 1.2 0.6 1.3 1.1

UK 1.2 0.9

Estonia 1.0 1.9 3.6

Luxemburg 1.0 1.3 0.7 0.6

Slovakia 0.8 0.4 0.7 1.5

Czech Republic 0.8 4.9 0.9 0.8

Lithuania 0.7 0.8

Latvia 0.7 0.7

Greece 0.5 0.5 0.9 0.3

EU average 2.3 2.0 2.3 2.2 Source: own calculations with data from Eurobarometer 413

22

Table 6b: Rank correlation of performance indicators

Employment Turnover Wage Added value

Employment 1

Turnover 0.471** 1

Wage 0.555*** 0.316 1

Added value 0.442* 0.358 0.740*** 1 Note: * indicate significant at the 10% level, ** significant at the 5% level and *** significant at the 1% level.

Additional computations to those shown in table 6a can be performed by comparing firms that are

doing cross-border e-commerce with firms that are exporting offline to other EU Member States. The

results of this exercise are presented in Table 7. In this case, the picture is not as clear as it was with e-

commerce. Here, 15 out of the 26 countries show overall positive premia, the values are in general

lower than those observed in the previous case, and the country rankings in each category are more

variable than in the previous case.

Table 7: Comparative performance: cross-border e-commerce

Employment Turnover Wage Added value

Ireland 2.0 0.6 1.4 0.3

Spain 1.6 0.9 1.5 1.5

Finland 1.5 2.2 1.6 1.0

Denmark 1.5 1.3 1.6 1.0

Netherlands 1.4 0.7 1.8

Poland 1.4 0.3 2.6 1.2

Austria 1.4 2.0 1.3 1.4

Romania 1.2 0.2 1.9 3.4

France 1.2 0.9 1.1 1.0

Slovenia 1.2 0.6 1.1 1.1

Slovakia 1.2 0.3 1.2 3.5

Croatia 1.1 6.7 1.2 1.0

Italy 1.0 0.4 0.9 0.8

Sweden 1.0 1.2 1.4 1.6

Belgium 1.0 2.0 1.0 0.7

Hungary 0.9 3.2 0.9 1.5

Portugal 0.8 0.2 0.8 0.7

Germany 0.8 0.4 0.3 0.3

Latvia 0.8 0.8

Estonia 0.8 0.7 1.2

Lithuania 0.6 0.9

Luxemburg 0.6 0.7 0.5 0.6

Bulgaria 0.5 1.2 0.6 0.6

Greece 0.3 0.2 1.5 0.6

Czech Republic 0.3 0.3 0.5 0.4

UK

EU average 1.0 1.0 1.1 1.1 Source: own calculations with data from Eurobarometer 413

23

Before entering into a more robust analysis, we computed the average performance of different groups

of firms and for a set of indicators more related to performance at the aggregate EU level. The results

are shown in Table 8. There, we defined four comparison groups: i) exporters vs. non-exporters; ii) firms

doing e-commerce vs. the rest; iii) firms doing cross-border e-commerce against the rest, and; iv) firms

doing cross-border e-commerce vs. firms doing cross-border traditional commerce. The table shows that

on average, exporters perform better than the rest. Firms doing e-commerce perform on average better

in terms of labour productivity and investment per employee, while they perform worse than other firms

in terms of R&D intensity and also show a lower average profit margin. Finally, firms doing cross-border

e-commerce perform better than the rest of firms in value added per employee, R&D intensity and

investment per employee. This group also performs better than firms doing traditional trade to other EU

Member States in terms of investment and profit rate. These indicators are simply weighted means.

Many factors – such as the size distribution of firms - can affect these ratios. A more robust

methodology would be required to effectively assess the premia associated to superior performance by

exporting firms.

Table 8: Relative performance between relevant firm groups

exporters vs. others

e-commerce vs. others

cross-border e-commerce

vs. others

cross-border e-commerce

vs. cross-border traditional commerce

Labour productivity 1.01 1.04 0.68 0.61

Value added per employee 1.21 1.09 1.04 0.96

R&D intensity 1.69 0.49 1.23 0.87

Investment per employee 1.57 1.09 1.42 1.25

Profit margin 1.03 0.91 0.97 1.11 Source: own calculations with data from Eurobarometer 413 and Orbis.

Since the contribution of Melitz (2003), literature on firm heterogeneity and international trade has

focused on the main determinants of exports at the firm level, based on productivity differentials. There

is a large amount of empirical literature assessing these different approaches, with an equivalently

large number of different methodologies, covering many industries and a great deal of countries.

However, the analysis of the factors explaining export performance for e-commerce firms has received

very little attention. This could be due to different factors such as the difficulties in accessing data on

this industry at the firm level, the high heterogeneity of the firms that form this industry, the

peculiarities of the products they produce, distribute -and eventually export- among many others.

As a second exercise, we take a closer look at the relationship between exports and productivity. Recent

empirical studies about exporting firms have accumulated a great deal of evidence showing that

exporters are more productive than non-exporters. The exporter productivity premium has become a

24

stylized fact, present in almost every country and industry for which there is firm-level data,

independently of the productivity measure used and, in some cases, it turns out to be robust when

controlling for unobserved firm characteristics.

In the literature on international trade and firm heterogeneity, the exporter productivity premium is

defined as the relative productivity differential between exporting and non-exporting firms of the same

size and from the same industry. Econometrically, it is estimated by regressing the log of productivity to

a dummy variable equal to one when the firm is an exporter and zero otherwise. In addition, the number

of employees and its squared value, and dummy variables for the industries (and years when panel

data at firm level is available) are included to control for firm size, industry affiliation and time trend.

In what follows, we apply this approach first to investigate the "e-commerce productivity premia",

comparing firms that are doing e-commerce with firms that are not doing e-commerce. Secondly, we

focus on cross-border e-commerce productivity premia, comparing in this case the sub-sample of firms

that are exporting online to other EU Member States with the rest. As a first step, the e-commerce

productivity premium is estimated using pooled data for the complete sample including dummy

variables to control for time and industry effects. The results obtained for the e-commerce productivity

premia with an OLS estimator are shown in column 1 of Table 10. Then, using a similar specification,

and in order to deal with some of the problems derived from having a dependent variable with extreme

values, we use quantile regressions to assess the validity of the results in different positions of the

distribution of the dependent variable. We run regressions at the 0.25, 0.5, 0.75 and 0.9 quantiles and

the results are shown in columns 2, 3, 4 and 5 of Table 10. In a further step, we look at robust

estimation9 of the e-commerce productivity premium, by means of which one can give a more precise

account of outliers in the database10.

Results are reported in Table 9. The first column of the table shows the standard OLS estimation and

indicates that the estimated export premium is positive, statistically significant and relevant from an

economic point of view –e-commerce firms are on average (all things being equal) 3.4% more

9 Full robustness in a regression based on cross-section data can be achieved by using the so-called MM-

estimator that can resist contamination of the data set of up to 50% of outliers (i.e., that has a breakdown point of 50 % compared to 0% for OLS (ordinary least squares)). A more detailed description of the method is beyond the scope of this report, but the interested reader can consult Verardi and Wagner (2011).

10 Productivity differences between firms can be related to other variables besides firm size and industry affiliation that are not included in the empirical model defined above to estimate the exporter productivity premium. Not controlling for these frequently unobserved factors can lead to biased estimates for the exporter premium. A standard solution for this problem that is widely used in the literature on the micro-econometrics of international firm activities is the estimation of fixed effects models for panel data. For obvious reasons, this approach is impossible for our analysis (Verardi and Croux, 2009).

25

productive than non-e-commerce firms11. When we move up in the productivity distribution, the

differences widen as well, passing from a premium of 0.7% in the 0.25 quantile to a 4% premium in the

0.9 quantile. When we use the robust estimator to avoid contamination from outliers, the estimated

premium is still positive and statistically significant, though much lower in value terms (0.9%).

Table 9: Productivity premia (in %)

OLS Quantile

MM Robust (0.25) (0.5) (0.75) (0.9) E-commerce 3.4 0.7 1.3 2.3 4.0 0.9 Online exporter 3.6 0.9 1.5 3.8 7.5 1.1 Source: own estimations with data from Flash Eurobarometer 413.

For the analysis of cross-border e-commerce premia, we follow a similar strategy. First, the online

exporter's productivity premium is estimated using OLS with the cross-section of firms. Second, we

estimate quantile regressions in order to assess the differential effects by means of parameter

estimates at different positions of the distribution of the dependent variable. Finally, we use robust

estimation to take into account more accurately the problem of outliers.

Estimation results for the online exporter productivity premium are shown in the second row of Table 9.

Results from the OLS estimator are in column 1 while results using quantile regressions are reported in

columns 2, 3, 4 and 5. Overall, the picture is similar to the results already presented. The estimated

exporter premium is statistically different from zero, positive, and large from an economic point of view

both for the OLS results (3.6%) and for the four considered quantiles, ranging from 0.9% for the lowest

quantile (0.25) to 7.5% for the largest one (0.9). Column 6 of Table 9 gives the estimated results from

the robust estimator. The estimated exporter productivity premium is again statistically highly

significant and very large from an economic point of view. When controlling for outliers, the point

estimate is smaller than the point estimate reported for the application of the non-robust standard

approach using OLS (1.1%) but still significant in economic terms. These results are in line with previous

findings concerning the exporter productivity premium in different countries and industries (Wagner,

2012; Vogel and Wagner, 2011; and Temouri et al., 2010).

Finally, Table 10 shows the results of the online exporter productivity premium by sector using the

robust estimator to control for the presence of outliers in the data. From the table we can see that the

11 The estimated coefficient for the e-commerce (cross-border e-commerce) dummy has been transformed

by 100(exp(ß)-1). The transformation shows the average percentage difference in labour productivity (all things being equal) between e-commerce (cross-border e-commerce) and non-e-commerce (non-cross-border e-commerce).

26

premium is positive and statistically significant for three of the four sectors considered. The only sector

where there are no productivity differences between online exporters and other firms is in

accommodation and food. For the other three, the premium amounts to 1% in the case of

manufacturing, 1.4% for wholesale and retail, and 1.1% in the information and communication

activities.

Table 10: Online exporters’ productivity premia, by sector (in %)

Manufacturing Wholesale and retail

Accommodation And food

Information and communication

Online exporter 1.0 1.4 0.3* 1.1 * Not statistically different from zero. Source: own estimations with data from Flash Eurobarometer 413.

An important limitation of this analysis is not being able to control for firm fixed effects. We would need

panel data to be able to take these into account. However, the findings described in this report are

relevant and novel. The productivity premium for e-commerce firms and, more interestingly, for online

exporters, is relevant. In sum, online exporters, according to these results, tend to be approximately 2%

more productive than offline-exporters. Although this figure seems plausible, productivity differences

between firms could be related to other variables besides firm size and industry affiliation that are not

included in the empirical model to estimate the exporter productivity premium, either because

information is missing or because they are unobservable to a researcher. More research would be

needed to address this issue.

6 Productivity and specialisation

In this section, we look into some sector specificities that have not been properly analysed in previous

sections. In particular, we look at the relationship between online revealed and estimated comparative

advantages at the sector and country levels. Previous empirical evidence on the internationalisation of

firms shows that countries generate more highly productive firms (and therefore internationalised firms)

in some industries than in others. In order to elucidate these differences, explanations rely on national

specificities of the entry and exit process at the industry level as a key driver of international

competitiveness.

So far, firm-level evidence (Melitz and Ottaviano, 2007) has shown that, while low-performing firms are

active in their own domestic markets only, better performing ones are also active in foreign markets. The

more efficient these firms are, the more they sell abroad thanks to richer product lines and more

numerous destinations. A country’s penetration of foreign markets is thus mainly driven by those

27

extensive margins. How do we reconcile this recent firm-level view with the traditional macro-view of

aggregate export performance as determined by countries’ inter-industry cost differentials?

In the traditional view, a country specialises in the production of those goods that its firms are able to

supply at a relatively low cost compared with their competitors in other countries. This pattern of

specialisation in production then implies a corresponding pattern of specialisation in exports: a country is

a (net) exporter of the products in which it exhibits a relative (‘comparative’) cost advantage.

Accordingly, the observed pattern of trade can be used to infer a country’s pattern of comparative

advantage (and disadvantage) across industries. This is the idea behind the ‘index of revealed

comparative advantage’ (henceforth, simply RCA) defined as:

(1)

where X refers to exports, c is the country label, s is the industry12 label and w is the label for the group

of countries under consideration (in our case the EU-26). The index is larger (smaller) than one if the

exports of country c are more (less) specialised in industry s than the exports of the other countries. In

this case, country c is said to exhibit a revealed comparative advantage (disadvantage) in industry s. In

addition, since this index is not bounded from above, it is common to normalise it to restrict its values to

between -1 and 1 as follows:

(2)

where SRCA stands for Symmetric RCA. Figure A6 in the Annex plots the SRCA calculated for online and

offline exports for the EU-26 countries included in the survey. Looking at the different panels of the

graph, some patterns emerge. For instance, one group of countries (Germany, Finland, Netherlands, and

Sweden, among others) show many sectors in the upper-left part of the graph. This means that its

online export specialisation is on average higher than the corresponding offline export specialisation in

those sectors. The opposite is true for the group of countries including Spain, France, Latvia, Lithuania,

and others. In these countries, the majority of sectors lie in the bottom-right area of the plot, meaning

that their offline export specialisation is higher than the corresponding online specialisation in those

sectors. The other EU countries included in the survey fall between these two extremes.

12 In this section, we use the NACE classification at two digit level, which in our case gives us information for

35 different sectors. A list of these sectors is included in Table A10 in the annex.

28

On the other hand, Figure A7 in the Annex plots the SRCA of selected countries against their index of

‘estimated comparative advantage’ (ECA) across several sectors included in the survey. The ECA is

defined as:

(3)

where P is productivity, c is the country label, s is the industry label and w is the label for the group of

the countries under consideration (the EU-26 as in the previous case). The index is larger (smaller) than

one if country c is relatively more (less) productive in industry s than the other countries. In this case,

country c is said to exhibit an estimated comparative advantage (disadvantage) in industry s. As in the

previous case, the index was normalised to restrict its values between -1 and 1 (see formula 2), and is

termed Symmetric ECA (SECA).

The different panels of the figure reveal a somewhat positive correlation between the online SRCA and

SECA. The latter is obviously only a crude measure of comparative advantage in online trade, as it does

not take into account important determinants of international competitiveness such as differences in

factor prices and accessibility across countries. Nevertheless, the figure creates a bridge between the

micro and the macro perspectives.

We have already established that countries generate larger numbers of highly productive firms in some

industries than in others. As these are the firms that will be able to compete in international markets,

the aggregate online export performance of a country is therefore better in some industries than in

others. This confirms that national specificities of the entry and exit process at the industry level also

constitute the key driver of international competitiveness in online internationalisation. Hence, we have

provided evidence that the relative online export performance of countries at the macro level is

positively correlated with the relative productivity of their firms measured at the micro level.

But what is the relationship between online and offline specialisation? In order to answer this question,

we rely on a regression methodology that relates both measures13. The degree of specialisation

similarity can be tested by means of the following regression equation (country by country):

13 The methodology will only briefly be presented in this paper, while the reader can consult Dalum, Laursen

and Villumsen (1998) in particular (or Cantwell, 1989), for further details.

29

The superscripts Online and Offline refer to the activities carried out by the firms, respectively. The

dependent variable, SRCA of online activities for firm i in sector j, is tested against the independent

variable which is the value of the offline SRCA. Here, α and β are standard linear regression parameters

and ϵ is a residual term. Basically, the size of β measures how similar the online specialisation pattern

of a country is with respect to its offline specialisation pattern. If β is low, one can talk about a small

degree of specialisation overlap, while the specialisation pattern can be said to be identical if β is not

significantly different from one. In addition, β/R, where R is the correlation coefficient from the

regression, would measure whether the level of specialisation is higher online than offline. If β/R>1,

online specialisation is higher, while offline specialisation is higher if β/R<1. Figure 5 shows the results

(which are also presented in Table A5 in the Annex).

Figure 5: Relationship between online and offline specialisation.

Source: own elaboration with data from Eurobarometer 413 and results from table A5.

The figure shows that there are three groups of countries:

i) those that show a degree of online specialisation similar to their offline specialisation (values of β

close to one);

ii) those that show slightly different specialisation patterns from their offline or traditional

specialisation (values of β between 0 and 0.5) and;

iii) those that have totally different specialisation configurations online than they do offline (negative

values of β).

The downward sloping relationship between the similarity of specialisation patterns and the overall

intensity of specialisation shows that those countries more specialised in online activities are those

countries where the similarity with the offline specialisation is lower. However, the majority of countries

0,000

0,500

1,000

1,500

2,000

2,500

-1,000 -0,800 -0,600 -0,400 -0,200 0,000 0,200 0,400 0,600 0,800 1,000

|β|/

R

β

30

tend to be more specialised online than offline, a result given by the fact that the scope of online

activities still tends to be more limited than that of traditional trade activities.

7. Barriers to cross-border e-commerce

In this section, we turn our attention to the effects of the barriers on cross-border e-commerce between

the different Member States. We follow a two-step strategy, as is typically done in traditional

international trade models (Mayer and Ottaviano, 2007). First, we estimate the impact of the barriers on

a firm's decision to sell across the border. This is equivalent to the number of firms that sell cross-

border– the extensive margin. The decision to sell implies a medium to long-term strategic decision by

the firm to be present in export markets. Clearly, perceived barriers will play an important role in this

decision. The decision is a binary variable, taking the value 1 if the firm is selling online cross border and

0 otherwise. The appropriate estimation methodology is a logit or probit regression model that assesses

the impact of a series of independent explanatory variables which include the respondents’ perceptions

of the barriers to the probability of engaging in online trade. Formally, we assume that there is an

auxiliary random variable, so that:

where . X is a vector that contains variables representing firms' characteristics and the

perceived barriers. Y can then be viewed as an indicator for whether this latent variable is positive:

{

Given data on these observations, the model is estimated by Maximum Likelihood. We expect to find

negative signs for the estimated coefficients for the barrier dummy variables in the regression analysis:

a barrier would normally reduce the propensity to sell across the border.

The second step seeks to explain the impact of these perceived barriers on the volume of cross border

e-commerce -the intensive margin of cross-border trade. Volume is measured as the share of total

cross-border e-commerce; hence it is a variable that can take any value in the interval [0-100]. Here we

also expect to see negative signs on the coefficients for the barrier dummy variables in the regression

analysis. We use a generalised linear regression model (Papke and Wooldridge, 1996) to allow non-

normally distributed errors, as is the case for shares. In a generalized linear model (GLM), each outcome

of the dependent variables, Y, is assumed to be generated from a particular distribution in the

exponential family (a large range of probability distributions that includes the normal, binomial, Poisson

31

and gamma distributions, among others). The mean, μ, of the distribution depends on the independent

variables, X, through:

where E(Y) is the expected value of Y; Xβ is the linear predictor, a linear combination of unknown

parameters β; g is the link function. As before, this model is usually estimated by Maximum Likelihood.

While both the extensive and intensive margin models use the same explanatory variables, we expect to

see differences in the coefficients between the two.

The right-hand side dependent variables in the regression models are more objectively measurable

factors while the explanatory variables include more subjective perceptions of the relative importance

of barriers to cross-border trade. By combining the two in a single regression, we check which perceived

barriers have a statistically significant impact on actual firm behaviour. Respondents may have flagged

some barriers as relevant but if this does not transpire in firms’ actual behavioural outcomes the

regression analysis will demonstrate the statistical insignificance of these barriers. We also run the

regressions separately for firms’ cross-border sales and purchases.

We run the regression analysis by firm size group because we expect to see differences in the relative

importance of barriers between small and large firms, based on the findings from traditional offline

trade. It is important to highlight that when the regressions are run on the weighted sample data, all

statistical significance disappears. We discard these results because we have no information on the

weightings carried out by TNS and prefer the original un-weighted data.

Small firms have a harder time coping with fixed cost barriers because they cannot write off these fixed

costs over a large market size. The traditional trade literature also finds that there are few exporting

firms and their distribution is highly skewed: exporters are bigger, generate higher value added, pay

higher wages, employ more capital per worker and more skilled workers and have higher productivity

than non-exporting firms (Mayer and Ottaviano, 2007). In theory, e-commerce should bring trade costs

down, in particular those related to transport (in terms of time), search costs, information costs, and

distribution costs. To what extent this expected reduction in trade costs suffices to engage more and

smaller firms in international trade or deepen their internationalisation is an empirical research question

that we address in this study.

32

7.1 Barriers to engage in cross-border e-commerce (extensive margin)

In this section, we will first refer to the results associated with the characteristics of firms, which are

common for the analysis of selling and buying online across the border. Secondly, we discuss the

specific results regarding the barriers firms face vis-à-vis their decision to engage in cross-border sales

or to carry out cross-border purchases. Tables A6 and A7 in the Annex, respectively, show the full set of

results. The first column displays the overall (aggregated) results; the following columns show the

results by firm size. Stars next to the coefficients show the level of statistical significance. As can be

observed, in general many coefficients are not significant and should be considered as zero (i.e., having

no effect on the decision to sell or buy across the border). The table shows the marginal effects or the

impact of each of the independent variables on the probability of doing cross-border e-commerce.14

Contrary to our expectations, Tables A6 and A7 show that size and age have no impact on the

companies' decisions to sell or buy cross-border. This is a somewhat strange result since the importance

of firm size in online exports has been confirmed by other studies (Alaveras & Martens, 2015). In this

respect, little is known about the role of size for online imports. Since we are dealing with marginal

effects, an alternative interpretation is that size and age do not affect the probability of engaging in

cross-border sales and purchases. Hence, factors other than size and age should have a more relevant

role in these decisions.

For instance, distribution channels do have an impact. Firms using their own websites or third-party

platforms are more likely to sell cross-border, particularly micro and small firms. This effect disappears

for medium-sized firms and even turns negative for large firms. Firms using their providers' websites are

more likely to be purchasing across the border. This factor is driven exclusively by micro firms. Firms

using EDI-type channels to purchase electronically are also more likely to buy across the border, a result

mainly driven by small firms. The use of small platforms seems to be correlated with the probability of

large firms buying from other EU countries online, consistent with anecdotal evidence on the role of B2B

platforms for intermediate goods transactions.

The type of products sold by firms also has no significant impact on cross-border e-commerce. This

result is surprising since we are considering specifically two categories in which firms sell digital services

online. Apparently, these firms are focused exclusively on their domestic markets, or other factors like

copyright laws restrict them from selling across the border. A possible alternative explanation is that

most firms declare they are engaged in several activities at the same time, and these multiple answers

could have an effect on the estimated coefficients. Here it would be interesting to explore the effects of

diversification on the decision to sell across the border. On the other hand, firms selling goods to

14 More detailed results are available from the authors upon request.

33

consumers are less likely to buy cross-border while firms selling goods to firms are more prone to cross-

border electronic purchases, again consistent with a B2B interpretation of these data. This last result is

driven mainly by micro firms.

Results by sector show that taking the manufacturing sector as a reference, firms in the accommodation

and food sector, basically related to tourism activities, are more likely to place their services across the

border. This result is consistent for any size class within this sector. No significant differences are found

for the remaining sectors, except for the case of large firms in the wholesale and retail trade sector,

which are less likely than their manufacturing counterparts to engage in cross-border online sales.

In terms of the barriers to cross-border online sales, the results including the full sample show that the

strategic decision to sell across the border is related to three barriers: high delivery costs; suppliers'

restrictions to selling abroad; and the cost of resolving complaints and disputes (see Table 11 for a

summary by firm size). According to these results, lowering the delivery costs could represent an

increase of 7.5% in the number of firms engaged in selling online across the border. Similarly, getting rid

of the vertical restrictions imposed by suppliers would boost the number of firms selling online by

10.6%. Finally, an additional 9% in the number of firms selling online cross-border could be achieved by

lowering the costs of resolving cross-border complaints and disputes. However, the incidence of these

barriers differs across firm size. For instance, delivery costs are more relevant for larger firms. Suppliers'

restrictions and dispute resolution costs matter only for smaller firms. Other statistically significant

barriers include copyright in the case of micro firms; and taxation and product labelling costs for small

firms. It should be noted that the effects by firm size are more important than the results at the

aggregated level. For instance, removing the restrictions imposed by suppliers could increase the

number of small firms selling online across the border by 40%.

Table 11: Statistically relevant barriers to cross-border e-commerce sales by firm size: extensive margin Firm size Barrier Micro Small Medium Large Delivery costs are too high -15.3% -17.8% Copyright prevents you from selling abroad or is too expensive to sell abroad

-14.6%

Dealing with foreign taxation is too complicated or too costly -21.1% Your product labelling has to be adapted -13.3% Your suppliers restrict or forbid you to sell abroad -40.2% Resolving complaints and disputes cross-border is too expensive -9.8% -10.5%

Source: own estimations with data from Flash Eurobarometer 413.

Turning to purchases, the three main relevant barriers for cross-border purchases are related to security

of payments, language skills and the cost of resolving complaints and disputes. The elimination of these

barriers would increase the number of firms engaging in online purchases across the border by 5%, 7%

34

and 12%, respectively. As before, these barriers are more relevant for smaller than for larger firms, with

the exception of dispute resolution, which was also found to be significant for large firms (see table 12).

Table 12: Statistically relevant barriers to cross-border e-commerce purchases by firm size: extensive margin Firm size Micro Small Medium Large Payments to other countries are not secure enough -7.7% You lack the language skills for dealing with foreign countries -6.7% Resolving complaints and disputes cross-border is too expensive -14.0% -9.6% -21.5% Source: own estimations with data from Flash Eurobarometer 413.

These results indicate that some barriers are statistically significant but with a puzzling positive sign. In

the aggregated results for online sales these are data protection concerns and firms' slow internet

connection; while in the case of online purchases this only occurs for product labelling. This also

happens for some other barriers with the results disaggregated by firm size. However, a positive sign

suggests that a barrier actually increases cross-border trade. This is implausible. We therefore discard

these results.15

7.2 Barriers that limit the volume of cross-border e-commerce (intensive

margin)

In this section, we look at the effects of barriers on the volume of cross-border sales and purchases. As

we did in the previous section, we first briefly offer an overview of the effects of the different firm's

characteristics and then we proceed to a more detailed analysis of the incidence of the different barriers

on cross-border volumes of sales and purchases, respectively. The main results are presented in Tables

A8 and A9 in the annex. As before, the first column shows the aggregated results, while the following

columns display the results by firm size. The table shows the marginal effects or the impact of each of

the independent variables on the share of cross-border e-commerce over total trade. Statistically

significant coefficients have stars attached to them.

Results from Tables A8 and A9 show that firm size is positively correlated with online export intensity.

Hence, larger firms generate a larger share of revenue from online exports than smaller firms. However,

this is not the case with online imports. In the case of the intensive margin, age is not significant in both

equations. In addition, in the overall results the different distribution channels have no effect. However,

medium-sized firms using large platforms are likely to have higher shares of cross-border e-commerce

sales. The same correlation occurs with EDI-type transactions and large firms.

15 This result could also arise from problems related to the appropriate definition of the sample.

Unfortunately, we are not able to appropriately control for biases included in the sample.

35

The different activities carried out by firms are seldom relevant. In the overall sample, only those firms

that sell goods to consumers are less likely to sell online across the border, a result that is mainly driven

by the smaller size classes (micro and small). In addition, medium-sized firms selling goods to firms are

also less likely to go online across the border. The rest of the activities do not show significant

correlations with the decision to engage in cross-border e-commerce. However, if we look at purchases,

the type of market matters more, since firms selling goods to consumers and firms selling traditional

services to firms show a significantly lower intensity of cross-border purchases than firms performing

other activities, and particularly traditional services to consumers. In terms of the differences by size-

class, these results are again mainly driven by micro and small firms, whereas medium-sized firms

selling services to consumers carry out more online cross-border purchases.

Taking the manufacturing sector as a reference, firms in the wholesale and retail trade and in the

information and communication industries are less likely to sell online across the border. This result

applies to all size classes except micro firms in the case of wholesale and retail and only to medium-

sized firms in the information and communication sector. However, firms in the accommodation and

food industry are more likely to do cross-border e-commerce, a result driven particularly by micro firms.

In terms of purchases, both accommodation and food and information and communication sectors are

less likely to purchase across the border (with respect to the baseline of the manufacturing sector),

while the opposite is true for wholesale and retail activities.

Looking at the effects of barriers on export intensity, the results indicate that five barriers are

statistically significant: i) delivery costs; ii) guarantees and returns; iii) foreign taxation; iv) suppliers'

restrictions to selling abroad; and v) product and/or services specificity. In this case too, these barriers

have more impact on smaller firms than on larger ones (See Table 13). Of these five relevant barriers,

only guarantees and returns are correlated with the export intensity of large firms. In the case of large

firms, there are two additional barriers: knowledge about the rules to be followed and interoperability

issues. Security of payments is a relevant barrier for medium-sized firms and language skills for small

firms.

By removing any of these barriers at the aggregate level, the volume of cross-border sales could

increase: from 3.2% in the case of lowering the costs of guarantees and returns, to 6% if the restrictions

imposed by suppliers were effectively eliminated.

36

Table 13: Relevant barriers to cross-border e-commerce sales by firm size: intensive margin Firm size Micro Small Medium Large Delivery costs are too high -4.2% -13.5% Guarantees and returns are too expensive -25.1% You don’t know the rules which have to be followed -45.1% Payments from other countries are not secured enough -15.1% Dealing with foreign taxation is too complicated or too costly -5.4% You lack the language skills to deal with foreign countries -7.1% Your suppliers restrict or forbid you to sell abroad -10.7% -21.8% For reasons of interoperability, you cannot provide your products/services

-17.2%

Your products and/or services are specific to your local market -11.4% Source: own estimations with data from Flash Eurobarometer 413.

The results from the analysis of cross-border online purchases show that in aggregate terms, language

skills and the cost of disputes are correlated with a low intensity of online cross-border purchases.

Language skills affect all size classes negatively except large firms. The costs of complaints and

disputes resolution have an effect on the intensity of cross-border electronic purchases of both micro

and large firms. As can be seen in Table 14 –a summary of the results by firm size- large firms also

face the additional barrier of high delivery costs and medium-sized firms' concerns about protection of

their data seem to be also negatively affecting their share of online purchases across the border.

Table 14: Relevant barriers to cross-border e-commerce purchases by firm size: intensive margin Firm size Micro Small Medium Large Delivery costs are too high -14.8% Payments to other countries are not secure enough -3.6% You lack the language skills for dealing with foreign countries -5.3% -7.1% -8.7% Resolving complaints and disputes cross-border is too expensive -9.9% -18.1% You are concerned your data is not well protected when purchasing abroad

-6.8%

Source: own estimations with data from Flash Eurobarometer 413.

8 Conclusions

The main objective of this study was to identify the barriers to cross-border e-commerce in the EU that

really matter from a firm’s perspective. We find that the most relevant barriers to cross-border sales,

including the firm’s decision to export and the resulting volume of online sales, are delivery costs and

suppliers' restrictions. The most important barriers to a firm’s cross-border online purchases are the lack

of language skills and the costs associated with resolving complaints and disputes across the border.

Other barriers appear to be less important or only significant for specific firm sizes, such as dealing with

foreign tax regulation, or the security of payments, among others. A fully-integrated Digital Single

Market would be facilitated by removing these barriers.

37

We have also examined several dimensions of the process of internationalisation of European online

firms. We have detected some stylised facts that correspond with evidence from the literature on offline

firms. We find that aggregate online exports are driven by a few top exporters, that the share of exports

in turnover remains limited and that the number of online exporters is one of the main determinants of

aggregate online exports. We have also provided original evidence related to online export productivity

premiums and the relationship between online and offline specialisation patterns. Many of these

findings are in line with those from offline cross-border trade research (Mayer and Ottaviano, 2007)

where smaller firms find it harder to meet the fixed costs associated with cross-border trade. According

to the present survey data, channelling sales through an established online platform does not really

seem to make it any easier for small firms to overcome these costs: they still need to deal with many

issues.

The results presented in this report help to identify relevant Digital Single Market policy implications:

What matters most for a country's online trade performance is the number of firms that engage in

exporting. Hence, policies should focus on expanding the online export base, particularly by making

it easier for small and medium-sized firms to export online.

Even though they seem to be few and small, the costs associated with online internationalisation

matter because they reduce the number of exporters significantly. Hence, measures that efficiently

remove these barriers would help to foster cross-border e-commerce.

Some sectors have greater unexploited export potential than others. They are characterised by a

larger presence of small, low-productivity firms. They are more likely to react to online import

competition through the exit of the worst-performing firms and therefore have greater potential for

unexploited productivity gains.

38

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39

Annex:

Table A1: Triprobit estimations of e-commerce target markets

E-commerce Domestic Cross-border Rest world Size (baseline: micro firms) Small 0.380*** 0.0354 -0.00717 (0.0766) (0.0534) (0.0581) Medium 0.382*** -0.0226 0.0325 (0.114) (0.0671) (0.0697) Large 0.572*** 0.109 0.0543 (0.188) (0.0948) (0.0990) Young 0.146 0.0938 -0.0917 (0.0934) (0.0610) (0.0684) Own website 0.259*** 0.236*** 0.196*** (0.0851) (0.0566) (0.0613) Small platform 0.200*** 0.182*** 0.169*** (0.0761) (0.0498) (0.0516) Large platform 0.579*** 0.272*** 0.185*** (0.0935) (0.0506) (0.0537) EDI type 0.335*** 0.0656 0.0597 (0.102) (0.0530) (0.0554) Goods to consumers 0.415*** -0.227*** -0.168*** (0.0708) (0.0525) (0.0591) Goods to firms -0.0659 0.0380 -0.00704 (0.0644) (0.0543) (0.0578) Digital services to consumers 0.268** 0.108 0.167* (0.123) (0.0838) (0.0908) Digital services to firms -0.159 0.113 0.0199 (0.110) (0.0836) (0.0910) Services to consumers -0.196*** 0.0939 0.00883 (0.0687) (0.0625) (0.0709) Services to firms 0.153** -0.0671 -0.00199 (0.0647) (0.0602) (0.0690) Structure (baseline: independent) National group 0.456** -0.0780 -0.107 (0.202) (0.0798) (0.0856) International group -0.113 0.0298 0.167** (0.116) (0.0754) (0.0781) Sector (baseline: manufacturing) Wholesale & retail 0.313*** -0.289*** -0.302*** (0.0649) (0.0568) (0.0627) Accommodation and food 0.558*** 0.570*** 0.651*** (0.140) (0.0797) (0.0811) Information and communication 0.303*** -0.0375 0.0804 (0.0954) (0.0805) (0.0829) Constant 0.661*** -0.233*** -0.798*** (0.110) (0.0870) (0.0956) Observations 3,489 3,489 3,489

-0.291*** (0.0538) -0.351*** (0.0475) 0.800*** (0.0366)

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

40

Table A2: E-commerce productivity premia

OLS Quantile

MM Robust (0.25) (0.5) (0.75) (0.9) E-commerce 0.0332*** 0.00697*** 0.0127*** 0.0230*** 0.0388* 0.00852*** (0.0113) (0.00191) (0.00334) (0.00736) (0.0202) (0.00212) Size -1.07e-05 2.49e-05*** 5.33e-05*** 0.000105*** 2.32e-05 0.000113*** (3.37e-05) (5.30e-06) (1.02e-05) (2.34e-05) (4.86e-05) (2.24e-05) Size2 -5.76e-10 -3.48e-09*** -6.91e-09*** -1.25e-08*** -6.54e-09 -3.53e-08*** (4.63e-09) (6.14e-10) (1.32e-09) (2.82e-09) (5.42e-09) (6.79e-09) Constant 0.187*** 0.0355*** 0.0837*** 0.163*** 0.344*** 0.0698*** (0.0119) (0.00205) (0.00356) (0.00737) (0.0190) (0.00215) Observations 6,922 6,922 6,922 6,922 6,922 6,922 Note: all regressions include sector fixed effects. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A3: Online exporters’ productivity premia

OLS Quantile

MM Robust VARIABLES (0.25) (0.5) (0.75) (0.9) Online exporter 0.0352** 0.00885*** 0.0152*** 0.0377*** 0.0727*** 0.0110*** (0.0141) (0.00232) (0.00410) (0.00934) (0.0262) (0.00284) Size -5.30e-06 2.64e-05*** 5.65e-05*** 0.000109*** 2.38e-05 4.74e-05*** (3.36e-05) (5.26e-06) (1.03e-05) (2.33e-05) (4.84e-05) (1.66e-05) Size2 -1.09e-09 -3.63e-09*** -7.25e-09*** -1.29e-08*** -6.61e-09 -5.27e-09*** (4.63e-09) (6.07e-10) (1.33e-09) (2.81e-09) (5.36e-09) (1.63e-09) Constant 0.194*** 0.0367*** 0.0859*** 0.165*** 0.346*** 0.0744*** (0.0114) (0.00196) (0.00341) (0.00702) (0.0181) (0.00200) Observations 6,933 6,933 6,933 6,933 6,933 6,933

Note: all regressions include sector fixed effects. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A4: Online exporters’ productivity premia, by sector

Manufacturing Wholesale and retail

Accommodation And food

Information and communication

Online exporter 0.0101*** 0.0140*** 0.00321 0.0110** (0.00380) (0.00509) (0.00199) (0.00466) Size 0.000200*** 0.000162*** 5.57e-05*** 4.16e-05 (2.72e-05) (4.26e-05) (1.63e-05) (2.78e-05) Size2 -1.94e-07*** -7.11e-08** -4.42e-08*** -6.70e-09 (3.39e-08) (2.91e-08) (1.01e-08) (4.44e-09) Constant 0.159*** 0.235*** 0.104*** 0.160*** (0.0122) (0.0259) (0.00636) (0.0159) Observations 1,877 3,099 810 1,147 Note: all estimations performed with the MM estimator. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

41

Table A5: Online vs. offline specialisation, by country

β |β|/R

Czech Republic 0.969 1.772

Portugal 0.841 1.409

Ireland 0.832 1.149

Netherlands 0.762 1.244

UK 0.697 1.240

Spain 0.612 1.068

Croatia 0.592 1.081

Latvia 0.562 1.362

Estonia 0.547 1.121

Belgium 0.515 1.019

France 0.507 1.506

Bulgaria 0.505 1.216

Slovenia 0.502 0.900

Greece 0.493 1.298

Austria 0.470 1.121

Italy 0.300 1.219

Denmark 0.291 0.586

Slovakia 0.250 0.657

Romania 0.187 0.753

Lithuania 0.115 1.183

Finland 0.090 0.639

Hungary -0.022 1.567

Poland -0.127 0.897

Germany -0.269 2.174

Sweden -0.399 1.630

Luxemburg -0.718 1.570 Note: All coefficients statistically different from zero and from one at the 90% except bold coefficients, which are not statistically different from zero and underlined coefficients, which are not statistically different from one. Source: own estimations with data from Eurobarometer 413.

42

Table A6. Barriers to cross-border e-commerce in the EU: decision to sell cross-border (1)Total (2)Micro (3)Small (4)Medium (5)Large Log(Size) 0.00458 Young -0.0354 -0.0539 0.0343 0.142 Channel: Own website 0.0645** 0.0773* 0.235*** 0.0238 -0.341** Small platform 0.0379 0.0632 0.140*** -0.0966 -0.0966 Large platform 0.0872*** 0.148*** 0.110* 0.0184 -0.0130 EDI type 0.0399 0.00997 0.0456 0.00384 0.0728 Market: Goods to consumers -0.0383 -0.0403 0.0113 -0.0635 -0.0153 Goods to firms -0.00599 0.0194 -0.120* -0.0260 0.0133 Digital services to consumers -0.0413 -0.102* -0.0374 0.124 -0.542 Digital services to firms 0.0391 0.0461 0.0701 -0.0275 0.754 Services to consumers 0.0178 0.0665 0.0128 0.0463 -0.200* Services to firms -0.0153 -0.0135 0.0235 -0.0356 -0.157* Sector (wrt manufacturing): Wholesale and retail trade -0.0121 0.0379 -0.00751 -0.00349 -0.235*** Accommodation and food 0.270*** 0.246*** 0.429*** 0.278** Information and communication -0.0387 -0.0290 0.00787 -0.0274 0.0863 Barriers: Delivery costs are too high -0.0747*** -0.0668 -0.0204 -0.153** -0.178* Guarantees and returns are too expensive -0.0157 -0.0564 0.113* -0.0844 0.0298 You don’t know the rules which have to be followed

-0.0260 -0.0303 0.0886 -0.155 0.0371

Payments from other countries are not secured enough

-0.0450 -0.0518 -0.00790 -0.161

Copyright prevents you from selling abroad or is too expensive to sell abroad

-0.0746 -0.146** 0.120 0.0318 -0.00921

Dealing with foreign taxation is too complicated or too costly

-0.0352 0.00892 -0.211*** -0.0473 -0.125

Your product labelling has to be adapted -0.0271 -0.0648 -0.133* 0.0361 0.0906 You lack the language skills to deal with foreign countries

-0.000585 -0.00504 -0.0340 0.0582 0.301

Your suppliers restrict or forbid you to sell abroad

-0.106** -0.0239 -0.402*** -0.139 0.171

Your suppliers do not allow you to use third platform to sell your products and/

-0.0229 0.00307 -0.109 0.144

Your suppliers request you to sell abroad at a different price

0.0804 0.00217 0.226* 0.396*** -0.0757

You are concerned your data is not well protected when selling abroad

0.120** 0.143** 0.382*** -0.0531 -0.153

For reasons of interoperability, you cannot provide your products and/or service

-0.0398 -0.0796 0.0129 -0.0867 -0.0925

Your products and/or services are specific to your local market

-0.0202 -0.0186 -0.0139 0.371* -0.167

Your company’s Internet connection is not fast enough

0.105** 0.198*** -0.0773 0.0541 -0.0251

Clients abroad do not have a fast enough Internet connection

-0.0100 -0.0163 -0.101 -0.120

Resolving complaints and disputes cross-border is too expensive

-0.0904*** -0.0980** -0.105* 0.00198 -0.0622

Observations 1,376 701 344 223 91

43

Table A7. Barriers to cross-border e-commerce in the EU: decision to buy cross-border (1)Total (2) Micro (3) Small (4)Medium (5) Large Log(Size) 0.00751 Young 0.00762 0.00833 0.0357 -0.0208 Channel: Provider website 0.0489*** 0.0497** 0.0369 0.0681 -0.0696 Small platform -0.00192 0.0127 -0.0590** 0.0301 0.126* Large platform 0.0291* 0.00647 0.108*** 0.0155 -0.0441 EDI type 0.0543*** 0.0396 0.0663* 0.0694 -0.0875 Market: Goods to consumers -0.0401** -0.0305 -0.0390 -0.0672 -0.0995 Goods to firms 0.0460** 0.0530** 0.0570 -0.0424 0.136 Digital services to consumers 0.0285 0.0232 0.129* -0.123 -0.181 Digital services to firms 0.0220 0.0216 -0.0581 0.141* 0.0352 Services to consumers 0.0209 0.0173 0.00892 0.0750 0.0264 Services to firms 0.00527 -0.00721 0.0723* -0.0275 -0.0393 Sector (wrt manufacturing): Wholesale and retail trade -0.00933 -0.0142 0.0348 -0.0206 0.0501 Accommodation and food -0.0185 -0.0108 0.0278 -0.0751 -0.00169 Information and communication -0.0240 -0.00898 -0.0281 -0.0611 -0.0111 Barriers: Delivery costs are too high 0.00151 0.00924 -0.0308 -0.00233 0.0503 Payments to other countries are not secure enough

-0.0508** -0.0773*** -0.0351 -0.0364 0.178**

The product labelling has to be adapted 0.129*** 0.144*** 0.105** 0.104* 0.106 You lack the language skills for dealing with foreign countries

-0.0670*** -0.0675*** -0.0562 -0.0495 -0.112

Copyright prevents foreign suppliers from delivering to your country or makes it

-0.0150 -0.0334 -0.00248 0.0665 0.0777

Foreign suppliers refuse to deliver to your country

-0.00981 0.0172 -0.0505 -0.0525 -0.142

Resolving complaints and disputes cross-border is too expensive

-0.118*** -0.140*** -0.0964*** -0.0565 -0.215***

You are concerned your data is not well protected when purchasing abroad

0.0305 0.0389 0.0651* -0.0537 0.0353

For reasons of interoperability, you cannot use foreign products and/or services

0.00668 -0.0107 0.0364 0.0305 0.0809

Observations 3,325 1,941 841 400 142

44

Table A8. Barriers to cross-border e-commerce in the EU: Volume of sales (1)Total (2)Micro (3)Small (4)Medium (5)Large Log(Size) 0.00851** Young 0.0116 0.0179 0.000929 -0.0312 Channel: Own website -0.0150 -0.0171 0.0651 -0.0351 -0.0435 Small platform -0.0149 0.00466 -0.00100 -0.0478 0.0122 Large platform 0.00219 -0.00358 -0.00898 0.0969*** 0.00297 EDI type 0.00770 -0.0188 0.00968 -0.00780 0.174*** Market: Goods to consumers -0.0603*** -0.0762*** -0.0334* -0.0248 0.0269 Goods to firms -0.00598 0.0174 -0.0259 -0.0976** -0.0132 Digital services to consumers -0.0311 -0.0343 -0.0292 -0.0839 0.0533 Digital services to firms 0.0260 0.0114 0.0673 -0.0184 0.0514 Services to consumers 0.0108 0.0436 -0.0339 -0.0441 -0.165 Services to firms -0.0169 -0.0254 0.0276 0.0240 -0.0560 Sector (wrt manufacturing): Wholesale and retail trade -0.0660*** -0.00784 -0.118*** -0.103** -0.140** Accommodation and food 0.0597* 0.0774** 0.0650 -0.0324 Information and communication -0.0727** -0.0464 -0.0568 -0.171** 0.0230 Barriers: Delivery costs are too high -0.0486** -0.0420** -0.00595 -0.135** -0.0390 Guarantees and returns are too expensive -0.0321* -0.0117 0.0104 -0.0995 -0.251*** You don’t know the rules which have to be followed

0.0116 0.000625 0.0631 -0.000154 -0.451***

Payments from other countries are not secured enough

-0.0256 -0.0253 0.0376 -0.151*

Copyright prevents you from selling abroad or is too expensive to sell abroad

0.00710 -0.0553 0.0735 0.181 0.312**

Dealing with foreign taxation is too complicated or too costly

-0.0497** -0.0536** -0.0784 -0.0376 0.138**

Your product labelling has to be adapted -0.0172 -0.0449 0.000166 -0.185 0.387*** You lack the language skills to deal with foreign countries

-0.0161 -0.0114 -0.0709* 0.0288 0.193***

Your suppliers restrict or forbid you to sell abroad

-0.0595** -0.0234 -0.107* -0.218*** 0.0815

Your suppliers do not allow you to use third platform to sell your products and/

0.0441 0.0261 0.00625 0.161**

Your suppliers request you to sell abroad at a different price

0.0497* 0.0216 0.0554 0.101* 0.253**

You are concerned your data is not well protected when selling abroad

0.0494** 0.0897*** 0.0445 0.0491 -0.00874

For reasons of interoperability, you cannot provide your products and/or service

0.0178 0.00930 0.00956 0.115 -0.172*

Your products and/or services are specific to your local market

-0.0517* -0.0255 -0.114*** -0.122 -0.204

Your company’s Internet connection is not fast enough

0.0488** 0.0676** 0.0361 0.0439 -0.175

Clients abroad do not have a fast enough Internet connection

-0.0194 -0.0339 -0.0401 0.0466

Resolving complaints and disputes cross-border is too expensive

-0.000105 0.00820 0.0107 -0.0288 -0.255

Observations 1,376 701 344 223 91

45

Table A9. Barriers to cross-border e-commerce in the EU: Volume of purchases (1)Total (2)Micro (3)Small (4)Medium (5)Large Log(Size) -0.00134 Young 0.0262 0.0240 0.0377 0.000779 Channel: Provider website 0.00235 0.0119 0.000419 0.00234 -0.0639 Small platform -0.0198 -0.0104 -0.0205 -0.0401 0.00173 Large platform -0.0131 -0.0138 0.00147 -0.0135 -0.0375 EDI type 0.00999 -0.0163 0.0313 0.0224 -0.0320 Market: Goods to consumers -0.0371** -0.0307 -0.0677*** -0.0329 -0.0375 Goods to firms 0.0218 0.0149 0.0740*** -0.00739 -0.0419 Digital services to consumers -0.0260 -0.0165 -0.0183 -0.0880 -0.0873 Digital services to firms 0.0213 -0.00433 0.0442 0.0487 0.0805 Services to consumers 0.0325* 0.0265 0.0236 0.146*** 0.0611 Services to firms -0.0282** -0.0458** 0.00876 -0.0366 0.0252 Sector (wrt manufacturing): Wholesale and retail trade 0.0330* 0.0376* 0.0841** -0.0405 0.00508 Accommodation and food -0.0516* -0.0206 -0.0316 -0.212*** -0.154 Information and communication -0.0521*** -0.0381 -0.0137 -0.138*** -0.172* Barriers: Delivery costs are too high -0.0133 -0.0143 0.00507 -0.00984 -0.148** Payments to other countries are not secure enough

-0.0175 -0.0365* -0.0201 0.0427 0.0663

The product labelling has to be adapted 0.0201 0.0181 0.0331 -0.00701 0.0789 You lack the language skills for dealing with foreign countries

-0.0620*** -0.0534*** -0.0713*** -0.0874** -0.0345

Copyright prevents foreign suppliers from delivering to your country or makes it

-0.0162 -0.0222 -0.0270 0.0486 -0.0240

Foreign suppliers refuse to deliver to your country

0.0282 0.0280 0.0162 0.0156 0.0955

Resolving complaints and disputes cross-border is too expensive

-0.0743*** -0.0987*** -0.00560 -0.0577 -0.181***

You are concerned your data is not well protected when purchasing abroad

0.00691 0.0103 0.0359 -0.0680** 0.0666

For reasons of interoperability, you cannot use foreign products and/or services

-0.00542 -0.00968 -0.0130 0.0113 0.0313

Observations 3,325 1,941 841 400 142

46

Table A10. Sectors at the 2 digit NACE classification

Code Sector

10 Manufacture of food products

11 Manufacture of beverages

12 Manufacture of tobacco products

13 Manufacture of textiles

14 Manufacture of wearing apparel

15 Manufacture of leather and related products

16 Manufacture of wood and of products of wood and cork

17 Manufacture of paper and paper products

18 Printing and reproduction of recorded media

19 Manufacture of coke and refined petroleum products

20 Manufacture of chemicals and chemical products

21 Manufacture of basic pharmaceutical products

22 Manufacture of rubber and plastic products

23 Manufacture of other non-metallic mineral products

24 Manufacture of basic metals

25 Manufacture of fabricated metal products

26 Manufacture of computer, electronic and optical products

27 Manufacture of electrical equipment

28 Manufacture of machinery and equipment n.e.c.

29 Manufacture of motor vehicles, trailers and semi-trailers

30 Manufacture of other transport equipment

31 Manufacture of furniture

32 Other manufacturing

33 Repair and installation of machinery and equipment

45 Wholesale and retail trade and repair of motor vehicles and motorcycles

46 Wholesale trade, except of motor vehicles and motorcycles

47 Retail trade, except of motor vehicles and motorcycles

55 Accommodation

56 Food and beverage service activities

58 Publishing activities

59 Motion picture, video and television production, sound recording and music publishing activities

60 Programming and broadcasting activities

61 Telecommunications

62 Computer programming, consultancy and related activities

63 Information service activities

Figure A1: Structure of the survey

A1

Sell

online

A2

Buy

online

Firms

(8705)

- country

- sector

- size (empl, turn)

- age

- type (org)

- category (mkt)

- sales trend

Yes

(3945)

No

(4743)

Yes

(7156)

No

(1503)

Missing (17)

Missing (46)

Own website (3107)

Small platform (1093)

Large platform (1038)

EDI (906)

Q1

Channels

(3827)

D7 Share online

sales (3504)

D7

Share of offline sales (7729)

Q2a: Online domestic (3445)

Q2c: Online RoW (918)

Q2b: Online other EU (1739)

Q4a: Offline domestic (7601)

Q4b: Offline other EU (3586)

Q4c: Offline RoW (1763)

Barriers to EU cross-border sales:

Q6a1-Q6a17 - Delivery (507)

- Guarantees (349)

- Rules (259)

- Payments (239)

- Copyright (136)

- Taxation (243)

- Labelling (153)

- Language (222)

- Suppliers forbid (125)

- Suppliers restrict (115)

- Suppliers dif price (135)

- Data (220)

- Interoperability (171)

- Product specificity (147)

-Own connection (279)

- Clients connection (148)

- Disputes (351)

Q3

In which countries do you sell?

Q5

Motives (1665)

Obstacles Q6c1-Q6c10

- Digital skills (945)

- Rules (1086)

- Delivery (1343)

- Guarantees (1271)

- Suppliers forbid (689)

- Higher price (952)

- Suppliers restrict (628)

- Risk prices down (979)

- Damaged image (763)

- Connection (1080)

0% (1787)

≠ 0% (1816)

Used to sell (56)

Tried but gave up (52)

Considering (438)

Trying (152) Barriers to EU

cross-border sales:

Q6b1-Q6b17 - Delivery (200)

- Guarantees (148)

- Rules (114)

- Payments (78)

- Copyright (63)

- Taxation (100)

- Labelling (59)

- Language (81)

- Suppliers forbid (61)

- Suppliers restrict (49)

- Suppliers dif price (33)

- Data (59)

- Interoperability (64)

- Product specificity (47)

-Own connection (63)

- Clients connection (45)

- Disputes (158)

Never (967)

Own website (4875)

Small platform (2793)

Large platform (2644)

EDI (1404)

Q7

Channels

(6807)

D8 Share online

purchases (6361)

Q8a: Online domestic (5936)

Q8c: Online RoW (1326)

Q8b: Online other EU (3129)

Barriers to EU

cross-border purchases

Q10a1-Q10a9 - Delivery (979)

- Payments (573)

- Labelling (457)

- Language (496)

- Copyright (537)

- Suppliers refuse (534)

- Disputes (996)

- Data (743)

- Interoperability (425)

Q9

Motives

(2916) 0% (3221)

≠ 0% (3354)

Used to buy (207)

Tried but gave up (145)

Considering (717)

Trying (112)

Barriers to EU cross-border

purchases

Q10b1-Q10b9 - Delivery (305)

- Payments (170)

- Labelling (165)

- Language (172)

- Copyright (192)

- Suppliers refuse (202)

- Disputes (339)

- Data (211)

- Interoperability (130)

Never (1735)

N of firms using: 1 channel: 2265 2 channels: 965 3 channels: 439 4 channels: 158

Provided D7 ≠ 100% (3339)

DSM Firms Survey Structure of the data Number of firms in parenthesis

N of firms using: 1 channel: 3881 2 channels: 1422 3 channels: 1025 4 channels: 479

Q11

Homogeneous rules

(2614)

61% answers favourably

48

Figure A2: Online export concentration

.2.4

.6.8

1.2

.4.6

.81

.2.4

.6.8

1.2

.4.6

.81

.2.4

.6.8

1

0 .5 1 0 . 5 1 0 .5 1 0 .5 1

0 . 5 1 0 .5 1

BELG IQUE DANM ARK D EU TSCHL AND EL LADA ESPANA SU OM I

FRANCE IRELAND ITALIA LU XEM BOURG NEDERLAND ÖSTERREICH

PO RTUG AL SVERIG E UK BALGARIJ A CESKA REPUBLIKA EESTI

M AG YARO RSZAG LATVI A LIETUVA POLSKA RO MANIA SL OVENSKA REPUBLIC

SL OVENIJA H RVATSKACu

mu

lative

sh

are

of

on

line

exp

ort

s

Cumulative share of firmsGraphs by B Country

49

Figure A3a: Structure of online sales by target markets and firm size

Figure A3b: Structure of online sales by target markets and sector

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

Micro Small

Medium Large

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

Manufacturing Wholesale and retai l

Accommodation and food Information and communication

50

Figure A4a: Structure of offline sales by target markets and firm size

Figure A4b: Structure of offline sales by target markets and sector

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

Micro Small

Medium Large

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

Manufacturing Wholesale and retai l

Accommodation and food Information and communication

51

Figure A5a: Structure of online purchases by target markets and firm size

Figure A5b: Structure of online purchases by target markets and sector

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

Micro Small

Medium Large

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

0

20

40

60

80

100 0

20

40

60

80

100

020406080100

Domestic Cross-border

Rest of the world

Manufacturing Wholesale and retai l

Accommodation and food Information and communication

52

Figure A6: Revealed comparative advantage: online vs. offline

56 10

46

25

55

4518 32

58

56 3218 10

13

25

23

10

58

25 59254528

46

47

28 10

46

10

46

55

45

4646

2547

56

46

10

46

47

4646

28

46

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46

55

46

55

46

4747

102562

46

18

55

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464646

28

46

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33

23

4747

1018

46

18

4646

1445

46

59

55

59

23

46

27

46

476247

10

4646

45

55

62 58

4545 27

5523

4646

6247

45

46

6262

45

46

10

47

4545

46

4756 10

47

46

23

464646

45

5862

55

14

46

55

62 58

46

20

4762

23

10

46

4747

1056 5959

46

5625

46

5555

46

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4646

23

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55

4525

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46

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-1 -.5 0 .5 1 -1 -.5 0 .5 1 -1 -.5 0 .5 1 -1 -.5 0 .5 1

-1 -.5 0 .5 1 -1 -.5 0 .5 1

BELGIQUE DANMARK DEUTSCHLAND ELLADA ESPANA SUOMI

FRANCE IRELAND ITALIA LUXEMBOURG NEDERLAND ÖSTERREICH

PORTUGAL SVERIGE UK BALGARIJA CESKA REPUBLIKA EESTI

MAGYARORSZAG LATVIA LIETUVA POLSKA ROMANIA SLOVENSKA REPUBLIC

SLOVENIJA HRVATSKA

Sym

metr

ic R

eveale

d C

om

para

tive

Ad

van

tage

- O

nlin

e

Symmetric Revealed Comparative Advantage - Offline

53

Figure A7: Revealed vs. estimated comparative advantage

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54

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European Commission

Joint Research Centre – Institute for Prospective Technological Studies

Title: Barriers to Cross-border eCommerce in the EU Digital Single Market

Authors: Néstor Duch-Brown and Bertin Martens

Spain: European Commission, Joint Research Centre

2015 – 53 pp. – 21.0 x 29.7 cm

EUR – Scientific and Technical Research series – ISSN 1831-9408 (online)

JRC Mission As the Commission’s in-house science service, the Joint Research Centre’s mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new methods, tools and standards, and sharing its know-how with the Member States, the scientific community and international partners.

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