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ICT as Enabler of Exports

Patricia Kotnik1

University of Ljubljana

[email protected]

Eva Hagsten

Statistics Sweden

[email protected]

August 2013

Abstract

Based on linked firm-level datasets available within the ESSLait project, this study investigates the

role of ICT use in the exporting activities of firms for a large group of European countries. Both the

decision to export and the value of export are studied. The results indicate that in a number of

European countries there exists a positive relationship between ICT use and the exporting of firms –

where ICT use is measured by online presence, use of online transactions, ICT-intensive human

capital and proportion of employees with access to fast internet capacity. However, what ICT tool

matters seems to vary.

1 Corresponding author.

2

Introduction

This study investigates the role of ICT use in the internationalisation of firms, based on the linked

firm-level datasets made available within the ESSLait project. The results indicate that in a number of

European countries there exists a positive relationship between ICT use and the exporting of firms –

where ICT use is measured by online presence, use of online transactions, ICT-intensive human

capital and proportion of employees with access to fast internet capacity. However, what ICT tool

matters seems to vary across countries.

The advantages of ICT use for the exporting activities of firms may be manifold. By establishing online

presence, firms can connect with a large number of customers both fast and quite likely also cheap.

The firms can thus establish virtual branches throughout the world, without an investment in

physical assets abroad and without the use of intermediaries. Especially for SMEs, the strategic use of

the internet can be a form of foreign market entry by which they compensate for disadvantages

connected to size or weaker physical presence and by which they can access parallel foreign markets

more quickly. A number of marketing tasks can be fulfilled through ICT, like international advertising

as well as communication with customers and order management. The internet can also be used to

gather information on foreign markets, thus decreasing the costs of information collection.

Research on the economic impact of ICT related to globalisation has so far mainly focused on

multinationals and the role of trade in technology adoption. The role of ICT in the exporting

behaviour of firms remains relatively unexplored. Some evidence is building up, particularly in

international marketing and entrepreneurship literature, showing ICT as relevant for

internationalisation of firms, especially smaller ones. Existing research has predominantly used

relatively small samples, the respondents’ perceptions rather than hard facts when measuring

relevant variables or focused on groups of firms already biased toward specific characteristics. The

data at hand for our analysis originate from the uniquely linked firm-level information made

accessible within the pioneering work by the ESSNet projects on ICT Impacts2. The project allowed us

to run the regressions for each country on the complete sets of data available to national statistical

offices, including production statistics, innovation data as well as ICT use data. Our study will add to

this area of research by empirically investigating the role of ICT in export activities of firms.

Literature review Cross-border e-commerce is gaining in importance. The Institute for Prospective Technological

Studies (IPTS) estimates that out of the EUR 240 billion total of EU e-commerce in 2011, EUR 44

billion constituted cross-border trade among EU Member States and EUR 6 billion relates to imports

from outside the EU (Martens, 2013). However, it is not only online trade that can contribute to

exports through the use of ICT, internet technology has also been increasingly integrated into

marketing activities. The internet can be used to establish direct customer contact and thus avoid

traditional market intermediaries (Lohrke, Franklin, & Frownfelter-Lohrke, 2006) and customer

service and support functions can be strengthened. Additionally, the internet may facilitate market

information gathering (Borges, Hoppen, & Luce, 2009), about competitors, the particular markets

2 Eurostat Grant agreements 49102.2005.017-2006.128 (ICT Impacts), 50701.2010.001-2010.578 (ESSLimit) and

50721.2013.001-2013.082 (ESSLait).

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and above all on the customers. Furthermore, it facilitates the build-up of valuable customer-related

information (for example by web-based market surveys, customer satisfaction measurements, and

by data-mining techniques applied at web site visits and transactions). Internet-related information

can also be used to adjust the marketing mix, as for example the methods used by Amazon when

suggesting new products to customers (Prasad, Ramamurthy, & Naidu, 2001).

Reuber and Fischer (2011) propose that for successful exporting you need both back-end integration

of a firm's online technology as well as front-end functionality. The former implies that web

applications are linked with back-office databases and that better analysis of data gives the firm an

advantage in discovering international opportunities. The front-end functionality refers to online

product information, transaction processing and the ability to customize the online experience for

specific markets. To sum up, ICT can be used for relationship building, information search and

provision, and as an online sales channel. When entering foreign markets, the use of internet may

thus result in lower costs of entry.

The empirical evidence on the role of the ICT in trade has so far been scarce. Freund and Weinhold

(2004), using data on bilateral trade from 1995-1999, found that growth in the number of websites in

a country helps to explain export growth the following year. Portugal-Perez and Wilson (2012) have

studied the impact of various types of infrastructure on the export performance of developing

countries and showed that an ICT infrastructure is relevant and that its impact on exports seems to

be increasingly important the richer a country becomes. Firm-level studies on the impact of ICT use

on exports can mostly be found in international marketing literature. Their empirical results imply

that online activities have an impact on export sales (Bennett, 1997), but with the emphasis that it is

how the internet technology is used that matters (Anna Morgan-Thomas & Bridgewater, 2004) and

that the combination with an offline strategy drives the export performance (Sinkovics, Sinkovics, &

Jean, 2013). Morgan-Thomas (2009) has distinguished between different types of online capabilities

and empirical results showed that the key benefit to internationalizing firms lies in supporting

customer relationships and not in online sales.

All of these firm-level studies suffer from a disadvantage of using the respondents’ subjective

evaluations of export performance as a measure of the dependent variable. A few recent studies

based on more reliable data include an exploration on the speed of internationalisation of SMEs that

has established a strong relationship between ICT use and rapid growth on international markets

(Anna Morgan-Thomas & Jones, 2009) and a study of the use of eBay sales data from five countries

that shows how this platform has opened up export markets to SMEs at lower cost (Martens, 2013,

p. 5).

Our research will add to this topic by empirically investigating the relationship between ICT use and

exporting of firms (both in terms of export propensity and export value) while controlling for other

determinants of exports. To capture different online capabilities that might affect exporting we study

the following ICT use variables:

i) Online presence (having a website), enabling a firm to share information with customers as

well as communicate with them,

ii) Online transactions (e-sales), facilitating economic exchange between buyers and sellers,

like customer ordering and possibly payment,

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iii) ICT-intensive human capital (proportion of ICT educated employees) and proportion of

workers with access to fast internet capacity, capturing complementary ICT resources

beneficial for exporting activities.

Conceptual model and estimation method

As Roper et al. (2006) point out, we can observe two conceptual approaches to modelling the

determinants of export performance, the first one grouping together productive resources of firms

that determine firms’ competitive advantage in international markets (labour, capital, firm size, and

also being a part of a group or in foreign ownership) and the second one focusing on the quality of

firms’ products or services as a basis of competitive advantage which is connected to technology

(development and implementation of new products and processes). In one of the seminal

contributions to empirical firm-level evidence on the export decision of firms, Bernard and Jensen

(2004) consider the effects of several characteristics of the firm as well as entry costs, effects of

spillovers from exporting activities of other firms in the same industry or region, and government

export promotion expenditures. They find that, in addition to favourable exchange rate shocks, size,

productivity, labour quality, ownership structure and introduction of product innovation, increase

the probability that a firm exports. Furthermore, they find that past successes in the export markets

are important for entry into exporting.3 Harris and Li (2009) examined determinants of exporting in

terms of entry into exporting as well as the export propensity and have captured technology by two

specific indicators - R&D activity and R&D undertaken continuously. They have reached conclusions

similar to those of Bernard and Jensen (2004) on the determinants of entry into exporting activities,

whereas the second part of results showed a strong negative relationship between size and export

intensity once the firm has entered the international markets (Harris & Li, 2009). Sector-specific

characteristics also matter in explaining whether exporting occurs or not and export performance.

Taking into account the findings of previous research on international trade, our model will include a

number of indicators of firms’ productive resources and innovation activities augmented by the ICT

use variables. To answer the question if a relationship exists between ICT use and export propensity

of a firm, we have estimated a probit model. The following model is adopted for the decision to

export:

where refers to export status (current and lagged measure, respectively), is a set of

indicators of firms’ productive resources (labour productivity, capital-labour ratio, firm size),

relates to firm’s other characteristics (age, foreign ownership), and is a set of indicators of

innovation activities (dummy for product and process innovations). is our set of ICT

indicators and illustrates the proportion of human capital in firms (depending on the

availability of data in each country: HKITpct as a proportion of employees with post-upper secondary

education in maths, physics, engineering, and IT, and HKNITpct capturing generally skilled human

3 Entering foreign markets will be connected to costs, so past export behaviour will be connected to probability

of exporting today.

5

capital; or HKpct as a proportion of employees with post-upper secondary education; or wages,

respectively). captures spillover effects due to exporting activities of firms in the industry

the firm belongs to, and is a vector of industry and year dummies.

In the second part of the analysis we model the determinants of exporting with value of exports as

dependent variable.4 We use a model similar to the one above except that the lagged export

measure, capital/labour ratio and export spillovers are excluded. This model has been estimated on

the full sample that includes exporters and non-exporters. Ordinary least squares (OLS) estimates will

be reported.

In both cases, pooled and unbalanced samples are used for each country, sourced from datasets

spanning over a number of years (the actual period depends on the availability of data in each

country). Due to the nature of available datasets it is not possible to include firm fixed effects in the

model. The simultaneity issue is alleviated by using lagged values of the explanatory variables in both

of the models. The main advantage of our empirical analysis is that it allows us to compare indicators

across countries based on harmonised datasets and identically performed estimating procedures.

Build-up and description of the dataset The data used in this study have been made available through the ESSLait (formerly ESSLimit-ICT

Impacts) Project. The underlying nationally linked datasets have been built up specifically for the

purpose of the project and include firm-level information for fourteen European countries, mainly

drawn from the business (BR), trade and education registers as well as from the surveys on

production (PS), ICT usage (EC) and innovation activities (IS) in firms. This linking procedure allows

data to appear in dimensions not earlier available. The smallest and most exclusive of the linked

datasets is the PSECIS, where data from all sources mentioned above are included.

Since firm-level data is classified and thus not feasible to stack in one single cross-country dataset, an

alternative method to reach the data was needed. Hence, the project chose to apply the means of

the Distributed Microdata Approach (DMD, Bartelsman, 2004; Eurostat, 2008), where code modules

for analyses and aggregation of indicators have been run on all national firm-level datasets locally.

Indicators are then aggregated to a level where disclosure becomes less of a problem and pooled

into a cross country dataset. This cross country micro-aggregated dataset is called the Micro

Moments Database. The DMD approach relies heavily on careful metadata analysis as a means to

harmonise the underlying datasets across countries. Detailed information about this procedure

within the ESSLimit-ESSLait project is presented in Gaganan (2012) and Hagsten et al. (2013).

4 The value of exports is chosen as a dependent variable over export intensity for two reasons. As a ratio of

exports and gross output, the changes in the value of export intensity do not necessarily imply that exports has

changed since gross output could also be reason for the change. Also, errors should be closer to normal

distribution in the case of export values as compared to export intensity values.

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A description of the variables highlighted in the model specification is described in Table 1, together

with information on how they have been sourced. Nominal values have been deflated by EUKLEMS

or WIOD 2-digit NACE 1 price series where needed.5

Table 1 Variable description and sources

Variable Description and source

EX Export decision Exporter = 1 (BR or PS)

Export value Nominal gross exports (Trade statistics, PS or VAT

register)

R Labour productivity (LPV) Nominal value added per employee (PS)

Capital-labour ratio (K/L) Capital stock or book value per employee (PS)

Firm size (E) Number of full time employees (BR or PS)

C Age (AGE) Firm age (BR)

Foreign ownership (FRGN_OWN) Foreign ownership = 1(BR or PS)

I Product innovation (INPD) Product innovator = 1 (IS)

Process innovation (INPS) Process innovator = 1 (IS)

ICT Online presence (WEB) Having website = 1 (EC)

Online transactions (e-sales, AESELL) Having e-sales = 1 (EC)

Proportion of employees with access

to broadband (BROADpct)

% of internet-enabled employees with access to

broadband (EC)

HK Skilled human capital:

1.ICT-intensive human capital

(HKITpct)

and

Non-ICT intensive human capital

(HKNITpct)

or

2. Human capital (HKpct)

or

3. Wages

Either:

Proportion of post upper secondary ICT educated

employees (Education Register, Occupation register or IS,

ISCED: maths, physics, engineering or ICT) and

Proportion of post upper secondary generally educated

employees

or

Proportion of employees with post upper secondary

education, or

Wage bill (PS) - if educational achievement not available

EXspill Export spillovers Ratio of exporters to total number of firms in an industry

(2-digit NACE); (Trade statistics, VAT)

S Time fixed effects

Industry fixed effects

Year (BR, PS, IS, EC)

Industry code (BR)

Note: BR=business register, PS=production survey, SBS or similar, IS=innovation survey (Community Innovation

Survey) and EC=E-commerce survey (ICT usage in firms). Data on exports derived from either the value added

tax register (VAT) or from Extrastat.

When linking a smaller sample survey to registered data or to a larger dataset the risk of selection

bias remains minimal. However, when more than one sample survey is included, it is reasonable to

assume that the representativeness can be affected. The sampling strategies underlying the

microdata in use here are developed by the national statistical agencies with the purpose of giving

good macro estimates, meaning that an emphasis is put on including as much of the production

value as possible rather than all firms. This implies that the proportion of employees covered is larger

than the share of firms and that there might be a certain bias towards larger firms, which generally

5 www.euklems.net and www.wiod.org.

http://www.euklems.net/

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are the only ones surveyed each time. In Table 2 an example of survey overlaps is given, based on

number of firms (N) as well as employment (E). The values in the table indicate the proportion of

firms – and the proportion of employment, respectively – from the PSEC sample that is included in

the PSECIS sample (where PSEC refers to dataset including production statistics and ICT use data, and

PSECIS to the sample where data on innovation is added).

Table 2 Survey overlaps in 2008

Country N_PSECIS/PSEC E_PSECIS/ PSEC

AT 0.22 n.a.

DE n.a. n.a.

DK 0.33 0.77

FI 0.25 n.a.

FR 0.33 n.a.

IE 0.26 0.30

IT 0.49 0.68

LU 0.15 n.a.

NL 0.55 n.a.

NO 0.40 0.62

SE 0.49 0.60

SI 0.22 0.64

Note: Due to disclosure issues, information on employment cannot be revealed for all countries.

Source: ESSLait dataset

Because the model specification chosen for the estimations requires the inclusion of innovation

variables, the smallest linked dataset PSECIS has to be used. The characteristics of this dataset are

presented in Table 3. The firms in this uniquely linked dataset are larger, with higher human capital

and higher labour productivity when compared with the population of firms (see Table I in the

Appendix). Our sample also somewhat overestimates the use of ICT as measured by the chosen

variables, especially so in the case of e-sales (Table I in the Appendix). However, according to Fazio et

al (2006) marginal analyses are not particularly sensitive to a certain degree of selection bias. The

industry structure of our PSECIS sample is similar to that of the population, with manufacturers being

a bit overrepresented. It is the manufacturing industry that contributes the most to total exports in

all countries (Table III in the Appendix).

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Table 3 Features of the PSECIS sample in 2008

Manufacturing and services firms

Note: The proportion of total exports per industry is only given for those countries where information is

available for both exports of goods and services. An asterisk (*) means exports of goods only. The export

intensity for Finland refers to 2007. XI signifies export intensity of exporters.

Source: ESSLait PSEC and PSECIS dataset

The linked datasets can be split in different sub-industries or by other firm characteristics.

Unfortunately, this also means that certain disclosure issues may appear, due to the size of the

sample. Because of this the estimations will be performed mainly on the total business sector,

including manufacturing as well as services firms. In addition to this, we show some general

descriptives by groups of industries to give a broader picture of the exporting activities of firms. For

this specific purpose the EUKLEMS alternative industry hierarchy will be used6, which separates the

ICT producing sector Electrical Machinery, Post and Telecommunications (Elecom) from the ICT users

in Market Services exclusive of Electrical Machinery, Post and Telecommunications (Mserv) and

Manufacturing exclusive of Electrical Machinery, Post and Telecommunications (MexElec), in the rest

of the paper referred to as ICT producing, manufacturing and services industries. These descriptives,

presented in the next section, are based on the larger samples (PS, PSEC and PSIS, respectively) and

are shown by export status of firms (for the latest available data in our datasets).

6 See www.euklems.net.

Country AT FI FR IE IT LU NO SE SI UK

XI 33 39 25 198 20 50 18 54 71 6

LPV (in 000 EUR) 82 62 77 89 59 66 79 92 35 87

Size (E) 406 301 1014 112 323 120 256 309 315 2173

INPD 48 46 50 35 34 38 33 44 35 37

INPS 49 49 49 42 38 37 27 39 40 22

WEB 92 97 82 80 78 82 94 92 86 97

AESELL 32 38 36 34 10 20 48 32 23 43

BROADpct 48 62 42 23 34 54 67 59 44 47

HKpct 30 17 23 17 15 11

HKNITpct 0 13 15 15 8 6

HKITpct 0 16 3 8 9 5

W/E (in 000 EUR) 55 42 53 46 43 53 46 60 23 33

Observations 762 907 2669 645 10842 330 1318 1546 454 903

Per cent of total exports:

Elecom 10 13 1 17 11 10

MServ 23 3 20 20 10 20

MexElec 67 84 79 63 80 70

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Cross-country comparisons of exporting and non-exporting firms

First, we start by comparing the export participation rate and export intensity of firms across

industries and countries (Table 3). The proportion of exporters is largest in the manufacturing sector,

with the ICT producing firms not far behind. Not unexpectedly, market services engage

internationally to a much lower degree. A comparison of the share of export sales in gross output,

that is, the export intensity, also follows this pattern. However, in some countries exporting services

firms earn a substantial share of their sales on foreign markets too, especially so in small open

economies like Slovenia, Luxembourg, Ireland, Sweden and Denmark. The relative picture needs to

be interpreted with certain care for Austria, Italy, the Netherlands and Norway, since only exports of

goods have been made available for the analyses of these countries. In the case of Ireland the export

intensity of manufacturing sector is unusually high (implying that exports exceed gross output.) This

is explained by the phenomenon of merchanting, where production and sales are not necessarily

reported in one and the same country. Table 4 also shows exporter productivity premium for each

country and sector. The data for most of the countries confirm the conclusions from theoretical and

empirical work that exporters are more productive than non-exporters. The productivity gap

between exporting and non-exporting firms seems to be the widest in ICT producing industries.

Table 4 Export participation, export intensity and productivity premium in 2009

Sorted by export participation rate

MexElec Mserv Elecom

2009 XP XI PB

2009 XP XI PB

2009 XP XI PB

LU 0.71 0.89 0.86

LU 0.76 0.47 1.17

LU 0.77 0.81 0.54

FR 0.66 0.36 1.52

FR 0.37 0.17 1.23

FR 0.66 0.35 1.71

IE 0.49 1.32 1.37

SI 0.16 0.56 1.46

IE 0.56 0.26 1.45

IT* 0.47 0.34 1.75

IT* 0.14 0.06 1.53

IT* 0.4 0.16 2.06

SI 0.24 0.7 1.49

UK 0.13 0.08 1.77

SE 0.27 0.55 2.08

SE 0.22 0.56 1.39

IE 0.11 0.4 1.56

FI 0.26 0.55 1.91

FI 0.21 0.52 1.29

SE 0.1 0.45 1.45

SI 0.24 0.48 1.06

AT* 0.19 0.47 2.1

NO* 0.06 0.06 1.91

AT* 0.19 0.32 1.73

NO* 0.16 0.3 1.67

AT* 0.05 0.11 1.39

UK 0.17 0.21 0.73

UK 0.14 0.33 1

FI 0.04 0.19 1.31

NO* 0.15 0.17 2.46

Mean 0.29 0.48 1.20

Mean 0.16 0.21 1.23

Mean 0.31 0.32 1.31

Note: XP signifies export participation rate, XI export intensity of exporters whereas XB is exporter premium

(LPV of exporters as compared to LPV of non-exporters). Data on exports include exports of goods as well as

services for all countries except those denoted with an asterisk (*) where only data on trade in goods is

available. Values for Finland refer to 2007.

Source: ESSLait PS Dataset

When exporters are compared with non-exporters in terms of their innovative activities (values in

the graph indicate a difference in percentage share of firms engaged in innovative activities between

exporters and non-exporters, where shares range between 0 and 1), the data confirm that innovative

activities are more frequent among exporters, especially so in the ICT-producing industries. Similar

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holds for process innovators where again the exporters significantly outperform non-exporters, with

the difference being most noticeable in the case of ICT-producing industries and least pronounced in

services firms.

Figure 1 Difference in shares of innovative firms between exporters and non-exporters by industry,

2008

Note: Data includes exports of goods as well as services for all countries except those denoted with an asterisk

(*) where only data on trade in goods are available.

Source: ESSLait PSIS Dataset

Next, we turn to the human capital of firms, more specifically to the ICT-intensive human capital as

the fourth important ICT variable to investigate. The ICT-intensive human capital, measured as a

percentage share of employees with post upper secondary education in the fields related to ICT, is

illustrated in Figure 2. Exporting firms have higher levels of formally educated ICT specialists in all

countries where this information is available, with the French and UK ICT producers being the only

exception. There is also an easily observable pattern across industries. Almost in all cases, the ICT

producers have higher shares of ICT specialists and this pattern is particularly visible in the Nordic

countries with Finland as leader. Among the UK exporting firms, a larger concentration of formally

educated ICT specialists is found in the services sector.

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Figure 2 Proportion of highly ICT educated employees in 2009, by export status and sector

Note: Norwegian data include exports of goods only. Information on formal educational achievement is only

available for this sub-group of countries.

Source: ESSLait PS Dataset

Online presence of firms, measured by firm having a website, is an indicator of ICT use that is coming

close to a level of saturation almost everywhere (Figure 3). In some countries, for example in Finland,

UK and Sweden, the share of firms having a website is reaching 100 per cent, especially in the ICT

producing industries. However, the proportion of firms with online presence is still higher among

exporters, particularly so in countries where saturation has not yet been met and in the

manufacturing and services firms.

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Figure 3 Proportion of firms with online presence (website) in 2009, by industry



Source: ESSLait PSEC Dataset

Recently, the proportion of firms with systems for online transactions has reached significant levels

(Figure 4), especially in countries like UK, Norway, Austria, Sweden and Finland. E-sales seem to be

important for all industries, and especially so for exporting firms. With the exception of UK, the share

of firms with e-sales is larger among exporters than non-exporters and this gap seems to be more

pronounced for manufacturing and services firms in countries where e-sales are more widespread.

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Figure 4 Proportion of firms with online transactions (e-sales) in 2009, by industry




Turning to the last characteristics of ICT use explored here, the proportion of broadband internet-

enabled employees, we can see that this indicator is the largest in the ICT producing and services

industries. Employees in manufacturing are generally connected to a lower degree than in other

firms. Again, the same countries as mentioned earlier in relation to high ICT intensity, Sweden, the

UK, Norway and Finland are establishing this pattern. Exporters from manufacturing and especially

services sector have in most countries much higher ICT connectivity than non-exporters. Among the

non-exporting firms, the Netherlands has a high level of broadband internet-enabled employees. In

the ICT producing industry the differences between exporters and non-exporters are smaller or

sometimes even reversed.

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Figure 5 Proportion of broadband internet-enabled employees in 2009




Summing up, on the basis of descriptive data, we have found that exporters are higher in labour

productivity, the likelihood to be innovative, the proportion of ICT intensive human capital, the

probability to engage in e-sales; the proportion of broadband internet-enabled employees (at least in

manufacturing and services sector) and the propensity to have a website, where the latter is more

pronounced in countries where saturation in online presence has not yet been reached. These

differences between exporters and non-exporters were observed on the datasets that are not the

same as the ones used for the regression estimates. As pointed out in the previous section, the firms

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in our sample will be somewhat larger, with higher labour productivity and human capital, and with

above-average ICT use therefore we can expect that the differences between exporters and non-

exporters will be smaller than observed here. This would imply that the conclusions based on our

results will underestimate the strength of the relationship between ICT use and exporting in the total

population of firms since they will be obtained on a sample of firms with above-average

performance.

Results and discussion

In this section we present and discuss the main results from our estimations. On the whole, ICT is

found to be related to the exporting activities of firms in most countries but what ICT tools matter

seems to vary.

Let us first comment on the export decision estimates (Table 5), pulling together the common results

for most countries included in our analysis. The online presence of a firm (having a website) increases

the probability of exporting in Austria, Ireland, Italy, Norway and Slovenia, while the e-sales variable

is statistically significant (with a positive sign) in Finland, France, Slovenia and Sweden. Firms with

higher proportion of broadband Internet enabled employees are more likely to export in Norway and

the United Kingdom.

When trying to interpret these results in the context of multi-country comparison we suggest that

having a website could be relevant for exports in those countries where this ICT tool has not yet

reached saturation. Once the saturation is reached and almost all firms have a website, other ICT

tools become more important, for example e-sales as is the case in Finland. In two countries with a

positive relationship between e-sales and exporting – Finland and Sweden – it is also the case that

ICT-intensive human capital is significant which might indicate a need for ICT-schooled employees to

accompany efforts in e-sales. Lastly, broadband Internet enabled employees are correlated with

exports, even after controlling for human capital of the firm, in two countries – Norway and the

United Kingdom – and both of these countries rank very highly in this type of ICT use when compared

with others (see Figure 5). This might suggest that a critical mass of employees to which ICT

resources are allocated is needed to have an effect on the export status. It might also be the case

that this variable captures efforts connected to organizational innovation. If so, the results would

indicate that firms with organizational innovation are more likely to export.

The most consistent result between countries among the control variables is that firms’ past export

behaviour (capturing sunk costs connected to entering foreign markets) increase the probability that

a firm exports. Other variables are statistically significant for a number of countries but not all. Larger

firms are more likely to export (with the exception of Ireland where smaller firms appear more likely

to export) as well as firms with product innovations. In the case of France, Sweden and Italy firms

with higher human capital have higher probability of exporting. In the case of Sweden both, ICT-

intensive and generally highly skilled human capital are significant, whereas in France only the latter

appears to be relevant.7 Export spillovers also appear to be relevant in most countries. Firms that

belong to industries with a higher proportion of exporters are more likely to export (with the

7 In Italy wages were used as a proxy for human capital, so a distinction between types cannot be made.

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exception of Luxembourg where the relationship is negative). Foreign owned firms are more likely to

export, except in Ireland and Sweden.

Turning to the specification explaining the value of exports (Table 6), the results suggest that in some

countries having a website is positively correlated with the value of exports (this holds for Austria,

France and Italy). Firms with e-sales have higher exports in Austria, France and Sweden. Finland is

again an exception, with results implying that firms with e-sales have lower exports. In 5 countries it

holds that firms with higher level of broadband Internet enabled employees have higher exports (in

Ireland, Italy, Norway, Sweden, and the United Kingdom). Austria presents an exception here (with a

statistically significant but negative coefficient). ICT-intensive human capital is highly statistically

significant and with high regression coefficients in 3 countries (where two of these – Finland and

Sweden – lead in this respect, having the highest proportion of this type of human capital amongst

countries for which data is available). The results for the last two variables (the broadband Internet

enabled employees and ICT-intensive human capital) could imply that higher export sales are

dependent upon the resources allocated to development of “online capabilities”, as Morgan-Thomas

(2009) called them. Both, human resources with IT skills and the equipment that is needed, seem to

be relevant.

The results for the control variables show, not surprisingly, that larger firms and firms with higher

labour productivity are found to have larger exports. Firms that have introduced product innovations

export more (as indicated by the results for Finland, France, Italy, Norway and Slovenia). Introduction

of process innovations also seems relevant in some countries (Austria, France, Italy, Slovenia and the

United Kingdom), where Finland provides an unusual example of a negative correlation. The older

the firms are the higher the exports – with the exception of Italy and also Ireland. Firms with foreign

ownership have higher values of exports in all countries for which this variable is available.

In all countries except Slovenia and the United Kingdom we find significant relation between human

capital and export sales. Let us comment first on the results for countries for which data on both

types of human capital exists. In Finland and Sweden both ICT-intensive and generally highly skilled

human capital are correlated with higher exports; in France only the latter is statistically significant

and in Norway the firms with higher ICT-intensive human capital export more whereas firms with

higher generally skilled human capital export less. In all countries where wages are used as a proxy

for human capital the results show a positive and significant correlation. Based on these results we

can conclude that a strong relationship exists between exports and human capital.


Table 5 Determinants of exports decision (pooled sample, 2002-2009)

Note: All explanatory variables (except age and ownership) are lagged one year. # means information only available for exports of goods. Levels of significance: 1% =*** 5%=** and 10%=*. Source: ESSLait PSECIS dataset

Dependent variable:

Exporting undertaken or not AT FI FR IE IT LU NO SE SI UK

Lagged exports 0.590*** 0.877*** 1.245*** 1.020*** 1.387*** 4.444*** 0.466*** 0.850*** -0.995 1.032***

(0.149) (0.097) (0.107) (0.244) (0.034) (1.346) (0.108) (0.203) (0.920) (0.067)

Log labour productivity (LPV) -0.000 -0.000 -0.000 0.001** -0.000 0.001 -0.000*** 0.000 0.004* 0.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.002) (0.000)

Log employment (E) 0.254*** 0.162*** 0.051* -0.140** 0.097*** 0.144 0.270*** 0.085 0.133 0.005

(0.078) (0.042) (0.029) (0.070) (0.015) (0.112) (0.039) (0.055) (0.109) (0.038)

Capital/labour ratio (K/L) -0.002 -0.000 -0.000* -0.000 -0.000*** -0.000 -0.000

(0.004) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

ICT-intensive human capital (HKITpct) 0.769* 0.546 0.377 4.427*** 0.322

(0.414) (0.346) (0.396) (1.402) (0.336)

Non-ICT intensive human capital (HKNITpct) -0.259 1.703*** -0.578* 1.282** -0.004

(0.449) (0.309) (0.317) (0.627) (0.206)

Human capital (HKpct) 1.804

(1.504)

Log wages (W) 0.418 0.100 0.341*** -0.738*

(0.272) (0.159) (0.055) (0.442)

Product innovation (INPD) 0.343** 0.197* 0.119 0.404** 0.102** -0.271 0.312*** 0.006 -0.152 0.088

(0.166) (0.101) (0.091) (0.173) (0.049) (0.270) (0.092) (0.151) (0.401) (0.093)

Process innovation (INPS) 0.152 -0.064 -0.085 -0.055 0.028 -0.045 0.153* -0.128 0.067 -0.081

(0.164) (0.096) (0.087) (0.162) (0.044) (0.264) (0.090) (0.153) (0.380) (0.102)

Firm has website (WEB) 0.494* -0.150 0.058 0.576*** 0.144*** 0.012 0.334*** 0.522 0.948** 0.265

(0.283) (0.188) (0.108) (0.157) (0.043) (0.261) (0.126) (0.492) (0.423) (0.231)

Online transactions (AESELL) 0.043 0.305*** 0.219** -0.012 0.056 0.135 0.018 0.318* 1.513*** 0.112

(0.157) (0.099) (0.089) (0.134) (0.068) (0.274) (0.075) (0.150) (0.428) (0.092)

% of emp. with access to broadband (BROADpct) -0.137 -0.091 0.190* 0.086 0.127* 0.137 0.366*** 0.371 -0.473 0.568***

(0.259) (0.165) (0.113) (0.275) (0.075) (0.434) (0.127) (0.230) (0.641) (0.126)

Age 0.001 0.001 0.004 -0.000 -0.000 0.027 0.010*** 0.010

(0.002) (0.002) (0.004) (0.001) (0.003) (0.010) (0.018) (0.006)

Foreign ownership 0.180* 0.225** 0.105 0.422*** 0.265 1.292** 0.161**

(0.107) (0.094) (0.166) (0.084) (0.164) (0.572) (0.081)

Export spillovers 1.315** 1.002*** 0.491 0.080 1.079*** -6.943** 1.296*** 1.691*** 6.755*** 2.202***

(0.517) (0.320) (0.353) (0.763) (0.126) (3.309) (0.388) (0.622) (2.538) (0.395)

Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes


Table 6 Determinants of exports value (pooled sample, 2002-2009)

Note: All explanatory variables (except age and ownership) are lagged one year. # means information only available for exports of goods. Levels of significance: 1% =***

5%=** and 10%=*.

Dependent variable:

Value of exports (log) AT FI FR IE IT LU NO SE SI UK

Log labour productivity (LPV) 0.000*** 0.000*** 0.002*** 0.000 0.000*** 0.002*** 0.000*** 0.000*** 0.011*** 0.003***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000)

Log employment (E) 0.795*** 1.228*** 0.874*** 1.199*** 1.052*** 1.106*** 0.644*** 0.723*** 1.029*** 0.770***

(0.064) (0.049) (0.038) (0.073) (0.016) (0.092) (0.053) (0.047) (0.056) (0.062)

ICT-intensive human capital (HKITpct) 3.352*** 0.161 3.906*** 2.711*** 0.560

(0.481) (0.401) (0.668) (0.497) (0.395)

Non-ICT intensive human capital (HKNITpct) 2.371*** 3.664*** -2.124 4.391*** 0.084

(0.627) (0.368) (0.553) (0.588) (0.358)

Human capital (HKpct) 0.610

(0.744)

Log wages (W) 1.655*** 1.173*** 1.292*** 0.720**

(0.255) (0.111) (0.070) (0.350)

Product innovation (INPD) 0.194 0.310*** 0.386*** 0.240 0.416*** -0.119 0.623*** 0.052 0.276* -0.164

(0.138) (0.109) (0.106) (0.175) (0.047) (0.211) (0.116) (0.106) (0.155) (0.131)

Process innovation (INPS) 0.420*** -0.171* 0.234** -0.078 0.114*** -0.210 0.134 0.056 0.298 0.319

(0.123) (0.104) (0.100) (0.170) (0.044) (0.202) (0.114) (0.100) (0.153)* (0.133)**

Firm has website (WEB) 0.658*** -0.127 0.410*** -0.084 0.552*** 0.109 -0.128 -0.079 0.162 -0.099

(0.279) (0.208) (0.152) (0.182) (0.058) (0.221) (0.211) (0.372) (0.241) (0.256)

Online transactions (AESELL) 0.663*** -0.355*** 0.317*** -0.144 0.014 0.158 0.029 0.386*** -0.047 -0.105

(0.105) (0.107) (0.095) (0.137) (0.057) (0.229) (0.108) (0.099) (0.136) (0.139)

% of emp. with access to broadband (BROADpct) -0.475** -0.281 0.036 0.548* 0.129 0.602* 0.418** 0.666*** 0.177 1.592***

(0.236) (0.199) (0.137) (0.281) (0.087) (0.327) (0.195) (0.183) (0.320) (0.218)

Age 0.006** 0.006*** -0.007* -0.005*** -0.001 0.007 0.019*** 0.017***

(0.003) (0.002) (0.004) (0.001) (0.004) (0.007) (0.006) (0.006)

Foreign ownership 0.504*** 0.581*** 0.898*** 0.632*** 0.309*** 0.699*** 0.321***

(0.107) (0.094) (0.159) (0.106) (0.097) (0.153) (0.116)

Degrees of freedom 1385 1666 2528 558 14171 492 2468 1564 686 1295

R2 .613 .508 .519 .657 .593 .518 .496 .582 .674 .439

Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

19

Our results suggest various avenues for future research. One of them is related to the limitations of

our study. The Distributed Microdata Approach that we are using to obtain estimates for a large

number of countries based on the same model specifications does not make it possible to use more

advanced econometrics that the access to each single dataset and the remote control allows.

Therefore we did not address the part of endogeneity that stems from unobserved firm

heterogeneity, but we did alleviate simultaneity issue to a certain extent by using lagged values of

explanatory variables in our model. Exports were found to be a significant determinant of ICT

adoption (see Giunta & Trivieri, 2007; Haller & Siedschlag, 2011), for example, so this issue needs to

be addressed. Using firm fixed effects in our case would be difficult since the datasets that include

innovation as well as ICT use variables resulted in a small unbalanced panel of firms that is not large

enough for this purpose. To explore the effects of ICT on exporting activities we therefore propose

further studies where this issue would be addressed. In our study we have instead built upon the

substantial advantages that the Distributed Microdata Approach brings, i.e. an opportunity to make

detailed and country specific adjustments to the datasets so that the outcome represents

harmonised data extracted by a set of identical codes. The resulting estimates are fully comparable

across countries and can be interpreted as comparable indicators of relationships between different

variables.

Another interesting avenue for further research would be to examine the reasons behind the

differences in the impact of ICT on export activities between countries. We have offered some

attempts at an explanation but this question needs to be explored further using appropriate

methodological approaches. Also, a number of question arises that could be explored through single

country studies. For example, we have found that in a number of countries e-sales do not contribute

to exporting. It would be interesting to see whether this is so because of the barriers for online

transactions (a topic addressed by EU Digital Agenda) or whether reasons lie elsewhere. Including the

data on export markets in the analysis would allow exploring to what extent are the effects of ICT use

in exporting activities affected by a wide variation in the e-commerce purchases among countries.

And lastly, the complementarities between ICT variables that we have explored and other variables

would be interesting to study.

20

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22

Appendix

Table I. Production and human capital variables in joint samples

Per cent or Euro, thousand (*)

PSEC 2008


HKpct

24 17

20 15 13 12

HKNITpct

14 14

15 9

7

HKITpct

10 3

5 6

5

LPV* 64 58 66 77 58 64 62 77 32 84

XI 26

24 182 18 35 14 54 63 9

W/E* 48 37 51 39 41 45 39 55 21 34

Nobs 2963 3299 7572 2457 22448 1618 3262 2932 1614 2026

PSECIS 2008


HKpct

30 17

23 17 15 11

HKNITpct

13 15

15 8

6

HKITpct

16 3

8 9

5

LPV* 82 62 77 89 59 66 79 92 35 87

XI 33 39 25 198 20 50 18 54 71 6

W/E* 55 42 53 46 43 53 46 60 23 33

Nobs 762 907 2669 645 10842 330 1318 1546 454 903

Note: PSEC means firm level linked information from business register, production survey and ICT usage survey.

When the innovation survey is included the dataset is called PSECIS. Source: ESSLait PSEC and PSECIS datasets

Table II. ICT usage variables in joint samples

Per cent

PSECIS 2008

PSEC 2008

Country AESELL BROADpct WEB Nobs

Country AESELL BROADpct WEB Nobs

AT 32 48 92 762 AT 21 44 88 2983

DE

DE 32 48 90 3411

DK 37 44 96 1266 DK 30 42 92 3592

FI 38 62 97 907 FI 25 63 89 3298

FR 36 42 82 2669 FR 27 42 73 7705

IE 34 23 80 645 IE 33 24 74 2417

IT 10 34 78 10842 IT 9 37 75 21720

LU 20 54 82 330 LU 12 48 71 1634

NL 25 52 93 2050 NL 26 54 92 1634

NO 48 67 94 1318 NO 44 62 84 3297

SE 32 59 92 1546 SE 31 59 91 3012

SI 23 44 86 454 SI 15 50 71 1626

UK 43 47 97 903 UK 43 51 96 1979

Source: ESSLait PSEC and PSECIS datasets

23

Table III. Proportion of total exports in industries across joint samples

Per cent

PSEC 2008 PSECIS 2008

Country Elecom Mserv MexElec Elecom Mserv MexElec

DK 13 34 53 13 32 55

FR 9 25 66 10 23 67

IE 18 7 76 13 3 84

LU 7 45 48 1 20 79

SE 14 30 56 17 20 63

SI 11 13 76 11 10 80

UK 9 17 74 10 20 70

Note: Shaded area means highest or equal value across samples.

Source: ESSLait PSEC and PSECIS datasets

Table IV. Industry structure across countries

Proportion of employees 2009

Country Elecom MServ MexElec

AT 5 71 24

DK 9 65 26

FI 8 65 27

FR 6 62 32

IE 12 64 25

IT 9 56 35

LU 4 77 19

NL 6 70 24

NO 5 76 19

SE 6 68 25

SI 8 53 39

UK 7 78 15

mean 7 67 26

Source: ESSLait PS dataset

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