Author: Dennis Cremers, Sarah Creemers, Loe Franssen, Marjolijn Jaarsma, Iryna Rud, Marcel van den Berg
Exporting services: challenges to start, and to expand

4. Exports of services to a new EU country: opportunities and barriers

Authors Dutch version: Dennis Cremers, Sarah Creemers, Loe Franssen, Marjolijn Jaarsma, Iryna Rud, Marcel van den Berg
Translated by CBS Vertaalbureau

Opening up a new destination for exports is not an easy step. It requires knowledge of the market, the local regulations and the culture, language and customs. In this chapter, we look at firms that start exporting services to a new destination market in the EU. What are the characteristics of these firms? How does previous familiarity with the market, for example through investments and trade in goods, contribute to the decision to start exporting? Is it easier to export to a new market if you are already active in several other countries or if you already trade with a neighbouring country? What barriers are encountered when starting up in a new market? This chapter addresses these and more questions.

4.1 Introduction

Many people have an intuitive idea of what barriers to international trade in goods are. Customs checks at the border, product requirements, overcoming physical distances, risks of damage or loss of consignments, import tariffs etc. (Van den Berg & Rooyakkers, 2021; Van den Berg et al., 2020). Such barriers largely fall into two types: natural barriers such as distance, language, degree of digitisation and cultural differences; and non-natural barriers such as tariffs and non-tariff barriers.

Barriers to international trade in services are generally more abstract than barriers to trade in goods. This subject was addressed in a recent edition of the Internationalisation Monitor (CBS, 2020). This mainly looked at the impact of distance to the destination market and the size of the destination market on the volume of Dutch service exports. These analyses showed that distance is a clear inhibiting factor in exports of services and that export values are lower in the case of more distant countries. For example, in 2019 almost two-thirds of service exports went to EU countries, a large proportion of them to Germany and Belgium. The size of the destination country’s economy, measured in terms of the country’s GDP, was closely related to the value of service exports (Cremers & Jaarsma, 2020).

Natural trade barriers include not only size and physical distance between trading countries. Factors such as language barriers, virtual proximity and the extent to which countries are culturally or digitally connected to each other also play an important role. In addition to these natural (geographic and cultural) barriers, there are also trade barriers created by the government and established by means of legislation and regulation (i.e.: non-tariff barriers). Quantifying all these barriers in international trade in services is a complex task, so as yet we have no comprehensive picture of the barriers faced by Dutch service exporters.
Barriers to the supply of a service to a foreign country often arise “behind the border” in the destination country. For example, many barriers to trade in services result from national regulation aimed at protecting local service providers from foreign operators (Mattoo et al., 2007). An example of this is the subordination of foreign operators in public procurements. A lack of regulation on intellectual property (including foreign intellectual property), inadequate access to the domestic financial system for foreign operators, requirements concerning permits or the use of local production resources, or mandating a physical presence or collaboration with local operators are examples of barriers that firms may face if they wish to provide their services abroad.

The WTO, the EU and other international organisations therefore work hard to reduce such barriers. Within the EU we have free movement of services, which means that every EU citizen in principle can work where he or she wishes. In addition, thanks to the 2009 EU Services Directive, European sole proprietorships and self-employed people can provide their services in another EU country just as easily as in their own country. More competition between member states should lead to better services and lower prices for the consumer. The Directive applies to all kinds of sole proprietorships, from plumbers to accountants and from pharmacists to architects. Nevertheless, there are still significant barriers to trade in services and many exceptions at national level (European Commission 2021; Barendregt & Wijffelaars, 2017). Recognition of qualifications in regulated occupations in other EU member states still poses challenges for firms. The administrative and regulatory barriers for firms wishing to export services remain in place and differ considerably between member states.

Research questions

It is therefore now appropriate to investigate the extent to which Dutch service exporters experience such barriers and the role these barriers play in the decision to start serving a new market. Our particular focus here is on firms that have started to serve new export markets in the EU (in contrast to Chapter 5, where we focus specifically on existing export relationships worldwide) and on the factors affecting the decision to open up a particular new market. What characterises firms that try to reach new EU markets? Which factors impede or actually assist them when starting in a new market? Are firms more likely to export to a new EU market if they are already active in other EU countries? What role does previous international experience, for example in goods exports or local investments, play in the decision to start exporting services? These research questions are central to this chapter.


This chapter focuses on firms that start to export services to a new destination market in the EU. Section 4.2 considers a number of characteristics of such new service exporters, such as their size, control structure and any previous experience of goods exports to the market concerned. Section 4.3 surveys the barriers encountered when starting exports. We look particularly at various natural and non-natural barriers that firms may encounter when exporting services within the EU. We measure non-tariff barriers to services by reference to the OECD’s Services Trade Restrictiveness Index (STRI), compiled specifically for countries in the European Economic Area (EEA). A conclusion is presented in Section 4.4.

4.2 What are the characteristics of the new service exporter?

This section also considers a large number of descriptive statistics that provide insight into the service exporters that have taken the step of adding a new sales market to their portfolio. We focus on firms starting service exports to EU countries in particular and therefore disregard countries outside the EU. We do this because the Statistics Netherlands source statistics on International Trade in Services do not include the necessary data on countries outside the EU. The box below explains in greater detail how new service exporters are defined and the parameters that have been specified.

Starting close to home

Table 4.2.1 shows that most firms that started service exports to a new EU trading partner in 2017, 2018 or 2019 did so to Belgium. This concerned an average of nearly 16,000 firms per year. Germany came second, with an average of over 13,000 firms exporting to this EU country for the first time in 2017, 2018 or 2019.The ranking of new service exporters can be understood intuitively: the more distant the EU country is from the Netherlands, the lower the number of firms that start exporting services to it. We can also see this in Table 4.2.1; the number of firms starting exports to countries such as Hungary, Slovenia or Latvia is low.
This picture remains unchanged during the years under review and is due in part to the fact that the costs and risks of doing business abroad are higher for countries that are further away (Helpman et al., 2004; Benz et al., 2020). Distance – in terms of transport and transaction costs – and risk and uncertainty, for example with regard to rules, culture or customs in a different country, have an inhibiting effect on firms’ export potential. The more distant the potential market is, the more likely it is that these costs and risks can only be borne by the most productive (possibly large) firms (Melitz, 2003). The size of the importing economy also plays a role; economically small countries with a relatively small population, such as the Baltic states, Malta and Cyprus, are also relatively low in the ranking. On the basis of the gravity theory, this can be explained by the fact that firms will be more strongly attracted to doing business with large, wealthy countries than with economically smaller partners. A total of around 80,000 EU destinations were added by over 47,000 firms in 2017, 2018 and 2019.

In this table we see what a typical new service exporter looks like for each EU country. A typical starter is defined by the number of firms, average number of persons employed, average number of destinations, average export value, median export value, and percentage of firms with trade with a neighboring country. 

Small firms mainly start close to home

Firms that start exporting to countries close to the Netherlands are on average smaller than firms that start exporting to more distant countries. This can also be seen from Table 4.2.1, which shows that the average number of employed persons is considerably lower in firms starting service exports to Belgium, Germany or the UK than in firms starting exports to eastern European member states or the Baltic states. This is consistent with the findings of Benz et al. (2020). Firms starting service exports to the Netherlands’ neighbouring countries employ an average of 27 people. This then rises steadily from an average of 68 employees in firms starting service exports to Spain to an average of 107 in the case of new exporters to Finland. The figure then jumps to more than 150 in firms starting to export services to the new member states in Eastern Europe. New exporters to Greece, Latvia and Slovenia top the ranking with an average of over 200 employed persons.

Another way of showing the effect of size on the start of exports to new destinations is by looking at the size and autonomy of the new service exporters. In this regard firms are divided into five categories: 1) foreign multinationals, where the firm is under foreign control; 2) large enterprises or Dutch multinationals, where the group to which the firm belongs has 250 or more employed persons, or fewer than 250 employed persons but foreign subsidiaries; 3) medium-sized enterprises with between 50 and 249 employed persons and no foreign parent company or subsidiary; 4) small enterprises, with between 10 and 49 employed persons, without a foreign parent company or subsidiary, and 5) self-employed workers and micro-enterprises, with between one and nine employed persons and no foreign parent company or subsidiary. Figure 4.2.2 shows the new service exporters to the 10 most important destination countries for Dutch service exports in the 2017-2019 period, broken down into these five categories. This also shows that small enterprises, self-employed workers and micro-enterprises make up by far the bulk of new service exporters, and that this share is largest to countries close to home, such as Belgium, Germany and the UK. The more distant the new export market, such as Poland, Ireland or Sweden, the larger the firm is and the more likely it is to operate as a multinational or as part of a foreign multinational.

Most new exporters already serve other EU member states

Table 4.2.1 also shows the extent to which firms that started service exports to an EU country in the 2017-2019 period already had service exports to one or more other EU countries. In general, firms that started service exports to more distant EU markets, such as Croatia, Slovenia, Slovakia or the Baltic states, during the period under review already serve many more countries than new exporters to nearby EU member states. For example, firms that started exporting services to a new country in the 2017-2019 period were on average already exporting to seven other markets. Firms that started service exports to Belgium or Germany usually had no other sales market. Firms that started exporting services to Croatia in 2017-2019 were already providing services to no fewer than eight other EU member states.

Average export value highest to Greece

Table 4.2.1 also provides insight into the average and median export value of the firms starting service exports to a specific EU country. The lowest average export values are recorded by new service exporters to the Baltic states and East European member states, such as Slovenia and Croatia. The median export value is also lowest for service exporters to these countries. Firms starting exports to Finland and Austria have a relatively low average export value of around €65,000, but a higher median export value than exporters to the Czech Republic, Romania and Spain, for example. This may indicate that service exports to the latter three countries were somewhat more dominated by larger firms (distributed unevenly) and those to Finland and Austria mainly by smaller exporters. The largest average export value is seen among new exporters to Greece (almost €400,000) and the UK (€256,000). In the case of the UK there is also a high median export value, while the average new service exporter to Greece has exports worth around €4,500. Exports to Greece are therefore also dominated by a few relatively large new service exporters. The highest average and median export values can be seen among firms starting service exports to the UK, Germany and France. A notable observation is the relatively low average export value to Belgium, of €87,000. This gives the impression that new exporters to Belgium are even smaller than new exporters to the Netherlands’ other neighbouring countries.

New service exports are often unrelated to goods exports

But geographic distance and firm size are not the only important factors. Previous experience in a market, for example in goods exports, may also be important. Hence the question: did a firm starting service exports to a particular EU country also export goods to that country in at least one of the three years1) before starting service exports? Not in most cases, according to Figure 4.2.3. Over 65-80 percent of new service exporters did not (yet) export goods to that EU country. The proportion of firms that did export goods was again largest among firms starting service exports to Belgium and Germany. New exporters to Italy, Romania, Poland, Spain and the Czech Republic, for example, also had a relatively large share of goods exports. Experience of goods exports is a less important factor for new service exports to Malta, Cyprus, the Baltic states and a number of Eastern European EU member states. The type of services exported may play a role here. If a firm wishes to start exporting financial services, it may not be logical or necessary to have experience of goods exports, whereas in the case of transport services that may well be a logical combination.

4.2.3 New service exporters who already exported goods to that EU country three years before the start, 2019
CountryNo goods exports (%)Goods exports (%)
Czech Republic70.829.2
United Kingdom71.128.9

New service exports are often unrelated to service imports

Service imports from an EU country also do not necessarily appear to go hand in hand with new exports to that destination country. Most firms starting service exports apparently do not import any services from the country in question, as can be seen from Figure 4.2.4. The percentages are highest the case of Ireland, Poland and Germany; 30-43 percent of new exporters also report imports of services from the country in the same year as the start of exports. In the case of more distant EU countries, for example in Southern and Eastern Europe and the Baltic states, a maximum of one in five new exporters also import services from the EU country in question.

4.2.4 New service exporters who already imported services from that EU country three years before the start, 2019
CountryNo services imports (%)Services imports (%)
Czech Republic75.524.5
United Kingdom79.520.5

New exporters operate particularly in the business services sector

Figure 4.2.5 shows that most new service exporters in the 2017-2019 period operated in business services, or in the trade, transport or accommodation and food services sectors. Here we focus on the 10 largest destination countries. New exporters in business services are found mostly in new exports to the UK and Denmark, while new exporters to Poland often operate in trade, transport or accommodation and food services. This may reflect the phenomenon seen in Figure 4.2.3, namely that firms starting to export services to Poland quite often also reported trade in goods three years earlier. The trade and transport sectors by their nature provide services for freight or passenger transport and are thus a logical combination. New exporters in the information and communication sector are strongly represented in service exports to Ireland, often concerning fees for the use of intellectual property (e.g. software), and among new exporters to the UK. Only a small proportion of new exporters are engaged in financial services.

4.3 What barriers do firms face when starting to export?

The decision to start exporting services to a particular country depends on various factors. The previous section showed that most new exporters in the Netherlands start exporting to countries close to home and to relatively large, wealthy countries. These were often the first international steps taken by the service exporter, but sometimes the firm already had some experience of trade in goods, exporting services to another country or importing services. Firms starting to export services to nearby EU destinations are smaller in terms of employed persons, but have a relatively high median export value. This is partly because most markets close to home are large, relatively wealthy countries compared to the more distant EU member states.

In this section, we enrich the information about new service exporters by simultaneously taking the above factors into account, among others. This section focuses on the barriers to new export activity at country level. Trade barriers can prevent firms from selling services to each other in foreign markets. Barriers to trade in services can broadly be divided into natural (geographic or cultural) barriers and non-natural (non-tariff) barriers.

Trade in services can be explained with the aid of gravity models. Over time researchers have included many of these trade barriers in their model, such as the GDP of the destination country, physical distance, cultural differences, a common language and the degree of digitisation. Information on language, culture and customs is not shown in our final results, because these factors did not result in any significant improvement in the model for the EU. Virtual proximity has recently been considered in the literature as a new, supplementary indicator for the proximity of countries. Its measurement is based on bilateral hyperlinks and bilateral website visits between countries (Chung, 2011; Hellmanzik & Schmitz, 2016). Particularly for trade in services, this would be a more relevant measure of distance than physical distance. In this section we therefore look specifically at the role of virtual proximity to a destination country and the likelihood that a firm will start exports to that country.

In addition to the natural barriers, there are the non-natural barriers. In the case of trade in services, these are non-tariff policy measures that can impede trade in services in some way, either knowingly or unknowingly. In this section we measure the non-tariff barriers to trade by reference to an external data source: the intra-EEA Services Trade Restrictiveness Index (intra-EEA STRI) of the OECD.

In examining the barriers faced by Dutch service exporters, we also take into account a whole series of background characteristics at both firm and country level. This makes it possible to take account of the size of the firm, whether it is part of a (possibly foreign) multinational, its productivity and the sector in which it operates. There are countless studies demonstrating a clear link between the productivity of a firm and exports (see for example Defever et al., 2015; Benz et al., 2020). Exporters are larger, more innovative, more productive and more profitable than non-exporters, for example, and that also applies to service exporters (see for example Bernard et al. 2007; Vos & Jaarsma, 2017). These characteristics therefore play a major role in the probability that a firm will enter a new market. In order to take this into account, we therefore examine a large number of business characteristics of these exporters. At country level we look at the size of the population of the destination country, the distance and the virtual distance. Finally, we also look at whether there have already been goods exports to the destination country in one of the three years prior to the start of exports, or whether there is already a participating interest in the destination country, at possible export experience (multiple destinations) and at the role of existing exports to a neighbouring country.

Concerning the data used in this chapter

To answer the research questions as fully as possible, a dataset was compiled of around 47,000 firms that started exporting to a new EU country in the period from 2017 to 2019 inclusive. Together these 47,000 firms accounted for around 80,000 export transactions to new EU destinations. CBS microdata on International Trade in Services were used as the source database for service exports. This is only available by country within the EU. Hence there are a maximum of 27 potential new export markets per firm.

Every firm that started exports in the 2017-2019 period is included in the dataset. For each firm, all EU destinations are included, i.e. those to which it did not export and those to which it started exporting. For example, if a firm started exporting to Belgium, but had already been exporting to Germany for a few years and did not export to any other EU country in the three years prior to the survey year, this firm appears in the data 26 times as a potential new exporter. This firm is defined as a new exporter to Belgium, but not as a new exporter to the other 25 EU countries (excluding Germany, where it was already active). This is because it is not possible to start exporting if there is already existing export activity. We thus obtain a dataset containing only firms that have proven interest in starting exports to new countries. Every firm has therefore specifically decided to start exporting or not to start exporting to a particular country. This enables us to make a pure measurement of the factors that affect the decision on whether to start exporting to a new country. Further information on the sources and methods used can be found in Section 4.5 Data and methods.

Concerning the method used in this chapter

In this chapter we use a probit model to measure the effect of the chosen variables on the probability that a firm will start exporting. This is a type of regression in which the dependent variable can only have two values (export start yes (1) or no (0)). It is widely used in the economic literature for similar studies. Further information on the methodology and software used can be found in Section 4.5 Data and methods. In the following paragraphs we gradually expand the base model by incorporating new variables. In each case a new model is built, since a slightly modified dataset is used each time. This is so as to be able to answer the relevant research questions in the purist way possible.


Table 4.3.1 shows the base model from which we start in this section. The significant positive coefficients clearly show that the bigger the foreign economy (larger population), the more likely it is that a firm will have service exports to this country ) In this case, the population has been included instead of GDP, since virtual proximity has been included for intensity (virtual proximity relative to GDP). This is in line with the literature on gravity models in services trade (e.g. Kimura & Lee, 2006; Nordås & Rouzet, 2017). On the other hand, the significantly negative relationship between distance and the probability of starting exports shows that distance to the destination country is a substantial inhibiting factor. The more distant a country is, the less likely it is that a Dutch service exporter will operate there. The literature also shows that the probability of survival for exporters in new destination countries diminishes as distance increases (Creusen & Lejour, 2011; Albornoz et al., 2016).

Virtual proximity has a positive effect on the start-up of exports, particularly for small firms

In addition, greater virtual connectedness between the Netherlands and the destination country is also correlated with a higher probability that a Dutch service exporter will operate there (see Table 4.3.1). Based on terms of interaction3), we see that this effect is more important for small firms than for large firms. For small firms it is therefore more difficult – ceteris paribus – to start exports to EU markets with which there is less virtual connectedness than for large firms or multinationals. In a similar vein, we find the same results with regard to productivity. Less productive (and often also smaller) firms benefit more from greater virtual connectedness than more productive (and often larger) firms.4) One reason for this may be that small firms have a smaller network and are more dependent on general ways of acquiring new customers in a new market. Large enterprises usually have a larger (possibly international) network and may be able to make better use of this to enter new markets, making them less dependent on cold acquisition than small firms. Firms operating in the communication and information service sectors also appear to be less inhibited by virtual distance. A possible explanation for this is that these firms are better able to cope with such barriers due to the nature of their activities.

More productive firms also start exporting more often, as in the case of firms that were already exporting goods to the destination in the three years prior to the starting year (see Table 4.3.1). Having a participating interest in the destination country prior to the starting year makes it more likely that a firm will become active as a service exporter to that country.

Finally, export experience is also important. We see, for example, that the larger the number of destinations to which services are already exported, the easier it appears to be to add a new destination to the export portfolio. This is consistent with the literature on goods exports (Creusen & Lejour, 2011). It is notable that firms that lack previous export experience are more likely to start service exports to a new EU destination than firms that are already internationally active in one to five other EU destinations. A possible reason for this is that there are two intertwined effects here. For example, it may become progressively harder to find suitable new destinations for export services, but more existing destinations also lead to more experience, which in turn means a higher likelihood of starting exports.

4.3.1 Base model for new service exporters by country, 2017-2019
Start of exports
Firm size
Large enterprise or Dutch-owned multinational0.188***
Medium-sized enterprise-0.082***
Small enterprise-0.143***
Self-employed worker and micro-enterprise-0.256***
Geographic barriers
Distance (ln)-0.492***
Population (ln)0.146***
Virtual proximity (intensity)0.216***
Background characteristics
Labour productivity (ln)0.058***
Affiliate in destination country (t-1)0.422***
Trade variables
Exports of goods (t-3 to t-1)0.655***
Imports (dummy)0.491***
Number of destinations (t-1) (0)0.545***
Number of destinations (t-1) (2 to 5)0.380***
Number of destinations (t-1) (6 to 10)0.829***
Number of destinations (t-1) (10+)1.262***

In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

Firms gradually expand their export markets

Table 4.3.2 provides information on “stepping-stone” behaviour (or sequential exporting), as studied previously by CBS (2019). The intuition here is that firms take advantage of exports to a country that is close to the intended new destination because it means they have already built up the necessary knowledge, information and experience. The probability that a firm will start exporting to a certain (possibly distant) country thus increases if it is already successfully exporting to a market close to that specific country (Eaton et al., 2008; Albornoz et al., 2012; Lejour & Creusen, 2015; Cremers et al., 2019). Defever et al. (2015), for example, find that the probability of a firm exporting to a new country increases by two percentage points for each export market in which it already operates and which shares a border with the new destination.

This breakdown analyses how previous exports to a neighbouring country affect the probability of starting exports to a country. Since both Belgium and Germany are neighbouring countries of the Netherlands, these countries are disregarded in this analysis. They are nevertheless still included as a stepping stone for other EU member states. According to our analyses, previous trade with a neighbouring country leads to a greater probability of starting exports to a new country with which it shares a border; in other words, there is a certain sequential pattern in the method of exporting. As previously already described in the base model, general export experience, measured by the number of destinations in the year prior to the start of exports, plays an important role. This also confirms the picture presented by previous studies (Helpman et al., 2004; Creusen & Lejour, 2011) in which firms with export experience export particularly to more distant destinations. Benz et al. (2020) also show that firms with more export experience can cope relatively better with trade barriers.

4.3.2 Stepping stones model for new service exporters by country, 2017-2019
Start of exports
Firm size
Large enterprise or Dutch-owned multinational0.181***
Medium-sized enterprise-0.107***
Small enterprise-0.179***
Self-employed worker and micro-enterprise-0.292***
Geographic barriers
Distance (ln)-0.385***
Population (ln)0.186***
Virtual proximity (intensity)0.110***
Background characteristics
Labour productivity (ln)0.064***
Affiliate in destination country (t-1)0.462***
Trade variables
Exports of goods (t-3 to t-1)0.683***
Imports (dummy)0.539***
Trade with neighbouring country (t-1)0.094***
Number of destinations (t-1) (0)0.454***
Number of destinations (t-1) (2 to 5)0.328***
Number of destinations (t-1) (6 to 10)0.714***
Number of destinations (t-1) (10+)1.110***
In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

It is more difficult to start exports to countries with more restrictive service sectors

Table 4.3.35) shows the results of the expansion of the base model. This breakdown also looks at the intra-EEA STRI. The intra-EEA STRI shows, both at country and sector level, to what extent a firm faces barriers in developing service activities in a specific sector in a specific country. Since the intra-EEA STRI characteristics are linked at sector level (SBI; see Table 4.6.1 in the annex), no separate sector dummies are included in the regression. In order nevertheless to check for the underlying sector differences between firms, it was decided to include the capital intensity variable. This variable has been defined at firm level, but it also varies greatly between sectors. Hence it is a suitable variable to control for the sector variability.6)

Table 4.3.3 shows that trade restrictions generally have a negative effect on the probability that a firm will start exporting services to the country in question. This is an overall effect that measures both the different intra-EEA STRI effects among the countries themselves and the sectors themselves. It therefore describes the overall effect of the intra-EEA STRI on the probability that a service exporter will select any particular EU country as a destination. Differences in the STRI between sectors appear to play a bigger role than differences in the STRI between countries.7) Hence relatively fewer firms start exports in sectors with higher barriers, whereas this is less of a factor in the case of countries with relatively high intra-EEA STRI scores. We find mixed results for individual sectors. Hence the coefficient of the intra-EEA STRI is sometimes positive (architectural services, construction and engineering services) and sometimes negative (legal services).8) This probably has to do with the very small differences in the intra-EEA STRI between countries in the sectors. Between sectors there is more variation in the index and we find that the intra-EEA STRI coefficient is negative almost across the board.9) For almost all countries, firms in sectors with a higher intra-EEA STRI have a lower probability of starting exports to a new destination than firms in a sector with a low intra-EEA STRI.

A country with relatively high restrictions in the EU is Belgium. In the case of Belgium we therefore see that firms in sectors with a higher intra-EEA STRI start exporting less often than firms in sectors where the intra-EEA STRI is low. Sectors with a relatively high intra-EEA STRI in Belgium are accounting and architectural services. A low intra-EEA STRI is found in the construction and engineering services, among others. Most forms of transport in Belgium also have a low intra-EAA STRI and therefore a slightly raised probability of new export activity.

Finally, we find no significant differences in the intra-EAA STRI with regard to the probability of a new export activity between low-productivity and high-productivity firms. A mixed picture also emerges if we analyse the effects of intra-EEA STRI on different size classes of firms and multinationality. Larger, but also more productive, firms appear to encounter more obstacles when starting in countries and sectors with more trade barriers. This may be explained by a selection effect among these firms as they focus on more difficult destinations, because they often already trade with countries that have few barriers. Additional research is needed to develop this hypothesis further.

4.3.3 Effect of intra-EEA STRI on new service exporters by country, 2017-2019
Start of exports
Firm size
Large enterprise or Dutch-owned multinational0.201***
Medium-sized enterprise-0.034*
Small enterprise-0.105***
Self-employed worker and micro-enterprise-0.235***
Geographic barriers
Distance (ln)-0.463***
Population (ln)0.138***
Virtual proximity (intensity)0.215***
Non-tariff barriers
Intra-EEA STRI-1.067***
Background characteristics
Labour productivity (ln)0.054***
Affiliate in destination country (t-1)0.439***
Capital/labour ratio0.007***
Trade variables
Exports of goods (t-3 to t-1)0.587***
Imports (dummy)0.503***
Number of destinations (t-1) (0)0.535***
Number of destinations (t-1) (2 to 5)0.384***
Number of destinations (t-1) (6 to 10)0.817***
Number of destinations (t-1) (10+)1.230***
In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

Main barriers with regard to competition in the destination country

It is important to consider the various types of trading restrictions separately (Jungmittag & Marschinski, 2020; Van der Marel & Shepherd, 2013). Such a separate assessment for each type of restriction shows more accurately where policy opportunities lie in order to reduce the restrictions on trade in services as efficiently as possible and thereby stimulate new investment projects. Jungmittag & Marschinski (2020) note that restrictions on trade in services constitute a significant barrier to greenfield FDI.10) In the accounting, computing, architectural and engineering service sectors, they find statistically significant evidence of a negative impact. Furthermore, the explanatory power of the models generally improves if the subtypes of the STRI are used instead of the aggregated STRI total score, although in this study too the results are less clear-cut if we look at the different types of restrictions within the EU. In the legal services sector the authors find that barriers to entry have a significant negative effect on FDI. The role of barriers to entry is less clear in the other service sectors.

Table 4.3.4 shows the regression results of the model with a further breakdown of the STRI index into different types of barriers. This distinguishes three types in the intra-EEA STRI: (1) restrictions on foreign entry; (2) barriers to competition; and (3) regulatory transparency.

Restrictions on foreign entry concern the difficulty of entering a new market in a specific sector in a specific country. This includes the degree of difficulty in establishing a subsidiary in a country because, among other things, there may be limits on foreign shareholders, nationality requirements for executive board members and limits on cross-border mergers.

Barriers to competition concern, for example, the extent to which the government can curb the influence of foreign firms, but also the extent to which a foreign firm can protest against certain rules which, it believes, lead to unfair competition. Regulatory transparency includes transparent legislative procedures, administrative procedures for firm formation, complexity of licence applications and conditions governing the awarding of contracts. Only these three types of barriers (out of five) are analysed, since within the EU the restrictions of the other two types (restrictions on the free movement of people and other discriminatory measures) are practically non-existent.

There are significant differences between the influence of different types of restrictions on the probability that a firm will start service exports, see Table 4.3.4. Restrictions on foreign entry, for example, are largely insignificant. Barriers to competition within a market have a negative effect on the start of an export activity. Finally, regulatory transparency has a positive coefficient. We must keep in mind that the intra-EEA STRI criterion measures trade barriers in the broad sense. It thus measures more than just barriers to exports. It may also include restrictions on the establishment of a foreign branch. Within the internal market, thanks to the considerable liberalisation efforts by EU member states hitherto, barriers faced by EU service providers are much lower than in non-EU countries (i.e. compared to third countries). There are nevertheless still barriers to cross-border trade in services within the EU. The severity of these barriers varies greatly between sectors, but varies notably less between countries. The intra-EEA STRI takes no account of the degree of similarity between regulatory systems in different markets. There is a general tendency whereby similarity of regulation is associated with regional proximity: the regulation in force in Germany is most similar to that of the Netherlands (Benz & Gonzales, 2019).

In various sectors there is also a great deal of heterogeneity among these different types of barriers. For firms in legal services, for example, there are restrictions on foreign entry, while the other sectors show insignificant results or even positive effects (architectural services and construction). Similarly, the effects are not clear-cut among the various countries. Belgium, for example, appears to have significant barriers to market entry, whereas Germany does not. The existence of more barriers to competition in the market has a fairly consistent, negative effect on the probability of a new export activity. Less transparent regulation actually appears to have a positive effect, which is counter-intuitive. A reason for this lies in the limited barriers that exist within the EU. Outside the EU there are much larger barriers, as discussed further in Chapter 5.

4.3.4 Effect of STRI types on new service exporters by country, 2017-2019
Start of exports
Firm size
Large enterprise or Dutch-owned multinational0.186***
Medium-sized enterprise-0.048***
Small enterprise-0.120***
Self-employed worker and micro-enterprise-0.246***
Geographic barriers
Distance (ln)-0.426***
Population (ln)0.158***
Virtual proximity (intensity)0.161***
Non-tariff barriers
Restrictions on foreign entry0,06
Barriers to competition-3.173***
Regulatory transparency10.603***
Background characteristics
Labour productivity (ln)0.051***
Affiliate in destination country (t-1)0.443***
Capital/labour ratio (ln)0.010***
Trade variables
Exports of goods (t-3 to t-1)0.615***
Imports (dummy)0.506***
Number of destinations (t-1) (0)0.536***
Number of destinations (t-1) (2 to 5)0.386***
Number of destinations (t-1) (6 to 10)0.810***
Number of destinations (t-1) (10+)1.222***
In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

Limitations of EU data for analysis of barriers

For a more comprehensive analysis of the effects of barriers on a new export activity, we prefer to use data on trade with countries outside the EU. In the countries outside the EU there are much bigger differences between the barriers in various countries and service sectors. Here, however, we have to contend with data limitations, as there is no complete source for all firms trading in services outside the EU. Chapter 5 looks more closely at the effects of these barriers on the expansion of business activities within existing markets based on a sample survey that is comprehensive for large enterprises but very incomplete for small service providers. The source used in Chapter 5 therefore contains more countries (including outside the EU) as well as breakdowns for types of services. This allows a better analysis of the effects of the barriers within the export markets, and the associated sectors within these countries. The dataset, however, consists of a select enterprise population and is therefore less suitable for a generic analysis of patterns in new export activities.

4.4 Summary and conclusion

This chapter has provided a general picture of firms that start exporting services to new destinations within the EU. Belgium and Germany are the undisputed number one and two when it comes to the most popular destinations for new exports. The United Kingdom is also an important destination for firms starting to export to a new country.

Latvia and Croatia, by contrast, are countries to which relatively few firms start exporting. The most popular destinations are also quite often served by smaller firms, with fewer employees; the least popular destinations, by contrast, are often served by the larger firms. In the case of the most popular countries, new service exporters are relatively more likely to have been exporting goods already in the preceding three years. The picture is less clear-cut when it comes to imports of services in the same year as the start of exports. Ireland and Poland stand out as countries from which services are relatively often imported, simultaneously with the start of exports. Finally, the least popular countries for an exporting country are also the last to be exported to. For example, firms enter an average of more than seven other export markets before starting exports of services to Croatia.

The probability of starting service exports is influenced by a large number of factors at both country and firm level. In the second part of this chapter a number of regressions were carried out showing the main factors. These analyses include background characteristics of both firms and countries. The size of the economy, in terms of population, is an important positive indicator for an export start. The distance to a country, on the other hand, leads to a smaller probability of starting exports of services to that country. More productive firms, firms with goods exports to the country in question in the three years prior to the research year and firms that already have a participating interest in the destination country have a greater probability of starting exports to that specific country.

Virtual connectedness with a destination country also plays a positive role in the decision on whether to start exporting to a particular country. This connectedness turns out to be more important for small firms than for larger firms. Virtual proximity is also a more important explanatory factor for less productive firms. In addition, having exports to a neighbouring country (of the potential destination country) in the year prior to a potential export start plays a role. Firms that already trade with a neighbouring country of the potential destination country have a greater probability of starting exports to that specific new destination country. Export experience (measured by the number of existing EU destinations) also plays a positive role in the probability of starting exports to a new EU country.

Various barriers relating to legislation and regulation, as measured by the intra-EEA STRI of the OECD, have a heterogeneous and inconclusive effect on the probability of an export start. In general (total intra-EEA STRI), Dutch firms are impeded/inhibited when starting exports to EU countries in which the service sectors are strictly regulated. But the effects differ greatly between sectors and to a lesser extent between countries. The barriers within the EU appear to be concentrated mainly in sectors and less in specific countries, so that is less clearly reflected in our dependent variable, the probability of starting to serve a particular export market. For individual sectors, for example, there is no clear negative (or positive) effect of the intra-EAA STRI on the probability of an export start. Hence there is no lower probability of starting exports to countries with a higher intra-EEA STRI, after we controlled for background characteristics. On the level of individual countries, however, where the effect of the differences in intra-EEA STRI between sectors is actually measured, there is a clearly negative effect. For the different types of barriers (market entry, competition and transparency) there are very mixed results. For example, the existence of more competition-related barriers has a fairly clear negative effect on the probability of an export start, less transparent regulation has a positive effect on the probability of an export start and the existence of more barriers to foreign entry has mixed results. This chapter has only considered trade in services with other EU countries. In Chapter 5 we focus on Dutch trade in services worldwide. Outside the EU there are more often non-tariff barriers to trade in services.

4.5 Data and methods

In order to answer the research questions, a micro-dataset was compiled for the 2014 reporting year up to and including 2020. The micro-dataset is a link between a number of internal CBS statistics and external data. The backbone of the micro-database is the business demographic framework (BDK). The BDK contains a variety of background characteristics of firms, such as the sector and size classification. Microdata for trade in services have been linked to the BDK. These microdata supplement the regular International Trade in Services (ITS) statistics, with data being added from a variety of other sources so as to provide information additionally on trade in services for small firms. These microdata are only available for EU countries. The International Trade in Goods (ITG) statistics have also been used. A supplemented version of the ITG has been used for this study, with intra-Community supplies (ICP) data also being used. The use of the ICP means more data are available on small exporting firms, mainly independent SMEs. Data on labour productivity and the relationship between capital and labour have also been added to the dataset. These mainly consist of data from the Statistics on the Finances of Enterprises (SFO) and Baseline. Information from CBS has also been added concerning firm subsidiaries. These data are obtained from corporation tax records.

A number of external sources have also been used. For example, country-specific data on GDP from the IMF and data on population, distance and shared borders from CEPII have been added. CEPII is a French institute that conducts research into international economics and has a large dataset with all kinds of country-specific variables. Finally, data on virtual proximity (see Chung, 2011) and the intra-EEA STRI (Services Trade Restrictiveness Index of the OECD) have been added. The software program R has been used for the regressions. Probit models have been used for all the regressions shown.

4.6 Annex

4.6.1 Expansion of virtual proximity interaction with type of firm, 2017-2019
Start of exports
Firm size * virtual proximity
Foreign-owned multinational0.092***
Large enterprise or Dutch-owned multinational0.155***
Medium-sized enterprise0.183***
Small enterprise0.227***
Self-employed worker and micro-enterprise0.232***
In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

4.6.2 Expansion of virtual proximity interaction with productivity measure, 2017-2019
Start of exports
Productivity (discrete) * virtual proximity
1st decile of productivity0.216***
2nd decile of productivity0.246***
3rd decile of productivity0.249***
4th decile of productivity0.239***
5th decile of productivity0.248***
6th decile of productivity0.226***
7th decile of productivity0.225***
8th decile of productivity0.214***
9th decile of productivity0.178***
10th decile of productivity0.146***
In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

4.6.3 Expansion of intra-EEA STRI to sector for 5 sectors, 2017-2019
Start of exports (accounting)Start of exports (architecture)Start of exports (construction)Start of exports (engineering)Start of exports (legal)
Intra-EEA STRI general0.7248.823***8.967***1.372*-4.467***
Intra-EEA STRI by type of restriction
Restrictions on foreign entry3.0707.021*15.320***0.683-4.586***
Barriers to competition-40.912***13.510-18.680***11.319***-2.774
Regulatory transparency20.823***17.798***26.120***13.758***10.684**
In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

4.6.4 Expansion of intra-EEA STRI for top 5 most important countries, 2017-2019
Start of exports (Belgium)Start of exports (Germany)Start of exports (France)Start of exports (Spain)Start of exports (Italy)
Intra-EEA STRI general-4.775***-0.674***-1.098***-1.0350.006
Intra-EEA STRI by type of restriction
Restrictions on foreign entry-3.322***2.295**-0.769-0.4251.054
Barriers to competition-6.906***-1.294**-1.461**-0.0940.552
Regulatory transparency-4.640***10.97431.439***16.153***0.413
In all model specifications we control for sector and year.
*p<0.1; **p<0.05; ***p<0.01

4.6.5 Link between OECD service sector and CBS SBI and service types
OECD service sectorISIC Rev 4CBS SBI
Broadcasting591 + 60260
Motion pictures591591
Sound recording592592
Construction41 - 4341 - 43
Computer62 + 6362 + 63
Distribution46 + 4746 + 47
Commercial banking6464
Insurance651 + 652651 + 652
Logistics cargo-handling52245224
Logistics customs brokerage52295229
Logistics freight forwarding52295229
Logistics storage and warehouse52105210
Road freight transport4923494
Air transport5151
Maritime transport5012502
Rail freight transport4912492

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1) A three-year period was chosen because this is also the number of years used to define new service exporters.

2) The Restrictiveness Indicator of the European Commission is not reported in this chapter, as the use of this index produced no clear-cut results.

3) See Table 4.6.1 for the results with regard to the relationship between virtual proximity and firm size. The table is an expansion of Table 4.3.1 and includes checks for the same variables.

4) See Table 4.6.2 for the results concerning the relationship between virtual proximity and productivity. The table is an expansion of Table 4.3.1 and includes checks for the same variables.

5) The model used here only includes the sectors and countries for which the intra-EEA STRI is defined. In short, this only concerns firms operating in a conventional service sector. Firms exporting services in non-regular sectors, such as manufacturing, are necessarily excluded from the analysis. The UK is also excluded from the analysis, because there is no intra-EEA STRI for the UK, only a standard STRI.

6) In the growth model of Solow (1957), capital and labour play an important role in explaining economic growth. Capital intensity at firm level is frequently used as a driver of economic growth and is therefore included as a control variable in our regression.

7) See Table 4.6.3 and Table 4.6.4 in the annex for further information. The biggest variation in the size of intra-EEA STRI is between sectors rather than between countries. If we run regressions for individual countries (and hence include sector variation), we see that there too a negative effect arises most often. The tables are an expansion of Table 4.3.3 and Table 4.3.4 and include checks for the same variables.

8) See Table 4.6.3 for five key sectors.

9) See Table 4.6.4 for five important countries.

10) A foreign investment in a foreign branch that is yet to be established from scratch.