Exporting services: challenges to start, and to expand

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.

Outline

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.

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.

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.

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.

Results

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.

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.

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.

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.

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

4.7 Literature

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Albornoz, F., Pardo, H. F. C., Corcos, G. & Ornelas, E. (2012). Sequential exporting. Journal of International Economics, 88(1), 17-31.

Barendregt, E. & Wijffelaars, M. (2017). De interne markt is een onvoltooid succes. ESB, 102(4754S), 73-76.

Benz, S. & Gonzales, F. (2019). Intra-EEA STRI Database: Methodology and Results. OECD Trade Policy Papers, No. 223. Paris: OECD Publishing.

Benz, S., Rouzet, D. & Spinelli, F. (2020). Firm heterogeneity in services trade: Micro-level evidence from eight OECD countries. The World Economy, 43(11), 2905-2931.

Berg, van den, M. & Rooyakkers, J. (2021). Niet-tarifaire maatregelen: een introductie. In S. Creemers, M. Jaarsma & J. Rooyakkers (Ed.), Internationalisation Monitor 2021, third quarter: Non-tariff measures. The Hague/Heerlen/Bonaire: Statistics Netherlands.

Berg, van den, M., Franssen, L. & Mounir, A. (2020). Europese importtarieven: wie betaalt de rekening? In M. Jaarsma & A. Lammertsma (Ed.), Internationalisation Monitor 2020, fourth quarter: Trade policy: Tariffs and Trade Agreements. The Hague/Heerlen/Bonaire: Statistics Netherlands.

Bernard, A. B., Jensen, J. B., Redding, S. J., & Schott, P. K. (2007). Firms in international trade. Journal of Economic perspectives, 21(3), 105–130.

CBS (2019). Internationalisation Monitor 2019, second quarter: Patterns in trade behaviour. The Hague/Heerlen/Bonaire: Statistics Netherlands.

CBS (2020). Internationalisation Monitor 2020, third quarter: International trade in services and R&D. The Hague/Heerlen/Bonaire: Statistics Netherlands.

Chung, J. (2011). The Geography of Global Internet Hyperlink Networks and Cultural Content Analysis. Dissertation, University at Buffalo.

Cremers, D. & Jaarsma, M. (2020). Dienstenhandel en zwaartekracht; anders dan goederenhandel? In S. Creemers & M. Jaarsma (Ed.), Internationalisation Monitor 2020, third quarter: International trade in services and R&D. The Hague/Heerlen/Bonaire: Statistics Netherlands.

Cremers, D., Jaarsma, M., Lammertsma, A., Polder, M. & Rook, van, R. (2019). De ontwikkeling in de exportportefeuille van startende goederenhandelaren. In M. Jaarsma (Ed.), Internationalisation Monitor 2019, second quarter: Patterns in trade behaviour. The Hague/Heerlen/Bonaire: Statistics Netherlands.

Creusen, H. & Lejour, A. (2011). Uncertainty and the export decisions of Dutch firms (No. 69). FIW Working Paper.

Defever, F., Heid, B. & Larch, M. (2015). Spatial exporters. Journal of International Economics, 95(1), 145-156.

Eaton, J., Eslava, M., Kugler, M. & Tybout, J. (2008). The margins of entry into export markets: evidence from Colombia. Helpman, E., Marin, D., & Verdier, T. (Ed.). The organization of firms in a global economy. Harvard University Press.

European Commission (2021). Mapping and assessment of legal and administrative barriers in the services sector. Brussels: European Commission, Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW).

FPS Economy (2020). Establishing oneself as a chartered accountant in Belgium. Brussels: FPS Economy, SMEs, Middle Classes and Energy.

Hellmanzik, C. & Schmitz, M. (2016). Gravity and international services trade: the impact of virtual proximity. ETSG 2016 Paper, Helsinki.

Helpman, E., Melitz, M. J. & Yeaple, S. R. (2004). Export versus FDI with heterogeneous firms. American economic review, 94(1), 300-316.

Jungmittag, A. & Marschinski, R. (2020). Service Trade Restrictiveness and Foreign Direct Investment: Evidence from Greenfield FDI in Business Services. JRC report, EUR 30399 EN. Luxembourg: Publications Office of the European Union.

Kimura, F. & Lee, H. H. (2006). The gravity equation in international trade in services. Review of world economics, 142(1), 92-121.

Lejour, A. & Creusen, H. (2015). Using Stepping Stones to Enter Distant Export Markets. Global Economy Journal, 15(1), 107-132.

Marel, van der, E. & Shepherd, B. (2013). Services Trade, Regulation and Regional Integration: Evidence from Sectoral Data. The World Economy, 36(11), 1393-1405.

Mattoo, A., Stern, R. M. & Zanini, G. (Ed.). (2007). A handbook of international trade in services. OUP Oxford.

Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica, 71(6), 1695-1725.

Nordås, H. K. & Rouzet, D. (2017). The impact of services trade restrictiveness on trade flows. The World Economy, 40(6), 1155-1183.

OECD (2022a). OECD Services Trade Restrictiveness Index: The Netherlands 2021.

OECD (2022b). Intra-EEA Services Trade Restrictiveness Index Regulatory Database. [Database]. Accessed on 7 June 2022.

Rouzet, D., Benz, S. & Spinelli, F. (2017). Trading firms and trading costs in services: Firm-level analysis. OECD Trade Policy Papers, No. 210. Paris: OECD Publishing.

Solow, R. M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics, 312-320.

Vos, S. & Jaarsma, M. (2017). Bedrijven met internationale handel in diensten; dezelfde prestaties als bedrijven met goederenhandel? In: M. Jaarsma & J. Voncken (Ed.), Internationalisation Monitor 2017 second quarter, International trade in services. The Hague/Heerlen/Bonaire: Statistics Netherlands.

Authors Dutch version: Sarah Creemers, Loe Franssen
Translated by CBS Vertaalbureau

5.1 Introduction

Barriers to trade can be defined as natural or man-made barriers that impede domestic and international transactions between economic entities. They may be geographic and cultural in nature or be erected as a result of policy. Geographic and cultural trade barriers encompass a wide range of factors, such as physical distance between trading countries, language barriers, cultural differences, virtual proximity and the degree of digitisation.

In the case of trade in services, it is more difficult to harmonise legislation and regulation between the various trading partners, and language and cultural barriers play a bigger role than in the case of trade in goods (Walsh, 2006). In contrast to trade in goods, services are not limited by tariff measures and border controls. Trade in services generally requires the movement of people and capital between countries. In most countries there are legal and regulatory requirements that must be satisfied in that regard (Walsh, 2006). Barriers to trade in services relate almost exclusively to regulatory measures or bureaucracy. National regulation – such as licences, quotas, professional qualifications and immigration rules – determine when and how foreign providers can enter a market. These are also referred to as non-tariff barriers (Jozepa et al., 2019). The many types of regulation make it harder for foreign service providers both to access markets and to expand activities in markets. In contrast to tariffs or transport costs, firms are not all impeded to the same extent by non-tariff barriers. That is because some firms appear better able than others to cope with regulatory obstacles in trade in services (Benz et al., 2020).

Whereas Chapter 4 examined the extent to which such trade barriers affect the initial entry into a foreign market, this Chapter takes a different perspective: to what extent do these trade barriers impede existing activities of Dutch service exporters in foreign markets? That requires us first to draw a clear distinction between barriers that impede international service providers’ access to foreign markets (also known as extensive margin of trade) and operational barriers that affect the expansion of their export portfolio in markets they already serve (also known as intensive margin of trade). On the basis of the firms in a comprehensive sample survey (see box in Section 5.2), a descriptive account is given of how the export value of Dutch services relates to various natural and non-natural trade barriers. Finally, we endeavour to provide a general picture of the significance of these trade barriers by means of econometric analyses.

Outline

Chapter 5 of this Internationalisation Monitor examines the extent to which geographic, cultural and non-tariff barriers affect exports by existing service providers. Section 5.2 discusses how non-tariffs trade barriers can be classified. We distinguish between barriers to entry and barriers to operations. Firms’ trading portfolios are discussed in Section 5.3. The difference between the extensive and the intensive export margin is central here. Trade barriers make it more difficult for firms to supply goods or services to foreign markets, sometimes even to the extent that they are completely prevented from exporting. The main barriers to international trade in services are natural barriers (geographic or cultural) and non-tariff barriers. Section 5.4 focuses on natural barriers. Section 5.5 examines non-tariff barriers to services, which we will measure on the basis of two external data sources: the Services Trade Restrictiveness Index (STRI) of the OECD and the Restrictiveness Indicator of the European Commission. The impact of barriers to service exports is examined econometrically in Section 5.6. The methodology and data used are described in Section 5.8.

5.2 Barriers to entry versus barriers to operations

Trade barriers come in different shapes and sizes. What they generally have in common is that they entail costs for a firm. These may be one-off or ongoing and may complicate or even completely preclude the activities of foreign service providers (Australian Government, 2015; Egger & Shingal, 2021). Some costs can be recouped, however, for example if they relate to a (possibly necessary) investment in quality (Van den Berg & Franssen, 2021). An important distinction can also be drawn between barriers that occur particularly at the border – at market entry – and barriers that mainly play a role behind the border and affect ongoing operations.

If trade barriers prevent service providers from entering certain international markets, we describe them as barriers to entry. These barriers to entry may distort investment decisions, including the decision on whether to open up a (possibly new) export market. Significant barriers to entry may mean that only the most productive firms are able to bear the fixed costs associated with entering foreign markets (Nordås et al., 2008). Examples of barriers to entry include start-up procedures involving high administrative costs and complex rules or the requirement to obtain the necessary licences and permits (Australian Government, 2015). Professional service providers, for example, generally require a permit in order to work abroad and they need to demonstrate that local qualification requirements have been satisfied (Nordås et al., 2008). The fact that legislation and regulation differ greatly between countries means that the fixed costs of complying with local regulations in a specific export market are in fact sunk costs for market access. The service provider has to decide whether or not to “invest” in market access and to bear these fixed costs (Kox & Lejour, 2005). In Chapter 4 of this Internationalisation Monitor we examine the extent to which trade barriers faced by Dutch service providers abroad affect their potential export start.

When a firm has overcome barriers to entry, it generally still has to contend with other barriers, the so-called barriers to operations (Australian Government, 2015; Nordås & Rouzet, 2017). Administrative formalities can negatively impact the activities of international service providers. An example of this kind of paperwork is that logistics service providers in some countries have to submit a certain set of documents to the local port authority for every ship entering coastal waters (Dincer & Tekin-Koru, 2020). It may be more complex for foreign service providers to fulfil local regulations, because it will require more effort to find the full, correct information concerning local legislation and practices than for local service providers (information asymmetries) (Crozet et al., 2016).

5.3 Extensive versus intensive export margin 

In parallel with the discussion on the classification of trade barriers, it is possible to characterise firms’ trade portfolios. The term extensive margin of trade is used when it comes to entering a new foreign market or exporting a new product. A firm can increase its extensive margin of trade, for example, by exporting to more countries, or by exporting additional products or services. The intensive margin, on the other hand, relates to the trade value involved in existing trade relationships. Hence when a firm successfully increases its sales of the same product or service to the same market from one year to the next, this is referred to as an intensification of this trade flow and growth in the intensive margin.

Figure 5.3.1 shows an overview of the extensive margin of trade portfolio of the 950 firms in our dataset as discussed in the box in Section 5.2. It can be seen, for example, that these firms trade services with an average of almost 60 countries, by means of both imports and exports. They have foreign subsidiaries in an average of 20 countries, while they export goods to almost 40 other countries. It should be noted, however, that the EU counts as a single country within these trade in goods statistics, whereas it is broken down into individual EU countries in the trade in services statistics. The actual number of countries will therefore be higher than the stated 40.

These exporters also export an average of five different types of services and they have been active as service exporters for an average of six of the eight years under review. This does not mean, however, that every service is exported to the same country every (or almost every) year. The average export spell, the number of years in which one and the same product or service is exported to one country without interruption, is four years. Previous CBS research (Boutorat et al., 2019; Van den Berg et al., 2022) has already shown that the Netherlands has many intermittent exporters. That is confirmed again in Figure 5.3.2 in the case of trade in services, since no fewer than one-quarter of all export relationships last only one year. Only 16 percent of all relationships were continuous or uninterrupted.

To gain a picture of the intensive margin of trade in services, we focus on the trade relationships that continue without interruption over the eight years under review (2014-2021). We then see in Figure 5.3.3 that the average amount involved in these trade relationships grows from €800 in 2014 to over €1,600 in 2021 for export transactions and from €700 to almost €1,500 for import transactions. In both imports and exports we therefore see growth in the intensive margin: the value of existing trade relations doubles in a period of eight years. At the firm level, we also see that the average export (import) value of services per firm grows from €72,000 (€66,000) in 2014 to over €160,000 (€145,000) in 2021.

5.4 Natural trade barriers

Trade barriers can be broadly divided into natural and non-natural trade barriers, such as import tariffs (see CBS, 2020a) or non-tariff measures (see CBS, 2021). Natural trade barriers concern geographic or cultural factors. We explain four of these in this section.

Having been traditionally developed to describe goods trade, the gravity model can explain trade in services flows between countries as effectively (see Kimura & Lee, 2006; Nordås & Rouzet, 2017). In the original gravity model of Tinbergen (1962), the distance between and the economic size of the destination country and the country of origin, measured in terms of GDP, are the main determinants of international trade. GDP makes a market attractive, whereas distance negatively impacts this relationship. Figure 5.4.1 shows that, in the case of trade in services too, the trade value is positively correlated with the size of the destination market.

In the case of geographic distance, it is known that this has a clearly negative effect on the value of goods trade. The relationship is less obvious in trade in services, because a service does not always have to be physically transported but can often be supplied virtually/digitally. Nevertheless, in the case of services too, the physical distance is still negatively correlated with the trade value: the greater the distance to the destination market, the smaller is the  trade value (see Figure 5.4.2).

Virtual proximity has recently been considered in the literature as an alternative indicator for the proximity of countries. Its measurement is based on bilateral hyperlinks and bilateral website visits between countries (Chung, 2011; Hellmanzik & Schmitz, 2016). This could be a more relevant measure of distance than geographic distance, particularly for trade in services. Virtual distance (or proximity) does indeed prove to be an important determinant: according to the Chung (2011) method, countries that are virtually close trade relatively more with each other (see Figure 5.4.3). This means that consumers purchase services more often from providers in countries about which they have more information and with which they feel more connected. Virtual connectedness can reduce uncertainty about the quality of services procured from abroad (Hellmanzik & Schmitz, 2016).

The rise of the internet and new digital technologies makes online service provision abroad easier (Loungani et al., 2017; WTO, 2019). The extent to which countries are able to adopt new technologies differs greatly, however. The World Bank’s Digital Adoption Index (DAI) surveys these differences and examines worldwide access to digital technologies. Digitisation has a major impact on international trade. It has simplified it, particularly in the case of e-commerce (Jayasooriya, 2021). We also see this in Figure 5.4.4: relatively more is exported to countries with higher scores in this digital adoption index.

5.5 Non-tariff barriers

As stated above, in addition to natural trade barriers there are also non-natural trade barriers. These are policy measures that can impede trade in services in some way, either knowingly or unknowingly. This section looks at non-tariff barriers to trade in services, which we will measure on the basis of two external data sources: the Services Trade Restrictiveness Index (STRI) of the OECD and the Restrictiveness Indicator of the European Commission. The many forms of regulation impede both access to (extensive margin) – as can be seen in Chapter 4 – and the expansion of existing export spells (intensive margin) for foreign service providers. Tables 5.9.1 and 5.9.2 in the annex contain information on the link between the external data sources of the OECD and the European Commission and Statistics Netherlands data sources.

Services Trade Restrictiveness Index measured by the OECD

The OECD Services Trade Restrictiveness Index (STRI) database combines information from more than 16,000 laws and legal provisions for 22 service sectors in 50 countries on an annual basis from 2014 to 2021 inclusive. This database provides an index value for each sector, each country and each year. STRI indices take the value from 0 to 1. Complete openness to trade and investment gives a score of 0, while being completely closed to foreign services providers yields a score of 1.

The OECD groups the barriers to trade in services into five policy areas (see box in Section 5.1 for an illustration of these barriers):

• Restrictions on foreign entry (such as limits on the number of foreign shareholders, nationality requirements for members of the executive board, restrictions on cross-border mergers);
• Restrictions on the free movement of people (such as visa requirements and permits to exercise certain professions);
• Other discriminatory measures (for example regarding taxes, grants or public procurement);
• Barriers to competition (such as anti-trust policy);
• Regulatory transparency.

Restrictions to market access for foreign service providers protect local firms against competition. In addition, barriers to operations, such as burdensome regulatory procedures and processes, impede both local and foreign firms. A high STRI score would therefore be expected to have a negative impact on the international performance of the service sector in question (Nordås & Rouzet, 2017).

Since the European Economic Area (EEA) constitutes a common market and thus involves deeper integration than a regular preferential trade agreement, which does not fall within the STRI, the OECD recently introduced an additional version of the STRI: the intra-EEA Services Trade Restrictiveness Index. This index covers the same five policy areas as the original STRI and is intended to provide an accurate picture of the trade restrictions in services between the 25 EEA countries. Benz & Gonzales (2019) show that trade in services in the EEA is considerably more liberal than the multilateral policy that the EEA member states apply to non-members. Certain trade barriers nevertheless remain within the internal market.

In this chapter we therefore use both the original and the intra-EEA STRI. More specifically, we have used the intra-EEA STRI value of the destination country if the destination country concerned, like the Netherlands, is a member of the EEA. If the destination country is not an EEA member, the original STRI of the destination country is used. We have thus adopted the same method as that described in Jungmittag & Marschinski (2020).

High STRI, many restrictions, less international trade in services

Nordås & Rouzet (2017) analyse the impact of the STRI on international trade in services. Here they use a (PPML) gravity model. A gravity model allows a distinction to be drawn between natural barriers – such as geographic and cultural differences – and barriers resulting from policy. The authors find sufficient statistical evidence to support the hypothesis that larger barriers (i.e.: a higher STRI) are associated with less trade in services. In most service sectors a higher STRI is associated with lower imports, indicating that the costs for foreign suppliers of entering and serving the host market are raised by trade-restrictive regulations as expected. The authors find in particular a strong connection between a high STRI and lower imports of legal services, telecommunications, commercial banking services, insurance, maritime transport services and courier services. This emphasises the importance of an open and competitive regulatory regime in strengthening the international competitiveness of service exporters (Nordås & Rouzet, 2017).

Rouzet et al. (2017) also find that service providers’ intensive margin of exports is inversely proportional to the legal restrictions in the importing country, as measured by the OECD STRI. Complex and restrictive regulation in the destination country limits the trade value. These trade restrictions on services reflect not only the operational trading costs but also one-off fixed and sunk costs.

We also note that the Dutch exporters in our dataset export less on average to countries with a higher STRI (see Figure 5.5.1). This relationship is not as pronounced, however, as the relationships we see in the case of natural barriers in the previous section. That may be because the effect of the non-natural barriers can differ between sectors, but also because the different types of barriers that make up the overall indicator may be related to trade in various ways.

Type of restriction and foreign direct investments

Jungmittag & Marschinski (2020) study the impact of trade barriers in services on bilateral greenfield investment projects (FDI)¹⁾ in four different business service sectors: computer, accounting, legal, architecture and engineering. In their econometric models they draw a distinction between the various STRI restrictions: restrictions on foreign entry, restrictions on the free movement of people and other trade restrictions on services. It is important to consider these different 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. For the accounting, computer and architecture and engineering service sectors, they find statistically significant evidence of a negative impact. The explanatory force of the models generally improves if the subtypes of the STRI are used instead of the aggregated STRI total score.

A new kid in town: Restrictiveness Indicator measured by European Commission

At specific times (for the years 2006, 2012, 2017), the European Commission has collected detailed legal data on existing restrictions in certain service sectors. It has examined the requirements imposed at national, regional or local level, as well as all rules set by professional bodies, associations or organisations in the exercise of their statutory autonomy to regulate access collectively to a specific service activity. The European Commission thus aims to gain an overview of the remaining barriers in the internal market and to maintain visibility on the evolution of these restrictions over time.

It documented each barrier, noting whether it existed in the country and service sector concerned. If the barrier was present, a numerical score between 0 and 2 was awarded, with 0 representing a complete absence of barriers and 2 representing the full presence of a barrier. These scores were then used to calculate the average per sector and per country(European Commission, 2021).

The EC Restrictiveness Indicator data were recently made available by the European Commission. Only a few studies have been conducted hitherto into the relationship between the EC Restrictiveness Indicator and trade. In accordance with the gravity model of Monteagudo et al. (2012), a 10 percent reduction in trade barriers leads to a 1.5 percent rise in trade. Using Statistics Netherlands and EC data we also see a negative relationship between barriers and trade, although at first sight this is not a very strong relationship (Figure 5.5.2).

The EC Restrictiveness Indicator is often used to analyse the economic impact of the Services Directive. The Services Directive was introduced in 2006 with the aim of promoting trade and investment in services within the EU by eliminating unjustified regulatory and administrative barriers. The Services Directive seeks more particularly to eliminate unnecessary and severe barriers to trade in services in the single market. A significant number of barriers nevertheless remain and there are many exceptions (European Commission, 2021; Barendregt & Wijffelaars, 2017). Barbero et al. (2022) use econometric and modelling techniques to quantify the macroeconomic impact of reforms of regulation that eliminated barriers in the European internal market for services between 2006 and 2017. The results of the modelling simulations show that the regulatory reforms between 2006 and 2017 will lead to an increase in GDP and a rise in employment.

Role of firm characteristics

Trade barriers do not affect all service providers to the same degree. Firm size, productivity and previous export experience appear to be decisive factors in tackling trade barriers at the border and behind the border (Benz et al., 2020; Rouzet et al., 2017).

The impact of regulatory obstacles on export values is considerably less negative for larger firms, which indicates that some of these barriers represent fixed export costs that prevent smaller firms from expanding exports to foreign markets. Internal legal expertise and wider existing networks of business partners at home and abroad illustrate why larger firms can cope better with complex and challenging regulatory environments (Benz et al., 2020). Very productive firms are more likely to expand into more restrictive markets, while less productive firms will tend to focus on more open markets (Rouzet et al., 2017).

5.6 Econometric results

This Internationalisation Monitor has already reviewed various factors that play a role in determining the value of service export flows. In this chapter we have seen, for example, that virtual proximity, a larger economy and digital adoption skills in the destination country are positively correlated with the value of service exports to that country. At the same time, barriers such as those classified and quantified by the OECD and the European Commission, but also, for example, physical distance, have a negative effect. As has already been discussed, the characteristics of firms may play a role. In order to weigh the relative role of each of those determinants, it is important to incorporate them simultaneously in an econometric regression. The set-up of the regression models and the detailed econometric results of these regressions are described in Section 5.8.

It is still more difficult to export to more restrictive sectors

Even after taking into account various observable and non-observable differences between firms, service types, destination countries and years, we see that the composite index of barriers to trade in services as defined by the OECD has a significant negative correlation with the value of Dutch service exports. This means that the average Dutch service exporter exports more (a higher value) of a particular service if fewer restrictive barriers arise in that sector, all other things being equal. These results thus confirm that the correlations in Figure 5.5.1 hold up, even after we incorporate other factors in the regression model. These regression results are shown in Table 5.8.1, Column 1. The European Commission Restrictiveness Indicator, by contrast, shows at first sight that trade barriers have no significant effect on the value of trade in services (Table 5.8.1, Column 2). However, this may be because different groups of firms are impacted in different ways by trade barriers, so that the “net” effect of all these groups combined is practically zero. This does indeed prove to be the case here. For firms with relatively low labour productivity, the effect of trade barriers (as quantified by the EC Restrictiveness Indicator) on the trade value is indeed significantly negative (Table 5.8.1, Column 4). These results therefore show that firms with low productivity find it more difficult to overcome trade barriers than firms with high productivity. Using the OECD STRI, however, we do not see such differences between groups of firms.

Differences between firms and barriers play a key role

The results discussed above relate to the overall index compiled by both the OECD and the EC. As discussed earlier, these are combined indices that include various detailed barriers. However, we can also examine the impact of these detailed barriers on the trade value. The results are shown in Table 5.8.2, Columns 1 and 2. Our analysis shows that the negative effect of barriers as measured by the OECD is caused particularly by the barriers to competition.

Barriers to competition include the extent to which the government can curb the influence of foreign firms, for example by setting limits on the percentage of foreign ownership, but also, for example, the extent to which a foreign firm can protest against certain rules which, it considers, lead to unfair competition. The telecommunication sector in China is strictly regulated, for example. Barriers to competition are the principal form of trade restrictions in this sector (OECD, 2022a). Hence both foreign and domestic telecom firms find it difficult to compete against Chinese government corporations.

Using the EC Restrictiveness Indicator, we see in particular that legal form requirements play a major role in barriers to trade. In Austria, for example, it is possible for architects to set up a firm, but only certain forms of company are permitted, including the form of private limited company.

Notably there are also tariff requirements. Tariff requirements refer to certain rules that mean firms do not have complete freedom to set their own prices. Germany, for example, lays down the method used to set the fees charged by architectural and engineering firms (see box in Section 5.1). In that sense, prices are therefore regulated and market participants are impeded in their activities in the German market.

To what extent can these barriers be linked to market entry or ongoing operational activities as discussed in Section 5.2? Nordas & Rouzet (2017) and Andrenelli et al. (2018) divide the various types of barriers into categories, with restrictions on foreign entry, international mobility of people and “other discriminatory rules” relating to market access, while barriers to competition and regulatory transparency relate particularly to ongoing operational activities. This can explain why in our analysis the barriers to competition emerge as a significant barrier to service exports in pre-existing trade partnerships.

The various restrictions identified in the European Commission database that have a significant effect on exports of services are also probably more likely to occur behind the border. This distinction is not clear-cut, however. In the case of these “specific” barriers too, there is always a high degree of heterogeneity, for example between sectors. There are also similarities between the various types of restrictions, so a simple division into barriers to entry and barriers to operations is not always straightforward.

The other variables in the models show the expected pattern. The Netherlands exports significantly more to countries with a high GDP and to countries located closer by. The importance of a control for GDP can be seen when this variable is not included in the regression. The significant negative effect of the STRI index on the value of service exports then becomes insignificant. This means that countries that have relatively strict service sectors are economically larger. If we disregard this, the positive effect of the higher GDP is partly absorbed by the STRI variable that has a negative effect. The combination of these two variables then results in an insignificant STRI index effect.

5.7 Summary and conclusion

The attractiveness of foreign market opportunities drives firms to sell across the border. Firms face all kinds of barriers, however. Exporters of both goods and services have to deal with natural barriers, such as the geographic distance to the destination country, or differences in language and culture. Tariff measures at the border are also important in the case of trade in goods. Statistics Netherlands has conducted previous studies of these (see for example CBS, 2020b).

In this Monitor we focus specifically on service providers, who face barriers particularly from regulations intended to restrict activities in a particular market. These restrictions may impede not only market entry, as highlighted in the previous chapter, but also existing activities in the market concerned. In this fifth chapter the emphasis is on these existing  trade relationships and the extent to which they are shaped by trade barriers. The OECD and the European Commission have tried in recent years to document and quantify these restrictions and thereby give an indication for each country and service sector of how restrictive the particular service sector is.

In this chapter this information is linked to information on the export activities of a small, select group of large Dutch service providers. It became clear that these barriers did indeed have a significant effect on the value of service exports: higher restrictions go hand-in-hand with lower export values. This relationship holds if at the same time we check for other key determinants (firm, country or sector) of service exports.

This kind of empirical study into the extent to which trade barriers affect service exports remains scarce. That is particularly due to the fact that data on trade in services at the firm level are only available for a limited number of countries, but also because this type of trade barrier is much more difficult to document than, for example, information on import tariffs. This complex data situation for trade in services means that it is not straightforward, for example, to  look closely at the heterogeneity of firms, sectors or types of barriers, as is commonly done in analyses of trade in goods. Regarding future research on this theme, it is therefore important that more detailed data become available on trade in services and the barriers that arise in it. That will provide opportunities to gain greater insight into the specific mechanisms that restrict or impede firms doing business internationally.

5.8 Data and methods

The main data sources that form the basis of this chapter are the so-called response data. This is a sample survey conducted every quarter by Statistics Netherlands in which detailed information on trade in services is gathered for the largest 950 or so firms (see Table 5.2.1 and the box in Section 5.2 for further information). We link various business characteristics to this as well as information on barriers to trade in services. These are obtained in particular from CEPII, the European Commission and the OECD, which have already been discussed in this Monitor.

We use these data to measure the effect of various trade barriers on exports of services by means of the following comparison inspired by the literature.

Where the exports by firm i and service j to country d in year t are explained by the following factors: a restriction index obtained from either the OECD or the EC (STR__jdt), the labour productivity of the firm, the number of employees, a dummy variable that states whether a firm also imports the service concerned, whether the firm exports goods, whether it is foreign owned and whether it has a foreign subsidiary in the destination country. We also control for the GDP and the distance to the destination country and whether it belongs to the European Union. For distance we use various measures, such as physical distance, virtual proximity, the time zone in which the country is located and the digital adoption capacity of the country (see also box in Section 5.6). In addition to these observable differences between firms, service types, destination countries and years, we also control for non-observable differences within these groups by means of firm, service, country and year fixed effects. The assumption is that these differences do not change significantly over time. This comparison is estimated by means of a pseudo poisson maximum likelihood (PPML) estimator in which standard errors are clustered at country level.

We also examine the extent to which exports of construction services, for example, are impeded by restrictions that apply in the construction sector in the destination country. This involves a more detailed identification than the one in Chapter 4. There we use the SBI of the firm as an indication of the type of service that the specific firm exports. In this section we actually know which service is exported to which country, so the barriers can be more closely linked to the service traded.

The results are shown in Tables 5.8.1 to 5.8.3. Table 5.8.1, Columns 1 and 2 show the results of the composite indices of the OECD and the European Commission, while Columns 3 and 4 interact this index with output. Table 5.8.2 shows the types of barriers involved in the composite indices. Finally, Table 5.8.3 shows the coefficients of different proxies for distance.

5.9 Annex

5.10 Literature

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