This paper examines whether restrictive data policies are related to trade in services. The authors have collected comparable information on a variety of policy measures that regulate data for a wide group of countries for the years 2006–2016. This information is compiled in a weighted index that assesses the restrictiveness of these countries’ data policies. They distinguish between policies regulating the cross-border movement of data and policies regulating the domestic use of data. Using econometric estimations, they show that strict data policies are negatively and significantly associated with imports of data-intense services. Therefore, countries applying restrictive data policies, in particular with respect to the cross-border flow of data, are likely to suffer from lower levels of services traded cross-border. The results of this analysis are significant and hold for various robustness checks.
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In WTO speak, the cross-border trade of services over the internet is commonly referred to as Mode 1 trade in services, which is defined as “services supplied from the territory of one Member into the territory of any other Member” pursuant to Article I:2 of the GATS. It is worth mentioning that WTO members have so far not agreed upon a clear determination of whether the electronic cross-border delivery of a service is a service supplied through GATS mode 1 (cross-border) or mode 2 (consumption abroad).
Restricting data flows limits countries’ ability to import digital services against lower prices and greater quality of new services and varieties, which then affects productivity. Moreover, the goods trade literature has shown that restricting intermediate inputs constrains countries’ potential to reach greater levels of productivity. See, for example, Amiti and Konings (2007) and Goldberg et al. (2010; 2009) in the case of goods.
Note that some coefficients show a positive result which may be due to omissions of control variables such as services regulations. However, due to insufficient observations when including these controls, regressions become impossible. This also provides us with a pragmatic reason to use our preferred identification strategy of data-intensities by sector. See further discussion in the paper.
Another recently developed report from the USITC (2017) has described and scrutinised the many ways in which digital trade takes place and ends this examination with a list of policy measures relevant for data flows. Examples include data protection and privacy and data localisation rules. Other examples the USITC report includes are more indirectly related to data flows such as cybersecurity measures, censorship and intellectual property rights measures. These measures are not included in our empirical assessment but are nonetheless picked up and discussed in Ferracane et al. (2018).
Other channels of trade in services are according to the WTO’s GATS Article 1:2: Mode 2, which covers services supplied in the territory of one country to the service consumer of any other country (also known as “consumption abroad”); Mode 3, which includes services supplied by a service supplier of one country, through commercial presence, in the territory of any other country (also known as “commercial presence”); and finally Mode 4, services supplied by a service supplier of one country, through the presence of natural persons of a country in the territory of any other country (also known as “presence of natural persons”).
The empirical trade literature in goods has also developed other types of intensities such as high-skilled labour intensity (H/L) and capital intensity (K/L) mainly for the manufacturing sector. These intensity measures are currently not available at detailed sector level for services and therefore are not used in our empirical specification. Consequently, the estimation results may exhibit a bias.
Moreover, one additional reason to look at the input-side of data and data-related services is that the recent economic literature connects the potential growth and productivity performance of countries notably to the input usage of data and digital services in the wider economy. See Jorgenson et al. (2011).
Note that 2007 is the most recent year the BEA report input–output tables for the US at a detailed level. In fact, these matrixes at 6-digit NAICS level are the most detailed in the world, which allows us to precisely determine which are the data-input sectors from where each services sector sources data from.
The concordance table between 4-digit NAICS and 2-digit BPM6 can be obtained upon request. Admittedly, the inclusion of intellectual property/royalties and license fees as a service is a BOP decision and some debate exists whether this is truly a service. In addition, for some countries, this may also reflect tax and transfer pricing as drivers of observable trade in this sector. However, since this sector is included in all publicly available data sources recording trade in services, we prefer to include it. Nonetheless, in our regression we have also dropped this sector entirely as additional (unreported) robustness checks. Results do not alter in any way apart from slight coefficient size changes. Results are available and can be obtained upon request.
Another non-ICT sector that is shown to be very data-intense is the retail sector. However, neither the US Census nor the BPM6 classification shows a separate entry for retail or wholesale distribution services, which is the reason why this sector is omitted in our analysis of intensities and is not covered in our regression analysis.
An additional convenient motivation for using US tables is that the US is often used as a benchmark country in similar cross-country studies using sector intensities, which makes our input coefficients on data usage exogenous. However, there is a debate in the economic literature about whether one should use the assumption of equal industry (or sector) technologies across countries or not. Equal technology coefficients seem reasonable if one assumes that the countries selected in the sample are reasonably similar in their economic structures and technology endowments. On the other hand, our fixed effects in the econometrical specification should take care of these technology differences. Moreover, in our case, we don’t have detailed country-specific IO tables for the 64 countries covered in our sample at such a disaggregated level, which we need. Practically, using US input–output shares only might as well form a convenient assumption if a suspicion exists that input–output tables at the country level are not always very well measured for some economies. This could be the case for less developed countries which often suffer from weak reporting capacities. Our country selection includes a substantial number of less developed countries where this could be the case.
Of note, both intensity proxies measure the use of software or data services as a flow. The way in which intensities are measured is in line with previous research (Arnold et al. 2015, 2011; Bourlès et al.,2013) in services. However, other literature mentioned in this work developing intensities proxy for a stock of a certain factor, such as labor and capital (i.e. Romalis, 2004). As such there is a mix, and some of the software technologies can also be considered a stock (see Corrado et al. 2012). The conceptual setup of our paper is closer to the first strand of the literature using software expenditures as a flow, but there where possible we try to capture both elements by using both capitalized and non-capitalized expenditures as some software services are also performed in-house.
The authors have contributed to the development of the database at ECIPE. The dataset comprises 64 economies and is publicly available on the website of the ECIPE at the link: www.ecipe.org/dte/database. Besides analysing the 28 EU member states and the EU economy as a single entity, this database also covers Argentina, Australia, Brunei, Canada, Chile, China, Colombia, Costa Rica, Ecuador, Hong Kong, Iceland, India, Indonesia, Israel, Japan, Korea, Malaysia, Mexico, New Zealand, Nigeria, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Russia, Singapore, South Africa, Switzerland, Taiwan, Thailand, Turkey, United States and Vietnam.
The authors have previously used this categorisation in Ferracane et al. (2018).
The EU adequacy decision typically goes one way and is not necessarily reciprocal by definition, which makes the preferential aspect somewhat complicated. For instance, the EU gives adequacy to Canada, which means that personal data can be transferred to Canada. However, this does not necessarily mean that Canadian data can be transferred to the EU. Yet, the decision to give adequacy to a country is done on a unilateral basis. One factor that needs further attention however is the existence of the preferential regime among EU states, which applies since 1995. Yet, we do not think that this fact impacts the robustness of our results as our paper shows that restrictions have a negative impact on trade in services, and this result emerges despite the fact that EU countries are listed as having some restrictions, also when trading with other EU countries. Intuitively, this makes us think that our results would be stronger if we would categorise EU countries as having no restrictions for intra-EU trade only, while keeping the same level of restrictions for other countries.
One should be aware, however, that Eq. (3) picks up international trade in services insofar these services are measured an EBOPS categories, not necessarily the sector in which the firm importing the service belong to. That is particularly true with respect to the servicification literature (Ariu et al., 2020; 2019). For instance, this strand of the literature points out that manufacturing firms can produce, sell and export services besides goods, which would render the “true” importing sector inconsistent with the EBOPS category.
The WTO-UNCTAD-ITC database also covers for services traded over Mode 2 and 4 although they do not form the main share of the total amount of services trade data recorded.
Since our NRI variable is demeaned, the statistical interpretation is that a negative sign on the coefficient result of this interaction variable means that countries with stronger digital networks compared to countries with an average NRI score experience a stronger reduction in services imports when having stricter levels of policy frameworks for data. By centring our variable first, effects can therefore be made more interpretable.
In total there are four modes of supply of which (1) Mode 1 represents cross-border – services supplied from the territory of one Member into the territory of another, e.g. software services through e-mail to another country; (2) Mode 2 represents consumption abroad – services supplied in the territory of one Member to the consumers of another, e.g. education services in another country; (3) Mode 3 represents commercial presence – services supplied through type of business or professional establishment of one Member in the territory of another; and finally (4) Mode 4: Presence of natural persons – services supplied by nationals of one Member in the territory of another, e.g. doctors moving to foreign country to provide temporary their service.
Note as well that the US Census ICT Survey only provides ICT expenditure data at 4-digit NAICS for the years 2010, 2011 and 2013 and so earlier years before 2010 are not possible to take into our research at detailed level.
A final services sector included in the World Bank’s STRI is the retail sector. However, since the EBOPS 2002 manual provides explanation of the difficulties of classifying trade in this sector (and their associated challenges of actually measuring trade in retail), we have omitted this sector in our analysis.
However, we also ran replicated Table 4 first to see whether initial results were holding, and no changes were observed apart from slight coefficient size changes. Results are unreported but can be obtained upon request.
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We thank Giorgio Garbasso, Nicolas Botton, Valentin Moreau and Cristina Rujan for their excellent research assistance. Comments from Bernard Hoekman, Ben Shepherd, Sébastian Miroudot, Sebastian Sàez, Cosimo Beveralli, Ruchita Manghnani and Rebecca Freeman are very much appreciated. We also thank the participants of the ADBI conference on development and services, the CEP-IMF-World Bank-WTO workshop on services, the EUI seminar on Empirical Investigations in Services Trade, the 2018 World Trade Forum and the ELSNIT seminar on Trade and Technology for their excellent feedback.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1: The category of Royalties and licenses & Intellectual property
The category of Royalties and license and Intellectual property are two different names that refer to the same variable which are found in WTO-UNCTAD-ITC and OECD-WTO (BaTIS) trade in services databases. In the WTO-ITC-UNCTAD database, to which we refer as BPM6, this category is called Intellectual property whereas in the BaTIS database this category is denoted as Royalties and licenses.
Unfortunately, no direct connection between the NAICS 2007 classification and the sectors Royalties and licenses nor Intellectual property can be made from where we have computed our data intensities, i.e. (D/L). Equally unfortunate is that no concordance table exists between NAICS and BPM6 and NAICS and EBOPS more generally. Therefore, we have constructed our own concordance tables and build them up from an extremely detailed 6-digit level. This is not too difficult when mapping each 6-digit NAICS code into a 2-digit BPM6 or EBOPS code. However, since no clear 6-digit NAICS code can be directly linked to the services category of Royalties and license or Intellectual property, we have extended our concordance scheme to include this sector. We have done so in an indirect way through other concordance systems. The result of this concordance process can be seen in Table 10.
The way to do so is not clear-cut and some assumptions need to be made. For starters, the WTO-UNCTAD-ITC trade in services database designates Intellectual Property as chapter “SH” following the 6th edition of the Balance of Payments (BPM6) while the OECD-WTO BaTIS denotes this category as S266 following EBOPS 2002. As said, both overlap and are therefore indicated as “SH/S266” in Table 10. To eventually arrive at the NAICS 2007 code, two sequential sources are needed. First, the Appendix 3 of the MSITS 2002 EBOPS classification provides a concordance table between EBOPS and CPC 1.0, which is used as a first step. Four sectors are classified under 266 Royalties and license fees, namely Patents, Trademarks, Copyrights and Other non-financial intangible assets. With the help of the United Nations correspondence tables website (https://unstats.un.org/unsd/classifications), a concordance can be made between CPC 1.0 and finally NAICS 2007 through five successive steps as outlined in Table 10.
Many different NAICS 2007 codes fall into one of the four original CPC 1.0 codes and therefore not all of them are equally relevant for Royalties and licenses or Intellectual property services. For that reason, we are not taking all 6-digit NAICS 2007 which eventually trace back to the two BPM6 and EBOP 2002 sectors as given in Table 10. The reason is that not all NAICS 2007 sectors are fully covered by the two intangible sectors. We only identify those which are not partially covered. These sectors are given in bold in column “NAICS 2002/07” of Table 10 and are not given an * under the column “P” (which stands for partial). The information on whether an item is covered partially or not also comes from the United Nations correspondence tables. To come up with 2-digit BPM6 and EBOPS 2002 sector intensities, we take the unweighted average of each data intensity of these designated non-partial NAICS 2007 sectors, which should give us eventually a good approximation of the level of data used in the two sectors of Royalties and license and Intellectual property.
As one can see, a mix of services sector fall under the two sectors, namely R&D services, some financial services, as well as cultural services such as motion pictures and sound recording. Also trust funds are fully covered under this category of Royalties and license/Intellectual property. Of note, the NAICS sector 515120 is not included under EBOPS, but is covered under BPM6 following their respective manuals.
Appendix 2: Methodology for the data policy index
The data policy index covers those data policies considered to impose a restriction on the cross-border movement and the domestic use of data. The methodology to build the index follows Ferracane et al. (2018) and covers the measures listed in the Digital Trade Estimates (DTE) database which is available on the ECIPE website (www.ecipe.org/dte/database). Starting from the DTE database, these policies are aggregated into an index using a detailed weighting scheme adapted from Ferracane et al. (2018). We expand the index released by Ferracane et al. (2018), which covered only the years 2016/2017, to create a panel for the years 2006–2016 that we can use in our regressions. In addition, the database and index are updated with new regulatory measures found in certain countries.
While certain policies on data flows can be legitimate and necessary to protect the privacy of the individual or to ensure national security, these policies nevertheless create a cost for trade and are therefore included in the analysis. The criteria for listing a certain policy measure in the DTE database are the following: (i) it creates a more restrictive regime for online versus offline users of data; (ii) it implies a different treatment between domestic and foreign users of data; and (iii) it is applied in a manner considered disproportionately burdensome to achieve a certain policy objective.
Each policy measure identified in any of the categories receives a score that varies between 0 (completely open) and 1 (virtually closed) according to how vast its scope is. A higher score represents a higher level of restrictiveness in data policies. The data policy index also varies between 0 (completely open) and 1 (virtually closed). The higher the index, the stricter the data policies implemented in the countries.
The index is composed of two sub-indexes that cover two main types of policy measures that we analyse in this paper: one sub-index covers policies on the cross-border movement of data and one sub-index covers policies on the domestic use of data. Analysing these two sub-indexes separately provides additional information on whether the impact of data policies on services trade varies according to the nature of the policies. The full data policy index is measured as the sum of these two sub-indexes. This appendix presents in detail how the two sub-indexes are composed. It shows which policy measures are contained in each of the sub-index and the scheme applied to weigh and score each measure.
The list of measures included in the two sub-indexes is summarised in Table 3 . As shown in the table, the sub-indexes are measures as a weighted average of different types of measures. The weights are intended to reflect the level of restrictiveness of the types of measures in terms of costs for digital trade. The first sub-index on cross-border data flows covers three types of measures, namely (i) a ban to transfer data or a local processing requirement for data; (ii) a local storage requirement, and (iii) a conditional flow regime. The second sub-index covers a series of subcategories of policies affecting the domestic use of data. These are: (i) data retention requirements, (ii) subject rights on data privacy, (iii) administrative requirements on data privacy, (iv) sanctions for non-compliance, and finally, (v) other restrictive practices related to data policies.
The main sources used to create the database are national data protection legislations. Otherwise, information is obtained from legal analyses on data policies and regulations from high profile law firms and from OECD (2015). Moreover, occasionally corporate blogs and business reports were also taken into consideration, as they can provide useful information on the de facto regime faced by the company when it comes to movement of data.
Sub-index on cross-border data flows
The first sub-index covers those policy measures restricting cross-border data flows. These measures are also referred to as “data localisation” measures and can be defined as government imposed measures which result in the localisation of data within a certain jurisdiction. Measures related to data localisation come in various forms and have different degrees of restrictiveness depending on the type of measure itself, but also on the sector and type of data affected.
We identify three types of measures, namely (i) a ban to transfer data or a local processing requirement for data; (ii) a local storage requirement, and (iii) a conditional flow regime. As shown in Table 3, the category of bans to transfer and local processing requirements has a score of 0.5, while the other two categories have a score of 0.25 each. The sum of the scores of these categories can go up to 1, that reflects a situation of virtually closed regime on cross-border data flows. This score is multiplied by 0.5 to create the final sub-index on cross-border data flows. The sub-index therefore goes from 0 (completely open) to 0.5 (virtually closed).
Bans to transfer data across the border and local processing requirements are the most restrictive measures on cross-border flow of data. In case of a ban to transfer data or a local processing requirement, the company needs to either build data centres within the implementing jurisdiction or switch to local service providers with a consequent increase in costs if these domestic service providers are less efficient than foreign providers. The difference between bans to transfer and local processing requirements is quite subtle. In the first case, the company is not allowed to even send a copy of the data cross-border. In the second case, the company can still send a copy of the data abroad—which can be important for communication between subsidiary and its parent company and in general for exchange of information within the company. In both cases, however, the main data processing activities need to be done in the implementing jurisdiction.
For the scoring of these measures, both the sectoral coverage of the measure as well as the type of data affected are taken into account. If the ban to transfer or local processing requirement applies to a specific subset of data (for instance, when it applies to health records or accounting data only), this measure receives a scoring of 0.5. A similar score is also assigned when the restriction only applies to specific countries (for instance, when data cannot be sent for processing only to a specific country). On the other hand, when the measure applies to all personal data or data of an entire sector (such as financial services or telecommunication sector), then a score of 1 is given. Measures targeting personal data also receive the highest score because it is often hard to disentangle personal information versus non-personal information, and therefore measures targeting personal data often end up covering the vast majority of data in the economy (MIT, 2015). The score, as always, goes from 0 (completely open) to 1 (virtually closed). Therefore, if there are two measures scoring 0.5, the score is 1. If there are more additional measures, the score for this category still remains one. This score is then weighted by 0.5 which is the weight assigned to the category of bans and local processing requirements (as presented in Table 3).
The second category covers local storage requirements. These measures require a company to keep a copy of certain data within the country. Local storage requirements often apply to specific data such as accounting or bookkeeping data. As long as the copy of the data remains within the national territory, the company can operate as usual. As for the scoring, when data storage is only for specific data as defined above, this measure receives a score of 0.5, whereas when the data storage applies to personal data or to an entire sector, it receives a score of 1. As mentioned before, the score goes up to 1 maximum and is then weighted by 0.25 which is the weight assigned to the category of local storage requirements (as presented in Table 3).
The third category of cost-enhancing measures related to cross-border flow of data is the case of conditional flow regime. These measures forbid the transfer of the data abroad unless certain conditions are fulfilled. If the conditions are stringent, the measure can easily result in a ban to transfer. The conditions can apply either to the recipient country (e.g. some jurisdictions require that data can be transferred only to countries with an “adequate” level of protection) or to the company (e.g. a condition might consist in the need to request the consent of the data subject for the transfer cross-border of his/her data). In terms of scoring, if a conditional flow regime is found, it receives a score of 0.5 in case it applies to specific data, but it receives a score of 1 in case conditions apply for personal data and or the entire sector. The final score is then weighted by 0.25, which is the weight assigned to the category of conditional flow regimes.
Of note, in certain cases it is not easy to discern whether a measure is a ban to transfer, a local processing requirement or a conditional flow regime. In fact, often cases of ban to transfer and local processing requirements have certain exceptions which might de facto result in a conditional flow regime. When the exceptions are quite wide (for example, if they include the request for consent from the data subject), then the measure has been categorized as a conditional flow regime.
Figure 7 shows a graphical representation of the various levels of data localisation measures taken up in this sub-chapter. The direction of the arrow indicates the increased level of restrictiveness. Note that conditional flow regime is put outside this conventional sequence of restrictiveness because it prevents the flow of data only when the conditions are not fulfilled. Also, note that in Table 3 the ban to transfer is put together with local processing requirements although these two measures have actually been separated in Fig. 7. The point is that the impact of those measures on trade is very similar and they are not always easy to discern. Yet, a ban to transfer is generally more restrictive than a local processing requirement.
Sub-index on domestic use of data
The sub-index on domestic use of data index covers a series of subcategories of policies affecting the domestic use of data. These are: (i) data retention requirements, (ii) subject rights on data privacy, (iii) administrative requirements on data privacy, (iv) sanctions for non-compliance, and finally, (v) other restrictive practices related to data policies. Given that each of these sub-categories contains, in turn, additional sub-categories, they will be presented separately. For the calculation of the sub-index, the weights assigned to the categories are shown in Table 3. The categories with the highest weights (and therefore those which are considered to create higher costs for digital trade) are data retention and administrative requirements on data privacy, which are assigned a weight of 0.15 each. The category of subject rights on data privacy is assigned a score of 0.1, while the other two categories of sanctions for non-compliance and other restrictive practices are assigned a score of 0.05.
The sum of the scores of these categories can go up to 0.5 that reflect a situation of virtually closed regime on domestic use of data. The sub-index therefore goes from 0 (completely open) to 0.5 (virtually closed). As mentioned above, the data policy index is measured as the sum of the two sub-indexes and therefore the score for the final data policy index goes from 0 to 1.
The first category belonging to the sub-index on domestic use of data deals with measures related to data retention, which are measures regulating how and for how long a company should keep certain data within its premises. Data retention measures can define a minimum period of retention or a maximum period of retention. In the first case, the companies (often telecommunication companies) are required to retain a set of data of their users for a certain period, which can go up to two years or more in some cases. These measures can be quite costly for the companies and they are assigned a weight of 0.7. On the other hand, the measures imposing a maximum period of retention are somewhat less restrictive and prescribe the company not to retain certain data when it is not needed anymore for providing their services. They are therefore given a weight of 0.3. The country receives a score of 1 in each of the two sub-categories when there is a one or more measures implemented, while 0 is assigned in case of absence of these measures. Therefore, if a country implements one or more data retention requirements for a minimum period of time and no data retention requirements for a maximum period of time, the score will be 0.7. Alternatively, if the country only implements one requirement of maximum period of data retention, the score will be 0.3.
Subject rights on data privacy
The second category belonging to the sub-index on domestic use of data includes measures related to subject rights on data privacy. The rights of the data subject are often a legitimate goal in itself, but they can nonetheless represent a cost for the firm when they are implemented disproportionately or in a discriminatory manner. This is the reason why they are covered in the database. However, they only form a smaller part of the sub-index with a weight of 0.1 as their cost on business is significantly low compared with other measures. Two categories of measures are identified regarding data subject rights, which are (i) the need for consent for the collection and use of data (with a weight of 0.5) and (ii) the right to be forgotten (with also a weight of 0.5).
If one of the measures applies, a score of 1 is given whereas a score of 0 is assigned otherwise. Regarding the first measure on the request of consent for the collection and use of data, a score of 1 is given only when the process for requesting consent is considered as disproportionately burdensome. This is the case when the consent has to be always written and explicit or when consent is required not only for the collection of data, but also for any transfer of data outside the collecting company. If this is not the case, then a score of 0 is assigned. Additionally, important to note is that, if the consent is required only in case of transfer across borders, this measure is instead reported in the first sub-index under conditional flow regime and scored accordingly.
Administrative requirements on data privacy
The third category belonging to the sub-index on domestic use of data covers administrative requirements on data privacy. Measures included in this category are (i) the requirement to perform a data privacy impact assessment (DPIA) (with a weight of 0.3), (ii) the requirement to appoint a data protection officer (DPO) (with as well a weight of 0.3), (iii), the requirement to notify the data protection authority in case of a data breach (with a weight of 0.1), and finally (iv) the requirement to allow the government to access the personal data that is collected (with also a weight of 0.3).
For the scoring, the first two measures receive a score of 1 when a measure applies and 0 otherwise. In the case of the fourth measure, which is the requirement to allow government to access collected personal data, a full score of 1 is assigned only when the government has an open access to data in at least one sector of the economy. However, if a government has only access to escrow or encryption keys, but still notifies access to the data, an intermediate score of 0.7 is assigned. Government direct access to data handled by the company or the use of escrow keys may in fact create remarkable consumer dissatisfaction that may lead to the user’s termination of service demand. Finally, if the government has to follow the same procedure that it would follow for offline access to data—that is, the presence of a court decision or a warrant, or when the request follows a judicial investigation process—then the score is 0.
Sanctions for non-compliance
The fourth category belonging to the sub-index on domestic use of data examines measures which impose a sanction for non-compliance. These measures cover both pecuniary and penal sanctions with a weight of 0.5 for each of them. The pecuniary sanctions are not considered a restriction per se, but their presence is listed in the database and accounted for in the sub-index when (i) they are above 250.000 EUR; (ii) companies have explicitly complained about disproportionately high fines or discriminatory enforcement of sanctions; (iii) they are expressed as a percentage of a company’s domestic or global turnover. In fact, in all these cases, the sanctions have the capacity of putting a company out of business and might play an important role in the economic calculation of a company. We also list under this section those instances in which the infringement of data privacy rules can be sanctioned by closing down the business. On the other hand, the application of penal sanctions such as jailtime as a result of infringement of data privacy rules is included in the database as a restriction. Instances in which penal sanctions are assigned as a result of identity theft and similar illegal actions are obviously not included. For what concerns the scoring, if these cases are identified, a score of 1 is assigned.
Finally, the last category takes up all those measures which are related to domestic use of data, but do not fit under any of the aforementioned categories. All these measures are assigned a score of 1.
Appendix 3: Concordance table between OECD STRI and BPM6 services classification
See Table 12.
Appendix 4: Data intensities with WTO-OECD BaTIS services classification
See Fig. 9.
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van der Marel, E., Ferracane, M.F. Do data policy restrictions inhibit trade in services?. Rev World Econ (2021). https://doi.org/10.1007/s10290-021-00417-2
- Data flows
- Digital technology