are applied. CB refers to the cross-border transfer of data, whereas DR refers to the domestic processing of data. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A4.

When both components are entered together, results are corroborated. That is, the conditional model obtains a positive and significant result on both CB and DR, whereas the limited model acquires a negative coefficient results for both components. In addition, the open model also obtains a negative and significant coefficient results for the DR component in most digital services sectors. However, additional significant results are now obtained, too. First, in column 2 (Digital+) and column 4 (IT & Info), the open model now also obtains a positive and significant result for CB. In contrast, the conditional model now receives a negative coefficient results for the CB element in the IT & Information service sector only (column 4).

As such, one preliminary conclusion from Table 4 is that trading partners sharing the open model for cross-border data transfers exhibit greater levels of digital services trade compared to country-pairs that have not the same data model in place. This result is particularly strong for IT and information services. In addition, trading partners sharing a conditional model also appear to show greater levels of digital services trade, but this conclusion is sharply reversed for IT and information services. In this sector, country-pairs sharing the conditional model trade less services with each other. Further, trading partners sharing the limited model seem to have lower levels of digital services.

A second conclusion from Table 4 is that country-pairs sharing the open model for domestic data processing exhibit lower levels of digital services trade compared to country-pairs where data models are not the same. This result is particularly strong for the publishing sector and IT and information services. In contrast, trading partners sharing the conditional model show greater levels of digital services trade compared to country-pairs with dissimilar data models. Regarding the limited model, country-pairs sharing this model seems to be again associated with lower trade in digital services than countries sharing different models.

# **5.1 Adding Control Variables**

A number of robustness checks are also conducted by adding several control variables, providing a panel dimension, and finally using the ITPD-E as an alternative source of trade data. For each subsequent table, results are reported when entering the two categories of CB and DR together.

Starting with the first robustness check, Table 5 shows the results when adding the data restrictiveness index as developed in Ferracane and van der Marel (2018) based on the ECIPE’s DTRI. This index measures level of data policy restrictiveness with respect to the cross-border transfers of data and the domestic processing of data of the partner country, namely the importer.<sup>24</sup> The table shows that results remain largely unchanged. In all instances, variables stay significant, indicating that sharing the open model on cross-border data transfers tends to be positively associated with digital services trade, while sharing the open model for domestic processing of data tends to be negatively associated. This pattern reverses for country-pairs sharing the conditional model. The

> 24 Note that the interpretation for both components CB and DR of the Data Restrictiveness Index in Ferracane and van der Marel (2018) is different than the one developed in this paper. The main differences are that the index more generally aims to measures the extent to which data transfers and data processing create restrictions on trade, while this paper simply categorizes the countries under different data models to estimate whether sharing such models has any impact on digital services trade.

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