used to identify the effects of any non-discriminatory policies on the side of exporters and importers, following Heid _et al_ . (2015), which to some extent are present in this case.<sup>22</sup>

# **4.2 Robustness Checks**

Several robustness checks are performed, mainly by including several additional control variables as part of the C ∗intlo term. More specifically, using equation (2), each regression model includes additional policy variables such as the ones developed by ECIPE’s Digital Trade Restrictiveness Index (DTRI), the OECD Digital Services Trade Restrictiveness Index (Digital STRI), as well as the WEF Technology Readiness Indicator. The first two variables are estimated on the importers side (country _d_ ), whereas the latter is estimated on the side of the exporter (country _o_ ). Finally, as said above, the study adopts a panel series and re-estimates the baseline regression equation (1) and (2), including by using alternative trade data from the ITPD-E.

# **5. Results**

The results of the baseline regressions following equation (1) are reported in Table 3. Column 1-7 follow the sectoral groupings as presented in Annex Table A2. In order to enhance readability of the main variable of interests, the results from the GRAVo and C ∗intlo vectors are featured in a separate table, which can be found in Annex Table A3.<sup>23</sup> Moreover, Table 3 first reports the coefficient results of CB and DR under MODELo when entered separately in order to avoid potential multicollinearity concerns that may arise by entering them simultaneously, even though countries do not necessarily follow the same model for the two components. In a second step the two components are nonetheless entered together.

The results in Table 3 show a positive and negative coefficient result for the MODEL CB variable, which covers cases in which country-pairs share _any_ model regarding the component of cross-border data transfers. However, none of the coefficient results are significant. In the second panel, the results on the MODEL DR for sharing any model regarding the component of domestic processing of data also show insignificant coefficient results which are either positive or negative in sign, except for publishing services which is negative and significant. When entered together, the analysis finds negative and significant correlation for the MODEL DR variable with digital trade in services in two sectors, namely in Digital+ and publishing services. Other sectors show insignificant coefficients. Given these varying outcomes, it may be the case that these two variables are too aggregate.

22 Given that exports is the main trade variable, the study controls the exporter’s data model interacted with the intlo dummy. Moreover, given that the dummy signifying each country data model is a square set of dummy variables across all countries, the analysis needs to exclude one of the three data models, which is used as a benchmark. The analysis omits the limited model and so coefficient results need to be seen against this model. 23 Note, however, that the coefficient results in Annex Table A4 are performed without the MODELo vector. The reason for doing so is that the coefficient results on the variables of interests in MODELo need to be set against the results of the country-specific control variables from vector C ∗intlo . Given that the variables in both vectors of MODELo and C ∗intlo are interaction variables, one needs to regress the latter without the model variables to determine the starting point of a country’s export that follows a certain data model. The coefficient results for the variables for both the gravity and control vectors for all other tables can be obtained from the authors upon request. These results are more reported for each table separately but referred to the Annex Table A4 given that only minor changes appear on each of the coefficient results.

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