Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter-year and importer-year are applied. CB refers to the cross-border transfer of data, whereas DR refers to the domestic data processing. Both ECIPE CB and ECIPE DR variables are taken for the importer’s side and are interacted with intl as part of the C ∗intlo term. The WEF NRI variable is taken for the exporter’s side and interacted with intl. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A6.

The regressions were also replicated by including the OECD Digital STRI instead. However, one major difficulty with this index is the relatively limited number of years. The OECD Digital STRI start in 2014 and given that the trade data ends in 2015, it would only allow to analyse two years. Nonetheless, the analysis only discusses the results without reporting them. The results show that all coefficient outcomes stay in line with the ones reported in Table 6, which therefore doesn’t alter the main conclusion of the cross-section analysis.

# **5.3 Using Alternative Data Source**

A final robustness check using the trade in services data from the ITPD-E is performed. This data source has recently been developed (see Borchert _et al_ ., 2020) and covers a large set of developing countries. Given that the sample of data models does not include many developing countries, using this source is interesting to check whether results are consistent. However, one disadvantage with this database is that it is not balanced, and many observations are missing for services, particularly for the sectors of interest. The data reported is administrative data which in the case of TiVA may not always be the case. It also includes internal trade, which is convenient for the gravity model. Another downside however is that this database groups all digital services sectors together, as shown in Annex Table A3. The years covered for the panel analysis is 2005-2016.

The results using the ITPD-E data with respect to the baseline regression following equation (3) are presented in Table 8, which means that it covers a panel data set and includes the two sets of control variables of the data restrictiveness index and digital enabling environment. As such, this table is readily comparable with Table 7, the more so because the sectoral grouping for Digital+ and business services are carefully cleaned and matched between the two data sources. The results are reported in Table 8. The results for GRAVo and C ∗intlo are reported in Annex Table A6. In Table 8, the total number of observations is lower than using TiVA data which is due to missing oo oo observations. The list of countries in this dataset is also different and not balanced compared to the TiVA data. The results reported in columns 1-3 are without any control variables included. A first finding is that none of the variables for the CB component have a significant coefficient outcome. That stands in contrast to some of the significant findings in the bottom panel of Table 4, to which these results need to be compared. When looking at the DR component, the results are largely in line with Table 4, except for the insignificance for the conditional model in column 2, even though the sign remains positive. The difference in results may be due to the fact that even though the sector groupings are matched as closely as possible, some sub-sectoral differences remain or cannot be captured adequately between the two data sources.

Column 4-6 repeat the baseline regressions and adds the two sets of control variables. Note that in case of adding the OECD DSTRI instead of the ECIPE DTRI, the results would omit a large number of developing countries.<sup>26</sup> The result that now stand out is the negative coefficient results for the

> 26 Interestingly, the panel results when using the OECD DSTRI would be in line with the results found in Table 6, particularly with respect to the Digital sector (column 2). However, the years covered by the OECD DSTRI index

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