correspondence between the policy data and the trade data. To measure policies that inhibit the free flow of data across borders, we use ECIPE’s **Digital Trade Restrictiveness Index (DTRI)** as developed by Ferracane et al. (2018). Specifically, we use the component of that database that captures restrictions on cross-border data flows in the importing country.

Moving towards more general measures of governance, we first take in the **World Bank’s Regulatory Governance** data. Specifically, we use the overall regulatory governance score based on the various components identified by the World Bank, again for the importing country. We also use data from the **Doing Business** project, specifically their index measures of the ease of doing business in three areas: **investor protection, contract enforcement,** and **resolution of insolvency** , all for the importing country.<sup>14</sup> Again, we cannot enter the measures individually in the regressions as they are strongly correlated. We therefore take a simple average to produce a single measure of the business environment, as we believe it is relevant to TFiS. Finally, we use the **regulatory quality** and **government effectiveness** indices from the **World Bank’s Worldwide Governance Indicators** , all for the importing country. For similar reasons as those previously mentioned, we take a simple average of the two indices and use the result as an aggregate indicator of governance as we believe it might be relevant to TFiS.

The final element of the gravity model data set is a set of controls for geographical and historical linkages between countries, all of which come from the CEPII distance data set. Specifically, we include the logarithm of bilateral distance, along with dummies for country pairs that were ever in a colonial relationship, those that had a common colonizer, and those that have a common language as measured on an ethnographic basis. Finally, we include a dummy for membership of the same regional trade agreement (RTA), taken from De Sousa (2012).

In the discussion above, it is important to note that many of the variables of interest—all except the STRI and the regulatory heterogeneity indicator—vary at the country level; they do not vary at the bilateral level. Without modification, it would therefore be impossible to identify them separately from the importer fixed effects in the gravity model. But given that the gravity model includes intra-national trade data, it is possible to introduce bilateral variation by interacting the country-specific policy variables with a dummy for international trade observations, as per the approach in Anderson et al. (2018). The final element of the data set is therefore a dummy variable equal to unity for international observations, and zero for intra-national observations.

Applying the estimation technique discussed above to these data can provide indications as to which factors are the most important in terms of promoting bilateral services trade, after controlling for the restrictiveness of market access conditions. However, parameter estimates are not easily comparable across measures, due to differences in scale from one indicator to another. We therefore convert statistically significant estimates to ad valorem tariff equivalents (AVEs), by making use of the relationship in the theoretical model between individual factors and overall bilateral trade costs. In other words, we convert meaningful (i.e. statistically significant) results into their trade cost equivalents, compared with a baseline of maximum possible performance, to give an indication at the country level of the impediments to services trade posed by different factors.

14 We have also run our preferred regression specification with Doing Business data on ease of paying taxes, as an additional factor that could plausibly affect cross-border trade in services. However, results are qualitatively unchanged. We therefore report the simpler version with fewer sub-variables here.

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