Where: _E0_ corresponds to the expenditure of the country corresponding to the omitted fixed effect (typically an importer fixed effect) in the empirical model, and the normalization of the corresponding price terms in the structural model.

For the above approach to work in a consistent way, it is important for the dependent variable, bilateral trade, to include all directions of trade. That is, it also has to include self-trade, production that is both produced and consumed in a given country, so that the estimated fixed effects in fact relate to the output and expenditure terms implied by theory. As a result, we use services trade data sourced from the OECD-WTO Trade in Value Added (TiVA) data set. To be clear, we do not use that data set’s estimates of value added components of trade, but instead simply take data on exports and production in gross shipments terms. The advantage of this source is that it contains harmonized trade and production data, so it is a simple matter to calculate self-trade: it is production less total world exports. Using the most recent version of the TiVA data set, we start with services trade and production data for 63 exporters and importers annually for the period 2005-2015.

In order to identify the impact of potential TFiS measures, it is important to control for the up-front policy restrictiveness in the sense of the set of measures that govern market access and national treatment in services markets. We use the OECD’s Services Trade Restrictiveness Index (STRI) for the importer to do this. Ideally, we would split out the sections of the STRI that deal with transparency and those that deal with market access in a pure sense; however, the two components of the index are strongly correlated, and it is not possible to obtain meaningful estimates when they are included separately. We therefore use the overall STRI. For the regressions using aggregate services trade data, we use PCA to produce a weighted average of STRIs across all sectors. We then also use selected sectoral STRIs where they correspond closely to sectoral definitions in the TiVA trade data.

The original STRI produced by the OECD only captured MFN policies in services (Geloso Grosso et al, 2015). Subsequently, however, the organization has produced measures of preferential policies for countries within the European Economic Area (EEA), essentially EU members, but not for other countries. We therefore merge these two data points into one STRI policy data index to take account of the special rules that govern services trade within the EEA, but include an RTA dummy as well (see below) to take account of the fact that coverage of preferential policies is not complete.

An important caveat for the data set assembled as described above is that it mainly includes developed countries and a small number of mostly large developing countries. Consistent trade and policy data are not available more broadly, due to consistent difficulties in reporting services trade data disaggregated by sector and partner country in many developing countries, as well as the continuing lack of a global database on services trade restrictiveness. We therefore use the broadest sample possible, but in interpreting results, this limitation in terms of sample breadth should be kept in mind.

# **_TFIS measures_**

We can then move to consider the various indicators relevant to TFiS, as per the discussion in the previous section. To measure regulatory heterogeneity, we use the **OECD’s index of regulatory heterogeneity** , based on differences in answers to the regulatory questionnaire used to produce the STRI (see Nordås, 2016). As for the STRI itself, as explained above, we use PCA to produce a weighted average of sectoral measures in aggregate regressions, otherwise we use sectoral data where there is a close 16