# **_Regulatory heterogeneity_**

As discussed, regulatory heterogeneity forms a significant part of the total costs in services trade. Regulatory heterogeneity covers the variability in the administration regarding the rules and requirements that apply to the delivery of the services and the regulatory processes to adhere to between countries. To date, the most sophisticated empirical counterpart for measuring regulatory heterogeneity is the OECD’s **Regulatory Heterogeneity index** in services based on the OECD STRI (see Nordås, 2016). Given that the in this index, the entire OECD index is used, it includes both the up-front or explicit barrier to trade related to market access and national treatment as well as to the extent that STRI covers for it the issue of regulatory transparency and barriers competition in services (see above).

The OECD’s STRI is specific to each country pair and indicates whether the countries have a similar regulatory frameworks in services or not. For each country pair and each sector, the indices reflect the (weighted) share of measures for which the two countries have different regulations. Note that in this respect, all categories of the OECD STRI are taken into account and not just specifically the areas of Regulatory transparency and / or Barriers to competition only. However, the OECD does have information on the level of regulatory heterogeneity for each of the five STRI components separately. Finally, the OECD developed two indicators of regulatory heterogeneity. One is computed on the basis of the qualitative answers as found in the database, the other one the basis of the actual index scoring.

# **3.2 Determinants of TFiS: Empirical Models and Results**

In this section, we develop an empirical approach to help identify the relative importance of different factors as potential drivers of TFiS, by focusing on the determinants of cross-border services trade as indicators of the sources of trade costs that arise in a cross-border setting. The empirical counterparts of the core dimensions of TFiS developed in the previous section serve as a basis for the analysis, but some compromise is necessary. These are that data are only available for a relatively small sample of countries, and that many of the indicators have strong correlations among themselves, which make it difficult to obtain precise estimates if all variables are included separately in regressions. We therefore use dimensionality reduction techniques, such as taking simple or weighted averages of individual indicators to group them into a smaller set of relevant factors, using principal component analysis to determine the weights where applicable.

The most commonly used setup for empirical international trade analysis is the gravity model. Current best practice in the literature is Anderson et al. (2018), where the authors develop a simple method for both estimating parameters econometrically and conducting counterfactual simulations, all perfectly consistently with the constraints imposed by standard trade theory. Their starting point is the familiar structural gravity model derived from CES preferences across countries for national varieties differentiated by origin (the Armington assumption). The model takes the following form: 14