exchange rate between the origin and destination currencies in year _t_ , and let _e_<sup>_US_</sup> _odt_<sup>betheaverage</sup> real exchange rate between the destination and the USD in year _t_ . Our bilateral exchange rate variables in logs are defined as follows: Finally, GDP data are from the World Bank’s World Development Indicators (WDI).

# **3.3 Trade policy variables**

We use data on both tariffs and non-tariff measures. For tariffs, we use data on Most Favored Nation (MFN) and preferential tariffs at the HS6 level from TRAINS. For each origindestination-product-year ( _odpt_ ) quadruplet, we compute the bilateral applied tariff. As we are mostly interested in the role played by differences in trade policy across markets, rather than in the effect of variations in trade policy in a given market over time, we also compute the average bilateral applied tariff over the period. This allows us to smooth out missing values without much loss of information. In the empirical analysis we will show that our results are robust to using time-varying bilateral tariffs.

For non-tariff measures we use ad-valorem equivalents (AVEs) at the destination countryproduct level estimated in Cadot and Gourdon (2014, 2016). The Ad-valorem equivalents were computed for 45 countries and three types of measures, type-A measures (SPS), type-B measures (TBT), and “other” measures (essentially quantitative restrictions).<sup>18</sup> The bulk of the variations in NTM AVEs (more than 80 percent) is attributable to the application of SPS and TBT regulations. Section B.1 of the online appendix provides more information on the NTM source data and how they were converted into estimated AVEs.

In Section B.2 we provide additional descriptive statistics on our trade policy measures. Table A.1 shows the (unweighted) average levels of bilateral applied tariffs and NTMs AVEs for the 45 countries covered by both tariff and NTM data. The lowest levels of tariffs are observed in developed countries (e.g. 0.8 percent in Japan). NTM AVEs also differ across countries, China having the highest (25 percent), which seems in accordance with anecdotal evidence of traderestrictive application of regulatory measures. Countries with similar regulations (e.g. members of the European Union) may nevertheless have different AVEs if they enforce them differently, which is the case for some of the Eastern European members (e.g. Hungary vs. the Czech Republic). Note that while some countries are characterized by high levels of both NTMs and tariffs, the overall correlation between the two is not statistically significant at common confidence levels.

The online appendix discusses other features of the NTMs and tariffs data. We show for instance that tariffs and NTMs data are observed in all 2-digit HS sections, with a larger coverage for tariffs (Table A.2). Different types of NTMs affect different sectors: SPS measures mostly impact animal, vegetable and processed food products, while TBT measures mostly affect

> 18These include for e.g. trade-related investment measures or intellectual property, although data on those is very scant. For more information on the MAST nomenclature, see: http://unctad.org/en/PublicationsLibrary/ditctab20122 ~~e~~ n.pdf.

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