countries, a convention that largely reflects the way most of them are administered.<sup>28</sup> In equation (6), _γ >_ 0 is our test for the combined hypothesis that NTMs are applied in non-discriminatory fashion but reduce competition increasing the fixed cost faced by producers.

**Applied tariffs** . To estimate the impact of tariffs on market structure (Testable prediction 2), we interact the exchange rate with the log of bilateral tariffs plus one. We use data on applied tariffs which vary across product-destination cells and take into account all preferential regimes. In order to make the exercise comparable to the one on non-tariff measures, for which we have no time-wise variation, we use a time-wise average of tariffs over the sample period.<sup>29</sup> The corresponding estimating equation is:

where **FE** is defined as before and Tariff _odp_ is the average tariff imposed by destination country _d_ on product _p_ imported from origin _o_ over the period. Prediction 2 implies an ambiguous effect of tariffs on foreign firms market shares and thus on their incentives to engage in PTM. How import tariffs in one destination market affect foreign firms market power is thus an empirical question. However, as previously discussed, since tariffs are discriminatory we would expect their rent-shifting effects to dominate and in net see them erode the market power of foreign exporters, i.e., empirically we would expect _θ_ to be negative.

# **5.2 Main results**

**NTMs** . Table 4 reports estimates from specification (6). In this table as in all the robustness exercises, we present estimations with origin _×_ year fixed effects (columns 1 to 4) or with firm _×_ product _×_ year fixed effects (columns 5 to 8). The number of observations is much lower than previously due to the incomplete availability of NTMs AVEs.<sup>30</sup> In spite of the reduced sample size and the attenuation bias due to the fact that AVEs are themselves econometric estimates, the results confirm the hypothesis of non-discrimination-cum-reduced competition. NTM reinforce the market power of non-exiting firms, domestic and foreign exporters alike, and thus their incentive to engage in PTM. As a result, PTM is significantly stronger quantitatively in markets with high levels of NTMs. Specifically, the coefficients on the exchange rate and its interaction with the NTM variable in column (1) suggest that moving from zero to a 10 percent ad valorem equivalent raises the price elasticity to exchange rate by a third from 0.5 percent to 0.7 percent. In columns (2) and (3), splitting the sample according to the level of NTMs yields similar results: The exchange rate coefficients is sixty percent larger in countries and sectors belonging to the top quartile of NTMs (column 3) than in those in the first quartile (column 2). Although the coefficients lose in precision and magnitude, the positive impact of NTMs on pricing-to-market

> 28Whereas applied tariffs are specific to origin-destination dyads, most non-tariff measures, in particular SPS and TBT regulations, are imposed on an “MFN” basis, i.e. specific to a destination and not a dyad. For instance, a maximum residual level of pesticides in horticulture products applies to _all_ imports, not just to imports from a particular country, and, unlike a tariff, will not be relaxed in the presence of a preferential trade agreement.

> 29An additional reason for doing this is that tariff data contain many missing values; using average values allows maximizing the number of observations. We however provide robustness exercises using time-varying tariffs.

> 30We confirmed that our main results of incomplete pass-through still hold despite the lower number of observations by re-estimating column (1) of Table 3 on this smaller sample. Results were unchanged (the exchange rate coefficient is estimated at 0.12, very close to our baseline of Table 3).

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