(2013) find that, as suggested by traditional theory, quota-based protection increases the market power of domestic producers while tariff-based protection does not. Kim (2000) provides similar results based on Korean sector-level data. Antidumping protection, which takes the form of tariffs (or, sometimes, price agreements), has also received some attention, although results are ambiguous.<sup>8</sup> Papers by De Loecker _et al._ (2016) and De Loecker _et al._ (2014) also provide evidence that the markups of domestic firms are affected by tariff reductions. These studies generally use measures of price-cost margins at the firm level to identify market-power effects.

The main problem faced by this literature is that estimating markups over marginal costs requires the use of detailed data on costs and/or strong identifying assumptions.<sup>9</sup> Using market shares as a measure of market power is also problematic, both because one has to define what the appropriate “market” is and because, when studying exporters, the sales of domestic firms are typically not observed. Most of the papers cited above use very detailed firm-level data, allowing for a precise identification of the channels through which trade policy affects markups; but this comes at a cost: these studies are typically limited to a single country—often a single industry— and focus exclusively on domestic producers. Using exchange rate variations to identify price discrimination and therefore market power across destinations has the advantage of requiring data on neither firm costs nor market shares. The idea that useful information on the extent of market power can be generated from price adjustments to exchange rate fluctuations is not new, as it was already present in early work such as Aw (1993), Goldberg and Knetter (1999) or Bernhofen and Xu (2000).<sup>10</sup> Our study, which is close in spirit to those papers, links the literature on PTM with that on trade policy and competition.

We also contribute to the recent literature documenting the effect of tariffs and NTMs on exporters’ performance. Fontagn ́e _et al._ (2015) use French firm-level data and show that SPS concerns raised at the WTO increase firms’ unit values and inhibit their entry. Interestingly, they show that these effects are not neutral on the distribution of firms, as smaller ones seem to react more strongly. Using a similar strategy, Fontagn ́e and Orefice (2018) study TBT concerns instead and show that firms’ exit probability increases in destinations with restrictive TBTs, an effect that is magnified in multi-destination firms that can divert their exports to TBT-free destinations. Fernandes _et al._ (2015) focus on the impact of pesticide standards on firms’ exports of agricultural products and show that more restrictive standards lower the firms’ probability to export as well as their export values and quantities. These papers help understand the mechanisms through which NTMs affect trade. However, they do not speak directly about their impact on market power, which cannot be directly measured in the absence of information on the entire set of firms, both foreign and domestic. In that sense, we view our paper as complementary to these works: we focus on a somewhat narrower question (we look at only one outcome), but we are able to cleanly identify the link between trade policy and market power.

Finally, our paper also relates to a vast literature on the determinants of PTM. We build on the predictions of a variety of models featuring endogenous markups, and heterogeneous PTM across firms. In these models, the perceived price elasticity of demand faced by each firm varies

> 8Blonigen _et al._ (2013) estimate a positive but mostly insignificant effect on market power. Konings and Vandebussche (2005) and Pierce (2011) find significant evidence of enhanced market power for EU and US firms respectively, while Nieberding (1999) and Rovegno (2013) find mixed results.

> 9See the recent survey by De Loecker and Van Biesebroeck (2018) which discusses the different methods (production-based or demand-based) which have been used to estimate mark-ups.

> 10See the survey by Goldberg and Knetter (1997).

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