we nonetheless control for a wide array of variables that might correlate with trade policy and affect PTM. We start by adding interactions between the exchange rate and the destination countries’ characteristics such as per capita GDP, GDP, and market potential in columns (2) and (7). In columns (3) and (8) we include interaction terms between the RER variable and a measure of market size varying by product and destination (total imports), as well as a set of standard gravity controls. In columns (4) and (9) we control for interaction terms between the real exchange rate and several product-specific characteristics: dummies for consumer goods (defined according to the Broad Economic Categories classification), manufacturing goods and differentiated goods (defined according to Rauch classification). Finally, in columns (5) and (10) we perform a more extreme exercise and include sets of interactions between the exchange rate and (i) destination group dummies; (ii) origin country dummies; as well as (iii) 2-digit sector dummies. This allows controlling for any time-invariant unobserved destination, origin or product factors that could be correlated with our trade policy variables and affect pricing-to-market.

Overall, our main results are largely unaffected throughout. We find weak evidence that pricing-to-market tends to be less prevalent in more competitive markets, where firms’ market power is lower (column (7)). Regarding product characteristics, when significant, the coefficients on the interaction terms tend to be negative (column (4)), but this result is not robust to the inclusion of firm _×_ product _×_ year FE (column (9)), to alternative currency specifications (online appendix Section C.8), nor to the use of the full sample of countries, i.e without the NTM data restriction. Taken together our results suggest no significant differences across categories of goods.<sup>31</sup> Finally, when including a full set of origin, destination group and sector interactions (columns (5) and (10)), although the coefficient on tariffs decreases in size and loses significance, the results are globally consistent with our baseline.

**Endogeneous trade policy.** A second potential concern is that trade policy may be endogenous to market structure. That is, importing countries might decide to set their levels of NTMs or tariffs depending on the market power of the foreign firms serving their markets. While this concern is mitigated by the fact that we are looking at trade policy in the destination countries, where firms from the set of developing countries in our sample are typically small players, we still provide two robustness checks to ensure that endogenous trade policy does not affect our results. We show that we obtain similar results when we drop from the sample exporting countries with high market shares in their destination-product (i.e. exporters that could affect foreign trade policy). Our results are also similar when we use an instrumental variable approach in which NTMs and tariffs in a product-destination are instrumented by the level of NTMs/tariffs set by the same destination on other products. These results are reported and discussed in Section C.6 of the online appendix.

**Further robustness.** The online appendix Sections C.7 to C.13 contain additional robustness exercises. These show that the results of Tables 4 to 6 are robust to: (i) the use of an alternative cleaning method of unit values (Section C.7); (ii) using export-currency denominated specifications (Section C.8); (iii) using alternative clustering strategies (Section C.9); (iv) boot-

> 31While this might appear surprising at first, these results are consistent with Rollo (2015) who find that firms in Tanzania do significant PTM even – especially – for goods classified as homogeneous. Overall, many so-called primary, homogeneous goods such as coffee are de facto differentiated.

20