Our main objective in this paper is not to study the precise channel through which trade policy affects market power. Rather, our goal is to provide clear-cut evidence about the sign of these theoretically ambiguous effects. Still, our results are informative about the channel. If NTMs affect mostly variable costs, we would expect their effect on PTM to be similar to that of tariffs. The fact that we find a negative effect of tariffs on PTM and a positive one of NTMs is a first indication that the latter are operating mostly through a fixed cost channel. We also discuss in Section 6 a number of additional estimations where we regress various indicators of trade margins (number of exporters, average price and size, concentration indices) on NTMs and tariffs to shed some additional light on those channels of transmission. We show in particular that while tariffs are associated with changes in firms’ performance at the intensive margin of trade, NTMs are mostly related to indicators at the extensive margin.<sup>14</sup>

# **3 Data and descriptive statistics**

Testing our predictions requires gathering three main types of data: (i) firm-level data on export flows, (ii) macroeconomic data and (iii) trade policy variables. Note that when testing the impact of trade policy on market power, the use of firm-level data is key because it enables us to control for firm characteristics which affect their reaction to exchange rate variations (e.g. marginal costs, size or other firm characteristics such as financial constraints) and can be correlated with trade protection.

## **3.1 Firm-level trade data**

Our data were obtained from the customs administrations of twelve developing countries. Data for Kenya, Rwanda, Tanzania and Uganda were obtained by the International Growth Center and data for Bangladesh, Chile, Jordan, Kuwait, Lebanon, Mexico, Morocco, and the Republic of Yemen were obtained by the Trade and Integration Unit of the World Bank Research Department, as part of the Exporters Dynamics Database (EDD) project described in Cebeci _et al._ (2012). Pooling data from different developing countries allows us to provide results on regions which have received little attention so far. The structure of our dataset also allows us to observe countries with different development levels, facing different trade policy arrangements (in particular preferential tariffs), which implies a gain in statistical power.

For each country, all export transactions are covered over a certain time period (see Table 1). For each firm and year, the data includes a firm identifier, as well as the value (in local currency) and quantity (expressed in kilograms) sold by the firm for each destination country and HS product (at country-specific HS8-equivalent levels).<sup>15</sup> For each firm-destination-productyear, unit values are computed as the ratio of export value to quantity. Note that unit values are an imprecise proxy for prices because there may be more than one distinct product within a 8-digit code. Prices changes may be due to compositional changes within a product code or due

> 14The theories mentioned in this section are heterogeneous firms models where each firm produces a single good. It might be the case that trade policy has an impact on PTM through their effect on multi-product firms. We come back to this point when discussing our empirical results in Section 6.

> 15Product classifications are not harmonized between countries at sub-HS6 levels of disaggregation (HS8 or HS10). This is not a problem in our estimations as all regressions have fixed effects at the firm-destination-product level. However, for comparability of descriptive statistics, we aggregate products up to the harmonized HS6 level.

8