# **4 Pass-through across countries and firms**

In this section, we empirically test on our data whether exchange rate pass-through is incomplete, implying that firms price to market, and that pass-through decreases with firm size, implying that larger firms price more to market. These results, which are predicted by the models reviewed in Section 2, have been documented for other countries, but checking that they hold on our sample is an important first step as our dataset comprises countries of various levels of development, including low income ones in which firms might not necessarily price to market.

## **4.1 Econometric strategy**

**Average PTM** . Let us denote by ln uv<sup>_USD_</sup> _fdpt_<sup>thelogoffirm</sup><sup>_f_’sproducerpricefora8-digit</sup> product _p_ exported to destination _d_ in year _t_ , proxied by its FOB unit value and expressed in USD.<sup>21</sup> Let _e_<sup>_US_</sup> _odt_<sup>betheaveragerealexchangeratebetweenthedestinationandtheUSDinyear</sup> _t_ as defined in equation (2). Finally, let **xdt** be a set of time-varying destination specific controls, including the destination’s GDP per capita and other controls used in our robustness exercises. Our baseline empirical specification estimates the effect of changes in the real exchange rate on ln uv<sup>_USD_withinfirm-destination-producttripletsovertime:</sup> _fdpt_<sup>,</sup>

where **FE** _fdp_ and **FE** _ot_ are respectively firm _×_ destination _×_ product and origin _×_ year fixed effects. In equation (3), an increase in _e_<sup>_USD_</sup> _odt_ is a depreciation of the USD, which we interpret as a depreciation in the exporter’s pricing currency (we come back below to the issue of currency denomination of prices and exchange rates in our estimated equations). The coefficient of interest _α_ is the elasticity of export prices to changes in the destination’s exchange rate and maps oneto-one onto an exchange rate pass-through elasticity into USD prices (pass-through is complete when _α_ = 0 and is zero when _α_ = 1).<sup>22</sup> Equation (3), as the rest of the specifications, is estimated by OLS with robust standard errors clustered at the dyad-product-year level. We will also report specifications using alternative clustering strategies (Section C.9).

**FE** _fpd_ accounts for all time-invariant firm, product or destination characteristics that might affect prices. **FE** _ot_ captures changes in economic conditions, in particular changes in costs at the origin country level – for instance supply shocks affecting prices across firms and correlated with exchange rates. We also systematically report results of specifications that include fixed effects in the firm _×_ product _×_ year dimension, i.e. those that capture entirely changes in costs at the firm-product level:<sup>23</sup>

> 21Note that as our dataset does not contain information on firm ownership, all firms in our sample are treated as independent entities. Thus in the presence of firm subscripts we omit the origin country subscripts.

> 22Note that _α_ may suffer from attenuation bias due to classical measurement error: because we use annual data, the exchange rates applied in our estimations are (potentially) not exactly the ones actually faced by the firms at the time they export. The only way to solve this issue would be to use higher frequency data which would allow us to match transactions with daily or monthly exchange rates. Indeed, Fosse (2012) shows using Danish data that moving from annual to monthly data increases the elasticity of unit values to the exchange rate (from 0.14 to 0.19 in his case – see also Mallick and Marques, 2010). This might explain why the literature using yearly trade customs data typically finds lower estimates of pass-through than those found in papers using direct price data at a higher frequency (e.g. Gopinath and Itskhoki, 2010).

> 23We use estimations in levels rather than first-difference for three main reasons. First, exchange rates appear to be stationary in our data (this result might be caused by the low power of unit root tests, as the time series

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