STEVEN PENNINGS 8 by 0.23%). As others have found, pass-through of the bilateral exchange rate is very low, even conditioning on a price change and over a long period, and is hard to justify using standard models.<sup>9</sup> Column 2 estimates the baseline specification for competitors’ exchange rates, where βc = 0.35 is large and highly statistically significant, verifying Testable Prediction 1. Moreover, pass-through of competitors’ exchange rates is larger than pass-through of the bilateral rate that is standard in the literature. This suggests that a fall in the USD against a range of competitor currencies is associated with a much larger increase in import prices than a fall in the USD relative to only the producers’ currency. Results in column 2 show that the addition of competitors’ RERs leads to a reduction of bilateral pass-through by around<sup>1</sup> /3 to βBI = 0.16, verifying Testable Prediction 2. That is, measured bilateral pass-through is likely to be upward biased (as defined in Section 2) if one does not control for competitors’ RERs.

3.3. **Pass-through of competitors’ exchange rates to producer prices.** I estimate Equation 9 with the results displayed in Table 1 columns 5-8 (change in log over the life of the good, conditional on a price change). p<sup>P P I</sup> R,jk<sup>isthereallogUSproducerprice,rerc,tislogcompetitors’RER(forallimportersinthe</sup> sector), p<sup>F CP</sup> R,jt<sup>arethelogrealforeigncompetitors’prices,pM</sup> Rjt<sup>arelogrealUSmaterialspricesinthesame</sup> sector j (some of which might be imported). X includes a time trend, the life-length of the good and the growth rate of US GDP as controls.

In the baseline specification (column 5), a 1% increase in competitors’ exchange rates in a sector is associated with a statistically significant 0.11% increase in US producer prices (verifying Testable Prediction 3; βC<sup>P P I</sup> = 0.11). A 1% increase in materials prices (a control variable) is associated with a 0.5% increase in US producer prices (δM<sup>P P I</sup> = 0.5). The average share of materials costs across sectors in the US Producer Price Index (PPI) data is about 50%, which is broadly consistent with the estimated coefficient.<sup>10</sup> Column 6 shows that when materials prices are removed as a control, βc<sup>P P I</sup> = 0.34, which is very large. Goldberg and Campa (2010) argue that the aggregate CPI in the US is insensitive to exchange rates, which puts an upper bound on the degree of pass-through to producer prices. The higher estimate might be due to other variables: movements in materials prices could be correlated with competitors’ RERs due to (i) imported intermediates or (ii) other shocks (e.g. oil prices) that jointly affect materials prices and RERs.<sup>11</sup> 3.4. **Pass-through of foreign competitors’ prices.** Consistent with the simple model, importing firms respond strongly to the prices of their foreign competitors, and this reduces the explanatory power of competitors’ exchange rates. In columns 3-4 and 7-8 of Table 1, I add foreign competitors’ prices to import and producer price regressions (respectively) as an additional control. A 1% increase in foreign competitors’ prices increases import prices by about 0.6%, verifying Testable Prediction 4(a).

US producer prices also respond to changes in the prices of foreign firms importing in the same sector. When I control for foreign competitors’ prices in the same sector for the PPI regressions (columns 7-8), I find that a 1% increase in the price of a producers’ foreign competitors is associated with a 0.16% increase in US producer prices (verifying Testable Prediction 4(b)). One explanation of the smaller coefficient (relative to that for importers) is that most US firms tend to be in sectors where they compete with other US firms, reducing the sensitivity of the sectoral price to foreign competitors’ prices (this is also consistent with the lower estimate of βC<sup>P P I</sup> ).

> 9GI2011 estimate lifelong pass-through at 0.31 for all goods, Burstein and Gopinath (2014) estimate 0.28, and Gopinath and Itskhoki (2010) estimate life-long pass-through between 0.21 (for low frequency goods) to 0.44 (for high-frequency goods). My estimate of βBI = 0.23, is at the low end of this range, but that is mostly due to the inclusion of controls for US GDP growth as well as time/length trends. Without these additional controls, βBI = 0.29 (t = 5.4), which is almost identical to the estimated coefficient from GI2011 and Burstein and Gopinath (2014). See the online appendix for additional robustness tests.

> 10Additional regressions (not reported) show that the coefficient on materials costs is larger for firms in high materials share sectors (0.78) than low materials share sectors (0.28), with the difference being statistically significant at the 1% level.

> 11The estimates of βC are much smaller (0.04) without controlling for GDP growth, date and life-length (see online appendix for further details). The coefficient on GDP is positive and significant, the coefficient on life-length is negative and significant and the time trend is usually insignificant which suggests that US producers might price differently over the business cycle or over time.