PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 7

# 3. Main Empirical Results

**Box 1:** Summary of four testable predictions from the simple model (Section 2) and associated empirical stylized facts (see Section 5 for a quantitative comparison of the full model and data). **Testable Prediction 1** : An appreciation of competitors’ exchange rates should increase US import prices.

Evidence: Significant pass-through of competitors’ exchange rates to US import prices, with a 1% increase in competitors’ exchange rates associated with a 0.35% increase in import prices in the preferred specification — larger than estimated bilateral pass-through.

**Testable Prediction 2** : The coefficient on the bilateral RER will be “biased upwards” in univariate regressions, with bilateral pass-through falling when competitors’ RERs are added as a control. Evidence: When competitors’ RERs are added as a control, bilateral pass-through falls by around a third (7ppts).

**Testable Prediction 3:** An appreciation of competitors’ exchange rates should increase US producer prices.

Evidence: Significant pass-through of competitors’ RERs to US producer prices: a 1% rise in competitors’ RERs increases US producer prices by about 0.11% in the preferred specification. **Testable Prediction 4:** An increase in foreign competitors’ prices should increase (a) US import prices and (b) US producer prices.

Evidence: A 1% increase in foreign competitors’ prices (in the same sector) raises import prices by 0.6% and US producer prices by 0.2% and lowers estimates of pass-through of competitors’ RERs.

3.1. **Data.** I use confidential monthly US import and producer price data from the Bureau of Labor Statistics (BLS), which are measured at the individual good level for manufacturing sectors (NAICS31-33). I calculate changes in log prices over the life of the good in the sample (conditional on a price change) which Gopinath and Itskhoki (2010) argue is able to uncover the long-run structural pass-through coefficients in a menu cost model.<sup>8</sup> The sample is 1992-2007 for import goods from the top 10 US trade partners (which represent around 2/3 of US imports) and 1994-2006 for US producer prices. All prices are real (deflated by the US CPI). I drop the top and bottom 1% outliers. For US producer prices regressions, I control for materials costs (domestic and imported) using NBER-CES materials costs index in the same NAICS 6 digit sector. Unfortunately, I have no good data on source imported materials prices for import prices regressions, though I tried a few unsatisfactory proxies (see Section 3.5 for a discussion).

Competitors’ RERs are the import-weighted average of bilateral RERs of all countries importing to the US in the same NAICS 6d sector (excluding the bilateral RER for import price regressions). The index of foreign competitors’ prices is constructed from import price microdata from the same NAICS 6d sector using a similar method as GI2011 (though I remove prices of other goods from the same exporting country). Compatriots’ prices are constructed from BLS microdata in a similar manner and aggregate US producer price indices by NAICS 6d sector are taken from the BLS website (both introduced in Section 6). See the appendix for more detail on data sources and the construction of key variables.

3.2. **Pass-through of competitors’ RER to import prices.** I estimate Equation 8, with the results shown in Table 1 in columns 1-4 (change in log prices over the life of the good, conditional on a price change). pR,ijk is the log real import price from country i of firm k in sector j, reri is the log bilateral RER (for country i with the US), rerc is log competitors’ RER index across competitor countries c (which varies by importer and sector), p<sup>F CP</sup> Rj is the log real foreign competitors’ price index. X includes a time trend, the life-length of the good and the growth rate of US GDP (as is common in the literature: Campa and Goldberg 2005; Gopinath et al 2010).

(8) ∆pR,ijkt = αi,j + βBI ∆rerit + βc∆rerct + βF CP ∆p<sup>F CP</sup> R,jt<sup>+ ΛX+ eijkt</sup> Table 1 (column 1) is the standard pass-through regression in the literature, with an estimate of measured bilateral pass-through of βBI = 0.23 (a real appreciation of the producer’s currency by 1% raises import prices

> 8The specification is designed to get the data as close to the model as possible: conditioning on price changes removes nominal rigidities in the firm’s own price, and having a long period removes the effect of other firms’ nominal rigidities through complementarities in pricing.