STEVEN PENNINGS 20

# Appendix A. Summary of Data Sources

- US import prices and US producer prices (dependent variables in regressions): confidential BLS microdata (see www.bls.gov/bls/blsresda.htm).

- Import and export values (for import shares): Feenstra Trade Database (http://cid.econ.ucdavis.edu/).

- Nominal bilateral exchange rates, country CPI, US CPI: Gopinath and Rigobon (2008) (http://www.aeaweb.org/articles.php?doi=10.1257/aer.100.1.304). Chinese CPI: IMF IFS.

- US Real Trade Weighted Exchange Rate (TWI), US GDP: Federal Reserve of St Louis Economic Database (FRED).

- Manf. output data and materials prices (series: PIMAT) from NBER-CES Manf. Ind. Database (http://www.nber.org/nberces/).

- NAICS6 measure of producer prices: bls.gov.

- Construction of Figure 1 (Aggregate Data). Manufacturing import prices index from Canada and Europe and all imports from Japan downloaded from bls.gov/mxp/. Annual average indices deflated by annual US CPI from FRED. Bilateral ER data and foreign CPI from FRED, annual averages. Competitors’ RERs data: Broad Real US TWI from FRED. Country trade weights for removing the bilateral rate and rescaling the TWI: Federal Reserve H.10 release.

# Appendix B. Data Construction

B.1. **Import price data.** The import price variable is constructed as the change in the log of the price of each individual good over its life in the sample (mean of around 2 years) in USD, from the first reported price to the last new price, which is essentially a pooled cross-section regression. The models in Sections 2 and 4 suggest I should deflate by unit labor costs (ULC). However, they are not available (or are poorly measured) for many of the countries in the sample, so I deflate all import prices with the CPI instead to remove the effects of inflation. I calculate the change in the log of each independent variable (such as the bilateral exchange rate and competitors’ exchange rate) over the life of the good. I also restrict the sample to USD priced goods. Restricting the sample to USD is common in the literature (e.g. GI2011), it only reduces the sample size by 10% and does not have much effect on the results. My model also deals with transactions between parties and so I remove intra-firm transactions. As in Gopinath and Rigobon (2008) and GI2011, I add country × BLS lower primary stratum fixed effects, and cluster standard errors at this level for import prices. I remove the 1% tails. I estimate the empirical model for goods from 10 countries/regions. I select the largest trading partners of the US in 2010, which together account for about two-thirds of US imports.<sup>27</sup> As the top 10 countries often compete against firms from countries outside the top 10, I construct the competitors’ exchange rate variable using data from all countries.

B.2. **Producer price data.** The construction of the producer price variable is similar to that for import prices, though more prices and firms are sampled as part of the PPI and so the sample size is larger. The PPI sample period is also slightly shorter, running from 1994-2006, and the life of goods is longer, with a mean life of around 4 years. As with the import data, non-market transactions are excluded. I drop all prices specifically for export. There are no fixed effects or clustering of standard errors by country (as all goods are produced by a single country). As a robustness test, in the online appendix I consider a specification with fixed effects and clustered standard errors at the NAICS3d level. The materials cost variables are also deflated by the CPI and reflect total materials costs (to which imported materials costs contribute).

B.3. **Measuring competitors’ exchange rates.** The primary measure of competitors’ exchange rates is the import-weighted average of the change in the log of exchange rates within the NAICS 6 digit sector. This is the measure suggested by the multi-country AB model in Section 4, and in a three-country model (for import prices) is equal to the third-country RER in Section 2. For import prices, I first remove the bilateral exchange rate of the country who manufactured the good (with weights rescaled so they add to 1),<sup>28</sup> though for US producer prices this is clearly not necessary. Ideally, I would measure competition at a lower level — AB argue their model is best applied at the “more disaggregated than ten-digit NAICS” level — but

> 27The countries/regions are: China, Canada, Mexico, Japan, Germany, France, United Kingdom, Republic of Korea, Brazil and Hong Kong SAR China. I replace Taiwan, China with Hong Kong SAR, China due to a lack of available data from Taiwan, China, and because many Chinese exports pass through Hong Kong SAR, China.

> 28That is, they are multiplied by 1/(1 − sBIIM<sup>).Theimportsharesusedtocalculatecompetitors’exchangeratesarethoseat</sup> the start of the life of the good in the BLS data.