PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 13 Table 2. Calibration

|**A. Universal Parameters**||||**Target/Source**|
|---|---|---|---|---|
|ρ= 10.5 (Within Sector Elasticity)<br>η = 1.24 (Between Sector Elasticity)||||EMX(2015) based on Taiwanese Manf. Data.|
|θ = 0.385 (Firm prod. log-normal SD)||||AB(2008)|
|Dj ∈{1,1.5,2}w.p <sup>1</sup>/3 (Sectoral Iceberg tra|de cost)|||Distrib. import shares (Feenstra & NBER Manf Data)|
|**B. Parameter/Equilibrium Object**|**US+10C Model**|**3C Model**|**Data/Target**|**Source**|
|KUS (US frms/Sector)|20|15|-|AveraesizeoftradeartnersGDPrelativetoUS|
|K (Foreign Firms per Country per sector)|3|15|-|g p|
|Sales-weighted ave markup|31%|32%|30%|AB (2008), value used in literature|
|Import share|28%|34%|28%|F&NBERMfD|
|Import-weighted ave import share|55%|58%|48%|eenstra an ata|
|Median Herfndahl|0.165|0.17|0.1-0.18; 0.26|DoJ “Moderately Concentrated” (AB); EMX#|
|Sales-weighted demand elasticity|5.3|5.2|≈5|Gopinath & Itskhoki (2010)/Broda & Weinstein (2006)|
|Mean share of largest dom. frm in sector|0.27|0.27|0.12-0.45|Amiti et al (2016) - EMX (2015)|

# calc from median inverse Herf

country, since the average of the top 10 US trade partners’ GDP is approximately 3/20th that of the US.<sup>18</sup> The second calibration is a three-country symmetric model which can be simulated in general equilibrium, and is used to study the correlation of exchange rates. In the body of the paper the three-country model is simulated in PE (GE in the online appendix).

I set K = KUS = 15 in the three-country model to keep the number of firms per sector close to the number considered in AB (45 here, 40 in AB). The standard deviation (and distribution) of firm productivity is taken from AB (where log(zjk) ∼ N (0, θ<sup>2</sup> ) and θ = 0.385). I set the iceberg cost of exporting to be D = 1, 1.5 or 2 per sector (with equal probability) in order to match the aggregate import share in the US manufacturing data of 0.28, and also (approximately) the distribution of import shares, as measured by the average importweighted import share of around 0.5.<sup>19</sup> The fact that the import-weighted average import share is higher than the total import share suggests that imports are concentrated in particular sectors, which is important for the mechanism in the model.<sup>20</sup> I calibrate the process of simulated RERs to match those in the data (in the GE model in the online appendix RERs are driven by TFP shocks). For the US+10 country calibration, I estimate the variancecovariance matrix of the RERs of the same top 10 trade partners as in the empirical work. For the symmetric three-country model, I calibrate the RER process to match the correlation of the bilateral RER and competitors’ RERs of ≈ 0.4 (with equal RER variances) in the sample of countries and time period in the empirical component.<sup>21</sup>

**Elasticities** Two of the most important parameters to calibrate in the AB model are the within-sector (ρ) and between-sector (η) elasticities, because the gap between η and ρ determines the strength of the variable markup mechanism (in trade models with fixed markups η = ρ). I discipline the size of this gap based on evidence from Edmond, Midrigan and Xu (2015; henceforth EMX), who note that Equation 10 and Equation 11 imply a linear relationship between the inverse markup μ<sup>−</sup> ijk<sup>1and market share sijk, such that μ−</sup> ijk<sup>1= b0+b1s</sup> where b0 = (ρ − 1)/ρ and b1 = − �η<sup>−1</sup> − ρ<sup>−1�</sup> . EMX estimate b0 and b1 using Taiwanese manufacturing data and use the ratio<sup>ˆ</sup> b1/<sup>ˆ</sup> b0 = −0.78 which pins down a tight relationship of η = [ρ<sup>−1</sup> + 0.78(ρ − 1)/ρ]<sup>−1</sup> . I take a

> 18The average of nominal GDP of Canada, China, Mexico, Japan, Germany, United Kingdom, Republic of Korea, France, Hong Kong SAR China, and Brazil in 2010 USD at market exchange rates (from IMF, World Economic Outlook Database, April 2011). The average is 3.6/20 of that of the US, but I round down to 3 firms per country per sector to keep the aggregate number of firms as close to AB who have 40 firms per sector.

> 19These two statistics are calculated using 2005 import and export data from Feenstra’s trade database and 2005 manufacturing production from the NBER manufacturing database for 371 matched NAICS 6d sectors which had import, export and US production data.

> 20In contrast, AB set D = 1.45 to match the average of import and export shares over 1987-2003 of 16.5%. The discrepancy between my figure and that of AB can be explained by (i) manufactured imports greater than exports, and (ii) the share of manufactured imports trending up over time.

> 21For the US+10 country sample, I calculate changes in RERs over overlapping three year periods, as this is halfway between the average life-length of goods in the IPP and PPI samples. The resulting correlation in the US+10 country model between bilateral and competitors’ RERs is 0.3, which is slightly lower than the empirical sample (and results in a smaller bias of univariate bilateral PT). Depending on weights of different countries in the sample, this correlation is between 1/4-1/2. As the competitors’ RER averages across multiple RERs, it usually has a SD 1/2-2/3 as large as the typical bilateral RER SD.