STEVEN PENNINGS 14 value of ρ = 10.5 from EMX, which implies η = 1.24 (similar to the values of ρ = 10 and η = 1.01 from AB). My model also does well in matching the sales-weighted demand elasticity of around 5 (targeted by Gopinath and Itskhoki (2010), based on Broda and Weinstein (2006)). The GE version of the model produces a trade elasticity in the range estimated in the literature (see the online appendix).

**Concentration** The value of ρ, combined with other parameters, yields a median sectoral Herfindahl index of around 0.165, in the “moderately concentrated” range of 0.1-0.18 according to the Department of Justice (as reported in AB). A much higher (lower) value of ρ leads to much higher (lower) concentration statistics, outside the DoJ’s moderate range. The level of concentration depends on the size of the sector one seeks to represent. In the empirical work I calibrate to 6 digit industries, so I would expect concentration statistics in between those of 4 digit industries of Amiti et al (2016) and 7 digit industries of EMX. The only concentration statistic reported by both papers is the mean share of the top domestic firm, which is around 0.45 in EMX and 0.12 in Amiti et al. In my calibration, the mean share of the top US firm is 0.27, which is almost exactly in the middle of EMX and Amiti et al’s estimates. My Herfindahl index is also lower than the 0.26 reported in EMX. As higher levels of concentration are associated with more variable markups, my calibration can be considered conservative.

# 5. Simulation Results (and comparison to data)

5.1. **PT of competitors’ RERs to simulated import and producer prices.** Estimates of exchange rate pass-through on simulated data are shown in Table 3. Using the preferred US+10 country model (Panel A), one can see that a 1% appreciation of the bilateral exchange rate raises import prices by 0.65% in the bivariate specification.<sup>22</sup> Note that bilateral pass-through is well above that estimated in the data, which is a well-known puzzle in the literature.

A 1% appreciation of competitors’ exchange rates in the model increases import prices by around 0.15% in the US+10 country specification (Table 3 column 2), showing that Testable Prediction 1 holds in the quantitative model. While this is a good deal lower than the estimate in the data of 0.35, (i) the estimates in the data might include some effect of imported intermediates, which are absent from the baseline model and (ii) estimates here are much larger than pass-through of competitors’ RERs in standard models (which is zero). As in the data, the addition of competitors’ exchange rates reduces bilateral pass-through, suggesting that measured pass-through is biased upwards (Testable Prediction 2). Quantitatively, the bias is 0.025 — a good deal smaller than in the data — mostly because pass-through of competitors’ RERs in the model is also smaller than in the data.

For US producer prices, a 1% increase in competitors’ RERs implies a 0.09% increase in simulated US producer prices (Table 3, column 4). These coefficients are very similar to those estimated in data, where a 1% increase in competitors’ exchange rates lead to a 0.11% increase in US producer prices.

In the model, pass-through of competitors’ RERs works through pass-through of foreign competitors’ prices. In the US+10 country model, a 1% increase in foreign competitors’ prices leads to a 0.19% increase in simulated import prices and a 0.11% increase in simulated producer prices (Table 3, columns 3 and 5 respectively). The estimates in the model are a good deal smaller than those in the data, which might reflect (i) a lower general responsiveness to competitors’ prices in the model (Γ/(1 + Γ) is too small) and/or (ii) the absence of sector specific-shocks in the model (for example, sector-specific technology shocks) which would be reflected in competitors’ prices, but not competitors’ RERs. The addition of these shocks would be an interesting area for future work.<sup>23</sup> Relative to the US+10 country calibration, the three-country symmetric model has (i) a larger own country import share (for importers), (ii) a smaller foreign competitors’ share (for importers) and (iii) a larger total import share. Panel B of Table 3 shows estimates in the three-country symmetric model that are generally similar to the US+10 country calibration, but differences (i)-(iii) respectively imply (i) slightly higher bilateral pass-through for importers, (ii) slightly lower pass-through of competitors’ RERs for importers, and (iii) slightly higher pass-through of competitors’ RERs for US producer prices.

The estimated rates of pass-through in Table 3 are very similar to the relevant structural β-coefficients averaged across individual firms. Figure 2 plots the structural βBI (LHS, bilateral PT) and β<sup> ̃</sup> C (RHS, competitors’ PT) for each sampled importing firm using the three-country model (vs the firm’s sector share).

> 22Standard errors are usually tiny (and arbitrary), so I don’t report them.

> 23As exchange rates are the only driver of prices in the model, competitors’ prices and competitors’ exchange rates are highly co-linear and I get over-fitting when trying to include both in the regression (not reported).