PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 17

# 6. Extension: PT of other competitors’ prices to import prices

While pass-through of foreign competitors’ exchange rates (and prices) are the focus of this paper, the simple model (Section 2) and the full model (Section 4) also suggest that import prices will respond to other components of the sectoral price: (i) to the US producer price index (βP P I ) and (ii) to other compatriots’ prices (βOCP ). In this section, I add measures of these two prices to the standard import price pass-through regression (Equation 8). GI2011 competitors’ price index is a combination of foreign competitors’ prices and other compatriots’ prices.

The first two columns of Table 4 show that a 1% increase in other compatriots’ prices (other firms from the same exporting country) is associated with an increase in US import prices of around 0.5% (significant at the 1% level). Controlling for compatriots’ prices also reduces pass-through of the bilateral RER (0.23 → 0.17) in column 1. However, once competitors’ RERs are included as a control, the addition of compatriots’ prices only has a small effect on estimates of bilateral pass-through, suggesting modest rates of “indirect” bilateral pass-through (Section 2).

Columns 3-4 of Table 4 show that pass-through of sectoral US producer prices to US import prices is extremely large: a 1% increase in US producer prices in the same sector raises US import prices by 0.9% (significant at the 1% level). The coefficient is much larger than pass-through of foreign competitors’ prices or GI2011’s measure of import competitors’ prices (both around 0.6). Controlling for the sectoral PPI substantially reduces pass-through of the bilateral RER (from 0.23 → 0.16) and also reduces PT of competitors’ RERs (0.35 → 0.10), which becomes insignificant.

Table 4. Pass-through of alternative measures of competitors’ prices to US import prices

||Dep|endent Vble|: Real US|Import Pric|es (∆p ove|r life of good)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Bilateral RER (βBI)|0.17***|0.13***|0.16***|0.14**|0.16**|0.15**|
||[4.0]|[3.0]|[2.6]|[2.1]|[2.5]|[2.2]|
|Competitors’ RERs||0.20***||0.10||0.06|
|(βC)||[3.0]||[1.4]||[0.8]|
|Foreign Comp. Prices|||||0.23***|0.22**|
|(βF P P)|||||[2.6]|[2.5]|
|Sectoral PPI|||0.92***|0.90***|0.48***|0.48***|
|(βP P I)|||[7.3]|[7.1]|[3.5]|[3.5]|
|Other Compatriot’s|0.50***|0.48***|||0.25***|0.25***|
|Prices (βOCP)|[6.7]|[6.7]|||[2.8]|[2.9]|
|Observations|4030|4028|3231|3228|2491|2489|

Notes: Robust t-statistics are in brackets. ***,**,* indicate significance at the 1, 5 and 10 per cent levels. 1% outliers dropped. All specification include BLS primary lower stratum × country fixed effects, with standard errors clustered at this level. Regressions include controls for US GDP growth, date and life-length.

As the different measures of competitors’ prices are correlated, including only one measure will bias upward the estimated coefficient. Columns 5-6 includes all three measures of competitors’ prices in the regression, which approximately halves the estimated coefficients βF CP , βOCP and βP P I relative to regressions with only one of these variables. It also makes βC small and insignificant, which is consistent with competitors’ RERs working through competitors’ prices.

How does this relate to the model’s predictions? In the quantitative model (Section 5), changes in exchange rates are the only shocks, which lead to overfitting when similar measures of competitors’ prices and exchange rates are included concurrently in the model.<sup>26</sup> When a measure of the sectoral PPI is added to a standard

> 26In order to separately identify the role of competitors’ prices and competitors’ exchange rates, the model likely needs sector-specific cost or demand shocks, which is an interesting area for future research. The model is not able to separately identify the effects of the bilateral exchange rate and compatriots’ prices due to overfitting.