is that there are no better detailed comprehensive choices available.<sup>8</sup> The Bureau of Labor Statistics (BLS) reports data on import and export price indexes for the US, but these indexes are available only at a fairly aggregate level: 129 commodities overall, and only 72 commodities at 3-digit SITC (Rev 3). Furthermore, the BLS indexes are reported using the third revision of the SITC, while the trade data described above use the second revision. Imputing BLS prices to trade data requires a concordance between these two classications. Since the concordance is not one to one (a 4-digit sector from SITC Rev 2 may belong to more than one 3-digit sector in Rev 3), the number of well mapped sectors would be even smaller. Working at this level of aggregation would miss the point of the paper that exposure to exchange rate risk varies across sectors. Nevertheless, to ease concerns and provide some further validation to the use of global unit values as proxies for prices, I computed the global unit values for 70 commodities that have BLS price data and where there was a clear map to SITC Rev 2 commodities and checked the within-commodity across-time correlation between the two price series by running a xed eect regression between the growth rates of prices from the two sources. Although the time span is short because BLS data are available only since 1993, the regression shows a positive and highly signicant relation between the two series. The results are summarized in the two plots reported in Figure 1 that show the partial regression between the two price growth series for the whole sample, and dropping the extremes. Each scatterplot also reports the estimated coecient in each subsample and shows that both relations are highly signicant. This evidence suggests that, despite their shortcomings, uctuations in global unit values are good proxies for uctuations in international prices.

The BLS data also shed some light on the validity of the assumption that the correlation between nominal exchange rate growth and the growth of the sectoral prices at monthly frequency is proportional to the correlation at annual frequency. Using the monthly BLS data it is possible to compute and compare the correlations at monthly (within year) and annual frequency, as well as the variance of relative prices ( _p_ ˆ _h − p_ ˆ _i_ ) and the standard deviation of sectoral prices ( _p_ ˆ _h_ ) at these two dierent frequencies. The results, summarized in Table 2, show that, within a country, the correlations, variances, and standard deviations at these two dierent frequencies are positively and signicantly related. Not surprisingly, the weakest relation is for the correlation measures, where the relation between the within and across year measures is signicant only at the 2 percent level. It is also the case that all the estimated coecients are much smaller than 1, which suggests that the coecients of the specication described in equation (7) estimated using annual frequency proxies will be smaller than the true coecients associated with the within year measures of correlations, variances, and standard deviations. Furthermore, it is clear from the gure that the relationship between annual and monthly correlations, while signicant, is imprecise and annual correlations explain only a fraction of monthly correlations ( _R_<sup>2</sup> is only 0.05). This means that the annual proxies

> 8I attempted using US import price indexes from BLS as an alternative, but doing so signicantly reduces the number of goods covered because of a higher level of aggregation and to an imperfect match between BLS sectors (based on SITC rev 3) and Feenstra sectors (based on SITC rev 2).

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