and take into account sector heterogeneity at a reasonably disaggregated level.<sup>2</sup> Denote Mijs the value of bilateral imports of country i from country j, in sector s, and let emissions per dollar of production in country j, sector s and pollutant k be given by gjs<sup>k. Then, the total(Z</sup> ij<sup>k) and average(Gk</sup> ij<sup>) PCI of country i from</sup> country j for pollutant k, are given by: where Mij ≡<sup>�</sup> s<sup>Mijsand μ</sup> ijs<sup>is the share of sector s in country i imports from</sup> country j.<sup>3</sup> This indicator covers the whole distribution of industrial sectors and its value is sensitive to differences in emissions across industries and pollutants.

The only comprehensive data on emissions are the 1987 US emissions coefficients computed for 79 4-digit ISIC industries. Note that, abstracting from the damage caused by the activity of transporting goods, if all countries had the same emission coefficients, the location of economic activity would not matter. However, it is clear that US coefficients cannot be applied to a large sample of countries that includes developed and developing countries. In order to estimate country-specific emissions, we draw on the results in Hettige et al (2000), who find that emissions per employee are roughly constant across countries.<sup>4</sup> Thus we can adjust the US per employee IPPS coefficients, h<sup>k</sup> US,s<sup>,byeachcountry’s</sup> industry labor-output ratio � XLjjss � ,i.e. gjs<sup>k=hk</sup> US,s � XLjjss �. This amounts to assuming that emissions per dollar are inversely proportional to labor productivity. While this adjustment to US coefficients seems reasonable, we abstain from extending it to other years since changes in emission intensities over time are not determined only by labor-productivity changes.<sup>5</sup> Changes in the volume of pollution embodied in worldwide trade (i.e. in PCI) is the simplest, most direct, and perhaps most reliable global indicator of the PH effect. Here we use average and total PCIs over 79 4-digit ISIC industries

> 2 The traditional approach relies on the less accurate comparison between “clean” and “dirty” sectors. It is true that, at the 3-digit ISIC level, the group of “dirty” sectors (reported in table A2 in Appendix 2) does not seem to change much across pollution criteria (e.g. actual emissions vs. abatement costs, see Mani and Wheeler (1997)). However, the definition of “clean” sectors is more ambiguous, and the intermediate sectors that are dropped from the usual analysis of clean vs. dirty industries often represent a substantial part of the PCI (up to 50% for some pollutants according to our estimates, see table A3 in Appendix 2). Moreover, at the 4-digit ISIC level, the classification of dirty sectors is not robust across pollutants. Two examples taken from the IPPS coefficients of the World Bank, establish the superiority of our approach. Take first the manufacture of leather products (ISIC 3233) which is one of the cleanest activities in terms of SO2 released in the air, but also one of the most polluting ones in terms of metal toxic pollution. Take next sugar factories and refineries (ISIC 3118): it has the opposite pattern, releasing much SO2 in the air, but negligible toxic pollution. Overall, more than a quarter of ISIC 4-digit categories exhibit a similar pattern, ranking in the top ten most polluting sectors according to one pollution criteria and among the ten cleanest according to another.

> 3 Muradian et al. (2002) and Ederington et al (2004) use a similar indicator.

> 4 We thank David Wheeler for this suggestion.

> 5 In an exploratory setting, using additional time-series data on SO2 emissions in China, Grether et al. (2006) construct a time-series of emissions for a similar sample of countries.

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