data. The advalorem tariff rates for the EU before and after the Uruguay Round (UR) are available through the WTO schedules of concessions at the 8-digit HS level and the 8-digit preferential tariff rates are obtained from UNCTAD.<sup>34</sup> The intra and extra-EU bilateral trade data is available at the same level of disaggregation through Eurostat. In order to construct the reciprocity variable for a given country we employ the data in Finger et al. (1999) who use the available tariff reductions for each WTO member for each product during the UR aggregated by country into averages and then weighted by own imports. They calculate both the tariff reductions from 1986-95 and 1992-95, which tend to differ mainly for the countries that undertook considerable unilateral liberalization between 1986-92. This is an important point in identifying the reciprocity effect as we describe above. From these we construct our own product weighted measures of reciprocity that account for the share of each main exporter to the EU in a particular good.

We also require data on production and other industry variables that we use as instruments for x. This data is typically available for EU members and we aggregate it in the way that is suggested by the theoretical model.<sup>35</sup> UNIDO’s industrial database provides the most comprehensive source covering all EU members and dating back to 1978. UNIDO’s data is collected at the industry level and hence it is more aggregated than the trade and tariff data, which could potentially introduce some measurement error. Although we can not rule out this possibility, we note that it may not be such an important concern for the following reason. If the EU negotiators use the data at the most disaggregated level available for most of its members, as we do, then our measure is actually the relevant one.<sup>36</sup> Furthermore, we argue that our use of instrumental variables reduces any measurement error problem generated by using industry data for this variable.

Using industry level data for x does imply that the interpretation of β is now as the average EU-wide extra weight taken over the different industries rather than products. We are comfortable with this interpretation, since producers tend to organize at the industry level to lobby for protection. This is particularly true in the EU, where there is more variation in protection across industries than within them. Therefore, to the extent that the extra weight re fl ects a political economy motive, the

> 34 We exclude products with a zero MFN tariff before the Uruguay Round for two reasons. First, when the MFN tariff is zero there is often more noise in the data about whether a preference exists or not, since it is in effect irrelevant. Second, all the tariffs in the sample that were initially zero remained unchanged and are likely to share an unobserved common characteristic. Thus, including those observations would bias the estimates if the proportion of zero tariffs is different for PTA goods relative to the rest of the goods.

> 35 In terms of production this entails simply adding it up over the EU members. The interpretation of the political economy weight estimated in this case is (ωi − 1) =<sup>�</sup> c<sup>(ω</sup> i<sup>c−1)ξc</sup> i<sup>whereωc</sup> i<sup>istheindividualmemberweightforagiven</sup> producer and ξ<sup>c</sup> i<sup>istheproductionshareintheEUasshowninsectionA.1oftheappendix.</sup>

> 36 In calculating the variable (XI /MI )/εI the remaining variables that we employ are at the same level of aggregation as the production data. Moreover, the import demand elasticity we calculate is for the EU as a whole, as required by the model. For this we use the structural estimates of the parameter ann for each 3-digit product from Kee, Nicita and Olarreaga (2004) along with the EU’s imports to GDP ratio in that industry.

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