best way to identify it is at the industry level.<sup>37</sup> Table 1 presents the tariff levels and their changes for our sample. Although our analysis is conducted at the product level we provide some statistics here aggregated by industry. The highest tariff rates before and after the UR appear in the tobacco sector (SIC-314): an average of 42 and 25 percent respectively. The lowest pre-UR tariffs are in the miscellaneous petroleum and coal products sector (SIC-354) with 3.9%, whereas the iron and steel industry (SIC-371) became the least protected in terms of tariffs after the UR, 0.4%. The footwear sector (SIC-324) experienced the least liberalization, 0.8 percentage points, and tobacco the highest, 17. Note also that there is a considerable amount of variation in tariff changes both within industries, with coefficients of variation between 0.28 and 1.5, and across industries, with a coefficient of variation of 0.44.

# 4.3 Estimation Results

The unconditional mean reduction in MFN tariffs by the EU was 4.4 percentage points for non-PTA products but only 2.9 for PTA products during the UR. A simple t-test con fi rms that the difference of 1.5 percentage points, with a standard error of 0.1, is statistically signi fi cant. This difference may be due to other factors that are correlated with the PTA variable. Therefore in table 2 we present the estimates of the parameters in (23). In order to address the endogeneity issues discussed above we employ an instrumental variables technique. More speci fi cally, we use the two-step efficient generalized method of moments estimator, which is robust to heteroskedasticity with an undetermined form. Furthermore, we allow for the errors to be correlated through clustering within each industry for which the production data is used to ensure the estimation is also robust to arbitrary within-industry correlation.<sup>38</sup> The variable I<sup>any0</sup> in table 2 takes the value one for goods imported at a duty-free preferential rate

> 37 The share of between-industry variation relative to total variation in the MFN tariff rates for EU is twice as high as in the US for instance. This is the case both in levels (0.62-0.69) and in changes (0.34). This might be partly due to the fact that the original CET in 1968 was an average of the tariffs of the founding members. When the averaging is done for each product across countries, the within variation for any given industry falls relative to what it was for any individual country. Thus, the share of the variation between industries after the averaging tends to increase relative to what it was for any given country. Moreover, from a political economy perspective, it is easier to build consensus for tariffs at the industry level (most countries are likely to produce some product in any given industry) rather than at the product level (some countries do not produce some products and would clearly loose when the CET on it is raised). In effect, the consumers are relatively organized in a CU because each government is likely to represent them when the country is a net importer. Producers from different countries that join for EU-wide lobbying on trade policy are more likely to maintain their coalition together if they focus on a level of protection that is similar for all of them, that is a level of protection with little within industry variation. A similar effect arises when ministers from different countries representing a given sector, say agriculture, must agree on an EU-wide policy. Each demand protection for some products and will support protection favored by other countries in other products, if they expect this to increase the probability of protection for their own products of concern, which Winters (1994) describes as the “restaurant bill” effect.

> 38 In section 4.5 we discuss formal tests of endogeneity and heteroskedasticity that justify this procedure and compare the results to those obtained using OLS.

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