stage antidumping investigation _outcome_ measure allows this information to affect the estimation, provided that additional investigations resulted in definitive antidumping measures on _different_ 6-digit HS products from those that received protection in the initial investigation. Second, when treating the estimation as a cross-section, it is not obvious that an industry with more AD cases over the 1995-2003 period has necessarily derived greater benefits from the policy than an industry that had pursued only one. For example, the “one case” industry could have benefited from imposition of a 1995 definitive measure and therefore face little competition from imports over the entire period. A different industry that pursued many cases over 1995-2003 could have faced a number of years of import competition before receiving protection as well as the cost of pursuing multiple legal proceedings.

We define our second stage antidumping _outcome_ variable as a coverage ratio, i.e., the fraction of 6-digit HS product imports within a 3-digit ISIC industry’s total imports that face definitive antidumping measures.<sup>14</sup> The construction of this variable also requires some explanation, as we construct a crosssectional measure intended to capture the _cumulative_ effects of an industry’s 1995-2003 antidumping use. First, we construct both the numerator and denominator of the coverage ratio from the _Comtrade_ 6-digit HS import data. The denominator, denoting the total value of 3-digit ISIC industry imports, is the sum of the maximum value of yearly imports for each 6-digit HS product over the 1995-2003 period. The numerator of the coverage ratio is the total value of the 6-digit HS imports in the year _prior_ to the initiation of the earliest AD investigation that led to a definitive AD measure affecting the product.<sup>15</sup> More formally, let _m_ denote the value of imports, _h_ denote a 6-digit HS product, _I_ be a 3-digit ISIC industry, _t_ be the year of the initiation of an antidumping case between 1995-2003 that ultimately concludes with the imposition of a definitive measure, and _A_ be the subset of 6-digit HS products in _I_ that ultimately face imposition of a new antidumping measure during 1995-2003. Omitting the country subscript, the dependent variable in regression equation (1) is the coverage ratio for industry _I_ defined as 14 Instead of a coverage ratio of affected imports, alternative approaches for the second stage would be to construct a measure of the size of the imposed final antidumping import restriction or to use a count measure of the number of measures imposed. The first alternative presents serious aggregation and averaging challenges given that most of these developing countries have not adopted the US model of implementing new antidumping measures almost exclusively in the form of an ad valorem duty. It is frequently the case for these developing countries that one subset of industry imports targeted by antidumping might be affected by an ad valorem duty, another subset might be affected by a specific duty, while a third subset might face a price undertaking. The second alternative of using a count measure also presents aggregation challenges given that many antidumping measures may be imposed on the same 6-digit HS product either across exporting countries and/or over time against the same exporter. See, for example, Staiger and Wolak (1994) for the caveats associated with constructing such measures in the case of the US.

> 15 There is potential mismeasurement of this variable for at least two reasons. First, we rely on the 6-digit HS data as this is the greatest level of disaggregation common across importing countries, although some countries impose antidumping measures at the 8, 10 or 12-digit level. Second, the numerator in the coverage ratio includes all 6-digit HS imports in a targeted product, and not just from those countries named in the investigation.

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