Where _g o jt_ and _g e jt_ stands for the growth rate of outputs and exports, respectively; _D j_ is a dummy<sup>_A_</sup> _t_ variable indicating ―dirty‖ industries; is a dummy variable indicating the period post-negotiations; and _X jt_ is a collection of variables that can help to explain a change in composition. Regressions for the Central American region might also include country-specific fixed effects. Table A.1 in annex 1 presents the variables included in the regression.

# _Data_

The exercises focus on four Central American countries (CA4) for which data are available: Costa Rica (CRI), El Salvador (SVL), Honduras (HND), and Nicaragua (NIC). The study covers a 10-year period (1999–2008) and takes 2004 (the beginning of DR-CAFTA negotiations) as the threshold (1999–2003 = before; 2005–08 = after). While the ratification and beginning of implementation took place between 2005 (for the United States) and 2007 (for Costa Rica), we believe that part of the changes in the patterns of production anticipate the actual ratification. Moreover, the choice of the beginning of negotiations as a threshold is convenient because it allows for additional observations in the post-agreement period.

Annual statistics on pollution emissions are constructed using pollution intensities from the Industrial Pollution Projection System (IPPS) of the World Bank. This database provides information on pollution intensity and abatement costs at the industry level<sup>8</sup> . More specifically, the IPPS reports the amount of each of 14 pollutants, in pounds per million dollars of value added, that are generated from each of 459 four-digit Standard Industrial Classification (SIC) codes. The predicted pollution levels are constructed by multiplying the industry’s value added by the industry’s IPPS coefficient. Industries are then classified into dirty and clean industries following Mani and Wheeler (1998). Annex 4 presents a list of industries by category.

While numerous studies use the results from IPPS for countries where data are insufficient (such as Mani and Jha 2006), the data have a few shortcomings. For example, IPPS takes pollution intensities in the United States in 1987 as the base. It represents a snapshot of the technique of production, held constant in a single year and place—that is, not accounting for country-specific factors or technical changes. This could affect the accuracy of IPPS estimates outside the United States and in different periods of time. However, if the intensity rankings by sector and relative magnitudes are similar across countries and time, IPPS can still be useful for identifying pollution problems even if it does not produce exact estimates of pollution. <u>In the context of our decomposition exercise, pollution estimates based on IPPS implicitly</u> assume no technical effect. While this might be a close approximation of the very short term after the trade agreement, the literature shows that technique effects play a crucial role in the long run. Therefore IPPS based analysis taking to account long period of time are likely to be biased. For example if instead technical changes are such that emission per unit of value added decreases with time, we would still be able to estimate the composition effects, but both the scale and the overall effects would have an upward bias<sup>9</sup> .

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