In order to address this shortcoming, we propose an alternative scenario where pollution intensities change across countries and time. The scenario is constructed taking as the base the technique effects estimated by Grether, Mathys, and de Melo (2007) for 62 developed and developing countries during 1990–2000.To our knowledge, this is the only study that estimates and identifies the technique effect for a large sample of countries<sup>10</sup> . It does so by combining different databases providing pollution estimates at the country level. For most countries the data are available only until 2000, which prevents us from developing a full decomposition exercise. We circumvent the data problem by regressing the estimate of Grether, Mathys, and de Melo against the possible determinants of technical changes (please see regression results in ~~the Appendix~~ <u>Annex 1). While initially we consider several determinants (such as</u> initial per capita GDP, trade-to-GDP, manufacturing output to GDP, growth in per capita income, and different measures of human capital), model selection analysis helps us to focus on two dependent variables—initial GDP per capita and ratio of trade to GDP—which allows us to project the rate of adjustment in pollution intensity for each of the CA-4 countries in the 1999–2008 period. The rates are then applied to IPSS pollution intensities in order to construct new emissions data series. We assume that technical changes are homogeneous across sectors.

The remaining data used in the analysis include the following indicators: value added, exports, imports, number of workers, wages of skilled workers, and wages of unskilled workers. All indicators are disaggregated at the two-digit industry level. Table A1 in the <u>Annex</u> ~~appendix~~ presents the summary statistics and source of each indicator. While value added and trade data are used directly in the analysis, labor indicators are used to calculate industry-specific factor shares. Following Grossman and Krueger (1992) we assume that each industry’s output is produced according to a constant returns to scale CobbDouglas technology using three main inputs: labor, human, and physical capital. For each industry, labor share is calculated as a product of average unskilled wages and total number of workers divided by industry output. Human capital share is calculated as the total wage bill divided by output minus the labor share. Finally, physical capital share is calculated as the residual.

While we acknowledge potential limitations of the pollution data and the analytical framework used, we believe that these exercises are in line with the literature and can help to provide insights for the ongoing policy debate.

# **V. Results**

This section presents and discusses the results from the two quantitative exercises. In each case we start by discussing the benchmark case (no technique effect) and move on to the alternative scenario. We present both individual-country results and the aggregate analysis for the region.

# _Decomposition Exercise_

Figure 1 compares the emissions from clean and dirty industries in the period before and after DRCAFTA assuming no technique effect. All countries experience a significant increase in pollution

> 10 Most papers in the literature estimate a combination of scale and technique effects.

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