constant and there is a fixed cost. Firms set prices such that marginal costs equal marginal revenue and there is free entry, which drives profits to zero. There is Chamberlinian largegroup monopolistic competition, which, together with the assumption that the ratio of fixed to marginal costs is constant, results in constant markups over marginal costs.

The 12-household version used for Tanzania has, however, been transformed into a one representative agent version for this current application. In Jensen et al. (2010) there were six rural and six urban households. Each category would receive the value added generated by the land, labor and agricultural capital as reported in the labor-income shares and agricultural capital and land returns available in the Household Budget Survey NBS (2002b). Further, within each of the rural and urban types of households there was one group of households below the food poverty line, another one between the food and basic poverty lines, while the other four categories were sorted according to the education of the head of the household. As can be seen this household categories did not include any information on gender. Therefore, they were converted into a single representative, which was also necessary in order to use the new factor shares we have derived with the rest of Thurlow and Wobst’s SAM (2003).

Jensen and Tarr (2010) have expanded the rest of the world region from the model of Jensen et al. (2010) to account for different areas sourcing imports and FDI in Tanzania. Of particular interest for our analysis is the fact that the multilateral liberalization scenario has the most important impact for Tanzania (i.e., Tanzania gains more when it lowers regulatory barriers to all regions, instead of lowering them to particular regions). Therefore, our single country analysis focusing on gender aspects seems a “suitable simplification” of the greatest impact that could be attained with the multiregional model.

Another aspect that merits comment is what could be the best approach to model gender differences in our model. We have seen that previous CGE gender-aware models have mostly concentrated on how to deal with “non-market activities” at the same time as they introduce shocks of trade liberalization. It seems clear that despite recent changes women are still primarily responsible for child care and housework duties. Although in developed countries these tasks now tend to interrupt fewer women in work careers, due to the increasing availability of child care facilities or better female pay (e.g., Light and Ureta, 1992; Frederiksen, 2008), the story is different in developing countries. This is certainly an important avenue for research. The task of trying to quantify its main determinants and effects, some of them ranging beyond the boundaries of economics, remains for the future.

How do we model wage formation in the presence of a wage gap? As noted above, we find evidence of the existence of a substantial wage gap. We do not know, however, to what extent that gap is related to discrimination or to other characteristics of women that could make them less productive than men, thus explaining their lower wages. One important question related to the wage setting mechanism in our model is the following: Would the policies we implement (increases in the number of foreign or domestic firms and changes in tariffs) directly impact on the degree of discrimination or change those characteristics that make women less productive than men? This does not seem to be the case. More openness could arguably reduce discrimination itself through changes in customs, new ideas, etc., but not immediately. It would 10