throughout the economy (0.71%). Agriculture makes very intensive use of female Laborers, by contrast.<sup>5</sup> The most capital intensive sectors are the “Dixit-Stiglitz goods” (83.12%>35.75%), although Business services are also very capital intensive (67.07%>35.75%). Finally, “Other CRTS” sectors also use female and male Professionals intensively, but make even more intensive use of male and female Technicians.

We identify a considerable gender wage gap in Tanzania. Under “Labor intensity (%)”in Table 1, the columns of “Young worker” and “Adult (females+males)” add up to 100, since they represent all the labor available for production in our model. A comparison of labor intensity measured in value added terms with that available in Table 2, which presents the actual number of workers (at the top) and their percentages over the total number of workers (below), provides an indication of the existing wage gaps between females and males. Comparing the row “All sectors” for labor intensities in value added and in physical units, we find that even though the percentage of “all females” and “all males” in total physical workers in Tanzania is very similar, 36.85% versus 36.10%, respectively, the former accounts for only 28.57%, while the latter accounts for 67.81% of all workers’ remunerations. This indicates the presence of a considerable gender gap.

However, the existence of this gender gap should be regarded with caution as evidence of discrimination. Non-discriminatory factors may explain part or, less likely perhaps, all of the wage differentials between men and women. Discrimination implies that some workers that have the same abilities, education, training, motivation, experience, etc. as others are accorded inferior treatment. Our broadly defined occupations, which are still more accurate than the usual skilled versus unskilled distinction, lack the detail needed for careful assessment of the exact portion of the pay gap due to labor market discrimination. For example, according to the ILFS (NBS, 2002a), women exhibit lower levels of education than men in Tanzania. This would support the idea of lower productivity (and therefore wages) of women with respect to that of men. Another factor that could (at least partly) explain the wage gap is the higher involvement of women in the low-paying sector of Agriculture. We do not know why women are more involved in agriculture, perhaps they have freely chosen to do so. In addition, we do not have detailed information on other factors: whether women are married, pregnant or have child care duties, whether they have different work preferences than men or less opportunity to migrate to urban areas, etc. Furthermore, in addition to individual attributes, sector sorting is another factor in creating the wage gap between men and women.

We have updated the estimates on the barriers to the entry of multinationals and domestic firms, while keeping the rest of model parameters and values in the SAM. Extensive documentation on the existing inefficiencies in Tanzanian specialized services has been developed elsewhere (Jensen and Tarr, 2010). The restrictiveness to the entry of multinationals and other regulatory barriers faced by domestic firms and multinationals into these services sectors have been translated into ad valorem equivalents by Jafari (2013), who updates the previous estimates of Mircheva (2008). Jafari’s (2013) estimates suggest a lower level of ad

> 5 Indeed, the category of “Laborers” is by far the most numerous in Tanzania because it includes “Skilled agricultural and fishery workers”, which is the main occupation in the country.

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