In Tables 6A-8A we provide the core results found. They offer factors’ remunerations at a 9-sector level by gender. They stem from the publicly available data in the ILFS (NBS, 2002a) (Table 6A), from the IFPRI SAM authors (Table 7A) and from the aggregation finally used in the simulations in the model by Jensen and Tarr (2010) (Table 8A). Note that Jensen and Tarr (2010) expanded de 43 sectors available in the IFPRI SAM to a broader sectoral disaggregation of 52 sectors, which is the same one we use in the present model. In constructing the data set presented in Table 6A, we keep the total estimation on factors remunerations (i.e., value of gross domestic product at factor cost) of Thurlow and Wobst (2003), which stems from the Tanzanian National Accounts. This total is disaggregated into “the subsistence factor”, capital<sup>10</sup> and labor (considering females, males and children). Regarding the “subsistence factor” we have also followed the approach of the IFPRI authors (Thurlow and Wobst, 2003). The “subsistence factor” is the one that produces the “home production”. Its value is available in the Tanzanian National Accounts and is very similar to the one reported in the Household Budget Survey 2000/01 (NBS, 2002b). This latter source offers the allocation of home consumption across sectors, since in that survey households were asked whether they had bought the goods they consumed or whether they had produced or gathered them. Households had also been asked to keep a journal of incomes and expenditures over a period of 30 days.

The rest of value added in Table 6A is disaggregated following the information from the ILFS (NBS, 2002a), where capital is calculated as a residual after subtracting from total value added the values of subsistence and all labor categories. It is with regard to capital and labor categories where the changes in shares arise. By comparing Tables 7A and 8A it can be seen that the modifications made by Jensen and Tarr (2010) (Table 8A) with respect to the IFPRI SAM were very small for the 9 sector level we are considering now. However, when we contrast the information in Table 6A (which contains the factor shares used in this study) with the one of the IFPRI SAM (Table 7A), we find important changes. The share of total female labor diminishes (from 11.06% to 9.84%) while the share of males increases (from 19.12% to 23.36%), compared with the IFPRI SAM. Most of the effect behind this reallocation takes place in the “Agriculture/Foresty/Fishing” sector which in the IFPRI SAM exhibits a share of 7.08% of total labor for females and a share of 6.89% of total labor for males. Using the information from the ILFS (NBS, 2002a), this turns out to be 5.82% and 10.25%. The share of women in “rest of services” is also smaller, compared to the IFPRI SAM. Besides, the percentage of workers in “Trade” and “Transport and communication” increases using the ILFS (NBS, 2002a). Table 1A reported a rather similar percentage of total female (50.63%) and male workers (49.37%), including female and male young workers. If we discount young workers, the number of workers remains quite balanced 51.38% are women while 48.62% are men. However, when taking into account wages we find that women account for 29.6% of total female and male remuneration, excluding young workers, while men account for the remaining 70.36%, according to NBS (2002a). The same calculations applied to the SAM derived by Thurlow and Wobst (2003) yield a 36.64% share of women (63.36% of men) in total female and male remunerations (also excluding young workers remunerations).

We have analyzed whether women could be working less hours than men. This does not seem to be the case. The issue of under-employment (i.e., working less than 40 hours per week) is explicitly analyzed in chapter 7 of the analytical report of the ILFS (NBS, 2002a). It affects only 5.3% of the workers of 10 years and more (i.e., including young workers). The number of underemployed is bigger for men (it affects 522,672 male workers which constitutes 6% of male workers) than women (427,755 or 4.7% of female workers). Although not much further detail is given regarding the exact amount of hours worked by gender, in principle, this would not support the fact that women work less than men. Another reason explaining the wage gap could be different skill levels between men and women, with women exhibiting lower skills. Table 9A gathers the mean monthly income of paid employees by occupation and sex showing that there is a considerable wage gap between men and women with the same occupation.

> 10 Capital includes land. The latter exhibit the same factor remunerations as in the IFPRI SAM, the elaboration of Jensen and Tarr (2010) and in our elaboration of the data.

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