Table 10A offers the number of workers (females, males, as well as, female and male young workers) classified according to their occupations at the 9-sector level. The definitions of occupations in the ILFS (NBS, 2002a) follow the Tanzanian National Standard Classification of Occupations (TASCO) which, in turn, follows ILO classifications<sup>11</sup> . According to the OECD (Elias, 1997, p. 7), those occupations can be assigned to four different skill levels. Only for one of the categories there is not an exact matching with skill levels. This category is “Legislators, administrators and managers”. In Tanzania it gathers a very similar number of male and women. Furthermore, the total amount of workers (male and women) included in that category is only 2.23% of all Tanzanian workers. Therefore we drop the data for this category and assign the wages to the four skill level categories following their weight in the total of the four categories. Wages by occupation (Table 9A) are only available for paid-employees (and not for self-employees). Neither do we know how skill levels are assigned across self-employees and paid-employees by sector and gender. So we provide just a crude approximation by assigning to the percentage of paid-employees in each sector (by sex), wages according to its corresponding occupations. This introduces a differentiation in levels of wages depending on skills by sector and gender. Although, the higher the levels of paid-employees in the sector, the stronger the different levels of wages according to skill levels are.

Looking at the country totals in percentages, at the bottom of the Table 10A, there is no big difference in the distribution of skills between women and men. We should keep in mind that the 2<sup>nd</sup> level of skills includes a category of “skilled agricultural and fisher workers”. Most labor is classified, then, in this occupation (86.8% of the Tanzanian labor force, 37% being females, 33.77% males and the rest young workers). Our skill level classification contrasts sharply with the one in the IFPRI SAM. As shown in Table 11A, Thurlow and Wobst (2003) have used education levels to proxy skill levels. They claim to be using information in the ILFS to assign men and women to different skill levels across sectors. According to the public information of the ILFS, skill levels are available only for the “traditional agriculture” sector, lacking any other detail for the rest of sectors. This can be seen in Table 12A which summarizes the information available for education levels in the ILFS (NBS, 2002a). Under this classification, the biggest percentages belong to the “not finished primary school” category, although an important share of labor is also allocated to the category of “no formal education”. This latter amount of workers appears in Table 10A behind the workers in agriculture.

Tables 13A and 14A concentrate on the shares of labor in total labor remuneration. They make even more explicit that our data set exhibits a higher wage gap (Table 13A) compared to the one of the IFPRI SAM (Table 14A). Looking at the bottom of Tables 13A and 14A (in Part 2 which offers the percentages), males and females account for 67.81% (62.72%) and 28.57% (36.28%) using the ILFS (original IFPRI SAM). Children between 10-14 years old also experience a considerable reduction when transformed into the remuneration of young workers. Females and male young workers weight around 20% on the total number of workers (Table 1A). However, their combined weight (i.e., for female and male children) is of only 3.62% in total labor remuneration (Table 13A or Table 15A).

There is no information on skill levels beyond the 9 sectors. However, as reported in Table 1A, there exists information for the number of females and males for a higher 16-sector disaggregation. We have, thus, assumed that skill levels in the 16-sectors are as in their broader corresponding 9-sector classification. Using this proportionality assumption we calculate the values in the Table 15A, which shows the labor shares across the 16-sectors. The resulting overall value added shares for all factors of production are presented in Table 16A. The one derived from the SAM of Thurlow and Wobst (2003) appear in Table 17A.

Finally, as already noted, the table of conversions of the 9-sectors (or 16-sectors) in the ILFS to the 52 sectors from the SAM in Jensen and Tarr (2010) is presented in Appendix 2.

> 11 The description of categories can be found in NBS (2012).

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