remunerations yields the “Total value added” column, whose value matches that in the Tanzanian National Accounts.<sup>2</sup> Relying on the publicly available data of the ILFS (NBS, 2002a), we have undertaken a thorough revision of labor shares in the SAM of Thurlow and Wobst using: 1) better proxies for workers’ skill levels, 2) a wider coverage of wage data, and 3) more accurate information on child labor.<sup>3</sup> In our new data set nine different occupational categories are converted into four different skill levels, following the OECD suggestions (Elias, 1997, p. 7). The new skills of workers are: “Unskilled”, “Laborers”, “Technicians” and “Professionals”. See the note in Table 1 for the exact conversions. Therefore, we do not proxy skill levels with educational categories, as Thurlow and Wobst (2003) do, because apart from “Traditional Agriculture”, there is no information on the skill of workers regarding their wages in other sectors. By contrast, we know occupations and wages for nine broad sectors, which are extrapolated into 52 sectors based on their value added weight. Furthermore, we assign different wages to the self-employed, which is one of the main difficulties in estimating labor shares (Arpaia, et al., 2009; Guerriero, 2012; ILO, 2010; Gollin, 2002). We also use children between 10 and 17 years old for whom there is data on their distribution across sectors with their corresponding wages and occupations, instead of the children between 10 and 14 years old of Thurlow and Wobst (2003), on whom that information is not available. The new category of children between 10 and 17 years old is labeled “young workers” in our new data set. In addition to labor, total value added is also composed of subsistence,<sup>4</sup> land and capital. Subsistence and land in our model are the same as in the SAM of Thurlow and Wobst (2003), while capital differs only slightly and is calculated (in our new data set) as a residual, subtracting from total value added the values of subsistence, land and all labor categories.

There are considerable differences in factor intensity in the four broad sectors we analyze in Table 1. As we move from the top to the bottom of that table, two measures of factor intensity appear. There has been a considerable debate on the best way to proxy factor intensity in the literature, particularly in the context of more than two factors of production (e.g., Bowen et al., 1987). We present first a rather standard measure of “Factor intensity”, in which the remuneration of each factor corresponds to its weight in total value added, the latter being normalized to 100 (see column “Total value added”). The factor intensity of a sector, e.g., “Business services”, is calculated by comparing each factor share in the services with the overall use of that factor in the whole economy (row “All sectors”). Thus, male Professionals are used very intensively in Business services (4.37%) in comparison with their overall use

> 2 This coincides with the total value added used in the Social Accounting Matrix (SAM) constructed by Thurlow and Wobst (2003), which, except for its original factor shares, will be the base for our simulations. By keeping total value added and using a model with one representative household, we ensure consistency with the rest of the SAM.

> 3 Appendix 1 offers a thorough explanation of how our new factor shares have been obtained, comparing them with those previously available in Thurlow and Wobst (2003).

> 4 The “subsistence factor” is a composite factor made up of labor, land and capital which produces the “home production”. Its overall value is available in the Tanzanian National Accounts.

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