mentioned, the module is solved sequentially (CGE module and household module). Let us now describe this sequence. We first specify an income equation that reflects income structure for each household in the ELIM-2006 survey. We assume that the endowments of factors are exogenous and are modified exogenously when necessary. We use the factor payment variations generated by the CGE module and apply them to the factor endowments. The two wage rates are also applied to labor endowments of households.<sup>23</sup> As in the CGE module, we assume that the transfers from other agents to the households are exogenous. When changes in factors such as land increases in the jatropha production are necessary, we computed the number of households concerned based on shares of land involved in the simulations. From there, we isolated farm households in our sample and performed random draws of households to select the ones benefiting from the growth in land under jatropha cultivation. We applied the growth to these households<sup>24</sup> .

This procedure provides us with the new household-specific income. We can then move on to the expenditure side. The demand functions are derived from a utility maximization process (Cobb-Douglas utility function), and this demand equation is a function of market prices and household income. The final step in the sequential resolution of the household module consists in computing the change in welfare. Implicitly, this allows us to take into account simultaneously the income and price effects on each household’s welfare.<sup>25</sup> As in Bourguignon and Savard (2008), we use the variation of the real income to measure the change in welfare. The household-specific value shares for consumption are computed from observed figures in ELIM-2006. These shares are then used to specify a household-specific price index that is in turn used in combination with the nominal income to obtain the change in real income. It is this real income that is used to compute welfare changes.

# **2.1.3 Poverty and distributional analysis**

After selecting our criteria for household decomposition (including rural/urban divide) we can apply any type of distributional analysis. The indices can be applied using the reference period directly from ELIM-2006 data and then for the various simulations. The indices selected for our distributive analysis are the Foster, Greer and Thorbecke<sup>26</sup> (F-G-T, 1984) _P_ α indices for poverty analysis and the Gini index for inequality changes. In addition to these indices, we applied pro-poor growth analysis with the growth incidence curves (GIC) proposed by Ravallion and Chen (2003).

> 23 The survey included information that allowed us to decompose the labor into qualified and non qualified labor. 24 We repeated this procedure a number of times and our distributional analysis was robust to this random draw procedure. This can be explained by the fact that poverty rates in rural areas are very large and the random draw produced sample of households with similar compositions.

> 25 This approach is different from the endogenous poverty line proposed by Decaluwé et al _._ (2005) as it captures the price effect of the simulation through specific household preference and not through a basic needs approach. For a discussion of the advantages and disadvantages of the two approaches, see Ravallion (1994).

> 26FGT poverty indices are additively decomposable; as such, they are interesting within the framework of this analysis and make it possible to measure the proportion of the poor among the population but also of this poverty depth and severity. The higher the degree of poverty aversion, the greater the importance granted to the poorest. For detailed information on this index family, read Ravallion (1994).

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