Let us begin with the analysis of poverty and inequality for the reference period at the national level and for the two groups of interest (Figure 2).<sup>35</sup> While poverty at the national level is slightly below from 40%, the rural area exhibits a poverty rate slightly higher at 42% while the poverty rate is much lower in urban zones at 14.5%. When analyzing the poverty depth (FGT1) and severity (FGT2), the indices for rural areas are just below the national level and the urban zones have much lower levels for these two indices. Mali is a country with relatively high level of inequality with a Gini value of 0.57 for the country. However, the decomposition analysis reveals lower inequality within the two zones at 0.53 for the urban areas and even lower in rural Mali at 0.49.

**Figure 2: Poverty and inequality analysis for Mali and the groups**

<!-- Start of picture text --> FGT1<br><!-- End of picture text --> <!-- Start of picture text --> 0,57 0,53<br>0,49<br><!-- End of picture text --> <!-- Start of picture text --> UrbanMali<br><!-- End of picture text --> <!-- Start of picture text --> RuralMali<br><!-- End of picture text --> <u>Source: Resutlts obtained by authors with DASP 2.1</u> For the distributional impact analysis for national poverty (see Table 8), we see that the country would experience a decrease in poverty for all indices and all simulations. In the first set of simulations, the one with the weakest positive impact is sim1b. Looking at poverty serverity, we observe the strongest reduction for the first simulation (sim1a) , while sim1c exhibits the highest reduction in poverty depth index. In the second set of simulations, the sim2a seems to be the most interesting in terms of the three poverty indices and the subsidy using idle land (sim2b) is the second best option. The sim2c produces the weakest positive results. The positive effects are a the results of increasing wages in most simulations and the decrease in price of key goods for poor households (food prices, forestry, fuel, manufactured goods). The returns to agricultral capital and land are qualitatively different between simulations and hence in some cases it will attenuate the positive effects of other variables and in other it will amplify the positive effects of other variables. The two simulations with endogenous labor supply contribute to significantly improving the situation of the

> 35 The welfare indicator used is the annual expenditures after private net transfers (monetary and non monetary), expressed per capita. The literature shows that income transfers between households can be considered a source of poverty reduction as well as a means to attenuate inequalities between households (Adams and He, 1995). Coulibaly (2010) shows that in the absence of transfers, poverty would be much higher in Mali and its regions. It is also worth noting that the national threshold chosen is the one suggested by the World Bank, based on the basic needs methodology. The poverty threshold is different in the two zones. The “Institut National de Statistique du Mali” computed 18 poverty lines that are decomposed between urban and rural areas for the 9 departments of the country. We computed rural and urban lines with the weighted averages on the nine rural and nine urban zones. This gave us a poverty line of 148 152 Fcfa for the urban and 120 091 Fcfa for the rural areas.

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