1% tax on fossil fuel plus the 20% subsidy are revenue neutral. We use this level of tax for other simulations in this set for comparative analysis purpose but this level is no longer revenue neutral given other general equilibrium effects that are different in simulations 2b, 2c and 2d.

We compare 4 case scenarios. The first one (sim2a) is a 20% subsidy and the 1% fossil fuel tax without an increase in land and capital in the jatropha and biofuel sectors. This allows us to isolate the impact of the tax and subsidy from other effects of the next simulations.

For the next simulation (sim2b), we combine the tax and subsidy with a tenfold increase in land for jatropha production where the land comes from idle land. The third simulation (sim2c) in the same as sim2b but the land is taken from other agricultural sectors and the last one (sim2d) in this group is sim2c with endogenous labor supply<sup>32</sup> .

The main challenge with performing sim1c and 2d with the endogenous labor supply is to attribute the increase in labor supply to specific households. For this procedure we randomly draw households from a group of selected households on the basis of characteristics found in the household survey. Table 1 summarizes these simulations.

**Table 1: Simulation descriptions**

|**Code**|**Simulations presentation**|
|---|---|
|**1a**|15 x increase of land for jatropha using idle land in Mali|
|**1b**|15 x increase of land for jatropha using agriculture land|
|**1c**|15 x increase of land for jatropha using agriculture land with endegenous labour supply|
|**2a**|1% tax on fossil fuels &20% subsidy to biofuel no exogenous increase in jatropha land|
|**2b**|1% tax on fossil fuels &20% subsidy to biofuel & 10 x jatropha land with idle land|
|**2c**|1% tax on fossil fuels &20% subsidy to biofuel & 10 x jatropha land with exploited land|
|**2d**|1% tax on fossil fuels &20% subsidy to biofuel & 10 x jatropha land with endegenous labour supply|

# **4 Results**

We proceed with a brief description of our macro and a few sectoral results. As we focus our analysis on a distributional impact of these scenarios, we select some key variables that play an important role in this distributional impact analysis<sup>33</sup> .

## **_4.1 Macroeconomic and sectoral impacts_**

From our results presented in Table 2 below, the first general statement we can formulate is that the impact on macro variables is relatively small. This is not surprising as the two main sectors of interest are relatively small at the reference period. In the first set of simulations, the first simulation is the one with the weakest impact at the macro level. Hence, using idle

> 32 For this set of simulations we used and 10 fold increase since the combination of the subsidy and 15 fold increase in land would not run as we encountered numerical resolution problems.

> 33 We can highlight that we performed extensive sensitivity analysis with respect to the value of parameters for our elasticities of substitution of our production structure. In reasonable ranges of parameter values our results were robust. Quantitative differences were observed but almost all qualitative variations were unchanged with very few exceptions. The general patterns observed and analyzed hereon were robust to our sensitivity analysis. For information detailing this sensitivity analysis, the reader can consult Olinga Megada (2012).

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