risks) and community level risks (covariate risks). Distinguishing between idiosyncratic and covariate risks is important for two reasons. First, informal risk sharing mechanisms taking place at the community level can be effective in partially insuring idiosyncratic risk but will remain vulnerable to the realization of aggregate risks (covariate risks). Second, making this distinction is relevant from a policy perspective to design the most appropriate safety net mechanisms. The targeting of the vulnerable population for example will differ according to whether shocks are dominantly idiosyncratic or covariate. The details of this methodology are presented in the annex.

# **B. Results**

We estimate two specifications for our empirical model of log consumption per capita. The set of explanatory variables Xij includes household demographics (household size, age dependency), head of household’s characteristics (gender, education level, activity), some indicators of wealth (livestock ownership, wealth index 3 ) and rainfall shocks experienced at the commune level in 2008 and in 2009.<sup>4</sup> The first specification is a simple OLS model (column 1 in Table 10). The sizes of the household and female headship each has a negative marginal contribution to welfare and is associated on average with 4% lower consumption per capita. Households where the head has completed primary education have on average a 3% premium on consumption per capita compared to households where the head has no education. For households where the head has completed secondary education, this premium is close to 12%. Wealth is positively associated with total consumption expenditure per capita, whether we look at livestock ownership or at measures based on durable goods ownership and housing.

Farming households living in districts where cotton production is an important activity are significantly worse off compared to the other households.<sup>5</sup> Consumption per capita is almost 30% lower for these households, everything else held equal. This is not a new finding since – as mentioned above - many studies have already documented the so-called “Sikasso paradox” situation in which poverty rates are surprisingly high in a fertile region dominated by cash crop production (Delarue etal. 2009, Dury and Bocoum 2012, World

> 3 The wealth index is based on a principal component analysis based on durable goods ownership and on housing conditions.

> 4 Rainfall data come from the NASA Tropical Rainfall Measuring Mission (TRMM) which provides a narrowly gridded (0.25x0.25 degrees of longitude and latitude) dataset of accumulated precipitation. Monthly data at aggregated for 2008 and 2009, and commune level rainfall is imputed from the 5 closest grid points using an Inverse Distance Weighting (IDW) algorithm. Rainfall shocks are then constructed based on the yearly standardized deviation from the 1998-2011 commune-specific precipitation average: zct = Rctσ−Rc,1998−2011R�����������������c,1998−2011 where Rct is the accumulated rainfall in commune c and

> year t, R���������������c,1998−2011 is the average yearly rainfall for commune c between 1998 and 2011, and σc,1998−2011R is the standard deviation of yearly accumulated rainfall for commune c between 1998 and 2011.

> 5 The ELIM 2010 survey did not ask directly to the respondents whether households were explicitly engaged in cotton production. Our cotton variable is constructed as the intersection between the set of households engaged in agriculture and the set of households living in districts where the Malian state owned cotton company (CMDT) operates (http://www.maliagriculture.org/services_tech/cmdt/page-cmdt.html).

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