Other co‐variates of interest are captured in the full regression results reported in Annex 1, Table 1. They capture the lower per capita consumption and income levels for larger households with more dependents mirrored in most other vulnerability studies. Female‐headed households do not have lower total or food consumption per capita, but do have lower income per capita. Finally, outside of the wealth variables, very few explanatory variables reduce the variance of outcome measures.

# **_5.2 Levels of Vulnerability_**

Given the above regressions, our next step is to calculate the probability that a household will fall below the poverty threshold. Because impacts of shocks on total consumption are similar to food consumption and income, and because shocks had no or a positive impact on maize harvest per capita, for the remainder of the paper, we will focus on shocks and total consumption per capita.

Table 4 displays the matrices for observed poverty in 2013 versus expected poverty in 2013 at the national level. The first scenario (upper matrix) assumes that expected shocks for households are all equal to zero. Rows give the observed number of households at or above the poverty line in 2013 (non‐poor), and those falling below the poverty line (poor). Columns contain the number of households who are expected to be at or above the poverty line (not vulnerable), and those expected to fall into poverty in 2013 (vulnerable), given characteristics prevailing in 2010. To the extent that households expect no deviations from long‐ term averages in terms of the shock variables, just 22.3% of households would be vulnerable, down from the 33.6% who are actually poor in 2013. Just 9.4% of the non‐poor are vulnerable, whereas 20.6% of the poor would expect to transition out of poverty.<sup>9</sup> But while each individual household may expect shocks to be zero when looking just one period ahead, risk averse households would still care about their probability of falling into poverty, and national‐level policymakers are also more interested in the likely incidence of poverty one year ahead. For instance, spatially, the rainfall deviation historically averaged about 21% nationally, ranging from 20% in Northern, 18% in Central, and 24% in Southern. We also note here the difference between average historical rainfall deviations versus the observed cross‐section deviations, which averaged about 12% nationally, to 8% in Northern, 10% in Central, and 16% in Southern. If we had been restricted to using the spatial variation observed for the 2012/2013 growing season, we would significantly underestimate the number of vulnerable households, since the 2012/2013 season exhibited lower deviations than the historical average in all regions.

> 9 The vulnerability ranking at the regional level in our sample switches depending on the shocks scenario used. Under the no shocks scenario, non‐poor households in Northern and Central are particularly vulnerable to becoming poor driven mainly by the possibility of future employment shocks.  Though Southern does have relatively high exposure to future rainfall variability, households there expect to be the least vulnerable.  When using regional‐level expected shocks, vulnerability is still highest in Northern, mirroring the results for households’ expectations, but Southern is now more vulnerable than Central.  This is primarily due to the fact that rainfall variability is higher in Southern than Central, and households there are more exposed to such shocks than those in Central.

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