This is equivalent to saying that a household is considered vulnerable if the probability of falling below the poverty line in any given year is 29%.<sup>10</sup> Ultimately the choice of the threshold has an impact on who is considered vulnerable. As the threshold increases (decreases), and the required probability of falling under the poverty line increases (decreases), fewer (more) households are going to be identified as vulnerable.<sup>11</sup> There are three important main caveats associated with the method that are important to bear in mind. First, the cross-sectional variance of consumption is assumed to approximate the intertemporal variance in consumption. Given that information on how household consumption varies due to shocks experienced over time is not available, the method assumes that the variability of household consumption over time can be approximated by how consumption at a given point in time varies across households with the same characteristics. The rationale behind this assumption is that differences in the consumption of households with the same characteristics at a given point in time is likely to be due to the different shocks experienced by these households. The extent to which this assumption is tenable or not can only be settled empirically by comparing the cross-sectional estimates of the variability of consumption among households with the same characteristics with the estimates of the variability of household consumption over time (and ideally with significantly more than the 2 or 3 survey rounds that could be found in short panel surveys). This issue will remain unsettled until a panel data set with multiple rounds sufficient for reliable estimation of householdspecific consumption over time becomes available. Second, potentially large consumption measurement errors can overestimate consumption variance, especially from idiosyncratic shocks, which may in turn result in misleading vulnerability estimates. These estimates can be tested by carrying out simulations based on alternative measurement error assumptions (see Appendix Table A.3 in Gunther and Harttgen, 2009). Third, this model can shed light on generic “covariate” or “idiosyncratic” sources of vulnerability but cannot identify effects from specific shocks, such as a

> vulnerability of 0.5. This implies that, in the limit, as the time horizon goes to zero, then being _in current poverty_ and being _currently vulnerable to poverty_ coincide.”

> 10 Let P i > l denote the probability of being above the poverty line in any given year. Assuming the poverty status of a household is independent over time, the probability of being vulnerable to poverty at least once in the next 2 years, (i.e. using the 50% threshold), is then given by PP= P �ln cc ll� i ≥0.5 . Solving this for yields which implies that the probability of falling below the poverty line in any given year is 0.29 (=1-0.71). ,tt+2 11 vv = 1 −PP<sup>2</sup> PP PP= 0.71 Take the extreme threshold of 0.  This threshold implies that for a household to be considered non-vulnerable there cannot be a scenario under which the household would be below the poverty line.  Although such threshold may seem desirable, it is impractical.  For most households, there is always some small positive probability that they will fall below the poverty line and thus a threshold of 0 would in fact categorize every household as vulnerable.  Such a categorization is not useful in identifying those with more immediate needs.

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