absence of panel data, some researchers have simply relied on cross‐sectional estimates alone, though Ligon & Schecter (2004) caution that such results can be quite biased. In other cases, researchers have used “pseudo‐panel” techniques, where repeated rounds of cross‐sectional survey data are collapsed at a supra‐household level where there are repeated observations (Christiaensen & Subbarao, 2005).

A recurring theme in the literature is the choice of the poverty line, and the choice of a probability threshold on which to categorize household vulnerability. Due to data constraints, few studies have been able to empirically test how these two choices affect the ability of estimated vulnerability to capture realized poverty in future periods. Zhang & Wan (2008), using three‐period panel data from China, estimate poverty and vulnerability using different poverty lines for the first two periods of the panel to get an estimate of vulnerability with which they then compare to observed poverty in the third period. The authors find that a relatively high poverty line closer to the mean observed income per capita does a better job at predicting the percent of households that stay in poverty and transition into poverty than a lower poverty line, as does setting the probability threshold at .5.<sup>3</sup> Cruces et al. (2010) also discuss the sensitivity of vulnerability classification depending on the threshold probability. They use two different threshold probabilities, the common “more likely than not” .5 probability, and the proportion of the population currently classified as poor. It is difficult to understand why the authors’ use the proportion currently classified as poor, but it does highlight difference in results, especially when comparing across countries. For countries with high poverty rates, a relatively large fraction of non‐poor households will be considered vulnerable, while in countries with relatively low poverty rates, relatively few non‐poor households will be classified as vulnerable. While these results are to be expected given the log‐normal distribution, as with the results in Zhang & Wan (2008), the ability of predicted vulnerability to actually capture vulnerable households may be more difficult in countries with very high or low poverty rates. Finally, Jha et al. (2010) have two periods of household data for Tajikistan, though they calculate the VEP vulnerability measure using cross‐sectional data in the earlier period and assess how well it predicts actual vulnerability in the second period. In their case, about 60% of households were below the poverty line in the first period, so that the poverty line is neither very high nor very low. And, the predicted vulnerable in the first period, 65%, was close to the observed poor in the second period, 62%.

Another point of interest from the extant empirical literature is the fact that, in most applications, the explanatory power of the variance equation estimates tends to be quite low. For instance, in Jha et al. (2010), estimated equations for different variance measures have an R<sup>2</sup> of around .05. This is similar to results found in Christiaensen & Subbarao (2005), Imai et al. (2011), and Bronfman (2014). Of the papers reviewed, the greatest explanatory power for the variance equation is found in Bogale (2012), with an R<sup>2</sup> of .20. Limited capacity to explain the variance would indicate either limited heteroskedasticity or omitted relevant variables, which gives some cause for concern about the use of this method to generate predicted variance and using this as a measure of risks households face.

In terms of empirical evidence, most studies find that poverty is the most important component of vulnerability, then followed by risk (Chaudhuri, Jalan, & Suryahadi, 2002; Ligon & Schechter, 2003; Christiaensen & Subbarao, 2005). Of the factors associated with vulnerability, almost all studies find that

> 3 From the paper, it appears that the authors only looked at aggregate percentages falling into different categories.  Given that vulnerability is a probabilistic measure, it would have been instructive to note the proportion of poor households predicted to remain poor who actually did so, the proportion of non‐poor households predicted to be vulnerable who actually fell below the poverty line.

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