individuals, households, or communities in the face of changing environment, can be considered as the net effect of three interrelated processes: non-stochastic determinants of poverty (e.g., household head education), exposure to risk (e.g., drought), and ability to cope with shocks (e.g., through insurance and credit markets). Thus in measuring vulnerability, not only current income or consumption of households should be take into account but their assets and changes in assets over time in order to gain a deeper understanding of chronic poverty beyond the moneymetric conceptualizations of poverty that have dominated the current poverty literature.

There are various approaches in the literature for measuring vulnerability to poverty, which differ in conceptualization and definition of vulnerability, in treatment of the state of world yielding non-poverty outcomes and the information base for their empirical application (e.g., panel versus cross-sectional data). Glewwe and Hall (1998) and Dercon and Krishnan (2000, 2003) define vulnerability as household’s ability to smooth consumption, by looking at observed changes in consumption over time in response to shocks. According to their approach, if household consumption expenditures co-vary with income shocks, then one may infer that the household lacks the means to smooth or insure away these shocks, and therefore vulnerable. Christiansen and Boisvert (2000) define vulnerability as the probability now of having a shortfall in the future. They measure vulnerability as the probability of falling below a pre-determined poverty line, multiplied by a conditional probability-weighted function of a shortfall below this poverty line. According to this definition, taking FGT poverty measure with _α = 0_ equates vulnerability to expected headcount poverty. But this is inadequate as it has the same shortcomings as headcount poverty measure in the way it threats household attitudes to risk. For α > 1, the measure accounts for household’s risk-aversion behavior such that households with a higher probability of large shortfalls become more vulnerable (Kamanou and Morduck, 2001). Even with α > 1, the Christiansen and Boisvert approach have serious weakness as the use of the FGT vulnerability aversion implies increasing absolute risk aversion with increasing consumption below the poverty line, which is inconsistent with risk preferences of poor households.

A milestone contribution to measurement of vulnerability is by Elbers and Gunning (2003). They present a more elaborate measure of vulnerability based on structural model of household’s consumption and saving behavior as an outcome of intertemporal optimization under uncertainty. A simulation-based vulnerability measure is then recursively estimated as a shortfall from the welfare attained if the household consumed permanently at the poverty line. Such an approach ensures that household’s responses to shocks (both _ex ante_ and _ex post_ ) are explicitly accounted for. This clearly is an improvement over vulnerability measures based on expected poverty such as that by Christiansen and Boisvert (2000) and regression-based methods such as the one by Glewwe and Hall (1998) that cannot adequately measure the impact of risk on household welfare. In the latter approaches household responses to shocks are not explicitly accounted for in estimations.

Elbers and Gunning (2003) illustrate their method by estimating Ramsey type model using panel data from Zimbabwe. A powerful implication of their approach is that vulnerability can change over time as a result of both growth and household’s adjustment to shocks. Households can become chronically poor because of their responses to shocks may lower their consumption permanently. There is ample evidence in the literature that shocks and household responses to 4