applying data reducing techniques, such as Principal Component Analysis (PCA) and Factor Analysis (FA).

One such paper considers resilience as a latent variable defined according to four main opportunities (i.e. income and food access; assets; access to public services; and social safety nets) and two additional dimensions (i.e. stability and adaptive capacity). The paper applies a factor analysis to these variables to obtain a resilience index which, together with other household covariates is entered into a regression of food security in the West Bank and Gaza (Alinovi et al. 2009). Regression results showed that resilience is one of the most important indicators in measuring vulnerability to food insecurity.

Ciani and Romano (2013) also developed a methodology to quantitatively assess resilience to food insecurity among rural households hit by Hurricane Mitch in 1999 in Nicaragua. The authors made use of a two-stage factor analysis on a nationally representative longitudinal data set of around 3,000 households, of which 540 were re-interviewed after the hurricane occurred. The resilience index resulting from the analysis seems to be a key element describing future households’ well-being attainments. The econometric analysis carried out by the authors on different outcome variables such as the growth rate of food expenditure and the transition both into and out of food poverty, and including the resilience index among the explanatory variables, shows a positive relationship between households’ resilience at time _t_ and the level of food security at _t+1_ and the probability of moving out of poverty between t and _t+1_ . At the same time, lower levels of resilience at time _t_ are shown to be related to higher probabilities of being food deficient at _t+1_ and of moving from being food sufficient to being food poor between _t_ and _t+1_ .

Demeke and Tefera (2011) use data from the Ethiopia Rural Household Survey to estimate household resilience to food insecurity. The authors derived a resilience index through the Principal Component Analysis on a set of variables such as food access, liquid assets, education, and social network. Determinants of resilience were then explored using a dynamic panel model. Results suggested that there is a significant state-dependence of the dynamics of resilience with food insecurity (i.e. current resilience status is affected by its past level). Furthermore, the probability of being resilient is also shown to depend on a number of physical, human, financial and social characteristics, as well as access to basic services, information, agricultural inputs, markets and income diversification. Access to productive assets seemed to play a crucial role in this context and to be positively associated with households’ resilience, whereas, access to credit in the form of short-term loans from informal sources showed a negative impact on the probability of being resilient, and traditional saving and credit arrangements both had a positive and significant effect.

In the next section we will provide a theoretical framework for understanding resilience and follow that with an empirical strategy for measuring it explicitly.

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