Early empirical applications of FAO RIMA (Alinovi et al. , 2008; Alinovi et al. , 2010) adopted  two-stage Factor Analysis (FA) with Bartlett’s prediction technique. In the first step resilience pillars were estimated through FA of observable variables and RCI was then estimated through FA of the pillars (for pillars aggregation process see Annex II). The last generation of RIMA applications (d’Errico et al. , 2015a; FAO et al. , 2014) employed factor analysis at the first stage and then estimated RCI by adopting a Structural Equation Model (SEM) at the second stage (Costello et al. , 2005; Scott, 1966). Root Mean Square Error of Approximation (RMSEA), Chi-squared tests, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI) and Standardized Root Mean Square Residual (SRMR) were estimated to evaluate goodness-of-fit and,  ultimately, correlation between residual errors. A modified RIMA-I approach was recently employed as a predictor of well-being variation over time (Ciani and Romano, 2011) to estimate rural household resilience in Nicaragua and the capacity of an RCI to predict future food consumption. In Burkina Faso Kozlowska et al. (2015) employed RIMA-I following the approach of Dang et al. (2014) and Moffit (1993) to estimate the RCI from synthetic panel data Ordinary Least Square (OLS) and Instrumental Variable (IV) adopted. D’Errico and Di Giuseppe (2016) used real panel data from Uganda to estimate resilience dynamics (fixed and random effect models adopted). Finally, in Mali, d’Errico et al. (2015b) estimated RIMA to determine, through regression analysis, the effect of resilience on a well-being indicator from the Mali Bureau of Statistics