Deriving multidimensional poverty indicators: methodological issues and an empirical analysis for Italy

Theoretical and empirical studies have recently adopted a multidimensional concept of poverty. There is considerable debate about the most appropriate degree of multidimensionality to retain in the analysis. In this work we add to the received literature in two ways. First, we derive indicators of multiple deprivation by applying a particular multivariate statistical technique, the non-linear principal component analysis (NLPCA), which overcomes traditional limits of many of the mostly used methodologies for poverty measurement. Second, on the basis of the aforementioned indicators, we provide an accurate identification of the poor in Italy by analyzing deprivation both as a distinct phenomenon in different life domains and as a single multidimensional concept. The main determinants of poverty in Italy are then investigated by estimating logit regressions and an ordered probit model. Our empirical analysis is based on data from the Italian component of European Statistics on Income and Living Conditions (EU-SILC-2004).