DEPRIVATION AND THE DIMENSIONALITY OF WELFARE: A VARIABLE-SELECTION CLUSTER-ANALYSIS APPROACH

We approach the problems of measuring the dimensionality of welfare and that of identifying the multidimensionally poor, by first finding the poor using the original space of attributes, and then reducing the welfare space. The starting point is the notion that the "poor" constitutes a group of individuals that are essentially different from the "non-poor" in a truly multidimensional framework. Once this group has been identified through a clustering procedure, we propose reducing the dimension of the original welfare space using recent blinding methods for variable selection. We implement our approach to the case of Latin America based on the Gallup World Poll, which contains ample information on many dimensions of welfare.