Measuring poverty as a fuzzy and multidimensional concept: Theory and evidence from the United Kingdom

摘要: Previous research shows that poor people define poverty not only in material terms, but also psychological and social though it has been consistently characterized by economic resources sciences. Using a method based on `fuzzy-set' theory can be uniquely placed to answer the question as allows us tackle problem of arbitrary line, integrate multiple dimensions into one index an intuitive way. It avoid line entirely introducing concept `membership function' which represents degree inclusion fuzzy subgroup poor. I therefore argue measures strong multidimensional alternative for centered around income. To support argument, two crucial points are clarfied. Firstly, difference between traditional needs discussed further since discussions new so far lean more toward fresh insights from measures, distinction policy-relevant information emphasized enough. From comparison, I present provide richer description phenomenon, enabling a more acceptable different sub-populations. Secondly, how behave statistically should considered depth because most frequent critiques measurements is methods depend too much decisions like setting line. Utilizing Monte Carlo simulation, find (Totally Fuzzy, Totally Fuzzy Relative, Integrated Relative) acknowledge points quite well: (i) concept, (ii) `poor' `non-poor' mutually exclusive sets `fuzzy'. turns out sampling distribution well-behaved, they robust choice estimation well reliable with relatively small sample size. Besides, show measurement errors. Finally, investigate identification performance each measure have consistency.