Fuzzy Set Approach to Multidimensional Poverty Measurement

Recent theoretical and empirical studies have concluded that in order to be accurate, poverty and deprivation must be measured within a multidimensional framework that is consistent, efficient, and statistically robust.

The fuzzy sets approach to poverty measurement was developed in the early 1990s and continues to be refined by scholars of economics and sociology who find the traditional "monetary-only" indicators to be inadequate and arbitrary.

This volume brings together advanced thinking on the multidimensional measurement of poverty, including the theoretical background, applications to cross-sections using contemporary European examples, and longitudinal aspects of multidimensional fuzzy poverty analysis that pay particular attention to the transitory, or impermanent, conditions that often occur during transitions to market economies.

This book will be of interest to scholars and researchers and will be a useful text on poverty for advanced students in applied statistics, urban planning, economics, and sociology.

Philosophical Accounts of Vagueness, Fuzzy Poverty Measures and Multidimensionality.- The Mathematical Framework of Fuzzy Logic.- An Axiomatic Approach to Multidimensional Poverty Measurement via Fuzzy Sets.- On the Convergence of Various Unidimensional Approaches.- Capability Approach and Fuzzy Set Theory: Description, Aggregation and Inference Issues.- Multidimensional and Longitudinal Poverty: an Integrated Fuzzy Approach.- French Poverty Measures using Fuzzy Set Approaches.- The “Fuzzy Set” Approach to Multidimensional Poverty Analysis: Using the Shapley Decomposition to Analyze the Determinants of Poverty in Israel.- Multidimensional Fuzzy Set Approach Poverty Estimates in Romania.- Multidimensional and Fuzzy Poverty in Switzerland.- A Comparison of Poverty According to Primary Goods, Capabilities and Outcomes. Evidence from French School Leavers’ Surveys.- Multidimensional Fuzzy Relative Poverty Dynamic Measures in Poland.- Modelling Fuzzy and Multidimensional Poverty Measures in the United Kingdom with Variance Components Panel Regression.

Achille Lemmi is Professor of Economic Statistics at the University of Siena. His areas of interest and research include personal income distribution models, poverty and living conditions estimation and analysis, and poverty dynamics.

Gianni Betti is Associate Professor of Economic Statistics at the University of Siena. His areas of interest and research include poverty and living conditions analysis, equivalence scales, small area estimation and poverty mapping.