Fuzzy Poverty Measures Research Articles

Estimating fuzzy measures of deprivation at local level in Tuscany.

In this paper we estimate monetary and non-monetary poverty measures at two sub-regional levels in the region of Tuscany (Italy) using data from the ad-hoc Survey on Vulnerability and Poverty held by Regional Institute from Economic Planning of Tuscany (IRPET). We estimate the percentage of households living in poverty conditions and three supplementary fuzzy measures of poverty regarding deprivation in basic needs and lifestyle, children deprivation, and financial insecurity. The key feature of the survey is that it was carried out after the COVID-19 pandemic, therefore, some of the items collected focus on the subjective perception of poverty eighteen months after the beginning of the pandemic. We assess the quality of these estimates either with initial direct estimates along with their sampling variance, and with a secondary small area estimation when the formers are not sufficiently accurate.

The Heavy Burden of “Dependent Children”: An Italian Story

This paper analyses multidimensional fuzzy monetary and non-monetary deprivation in households with children by using two different definitions: households with children under 14 years old, and the EU definition of households with dependent children. Eight dimensions of non-monetary deprivation were found using 34 items from the EU-SILC 2016 survey. Dealing with subpopulations, it is essential to compute standard errors for the presented estimators. Thus, a relevant added value of the paper is fuzzy poverty measures and associated standard errors, which were also computed. Moreover, a comparison was made between the measures obtained concerning the two subpopulations across countries. With a focus on Italy, an Italian macro-region is presented.

Poverty: fuzzy measurement and crisp ordering

In this paper we make several contributions to the literature of poverty measurement. We first identify a plausible source for the fuzziness in poverty identification which renders a natural and direct estimation of the poverty membership function; we then provide an axiomatic characterization for an important class of fuzzy poverty measures; and we finally derive a set of crisp dominance conditions for fuzzy partial poverty orderings. We also illustrate the results with the US CPS data.

Fuzzy Multidimensional Poverty Measurement: An Analysis of Statistical Behaviors

Using the 2006 round of the British Household Panel Study dataset, I explore the statistical behavior of three fuzzy measures of poverty through a simulation (Monte Carlo) method. The measures [totally fuzzy (TF), totally fuzzy and relative (TFR), and integrated fuzzy and relative (IFR)] acknowledge that (1) poverty is a multidimensional concept, and (2) the ‘poor’ and ‘non-poor’ are not two mutually exclusive sets and the distinction can be ‘fuzzy’. I find that the sampling distributions of the fuzzy measures are quite normally distributed, and they are robust to arbitrary choice in the estimation as well as reliable with relatively small sample size, though there is some differences between the methods. Also, I show that they are robust to measurement errors: allowing random measurement errors in all indicators, the measures still yield strongly reliable results. Finally, I investigate the identification performance of each measure and show that IFR measure has strong consistency, while both TF and TFR measures significantly underestimate the number of people whose fuzzy index values are very high.

New fuzzy indices of poverty by distinguishing three levels of poverty

In order to avoid a rigid poor/non-poor dichotomy (see e.g. Hagenaars, 1986), the fuzzy sets approach to poverty measurement has been used. The aim of this paper is to propose fuzzy measures of unidimensional and multidimensional poverty by distinguishing three levels of poverty. A methodological research is proposed as follows: first, the poor are analyzed by partitioning the total population in three mutually exclusive groups around the poverty line, and three levels of poverty are distinguished. Second, a general rule for the construction of different fuzzy measure unions ( Zadeh, 1975) is proposed, that is, rules for the construction of overall poverty starting from different levels of poverty. Finally, classes of fuzzy measures of poverty referring to the overall population are suggested. An application using individual well-being data from Tunisian households in 1990 is presented to illustrate use of one of the proposed concepts.

Multidimensional approach to poverty measurement: fuzzy measures of the incidence and the depth of poverty

The paper presents a methodology for study of multi-dimensional aspects of poverty. In addition to the traditional uni-dimensional measures of poverty, exclusively estimated on the basis of the monetary variable (income or expenditure), other non-monetary variables are incorporated in analysis of the poverty phenomenon. The multidimensional approach has been based on the fuzzy sets theory in which the conventional poor/non-poor dichotomy is replaced by assessment of the degree of household poverty threat. The same methodology facilitates comprehensive monetary and non-monetary poverty analysis. In order to provide effective assessment of poverty the fuzzy measures of poverty were applied. The study employs the fuzzy measures to compare the degree of monetary poverty and deprivation threat in Polish voivodships in 2007 using micro data from the EU-SILC survey. The results of estimation of the fuzzy measures show that poverty in Poland has many dimensions and that its measurement solely from income standing vantage point is highly insufficient.

The Capability Approach and Fuzzy Poverty Measures: An Application to the South African Context

One way of making the capability approach (CA) operational uses fuzzy poverty measures. In this paper, we present a new approach to applying these measures in the South African context using responses to a questionnaire on ‘The Essentials of Life’ in conjunction with a methodology for dealing with the vagueness of poverty. Our results suggest very low cut-offs for people or households to classify as definitely poor for some social indicators. These cut-offs are far lower than those Klasen used in his application of the CA. The attempt to apply the CA using Cheli and Lemmi’s ‘totally fuzzy and relative’ poverty measure in conjunction with our approach to specifying cut-offs can lead to incoherence. This measure can, nonetheless, be useful when social indicators have a ‘relativist component’. While the Cerioli and Zani measure does not lead to such incoherence, it also has a serious weakness.