Search results

A growing polarization of society accompanied by an erosion of the middle class is receiving increasing attention in recent German economic and social policy discussion. Our study contributes to this discussion in two ways: First, on a theoretical level we propose extended multidimensional polarization indices based on a constant elasticity of substitution (CES)-type well-being function and present a new measure to multidimensional polarization, the mean minimum polarization gap, 2DGAP. This polarization intensity measure provides transparency with regard to each single attribute, which is important for targeted policies, while at the same time respecting their interdependent relations. Second, in an empirical application, time is incorporated, in addition to the traditional income measure, as a fundamental resource for any activity. In particular, genuine personal leisure time will account for social participation in the sense of social inclusion/exclusion and Amartya Sen’s capability approach.

Instead of arbitrarily choosing the attribute parameters in the CES well-being function, the interdependent relations of time and income are evaluated by the German population. With the German Socio-Economic Panel (GSOEP) and detailed time use diary data from the German Time Use Surveys (GTUSs) 1991/1992 and 2001/2002, we quantify available and extended multidimensional polarization measures as well as our new approach to measuring the polarization of the working poor and affluent in Germany.

There are three prominent empirical results: Genuine personal leisure time in addition to income is an important and significant polarization attribute. Compensation is of economic and statistical significance. The new minimum 2DGAP approach reveals that multidimensional polarization increased in the 1990s in Germany.

Latin America has a longstanding tradition in multidimensional poverty measurement through the unsatisfied basic needs (UBN) approach. However, the method has been criticized on several grounds, including the selection of indicators, the implicit weighting scheme and the aggregation methodology, among others. The estimates by the UBN approach have traditionally been complemented (or replaced) with income poverty estimates. Under the premise that poverty is inherently multidimensional, in this chapter we propose three methodological refinements to the UBN approach. Using the proposed methodology we provide a set of comparable poverty estimates for six Latin American countries between 1992 and 2006.

Looking at the Jewish population in Israel in 1995 this paper compares three multidimensional approaches to poverty measurement and checks to what extent they identify the same households as poor. Logit regressions are then estimated to understand which variables have an impact on poverty. Finally, the so-called Shapley decomposition is introduced to estimate the exact marginal impact of these determinants of poverty.

Of particular interest to this study was the combined effect of the generation to which the head of the household belongs and his/her place of birth. It turns out that the ethnic origin has a significant impact on multidimensional poverty in Israel insofar as being a head of household born in Asia or Africa, whatever the generation to which one belongs, increases, ceteris paribus, the probability of being poor.

The purpose of this paper is to compare the assessment of poverty/deprivation using different conceptions of this phenomenon including the traditional money‐metric measure and different forms of multidimensional constructs.

The data were drawn from a household survey conducted in Nsukka, Nigeria. Interviewer‐administered questionnaires were used in data collection from about 410 households across urban and rural localities. The counting and FGT methodologies were used to assess impoverishment, while regression analyses were used to assess the determinants of deprivation across different constructs.

Between 70 per cent and 78 per cent of the study population were identified as poor/deprived. However, more than 11 per cent of those living on less than USD1.25/day were classified as non‐poor using different measures of multidimensional poverty. Similarly, more than 62 per cent of individuals who live on more than 1.25USD/day (i.e. non‐poor) are classified as poor using different measures of multidimensional deprivation. There is some level of correlation between measures, some inevitably stronger than others. The major determinants of deprivation across the various constructs of deprivation include large family size, low level of education, poor employment, rural location, and poor health.

This paper uses novel datasets that incorporate variables relating to the capability approach in understanding deprivation. Specifically, it analyses the so‐called missing dimensions of poverty. It also applies a new methodology for the assessment of impoverishment and deprivation. It highlights the importance of the capability approach in explaining poverty.

A number of multidimensional poverty measures that respect the ordinal nature of dimensions have recently been proposed within the counting approach framework. Besides ensuring a reduction in poverty, however, it is important to monitor distributional changes to ensure that poverty reduction has been inclusive in reaching the poorest. Distributional issues are typically captured by adjusting a poverty measure to be sensitive to inequality among the poor. This approach, however, has certain practical and conceptual limitations. It conflicts, for example, with some policy-relevant measurement features, such as the ability to decompose a measure into dimensions post-identification and does not create an appropriate framework for assessing disparity in poverty across population subgroups. In this chapter, we propose and justify the use of a separate decomposable inequality measure – a positive multiple of “variance” – to capture the distribution of deprivations among the poor and to assess disparity in poverty across population subgroups. We demonstrate the applicability of our approach through two contrasting inter-temporal illustrations using Demographic Health Survey data sets for Haiti and India.

This paper aims to propose a dynamic multidimensional approach to identify the middle class and then to reliably study the structural changes that have marked it in terms of demographic and socioeconomic characteristics and aspirations.

This study uses Moroccan data from the 2007 and 2014 household expenditure surveys. The method consists in applying a factor analysis of mixed data on a set of variables inspired by Bourdieu’s concepts of social space and forms of capital, then performing a hierarchical ascending classification consolidated by the k-means clustering, along with adopting the same indicators and weighting for both years studied to ensure reliable comparison.

The classification results identified three social classes whose changing size reveals a decline of the lower class and an expansion of the upper and middle classes. Some characteristics of the middle class are becoming close to those of the upper class, like fertility behavior, while a significant gap remains between the two classes in other characteristics, like education. Moreover, middle-class perceptions reflect their downgrading, confirming that the so-called decline of the middle class is more related to feelings than to objective realities.

Middle class studies are generally based on a single criterion (income or consumption) with somewhat arbitrary boundaries that are often ill-suited to developing countries. This paper proposes a new dynamic multidimensional approach to overcome these problems while adopting a new technique for reliable intertemporal comparisons.

In recent years, there is growing recognition of the importance of geography and space in the analysis of economic convergence by focusing on the dynamics of monetary indicators. The analysis of spatial convergence based on socio-economic indicators are rare. These variables present a complement to understand the spatial dynamics of territorial units. The purpose of this paper is, first, to analyzes and describes trends in multidimensional poverty in Morocco and second it explores the convergence hypothesis.

Data are driven from HCP (2017). It concerns 75 provinces over the period 2004 and 2014. In addition to the availability of data, this period corresponds to significant changes in public policy. The nature of the observations necessitates the use of the spatial analysis techniques.

The results show that poverty is a geographical phenomenon with low speed of convergence. The paper propose some solutions to help policymakers implement an effective targeting policy aimed at reducing spatial inequalities in terms of multidimensional poverty in Morocco.

The analysis of spatial convergence based on socio-economic indicators are rare. This paper will focus on the convergence of the poverty index for a developing country.

The multidimensional nature of well-being is now widely recognized. However, multidimensional poverty measurement is still an expanding field of research and a consensus about the “best” composite indicator has not yet emerged. In this chapter, we provide an empirical analysis using three existing methodologies: Bourguignon and Chakravarty (2003), Alkire and Foster (2007), and Lemmi (2005); Chiappero Martinetti (2000). We present an empirical study of the convergence and divergence of poverty profiles for children in Uruguay considering the following dimensions: nutritional status, child educational achievement, housing condition, and household income. Our data gather information of 1,185 children attending public schools in Montevideo and the surrounding metropolitan area, and were specially gathered to carry out a multidimensional analysis of poverty.

Our results indicate that the three families of indexes yield very different cardinalizations of poverty. At the same time, the correlation coefficients among the three groups of measures for the generalized headcount ratio also highlight important differences in the children labeled as “more deprived.” For the generalized severity and intensity indexes the correlation coefficients increase significantly suggesting a high level of concordance among the three measures, particularly among the Bourguignon and Chakravarty methodology and the Alkire and Foster one.

The purpose of this paper is to measure the unidimensional and multidimensional inequality in the case of Pakistan and compare their results at the provincial as well as regional (urban and rural areas) level. The authors collected data from Pakistan Social and Living Standard Measurement and Household Integrated Economic Survey for fiscal years of 1998–1999 and 2013–2014.

The authors used Gini coefficient for unidimensional inequality and multidimensional indexing approach of Araar (2009) for multidimensional inequality.

The findings predicted that unidimensional inequality is relatively high in the urban area due to uneven dissemination of income, but multidimensional inequality is quite high in rural areas because of higher disparities among all dimensions. At the provincial level, Punjab has relatively high-income inequality followed by Sindh, KPK and Baluchistan.

This study is a pioneering effort to compare two time periods to explore unidimensional and multidimensional inequality in all provinces of Pakistan and their representative rural-urban regions by applying Araar and Duclos’s (2009) approach. Further, this study opens some new insights for policy makers.