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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.

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 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.

This paper evaluates the impact of socio and demographic factors on the multidimensional poverty of smallholder arable crop farming households in Nigeria.

Data were drawn from the second wave of the LSMS-Integrated Surveys on Agriculture General Household Survey Panel 2012/2013. The methods adopted in analysing the data were descriptive statistics, Alkire and Foster Method (AFM) and logit regression model.

The result shows that 84.34% of the households were headed by a male while 80.26% of the respondents were married with a mean household size of seven persons. The multidimensional poverty of arable crop farm households in Nigeria is 0.60, while the adjusted headcount ratio (MPI) is 0.27, with an average intensity of 0.45. We found that deprivation in the dimension of living standard accounted for 45.5% of the overall multidimensional poverty index (MPI). The result of the logistic regression indicates that household location, gender, household size and non-farm income are negatively correlated to poverty. The factors that increase poverty among households are the age of the household head and access to extension services.

The study presents an alternative means of assessing poverty among smallholder arable crop farming households in Nigeria. This study recommends that policymakers should focus more on improving the living standard of arable crop farming households to reduce poverty in rural areas. Similarly, concerted efforts should be made towards providing adequate health care and improved sanitation, supply of electricity and educational training that goes beyond primary education for farming household members.

The purpose of this study is to analyse the influence of household and community characteristics on multidimensional poverty in communities hosting public universities in Ondo state, Nigeria.

The study constructs Global Multidimensional Poverty Index (MPI) using Alkire-Foster methodology and uses logistic regression to analyse the likelihood of experiencing multidimensional poverty.

Findings from the study suggest that child schooling attendance, child mortality and asset ownership are the indicators in which households are mostly deprived in education, health and living standards consecutively. In addition, using logistic regression, the study finds multidimensional poverty reducing effects of education, age (before old), household size (having more economically active members), income and residing in urban areas. The study, however, documents that living far away from the universities increases the likelihood of experiencing multidimensional poverty in those communities.

To reduce multidimensional poverty in the communities of study, there is need to implement policies that will improve child schooling, reduce infant mortality, increase gainful employment and create enabling environment for asset ownership. This is in addition to upgrading infrastructure in those communities especially in their fringe areas so that development can spread, and multidimensional poverty reduction can follow in no distant future.

Unlike capturing the effect of location on possibility of experiencing poverty using rural or urban dummy, the authors, in addition to that, incorporate distance to university variable on the premise of distance decay mechanism.

This paper aims to investigate the effectiveness of Qard-al-Hasan (QH) or interest-free loan in reducing multidimensional poverty through examining Fael Khair Waqf (FKW) program as a case.

A Multidimensional Poverty Index (MPI) approach has been used in this study, which was conducted over 1,600 households including 1,200 program households in 40 villages and 400 control households in 20 villages of 8 districts in the south-western region of Bangladesh.

It is found that 38.5% of sample population was multidimensional poor with deprivation of above the cutoff score. However, FKW participants were relatively less multidimensional poor. It was 35.3%. Hence, it is argued that the FKW is an effective program in terms of poverty reduction as it has contributed to higher economic outcomes for their participating households. The econometric result also suggests that the likelihood of the participants of FKW to be MPI poor is around 1.5 times lower than the nonparticipants, and the result is significant at 1% level. This indicates that program has a positive impact in reducing multidimensional poverty.

The analysis in this paper would fill the literature gap by investigating the link between application of QH and poverty alleviation. It will also guide academicians, researchers and decision-makers to design evidence-based policy to alleviate poverty.

To the best of the authors’ knowledge, there has been no empirical work in Bangladesh on the effectiveness of QH in poverty reduction considering an MPI approach. Hence, this study is a unique contribution to the literature of Islamic social finance.

This paper is an extended version of Hireche and Drias (2018) presented at the WORLD-CIST’18 conference. The major contribution, in this work, is defined in two phases. First of all, the use of data mining technologies and especially the tools of data preprocessing for instances of hard and complex problems prior to their resolution. The authors focus on clustering the instance aiming at reducing its complexity. The second phase is to solve the instance using the knowledge acquired in the first step and problem-solving methods. The paper aims to discuss these issues.

Because different clustering techniques may offer different results for a data set, a prior knowledge on data helps to determine the adequate type of clustering that should be applied. The first part of this work deals with a study on data descriptive characteristics in order to better understand the data. The dispersion and distribution of the variables in the problem instances is especially explored to determine the most suitable clustering technique to apply.

Several experiments were performed on different kinds of instances and different kinds of data distribution. The obtained results show the importance and the efficiency of the proposed appropriate preprocessing approaches prior to problem solving.

The proposed approach is developed, in this paper, on the Boolean satisfiability problem because of its well-recognised importance, with the aim of complexity reduction which allows an easier resolution of the later problem and particularly an important time saving.

State of the art of problem solving describes plenty of algorithms and solvers of hard problems that are still a challenge because of their complexity. The originality of this work lies on the investigation of appropriate preprocessing techniques to tackle and overcome this complexity prior to the resolution which becomes easier with an important time saving.

This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.

A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of risk a priori categorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.

Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.

The results need a qualitative or case study validation at the community/district level.

We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.

Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.

Italy has become a migrant receiving country and it has to face with the problem of social inclusion of immigrants. The purpose of this paper is to measure the gap on poverty and deprivation between immigrants and natives since manifest conditions of both of them are an important signal, although not exclusively, of social exclusion.

Poverty analysis typically relies on a single monetary variable such as income and it is characterized by a simple dichotomization of the population into poor and non-poor. In this paper the authors stress the importance of using a multidimensional and fuzzy approach in order to study disparities between immigrants and natives. The authors cover several of the multifaceted aspects of resources necessary to maintain adequate living standards in a developed country. With the fuzzy methodology, the authors also overcome any limitation of the conventional approach based on the simple dichotomization of the phenomenon.

The empirical analysis is based on data from two official surveys. The authors find that between Italian and immigrant households there are significant differences in poverty and deprivation levels, with a strong disadvantage for the latter. The authors argue that any serious attempt to reduce poverty and deprivation must now include comprehensive reforms in the nation's immigration policies if they are to be taken seriously.

The paper makes an original contribution to the understanding of inequality between immigrants and natives, by studying a complex phenomena such as poverty and deprivation in a multidimensional perspective using a fuzzy approach.