Search results

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.

Using data on food insecurity in Israel, this chapter suggests borrowing techniques from the literature on multidimensional poverty to measure food insecurity, a distinction being made between “nominal” and “real” food insecurity. Various counting techniques are then implemented, including the well-known approach of Alkire and Foster. The chapter ends with a section where, following recent work by Dhongde, Li, Pattanaik, and Xu (2016), a distinction is also made between “basic” and “non-basic” dimensions of food insecurity.

This paper compares two alternative methods for measuring multidimensional poverty. This question has become extremely important in recent years, both in the scientific literature and in social policy. We propose to use latent class analysis to evaluate poverty in Spain. We make use of the “fuzzy set” approach, and compare the results achieved from these two methodologies.

Purpose – A counting approach based on the number of deprivations suffered by the poor is quite an appropriate framework to measure multidimensional poverty with ordinal or categorical data. A method to identify the poor and a number of poverty indices have been proposed to take this framework into account. The implementation of this methodology involves the choice of a minimum number of deprivations required in order for an individual to be identified as poor. This cutoff and the choice of a poverty measure to aggregate the data are two sources of arbitrariness in poverty comparisons. The aim of this chapter is twofold. We first explore properties that characterize an identification method which allows different weights for different dimensions. Then the chapter examines dominance conditions in order to guarantee unanimous poverty rankings in a counting framework.

Design/methodology/approach – In the unidimensional poverty field, one branch of the literature is devoted to establishing dominance criteria that guarantee unanimous orderings at a variety of poverty thresholds and indices. This chapter takes this literature as a starting point, and investigates circumstances in which these ordering conditions may be applied in a weighted counting framework.

Findings – Necessary and sufficient conditions are obtained that guarantee that two vectors, which represent the weighted sum of the deprivations felt by each person, may be unanimously ranked regardless of the identification cutoff and of the poverty measure.

Originality/value – Since most of the data available for measuring capabilities or dimensions of poverty is either ordinal or categorical, the counting approach provides an alternative framework that suits these types of variables. The implementation of the ordering conditions derived in this chapter is based on simple graphical devices that we call dimension deprivation curves. These curves become a useful way to check the robustness of poverty rankings to changes in the identification cutoff. They also provide a tool for determining nonambiguous poverty rankings in a wide set of multidimensional poverty indices that suit ordinal and categorical data.

In this chapter, we examine the notion of ethnic diversity with a view to explore Europe-wide differences in defining and managing ethnic diversity and equality. When compared to gender diversity, ethnic diversity does not enjoy similar level of success in Europe. Our analyses show that this is due to the fact that ethnicity and ethnic categories are national. In fact, there are different levels of discussion on ethnicity, where the debate is limited due to historical, cultural and legal differences.

The existing literature on fatness has critically discussed meanings and morals associated with body weight and explored people’s experiences of weight loss attempts. However, little attention has been paid to the practices of dieting – how it is ‘done’. Based on an interview study involving 31 participants, who shared their self-tracking experience of using the MyFitnessPal calorie counting app, we focus on the practices of ‘doing’ calories. First, we discuss the practices of temporality of logging food, showing that the use of MyFitnessPal not only has to be fitted into daily routines but can also transform them. Then, we look at the practices of precision or users’ various ways of turning the ‘messiness’ of food into precise numbers. Lastly, we explore users’ practices of adjustments – their attitudes to adherence to their daily calorie goal and ways of dealing with going above it. Based on our findings we suggest calorie counting is not a straightforward data collection, but one that involves constant practical strategies and negotiations, and can both influence and be influenced by other everyday practices.