The necessary requirement of median independence for relative bipolarisation measurement

The relative bipolarisation literature features examples of indices which depend on the median of the distribution, including the renowned Foster–Wolfson index. This study shows that the use of the median in the design and computation of relative bipolarisation indices is both unnecessary and problematic. It is unnecessary because we can rely on existing well-behaved, median-independent indices. It is problematic because, as the study shows, median-dependent indices violate the basic transfer axioms of bipolarisation (defining spread and clustering properties), except when the median is unaffected by the transfers. The convenience of discarding the median from index computations is further illustrated with a numerical example in which median-independent indices rank distributions according to the basic transfer axioms while median-dependent indices do not

The benchmark of maximum relative bipolarisation

Relative bipolarisation indices are usually constructed making sure that they achieve their minimum value of bipolarisation if and only if distributions are perfectly egalitarian. However, the literature has neglected discussing the existence of a benchmark of maximum relative bipolarisation. Consequently there is no discussion as to the implications of maximum bipolarisation for the optimal normalisation of relative bipolarisation indices either. In this note we characterize the situation of maximum relative bipolarisation as the only one consistent with the key axioms of relative bipolarisation. We illustrate the usefulness of incorporating the concept of maximum relative bipolarisation in the design of bipolarisation indices by identifying, among the family of rank-dependent Wang–Tsui indices, the only subclass fulfilling a normalisation axiom that takes into account both benchmarks of minimum and maximum relative bipolarisation

On the Robustness of Multidimensional Counting Poverty Orderings

Counting poverty measures have gained prominence in the analysis of multidimensional poverty in recent decades. Poverty orderings based on these measures typically depend on methodological choices regarding individual poverty functions, poverty cut-offs, and dimensional weights whose impact on poverty rankings is often not well understood. In this paper we propose new dominance conditions that allow the analyst to evaluate the robustness of poverty comparisons to those choices. These conditions provide an approach to evaluating the sensitivity of poverty orderings superior to the common approach of considering a restricted and arbitrary set of indices, cut-offs, and weights. The new criteria apply to a broad class of counting poverty measures widely used in empirical analysis of poverty in developed and developing countries including the multidimensional headcount and the adjusted headcount ratios. We illustrate these methods with an application to time-trends in poverty in Australia and cross-regional poverty in Peru. Our results highlight the potentially large sensitivity of poverty orderings based on counting measures and the importance of evaluating the robustness of results when performing poverty comparisons across time and regions

When more does not necessarily mean better: Health-related illfare comparisons with non-monotone wellbeing relationships

Most welfare studies assume that wellbeing is monotonically related to the variables used for the analysis. While this assumption is reasonable for many dimensions of wellbeing like income, education, or empowerment, there are some cases where it is definitively not relevant, in particular with respect to health. For instance, health status is often proxied using the Body Mass Index (BMI). Low BMI values can capture undernutrition or the incidence of severe illness, yet a high BMI is neither desirable as it indicates obesity. Usual illfare indices derived from poverty measurement are then not appropriate. This paper proposes illfare indices that are consistent with some situations of non-monotonic wellbeing relationships and examines the partial orderings of different distributions derived from various classes of illfare indices. An illustration is provided for child health as proxied by a weight-for-age indicator using DHS data for Bangladesh, Colombia and Egypt during the last few decades

Inequality of ratios

Some socioeconomic indicators can be represented in alternative ways. For example, as either attainments (e.g. child survival rates) or shortfalls (e.g. child mortality rates) if the variables are bounded. The literature has long been concerned with the consistency of inequality comparisons across such alternative representations. The case of bounded variables and their two alternative representations (attainments versus shortfalls) has been largely settled. This paper addresses the extent of the consistency problem in inequality comparisons involving ratio indicators which also have two potential representations (e.g. number of people per room or number of rooms per capita in the case of overcrowding). First, we probe welfare comparisons based on the generalised Lorenz curve and find that consistency can be secured in the presence of rank dominance. Second, we show that robust inequality comparisons based on all possible Zoli partial orderings (which include all relative and absolute inequality partial orderings, among others) are inconsistent across alternative representations of ratios. Third, with the identification of a class of inequality indices based on the ratio of the harmonic to the arithmetic mean, we show that complete orderings consistent across alternative representations of ratios do exist. Then we consider and ponder the pros and cons of three alternative solutions: defending one representation, using inequality indices that combine both representations, and functional transformations of the ratio variable. We find that the costs of these alternatives render them inferior to the class of indices based on the harmonic and arithmetic means. Both the consistency problem and its preferred proposed solution are illustrated with an empirical study of intergenerational changes in overcrowding inequality in Mexico

Assessing deprivation with an ordinal variable: theory and application to sanitation deprivation in Bangladesh

The challenges associated with poverty measurement using a cardinal variable have received much attention over the past four decades, but there is a dearth of literature on how to meaningfully assess poverty with an ordinal variable. This article proposes a class of simple, intuitive, and policy-relevant poverty measures for ordinal variables. The measures are sensitive to the depth of deprivations, unlike the headcount ratio. Moreover, under appropriate restrictions, the measures ensure that priority is given to the poorest among the poor when targeting, monitoring, and evaluating poverty alleviation programs. To assess the robustness of poverty comparisons to alternative choices of parameters, the article develops various stochastic dominance tests (some of which are novel contributions to the stochastic dominance literature). The empirical illustration documenting changes in sanitation deprivation in Bangladesh showcases the measures’ ability to identify instances in which overall sanitation deprivation improved while leaving the poorest behind

Inequality and welfare in quality of life among OECD countries: non-parametric treatment of ordinal data

The last few years have witnessed an increasing emphasis on going beyond GDP per capita when measuring a nation’s quality of life. Countries (e.g. UK, France, Canada, Germany, Italy, Japan, Korea, Spain) and international organizations (e.g. OECD) have been developing methods suitable for non-income indicators. However, this involves serious measurement challenges due to: (a) multidimensionality, and (b) ordinality (i.e. unlike income these indicators do not have a natural scale). This paper is the first summary of the methods developed in the last decade in the field of inequality and welfare measurement to address these challenges. Next, we utilize the presented methodology and provide evidence on the ranking of OECD countries in terms of welfare and inequality in education and happiness. We find that when dimensions are analysed separately, welfare dominance is frequent (42% of all comparisons in education and 31% in life satisfaction). The number drops to only 4.4% for bivariate dominance, which highlights the empirical relevance of multidimensional analysis. Greece, Portugal and Hungary feature the lowest joint welfare. Northern European countries are most often dominating and Southern European countries are most often dominated in both inequality and welfare analyses

Corruption can cause healthcare deprivation: Evidence from 29 sub-Saharan African Countries

The WHO estimates that nearly half of the world’s population lacks access to essential healthcare, and that the proportion of the population with catastrophic out-of-pocket health spending (10% or more of the household budget) is on the rise. Meanwhile, the United Nations’ General Assembly has recently identified corruption as a vital factor undermining efforts to accomplish universal health coverage. We examine how corruption may lead to healthcare deprivation in the context of 29 sub-Saharan African countries, employing the fifth, sixth and seventh waves of the Afrobarometer survey spanning 2011–2018. Applying an instrumental variable framework, we find that the experience of corruption in the form of bribe payments as well as the frequency of bribe payments within the healthcare sector increases the likelihood of healthcare deprivation. Moreover, corruption experienced in other sectors, such as education, the police, public utilities and identification authorities, have spill-over effects affecting healthcare deprivation adversely. Further analysis reveals that the experience of corruption in multiple sectors simultaneously worsens healthcare deprivation. Our findings suggest that mitigating corruption in the healthcare sector alone may not be sufficient to end the adverse effect of corruption on effective healthcare access in SSA countries. Finally, through mediation analysis, we show that loss of income and loss of trust are two channels through which corruption influences healthcare deprivation

Measuring educational inequality of opportunity: pupil’s effort matters

The distinction between effort and other factors, such as family background, matters for correcting policies and normative reasons when we appeal to inequality of opportunity. We take advantage of a purposefully designed survey on secondary schools in rural Bangladesh to offer a comprehensive view of the importance of overall effort when measuring inequalities of opportunity in education. The analysis comprises decomposition exercises of the predicted variance of student performance in mathematics and English by source (effort, circumstances, etc.) and subgroup (within- and between-schools) based on parametric estimates of educational production functions. Pupils’ effort, preferences, and talents contribute between 31% and 40% of the total predicted variances in performance scores. The contribution of overall effort falls by 10% when the correlation between effort and circumstances is taken into account. These findings are robust to the choice of estimation strategy (i.e. combined within- and between-schools variation models versus multilevel random-effect models). All in all, these results advocate that social determinism in education can be mitigated by individual effort at school

First Order Dominance Techniques and Multidimensional Poverty Indices:An Empirical Comparison of Different Approaches

In this empirically driven paper we compare the performance of two techniques in the literature of poverty measurement with ordinal data: multidimensional poverty indices and first order dominance techniques (FOD). Combining multiple scenario simulated data with observed data from 48 Demographic and Health Surveys around the developing world, our empirical findings suggest that the FOD approach can be implemented as a useful robustness check for ordinal poverty indices like the multidimensional poverty index (MPI; the United Nations Development Program's flagship poverty indicator) to distinguish between those country comparisons that are sensitive to alternative specifications of basic measurement assumptions and those which are not. To the extent that the FOD approach is able to uncover the socio-economic gradient that exists between countries, it can be proposed as a viable complement to the MPI with the advantage of not having to rely on many of the normatively binding assumptions that underpin the construction of the index