Research on Economic Inequality: Volume 25

A transfer from a richer individual to a poorer one seems to be the most intuitive and straightforward way of reducing income inequality in a society. However, can such a transfer reduce the welfare of the society? We show that a rich-to-poor transfer can induce a response in the individuals’ behaviors which actually exacerbates, rather than reduces, income inequality as measured by the Gini index. We use this result as an input in assessing the social welfare consequence of the transfer. Measuring social welfare by Sen’s social welfare function, we show that the transfer reduces social welfare. These two results are possible even for individuals whose utility functions are relatively simple (namely, at most quadratic in all terms) and incorporate a distaste for low relative income. We first present the two results for a population of two individuals. We subsequently provide several generalizations. We show that our argument holds for a population of any size, and that the choice of utility functions which trigger this response is not singular – the results obtain for an open set of the space of admissible utility functions. In addition, we show that a rich-to-poor transfer can exacerbate inequality when we employ Lorenz-domination, and that it can decrease social welfare when we draw on any increasing, Schur-concave welfare function.

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.

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.

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.

There is a paradox in the normative foundations for chronic and intertemporal poverty measurement. Measures that reflect particular aversion to chronicity of poverty cannot also reflect particular aversion to fluctuations in the level of poverty when poverty is intense, yet good arguments are made in favour of each of these properties. I argue that the paradox may be explained if the poverty analyst implicitly predicts that an individual observed to experience persistent poverty will continue to experience poverty when unobserved. The paradox may then be resolved by separating the normative exercise of evaluation, applying a measure that reflects particular aversion to fluctuations, from a positive exercise of modelling and prediction. This proposal is illustrated by application to panel data from rural Ethiopia, covering the period 1994–2004. Several dynamic models are estimated, and a simple model with household-specific trends is found to give the best predictions of future wellbeing levels. Appropriately normalised measures of intertemporal poverty are applied to the predicted and observed trajectories of wellbeing, and results are found to differ substantially from naïve application of the measures to observed periods only. While similar results are obtained by naïve application of the measures that embody particular aversion to chronicity, separation of the normative and positive exercises maintains conceptual clarity.

This chapter examines the impoverishment process in three South Caucasian states: Armenia, Azerbaijan and Georgia. It uses the concept of ‘order of curtailment’ of consumption expenditures to detect the order of curtailment of expenditures in the Caucasus region. It then suggests computing poverty rates on the basis of a threshold corresponding to the curtailment of a certain number of consumption expenditures categories and compares the poverty rates obtained with those derived from more traditional approaches to the unidimensional measurement of poverty. The empirical illustrations are based on the Caucasus Barometer surveys of 2009 and 2013.

Most poverty research has explored monetary poverty. This chapter presents and analyzes the global multidimensional poverty index (MPI) estimations for China. Using China Family Panel Studies (CFPS), we find China’s global MPI was 0.035 in 2010 and decreased significantly to 0.017 in 2014. The dimensional composition of MPI suggests that nutrition, education, safe drinking water, and cooking fuel contribute most to overall non-monetary poverty in China. Such analysis is also applied to subgroups, including geographic areas (rural/urban, east/central/west, provinces), as well as social characteristics such as gender of the household heads, age, education level, marital status, household size, migration status, ethnicity, and religion. We find the level and composition of poverty differs significantly across certain subgroups. We also find high levels of mismatch between monetary and multidimensional poverty at the household level, which highlights the importance of using both complementary measures to track progress in eradicating poverty.

During the last two decades the share of foreign-born residents in Italy has grown considerably, from just over 1 percent to about 8 percent. This chapter seeks to clarify the status of immigrants in Italy by examining the evolution of their economic situation and, in particular, the presence of economic hardship. Poverty is measured by considering not only the usual income-based indicators but also others that take into account households’ real and financial wealth. The picture that emerges is one of a higher incidence of economic hardship among immigrant households that strongly affects the dynamics of poverty nationwide. The economic gap with respect to natives appears to increase in the years considered, but the condition of poverty is not more persistent for immigrants than for Italians.

Conditional cash transfers (CCT) have been adopted in many countries over the last two decades. Although the impacts of these programs have been studied extensively, understanding of the economic mechanisms through which cash and conditions affect household decisions remains incomplete. In particular, relatively little is known about the effects of these programs on intra-household allocation decisions. This chapter uses evidence from a program in Cambodia, where eligibility varied substantially among siblings in the same household, to illustrate these effects. A simple model of schooling decisions highlights three different effects of a child-specific CCT: an income effect, a substitution effect, and a displacement effect. The model predicts that such a CCT should unambiguously increase enrollment for eligible children, but have an ambiguous effect on ineligible siblings. The ambiguity arises from the interaction of a positive income effect with a negative displacement effect. These predictions are shown to be consistent with evidence from Cambodia, where the CESSP Scholarship Program (CSP) makes modest transfers, conditional on school enrollment for children of middle-school age. Scholarship recipients were more than 20 percentage points more likely to be enrolled in school, and 10 percentage points less likely to work for pay. However, the school enrollment and work of ineligible siblings was largely unaffected by the program. A possible fourth effect, operating through non-pecuniary spillovers of the intervention among siblings, remains largely outside the scope of the analysis, although there is some tentative evidence to suggest that it might also be at work.

This chapter assesses the extent to which historical levels of inequality affect the creation and survival of businesses over time. To this end, we use the Global Entrepreneurship Monitor survey across 66 countries over 2005–2011. We complement this survey with data on income inequality dating back to early 1800s and current institutional environment, such as the number of procedures to start a new business, countries’ degree of financial inclusion, corruption and political stability. We find that, although inequality increases the number of firms created out of need, inequality reduces entrepreneurial activity as in net terms businesses are less likely to be created and survive over time. These findings are robust in using different measures of inequality across different points in time and regions, even if excluding Latin America, the most unequal region in the world. Our evidence then supports theories that argue early conditions, crucially inequality, influence development path.