Understanding Wealth Inequalities in Education Access in Urbanizing Sub-Saharan Africa

Introduction: Urbanization is a double-edged sword, while it is transforming the world, it is also creating spaces that pose threats to its benefits. In sub-Saharan Africa, urbanization is occurring amidst slowed economic growth and into spaces that are already strained. This is resulting in the growth of urban poverty and possibly increasing inequalities. It is thus imperative to understand the effects of urbanization in realizing inclusive and equitable education for all.Objective: We examine inequalities in enrolment of schooling going children aged 6–17 years living in urban areas using the latest Demographic and Health Surveys data from 24 SSA countries.Methods: We utilize three measures of inequality: Rate difference, rate ratio, and relative concentration index to examine inequalities in education access. Using wealth status as the key inequality indicator, we compute and compare school enrollment of children living in urban poor households with that of those living in urban rich households for each measure of inequality. Where appropriate, we stratify the results by country, age, and gender.Results: The results show high levels of inequalities in education access in urban settings. Across all the measures of inequality, in most countries, children from urban poor households were significantly less likely to be in school compared to those from the richest ranked households. The degree of inequality varied considerably between countries and the age groups. Among children aged 6–11 years, Tanzania, Burundi, Nigeria, and Uganda had the highest degree of inequality favouring the urban rich. We also find intriguing results in few countries such as Ethiopia, Benin, Senegal and Mali, which the urban poor had, better school enrolments than the urban rich. We do not find a clear pattern to suggest girls from poor households are overly disadvantaged than boys from similar households.Conclusion: Our study shows a high level of inequalities in education access in an urban setting, with children age in urban poor settings hugely disadvantaged. There is a need for strategic efforts in terms of deliberate interventions and policy frameworks to combat the apparent inequalities that disadvantage children from poor families from accessing education.

Harnessing the Fifth Element of Distributional Statistics for Psychological Science: A Practical Primer and Shiny App for Measures of Statistical Inequality and Concentration

Although distributional inequality and concentration are important statistical concepts in many research fields (including economics, political and social science, information theory, and biology and ecology), they rarely are considered in psychological science. This practical primer familiarizes with the concepts of statistical inequality and concentration and presents an overview of more than a dozen useful, popular measures of inequality (including the Gini, Hoover, Rosenbluth, Herfindahl-Hirschman, Simpson, Shannon, generalized entropy, and Atkinson indices, and tail ratios). Additionally, an interactive web application (R Shiny) for calculating and visualizing these measures, with downloadable output, is described. This companion Shiny app provides brief introductory vignettes to this suite of measures, along with easy-to-understand user guidance. The Shiny app can readily be used as an intuitively accessible, interactive learning and demonstration environment for teaching and exploring these methods. We provide various examples for the application of measures of inequality and concentration in psychological science and discuss venues for further development.

Educational disparities in hypertension, diabetes, obesity and smoking in Brazil: a trend analysis of 578 977 adults from a national survey, 2007–2018

ObjectivesOur study aimed to assess social inequality trends for hypertension, diabetes mellitus, smoking and obesity from 2007 to 2018 in adults from Brazilian capitals.SettingData from the Surveillance of Risk and Protection Factors for Chronic Diseases by Telephone Survey study, a cross-sectional telephone survey, conducted annually from 2007 to 2018.ParticipantsWe used data from 578 977 Brazilian adults (≥18 years).DesignCross-sectional surveys conducted annually from 2007 to 2018.Primary outcome measuresParticipants responded to a questionnaire about medical diagnosis of hypertension and diabetes, smoking status, weight and height. Educational inequalities (0–3, 4–8, 9–11 and 12 or more years of study) by sex and skin colour were assessed trough absolute, Slope Index of Inequality (SII) and relative measures of inequality, Concentration Index and trends were tested by Prais-Winsten.ResultsAll outcomes were more prevalent in the least educated. The largest absolute educational inequality was observed for hypertension (SIItotal=−37.8 in 2018). During 2007–2018, the total educational disparity remained constant for hypertension, increased for diabetes and smoking, and decreased for obesity. Overall, inequality was higher among women and non-whites, compared with men and whites. We found a reduction in absolute inequality for hypertension among non-whites, an increase for diabetes in all strata, and an increase for smoking in women and non-whites. The relative inequality decreased in women and whites and increased for smoking in all strata, except among men.ConclusionThe educational inequality reduced for obesity, remained constant for hypertension and increased for diabetes and smoking from 2007 to 2018 in Brazilian adults.

The Construct of Subjective Economic Inequality

Economic inequality has been associated with a host of social ills, but most research has focused on objective measures of inequality. We argue that economic inequality also has a subjective component, and understanding the effects of economic inequality will be deepened by considering the ways that people perceive inequality. In an American sample ( N = 1,014), we find that some of the key variables that past research has found to correlate with objective inequality also correlate with a subjective measure of inequality. Across six countries ( N = 683), we find that the relationship between subjective inequality and different psychological variables varies by country. Subjective inequality shows only modest correlations with objective inequality and varies by sociodemographic background.

Inequality in late colonial Indonesia: new evidence on regional differences

This paper adds to a growing literature that charts and explains inequality levels in pre-industrial societies. On the basis of a wide variety of primary documents, the degree of inequality is estimated for 32 different residencies, the largest administrative units and comparable to present-day provinces, of late colonial Indonesia. Four different measures of inequality (the Gini, Theil, Inequality Extraction Rate and Top Income Rate) are employed that show consistent results. Variation in inequality levels across late colonial Indonesia is very large, and some residencies have much higher levels of inequality (with, for example, Ginis above 60) than others (with Ginis below 30). This suggests that even within a single colony, levels of inequality may vary substantially and this puts some doubts on the representativeness of using a single number to capture the level of inequality in a large economy. In order to explain the variation across residencies and over time, this paper investigates the role of exports and plantations, so frequently mentioned in the literature. It is shown that both explain a part of the variation in levels of inequality across colonial Indonesia, but that only the rise of plantations can explain changes in inequality levels over time. This points to the importance of the institutional context in which global export trade takes place for the rise of inequality.

Interregional inequality in environmental and economic development in the Russian Federation

Using the most popular measures of inequality and recent statistical data, the authors estimate interregional inequalities in Russia. The specific aim of this study is to identify inequality in well-being indicators, which were calculated using a multiplicative model based on the A. Sen extended function. Our calculations have revealed that there is a significant interregional differentiation in the social well-being level. The environmentally adjusted well-being exhibits higher levels of inequality than the indicator that does not take into account the environmental component. The paper shows that from 2008 to 2018 the gaps between Russian regions had in some cases increased.