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Journal articles on the topic 'Socioeconomic inequality'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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1

Schwendicke, F., C. E. Dörfer, P. Schlattmann, L. Foster Page, W. M. Thomson, and S. Paris. "Socioeconomic Inequality and Caries." Journal of Dental Research 94, no. 1 (November 13, 2014): 10–18. http://dx.doi.org/10.1177/0022034514557546.

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2

Budría, Santiago. "The Socioeconomic determinants of economic inequality. Evidence from Portugal." Revista Internacional de Sociología 68, no. 1 (December 21, 2009): 81–124. http://dx.doi.org/10.3989/ris.2008.10.24.

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3

Tran, Van C. "Immigration, poverty and socioeconomic inequality." Journal of Children and Poverty 20, no. 1 (January 2, 2014): 69–70. http://dx.doi.org/10.1080/10796126.2014.890095.

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4

Gleeson, Shannon. "Immigration, Poverty, and Socioeconomic Inequality." Contemporary Sociology: A Journal of Reviews 44, no. 6 (October 28, 2015): 786–88. http://dx.doi.org/10.1177/0094306115609925g.

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5

Whiteford, Peter. "Inequality and Its Socioeconomic Impacts." Australian Economic Review 48, no. 1 (February 25, 2015): 83–92. http://dx.doi.org/10.1111/1467-8462.12099.

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6

Noble, Kimberly G., and Melissa A. Giebler. "The neuroscience of socioeconomic inequality." Current Opinion in Behavioral Sciences 36 (December 2020): 23–28. http://dx.doi.org/10.1016/j.cobeha.2020.05.007.

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Fateh, Mansooreh, Mohammad Hassan Emamian, Fereshteh Asgari, Ali Alami, and Akbar Fotouhi. "Socioeconomic inequality in hypertension in Iran." Journal of Hypertension 32, no. 9 (September 2014): 1782–88. http://dx.doi.org/10.1097/hjh.0000000000000260.

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Thorbecke, Erik, and Chutatong Charumilind. "Economic Inequality and Its Socioeconomic Impact." World Development 30, no. 9 (September 2002): 1477–95. http://dx.doi.org/10.1016/s0305-750x(02)00052-9.

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Wolniak, Gregory C., Ryan S. Wells, Mark E. Engberg, and Catherine A. Manly. "College Enhancement Strategies and Socioeconomic Inequality." Research in Higher Education 57, no. 3 (October 5, 2015): 310–34. http://dx.doi.org/10.1007/s11162-015-9389-4.

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Wilde, Melissa J., Patricia Tevington, and Wensong Shen. "Religious Inequality in America." Social Inclusion 6, no. 2 (June 22, 2018): 107–26. http://dx.doi.org/10.17645/si.v6i2.1447.

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Sociology has largely ignored class differences between American religious groups under the assumption that those differences “are smaller than they used to be and are getting smaller all of the time” (Pyle & Davidson, 2014, p. 195). This article demonstrates that profound class differences remain amongst American religious groups. These differences are as large as—or larger than—commonly examined forms of inequality such as the gender pay gap and the race achievement gap. Using the most popular categorization of American religious groups, we find that regardless of the particular measure examined (years of education, income, socioeconomic index score, and proportion of members with at least a bachelor’s degree) Jews and Mainline Protestants are at the top of the socioeconomic ladder and Evangelical Protestants, both black and white, are at the bottom. Furthermore, religious group significantly predicts both years of education and the overall socioeconomic standing of respondents by itself with basic controls. Likewise, both socioeconomic indicators and education significantly predict the likelihood of being in a specific religious tradition on their own with basic controls. Some religious groups, namely Evangelical Protestants at the low end and Jews and the high end, are relatively educationally homogeneous. Others, such as Catholics, Mainline Protestants and the nonreligious are much more educationally heterogeneous. The picture is the same when socioeconomic heterogeneity is examined, except that Mainline Protestants emerge as more clearly advantaged socioeconomically. In sum, religious inequality remains in America, it is robust, and it appears to be quite durable.

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Jamal, Haroon. "Spatial Disparities in Socioeconomic Development: The Case of Pakistan." Pakistan Development Review 55, no. 4I-II (December 1, 2016): 421–35. http://dx.doi.org/10.30541/v55i4i-iipp.421-435.

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Rising spatial disparities in socioeconomic development is a major concern in developing economies. Regional inequality is a dimension of overall inequality, but it has added significance when spatial and regional divisions align with political and ethnic tensions to undermine social and political stability. This paper is an attempt to document the extent and nature of regional inequality in the level of socioeconomic development. Seventeen indicators related to human resources and standards of living are developed from the district representative household data of Pakistan Social and Living-Standard Measurement (PSLM) survey for the year 2012-13. Development Indicators, for the first time in Pakistan are also aggregated at sub-district level to incorporate the intra-district inequalities in the analysis of spatial disparities. The study provides provincial multidimensional Gini coefficients and district development ranking to evaluate inter and intra provincial disparities respectively in the selected dimensions of socioeconomic development with the help of Inequality-adjusted Socioeconomic Development Index (ISDI). JEL Classification: D63, I31 Keywords: Multidimensional Inequality, Inequality Adjusted Socioeconomic Development Index, Development Ranking, Pakistan

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Pulok, Mohammad Habibullah, Md Nasim-Us Sabah, and Ulrika Enemark. "Socioeconomic inequalities of child malnutrition in Bangladesh." International Journal of Social Economics 43, no. 12 (December 5, 2016): 1439–59. http://dx.doi.org/10.1108/ijse-03-2015-0065.

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Purpose The purpose of this paper is to investigate how socioeconomic status and demographic factors were associated with child malnutrition as well as how these factors accounted for socioeconomic inequality of child malnutrition in Bangladesh during 2007-2011. Design/methodology/approach Data of this study come from two cross-sectional rounds (2007 and 2011) of the Bangladesh Demographic and Health Survey. This paper uses ordinary least square models to estimate the correlates of child malnutrition. The study further uses the concentration curve and the concentration index to estimate socioeconomic inequality of child malnutrition in Bangladesh. Finally, the decomposition method is used to explain socioeconomic inequality of child malnutrition in the study period. Findings Regression analysis indicates that child’s age, breast feeding, child’s birth order, the number of under-five child in the household, household wealth and parental education were strongly correlated with child malnutrition in Bangladesh. This study finds that absolute level of child malnourishment slightly decreased between 2007 and 2011, but socioeconomic inequality increased during this period. Children from the poorest household endured the burden of malnourishment more than those from the wealthiest households. The level of inequality also increased among the rural children, although it remained stagnant among the urban children. Decomposition analysis highlights that parental education had a significant negative relation with the average level of malnutrition, but its role was primarily centred among children from wealthier households. Practical implications An approach linking the ministry of health and education with other ministries may speed up the reduction of inequalities in social determinants of childhood undernourishment. Most importantly, there is a need for comprehensive government policies to reduce growing economic inequality and increase the relative income of the poor in Bangladesh. Originality/value This study is the first of its kind to apply the decomposition method to explain the socioeconomic inequality of child malnutrition in Bangladesh. This paper presents an enriched understanding of socioeconomic inequality of child malnutrition in Bangladesh during 2007-2011.

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13

Ali, Omer, William A. Darity, Avra Janz, and Marta Sánchez. "The Association between Wealth Inequality and Socioeconomic Outcomes." AEA Papers and Proceedings 111 (May 1, 2021): 211–15. http://dx.doi.org/10.1257/pandp.20211114.

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We investigate whether inequality in wealth distribution is broadly associated with adverse socioeconomic outcomes across countries. There are few studies that investigate these relationships, likely due to data limitations. On the other hand, a substantial body of work suggests that income inequality correlates with undesirable outcomes. This paper evaluates the relationship between measures of wealth inequality and a number of outcomes across countries in a unified estimation framework that circumvents publication bias. While we fail to find support for a broad negative association, we do find a negative correlation between wealth inequality and institutional quality.

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14

Wohlfarth, Tamar. "Socioeconomic inequality and psychopathology: Are socioeconomic status and social class interchangeable?" Social Science & Medicine 45, no. 3 (August 1997): 399–410. http://dx.doi.org/10.1016/s0277-9536(96)00355-3.

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15

Cho, Minha, Emily A. Impett, Belinda Campos, Serena Chen, and Dacher Keltner. "Socioeconomic inequality undermines relationship quality in romantic relationships." Journal of Social and Personal Relationships 37, no. 5 (March 3, 2020): 1722–42. http://dx.doi.org/10.1177/0265407520907969.

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The present research tests how socioeconomic inequality (within romantic relationships) predicts relationship quality and observable expressions of emotion—examining longitudinal self-report and behavioral data from both partners of romantic couples. In Part 1, we examined the romantic partners’ self-reports of relationship quality at baseline and after three and a half months. In keeping with prior research on how power inequality undermines relationship satisfaction, couples defined by greater socioeconomic inequality showed a decline in relationship quality over time. In Part 2, we examined observer ratings of emotional expressions in the romantic partners’ conversations in the laboratory. Here, we found greater expression of negative emotion in the conversations of couples defined by greater inequality. Importantly, it was inequality itself—rather than whether one has higher or lower socioeconomic status than the partner—that significantly predicted changes in relationship quality and observable expressions of negative emotion. These findings have implications for how deepening economic inequality may manifest in the weakening of intimate bonds—the quality of which is central to the well-being of individuals and broader society.

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Warren, John Robert. "Does Growing Childhood Socioeconomic Inequality Mean Future Inequality in Adult Health?" ANNALS of the American Academy of Political and Social Science 663, no. 1 (December 10, 2015): 292–330. http://dx.doi.org/10.1177/0002716215596981.

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Over the past half century, American children have experienced increasingly unequal childhoods. The goal of this article is to begin to understand the implications of recent trends in social and economic inequalities among children for the future of inequalities in health among adults. The relative importance of many of the causal pathways linking childhood social and economic circumstances to adult health remains underexplored, and we know even less about how these causal pathways have changed over time. I combine a series of original analyses with reviews of relevant literature in a number of fields to inform a discussion of what growing childhood inequalities might mean for future inequalities in adult health. In the end, I argue that there is good reason to suppose that growing inequalities in children’s social and economic circumstances will lead to greater heterogeneity in adults’ morbidity and mortality.

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17

Yang, Fan, Colin Angus, Ana Duarte, Duncan Gillespie, Simon Walker, and Susan Griffin. "Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis." Medical Decision Making 40, no. 5 (July 2020): 606–18. http://dx.doi.org/10.1177/0272989x20935883.

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Public health decision makers value interventions for their effects on overall health and health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates health inequality concerns into economic evaluation by accounting for how parameters, such as effectiveness, differ across population groups. A good understanding of how and when accounting for socioeconomic differences between groups affects the assessment of intervention impacts on overall health and health inequality could inform decision makers where DCEA would add most value. We interrogated 2 DCEA models of smoking and alcohol policies using first national level and then local authority level information on various socioeconomic differences in health and intervention use. Through a series of scenario analyses, we explored the impact of altering these differences on the DCEA results. When all available evidence on socioeconomic differences was incorporated, provision of a smoking cessation service was estimated to increase overall health and increase health inequality, while the screening and brief intervention for alcohol misuse was estimated to increase overall health and reduce inequality. Ignoring all or some socioeconomic differences resulted in minimal change to the estimated impact on overall health in both models; however, there were larger effects on the estimated impact on health inequality. Across the models, there were no clear patterns in how the extent and direction of socioeconomic differences in the inputs translated into the estimated impact on health inequality. Modifying use or coverage of either intervention so that each population group matched the highest level improved the impacts to a greater degree than modifying intervention effectiveness. When local level socioeconomic differences were considered, the magnitude of the impacts was altered; in some cases, the direction of impact on inequality was also altered.

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18

Wilkinson, Richard G., and Kate E. Pickett. "Income Inequality and Socioeconomic Gradients in Mortality." American Journal of Public Health 98, no. 4 (April 2008): 699–704. http://dx.doi.org/10.2105/ajph.2007.109637.

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강영준. "Empirical Analysis for Socioeconomic Inequality in Smoking." Journal of Social Science 41, no. 1 (April 2015): 59–83. http://dx.doi.org/10.15820/khjss.2015.41.1.003.

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20

계봉오. "Population aging, socioeconomic development and social inequality." Economy and Society ll, no. 106 (June 2015): 41–72. http://dx.doi.org/10.18207/criso.2015..106.41.

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21

Van de Poel, Ellen. "Socioeconomic inequality in malnutrition in developing countries." Bulletin of the World Health Organization 86, no. 4 (April 1, 2008): 282–91. http://dx.doi.org/10.2471/blt.07.044800.

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22

Giulietti, Corrado. "Book Review: Immigration, Poverty, and Socioeconomic Inequality." ILR Review 67, no. 3_suppl (May 2014): 788–89. http://dx.doi.org/10.1177/00197939140670s315.

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23

Mustard, C. A. "Gender differences in socioeconomic inequality in mortality." Journal of Epidemiology & Community Health 57, no. 12 (December 1, 2003): 974–80. http://dx.doi.org/10.1136/jech.57.12.974.

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24

FOSSETT, MARK A. "Community-Level Analyses of Racial Socioeconomic Inequality." Sociological Methods & Research 16, no. 4 (May 1988): 454–91. http://dx.doi.org/10.1177/0049124188016004002.

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25

Newton, John N. "Counting early deaths due to socioeconomic inequality." Lancet Public Health 5, no. 1 (January 2020): e6-e7. http://dx.doi.org/10.1016/s2468-2667(19)30242-7.

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26

Asaria, Miqdad, Sumit Mazumdar, Samik Chowdhury, Papiya Mazumdar, Abhiroop Mukhopadhyay, and Indrani Gupta. "Socioeconomic inequality in life expectancy in India." BMJ Global Health 4, no. 3 (May 2019): e001445. http://dx.doi.org/10.1136/bmjgh-2019-001445.

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IntroductionConcern for health inequalities is an important driver of health policy in India; however, much of the empirical evidence regarding health inequalities in the country is piecemeal focusing only on specific diseases or on access to particular treatments. This study estimates inequalities in health across the whole life course for the entire Indian population. These estimates are used to calculate the socioeconomic disparities in life expectancy at birth in the population.MethodsPopulation mortality data from the Indian Sample Registration System were combined with data on mortality rates by wealth quintile from the National Family Health Survey to calculate wealth quintile specific mortality rates. Results were calculated separately for males and females as well as for urban and rural populations. Life tables were constructed for each subpopulation and used to calculate distributions of life expectancy at birth by wealth quintile. Absolute gap and relative gap indices of inequality were used to quantify the health disparity in terms of life expectancy at birth between the richest and poorest fifths of households.ResultsLife expectancy at birth was 65.1 years for the poorest fifth of households in India as compared with 72.7 years for the richest fifth of households. This constituted an absolute gap of 7.6 years and a relative gap of 11.7 %. Women had both higher life expectancy at birth and narrower wealth-related disparities in life expectancy than men. Life expectancy at birth was higher across the wealth distribution in urban households as compared with rural households with inequalities in life expectancy widest for men living in urban areas and narrowest for women living in urban areas.ConclusionAs India progresses towards Universal Health Coverage, the baseline social distributions of health estimated in this study will allow policy makers to target and monitor the health equity impacts of health policies introduced.

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D’Onofrio, Alexandra, Raoul Minetti, and Pierluigi Murro. "Banking development, socioeconomic structure and income inequality." Journal of Economic Behavior & Organization 157 (January 2019): 428–51. http://dx.doi.org/10.1016/j.jebo.2017.08.006.

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28

Sun, Jing-Shui, and Ling-Jie Huang. "Socioeconomic Status and Inequality of Residents’ Income." IOP Conference Series: Earth and Environmental Science 237 (March 19, 2019): 052016. http://dx.doi.org/10.1088/1755-1315/237/5/052016.

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Bukowski, William M., Melanie Dirks, Ryan J. Persram, Leah Wright, and Erika Infantino. "Peer relations and socioeconomic status and inequality." New Directions for Child and Adolescent Development 2020, no. 173 (September 2020): 27–37. http://dx.doi.org/10.1002/cad.20381.

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Barratt, Helen, Miqdad Asaria, Jessica Sheringham, Patrick Stone, Rosalind Raine, and Richard Cookson. "Dying in hospital: socioeconomic inequality trends in England." Journal of Health Services Research & Policy 22, no. 3 (January 11, 2017): 149–54. http://dx.doi.org/10.1177/1355819616686807.

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Objective To describe trends in socioeconomic inequality in the proportion of deaths occurring in hospital, during a period of sustained effort by the NHS in England to improve end of life care. Methods Whole-population, small area longitudinal study involving 5,260,871 patients of all ages who died in England from 2001/2002 to 2011/2012. Our primary measure of inequality was the slope index of inequality. This represents the estimated gap between the most and least deprived neighbourhood in England, allowing for the gradient in between. Neighbourhoods were geographic Lower Layer Super Output Areas containing about 1500 people each. Results The overall proportion of patients dying in hospital decreased from 49.5% to 43.6% during the study period, after initially increasing to 52.0% in 2004/2005. There was substantial ‘pro-rich’ inequality, with an estimated difference of 5.95 percentage points in the proportion of people dying in hospital (confidence interval 5.26 to 6.63), comparing the most and least deprived neighbourhoods in 2011/2012. There was no significant reduction in this gap over time, either in absolute terms or relative to the mean, despite the overall reduction in the proportion of patients dying in hospital. Conclusions Efforts to reduce the proportion of patients dying in hospital in England have been successful overall but did not reduce inequality. Greater understanding of the reasons for such inequality is required before policy changes can be determined.

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Kim, jiyoun, and Soon-Yong Pak. "A Study on the Changes in Educational Inequalities amidst the Expansion of Higher Education Opportunities: A Systematic Review of Korean Literature." Institute for Educational Research 35, no. 4 (December 31, 2022): 79–108. http://dx.doi.org/10.35283/erft.2022.35.4.79.

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The purpose of this study is to comprehensively review the patterns and mechanisms of educational inequalities that have changed in the context of expanding and diversifying accessibility to higher education since the 1990s. Undergoing systematic literature review, the researchers selected 38 documents that dealt with the theme on educational inequality, and then proceeded to analyze and integrate the findings. First, educational inequality was found to coexist in two types: vertical inequality and horizontal inequality. The impact of parents' socioeconomic status on children's academic achievement increased, as the educational gap between socioeconomic classes widened. Second, as a factor of deepening educational inequality, the lower the parents' socioeconomic status, the less investment were made in private education, and the formation of social and cultural capital that promotes academic achievement was also low. This study expanded the understanding of educational inequalities in Korea by systematically integrating research on educational inequality, and based on the findings, suggested measures to alleviate educational inequality.

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Baigi, Vali, Saharnaz Nedjat, Ahmad Reza Hosseinpoor, Majid Sartipi, Yahya Salimi, and Akbar Fotouhi. "Socioeconomic inequality in health domains in Tehran: a population-based cross-sectional study." BMJ Open 8, no. 2 (February 2018): e018298. http://dx.doi.org/10.1136/bmjopen-2017-018298.

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ObjectiveReduction of socioeconomic inequality in health requires appropriate evidence on health and its distribution based on socioeconomic indicators. The objective of this study was to assess socioeconomic inequality in various health domains and self-rated health (SRH).MethodsThis study was conducted using data collected in a survey in 2014 on a random sample of individuals aged 18 and above in the city of Tehran. The standardised World Health Survey Individual Questionnaire was used to assess different health domains. The age-adjusted prevalence of poor health was calculated for each health domain and SRH based on levels of education and wealth quintiles. Furthermore, the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII) were applied to assess socioeconomic inequality in each of the health domains and SRH.ResultsThe age-adjusted prevalence of poor health was observed in a descending order from the lowest to the highest wealth quintiles, and from the lowest level of education to the highest. RII also showed varying values of inequality among different domains, favouring rich subgroups. The highest wealth-related RII was observed in the ‘Mobility’ domain with a value of 4.16 (95% CI 2.01 to 8.62), and the highest education-related RII was observed in the ‘Interpersonal Activities’ domain with a value of 6.40 (95% CI 1.91 to 21.36).ConclusionsSubstantial socioeconomic inequalities were observed in different health domains in favour of groups of better socioeconomic status. Based on these results, policymaking aimed at tackling inequalities should pay attention to different health domains as well as to overall health.

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Øvrum, Arnstein, and Kyrre Rickertsen. "Inequality in health versus inequality in lifestyle choices." Nordic Journal of Health Economics 3, no. 1 (October 3, 2015): 18–33. http://dx.doi.org/10.5617/njhe.972.

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Repeated Norwegian cross-sectional data for the period 2005 to 2011 are used to compare sources of inequality in health, as represented by self-assessed health and obesity, with sources of inequality in lifestyles that are central to the production of health, as represented by physical activity, cigarette smoking and dietary behavior. Sources of overall inequality and socioeconomic inequality in these lifestyle and health indicators are compared by estimating probit models, and by decomposing the explained part of the associated Gini and concentration indices with respect to education and income. As potential sources of inequality, we consider education, income, occupation, age, gender, marital status, psychological traits and childhood circumstances. Our results suggest that sources of inequality in health are not necessarily representative of sources of inequality in underlying lifestyles. While education is generally an important source of overall inequality in both lifestyles and health, income is unimportant in all lifestyle indicators except physical activity. In several cases, education and income are clearly outranked by other factors in terms of explaining overall inequality, such as gender in eating fruits and vegetables and age in fish consumption. These results suggest that it is important to decompose both overall inequality and socioeconomic inequality in different lifestyle and health indicators. In indicators where other factors than education and income are clearly most important, policy makers should consider to target these factors to efficiently improve overall population health.Published: Online October 2015. In print December 2015.

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Alves, Luciana Correia, and Natália Martins Arruda. "Socioeconomic differentials and disease-free life expectancy of the elderly in Brazil." International Journal of Population Studies 3, no. 1 (August 30, 2017): 64. http://dx.doi.org/10.18063/ijps.2017.01.001.

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The objective of this study was to estimate life expectancy with and without a specific chronic disease among the Brazilian elderly population, by sex and socioeconomic factors, for the years 1998 and 2008. Life expectancy with and without hypertension, diabetes, bronchitis/asthma, and heart disease were calculated using the Sullivan method and prevalence estimates from data collected in the two years through the Brazilian National Household Survey (PNAD). Hypertension was the chronic disease with the largest effect on life expectancy. Among socioeconomic determinants, education proved more relevant than income. Having more years of education increased the average healthy time. Socioeconomic inequality negatively affected the health of women more than men. Despite the social changes in Brazil in recent decades with a reduction in inequality and poverty, the effect of socioeconomic inequality in the country on the health status of the elderly remains evident.

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Minkov, Zhivko. "Attitudes towards Democracy in Bulgaria: The Importance of Social Inequalities." English Studies at NBU 5, no. 2 (December 30, 2019): 323–49. http://dx.doi.org/10.33919/esnbu.19.2.8.

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This research is an attempt to examine the developmental relationship between democracy and the socioeconomic conditions in Bulgaria. The assumption is that one of the factors contributing to the negative attitude towards democracy in Bulgaria is the high level of social inequality. After discussing the relevance of studying democracy and socioeconomic conditions from a developmental perspective, the paper traces the socioeconomic development of Bulgaria from 1989 to date and examines public perceptions of social inequality in the country. The research has not revealed any overall negative public attitude towards democracy in Bulgaria. However, the findings have demonstrated a clear tendency for the public support for democracy to decline reflecting the growth of social inequality.

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Hosseinkhani, Zahra, Farzad Khodamoradi, and Sara Sheikh. "Socioeconomic Inequality of Elder Abuse in Qazvin, Iran." Social Health and Behavior 2, no. 2 (2019): 47. http://dx.doi.org/10.4103/shb.shb_2_19.

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Hosseinkhani, Z., S. Nedjat, A. Aftatouni, M. Mahram, and R. Majdzadeh. "Socioeconomic inequality and child maltreatment in Iranian schoolchildren." Eastern Mediterranean Health Journal 21, no. 11 (November 1, 2015): 819–27. http://dx.doi.org/10.26719/2015.21.11.819.

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Boing, Alexandra Crispim, Andréa Dâmaso Bertoldi, Aluísio Jardim Dornellas de Barros, Leila Garcia Posenato, and Karen Glazer Peres. "Socioeconomic inequality in catastrophic health expenditure in Brazil." Revista de Saúde Pública 48, no. 4 (August 2014): 632–41. http://dx.doi.org/10.1590/s0034-8910.2014048005111.

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OBJECTIVE To analyze the evolution of catastrophic health expenditure and the inequalities in such expenses, according to the socioeconomic characteristics of Brazilian families.METHODS Data from the National Household Budget 2002-2003 (48,470 households) and 2008-2009 (55,970 households) were analyzed. Catastrophic health expenditure was defined as excess expenditure, considering different methods of calculation: 10.0% and 20.0% of total consumption and 40.0% of the family’s capacity to pay. The National Economic Indicator and schooling were considered as socioeconomic characteristics. Inequality measures utilized were the relative difference between rates, the rates ratio, and concentration index.RESULTS The catastrophic health expenditure varied between 0.7% and 21.0%, depending on the calculation method. The lowest prevalences were noted in relation to the capacity to pay, while the highest, in relation to total consumption. The prevalence of catastrophic health expenditure increased by 25.0% from 2002-2003 to 2008-2009 when the cutoff point of 20.0% relating to the total consumption was considered and by 100% when 40.0% or more of the capacity to pay was applied as the cut-off point. Socioeconomic inequalities in the catastrophic health expenditure in Brazil between 2002-2003 and 2008-2009 increased significantly, becoming 5.20 times higher among the poorest and 4.17 times higher among the least educated.CONCLUSIONS There was an increase in catastrophic health expenditure among Brazilian families, principally among the poorest and those headed by the least-educated individuals, contributing to an increase in social inequality.

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Jaret, Charles, Lesley Williams Reid, and Robert M. Adelman. "Black-White Income Inequality and Metropolitan Socioeconomic Structure." Journal of Urban Affairs 25, no. 3 (August 2003): 305–34. http://dx.doi.org/10.1111/1467-9906.00167.

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Brekke, Mette, Per Hjortdahl, and Tore K. Kvien. "Severity of musculoskeletal pain: relations to socioeconomic inequality." Social Science & Medicine 54, no. 2 (January 2002): 221–28. http://dx.doi.org/10.1016/s0277-9536(01)00018-1.

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Kozlowski, Karen Phelan. "Socioeconomic Inequality in Decoding Instructions and Demonstrating Knowledge." Qualitative Sociology 43, no. 1 (January 9, 2020): 43–66. http://dx.doi.org/10.1007/s11133-019-09442-y.

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42

Caston, Richard J. "Dimensions of occupational inequality and Duncan's socioeconomic index." Sociological Forum 4, no. 3 (September 1989): 329–48. http://dx.doi.org/10.1007/bf01115013.

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43

Dummer, T. J. B. "Changing socioeconomic inequality in infant mortality in Cumbria." Archives of Disease in Childhood 90, no. 2 (February 1, 2005): 157–62. http://dx.doi.org/10.1136/adc.2003.036111.

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Lofstrom, Magnus, and Steven Raphael. "Crime, the Criminal Justice System, and Socioeconomic Inequality." Journal of Economic Perspectives 30, no. 2 (May 1, 2016): 103–26. http://dx.doi.org/10.1257/jep.30.2.103.

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Crime rates in the United States have declined to historical lows since the early 1990s. Prison and jail incarceration rates as well as community correctional populations have increased greatly since the mid-1970s. Both of these developments have disproportionately impacted poor and minority communities. In this paper, we document these trends. We then assess whether the crime declines can be attributed to the massive expansion of the US criminal justice system. We argue that the crime rate is certainly lower as a result of this expansion and in the early 1990s was likely a third lower than what it would have been absent changes in sentencing practices in the 1980s. However, there is little evidence that further stiffening of sentences during the 1990s—a period when prison and other correctional populations expanded rapidly—have had an impact. Hence, the growth in criminal justice populations since 1990s has exacerbated socioeconomic inequality in the United States without generating much benefit in terms of lower crime rates.

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Hosseinpoor, Ahmad Reza, Eddy Van Doorslaer, Niko Speybroeck, Mohsen Naghavi, Kazem Mohammad, Reza Majdzadeh, Bahram Delavar, Hamidreza Jamshidi, and Jeanette Vega. "Decomposing socioeconomic inequality in infant mortality in Iran." International Journal of Epidemiology 35, no. 5 (September 19, 2006): 1211–19. http://dx.doi.org/10.1093/ije/dyl164.

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Abbas, Motevalian, Safari Roya, and Asadi-Lari Mohsen. "Socioeconomic Inequality in road traffic injuries in Tehran." Injury Prevention 18, Suppl 1 (October 2012): A212.1—A212. http://dx.doi.org/10.1136/injuryprev-2012-040590u.28.

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Aizawa, Toshiaki, and Matthias Helble. "Socioeconomic inequality in excessive body weight in Indonesia." Economics & Human Biology 27 (November 2017): 315–27. http://dx.doi.org/10.1016/j.ehb.2017.09.005.

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Wondimu, Abrham, Jurjen van der Schans, Marinus van Hulst, and Maarten J. Postma. "Inequalities in Rotavirus Vaccine Uptake in Ethiopia: A Decomposition Analysis." International Journal of Environmental Research and Public Health 17, no. 8 (April 14, 2020): 2696. http://dx.doi.org/10.3390/ijerph17082696.

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A previous study in Ethiopia reported significant variation in rotavirus vaccine uptake across socioeconomic strata. This study aims to quantify socioeconomic inequality of rotavirus vaccine uptake in Ethiopia and to identify the contributing factors for the inequality. The concentration curve (CC) and the Erreygers Normalized Concentration Index (ECI) were used to assess the socioeconomic related inequality in rotavirus vaccine uptake using data from the 2016 Ethiopian Demographic and Health Survey. Decomposition analysis was conducted to identify the drivers of inequalities. The CC for rotavirus vaccine uptake lay below the line of equality and the ECI was 0.270 (p < 0.001) indicating that uptake of rotavirus vaccine in Ethiopia was significantly concentrated among children from families with better socioeconomic status. The decomposition analysis showed that underlining inequalities in maternal health care services utilization, including antenatal care use (18.4%) and institutional delivery (8.1%), exposure to media (12.8%), and maternal educational level (9.7%) were responsible for the majority of observed inequalities in the uptake of rotavirus vaccine. The findings suggested that there is significant socioeconomic inequality in rotavirus vaccine uptake in Ethiopia. Multi-sectoral actions are required to reduce the inequalities, inclusive increasing maternal health care services, and educational attainments among economically disadvantaged mothers.

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Farro-Maldonado, Marioli Y., Glenda Gutiérrez-Pérez, Akram Hernández-Vásquez, Antonio Barrenechea-Pulache, Marilina Santero, Carlos Rojas-Roque, and Diego Azañedo. "Socioeconomic inequalities in abdominal obesity among Peruvian adults." PLOS ONE 16, no. 7 (July 21, 2021): e0254365. http://dx.doi.org/10.1371/journal.pone.0254365.

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Objectives Abdominal obesity (AO) has become a public health issue due to its impact on health, society and the economy. The relationship between socioeconomic disparities and the prevalence of AO has yet to be studied in Peru. Thus, our aim was to analyze the socioeconomic inequalities in AO distribution defined using the International Diabetes Federation (IDF) cut-off points in Peruvian adults in 2018–2019. Methods This was a cross-sectional study using data from the 2018–2019 Demographic and Family Health Survey (ENDES) of Peru. We analyzed a representative sample of 62,138 adults over 18 years of age of both sexes from urban and rural areas. Subjects were grouped into quintiles of the wealth to calculate a concentration curve and the Erreygers Concentration Index (ECI) in order to measure the inequality of AO distribution. Finally, we performed a decomposition analysis to evaluate the major determinants of inequalities. Results The prevalence of AO among Peruvian adults was 73.8%, being higher among women than men (85.1% and 61.1% respectively, p < 0.001). Socioeconomic inequality in AO was more prominent among men (ECI = 0.342, standard error (SE) = 0.0065 vs. ECI = 0.082, SE = 0.0043). The factors that contributed most to inequality in the prevalence of AO for both sexes were having the highest wealth index (men 37.2%, women 45.6%, p < 0.001), a higher education (men 34.4%, women 41.4%, p < 0.001) and living in an urban setting (men 22.0%, women 57.5%, p < 0.001). Conclusions In Peru the wealthy concentrate a greater percentage of AO. The inequality gap is greater among men, although AO is more prevalent among women. The variables that most contributed to inequality were the wealth index, educational level and area of residence. There is a need for effective individual and community interventions to reduce these inequalities.

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Sengupta, Susmita, and Sanat Kumar Guchhait. "Inequality in Contemporary India: Does Caste Still Matter?" Journal of Developing Societies 37, no. 1 (March 2021): 57–82. http://dx.doi.org/10.1177/0169796x21998387.

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Are the issues of socioeconomic inequalities related to specific caste biases in Contemporary India? Why are some social groups in the socioeconomic ladder lagging compared to others? How much have the poor people living in rural areas in India achieved their esteemed goals in the present economic arena of development? Is it the limited educational attainment or lack of physical and social capital of the poor households that begets inter-group economic inequality? Or is it a social identity that impedes the well-being of a certain social group? In search of answering these questions, this article explores the role of castes in explaining socioeconomic inequalities in the rural settings of contemporary India. The limited access to basic amenities and the poor educational attainment of the lower castes and the dominance of the upper castes in these regards show the persistence of social group inequalities. Four remote villages of Purulia district, one of the most backward tribal districts of West Bengal were systematically selected for scrutiny to explore socioeconomic inequality within the caste structure. Two are tribal villages with low inequality and the other two are multi-caste-oriented villages with high inequality.

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