An Inequality Income Distribution in Kabupaten Banyumas Central Java

The central theme of this paper is the measurement of income inequality by investigating its present time in Banyumas. It is important to know the impact of local development process on income inequality. Not only the impacts of local development, but also other factors determining the trend of income inquality (i.e.fuel price raises, price and income adjustment policies) give impact on present income inequality. Nowaydays, the local development policies and the other factors are predicted that they give impact on the raise of recent income inquality in Banyumas. Based on Gini coefficient measurement method, we obtain startling result. The result presented in this paper is based on a sample of 180 households including urban area, sub urban area and rural area. The Gini coefficient result is 0,603. This result shows the sharp raise of income inequality from past time to recent in Banyumas. The imbalance of development between modern sectors (i.e services, contruction, finance, trade and manufacture) and traditional sectors (i.e agriculture and informal sector) causes the raise of income inequality. Based on this trend of the raise of income inequality, local government must practice pro poor development policies without ignoring sustainable economic growth and especially focusing on agriculture, rural economy, informal sector, small and medium industries. Keywords: income inequality, Gini coefficient, pro-poor policy, sustainable growth

This paper reviews conceptual differences between popular measures of relative intergroup income inequality. To organize our review we introduce a set of guidelines identifying desirable characteristics of measures of relative intergroup income inequality and then evaluate different measures in terms of these guidelines. Significantly, we find that many popular measures (e.g., the ratio of median incomes, the Index of Dissimilarity, and the Index of Net Difference) have one or more logical weaknesses which prevent them from fully satisfying our guidelines. On the other hand, comparisons of group mean incomes (e.g., the ratio of mean incomes or the difference of the means for the logarithm of income) satisfy most of the guidelines we set forth. Additionally, we introduce a new measure of relative intergroup income inequality, the Index of Average Relative Advantage, which meets our guidelines and has an appealing interpretation.

Economic development and income distribution

In his paper, Dr. Joseph Gastwirth argues that an income inequality indicator that combines the Gini coefficient with a measure that captures right skewness is a better measure to detect changes in the distribution that disproportionately favor upper income levels. Gastwirth proposes the indicator, G2, in which the mean/median is multiplied by the Gini coefficient, as a better measure of income inequality in such circumstances. In this paper, I consider whether G2 is a useful measure in Brazil and Mexico, two upper middle income countries that have historically high income inequality that appears to have decreased in the recent past. This paper suggests that G2 may be somewhat better than the Gini coefficient at capturing changes both in increasing income inequality and decreasing income inequality. In order to provide context for interpreting these results, I examine the quality of data on income inequality in both countries by analyzing the income-related questionnaires of the main household income and expenditure surveys for both countries. I find that although Mexico's household income survey does have more detailed questions on non-wage income than Brazil's household income survey, income estimates still fall below consumption estimates in both countries, which suggests consumption may be a better source of information on income inequality in both Brazil and Mexico.

PurposeThe objectives of this study are threefold: firstly, to measure the impact of educational inequality on income inequality, and per capita income; secondly, to measure the impact of gender inequality in education on income inequality, per capita income and educational inequality; and lastly, to test the Kuznets inverted U-shape hypothesis between inequality in education and average year of schooling.Design/methodology/approachThe study has adopted the Marin and Psacharopoulos (1976) model of human capital in which income earned by an individual can be estimated as a function of number of year spent in schooling or education. Gini coefficient is used as a measure of income inequality, while inequality in education is measured by Gini index of educational inequality. Gender inequality in education is measured by the difference between male and female enrolment ratios as a proportion of male enrolment. The study utilizes the data of six South Asian countries, i.e. Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka from 1980 to 2010 at five-year average and employs fixed effect model (FEM) and random effect model (REM) for estimation.FindingsResult suggests that educational inequality and average year of schooling have positive and significant impact on income inequality. Primary (basic) education and tertiary (higher) education reduce income inequality, while secondary education widens income inequality. Negative relationship exists between educational inequality and per capita income. Unequal distribution of education among boys and girls at primary level increases income inequality, while reduces income inequality at tertiary level. Gender inequality in secondary and tertiary level of education reduces per capita income, while unequal distribution of education among boys and girls further increases the educational inequality. Kuznets inverted U-shape hypothesis does not hold between education expansion and educational inequality, while weak U-shape relationship exists in South Asian countries.Practical implicationsGovernment has to provide free education in poor regions and makes employment programs to reduce the income and educational inequality respectively, while to remove gender inequality in education it is necessary to build more schools especially for girls. Government has to launch different online education programs for expansion in education at all levels.Originality/valueThis study adds to the literature by analyzing whether the inequality in income increases (decreases) due to increase (decrease) in educational and gender inequality in South Asian countries. This study contributes in the existing literature by developing a measure of educational and gender inequality in education in South Asian countries.Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2020-0226.

INTRODUCTION The idea that the poor – through their vote – represent a threat to democracy and property has inspired research on regime change for decades. From Lipset (1959, 31) to Boix (2011), scholars have expected growth, equality, and democracy to run together. Although it has gone largely unchallenged over decades, the notion that the poor threaten property and democracy is fundamentally mistaken on both empirical and theoretical grounds. The roots and persistence of this error lie with a failure to properly connect social scientists' standard quantitative measure of income inequality – the Gini coefficient – to equally standard sociological understandings of class structure, in terms of the relative sizes and incomes of different social groups: the incumbent autocratic elite (which we assumes includes large landowners if they exist), rising (relative) economic elites, including the bourgeoisie and in many cases industrial workers, and the poor. In this chapter, we show that properly connecting Gini coefficients to social structures supports our contention that competition over regime change tends to occur between relative economic elites. We start from the observation that income inequality is typically very low in preindustrial societies. Historically, the onset of sustained economic growth – a secular shift from agricultural to nonagricultural sectors – tends to generate a rapid increase in income inequality. Simon Kuznets (1955) noted this phenomenon decades ago, but students of regime change have not teased out this correlation's political implications. The key theoretical point is that the combination of growth and inequality in a developing autocracy does not imply a growing redistributive threat from the median voter. As Kuznets noted, economic development has the effect of increasing intergroup variation in incomes, which is what the Gini coefficient actually measures. That is, inequality does not increase simply because the rich are further distancing themselves from everyone else, as Lipset and others appear to assume, but because of the emergence and growth of the bourgeois, middle, and working classes. Members of these groups tend to earn far more than the future median voter, who in nearly all historical cases tends to remain relatively poor. And because they have far more to lose, members of these rising economic groups are also increasingly likely to mobilize to press for political reforms.

Economic growth is insufficient to be a sole indicator of the population’s welfare. Specifically, high economic growth does not necessarily imply that the population is generally prosperous. Equal income distribution is crucial to achieving sustainable economic growth. Since 2000, the Gini index as a measure of income inequality in Indonesia showed an increasing trend. On the other side, financial technology 3.0 started to develop. This paper seeks to investigate the impact of fintech 3.0 development on income inequality in Indonesia and to identify the determining factors of income inequality in Indonesia. By using the partial adjustment model (PAM) with the observation period of 1990-2017, the study empirically shows that fintech 3.0 development that started in 2000 had a significant impact on income inequality in Indonesia. Besides, the investment variable also positively affect income inequality in Indonesia. Thus, the findings indicate that the Indonesian population did not equally utilize fintech development.
 Keywords: income inequality, financial technology, Indonesia, partial adjustment model

Determining the level of income inequality requires the adoption of a specific measurement methodology. The aim of the study was to review and discuss the methodologies used to measure income inequality. Four measures are presented, each based on different assumptions. These measures were the Gini coefficient, Theil coefficient, Kukuła coefficient and unevenness coefficient. The first three measures, and in particular the Gini coefficient, are commonly described in the literature, while the unevenness coefficient is the author’s proposal for measuring income inequality. The empirical material for the research consists of data on the distribution of disposable income by decile groups in households in Poland for the years 2005–2017. The most important issue in practice regarding the measurement of income inequality was the transfer principle. Depending on the methodology adopted, the transfer of income is treated differently. The Gini, Theil and Kukula coefficients respond to any change in the income distribution, while the unevenness coefficient only to changes above the average. In a situation where the Gini coefficient (Theil and Kukula) decreases (increases), the level of inequality decreases (increases), but it is not known which transfers led to such a result. The decreasing (growing) unevenness coefficient means that these were transfers from groups with shares in income above (below) the average for groups with shares below (above) the average.

Is income inequality related to childhood dental caries in rich countries?

Income inequality and nations' altruism

Purpose — This paper aims to examine the relationship between the shadow economy and income inequality in Nigeria.Method — The paper employed Autoregressive Distributed Lag (ARDL), Fully Modified Ordinary Least Square (FMOLS), and Granger causality. This methodology is used to avoid endogeneity and heterogeneity in the model. This paper gauged income inequality using two diverse indicators of the Gini coefficient: the Gini index in proportion to household disposable income and the Gini index in proportion to household market income. In accordance with the literature, our empirical analysis draws on data from the Standardized World Income Inequality Database (SWIID), the World Bank, World Development Indicators, and the International Country Risk Guide (ICRG) for Nigeria from 1991 to 2018.Result — The findings of ARDL and FMOLS suggested a positive relationship between income inequality and the shadow economy, based on both measures of income inequality. In the short term, however, the shadow economy and income inequality are negatively correlated. Furthermore, we discovered a one-way causal relationship exists in Nigeria between the shadow economy, household disposable income, institutional democracy, household market income, and corruption control (CCI).Recommendation — Shadow economy has been regarded as an avenue to create job opportunities and raise poverty-income levels. It is critical that, for the shadow economy to reduce income inequality in Nigeria, policymakers should develop much better policies aimed at addressing income inequality.Contribution — In order to understand the relationship between income inequality and shadow economy activities in Nigeria, this study employed three methodologies, namely: Autoregressive Distributed Lags (ARDL), Fully Modified Ordinary Least Squares (FMOLS), and Granger Causality. The result offers reliable recommendations for pro-poor interventions that aim to limit the growth of informality via redistributing incomes.

The paper seeks to establish the implication of occupational distribution on income inequality. Data was collected from five independent sub-groups, two from the formal and the other two from the informal sectors with a fifth sample generated from these four samples. Lorenz curves and Gini coefficients were used to measure income inequality. The results show that though formal sector income sub-groups generated higher incomes they produced lower inequalities than the informal sector income sub-groups which induced lower incomes due to salary harmonization using the single spine salary structure policy introduced in 2010. The paper also reveals that even though the Lorenz curve and Gini index are very useful in establishing intra sub-group inequalities their application to inter sub-group inequalities for comparative purposes is problematic since both tools are unable to deal with variations in income levels across income sub-groups. The bootstrap confidence intervals were constructed to deal with the statistical testability enigma of the Gini index. The results show that the empirical statistics and by extension Gini coefficients were significant at 95% confidence interval. The paper recommends that policies targeted at solving the inequality puzzle in low income countries should always be accompanied with growth generating policies for the expansion and sustained poverty and income inequality reduction. Keywords: Income inequality, Poverty, Lorenz Curve, Gini coefficient, Nadowli-Kaleo District

Rising U.S. income inequality, gender and individual self-rated health, 1972–2004

The effect of income inequality on health has been a contested topic among social scientists. Most previous research is based on cross-sectional comparisons rather than temporal comparisons. Using data from the General Social Survey and the U.S. Census Bureau, this study examines how rising income inequality affects individual self-rated health in the U.S. from 1972 to 2004. Data are analyzed using hierarchical generalized linear models. The findings suggest a significant association between income inequality and individual self-rated health. The dramatic increase in income inequality from 1972 to 2004 increases the odds of worse self-rated health by 9.4 percent. These findings hold for three measures of income inequality: the Gini coefficient, the Atkinson Index, and the Theil entropy index. Results also suggest that overall income inequality and gender-specific income inequality harm men's, but not women's, self-rated health. These findings also hold for the three measures of income inequality. These findings suggest that inattention to gender composition may explain apparent discrepancies across previous studies.

Trends in the association between average income, poverty and income inequality and life expectancy in Spain