scholarly journals How do mean division shares affect growth and development

2017 ◽  
Vol 64 (5) ◽  
pp. 525-545 ◽  
Author(s):  
Liang Shao
Keyword(s):  
Panel Data ◽  
Gini Coefficient ◽  
Growth And Development ◽  
Income Share ◽  
Lorenz Curves ◽  
Growth Outcomes ◽  
The Gini Coefficient ◽  
Household Disposable Income ◽  
The Mean ◽  
Per Capita

The Gini coefficient is widely used in academia to discuss how income inequality affects development and growth. However, different Lorenz curves may provide different development and growth outcomes while still leading to the same Gini coefficient. This paper studies the development effects of ?mean division shares?, i.e., the share of income (mean income share) held by people whose household disposable income per capita is below the mean income and the share of the population (mean population share) with this income, using panel data. Our analysis explores how this income share and population share impact development and growth. It shows that the income and population shares affect growth in significantly different ways and that an analysis of these metrics provides substantial value compared to that of the Gini coefficient.

scholarly journals The labor share and income inequality: some empirical evidence for the period 1990-2015

2020 ◽  
Vol 28 (84) ◽  
pp. 173-195
Author(s):  
Iñaki Erauskin
Keyword(s):  
Labor Market ◽  
Panel Data ◽  
Income Inequality ◽  
Gini Coefficient ◽  
Content Type ◽  
Labor Share ◽  
Income Share ◽  
The Gini Coefficient ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to analyze empirically the relationship between the labor share and income inequality, as measured by the Gini coefficient and by the income shares for different quintiles, during the period 1990–2015 for 62 developed and developing countries. Design/methodology/approach This study uses panel data techniques to analyze empirically the relationship between the labor share and income inequality. Findings This paper finds that a lower labor share is associated with a higher Gini coefficient. A lower labor share is found to be strongly associated with a smaller income share for the lowest two quintiles and larger income share for the highest quintile and weakly associated with a smaller income share for the third and fourth quintiles. Moreover, this paper finds that the lower the quintile, the stronger the impact of the labor share on the income share of the quintile. Social implications Policymakers should take into account the evolution of the labor share. Public policies that improve labor market outcomes, such as those aimed to promote participation in the labor market and strengthen the human capital of low-income groups, seem necessary to prevent the rise in economic inequalities. Moreover, as the digital transformation of society progresses, policies to promote skill deepening may have an important role in reversing excessive inequalities. Originality/value How changes in the labor share are associated with changes in the Gini coefficient, and how this is driven by income shares for different quintiles, for a broad range of countries during the most recent period, has not been comprehensively studied using panel data techniques.

Anything Lorenz Curves Can Do, Top Shares Can Do: Assessing the TopBot Family of Inequality Measures

2018 ◽  
Vol 49 (4) ◽  
pp. 947-981 ◽  
Author(s):  
Guillermina Jasso
Keyword(s):  
Gini Coefficient ◽  
Probability Distributions ◽  
The United States ◽  
Current Evidence ◽  
Inequality Measure ◽  
Lorenz Curves ◽  
Inequality Measures ◽  
Top Incomes ◽  
The Gini Coefficient ◽  
And Shifts

Newly precise evidence of the trajectory of top incomes in the United States and around the world relies on shares and ratios, prompting new inquiry into their properties as inequality measures. Current evidence suggests a mathematical link between top shares and the Gini coefficient and empirical links extending as well to the Atkinson measure. The work reported in this article strengthens that evidence, making several contributions: First, it formalizes the shares and ratios, showing that as monotonic transformations of each other, they are different manifestations of a single inequality measure, here called TopBot. Second, it presents two standard forms of TopBot, which satisfy the principle of normalization. Third, it presents a new link between top shares and the Gini coefficient, showing that properties and results associated with the Lorenz curve pertain as well to top shares. Fourth, it investigates TopBot in mathematically specified probability distributions, showing that TopBot is monotonically related to classical measures such as the Gini, Atkinson, and Theil measures and the coefficient of variation. Thus, TopBot appears to be a genuine inequality measure. Moreover, TopBot is further distinguished by its ease of calculation and ease of interpretation, making it an appealing People’s measure of inequality. This work also provides new insights, for example, that, given nonlinearities in the (monotonic) relations among inequality measures, Spearman correlations are more appropriate than Pearson correlations and that weakening of correlations signals differences and shifts in distributional form, themselves signals of income dynamics.

scholarly journals Intersecting Lorenz curves and aversion to inverse downside inequality

2020 ◽  
Author(s):  
W. Henry Chiu
Keyword(s):  
Gini Coefficient ◽  
Linear Inequality ◽  
The Other ◽  
Inequality Aversion ◽  
Inequality Index ◽  
Lorenz Curves ◽  
Income Distributions ◽  
The Gini Coefficient ◽  
Precise Condition

Abstract This paper defines and characterizes the concept of an increase in inverse downside inequality and show that, when the Lorenz curves of two income distributions intersect, how the change from one distribution to the other is judged by an inequality index exhibiting inverse downside inequality aversion often depends on the relative strengths of its aversion to inverse downside inequality and inequality aversion. For the class of linear inequality indices, of which the Gini coefficient is a member, a measure characterizing the strength of an index’s aversion to inverse downside inequality against its own inequality aversion is shown to determine the ranking by the index of two distributions whose Lorenz curves cross once. The precise condition under which the same result generalizes to the case of multiple-crossing Lorenz curves is also identified.

scholarly journals The Robin Hood Index Adjusted for Negatives and Equivalised Incomes

2021 ◽  
Vol 37 (4) ◽  
pp. 1047-1058
Author(s):  
Marion van den Brakel ◽  
Reinder Lok
Keyword(s):  
Gini Coefficient ◽  
Wealth Inequality ◽  
Income Transfer ◽  
Income Share ◽  
Robin Hood ◽  
Inequality Measures ◽  
The Poor ◽  
The Gini Coefficient ◽  
The Rich ◽  
Original Index

Abstract Indisputable figures on income and wealth inequality are indispensable for politics, society and science. Although the Gini coefficient is the most common measure of inequality, the straightforward concept of the Robin Hood index (namely, the income share that has to be transferred from the rich to the poor to make everyone equally well off) makes it a more attractive measure for the general public. In a distribution with many negative values – particularly wealth distributions – the Robin Hood index can take on values larger than 1, indicating an intuitively impossible income transfer of more than 100%. This article proposes a method to normalise the Robin Hood index. In contrast to the original index, the normalised Robin Hood index always takes on values between 0 and 1 and ends up as the original index in a distribution without negatives. As inequality measures are commonly applied to equivalised income, we also introduce a method for adequately transferring equivalised incomes from the rich to the poor within the framework of the (normalised) Robin Hood index. An empirical application shows the effect of normalisation for the Robin Hood index, and compares it to the normalisation of the Gini coefficient from previous research.

scholarly journals Fracture Network Localization Preceding Catastrophic Failure in Triaxial Compression Experiments on Rocks

2021 ◽  
Vol 9 ◽  
Author(s):  
Jessica McBeck ◽  
Yehuda Ben-Zion ◽  
François Renard
Keyword(s):  
Gini Coefficient ◽  
Triaxial Compression ◽  
Fracture Network ◽  
Catastrophic Failure ◽  
Fracture Networks ◽  
Confining Stress ◽  
Network Localization ◽  
The Gini Coefficient ◽  
The Mean ◽  
Total Fracture

We quantify the spatial distribution of fracture networks throughout six in situ X-ray tomography triaxial compression experiments on crystalline rocks at confining stresses of 5–35 MPa in order to quantify how fracture development controls the final macroscopic failure of the rock, a process analogous to those that control geohazards such as earthquakes and landslides. Tracking the proportion of the cumulative volume of fractures with volumes >90th percentile to the total fracture volume, ∑v90/vtot indicates that the fracture networks tend to increase in localization toward these largest fractures for up to 80% of the applied differential stress. The evolution of this metric also matches the evolution of the Gini coefficient, which measures the deviation of a population from uniformity. These results are consistent with observations of localizing low magnitude seismicity before large earthquakes in southern California. In both this analysis and the present work, phases of delocalization interrupt the general increase in localization preceding catastrophic failure, indicating that delocalization does not necessarily indicate a reduction of seismic hazard. However, the proportion of the maximum fracture volume to the total fracture volume does not increase monotonically. Experiments with higher confining stress tend to experience greater localization. To further quantify localization, we compare the geometry of the largest fractures, with volumes >90th percentile, to the best fit plane through these fractures immediately preceding failure. The r2 scores and the mean distance of the fractures to the plane indicate greater localization in monzonite than in granite. The smaller mean mineral diameter and lower confining stress in the granite experiments may contribute to this result. Tracking these various metrics of localization reveals a close association between macroscopic yielding and the acceleration of fracture network localization. Near yielding, ∑v90/vtot and the Gini coefficient increase while the mean distance to the final failure plane decreases. Macroscopic yielding thus occurs when the rate of fracture network localization increases.

scholarly journals A Meta-Analysis of the Association between Income Inequality and Intergenerational Mobility

2019 ◽  
Vol 5 ◽  
pp. 237802311988128 ◽  
Author(s):  
Ernesto F. L. Amaral ◽  
Shih-Keng Yen ◽  
Sharron Xuanren Wang-Goodman
Keyword(s):  
Income Inequality ◽  
Gini Coefficient ◽  
Intergenerational Mobility ◽  
Meta Analysis ◽  
The United States ◽  
Income Share ◽  
Income Elasticities ◽  
Great Gatsby ◽  
The Gini Coefficient ◽  
Over Time

We provide an overview of associations between income inequality and intergenerational mobility in the United States, Canada, and eight European countries. We analyze whether this correlation is observed across and within countries over time. We investigate Great Gatsby curves and perform metaregression analyses based on several papers on this topic. Results suggest that countries with high levels of inequality tend to have lower levels of mobility. Intergenerational income elasticities have stronger associations with the Gini coefficient compared to associations with the top 1 percent income share. Once models are controlled for methodological variables, country indicators, and paper indicators, correlations of mobility with the Gini coefficient lose significance but not with the top 1 percent income share. This result is an indication that recent increases in inequality at the top of the distribution might be negatively affecting mobility on a greater magnitude compared to variations across the income distribution.

Kentucky Wealth at the End of the Eighteenth Century

1983 ◽  
Vol 43 (3) ◽  
pp. 617-633 ◽  
Author(s):  
Lee Soltow
Keyword(s):  
Eighteenth Century ◽  
Civil War ◽  
Gini Coefficient ◽  
Relative Distribution ◽  
Relative Inequality ◽  
Distribution Of Wealth ◽  
The Gini Coefficient ◽  
Per Capita ◽  
Real Wealth

The Kentucky tax lists from 1800 to 1860 permit a study of both the trend in per capita wealth and the change in the relative distribution of wealth in this strategic state from the time of its inception to the Civil War. Real wealth per capita is judged to have increased 2.4 percent a year from 1800 to 1840, and 2.8 percent from 1840 to 1860. Relative inequality remained roughly constant, with the Gini coefficient .80 in both 1800 and 1860 for adult free males. The number of properties held by various individuals was surprisingly unequal in distribution in 1800 and 1820.

Inequality and COVID-19 Deaths and Cases in the First Wave, a Cross-Country Analysis Economic

2020 ◽  
Vol COVID-19 ◽  
pp. e2020157
Author(s):  
James B. Davies
Keyword(s):  
Gini Coefficient ◽  
The United States ◽  
Significant Positive Effect ◽  
Poverty Measure ◽  
Full Sample ◽  
Positive Effects ◽  
The Gini Coefficient ◽  
Per Capita ◽  
Cross Country ◽  
Positive Effect

The cross-country relationship of COVID-19 case and death rates with previously measured income inequality and poverty in the pandemic’s first wave is studied, controlling for other underlying factors, in a worldwide sample of countries. If the estimated associations are interpreted as causal, the Gini coefficient for income has a significant positive effect on both cases and deaths per capita in regressions using the full sample, and for cases although not for deaths when OECD and non-OECD subsamples are treated separately. The Gini coefficient for wealth has a significant positive effect on cases, but not on deaths, in both subsamples and the full sample. Poverty generally has weak positive effects in the full and non-OECD samples, but a relative poverty measure has a strong positive effect on cases in the OECD sample. Analysis of the gap between COVID-19 first-wave cases and deaths per capita in Canada and the higher rates in the United States indicates that 37% of the cases gap and 28% of the deaths gap could be attributed to the higher income Gini in the U.S. according to the full sample regressions.

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