scholarly journals Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Lifeng Wu ◽  
Kai Cai ◽  
Yan Chen
Keyword(s):  
Gini Coefficient ◽  
Northwest China ◽  
Grey Model ◽  
Effective Control ◽  
Grey Prediction ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
The Rich ◽  
The Relationship ◽  
Pm2.5 Concentration

To explore the relationship between the PM2.5 concentration and the gap between the rich and the poor, the PM2.5 concentration in 26 provincial regions of China is predicted by using the Gini coefficient as the independent variable. The nonequigap fractional grey prediction model (CFNGM (1, 1)) is used for data fitting and predicting. The validity of the model is verified by comparing with the traditional nonequidistant grey model. The predicting results show that the PM2.5 concentration in many provinces of China presents a roughly downward trend. In the past nine years, the Gini coefficients have declined in more than 70% of the 26 provinces. However, the development of the Gini coefficient in Northwest China fluctuates greatly and even has an upward trend in recent years. According to the predictive results, reasonable suggestions can be put forward for the effective control of PM2.5 emission in China.

scholarly journals Commentary: Measuring the shape of degree distributions

2013 ◽  
Vol 1 (2) ◽  
pp. 213-225 ◽  
Author(s):  
JENNIFER M. BADHAM
Keyword(s):  
Gini Coefficient ◽  
Degree Distribution ◽  
Fundamental Property ◽  
Standard Measure ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
Different Types ◽  
Network Properties ◽  
The Relationship

AbstractDegree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focuses on degree, the distribution of other node-based network properties could also be described with Gini coefficients.

scholarly journals The Gini Coefficient as a useful measure of malaria inequality among populations

2020 ◽  
Author(s):  
Jonathan Abeles ◽  
David Conway
Keyword(s):  
Global Health ◽  
Confidence Intervals ◽  
Gini Coefficient ◽  
Malaria Incidence ◽  
Potential Range ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
World Regions ◽  
Village Communities ◽  
High Level

BACKGROUND: Understanding inequality in infectious disease burden requires clear and unbiased indicators. The Gini coefficient, conventionally used as a macroeconomic descriptor of inequality, is potentially useful to quantify epidemiological heterogeneity. With a potential range from 0 (all populations equal) to 1 (populations having maximal differences), this coefficient is used here to show the extent and persistence of inequality of malaria infection burden at a wide variety of population levels. METHODS: We first applied the Gini coefficient to quantify variation among WHO world regions for malaria and other major global health problems. Malaria heterogeneity was then measured among countries within the geographical sub-region where burden is greatest, among the major administrative divisions in several of these countries, and among selected local communities. Data were analysed from previous research studies, national surveys, and global reports, and Gini coefficients were calculated together with confidence intervals using bootstrap resampling methods. RESULTS: Malaria showed a very high level of inequality among the world regions (Gini coefficient, G = 0.77, 95% CI 0.66-0.81), more extreme than for any of the other major global health challenges compared at this level. Within the most highly endemic geographical sub-region, there was substantial inequality in estimated malaria incidence among countries of West Africa, which did not decrease between 2010 (G = 0.28, 95% CI 0.19-0.36) and 2018 (G = 0.31, 0.22-0.39). There was a high level of sub-national variation in prevalence among states within Nigeria (G = 0.30, 95% CI 0.26-0.35), but more moderate variation within Ghana (G = 0.18, 95% CI 0.12-0.25) and Sierra Leone (G = 0.17, 95% CI 0.12-0.22). There was also significant inequality in prevalence among local village communities, generally more marked during dry seasons when there was lower mean prevalence. The Gini coefficient correlated strongly with the Coefficient of Variation which has no finite range. CONCLUSIONS: The Gini coefficient is a useful descriptor of epidemiological inequality at all population levels, with confidence intervals and interpretable bounds. Wider use of the coefficient would give broader understanding of malaria heterogeneity revealed by multiple types of studies, surveys and reports, providing more accessible insight from available data.

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 Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity

2018 ◽  
Author(s):  
Sandro Gsteiger ◽  
Nicola Low ◽  
Pam Sonnenberg ◽  
Catherine H Mercer ◽  
Christian L Althaus
Keyword(s):  
Sexual Activity ◽  
Gini Coefficient ◽  
Treatment Strategies ◽  
Mycoplasma Genitalium ◽  
Sti Prevention ◽  
Sexually Transmitted ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
Hpv Types ◽  
Different Levels

AbstractObjectivesGini coefficients have been used to describe the distribution of Chlamydia trachomatis (CT) infections among individuals with different levels of sexual activity. The objectives of this study were to investigate Gini coefficients for different sexually transmitted infections (STIs), and to determine how STI control interventions might affect the Gini coefficient over time.MethodsWe used population-based data for sexually experienced women from two British National Surveys of Sexual Attitudes and Lifestyles (Natsal-2: 1999-2001; Natsal-3: 2010-2012) to calculate Gini coefficients for CT, Mycoplasma genitalium (MG), and human papillomavirus (HPV) types 6, 11, 16 and 18. We applied bootstrap methods to assess uncertainty and to compare Gini coefficients for different STIs. We then used a mathematical model of STI transmission to study how control interventions affect Gini coefficients.ResultsGini coefficients for CT and MG were 0.33 (95% confidence interval (CI): 0.18-0.49) and 0.16 (95% CI: 0.02-0.36), respectively. The relatively small coefficient for MG suggests a longer infectious duration compared with CT. The coefficients for HPV types 6, 11, 16 and 18 ranged from 0.15-0.38. During the decade between Natsal-2 and Natsal-3, the Gini coefficient for CT did not change. The transmission model shows that higher STI treatment rates are expected to reduce prevalence and increase the Gini coefficient of STIs. In contrast, increased condom use reduces STI prevalence but does not affect the Gini coefficient.ConclusionsGini coefficients for STIs can help us to understand the distribution of STIs in the population, according to level of sexual activity, and could be used to inform STI prevention and treatment strategies.Key messagesThe Gini coefficient can be used to describe the distribution of STIs in a population, according to different levels of sexual activity.Gini coefficients for Chlamydia trachomatis (CT) and human papillomavirus (HPV) type 18 appear to be higher than for Mycoplasma genitalium and HPV 6, 11 and 16.Mathematical modelling suggests that CT screening interventions should reduce prevalence and increase the Gini coefficient, whilst condom use reduces prevalence without affecting the Gini coefficient.Changes in Gini coefficients over time could be used to assess the impact of STI prevention and treatment strategies.

Some Variations on Standard Income Measurement Error Models

2004 ◽  
Author(s):  
Brigham B. Frandsen ◽  
James B. McDonald
Keyword(s):  
Measurement Error ◽  
Gini Coefficient ◽  
Measurement Error Models ◽  
The Gini Coefficient ◽  
Parametric Specification ◽  
Income Measurement ◽  
The Difference ◽  
Multiplicative Measurement Error ◽  
The Relationship ◽  
Measures Of Inequality

Measurement error can have a significant impact on measures of inequality. Using a fairly flexible parametric specification of an independent multiplicative measurement error (IMME) model we explore the relationship between changes in the variance of measurement error, for a given mean of measurement error, on the Gini Coefficient. While the measured Gini is greater than the true Gini, the difference decreases as the variance of measurement error decreases. Copulas are used to relax the assumption of independence of measurement error and true income. In this case the measured Gini can be larger or smaller than the true Gini, depending on the correlation between true income and measurement error. Using the same approach with simulations the effect of a different distribution of measurement error is investigated.

Frequency distribution of shoot weight of plants in populations ofTrifolium repenspersisting by clonal growth in grazed pastures

1990 ◽  
Vol 115 (1) ◽  
pp. 41-47 ◽  
Author(s):  
M. J. M. Hay ◽  
V. J. Thomas ◽  
J. L. Brock
Keyword(s):  
Gini Coefficient ◽  
White Clover ◽  
Clonal Growth ◽  
Dry Weight ◽  
Plant Size ◽  
Size Hierarchy ◽  
Gini Coefficients ◽  
Shoot Dry Weight ◽  
The Gini Coefficient ◽  
Variable Nature

SUMMARYOver two years (1984/85 and 1986/87), monthly sampling of shoots of white clover plants compared the populations of white clover in mixed swards at Palmerston North, New Zealand, under set stocking, rotational grazing and a combination of both systems, at a common stocking rate of 22·5 ewe equivalents/ha.The frequency distributions of shoot (or stolon) dry weight per plant in each population over the study period was described by a log-normal model, which indicated that the populations consisted of many small individuals and few large individuals. Such inequality of shoot dry weight within populations is commonly termed size hierarchy; a statistic giving a measure of such size hierarchy is the Gini coefficient. The populations under different managements had similar Gini coefficients which differed little among seasons or between years. Lack of significant correlation between the Gini coefficient and mean shoot dry weight per plant of each population indicated that, in these white clover populations, size hierarchy was independent of mean plant size.These results were considered in relation to the clonal growth of white clover in grazed swards and it is suggested that the variable nature of death of older basal stolons makes an important contribution to the variability in size of individual plants and hence to size hierarchy. As size hierarchy, as assessed by Gini coefficients, was relatively stable in these populations over 3½ years, it appears that clonal growth of white clover incorporates sufficient variability within the growth and death processes at the individual plant level to maintain the size hierarchy, irrespective of variations in mean plant size of populations.

scholarly journals Relationship between Event Prevalence Rate and Gini Coefficient of Predictive Model

2022 ◽  
Vol 14 (1) ◽  
pp. 46
Author(s):  
Fei Han ◽  
Ian Stockwell
Keyword(s):  
Gini Coefficient ◽  
Predictive Models ◽  
Prevalence Rate ◽  
Development Process ◽  
Predictive Performance ◽  
Gini Coefficients ◽  
Quantitative Indices ◽  
C Statistic ◽  
The Relationship ◽  
Crucial Part

Predictive models are currently used for early intervention to help identify patients with a high risk of adverse events. Assessing the accuracy of such models is a crucial part of the development process. To measure the predictive performance of a scoring model, quantitative indices such as the K-S statistic and C-statistic are used. This paper discusses the relationship between Gini coefficients and event prevalence rates. The main contribution of the paper is the theoretical proof of the relationship between the Gini coefficient and event prevalence rate.

Inequalities and political populism: The case of Bulgaria

SEER ◽  
2020 ◽  
Vol 23 (2) ◽  
pp. 233-244
Author(s):  
Lyuboslav Kostov
Keyword(s):  
Eastern Europe ◽  
Gini Coefficient ◽  
Central And Eastern Europe ◽  
Political Rhetoric ◽  
Macroeconomic Shocks ◽  
Theoretical Literature ◽  
The Gini Coefficient ◽  
The Relationship

The article evaluates and analyses the dynamics of inequalities in Bulgaria during 2010-2020 as quantified by a set of particular indicators including the Gini coefficient, the S80/S20 indicator and the share of income held by the richest five per cent. The article examines the relationship between these inequalities and the growth of a certain type of political rhetoric which the literature clearly categorises as populism and which has been rising in central and eastern Europe as in other places elsewhere. In addition, the most up-to-date theoretical literature on these issues is studied and summarised. Social and macroeconomic shocks evidently affect the development of inequalities and, with the global Covid-19 pandemic, we are in the middle of one such set of shocks. The article concludes that a broad public and expert debate is overdue on the problems of inequalities and the consequences of their growth - namely: the development of populist rhetoric - and that reforms are required to reduce inequalities to within parameters that are more socially acceptable as a means of reducing the incidence of populism.

scholarly journals Inequality Measures: The Kolkata Index in Comparison With Other Measures

2020 ◽  
Vol 8 ◽  
Author(s):  
Suchismita Banerjee ◽  
Bikas K. Chakrabarti ◽  
Manipushpak Mitra ◽  
Suresh Mutuswami
Keyword(s):  
Gini Coefficient ◽  
Gini Index ◽  
Empirical Studies ◽  
Nonlinear Function ◽  
Hirsch Index ◽  
Inequality Measures ◽  
Income Distributions ◽  
The Gini Coefficient ◽  
The Rich ◽  
Total Wealth

We provide a survey of the Kolkata index of social inequality, focusing in particular on income inequality. Based on the observation that inequality functions (such as the Lorenz function), giving the measures of income or wealth against that of the population, to be generally nonlinear, we show that the fixed point (like Kolkata index k) of such a nonlinear function (or related, like the complementary Lorenz function) offer better measure of inequality than the average quantities (like Gini index). Indeed the Kolkata index can be viewed as a generalized Hirsch index for a normalized inequality function and gives the fraction k of the total wealth possessed by the rich 1−k fraction of the population. We analyze the structures of the inequality indices for both continuous and discrete income distributions. We also compare the Kolkata index to some other measures like the Gini coefficient and the Pietra index. Lastly, we provide some empirical studies which illustrate the differences between the Kolkata index and the Gini coefficient.

Sign in / Sign up

Export Citation Format

Share Document