Using the Gini coefficient with BIOLOG substrate utilisation data to provide an alternative quantitative measure for comparing bacterial soil communities

A measure quantifying unequal use of carbon sources, the Gini coefficient (G), has been developed to allow comparisons of the observed functional diversity of bacterial soil communities. This approach was applied to the analysis of substrate utilisation data obtained from using BIOLOG microtiter plates in a study which compared decomposition processes in two contrasting plant substrates in two different soils. The relevance of applying the Gini coefficient as a measure of observed functional diversity, for soil bacterial communities is evaluated against the Shannon index (H) and average well colour development (AWCD), a measure of the total microbial activity. Correlation analysis and analysis of variance of the experimental data show that the Gini coefficient, the Shannon index and AWCD provided similar information when used in isolation. However, analyses based on the Gini coefficient and the Shannon index, when total activity on the microtiter plates was maintained constant (i.e. AWCD as a covariate), indicate that additional information about the distribution of carbon sources being utilised can be obtained. We demonstrate that the Lorenz curve and its measure of inequality, the Gini coefficient, provides not only comparable information to AWCD and the Shannon index but when used together with AWCD encompasses measures of total microbial activity and absorbance inequality across all the carbon sources. This information is especially relevant for comparing the observed functional diversity of soil microbial communities.

Comparative Analysis of Evolving Software Systems Using the Gini Coefficient

Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics --- for example, in terms of "average" values --- can be highly misleading. Many metrics, it turns out, are distributed like wealth --- with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.

Gini coefficient, dissimilarity index and Lorenz curve for the spanish port system by type of goods

This paper shows the Gini Coefficient, the dissimilarity Index and the Lorenz Curve for the Spanish Port System by type of goods from 1960 to the year 2010 for business units: Total traffic, Liquid bulk cargo, Solid bulk cargo, General Merchandise and Container (TEUs) with the aim of carcaterizar the Spanish port systems in these periods and propose future strategies.

An R script to plot the conditioned Gini (or skewness) coefficient in the bivariate case

In the cs.index.zip file we provide an R script which let us plot the conditioned Gini (or skewness) coefficient used in the working paper entitled "On conditional skewness with applications in environmental data" submitted to Environmental and Ecological Statistics. On the other hand, the ReadMe.pdf explains how to use the cs.index.R script.

Aportes para examinar las desigualdades educativas en los grandes centros urbanos : El uso del coeficiente de Gini para analizar la distribución del abandono escolar entre las escuelas secundarias de la Ciudad Autónoma de Buenos Aires

La Ley de Educación Nacional promulgada en 2006 en Argentina establece la obligatoriedad de la educación secundaria en todo el país, la Ciudad Autónoma de Buenos Aires ha sido pionera al respecto estableciendo esta meta en el año 2002. En este marco, se propone analizar los desafíos que este tipo de política enfrenta en la actualidad, centrando la atención en la capacidad de los sistemas e instituciones educativas para garantizar este derecho en términos de acceso, permanencia y promoción de los alumnos en el nivel secundario. Este trabajo tiene por objeto plantear un ejercicio de análisis del abandono escolar que ocurre durante el ciclo lectivo en las escuelas a partir de la consideración del análisis del porcentaje de alumnos "salidos sin pase", e incorporar, a la vez, una medida de dispersión (coeficiente de Gini) que permita visibilizar las desigualdades entre escuelas a nivel territorial en la Ciudad Autónoma de Buenos Aires

Aportes para examinar las desigualdades educativas en los grandes centros urbanos : El uso del coeficiente de Gini para analizar la distribución del abandono escolar entre las escuelas secundarias de la Ciudad Autónoma de Buenos Aires

La Ley de Educación Nacional promulgada en 2006 en Argentina establece la obligatoriedad de la educación secundaria en todo el país, la Ciudad Autónoma de Buenos Aires ha sido pionera al respecto estableciendo esta meta en el año 2002. En este marco, se propone analizar los desafíos que este tipo de política enfrenta en la actualidad, centrando la atención en la capacidad de los sistemas e instituciones educativas para garantizar este derecho en términos de acceso, permanencia y promoción de los alumnos en el nivel secundario. Este trabajo tiene por objeto plantear un ejercicio de análisis del abandono escolar que ocurre durante el ciclo lectivo en las escuelas a partir de la consideración del análisis del porcentaje de alumnos "salidos sin pase", e incorporar, a la vez, una medida de dispersión (coeficiente de Gini) que permita visibilizar las desigualdades entre escuelas a nivel territorial en la Ciudad Autónoma de Buenos Aires

Aportes para examinar las desigualdades educativas en los grandes centros urbanos : El uso del coeficiente de Gini para analizar la distribución del abandono escolar entre las escuelas secundarias de la Ciudad Autónoma de Buenos Aires

La Ley de Educación Nacional promulgada en 2006 en Argentina establece la obligatoriedad de la educación secundaria en todo el país, la Ciudad Autónoma de Buenos Aires ha sido pionera al respecto estableciendo esta meta en el año 2002. En este marco, se propone analizar los desafíos que este tipo de política enfrenta en la actualidad, centrando la atención en la capacidad de los sistemas e instituciones educativas para garantizar este derecho en términos de acceso, permanencia y promoción de los alumnos en el nivel secundario. Este trabajo tiene por objeto plantear un ejercicio de análisis del abandono escolar que ocurre durante el ciclo lectivo en las escuelas a partir de la consideración del análisis del porcentaje de alumnos "salidos sin pase", e incorporar, a la vez, una medida de dispersión (coeficiente de Gini) que permita visibilizar las desigualdades entre escuelas a nivel territorial en la Ciudad Autónoma de Buenos Aires

Practical Modified Gini Index. ACES Working Papers, 2012

The Gini index is the most common method for estimating the level of income inequality in countries. In this paper we suggest a simple modification that takes into account the moderating effect of in-kind government benefits. Unlike other studies that use micro level data that is rarely available for many countries or over a period of time, the proposed modified Gini index could be calculated using just the regularly available data for each country. Such data includes the original Gini coefficient, government consumption expenditures, GDP and total tax revenue as a percentage of GDP. This modified version of the Gini index allows us to calculate the level of inequality more precisely, and make better comparisons between countries and over time.

Global, regional, and national age–sex specifi c all-cause and cause-specifi c mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Background Up-to-date evidence on levels and trends for age-sex-specific all-cause and cause-specific mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specific all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specific causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65·3 years (UI 65·0–65·6) in 1990, to 71·5 years (UI 71·0–71·9) in 2013, while the number of deaths increased from 47·5 million (UI 46·8–48·2) to 54·9 million (UI 53·6–56·3) over the same interval. Global progress masked variation by age and sex: for children, average absolute differences between countries decreased but relative differences increased. For women aged 25–39 years and older than 75 years and for men aged 20–49 years and 65 years and older, both absolute and relative differences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10·7%, from 4·3 million deaths in 1990 to 4·8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specific mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.

Hyvinvointivaltion optimaalisen köyhyyspolitiikan määrittelyä

The purpose of this research is to identify the optimal poverty policy for a welfare state. Poverty is defined by income. Policies for reducing poverty are considered primary, and those for reducing inequality secondary. Poverty is seen as a function of the income transfer system within a welfare state. This research presents a method for optimising this function for the purposes of reducing poverty. It is also implemented in the representative population sample within the Income Distribution Data. SOMA simulation model is used. The iterative simulation process is continued until a level of poverty is reached at which improvements can no longer be made. Expenditures and taxes are kept in balance during the process. The result consists of two programmes. The first programme (social assistance programme) was formulated using five social assistance parameters, all of which dealt with the norms of social assistance for adults (€/month). In the second programme (basic benefits programme), in which social assistance was frozen at the legislative level of 2003, the parameter with the strongest poverty reduction effect turned out to be one of the basic unemployment allowances. This was followed by the norm of the national pension for a single person, two parameters related to housing allowance, and the norm for financial aid for students of higher education institutions. The most effective financing parameter measured by gini-coefficient in all programmes was the percent of capital taxation. Furthermore, these programmes can also be examined in relation to their costs. The social assistance programme is significantly cheaper than the basic benefits programme, and therefore with regard to poverty, the social assistance programme is more cost effective than the basic benefits programme. Therefore, public demand for raising the level of basic benefits does not seem to correspond to the most cost effective poverty policy. Raising basic benefits has most effect on reducing poverty within the group of people whose basic benefits are raised. Raising social assistance, on the other hand, seems to have a strong influence on the poverty of all population groups. The most significant outcome of this research is the development of a method through which a welfare state’s income transfer-based safety net, which has severely deteriorated in recent decades, might be mended. The only way of doing so involves either social assistance or some forms of basic benefits and supplementing these by modifying social assistance.

Income Inequality and Healt: A Multi-Country Analysis

This paper investigates the effect of income inequality on health status. A model of health status was specified in which the main variables were income level, income inequality, the level of savings and the level of education. The model was estimated using a panel data set for 44 countries covering six time periods. The results indicate that income inequality (measured by the Gini coefficient) has a significant effect on health status when we control for the levels of income, savings and education. The relationship is consistent regardless of the specification of health status and income. Thus, the study results provide some empirical support for the income inequality hypothesis.

Aproximación a las causas de la desigualdad económica

Resumen: El siguiente ensayo buscará presentar un enfoque alternativo al problema de la desigualdad económica. Abarcando cuestiones relacionadas con el análisis macroeconómico del desarrollo económico, la distribución de la renta entre trabajadores; la visión regional de la economía y las conexiones internacionales del desarrollo. Para ello, se establecieron como hipótesis el crecimiento económico y la inflación. Además, se analizará el impacto de un aumento en la PEA sobre la distribución del ingreso. Para terminar, el autor arribará a numerosas conclusiones, entre ellas, la dicotomía entre corto y largo plazo para la inflación, el aumento de la desigualdad ante el aumento del PBI en países subdesarrollados, y la relación inversa entre la PEA y el Índice de Gini.

Income, inequality, and criteria air pollutants in the CAMA counties

Socioeconomic factors have long been incorporated into environmental research to examine the effects of human dimensions on coastal natural resources. Boyce (1994) proposed that inequality is a cause of environmental degradation and the Environmental Kuznets Curve is a proposed relationship that income or GDP per capita is related with initial increases in pollution followed by subsequent decreases (Torras and Boyce, 1998). To further examine this relationship within the CAMA counties, the emission of sulfur dioxide and nitrogen oxides, as measured by the EPA in terms of tons emitted, the Gini Coefficient, and income per capita were examined for the year of 1999. A quadratic regression was utilized and the results did not indicate that inequality, as measured by the Gini Coefficient, was significantly related to the level of criteria air pollutants within each county. Additionally, the results did not indicate the existence of the Environmental Kuznets Curve. Further analysis of spatial autocorrelation using ArcMap 9.2, found a high level of spatial autocorrelation among pollution emissions indicating that relation to other counties may be more important to the level of sulfur dioxide and nitrogen oxide emissions than income per capita and inequality. Lastly, the paper concludes that further Environmental Kuznets Curve and income inequality analyses in regards to air pollutant levels incorporate spatial patterns as well as other explanatory variables. (PDF contains 4 pages)

Analysis of competition in the wholesale and retail markets for fish in Kisumu, Kenya

This study analyses competition in the wholesale and retail fish marketing system in Kisumu, which is Kenya's largest fish market. It is based on cross sectional and time series primary data collected in a survey involving 88 retailers and 47 wholesale traders of fish in the town. Stratified random sampling method was used in selecting the respondents, Concentration ratios, Lorenz curves and Gini coefficients are derived and evaluated for both markets. They demonstrate that market shares are unequally distributed among the wholesalers and retailers. The Gini coefficients are 0.37 and 0.45 for the whole and retail markets respectively. Based on a Gini coefficient cut-off level of 0.4, it is concluded that the wholesale fish market exhibits effective competition while the retail outlet has oligopolistic tendencies. The implication of this level of competition to price efficiency is discussed. Intervention measures to enhance competition in the market are recommended.

Differences in performance among four indices used to evaluate diversity in planktonic ecosystems

The performance of four common estimators of diversity are investigated using calanoid copepod data from the Continuous Plankton Recorder (CPR) survey. The region of the North Atlantic and the North Sea was divided into squares of 400 nautical miles for each 2-month period. For each 144 possible cases, Pielou's pooled quadrat method was performed with the aims of determining asymptotic diversity and investigating the CPR sample-size dependence of diversity estimators. It is shown that the performance of diversity indices may greatly vary in space and time (at a seasonal scale). This dependence is more pronounced in higher diverse environments and when the sample size is small. Despite results showing that all estimators underestimate the `actual' diversity, comparison of sites remained reliable from a few pooled CPR samples. Using more than one CPR sample, the Gini coefficient appears to be a better diversity estimator than any other indices and spatial or temporal comparisons are highly satisfactory. In situations where comparative studies are needed but only one CPR sample is available, taxonomic richness was the preferred method of estimating diversity. Recommendations are proposed to maximise the efficiency of diversity estimations with the CPR data.