Application of Salagean and Ruscheweyh Operators on Univalent Holomorphic Functions with Finitely many Coefficients

MSC 2010: 30C45, 30C50

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.

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.

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)

Estimating male and female height inequality

This study investigates the coefficient of variation (CV) of height of males and females as a measure of inequality. We have collected a data set on corresponding male and female height CVs from 124 populations, spanning the period between the 1840s and 1980s. The results suggest that the R2 between the two CVs is 0.39, with the male CV being greater, indicating higher plasticity.

Inequality and GM crops: a case study of Bt cotton in India

Critics of genetically modified (GM) crops often contend that their introduction enhances the gap between rich and poor farmers, as the former group are in the best position to afford the expensive seed as well as provide other inputs such as fertilizer and irrigation. The research reported in this paper explores this issue with regard to Bt cotton (cotton with the endotoxtin gene from Bacillus thuringiensis conferring resistance to some insect pests) in Jalgaon, Maharashtra State, India, spanning the 2002 and 2003 seasons. Questionnaire–based survey results from 63 non–adopting and 94 adopting households of Bt cotton were analyzed, spanning 137 Bt cotton plots and 95 non–Bt cotton plots of both Bt adopters and non–adopters. For these households, cotton income accounted for 85 to 88% of total household income, and is thus of vital importance. Results suggest that in 2003 Bt adopting households have significantly more income from cotton than do non–adopting households (Rp 66,872 versus Rp 46,351) but inequality in cotton income, measured with the Gini coefficient (G), was greater amongst non–adopters than adopters. While Bt adopters had greater acreage of cotton in 2003 (9.92 acres versus 7.42 for non–adopters), the respective values of G were comparable. The main reason for the lessening of inequality amongst adopters would appear to be the consistency in the performance of Bt cotton along with the preferred non–Bt cultivar of Bt adopters—Bunny. Taking gross margin as the basis for comparison, Bt plots had 2.5 times the gross margin of non–Bt plots of non–adopters, while the advantage of Bt plots over non–Bt plots of adopters was 1.6 times. Measured in terms of the Gini coefficient of gross margin/acre it was apparent that inequality was lessened with the adoption of Bunny (G = 0.47) and Bt (G = 0.3) relative to all other non–Bt plots (G = 0.63). Hence the issue of equality needs to be seen both in terms of differences between adopters and non–adopters as well as within each of the groups.

Growth and Inequality : A study of Swedish municipalities

This paper explores the relationship between the growth rate of the average income and income inequality using data at the municipal level in Sweden for the period 1992-2007. We estimate a fixed effects panel data growth model where the within-municipality income inequality is one of the explanatory variables. Different inequality measures (Gini coefficient, top income shares, and measures of inequality in the lower and upper ends of the income distribution) are also examined. We find a positive and significant relationship between income growth and income inequality, measured as the Gini coefficient and top income shares, respectively. In addition, while inequality at the upper end of the income distribution is positively associated with the income growth rate, inequality at the lower end of the income distribution seems to be negatively related to the growth rate. Our findings also suggest that increased income inequality enhances growth more in municipalities with a high level of average income than in those with a low level of average income.

Inflation and income inequality: a shopping-time aproach: (Forthcoming, Journal of Development Economics)

Our work is based on a simpliÖed heterogenous-agent shoppingtime economy in which economic agents present distinct productivities in the production of the consumption good, and di§erentiated access to transacting assets. The purpose of the model is to investigate whether, by focusing the analysis solely on endogenously determined shopping times, one can generate a positive correlation between ináation and income inequality. Our main result is to show that, provided the productivity of the interest-bearing asset in the transacting technology is high enough, it is true true that a positive link between ináation and income inequality is generated. Our next step is to show, through analysis of the steady-state equations, that our approach can be interpreted as a mirror image of the usual ináation-tax argument for income concentration. An example is o§ered to illustrate the mechanism.

On the positive correlation between income inequality and unemployment

Several empirical studies in the literature have documented the existence of a positive correlation between income inequalitiy and unemployment. I provide a theoretical framework under which this correlation can be better understood. The analysis is based on a dynamic job search under uncertainty. I start by proving the uniqueness of a stationary distribution of wages in the economy. Drawing upon this distribution, I provide a general expression for the Gini coefficient of income inequality. The expression has the advantage of not requiring a particular specification of the distribution of wage offers. Next, I show how the Gini coefficient varies as a function of the parameters of the model, and how it can be expected to be positively correlated with the rate of unemployment. Two examples are offered. The first, of a technical nature, to show that the convergence of the measures implied by the underlying Markov process can fail in some cases. The second, to provide a quantitative assessment of the model and of the mechanism linking unemployment and inequality.

A theoretical connection between inflation and income inequality

This work investigates the effects of inflation on income distribution. We use a dynamic shopping-time model to show that a differentiated access to transacting technologies by poor and rich consumers is enough to generate a positive link between inflation and the Gini coefficient of income distribution.

Measurements of income inequality: a critique. Inflation and income inequality: a link through the job- search process

In this paper I claim that, in a long-run perspective, measurements of income inequality, under any of the usual inequality measures used in the literature, are upward biased. The reason is that such measurements are cross-sectional by nature and, therefore, do not take into consideration the turnover in the job market which, in the long run, equalizes within-group (e.g., same-education groups) inequalities. Using a job-search model, I show how to derive the within-group invariant-distribution Gini coefficient of income inequality, how to calculate the size of the bias and how to organize the data in arder to solve the problem. Two examples are provided to illustrate the argument.

Determinants of unfair inequality in Brazil, 1995 and 2009

This article analyses the trend of unfair inequality in Brazil (1995-2009) using a nonparametric approach to estimate the income function. The entropy metrics introduced by Li, Maasoumi and Racine (2009) are used to quantify income differences separately for each effort variable. A Gini coefficient of unfair inequality is calculated, based on the fitted values of the non-parametric estimation; and the robustness of the estimations, including circumstantial variables, is analysed. The trend of the entropies demonstrated a reduction in the income differential caused by education. The variables “hours worked” and “labour-market status” contribute significantly to explaining wage differences imputed to individual effort; but the migratory variable had little explanatory power. Lastly, the robustness analysis demonstrated the plausibility of the results obtained at each stage of the empirical work.

Income inequality and elderly self-rated health in Sao Paulo, Brazil

Purpose: To test the association between income inequality and elderly self-rated health and to propose a pathway to explain the relationship. Methods: We analyzed a sample of 2143 older individuals (60 years of age and over) from 49 distritos of the Municipality of Sao Paulo, Brazil. Bayesian multilevel logistic models were performed with poor self-rated health as the outcome variable. Results: Income inequality (measured by the Gini coefficient) was found to be associated with poor self-rated health after controlling for age, sex, income and education (odds ratio, 1.19; 95% credible interval, 1.01-1.38). When the practice of physical exercise and homicide rate were added to the model, the Gini coefficient lost its statistical significance (P>.05). We fitted a structural equation model in which income inequality affects elderly health by a pathway mediated by violence and practice of physical exercise. Conclusions: The health of older individuals may be highly susceptible to the socioeconomic environment of residence, specifically to the local distribution of income. We propose that this association may be mediated by fear of violence and lack of physical activity. (C) 2012 Elsevier Inc. All rights reserved.

Cause-specific mortality and income inequality in Sao Paulo, Brazil

OBJECTIVE: To analyze cause-specifi c mortality rates according to the relative income hypothesis. METHODS: All 96 administrative areas of the city of Sao Paulo, southeastern Brazil, were divided into two groups based on the Gini coefficient of income inequality: high (>= 0.25) and low (<0.25). The propensity score matching method was applied to control for confounders associated with socioeconomic differences among areas. RESULTS: The difference between high and low income inequality areas was statistically significant for homicide (8.57 per 10,000; 95% CI: 2.60; 14.53); ischemic heart disease (5.47 per 10,000 [95% CI 0.76; 10.17]); HIV/AIDS (3.58 per 10,000 [95% CI 0.58; 6.57]); and respiratory diseases (3.56 per 10,000 [95% CI 0.18; 6.94]). The ten most common causes of death accounted for 72.30% of the mortality difference. Infant mortality also had signifi cantly higher age-adjusted rates in high inequality areas (2.80 per 10,000 [95% CI 0.86; 4.74]), as well as among males (27.37 per 10,000 [95% CI 6.19; 48.55]) and females (15.07 per 10,000 [95% CI 3.65; 26.48]). CONCLUSIONS: The study results support the relative income hypothesis. After propensity score matching cause-specifi c mortality rates was higher in more unequal areas. Studies on income inequality in smaller areas should take proper accounting of heterogeneity of social and demographic characteristics.

Cause-specific mortality and income inequality in São Paulo, Brazil

OBJECTIVE: To analyze cause-specific mortality rates according to the relative income hypothesis. METHODS: All 96 administrative areas of the city of São Paulo, southeastern Brazil, were divided into two groups based on the Gini coefficient of income inequality: high (>0.25) and low (<0.25). The propensity score matching method was applied to control for confounders associated with socioeconomic differences among areas. RESULTS: The difference between high and low income inequality areas was statistically significant for homicide (8.57 per 10,000; 95%CI: 2.60;14.53); ischemic heart disease (5.47 per 10,000 [95%CI 0.76;10.17]); HIV/AIDS (3.58 per 10,000 [95%CI 0.58;6.57]); and respiratory diseases (3.56 per 10,000 [95%CI 0.18;6.94]). The ten most common causes of death accounted for 72.30% of the mortality difference. Infant mortality also had significantly higher age-adjusted rates in high inequality areas (2.80 per 10,000 [95%CI 0.86;4.74]), as well as among males (27.37 per 10,000 [95%CI 6.19;48.55]) and females (15.07 per 10,000 [95%CI 3.65;26.48]). CONCLUSIONS: The study results support the relative income hypothesis. After propensity score matching cause-specific mortality rates was higher in more unequal areas. Studies on income inequality in smaller areas should take proper accounting of heterogeneity of social and demographic characteristics.