The Gini Coefficient is a popular and a widely used index for measuring inequality. But it does not express explicitly a parameter or a value judgment to reflect the opinion of authorities or researches. A generalization of this index known as extended Gini Coefficient is introduced to explain different versions of inequality. For decomposing of inequality there are two approaches. First, using a decomposition of Gini Coefficient to income components dissent the relative importance of the major income components. Following such decomposition, the marginal impact on inequality due to a change in income or consumption for specific source estimates the Gini income elasticity. The second approach is decomposition of Gini Coefficient by subgroups of population. This approach is applicable when the subgroups do not overlap. This article in tends to analyze the structure of distribution of income in Iran by using these approaches.

Background: Each country’s health system is tasked with providing services to ensure the health of its population. Dental services are among these essential services. However, dental service utilization is unevenly distributed worldwide. This study’s findings will give health sector officials and policymakers valuable insights to enhance system performance.Methods: This descriptive-analytical study, conducted in 2021, aimed to determine the distribution of dentists worldwide. The analysis utilized the Pareto curve, the dentist-to-population ratio (DPR), the Gini Coefficient (GC), and the Lorenz curve. The goal was to identify the countries with the highest number of dentists.Results: There are over 2,500,000 dentists globally, averaging 3.6 dentists per 10,000 people. However, the ratios vary significantly, with about 6.8 per 10,000 in countries with very high Human Development Index (HDI) groups and 0.19 per 10,000 in low HDI groups. The data indicates that more than 80% of dentists work in countries with very high and high HDI.Conclusion: This study suggests that job concentration is higher in areas with superior welfare and facilities, and dentistry is no exception. Therefore, creating better welfare in more disadvantaged areas, implementing mandatory government plans, and strategic government planning can help mitigate distribution inequality.

Reducing income inequality is one of the important goals of every country's economic decision makers. But proper policy making requires exact and correct information about the severity of this inequality. Since usual income inequality indices have some kind of deficiencies, two new indices namely, S-Gini Coefficient and Atkinson-Gini indices are being introduced, their properties reviewed and then estimated based on raw household budget data for 1997-2007. Our findings indicate that household income inequality in rural areas is more severe than urban, while the reverse is true when it comes to the per capita (individual) basis. Furthermore, although the severity of income inequality has been reduced till 2003, but increased afterwards.

Objective The Lorenz curve and the Gini Coefficient were first proposed as tools to measure inequality in the wealth distribution. The increasing application of these tools and their generalized versions in various sciences led to numerous studies of the Lorenz curve and its applications. The main objective of this research is to present and localize the applications of the generalized Lorenz curve and the Gini Coefficient in the insurance industry for the management of insurance portfolio risk, ranking of insurance risk factors, fair pricing of insurance policies, and selection of appropriate insurance policy pricing models.   Methods Data on available compensation and insurance premiums from Bimah-DAY's supplementary health insurance contracts, with start dates ranging from January 21, 2018, to May 12, 2018, were used to validate the study. Insurance risk analysis was performed based on the characteristics of the insured by comparing the relativity index (the ratio of claims paid to premiums received) of the insurance portfolio. The relative importance of the factors for the level of compensation for the insured person was determined by calculating the concentration Coefficient and plotting the Lorenz curves and the corresponding concentration curves. Several pricing models based on insured features were estimated using the generalized least squares (GLM) method. The appropriate model was then selected using the ABC criterion (area enclosed between the Lorenz curve and the concentration curve).   Results The results showed that the relativity index of insurance portfolios varies depending on the characteristics of the insured, such as gender, age, and province of residence. The insurance company's losses from coverage under the treatment completion insurance contracts for 371, 403 policyholders amounted to over 127 billion riyals during the reporting period. About 28 percent of the damage amount is attributed to the male gender (men) and the remaining 72 percent to the female gender (women). An insurance portfolio covering age groups from 0 to 21 years was profitable for the company, whereas it resulted in a loss for individuals over 68 years of age. In five Iranian provinces of Qom, Hormozgan, East Azerbaijan, Razavi-Khorasan, and Kurdistan, the ratio was below one. In other provinces, it was above one. The insurance company's loss per insured individual in Gilan Province is higher than in other provinces, with a ratio of 3. 046. The dependence of the compensation paid on the level of coverage of the insurance contracts is stronger than on the characteristics of the insured person. The gender and place of residence of the insured person had an inconsistent impact on the compensation paid and the insurance risk. In the selected pricing model, the effect of all characteristics of the insured was significant. The influence of age on the insurance company's risk was greater than the influence of the gender of the insured, while the influence of province of residence was greater than the influence of age and gender in some provinces.   Conclusion Due to the significance of the Coefficient of all three factors of gender, age and place of residence in the chosen pricing model and the non-uniformity of the index of relativity of insurance portfolios according to the characteristics of the insured, we concluded that to reduce the losses of the insurance company in the field of supplementary treatment insurance, the application price discrimination policies based on age, gender, etc. required.

This paper is looking to answer the question that does the increase of women's employment has impact on impartial income distribution and standard of living and welfare in Iranian society. In order to answer this question, an appropriate model for the Gini Coefficient, as the most commonly index of income inequality, is used and the effect of women's employment on the Gini Coefficient in Iran is examined for the period 1991-2008 using Vector Autoregressive (VAR) Method. The results show that an increase in women's employment, and increase of governmental tax revenues and its consumption spending lead to a reduction of Gini Coefficient and actually results in unfair distribution of income. In fact, considering the increase of educated women in Iran’s society, by increasing the employment opportunities for women, we can see a more equitable distribution of income in society and an increase of the level of welfare.

Unidimensional inequality has been estimated many times; but individual well-being is a multi-dimensional concept that depends not only on income but also on other benefits such as education, health, and housing. The main aim in this research has been to evaluate the inequality concerning commodity groups in the household basket and compare the multi-dimensional inequality with that of one-dimensional (total expenditure) inequality for urban, and rural areas as well as the country as a whole. Therefore, the Multidimensional Gini Coefficient is estimated according Kumar Banerjee (2010) during the period 1984-2021. Doing so, we have used the household income-expenditure microdata collected in the 9 commodity groups of food, clothing, housing, services, health, recreation and entertainment, education, transportation and communication, and others by Statistical Center of Iran.  The results indicate that the multi-dimensional inequality trend have been increasing for urban and rural so the country as a whole, while the one-dimensional expenditure(unidimensional) inequality shows a decreasing trend and the gap between trends were also increasing. The Multidimensional inequality headways been more than that of unidimensional inequality level as has been expected, which more corresponds with real evidence in Iran.JEL Classification: D3, I14, I24, I3

The main reason of this study is investigation in effect of the number of large industrial segments on Gini Coefficient from 1974 to 2012. To this end, linear and nonlinear models are estimated. The results show that nonlinear model has more explanatory power than linear model. We defined three threshold include up, middle and down threshold, with regard to acceptance of nonlinear model. The results also show that variation in the number of industrial segment of last period has the most effect on current Gini Coefficient in low threshold and is reduced with moving toward up threshold. The results also show that variation in the number of industrial segment of last period has the most effect on current Gini Coefficient in low threshold and is reduced with moving toward up threshold.

The aim of this study is to investigate the short-run asymmetric effect of Gini Coefficient, unemployment and divorce on the prior period robbery, using Error Correction Smooth Transition Model for the Period 1984-2012. For this purpose, at first we investigate data constant and long-run relationship using Engel  and Granger hypothesis. The results show that non-linear model has higher explanatory power than the linear model, so that the change in the Gini Coefficient and unemployment of prior period on the current period's robbery in middle and high threshold for both variables have the greatest effect. However, for divorce cases, based on the results of the three thresholds, there is a positive effect on robbery, though, in high threshold it has the greatest effect.

This paper proposes a very simple way to estimate the Gini Coefficient, particularly when the data are divided into K open-ended income intervals without information about the interval mean incomes: The measure proposed is based upon the assumption that income is distributed according to the log-logistic distribution. This assumption can be readily checked using a simple plotting procedure and the Gini can then be estimated by applying OLS to the plot. The model is shown to work very well for two very different sets of data from the UK and Iran. The log-logistic has the following important implication in the present context. Any income level (y) divides the population into two parts. The ratio of the population with income less than or equal to y to those with income more than y is a log-linear function of the y .The slope of the line can be interpreted as an equality index and the inverse of the slope is the well-known Gini Coefficient. This can be compared with the Pareto Law of income distribution, which states that the proportion of people' with income greater than y will be a linear function of y on double log scale. However, whereas the Pareto Law applies merely to the upper-tail segment of the income distribution, the log-logistic constitutes a model, which pertains to the complete range of incomes.

In this paper a general view of the Gini decomposition is presented. In order to decompose the Gini Coefficient into within group inequality, between group inequality and transvariation inequality of the Iranian urban and rural areas, the 2006 and 2010 micro-data and Dagum (1997) method is used. The results indicate that about 48 percent of the inequality in the country as a whole has been due to within group (urban and rural) inequality. About 26 (2006) and 22 (2010) percent of the inequality has been due to the inequality between urban and rural, while 27 (2006) and 29 (2010) percent of the inequality has been because of the transvariation inequality. Although more than half of the inequality has been caused by the gross inequality between urban and rural areas, ignoring the transvariation inequality, urban inequality has the most contribution of total inequality.