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.

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.

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.

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.