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This file contains additional information such as Exif metadata which may have been added by the digital camera, scanner, or software program used to create or digitize it. If the file has been modified from its original state, some details such as the timestamp may not fully reflect those of the original file. The timestamp is only as accurate as the clock in the camera, and it may be completely wrong. the Gini coefficients of 1980 and 1988, and they were 0.25 and 0.30. [4] estimated the Gini coefficients for 1993 and 1997, which were 0.375 and 0.39. [5] calcu- lated the Gini coefficients of 1988 and 1995 based on the household sampling survey, and the values were 0.382 and 0.445. The study empirically analyzed Chinese in- The Gini coefficient (Gini 1912) is a popular measure of statistical dispersion, especially used for analyzing inequality or concentration of income, wealth or sales turnover of competing firms. The coefficient ( G) varies between 0 (no inequality/concentration) and 1 (complete inequality/concentration). The more we introduce different social groups in the distribution, the higher is the corresponding Gini coefficient. See formulas and technical appendix. 0.20 0.60 0.80 Gini coefficient: 2 group distribution (bottom 90%, top 10%) 0.40 0.50 Gini coefficient: 3 group distribution (bottom 50%, following 40%, top 10%) 0.33 0.56 I would recommend that you start with Stata's own documentation, including the help files and PDF documents. Most user-written packages for Stata, such as those found with findit gini, also come with a help file that explains how they should be used. For the packages mentioned by Carlo and William, you can read the help files with the following The Gini coefficients of the main developed countries are between 0.24 and 0.36, and the United States' is a little higher to 0.4. China's Gini coefficient was 0.412 in 2000, and the value climbed up to 0.465 in 2004 as reported by National Statistic Bureau (NSB). In June 2006, China Economic Weekly reported, "Our nation's Gini recent years, the Gini coefficient has been used to study cases of uneven distribution of resources and inequality in energy consumption. The importance of the measure in public health and epidemiology has also been recognized. In this study, Gini coefficients are calculated using the following formula: ∑n i=1 i i-1 i i-1 G =1- (N +N )(M -M ) [1] The graph shows that the Gini coefficient is equal to the area marked A divided by the sum of the areas marked A and B, that is, Gini = A/ (A + B). It is also equal to 2 A and to 1 − 2B due to the fact that A + B = 0.5 (since the axes scale from 0 to 1). The process includes selecting the maximal number of connections for a node (or an entity) of each cluster; selecting the top 5 of the data elements in clusters obtained by performing first step; and using the abovementioned Gini formula to determine the depth (≥0.4) or the breadth (<0.4) of a specific author research domain. Interested readers are suggested to click the hyperlink for calculating the Gini coefficient on their own at the reference. From the Lorenz cur