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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Rodríguez Esparza, Luz Judith, Dolly Anabel Ortiz Lazcano, Julio César Macías Ponce, and Octavio Martín Maza Díaz Cortés. "Bilateral Gini Index." RBEST: Revista Brasileira de Economia Social e do Trabalho 2 (November 26, 2020): e020010. http://dx.doi.org/10.20396/rbest.v2i..13481.

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In this paper, we have considered a new contextualization of the Gini index, giving a particular interpretation of inequality. The Gini index is the most widely used measure of inequality in the world, however, this index does not meet some desirable properties of an inequality indicator, even so, as it is a measure adopted by most countries through the years, makes it a valuable statistical input; which requires adjustments that provide information, making the study of inequality more robust by adding different indicators that can account for their economic, political, and social environment. This article provides a variation of the Gini index with the purpose of compare and classify different territories (the States of Mexico, as well as some countries) with similar Gini index. The classification is carried out into groups (called turbines) with either positive equality, negative equality, positive inequality or negative inequality. The main contribution of this paper lies on distinguish territories with similar Gini index but different in how privileges and facilities are distributed into them.

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2

Lesser, Lawrence M. "Gini Index; Lottery Lunacy." Mathematics Teacher 106, no. 2 (September 2012): 92–94. http://dx.doi.org/10.5951/mathteacher.106.2.0092.

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Students analyze items from the media to answer mathematical questions related to the article. The Lorenz curve and income disparity are discussed, and some popular misconceptions about the lottery are debunked.

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3

Banerjee, Asis Kumar. "A multidimensional Gini index." Mathematical Social Sciences 60, no. 2 (September 2010): 87–93. http://dx.doi.org/10.1016/j.mathsocsci.2010.06.001.

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4

Malul, Miki, Daniel Shapira, and Amir Shoham. "Practical modified Gini index." Applied Economics Letters 20, no. 4 (March 2013): 324–27. http://dx.doi.org/10.1080/13504851.2012.699182.

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5

Yuan, Ye, Liji Wu, and Xiangmin Zhang. "Gini-Impurity Index Analysis." IEEE Transactions on Information Forensics and Security 16 (2021): 3154–69. http://dx.doi.org/10.1109/tifs.2021.3076932.

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6

Domicolo, Carly, and Hosam Mahmoud. "DEGREE-BASED GINI INDEX FOR GRAPHS." Probability in the Engineering and Informational Sciences 34, no. 2 (March 14, 2019): 157–71. http://dx.doi.org/10.1017/s0269964819000044.

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AbstractIn Balaji and Mahmoud [1], the authors introduced a distance-based Gini index for rooted trees. In this paper, we introduce a degree-based Gini index (or just simply degree Gini index) for graphs. The latter index is a topological measure on a graph capturing the proximity to regular graphs. When applied across the random members of a class of graphs, we can identify an average measure of regularity for the class. Whence, we can compare the classes of graphs from the vantage point of closeness to regularity.We develop a simplified computational formula for the degree Gini index and study its extreme values. We show that the degree Gini index falls in the interval [0, 1). The main focus in our study is the degree Gini index for the class of binary trees. Via a left-packing transformation, we show that, for an arbitrary sequence of binary trees, the Gini index has inferior and superior limits in the interval [0, 1/4]. We also show, via the degree Gini index, that uniform rooted binary trees are more regular than binary search trees grown from random permutations.

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7

Guiasu, Radu Cornel, and Silviu Guiasu. "The Weighted Gini-Simpson Index: Revitalizing an Old Index of Biodiversity." International Journal of Ecology 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/478728.

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The distribution of biodiversity at multiple sites of a region has been traditionally investigated through the additive partitioning of the regional biodiversity into the average within-site biodiversity and the biodiversity among sites. The standard additive partitioning of diversity requires the use of a measure of diversity, which is a concave function of the relative abundance of species, such as the Gini-Simpson index, for instance. Recently, it was noticed that the widely used Gini-Simpson index does not behave well when the number of species is very large. The objective of this paper is to show that the new weighted Gini-Simpson index preserves the qualities of the classic Gini-Simpson index and behaves very well when the number of species is large. The weights allow us to take into account the abundance of species, the phylogenetic distance between species, and the conservation values of species. This measure may also be generalized to pairs of species and, unlike Rao’s index, this measure proves to be a concave function of the joint distribution of the relative abundance of species, being suitable for use in the additive partitioning of biodiversity. The weighted Gini-Simpson index may be easily transformed for use in the multiplicative partitioning of biodiversity as well.

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8

Larraz, Beatriz. "Decomposing the Gini Inequality Index." Sociological Methods & Research 44, no. 3 (August 26, 2014): 508–33. http://dx.doi.org/10.1177/0049124114546046.

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9

Moothathu, T. S. K. "Lorenz Curve and Gini Index." Calcutta Statistical Association Bulletin 40, no. 1-4 (January 1990): 307–24. http://dx.doi.org/10.1177/0008068319900524.

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10

ZHANG, YUJIE, and MINGFENG HE. "THE WEALTH DISTRIBUTION MODEL WITH THE KICKBACK RATE." International Journal of Modern Physics C 19, no. 10 (October 2008): 1555–62. http://dx.doi.org/10.1142/s0129183108013096.

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We define an asset exchange model by adding the kickback rate to the trade, and discuss the Gini index with different kickback rates. It is found that for every kickback rate, the related Gini index tends to be steady; thus, the kickback rate — Gini index curve may be obtained. Furthermore, it is shown that the Gini index decreases when the kickback rate increases, so that the fair degree of social wealth distribution gets better. The Gini index reaches a minimum when the kickback rate is 0.58, and then it increases, as the accretion of the kickback rate destroys the fair degree of social wealth distribution. However, in all situations, the Gini index with kickback rate is less than the one without kickback. This means that the introduction of kickback rate is favorable to the raising of the fair degree of wealth distribution. We also define a moral index similar to the Gini index to weigh the differences of social moral level, and find that the differences of social moral level increase with time for the model with kickback rate.

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11

Setyaningsih, Purbawati, Roikhan Mochamad Aziz, and Puji Hadiyati. "Analysis of Ziswaf, Production Index and Sharia Stock Index on Economic Growth." Jurnal Pasar Modal dan Bisnis 1, no. 2 (December 30, 2019): 221–42. http://dx.doi.org/10.37194/jpmb.v1i2.32.

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This study analyzes the influence ZISWAF, Gini ratio, the total export value, the index of industrial production, sharia stock index investment to GDP growth, in the short and long term. Qualitative data were taken from BPS, Baznas, ACT Global Waqf, the FSA from March 2006 until December 2017 using the methodology of The Error Correction Model (ECM). The results of this study indicate that the variable Gini Ratio, Ziswaf, Total exports, Production Index and Sharia Stock Index on GDP economic growth have significant and positive effects in the long term and the short term. Meaning that these variables have a relationship with GDP economic growth. If the variable decreases or slows down-then GDP economic growth also. While total exports have insignificant effects and negative effects on GDP economic growth. The R-square regression value of the long-term model produces a proportion of 96 percent, the short-term model produces a proportion of 97 percent. Both in the long-run and short-run models, the highest coefficient value is the value of the Gini ratio with 4.941522 and 0.348043. All positive coefficients, Gini ratio variables, ziswaf and production index have a significant effect on gdp, total exports and sharia stock indexes do not have a significant effect on gdp both in the long and short-term models. It implies in the future, fiscal economic policy makers to economic growth that opened a lot of employment, by encouraging resource based economic activities of Indonesia's largest export-oriented agriculture and mining. Good Corporate goverment should do so gini ratio of the areas surrounding the economy improved and people kesejahtaeraan increase.

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12

Jan, Kadavý, Kneifl Michal, Fedorová Barbora, and Bartůněk Jaroslav. "Evaluation of transformation from even-aged to selection forest by means of Gini index." Journal of Forest Science 63, No. 10 (October 20, 2017): 476–84. http://dx.doi.org/10.17221/48/2017-jfs.

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We used the Gini index for evaluation of the 40-year transformation to selection forests in the Training Forest Enterprise Masaryk Forest Křtiny (Czech Republic). The Gini index values for particular forest stands were compared with the reference values derived from the diameter distribution model curve (type E) by Meyer. From a total of sixteen stands which were evaluated in 2013 (the last periodic inventory), only two stands reached the desired diameter structure. Four other stands reached the desired diameter structure at least once during the transformation period. We recommend the application of Gini index for determination of a success rate of even-aged stand transformation to selection forest.

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13

La Haye, Roberta, and Petr Zizler. "The Gini Index and Grayscale Images." College Mathematics Journal 49, no. 3 (April 13, 2018): 205–11. http://dx.doi.org/10.1080/07468342.2018.1426330.

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14

Frees, Edward W. Jed, Glenn Meyers, and A. David Cummings. "Insurance Ratemaking and a Gini Index." Journal of Risk and Insurance 81, no. 2 (March 14, 2013): 335–66. http://dx.doi.org/10.1111/j.1539-6975.2012.01507.x.

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15

Furman, Edward, Yisub Kye, and Jianxi Su. "Computing the Gini index: A note." Economics Letters 185 (December 2019): 108753. http://dx.doi.org/10.1016/j.econlet.2019.108753.

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16

Chandra, B., and P. Paul Varghese. "Fuzzifying Gini Index based decision trees." Expert Systems with Applications 36, no. 4 (May 2009): 8549–59. http://dx.doi.org/10.1016/j.eswa.2008.10.053.

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17

Lv, Xiaofeng, Gupeng Zhang, and Guangyu Ren. "Gini index estimation for lifetime data." Lifetime Data Analysis 23, no. 2 (January 25, 2016): 275–304. http://dx.doi.org/10.1007/s10985-016-9357-0.

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18

Davidson, Russell. "Reliable inference for the Gini index." Journal of Econometrics 150, no. 1 (May 2009): 30–40. http://dx.doi.org/10.1016/j.jeconom.2008.11.004.

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19

Balaji, Hrishikesh, and Hosam Mahmoud. "The Gini index of random trees with an application to caterpillars." Journal of Applied Probability 54, no. 3 (September 2017): 701–9. http://dx.doi.org/10.1017/jpr.2017.28.

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Abstract We propose two distance-based topological indices (level index and Gini index) as measures of disparity within a single tree and within tree classes. The level index and the Gini index of a single tree are measures of balance within the tree. On the other hand, the Gini index for a class of random trees can be used as a comparative measure of balance between tree classes. We establish a general expression for the level index of a tree. We compute the Gini index for two random classes of caterpillar trees and see that a random multinomial model of trees with finite height has a countable number of limits in [0, ⅓], whereas a model with independent level numbers fills the spectrum (0, ⅓].

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20

Hong, Long, Guido Alfani, Chiara Gigliarano, and Marco Bonetti. "Giniinc: A Stata Package for Measuring Inequality from Incomplete Income and Survival Data." Stata Journal: Promoting communications on statistics and Stata 18, no. 3 (September 2018): 692–715. http://dx.doi.org/10.1177/1536867x1801800309.

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Often, observed income and survival data are incomplete because of left- or right-censoring or left- or right-truncation. Measuring inequality (for instance, by the Gini index of concentration) from incomplete data like these will produce biased results. We describe the package giniinc, which contains three independent commands to estimate the Gini concentration index under different conditions. First, survgini computes a test statistic for comparing two (survival) distributions based on the nonparametric estimation of the restricted Gini index for right-censored data, using both asymptotic and permutation inference. Second, survbound computes nonparametric bounds for the unrestricted Gini index from censored data. Finally, survlsl implements maximum likelihood estimation for three commonly used parametric models to estimate the unrestricted Gini index, both from censored and truncated data. We briefly discuss the methods, describe the package, and illustrate its use through simulated data and examples from an oncology and a historical income study.

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21

Zeffora, Joylin, and Shobarani Shobarani. "Optimizing random forest classifier with Jenesis-index on an imbalanced dataset." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 505. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp505-511.

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Random <span>forest is an ensemble algorithm for machine learning. In decision trees, the splitting criteria is built on the prediction of the nodal points and formation of rules by Gini index and Information Gain. Gini index is a measure of inequality. Gini index does not take into consideration the structural changes in the dataset, and inaccurate data can distort the validity of the gini-coefficient. For data with the same feature but different outcomes, the gini-coefficient remained the same. The proposed method for attribute selection measure takes into consideration that there may be structural changes in the dataset overtime and it adapts to such expected changes and maintain the accuracy of the algorithm avoiding under-fitting and over-fitting. A dataset on myocardial infarctions was taken for the study and the results were promising.</span>

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22

Mahinpei, Romina. "A Comparative Analysis of the Gini Index." STEM Fellowship Journal 6, no. 1 (December 1, 2020): 34–52. http://dx.doi.org/10.17975/sfj-2020-010.

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Around the world, the Gini index is used to represent income inequality and is compared between regions. Proposed by Corrado Gini in 1912, the index summarizes the income disparity of an area into a single value that falls between zero and one [1]. There are numerous methods for evaluating the Gini index [2]. Considering its global use, it is essential for these different approaches to provide consistent results for a region. This paper compares the Gini indices obtained using three of the earliest developed methods. These methods include Gini’s original method, the relative mean difference method, and the geometric method. The geometric method, specifically, can be applied either algebraically or geometrically. In this report these three approaches were applied to the 2017 Canadian income distribution from Statistics Canada. To ensure a fair analysis, the methods were also applied to the Canadian income distributions from 1999 and 2010, with their calculations being summarized in Appendices A and B respectively.From the investigation, it was discovered that Gini’s original method and the relative mean difference method, (collectively referred to as the algebraic methods), provided identical results for all three data sets. However, the geometric methods, referring to the Trapezoid Rule and Logger Pro technology, provided values that differed from one another and the algebraic methods. This highlights the importance of acknowledging the method used to derive the Gini Index to ensure consistency and to allow a valid interpretation. The results of this paper also suggest that the algebraic methods should be preferred over the geometric methods when dealing with discrete data to ensure consistent results.

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23

Aliluiko, Andrii, Valerii Yeromenko, and Natalia Stefurak. "ASSESSMENT OF INEQUALITY OF THE POPULATION OF UKRAINE BY SOURCES OF INCOME." INNOVATIVE ECONOMY, no. 3-4 (2021): 98–105. http://dx.doi.org/10.37332/2309-1533.2021.3-4.14.

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Purpose. The aim of the article is the analysis of inequality of the population of Ukraine by sources of income. Methodology of research. A set of general scientific and specific methods of economic research was used in the course of the research, in particular, methods of theoretical generalizations: analysis, abstract and logical method (at substantiation of modern tendencies of inequality of the population of Ukraine on incomes); economic and statistical methods (in assessing the dynamics of inequality of the population of Ukraine in terms of monetary income); methods of mathematical statistics (when calculating the Gini index and the decomposition of income); methods of graphic display of the received results of research. Findings. The Gini index was calculated using the spline interpolation method to construct the Lorentz function. The Gini index is compared with known calculation methods, the description of the extended method of decomposition of the Gini index is carried out, and also this method is applied on statistical data of distribution of households of Ukraine on the level of monetary incomes. The influence of changes in sources of income on the general level of differentiation is analysed. According to the results of the study, the main factors influencing the level of income distribution and the possibility of reducing the level of stratification of the population of Ukraine were identified. Originality. It is proposed to use the spline interpolation method to construct the Lorentz function, which involves the calculation of the Gini index. The method of the Gini index decomposition identifies the sources of income that have the greatest impact on the growth of income inequality in Ukraine in the period 2014-2020. Practical value. The obtained results of the research are useful in the study of determining the reasons for the influence of certain factors on the level of income distribution and the possibility of reducing the level of stratification of the population of Ukraine. Key words: population inequality, Lorentz curve, Gini index, Gini index decomposition, spline interpolation.

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24

Ogwang, Tomson. "The Marginal Effects in Subgroup Decomposition of the Gini Index." Journal of Official Statistics 32, no. 3 (September 1, 2016): 733–45. http://dx.doi.org/10.1515/jos-2016-0037.

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Abstract In this article, we derive the elasticity of the Gini index with respect to changes in subgroup incomes for subgroups that are characterized by significant income separation. The resulting elasticity, which is structurally similar to that of the empirically popular Lerman and Yitzhaki’s (1985) elasticity for Gini income-source decomposition, entails easy and transparent computations. Some possible checks for income separation are described and an illustrative example using Canadian data is provided. The advantages of the proposed methodology over the Shapley value approach to Gini subgroup decomposition are stated.

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25

Kohne Rooz, Saeed Baghban, Mohammad Moradi, and Hossein Jabbari Khamnei. "Ranking and Selection Procedure for Gini Index." Bulletin of Mathematical Sciences and Applications 3 (February 2013): 1–14. http://dx.doi.org/10.18052/www.scipress.com/bmsa.3.1.

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Gini index, which is derived from the Lorenz curve of income inequality and shows income inequality in different populations, can be applied to ranking and selectionpopulations. Many procedures are available for ordering and ranking income distributions where the ordering is not linear. However, the researchers often are not interested in ordering the populations but selecting the best (or worst) of available populations indicating a lower (or higher) level of disparities in incomes within the population. Madhuri S. Mulekar (2005) discussed the estimation of overlap ofincome distributions and selection in terms of Gini Measure of income inequality. In this paper, we simulate populations ranking and selection based on Gini index of income inequality for case that the variances are equal but known in income distributions and for case that the variances are unequal but known in income distributions.

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26

Biró, Tamás S., and Zoltán Néda. "Gintropy: Gini Index Based Generalization of Entropy." Entropy 22, no. 8 (August 10, 2020): 879. http://dx.doi.org/10.3390/e22080879.

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Entropy is being used in physics, mathematics, informatics and in related areas to describe equilibration, dissipation, maximal probability states and optimal compression of information. The Gini index, on the other hand, is an established measure for social and economical inequalities in a society. In this paper, we explore the mathematical similarities and connections in these two quantities and introduce a new measure that is capable of connecting these two at an interesting analogy level. This supports the idea that a generalization of the Gibbs–Boltzmann–Shannon entropy, based on a transformation of the Lorenz curve, can properly serve in quantifying different aspects of complexity in socio- and econo-physics.

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27

Daniya, T., M. Geetha, and K. Suresh Kumar. "CLASSIFICATION AND REGRESSION TREES WITH GINI INDEX." Advances in Mathematics: Scientific Journal 9, no. 10 (September 23, 2020): 8237–47. http://dx.doi.org/10.37418/amsj.9.10.53.

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28

Peng, Liang. "EMPIRICAL LIKELIHOOD METHODS FOR THE GINI INDEX." Australian & New Zealand Journal of Statistics 53, no. 2 (June 2011): 131–39. http://dx.doi.org/10.1111/j.1467-842x.2011.00614.x.

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29

Frees, Edward W., Glenn Meyers, and A. David Cummings. "Summarizing Insurance Scores Using a Gini Index." Journal of the American Statistical Association 106, no. 495 (September 2011): 1085–98. http://dx.doi.org/10.1198/jasa.2011.tm10506.

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30

Plata-Pérez, L., J. Sánchez-Pérez, and F. Sánchez-Sánchez. "An elementary characterization of the Gini index." Mathematical Social Sciences 74 (March 2015): 79–83. http://dx.doi.org/10.1016/j.mathsocsci.2015.01.002.

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31

Frank A. Farris. "The Gini Index and Measures of Inequality." American Mathematical Monthly 117, no. 10 (2010): 851. http://dx.doi.org/10.4169/000298910x523344.

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32

De, Shyamal K., and Bhargab Chattopadhyay. "Minimum Risk Point Estimation of Gini Index." Sankhya B 79, no. 2 (August 11, 2017): 247–77. http://dx.doi.org/10.1007/s13571-017-0140-3.

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33

Barabesi, Lucio, Giancarlo Diana, and Pier Francesco Perri. "Gini index estimation in randomized response surveys." AStA Advances in Statistical Analysis 99, no. 1 (March 20, 2014): 45–62. http://dx.doi.org/10.1007/s10182-014-0230-8.

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34

Hu, Baiding. "A note on calculating the Gini index." Mathematics and Computers in Simulation 39, no. 3-4 (November 1995): 353–58. http://dx.doi.org/10.1016/0378-4754(95)00083-8.

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35

Puerta, Carmen, and Ana Urrutia. "A characterization of the Gini segregation index." Social Choice and Welfare 47, no. 3 (July 25, 2016): 519–29. http://dx.doi.org/10.1007/s00355-016-0980-1.

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36

Burrell, Q. L. "The Bradford distribution and the Gini index." Scientometrics 21, no. 2 (June 1991): 181–94. http://dx.doi.org/10.1007/bf02017568.

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37

Trannoy, Alain. "On Thon's axiomatization of the Gini index." Mathematical Social Sciences 11, no. 2 (April 1986): 191–94. http://dx.doi.org/10.1016/0165-4896(86)90025-9.

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38

Carrascal, Ursicino. "The Gini index: A proposal for revision." Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA 21, no. 1 (December 31, 2020): 1–7. http://dx.doi.org/10.24309/recta.2020.21.1.01.

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Анотація:

The Gini index is widely used in statistics for the study of equity in the distribution of a variable. However, its definition has several formulations and its use sometimes has hidden problems that can lead to incorrect conclusions. That is the reason why it is necessary to make some remarks this regard and even formulate some proposals to clarify some errors on its definition and use. We present an alternative to solve this error in one of the most popular formulas of the Gini index.

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39

Mirzaei, Shahryar, Gholam Reza Mohtashami Borzadaran, and Mohammad Amini. "A comparative study of the Gini coefficient estimators based on the linearization and U-statistics Methods." Revista Colombiana de Estadística 40, no. 2 (July 1, 2017): 205–21. http://dx.doi.org/10.15446/rce.v40n2.53399.

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In this paper, we consider two well-known methods for analysis of the Gini index, which are U-statistics and linearization for some incomedistributions. In addition, we evaluate two different methods for some properties of their proposed estimators. Also, we compare two methods with resampling techniques in approximating some properties of the Gini index. A simulation study shows that the linearization method performs 'well' compared to the Gini estimator based on U-statistics. A brief study on real data supports our findings.

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40

Preda, Vasile, and Luigi-Ionut Catana. "Tsallis Log-Scale-Location Models. Moments, Gini Index and Some Stochastic Orders." Mathematics 9, no. 11 (May 27, 2021): 1216. http://dx.doi.org/10.3390/math9111216.

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In this article we give theoretical results for different stochastic orders of a log-scale-location family which uses Tsallis statistics functions. These results describe the inequalities of moments or Gini index according to parameters. We also compute the mean in the case of q-Weibull and q-Gaussian distributions. The paper is aimed at analyzing the order between survival functions, Lorenz curves and (as consequences) the moments together with the Gini index (respectively a generalized Gini index). A real data application is presented in the last section. This application uses only the survival function because the stochastic order implies the order of moments. Given some supplementary conditions, we prove that the stochastic order implies the Lorenz order in the log-scale-location model and this implies the order between Gini coefficients. The application uses the estimated parameters of a Pareto distribution computed from a real data set in a log-scale-location model, by specifying the Kolmogorov–Smirnov p-value. The examples presented in this application highlight the stochastic order between four models in several cases using survival functions. As direct consequences, we highlight the inequalities between the moments and the generalized Gini coefficients by using the stochastic order and the Lorenz order.

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Teixeira, Tatiana Rodrigues de Araujo, and Oswaldo Gonçalves Cruz. "Spatial modeling of dengue and socio-environmental indicators in the city of Rio de Janeiro, Brazil." Cadernos de Saúde Pública 27, no. 3 (March 2011): 591–602. http://dx.doi.org/10.1590/s0102-311x2011000300019.

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This study analyzed the spatial distribution of dengue in Rio de Janeiro, Brazil, in 2006, and associations between the incidence per 100,000 inhabitants and socio-environmental variables. The study analyzed reported dengue cases among the city's inhabitants, rainfall, Breteau index (for Aedes aegypti and Aedes albopictus), Gini index, and social development index. We conducted mapping and used the global Moran index to measure the indicators' spatial autocorrelation, which was positive for all variables. The generalized linear model showed a direct association between dengue incidence and rainfall, one-month rainfall time lag, Gini index, and Breteau index for A. albopictus. The conditional autoregressive model (CAR) showed a direct association with rainfall for four months of the year, rain time lag in July, and Gini index in February. The results demonstrate the importance of socio-environmental variables in the dynamics of dengue transmission and the relevance for the development of dengue control strategies.

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Pavlovic, Sanja, Nikola Todorovic, Jelena Bolovic, and Marina Vesic. "Variations in seasonality in spa centres of Serbia." Glasnik Srpskog geografskog drustva 101, no. 1 (2021): 89–110. http://dx.doi.org/10.2298/gsgd2101089p.

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Seasonality of tourist demand imposes a number of issues related to the destination?s carrying capacity and business activities of the individual service providers in tourism. As one indicator of seasonal fluctuation of tourist demand, the Gini index is identified in order to establish monthly concentration of tourists. In this study, the Gini index was calculated for four spa tourism destinations in Serbia (Vrnjacka Banja, Sokobanja, Niska Banja, and Prolom Banja), in order to establish variability in the seasonality. The research took into consideration the period 2010-2019. Research results indicate that Niska Banja Spa has the lowest values of the Gini index, while Vrnjacka Banja Spa and Sokobanja Spa have the highest values, whereby no values are higher that .45 (mostly between .30 and .39). Given that the theoretically lowest value of the Gini index is 0 (smallest seasonal concentration) and the highest is 1 (biggest concentration), the selected spas still do not have particularly high degree of seasonal concentration of tourists. Possibilities for reduction of tourist demand seasonality are pointed out, given its impact on tourism planning and its economic effects.

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43

Sсhmerling, Dmitry S. "New model of understanding income distribution through graduation of normalized Gini Mean Difference." Scholedge International Journal of Management & Development ISSN 2394-3378 2, no. 9 (October 12, 2015): 1. http://dx.doi.org/10.19085/sijmd.020901.

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<p>This work covers such a topical problem as the study of income and wealth inequality. It introduces the notion of “graduation”, i.e. rate of wage rate scale customary at time wage system. Graduation means introduction of the index m as a degree of the polynomial based on which the wage rate scale is distributed. In other words, using m allows to graduate Gini mean difference (0<G<1) by assigning integral number or fraction m, 0<m<∞ to every value of Gini index. Similarly, for each m we can calculate and estimate corresponding normalized Gini mean difference (the analog of Gini index used for simplicity of calculations).<br />Note that in this case we are not talking about distribution in real society, but in the simplest model of this society – a metaphoric community with only one person at each income level. This way we get the most distinct impression of the mechanism of income distribution that gives us the chance to assess scale of inequality. To some extent it clarifies the wide-spread Gini coefficient and “the nature of populations’ wealth”.</p>

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44

Allen, Edward. "Relation Between Two Income Inequality Measures: The Gini coefficient and the Robin Hood Index." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 19 (March 8, 2022): 760–70. http://dx.doi.org/10.37394/23207.2022.19.67.

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The objective of this investigation is to study the relation between two common measures of income inequality, the Gini coefficient and the Robin Hood index. An approximate formula for the Robin Hood index in terms of the Gini coefficient is developed from 100,000 Lorenz curves that are randomly generated based on 100 twenty-parameter families of income distributions. The approximate formula is tested against Robin Hood indexes of commonly-used one-parameter Lorenz curves, income data of several countries, and reported results of Robin Hood indexes. The approximate formula is also tested against results of a stochastic income-wealth model that is introduced in the present investigation. The formula is useful conceptually in understanding why Gini coefficients and Robin Hood indexes are correlated in distribution data and is useful practically in providing accurate estimates of Robin Hood indexes when Gini coefficients are known. The continuous piecewise-linear approximation is generally within 5% of standard one-parameter Lorenz curves and income distribution data and has the form: R ≈ 0.74G for 0 ⩽ G ⩽ 0.5, R ≈ 0.37+0.90(G-0.5) for $$0.5 ⩽ G ⩽ 0.8 and $$ R ≈ 0.64 + 1.26 (G- 0.8) for 0.8 ⩽ G ⩽ 0.95$$ where R is the Robin Hood index and G is the Gini coefficient.

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45

Budiono, Budiono. "PENURUNAN INDEKS GINI PENDIDIKAN: INDONESIA, JAWA TENGAH DAN MAGELANG." Buletin Ekonomi: Manajemen, Ekonomi Pembangunan, Akuntansi 18, no. 2 (October 23, 2021): 251. http://dx.doi.org/10.31315/be.v18i2.5641.

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This policy paper aims to determine the relationship between educational inequality and the average length of schooling in Indonesia, Central Java Province and Magelang District. The data source is the national socio-economic survey (Susenas / BPS) in 2015 and 2020, especially the table: the percentage of the population aged 15 years and over according to the type of area and the highest level of education completed. To determine education inequality, it is calculated using data on the average length of schooling of the population aged 15 years and over, the Lorenz curve and the Gini index of education. The average length of schooling in Indonesia, Central Java Province, Magelang Regency increased during the 2015–2020 period, while education inequality decreased. The Gini index calculation results show that Indonesia is more evenly distributed than Central Java, and Magelang is the least even. A negative relationship was found between mean years of schooling and the Gini index of education. a positive relationship was found between the rate of increase in mean years of schooling and a decrease in the Gini index of educationNaskah kebijakan ini bertujuan untuk mengetahui hubungan antara ketimpangan pendidikan dengan rata-rata lama sekolah di Indonesia, Provinsi Jawa Tengah dan Kabupaten Magelang. Sumber data adalah surve social ekonomi nasional (Susenas / BPS) tahun 2015 dan 2020, khusunya tabel: persentase penduduk usia 15 tahun ke atas menurut tipe daerah dan jenjang pendidikan tertinggi yang ditamatkan. Untuk menentukan ketimpangan pendidikan dihitung melalui, data rata-rata lama sekolah penduduk usia 15 tahun ke atas, kurva Lorenz dan indeks Gini pendidikan. Rata-rata lama sekolah di Indonesia, Provinsi Jawa Tengah Kabupaten Magelang meningkat selama periode 2015–2020 sedangkan ketimpangan pendidikan menurun. Hasil perhitungan indeks gini menunjukan bahwa Indonesia lebih merata daripada Jawa Tengan, dan Magelang paling tidak merata. Hubungan negatif ditemukan antara rata-rata tahun sekolah dan indeks Gini pendidikan. hubungan positif ditemukan antara tingkat kenaikan rata-rata tahun sekolah dan penurunan indeks Gini pendidikan.

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Bezerra, Héllyda de Souza, Tatiana de Medeiros Carvalho Mendes, Talita Araujo de Souza, and Fabia Barbosa de Andrade. "Mortality rate for cervical cancer in Brazil and socioeconomic indicators: a spatial study." Acta Scientiarum. Health Sciences 41 (July 4, 2019): e45388. http://dx.doi.org/10.4025/actascihealthsci.v41i1.45388.

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The objective of the study was to analyze the correlation between the mortality rate from cervical cancer with schooling, Gini index and Human Development Index (HDI). This is an epidemiological, retrospective, observational and ecological study carried out in the 161 intermediate regions of urban articulation in Brazil. The study was based on secondary data from DATASUS and the United Nations Development Program. The spatial analysis of mortality versus schooling, Gini index and HDI was performed. Most municipalities in Brazil with high mortality rate are located in the North and Northeast. In addition, it was observed that schooling, Gini index and HDI directly influence the increase or reduction of mortality. Knowing these fragilities and their distribution in Brazil is necessary to solve the problems and improve the indicators such as the mortality rate.

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47

Ceriani, Lidia, and Paolo Verme. "The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini." Journal of Economic Inequality 10, no. 3 (June 10, 2011): 421–43. http://dx.doi.org/10.1007/s10888-011-9188-x.

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48

Berrebi, Z. M., and Jacques Silber. "Dispersion, Asymmetry and the Gini Index of Inequality." International Economic Review 28, no. 2 (June 1987): 331. http://dx.doi.org/10.2307/2526728.

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49

Reddy, S. V. G., K. Thammi Reddy, V. Valli Kumari, P. Sanjay Varma, and S. V. S. Nitish Kumar Gupta. "A Recurrent Gini Index based Fuzzy Neural Network." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 521–25. http://dx.doi.org/10.26438/ijcse/v7i4.521525.

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50

Cordonnier, Thomas, and Georges Kunstler. "The Gini index brings asymmetric competition to light." Perspectives in Plant Ecology, Evolution and Systematics 17, no. 2 (April 2015): 107–15. http://dx.doi.org/10.1016/j.ppees.2015.01.001.

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