Relevant bibliographies by topics / Gini coefficiency

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gini coefficiency'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Gini coefficiency"

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Gsteiger, Sandro, Nicola Low, Pam Sonnenberg, Catherine H. Mercer, and Christian L. Althaus. "Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity." PeerJ 8 (January 20, 2020): e8434. http://dx.doi.org/10.7717/peerj.8434.

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Objectives Gini coefficients have been used to describe the distribution of Chlamydia trachomatis (CT) infections among individuals with different levels of sexual activity. The objectives of this study were to investigate Gini coefficients for different sexually transmitted infections (STIs), and to determine how STI control interventions might affect the Gini coefficient over time. Methods We used population-based data for sexually experienced women from two British National Surveys of Sexual Attitudes and Lifestyles (Natsal-2: 1999–2001; Natsal-3: 2010–2012) to calculate Gini coefficients for CT, Mycoplasma genitalium (MG), and human papillomavirus (HPV) types 6, 11, 16 and 18. We applied bootstrap methods to assess uncertainty and to compare Gini coefficients for different STIs. We then used a mathematical model of STI transmission to study how control interventions affect Gini coefficients. Results Gini coefficients for CT and MG were 0.33 (95% CI [0.18–0.49]) and 0.16 (95% CI [0.02–0.36]), respectively. The relatively small coefficient for MG suggests a longer infectious duration compared with CT. The coefficients for HPV types 6, 11, 16 and 18 ranged from 0.15 to 0.38. During the decade between Natsal-2 and Natsal-3, the Gini coefficient for CT did not change. The transmission model shows that higher STI treatment rates are expected to reduce prevalence and increase the Gini coefficient of STIs. In contrast, increased condom use reduces STI prevalence but does not affect the Gini coefficient. Conclusions Gini coefficients for STIs can help us to understand the distribution of STIs in the population, according to level of sexual activity, and could be used to inform STI prevention and treatment strategies.
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Everett, Theodore J., and Bruce M. Everett. "Justice and Gini coefficients." Politics, Philosophy & Economics 14, no. 2 (April 21, 2014): 187–208. http://dx.doi.org/10.1177/1470594x14528653.

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Matsumoto, Kunichika, Kanako Seto, Shigeru Fujita, Takefumi Kitazawa, and Tomonori Hasegawa. "Population aging and physician maldistribution: A longitudinal study in Japan." Journal of Hospital Administration 5, no. 1 (October 21, 2015): 29. http://dx.doi.org/10.5430/jha.v5n1p29.

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Background: Over the past two decades, population aging and the introduction of the new postgraduate medical education program in 2004 have impacted on the geographic maldistribution of physicians in Japan. The purpose of this study was to evaluate recent changes in physician distribution across municipalities from 1996 to 2012 using Gini coefficients and to clarify the impact of the new medical education program on physician distribution.Methods: We extracted the number of physicians classified by type of medical institution and municipal bodies. Gini coefficients were calculated using both population and demand for medical services. We calculated the contribution ratio (CR) of maldistribution within each type of medical institution to the whole maldistribution using Rao’s method. In addition, we calculated the incremental difference in Gini coefficients between 2002 and 2010, and calculated the CR of the incremental Gini coefficient difference for each medical institution type using Seki’s method.Results: Both Gini coefficients decreased from 1996 to 2002, and increased after 2006. The CR of other hospitals increased from 2004. The incremental difference in the Gini coefficient using demand between 2002 and 2010 was 0.012, and the CR of each type of medical institution was -25.1% (university hospitals), 131.0% (other hospitals) and -5.9% (clinics).Conclusions: Our analysis showed that the geographic maldistribution of physicians has worsened since the introduction of the new postgraduate medical education program, and the CR of maldistribution in other hospitals was high. Our study suggested that new medical resource distribution policies should be discussed to improve maldistribution.
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Ghosh, Sucharita. "Computation of Spatial Gini Coefficients." Communications in Statistics - Theory and Methods 44, no. 22 (November 17, 2015): 4709–20. http://dx.doi.org/10.1080/03610926.2013.823211.

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Furman, Edward, and Ričardas Zitikis. "BEYOND THE PEARSON CORRELATION: HEAVY-TAILED RISKS, WEIGHTED GINI CORRELATIONS, AND A GINI-TYPE WEIGHTED INSURANCE PRICING MODEL." ASTIN Bulletin 47, no. 3 (August 7, 2017): 919–42. http://dx.doi.org/10.1017/asb.2017.20.

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AbstractGini-type correlation coefficients have become increasingly important in a variety of research areas, including economics, insurance and finance, where modelling with heavy-tailed distributions is of pivotal importance. In such situations, naturally, the classical Pearson correlation coefficient is of little use. On the other hand, it has been observed that when light-tailed situations are of interest, and hence when both the Gini-type and Pearson correlation coefficients are well defined and finite, these coefficients are related and sometimes even coincide. In general, understanding how these correlation coefficients are related has been an illusive task. In this paper, we put forward arguments that establish such a connection via certain regression-type equations. This, in turn, allows us to introduce a Gini-type weighted insurance pricing model that works in heavy-tailed situations and thus provides a natural alternative to the classical capital asset pricing model. We illustrate our theoretical considerations using several bivariate distributions, such as elliptical and those with heavy-tailed Pareto margins.
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Hong, Minki. "Corrected Gini Coefficient in Korea." Korean Development Economics Association 23, no. 3 (September 30, 2017): 1–22. http://dx.doi.org/10.20464/kdea.2017.23.3.1.

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Ryu, Hang K., Daniel J. Slottje, and Hyeok Y. Kwon. "A New Logit-Based Gini Coefficient." Entropy 21, no. 5 (May 13, 2019): 488. http://dx.doi.org/10.3390/e21050488.

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The Gini coefficient is generally used to measure and summarize inequality over the entire income distribution function (IDF). Unfortunately, it is widely held that the Gini does not detect changes in the tails of the IDF particularly well. This paper introduces a new inequality measure that summarizes inequality well over the middle of the IDF and the tails simultaneously. We adopt an unconventional approach to measure inequality, as will be explained below, that better captures the level of inequality across the entire empirical distribution function, including in the extreme values at the tails.
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Dixon, Philip M., Jacob Weiner, Thomas Mitchell-Olds, and Robert Woodley. "Bootstrapping the Gini Coefficient of Inequality." Ecology 68, no. 5 (October 1987): 1548–51. http://dx.doi.org/10.2307/1939238.

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Dixon, Philip, Jacob Weiner, Thomas Mitchell-Olds, and Robert Woodley. "Bootstraping the Gini Coefficient of Inequality." Ecology 69, no. 4 (August 1988): 1307. http://dx.doi.org/10.2307/1941290.

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Matsumoto, Kunichika, Kanako Seto, Eijiro Hayata, Shigeru Fujita, Yosuke Hatakeyama, Ryo Onishi, and Tomonori Hasegawa. "The geographical maldistribution of obstetricians and gynecologists in Japan." PLOS ONE 16, no. 1 (January 12, 2021): e0245385. http://dx.doi.org/10.1371/journal.pone.0245385.

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Background In Japan, there is a large geographical maldistribution of obstetricians/gynecologists, with a high proportion of females. This study seeks to clarify how the increase in the proportion of female physicians affects the geographical maldistribution of obstetrics/gynecologists. Methods Governmental data of the Survey of Physicians, Dentists and Pharmacists between 1996 and 2016 were used. The Gini coefficient was used to measure the geographical maldistribution. We divided obstetricians/gynecologists into four groups based on age and gender: males under 40 years, females under 40 years, males aged 40 years and above, and females aged 40 years and above, and the time trend of the maldistribution and contribution of each group was evaluated. Results The maldistribution of obstetricians/gynecologists was found to be worse during the study period, with the Gini coefficient exceeding 0.400 in 2016. The contribution ratios of female physicians to the deterioration of geographical maldistribution have been increasing for those under 40 years and those aged 40 years and above. However, there was a continuous decrease in the Gini coefficient of the two groups. Conclusions The increase in the contribution ratio of the female physician groups to the Gini coefficient in obstetrics/gynecology may be due to the increased weight of these groups. The Gini coefficients of the female groups were also found to be on a decline. Although this may be because the working environment for female physicians improved or more female physicians established their practice in previously underserved areas, such a notion needs to be investigated in a follow-up study.
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Dissertations / Theses on the topic "Gini coefficiency"

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Pillay, Nalini Sooknanan. "Using system dynamics to explore gini coefficient parametrics." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/9143.

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Modern economies are dependent on a reliable electricity supply for sustaining economic health and development, enabled by adequate energy planning and/or investment in capacity. Identifying drivers such as changes in income distribution that impact electricity demand is thus critical. This project made use of a system dynamics methodology with feedback loops to provide an insightful alternative to the conventional linear statistical empirical approaches such as multiple regression analysis and principal component analysis, generally used to explore the sensitivities of key driving forces which affect income distribution. The system dynamics simulation tool highlighted the direct causal influence of Gini coefficient on residential electricity consumption, by using equations as opposed to correlations. Results show that for a GDP growth rate of 2, by year 2035, a Gini coefficient of 0.5 is linked to a 3.14 increase in residential electricity demand while a Gini coefficient of 0.4 means a 4.73 increase in residential electricity demand. This dynamic is an important consideration for energy planners since government has (and continues to) introduce policies and mechanisms to ensure a more equal income distribution and hence a decrease in Gini coefficient from 0.67 to lower values.
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Yusuf, Sulman. "Income Inequality and Economic Growth : The Effect of Gini Coefficient on GNI." Thesis, Umeå universitet, Nationalekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-162577.

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Using panel data from 1960-2000 for 28 countries, this paper explores the relationship between inequality and economic growth.To mitigate the problem of endogeneity, dynamic panel data estimation technique such as difference GMM was used. Estimated results showed positive significant relationship in short to medium term between income inequality and economic growth. The results are robust to sensitivity analysis when the estimation was carried out by running the regression on sub samples to check if the sample selection influnces the results. The results obtained in this study oppose the common belief that growthwill increase by bringing down inequality.
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Johnson, Kelly Singleton. "Quantitative Poverty: Relationship Between Poverty Level and Population Size, GDP, and Gini Coefficient." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4455.

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This research examines why poverty has been persistent in all regimes that have tried to use public policy to eradicate it with no success. This research begins to examine the economic, fiscal, and current Federal Reserve monetary policy for an understanding of why poverty persists. The purpose of this experimental, cross-sectional design is to test the relationship between poverty level, population size, gross domestic product and the Gini coefficient. The most important outcome of the research is to understand if poverty is an unintended consequence of economic activity and not individual circumstance. In the dissertation, 5 U.S. states are examined in the year 2014. The data were collected using the U.S. Census Bureau and American Community Surveys. Using multiple regression, this research aimed to establish the minimum amount of expected poverty in the sample's population and gross domestic product (GDP). Using the results and further research, a predictive model could be created to understand how poverty, population, and GDP intersect to create stable economies. The key results yielded the Gini coefficient has no effect in predicting expected poverty levels. As determined by the model, Arizona would have a poverty decrease of 17.1% and Illinois' poverty would decrease by 7.7%. Georgia and Washington would increase by 9.4% and 21.8%, respectively. New York's levels would remain the same. One of the recommendations is continuing research to understand other quantitative factors that reduce or increase poverty numbers. These results help promote social change by possibly informing monetary policymakers more targeted solutions to mitigating poverty levels.
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Ismail, Abdullahi Abdi, and Muna Muse. "The effects of Income Inequality on Economic Growth." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-29334.

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The effect of income inequality has been controversial issue for decades, which researchers have concluded conflicting results. Many researchers have found that income inequality is conducive on economic growth, while others found harmful effect. Hence, this paper investigates the impact of income inequality on economic growth by using the cross sectional analysis. The averaged data from periods of 2002-2006 were used and observations from 90 developed and developing countries were also used. We find that income inequality is negatively associated in economic growth.
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Berhanu, Samuel. "Econometric analysis of household expenditures." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=474.

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Thesis (Ph. D.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains ix, 189 p. : ill. Includes abstract. Includes bibliographical references (p. 131-140).
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Misra, Kaustav. "Determinants of Young Adult Poverty: A ZIP Code Level Analysis." Lexington, Ky. : [University of Kentucky Libraries], 2007. http://lib.uky.edu/ETD/ukyagec2007t00616/Thesis_July9.pdf.

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Thesis (M.S.)--University of Kentucky, 2007.
Title from document title page (viewed on August 6, 2007). Document formatted into pages; contains: viii, 61 p. : ill. (some col.). Includes abstract and vita. Includes bibliographical references (p. 56-59).
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Ho, Ki-hiu, and 何其曉. "Extracting real market behavior in complex adaptive systems through minority game." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30163705.

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Castoe, Minna, and Aalekhya Sanyal. "The Impact of Immigration on Income Inequality : Evidence from Sweden and the United States." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44069.

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This paper studies data from a 25-year period in the history of Sweden and the US, ranging from 1993 to 2017. The aim of the thesis was to investigate the impact of immigration on the income inequality of the respective countries while also considering the impact of other specific variables. In order to estimate the impact of the variables, both static and dynamic models were used, with the Gini coefficient being the dependent variable. With the ordinary least square giving short-run estimates and the error correction model providing short- and long- run estimates simultaneously, the main variable for immigration, being the foreign-born population, showed a strong positive relationship with income inequality. For the estimation, the immigration variable was also split into high- and low-skilled immigrants as well as different age groups. In conclusion, we find that immigration in Sweden and the US has high levels of influence on the income inequality for both countries.
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Hult, Amanda. "Income inequality and economic growth : An investigation of the OECD countries." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Nationalekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-47283.

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Income inequality is in a majority of earlier studies more or less affirmatively agreed to be negatively related to economic growth. The underlying complexity of the connection lacks well-tried backing in the modern time. The main purpose of this research is to identify the relationship between income inequality and economic growth, but also the effects of other factors, such as human capital and investment. This is conducted with a panel data approach on 34 OECD countries with data over the period 1990-2010. Aggregate income inequality, represented by the Gini coefficient is used in the empirical estimation, together with two other variables to control for the income inequality at the bottom and top end of the income distribution. The results indicate the aggregate inequality level to be significantly and positively related to growth, while bottom end and top end inequality is seen to have a significant and negative relationship with growth. The level of GDP per capita, education and population growth is also seen to have an impact on economic growth.
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Lee, Dylan B. "Occupy This: The Effect of Income Inequality on GDP Per Capita Growth Using Panel Data in the United States from 1963 to 2009." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/cmc_theses/311.

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Income inequality and its relationship to long-term GDP per capita growth has been researched for decades since the development of the Kuznet’s Curve. Theoretical and empirical research has shown mixed results including positive, negative, non-existent, or statistically insignificant relationships. Empirical research on income inequality and economic growth in the United States has also shown mixed results. In addition to using existing data, this paper uses originally-constructed Gini Coefficients from 2005 to 2009. A statistically significant negative correlation between income inequality, and both short-term growth and long-term growth is found in the analysis of this data. Finally, this paper attempts to justify a causal relationship between income inequality and long-term growth.
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Books on the topic "Gini coefficiency"

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Thomas, Vinod. Measuring education inequality: Gini coefficients of education. Washington, D.C: World Bank Institute, Office of the Vice President, and Economic Policy and Poverty Reduction Division, 2001.

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Zhongguo ji ni xi shu ji qi fen jie fen xi: Li lun, fang fa he ying yong. Beijing: Zhongguo jing ji chu ban she, 2013.

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Arkhiereev, S. I. Za mez︠h︡amy vyboru miz︠h︡ efektyvnisti︠u︡ ta spravedlyvisti︠u︡: Miz︠h︡narodnyĭ pidkhid = Beyond the limits of the equity-efficiency trade-off : an international approach = Za predelami vybora mezhdu ėffektivnostʹi︠u︡ i spravedlivostʹi︠u︡ : mezhdunarodnyĭ podkhod. Kharkiv: KhNU imeni V.N. Karazina, 2008.

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Ramachandran, K. S. Marketing strategies vis-à-vis income disparities: Gini coefficient for managers. Gurgaon: J.K. Business School, 2009.

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The dynamics of inequality and poverty: Comparing income distributions. Cheltenham, UK: E. Elgar, 1998.

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Leibbrandt, M. V. The contribution of income components to income inequality in South Africa: A decomposable Gini analysis. Washington, D.C: World Bank, 1996.

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S, Ramachandran K. The economy of Gurgaon: The development agenda : human development indicators - standard of living - Gini index. Gurgaon: JK Business School, 2010.

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Fang fa, zhi du, shu ju: Dui wai jing ji mao yi tong ji yan jiu. Beijing Shi: Dui wai jing ji mao yi da xue chu ban she, 2001.

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Lorenzkurvendisparität: Neuere Entwicklungen, Erweiterungen und Anwendungen. Frankfurt am Main: P. Lang, 1997.

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Zhu, Susan Chun. Ginis in general equilibrium: Trade, technology and southern inequality. Cambridge, MA: National Bureau of Economic Research, 2001.

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Book chapters on the topic "Gini coefficiency"

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Kalmijn, Wim. "Gini Coefficient." In Encyclopedia of Quality of Life and Well-Being Research, 2559–61. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1168.

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Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, Zhao Xu, Yaakov Engel, Claude Sammut, Mark Reid, et al. "Gini Coefficient." In Encyclopedia of Machine Learning, 457–58. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_343.

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Yang, Tianyu, and Zhinan Cao. "Why China’s Gini Coefficient Has Been Falling?" In A New Era, 59–82. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8357-0_3.

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Zhang, Yan, and JingTao Yao. "Determining Three-Way Decision Regions with Gini Coefficients." In Lecture Notes in Computer Science, 160–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08644-6_17.

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Yitzhaki, Shlomo, and Edna Schechtman. "Social Welfare, Relative Deprivation, and the Gini Coefficient." In Springer Series in Statistics, 253–73. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4720-7_13.

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Hipp, John R., and Jae Hong Kim. "Income Inequality and Economic Segregation in Los Angeles from 1980 to 2010." In The Urban Book Series, 371–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64569-4_19.

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AbstractRising income inequality is a critical problem in both the global North and South. In the United States, the Gini coefficient measuring nationwide income inequality rose from 0.403 in 1980 to 0.480 in 2014 (US Census), and residential segregation by income has increasingly occurred in many metropolitan regions and is particularly reflected in the spatial separation of the wealthiest households. This chapter focuses on the change in the level of income inequality in the Los Angeles region since 1980 and how it is related to changes in residential segregation between economic groups over that same time period. We use data from the US Census collected in 1980, 1990, 2000, and 2010. We measure residential segregation between economic groups based on occupational structure, and measure ‘neighbourhoods’ using Census tracts: these are units defined by the US Census and typically average about 4,000 residents. The overall level of inequality in the region is measured at each decade point using the Gini coefficient for household income. Maps demonstrate where different socioeconomic status groups have tended to locate and how economic segregation has changed in Los Angeles over this time period. We also assess the extent to which changes in inequality are related to changes in economic segregation over the last four and a half decades.
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Wagner, Katarzyna. "Wealth inequalities in cities of the Polish-Lithuanian Commonwealth and their changes during the seventeenth century. Sources and methods of measure." In Disuguaglianza economica nelle società preindustriali: cause ed effetti / Economic inequality in pre-industrial societies: causes and effect, 131–44. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-053-5.12.

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I would like to determine the evolution of wealth concentration in main cities of the Polish-Lithuanian Commonwealth by comparing the data from different benchmark years. Moreover, I will analyze whether the Gini coefficient value indeed refers to the communities who are at a threshold of economic growth, and what is the correlation between the value of the coefficient and the town or city’s economic situation. Also, it is worthwhile to ponder the question: is there any correlation – noted by both Jan Luiten van Zanden and Guido Alfani – whereby the larger the town/city, the more visible the inequalities. Finally, how do the towns/cities of the Polish-Lithuanian Commonwealth compare to those in Western Europe.
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Su, Fei, Xiaofang Zou, Yong Qin, Shaoyi She, and Hang Su. "Road Network Equilibrium Analysis Based on Section Importance and Gini Coefficient." In Green, Smart and Connected Transportation Systems, 1119–34. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0644-4_86.

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Yang, Xin, and Lurong Fan. "The Allocation of Carbon Emission Allowances for Power Plants with Gini Coefficient." In Advances in Intelligent Systems and Computing, 1557–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47241-5_130.

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Yitzhaki, Shlomo, and Edna Schechtman. "The Gini Equivalents of the Covariance, the Correlation, and the Regression Coefficient." In Springer Series in Statistics, 33–49. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4720-7_3.

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Conference papers on the topic "Gini coefficiency"

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Hai, Xiaohui. "Dynamic Research on China's Gini Coefficient." In International Conference on Humanities and Social Science 2016. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/hss-26.2016.98.

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Giger, Emanuel, Martin Pinzger, and Harald Gall. "Using the gini coefficient for bug prediction in eclipse." In the 12th international workshop and the 7th annual ERCIM workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2024445.2024455.

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Li, Gang, and Guotai Chi. "A New Determining Objective Weights Method-Gini Coefficient Weight." In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.84.

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Tabassum, Mujahid, and Jeffery Jeselee Sijore. "Growth Analysis of Eclipse and NetBeans using Gini Coefficient." In the 2017 International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3077584.3077607.

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Sivagama Sundhari, S. "A knowledge discovery using decision tree by Gini coefficient." In 2011 International Conference on Business, Engineering and Industrial Applications (ICBEIA). IEEE, 2011. http://dx.doi.org/10.1109/icbeia.2011.5994250.

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Liu, Yun, Siyu Jiang, and Jiuming Huang. "Anomaly Detection for DDoS Attacks Based on Gini Coefficient." In 2013 International Conference on Advanced ICT. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icaicte.2013.129.

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Lopes, Giseli Rabello, Roberto da Silva, and J. Palazzo M. de Oliveira. "Applying Gini coefficient to quantify scientific collaboration in researchers network." In the International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1988688.1988767.

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Vasa, Rajesh, Markus Lumpe, Philip Branch, and Oscar Nierstrasz. "Comparative analysis of evolving software systems using the Gini coefficient." In 2009 IEEE International Conference on Software Maintenance (ICSM). IEEE, 2009. http://dx.doi.org/10.1109/icsm.2009.5306322.

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Wang, Guangming, and Jinxin Xu. "A Decision Tree Algorithm Based on Coordination Degree and Gini-Coefficient." In 2008 International Conference on MultiMedia and Information Technology (MMIT). IEEE, 2008. http://dx.doi.org/10.1109/mmit.2008.208.

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Jianjun, Wang, Ren Chaojun, He Qianqian, and Liu Ping. "Gini Coefficient Used in the Post-evaluation Theory of Highway Construction." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.725.

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Reports on the topic "Gini coefficiency"

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Shkolnikov, Vladimir M., Evgueni M. Andreev, and Alexander Z. Begun. Gini coefficient as a life table function: computation from discrete data, decomposition of differences and empirical examples. Rostock: Max Planck Institute for Demographic Research, June 2001. http://dx.doi.org/10.4054/mpidr-wp-2001-017.

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Andreev, Evgueni M., Vladimir M. Shkolnikov, and Alexander Z. Begun. Algorithm for decomposition of differences between aggregate demographic measures and its application to life expectancies, Gini coefficients, health expectancies, parity-progression ratios and total fertility rates. Rostock: Max Planck Institute for Demographic Research, August 2002. http://dx.doi.org/10.4054/mpidr-wp-2002-035.

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