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arXiv 1406.2874physics.soc-ph
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用定量方法衡量社会不平等:基尼系数和$k$指数的分析估计和经验数据分析

Measuring social inequality with quantitative methodology: analytical estimates and empirical data analysis by Gini and $k$ indices

Jun-ichi Inoue, Asim Ghosh, Arnab Chatterjee, Bikas K. Chakrabarti

摘要:

在人类存在的不同阶层中表现出来的社会不平等可以用几种方式来量化。在这里,我们计算非熵的不等式的措施,如洛伦兹曲线,基尼指数和最近推出的$k$指数分析从已知的分布函数。我们描述了不同数量的分布函数,如投票,期刊引用,城市规模等,与适当的拟合,计算其不等式的措施,并与分析结果进行比较。单个解析函数通常不足以拟合经验数据的整个概率分布范围,并且更好地拟合具有单个交叉点的两个不同函数。在这里,我们提供了一般公式来计算这些不等式的措施,为上述情况。我们试图通过最小化措施的实证和分析评估之间的差距来指定交叉点。将$k$指数视为一个“额外维度”,基尼指数的上下界都是根据$k$指数得出的。不平等指数之间的这种不平等关系可能有助于我们检验对这些指数的实证和分析评估的有效性。

英文摘要:

Social inequality manifested across different strata of human existence can be quantified in several ways. Here we compute non-entropic measures of inequality such as Lorenz curve, Gini index and the recently introduced $k$ index analytically from known distribution functions. We characterize the distribution functions of different quantities such as votes, journal citations, city size, etc. with suitable fits, compute their inequality measures and compare with the analytical results. A single analytic function is often not sufficient to fit the entire range of the probability distribution of the empirical data, and fit better to two distinct functions with a single crossover point. Here we provide general formulas to calculate these inequality measures for the above cases. We attempt to specify the crossover point by minimizing the gap between empirical and analytical evaluations of measures. Regarding the $k$ index as an `extra dimension', both the lower and upper bounds of the Gini index are obtained as a function of the $k$ index. This type of inequality relations among inequality indices might help us to check the validity of empirical and analytical evaluations of those indices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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