隔离单调性与社交网络中的不平等度量
Segregation Monotonicity and the Measurement of Inequality in Social Networks
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- University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
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中文总结 AI 辅助
本文提出隔离单调性准则，证明基于相对剥夺的网络不平等度量满足该性质，而基于总体验的度量通常不满足，并揭示不同网络不平等度量的根本差异。
中文摘要 AI 辅助
本文引入隔离单调性作为评估社交网络不平等度量的一项准则。若在收入分配保持不变的情况下，当网络根据指定的网络结构变换变得更加隔离时，网络不平等度量弱增加，则该度量满足隔离单调性。我利用一类层级为k的星型网络来研究该性质，这类网络在以下意义上代表日益加剧的社会隔离：相对较贫困的个体彼此之间日益孤立，其社会比较日益集中于较富裕的个体之间。我证明，基于相对剥夺的不平等度量（该度量汇总与较富裕网络邻居的比较）满足隔离单调性。相比之下，基于总体验的度量（该度量汇总所有网络邻居间绝对收入差异）通常不满足隔离单调性，并且可能随着隔离程度上升而下降。我还建立了基于总体验的度量与标准基尼系数之间的若干关系。结果表明，不同的基于网络的不平等度量可能体现对社会相关比较的根本不同概念。
英文摘要
This paper introduces segregation monotonicity as a criterion for evaluating measures of inequality in social networks. A network inequality measure satisfies segregation monotonicity if, holding the distribution of income fixed, it weakly increases as the network becomes more segregated according to a specified transformation of network architecture. I investigate this property using a class of level-$k$ star networks that represent increasing social segregation in the following sense: relatively poorer individuals become increasingly isolated from one another and their social comparisons become increasingly concentrated among richer individuals. I show that a relative deprivation-based measure of inequality, which aggregates comparisons with richer network neighbors, satisfies segregation monotonicity. By contrast, total experience-based measures, which aggregate absolute income differences among all network neighbors, do not generally satisfy segregation monotonicity and can decline as segregation rises. I also establish several relationships between the total experience-based measures and the standard Gini coefficient. The results show that alternative network-based inequality measures can embody fundamentally different conceptions of socially relevant comparisons.
