The article contains a methodology for social statistics assessing. The significance of minorities (groups that differ in their attributes from the majority) has grown substantially in the modern postindustrial economy and society. In the multidimensional characteristics space distribution analysis subjects that not included in the sample can be negligible, but they may be metrically significant. For example, they can be located compactly and remotely from the main mass of subjects, i.e. have similar characteristics and significantly influence the dynamics of socio-economic systems. In addition, it is necessary to evaluate not the probability of errors in each characteristic separately, but the probability of errors in their combinations. The projection of a multidimensional space into two-dimensional or three-dimensional space, usually used to analyze such data, leads to the loss of information about compact minorities that merge into a "false majority" or the appearance of distribution. Thus, the posed task can not be reliably solved without analysis in a multidimensional space. This problem is especially relevant in states aggregated social statistics analysis for which relatively small number of subjects (countries) are represented. The aim of the article is to develop tools for the metric analysis of aggregated social statistics, which is characterized by a small number of measurements. It is developed to check the existence of stable metric and topology structures (clusters) and distribution of countries based on UN data on countries social development characteristics.
Extending the Measurement of Composite Indicators Towards a Non-convex Approach: Corporate Social Responsibility for the Food and Beverage Manufacturing Industry
District-Level Food Environment Indicators and Social Vulnerability in São Paulo
Instituto de Informática, Universidade Federal de Goiás（戈亚斯联邦大学信息学院）；Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo（圣保罗大学数学与计算机科学学院）；Escola Superior de Agricultura Luiz de Queiroz da Universidade de São Paulo（圣保罗大学路易斯·德·奎罗斯高等农业学院）；Faculdade de Saúde Pública, USP（圣保罗大学公共卫生学院）
Social networks and labour productivity in Europe: An empirical investigation
本文使用AMADEUS数据库中的企业层面数据，对欧洲各国的劳动生产率分布进行了调查。我们发现，经验生产率分布的上尾遵循衰减幂律，其指数$α$是由Clementi等人（2006）最近开发的半参数估计技术获得的。生产率分布中“肥尾”的出现已经在Di Matteo et al.（2005）中被发现，并通过社会网络模型进行了解释。在这里，我们表明，这个模型是测试在更广泛的国家样本具有不同的模式的社会网络结构。这些不同的社会态度，使用社会资本指标衡量，反映在幂律指数估计，以这种方式验证企业的生产力绩效和社会网络之间的联系的存在。
