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In order to study the structure of society, sociologists usually distinguish several homogeneous social groups, or classes. The most common division consists of three groups: upper, middle and lower classes. Such a partition is traditionally based on a subjective (exogenous) criteria adopted by a particular researcher. In this paper, the distribution of households in Russian federal districts is modeled as a mixture of three lognormal distributions. The mixing proportions (probabilities) of the mixture components and the corresponding distribution parameters are modeled as functions of the individual characteristics of households. The result is an endogenous decomposition of household sample into three clusters (lower, middle, upper). This classification allows analyzing the difference between regions and the patterns of intergroup dynamics in the period 2014—2018. The approach used in this work has demonstrated great flexibility in analyzing the distribution of income, the dynamics of this distribution over time, as well as a migration between relatively homogeneous clusters. The use of mixture density function with endogenously determined probabilities allows for precise detection of the effects of the income heterogeneity determinants within each cluster.

Performance of Russian homeowners associations (HOAs) could serve as a litmus test for the Russian societys capacity for self-organization. Efficiency of Russian HOAs is gauged in the paper by using the stochastic frontier technique. The variation of obtained performance indexes reflects observed HOA successes and failures. HOA are shown to be a superior alternative to municipal management of common property in residential housing only if there is a sufficient stock of a special kind of social capital - technical civic competence - in the community of tenants. Lack of social capital and insufficient competition between management companies prevent Russian HOAs from fully realizing their potential.

Is it generally true that being an exporter for a firm is associated with a higher productivity? We study the relationship between firms' export status and their technical efficiency in Russian manufacturing sector in 2004-2013 using the data from the Bureau van Dijk database "Ruslana". To estimate the exporter status effect we apply two methods, both based on the stochastic frontier analysis. The first approach estimates the effect as the marginal effect of the exporter status on technical efficiency. The second approach is based on the propensity score matching of exporting firms with nonexporting firms. We obtain robust results which suggest that the relationship between the exporter status and technical efficiency is positive. In both cases the exporter status' effect is up to 0.03 (3 p. p.).