Journal section "Social development"

Hierarchical Pareto Classification of the Russian Regions by the Population’s Quality of Life Indicators

Mironenkov A.A.

Volume 13, Issue 2, 2020

Mironenkov A.A. Hierarchical Pareto classification of the Russian regions by the population’s quality of life indicators. Economic and Social Changes: Facts, Trends, Forecast, 2020, vol. 13, no. 2, pp. 171–185. DOI: 10.15838/esc.2020.2.68.11

DOI: 10.15838/esc.2020.2.68.11

Abstract   |   Authors   |   References
Improving population’s quality of life is a key goal of the state. In this regard, it is very important to correctly measure its level and, accordingly, classify the country's regions by quality of life indicators. Most research in this area involves dividing variables into groups, unifying variables in each group and building an integral indicator, grouping or clustering objects as a linear convolution of variables with weights. Such approaches have their drawbacks due to the subjectivity of expert estimates, instability of the coefficients of the main component, inability to work with ordinal data, etc. Thus, the purpose of this study is to build a methodology for classifying the regions of the Russian Federation by quality of life indicators devoid of the above disadvantages. The proposed method is based on the concept of Pareto optimality well-known in Economics according to which all the regions are divided into disjoint classes. After dividing variables into groups we recommend using Pareto class as a representative of the category instead of the traditional unification and construction of intra-group convolutions, which is obtained after the intra-group Pareto classification, and building the final Pareto classification of the regions of the Russian Federation on the basis of the obtained intra-group Pareto classes. The advantage of the proposed approach is that it can be applied on the ordinal data, that is, when some variables are characterized only by their order and there are no exact values for each region. In addition, the algorithm is undemanding for computing power and does not use expert estimates, except for the selection of research variables. The main results of the study are the construction of a classification of the Russian Federation regions by quality of life indicators, comparison with traditional approaches and analysis of the features of the proposed methodology


quality of life, stratification, regional ranking, population’s quality of life indicators, pareto ratio, pareto dominance, pareto classification, pareto optimum

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