Cluster Analysis Of Indicators Research Articles

КЛАСТЕРНЫЙ АНАЛИЗ РЕГИОНОВ РФ ДЛЯ ВЫЯВЛЕНИЯ ТЕРРИТОРИЙ – ДРАЙВЕРОВ УСТОЙЧИВОГО РАЗВИТИЯ: НАЛОГОВАЯ КОМПОНЕНТА

The relevance of tax clustering is due to the need for a competent scientifically grounded definition of territories that are drivers of economic growth. The aim of the study was to identify, on the basis of econometric methods, clusters of the regions of the Russian Federation by a set of indicators reflecting their tax status, tax administration, informatization of the tax environment. The Russian regions were grouped into clusters by a set of tax indicators based on official statistical data for 2018 using SPSS, Rstudio, Anaconda Navigator software. As a result of the anomalous values, five federal subjects were excluded from the analysis: Moscow, Sevastopol, Ingushetia, Khanty-Mansi and Yamalo-Nenets Autonomous Okrugs. Econometric analysis made it possible to conclude that there are three clusters of regions according to the analyzed parameters: 1) the least functionally proportional (7 regions), which have the lowest tax intensity of the gross regional product, the highest debt intensity of the gross regional product and the highest level of tax debt of the employed population, companies, and individual entrepreneurs; 2) medium functionally proportional (50 regions) with the lowest efficiency of tax administration, the highest coefficient of tax collection, the lowest level of taxation of the employed population and individual entrepreneurs (but not companies), the lowest level of tax debt of all analyzed subjects, and the lowest additional tax charges and sanctions for violation of tax legislation from tax audits, 3) the most comprehensively successful (22 regions), which are characterized by the highest tax intensity of the gross regional product and the highest level of tax revenues generated by the employed population, companies, and individual entrepreneurs. The regions of this cluster have the most effective taxation of value added and financial results of organizations. Among the regions of the third group, the leaders in terms of digital indicators are: Tyumen Oblast, Murmansk Oblast, Republic of Tatarstan, Leningrad Oblast. The study can develop in the following promising directions: 1) inclusion in the cluster analysis of indicators, not typical for the characteristics of the tax environment, that most fully reflect the influence of external diverse factors on the tax state of the regions; 2) extrapolation of the results to assess the tax status of the territories of other states; 3) the need to improve the tax clustering method based on artificial intelligence technology.

Determining the conditions for sustainable development of the Russian economy

Subject. The article addresses conditions for sustainable development of the Russian economy. Objectives. The purpose is to link the limit of sustainability of the Russian economy with required conditions. Methods. The study draws on the systems approach, using the methods of statistical, neural network, and cluster analysis. Results. I describe scientific ideas about sustainable development of economy, which have been developed in theoretical and practical activities. The World Bank data on environment, social affairs and governance on 17 key sustainable development topics, covering environmental, social and management categories, are used to determine conditions for sustainability of the Russian economy. The neural network analysis of the importance of indicators’ rate of increment shows a hierarchy of priorities. The cluster analysis of indicators’ rate of increment identifies significant clusters. The analysis of key priorities of sustainable development of the Russian economy enables to reveal significant deviations in political stability and in access to electricity. Conclusions. Conditions for sustainable development of the Russian economy are determined by their proximity to the United Nations Sustainable Development Goals. Identifying these conditions in the globalizing world creates an understanding of the real limit and opportunities of the country's development.

Neighborhoods and Homicide

A number of prevailing themes in lethal violence research are explored in this study of homicide for the period 1985 to 1994 in Chicago, Houston, and Miami. Homicide and census tract data from the National Consortium on Violence Research Data Center are combined to analyze 14,443 homicides across 1,409 census tracts. Cluster analysis of indicators of social disorganization and social control from studies at larger aggregations (i.e., cities, standard metropolitan statistical areas, and states) results in the classification of three distinct neighborhood types. This results in a rich description of overall and sex-specific rates of adult homicide victimization and offending in expressive and instrumental incidents. Implications for future research are discussed.