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A comprehensive analysis of Russian crime and conviction records

Vol. 15, Iss. 2, FEBRUARY 2019

Received: 10 October 2018

Received in revised form: 26 October 2018

Accepted: 14 November 2018

Available online: 15 February 2019


JEL Classification: D63, K42

Pages: 359–375

Smirnov V.V. I.N. Ulianov Chuvash State University (ChuvSU), Cheboksary, Chuvash Republic, Russian Federation

Mulendeeva A.V. I.N. Ulianov Chuvash State University (ChuvSU), Cheboksary, Chuvash Republic, Russian Federation

Subject The article discusses the crime and punishment in Russia.
Objectives The research aims to determine the sequence of agglomeration and importance of crime and conviction rates in the Russian Federation.
Methods The research draws upon a comprehensive approach involving cluster and neural network analysis.
Results Using the cluster and neural network analysis, we determined the agglomeration sequence and importance of criminality and conviction indicators in the Russian Federation. The findings allow to systemically consider the issue of decreasing the crime rate with the synergistic effect which is reciprocally generated by respective crimes and commensurate punishment. The comprehensive approach broadens hands-on knowledge of possibilities to decrease crimes and enhance the law and security protection activities inter alia by reducing a share of federal expenditures.
Conclusions and Relevance We identified key areas for preventing the main types of crime in terms of ascending hierarchical severity among alumni and students, men aged 30–49 and the unemployed, and determined the principal punishment. Governmental authorities can use the findings to plan crime curbing activities.

Keywords: cluster analysis, measure of punishment, agglomeration order, crime


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