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Foundation of theory plus the algorithm for computing

Treshchevskii Yu.I. Voronezh State University, Voronezh, Russian Federation ( utreshevski@yandex.ru )

Journal: Economic Analysis: Theory and Practice, #7, 2016

Importance The peer-reviewed textbook by N.P. Lyubushin and N.E. Babicheva "Economics of the organization" is considered in terms of its contribution to the economic science in general and its relevance in the teaching process of higher educational institutions.
Objectives The purposes of the article are to overview the contents of the textbook, to consider methodological approaches to the presentation of the material, to give an adequate assessment of the textbook from the perspective of teaching of such disciplines as Economics, Management, and of its compliance with the Federal State Education Standard of Higher Education of a new generation (FSES of HE 3+).
Methods The research methodology draws upon the dialectical method of cognition, which is based on systems and integrated approaches.
Results The article highlights fundamental differences of the authors' approach to writing the textbook. In particular, unlike most of textbooks and manuals on economics of the organization, it combines theoretical tenets with streamlined computation algorithms. The textbook is illustrated with graphical materials and statistical data that complement the text. Furthermore, it is well structured, the material is presented from the general to the special, thus allowing the readers to specify economic problems as they proceed to next section.
Conclusions The textbook is written in plain language, it complies with the FSES of HE 3+ requirements. The authors' approach to theory presentation facilitates the students' assimilation of the training material and expands their interests. This will help avoid the dogmatic perception of economic operations and be prepared for practical activities.


The impact of capital investment, number of people employed in the economy and hydrocarbon extraction on sustainable development of industrial production in Russia

Lyubushin N.P. Voronezh State University, Voronezh, Russian Federation ( lubushinnp@mail.ru )

Babicheva N.E. Voronezh State University, Voronezh, Russian Federation ( sigaeva@mail.ru )

Kozinova A.T. National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation ( antonina.kozinova@gmail.com )

Kupryushina O.M. Voronezh State University, Voronezh, Russian Federation ( olgakupryushina@umc.vsu.ru )

Journal: Economic Analysis: Theory and Practice, #11, 2016

Subject The article addresses econometric analysis of relationship of macroeconomic indicators defining the sustainable development of industrial production in Russia, i.e. industrial production index, capital investment, number of people employed in the economy, coal mining, oil and gas production.
Objectives The purpose of the work is to create models of analytical quantification of industrial production index of Russia, taking into account periods of economic turbulence.
Methods We apply correlation and regression methods to analyze statistical data from 2003 through 2015. Dummy indicators are included in the models for quantification of abnormal changes in Russia's industrial production index during periods of economic instability.
Results The quantitative analysis suggests that the elasticity of industrial output is very small (less than 0.1) for capital investment, testifying to low efficiency of investment process; it is higher for oil production as compared with coal and gas, and is indicative of greater dependence of the Russian economy on oil; and it is significantly higher for the number of people employed in the economy as compared with other macroeconomic indicators, and this implies that the physical infrastructure of the country is obsolete.
Conclusions and Relevance The use of dummy indicators in the models enabled to neutralize the impact of periods of economic volatility on the model quality; to evaluate abnormal changes in industrial production index of Russia during crises that are not related to macroeconomic indicators included in the regression function; to predict changes in the industrial production of Russia depending on capital investment, number of people employed and production of hydrocarbons.


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