Regional Economic Development in Russia: Institutions, Regulations, and Structural Transformations (Springer Proceedings in Business and Economics) 3030398587, 9783030398583

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Springer Proceedings in Business and Economics

Niyaz Kamilevich Gabdrakhmanov Lenar Nailevich Safiullin   Editors

Regional Economic Development in Russia Institutions, Regulations, and Structural Transformations

Springer Proceedings in Business and Economics

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Niyaz Kamilevich Gabdrakhmanov Lenar Nailevich Safiullin

•

Editors

Regional Economic Development in Russia Institutions, Regulations, and Structural Transformations

123

Editors Niyaz Kamilevich Gabdrakhmanov Institute of Education National Research University Higher School of Economics Moscow, Russia

Lenar Nailevich Safiullin Institute of Management, Economics and Finance Kazan Federal University Kazan, Russia

ISSN 2198-7246 ISSN 2198-7254 (electronic) Springer Proceedings in Business and Economics ISBN 978-3-030-39858-3 ISBN 978-3-030-39859-0 (eBook) https://doi.org/10.1007/978-3-030-39859-0 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Introduction

The world has changed significantly because of the active development of scientific and technological advances over the past decade. A new emerging “economic reality” is characterized by the modernization of economic systems, globalization, computerization, informatization, humanization, ecologization, formation of a new model of socio-economic development of human society, etc. These changes require a new approach to the solution of various political, economic and social problems. The present-day economic reality provides a variety of forms and models of economic activity. Each country or region has its unique experience in solving the above-mentioned problems. Nowadays, the Russian economy is in recession. This inevitably has influenced the economic potential of the regions, which has led to worsening of the living standards of the population. In this regard, a review of the model of the regional socio-economic development and redesign of the regional economic policy is must. The formation of the new economy should include new ideas about the sources and factors of the economic development, including the potential of international cooperation, strengthening connection between science and education, knowledge transfer and investment in human capital. This book summarizes the results of the Russian studies in regional economics and aims to solve the following tasks: • to determine institutional peculiarities of the Russian model of the regional development; • to consider the mechanisms of innovative development of the Russian regions; • to analyze the main trends of the regional policy that provides the complex, balanced development of the region; • to analyze structural changes in regional economies in the context of global challenges. This book includes materials of the International Scientific Conference “Economics in the Changing World” held on June 26–27, 2018, at the Institute of Management, Economics and Finance of Kazan Federal University (Kazan, Russia). This conference combines the contributions of leading specialists in the field of v

vi

Introduction

management, territorial development and state, regional and municipal management. It also covers modern trends in the development of economic complexes and firms, economics of innovative processes, social policy, financial analysis, mathematical methods in economic research, etc. The presentations of the conference are grouped into three main categories, which form the basis of the parts of this book. The first part “Regional Economic Development” is devoted to the problems of transport development, agroindustrial complexes, investment policy and formation of methodological approaches to the effective work of small innovative enterprises of regional importance. The leading work “Institutions in the Context of the Regional Development of Russia” by scientists D. Vuković, N. Y. Vlasova and O. T. Ergunova is devoted to the role of the regional development institutions in the socio-economic regional development of the Russian Federation. The authors summarize the experience of managing the regional development institutions. They also conclude that it is necessary to increase the degree of influence on the development of the economic and social infrastructure of the regions of the Russian Federation. The work clarifies the nature and the content of the concept “regional institution of development,” as well as the main groups of the regional development institutions existing in Russia. The scientists develop trends toward increasing the efficiency of their functioning based on the survey. This survey is conducted for the heads of the development institutions the entities of the Russian Federation in terms of authority and degree of independence aimed to support investors and interact with them. The authors consider priorities in choosing current projects to develop the investment and social infrastructure, as well as the problems that hinder their efficient operation. The article “Infrastructure Projects and Transport Systems Financing in Russia” by L. R. Ikhsanova et al. is essential for this part. It is devoted to the problem of project financing of transport system development. Based on the analysis of Russian transport systems financing, the authors prove that during the economic development, the importance of budget financing is gradually decreasing. And nowadays, private and foreign investments and public-private partnership in financing of the infrastructure projects are becoming increasingly important. Using econometric tools, the authors evaluate the impact of the transport sector on the Russian GDP. Also, it is worth noting the work “The Influence of the Russian Economic Crisis on the Regional Peculiarities of Investment Activity” by M. N. Mironova, U. V. Mizerovskaya, E. V. Zhigalina and L. V. Shubtsova from the Peoples’ Friendship University of Russia. The authors present a comprehensive analysis of the negative trends of investments in the economy of Russia and its regions, caused by the economic recession. The analysis of the investment structure shows a decline in the share of state property and an increase in the private property. This is associated with an increase in own funds and the growth of investments in mining operations and real estate. The authors compare the regional priorities of domestic and foreign investors and show the general level of investment activity in the regions and the prospects for the regional development of the Russian economy.

Introduction

vii

The second part “Problems of Contemporary Regional Policy” is devoted to the government support for the regions, small enterprises and banks. The work “Food Stamps as a Method of the Parallel Government Support” by A. Nechaev, E. Ilina and M. Li is of particular interest. The scientists propose using food cards to support not only the population but also the regional agricultural commodity producers. It is proposed to limit the trade increment to 10% for essential products, which will allow developing the enterprises to find permanent market outlets. The low level of the trading margin will allow increasing the selling price for the manufacturer. This will contribute to the growth of the profitability of the main activity and indirectly stimulate the introduction of innovations. The authors believe that the proposed tool of support is promising in the context of the financial deficit since the funds allocated to the population stimulate agricultural production and increase the profitability of enterprises. The problem of investments and development of high-tech industries is one of the key subjects in this book. Thus, scientists O. I. Koloskova, I. V. Somina and M. Radosavljevic attempt to model the innovative activity of the Russian enterprises. They conclude that the most significant determinants of the innovation process in Russia are the market demand factor, investment-technological and business factors. The third part “Structural Changes in the Regional Economy” is devoted to the problems of particular sectors development in the regional economy. Efficiency assessment of the regional economy sectors is in the spotlight of I. A. Kabasheva, I. A. Rudaleva, A. V. Gorbatov and O. A. Krioshina in their work “The Effectiveness of the Regional Healthcare System: The Evidence from the Republic of Tatarstan (Russia).” The authors model the system of indicators for the assessment of the working efficiency of the health system in the Republic of Tatarstan. The authors assert that this model can be used for revising government programs and subprograms in health care. A number of works are dedicated to the development of the fuel and energy complex. E. A. Potapova, E. I. Bulatova, A. N. Kiryushkina and T. V. Polteva consider the development of the oil and gas industry through the prism of environmental safety. They note the existing paradoxical situation: The increase in the internal R&D expenses of the Russian oil and gas companies does not lead to the reduction in the pollutant emissions to the environment. Xiang Li, Aleksandr S. Bovkun, Galina M. Beregova, Aleksandr F. Schupletsov and Yullia A. Skorobogatova define approaches to building an effective system of project management in the field of renewable energy sources. They are convinced that the potential of renewable sources of energy in Russia is undervalued. But the use of modern management technologies will allow realizing power-efficient projects in this field. The work “Mathematical and Cartographic Modeling and Demographic Analysis of Rural Settlements” written by V. A. Rubtzov, N. K. Gabdrakhmanov, N. M. Biktimirov, M. R. Mustafin and R. R. Nurmieva (Institute of Management, Economics and Finance of Kazan Federal University) is also of relevance. The authors pay special attention to considering the issues of complex presentation of

viii

Introduction

spatial and coordination information about the population. The modern methods and solutions used in the demographic analysis are discussed in this work. The scientists show that the geographic and cartographic components often become the main way of studying natural or socio-economic systems, including the population at the regional level. The efforts of the authors, reviewers, participants and organizers of the conference make it possible to discuss the issues devoted to the economic development of the constituent entities of the Russian Federation. All the conference participants have made a great contribution to holding of the conference. The chairpersons of the program committee of the conference are N. G. Bagautdinova (Doctor of Economics, Professor, director of the Institute of Management, Economics and Finance of Kazan Federal University) and Yu. N. Moseikin (Doctor of Economics, Professor, head of the Department of National Economics of the Peoples’ Friendship University of Russia). The gratitude should also be expressed to the conference organizers from the higher educational institutions of Kazan Federal University and RUDN University. Kazan, Russia June 2018

Niyaz Kamilevich Gabdrakhmanov Lenar Nailevich Safiullin

Contents

Regional Economic Development Regional and Country Aspects of Compensating for Environmental Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. N. Kaigorodova, G. K. Pyrkova, A. A. Mustafina and D. P. Alyakina

3

Modeling of Transport and Logistical Processes at the Local Level: The Case of Tatarstan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. S. Nikolaev and D. O. Egorov

15

Management of Investment Attractiveness of the Russian Federation Regions: Practical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irina S. Glebova, Svetlana S. Berman and Ruslan R. Galiakhmetov

31

Infrastructure Projects and Transport System Financing in Russia . . . . Liliana R. Ikhsanova, Rezeda R. Shigapova, Joanna Koczar, Zarina I. Agliullina, Madina I. Agliullina and Maria E. Syslova

41

The Influence of the Russian Economic Crisis on the Regional Peculiarities of Investment Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marina N. Mironova, Uliana V. Mizerovskaya and Ludmila V. Shubtsova

53

The Mechanism of Integration of Innovations in Technological Processes (by the example of the Construction Sector) . . . . . . . . . . . . . . Petr I. Ospishchev and Maja Andjelkovic

67

Tax Capacity of the Russian Federation Constituent Entities: Problems of Assessment and Unequal Distribution . . . . . . . . . . . . . . . . N. M. Sabitova, Ch. M. Shavaleyeva, E. N. Lizunova, A. I. Khairullova and A. Zahariev Key Indicators of Regional Bank Activities: From Theory to Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. R. Tagirova

79

87

ix

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Contents

Development of Agroindustrial Companies: The Stakeholder Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Maria Tyapkina, Maria Vinokurova, Sergey Vinokurov and Huizi Li Theoretical Aspects of the Optimization of the Regional Innovative Enterprises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Sergey V. Zakharov, Evgeniya V. Bibaeva and Olga V. Nikitina Institutions in the Context of the Regional Development of Russia . . . . 123 Darko Vuković, Natalya Y. Vlasova and Olga T. Ergunova The Problems of Contemporary Regional Policy The Formation of the Single Stock Market by Russia and Kazakhstan in the Conditions of Development of the Eurasian Economic Union . . . 135 Tatiana K. Blokhina, Elena Y. Goryunova and Dmitry V. Kovalevich Financial Engineering Tools for Stress Testing in Banks . . . . . . . . . . . . 147 Vadim L. Babur, Adel A. Daryakin, Nailya F. Yalalova, Aliya A. Ahmadullina and F. A. Feras The Public–Private Partnership as a Part of High Technologies of Modern Russia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Yury A. Doroshenko, Lyudmila A. Minayeva, Irina O. Malykhina, Zhanna N. Avilova and Zivota Radosavljevic Food Stamps as a Method of the Parallel Government Support . . . . . . . 171 Andrey Nechaev, Elena Ilina and Miaomiao Li Efficiency Factors of the Innovative Activity in High-Tech Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Olga I. Koloskova, Irina V. Somina and Milan Radosavljevic Establishing the Innovative Economic System of Russia Through Gaining Foreign Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 T. A. Sibgatullin, M. F. Musallyamova and C. Schnöller Inter-budgetary Transfers from the Standpoint of Russian Federalism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Irina I. Zedgenizova and Elena L. Vlasova Leasing as a Tool for Financing of Innovative Projects . . . . . . . . . . . . . 223 Y. N. Barykina, E. I. Gavrikova and M. L. Tang Structural Changes in the Regional Economy The Effectiveness of the Regional Healthcare System: The Evidence from the Republic of Tatarstan (Russia) . . . . . . . . . . . . . 233 Irina A. Kabasheva, Irina A. Rudaleva, Alexandr V. Gorbatov and Olga A. Krioshina

Contents

xi

A Transition to the Innovative Model of the Oil and Gas Industry Development as an Integral Part of Environmental Safety . . . . . . . . . . . 243 E. A. Potapova, E. I. Bulatova, A. N. Kiryushkina and T. V. Polteva World Manufacturing Industries in the Post-industrial Society: Tendencies and Regional Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Irina Rodionova, Tatiana Kokuytseva and Cezary Mądry Mathematical and Cartographic Modeling and Demographic Analysis of Rural Settlements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 V. A. Rubtzov, N. K. Gabdrakhmanov, N. M. Biktimirov, M. R. Mustafin and R. R. Nurmieva Development of the Project Management Mechanism Based on Renewable Energy Sources for the Northern Regions of Russia . . . . 275 Xiang Li, Aleksandr S. Bovkun, Galina M. Beregova, Aleksandr F. Schupletsov and Yullia A. Skorobogatova Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285

Regional Economic Development

Regional and Country Aspects of Compensating for Environmental Damage G. N. Kaigorodova, G. K. Pyrkova, A. A. Mustafina and D. P. Alyakina

Abstract In this chapter, the issues of the level of environmental pollution in Russia during the economic crisis are studied. We bring the results of the data analysis of the Russian population morbidity rate, the cases of environmental pollution of extreme nature, and the structure of environmental costs. The work analyzes the relationship between the values of environmental protection investment and the level of GDP.

1 Introduction The reproduction of the environment should act as an important economic principle. However, the implementation of this principle affects the policy of prices and taxes and, ultimately, the entire economic policy of the state. To implement the state policy in the field of environmental protection and formation, a whole range of measures is necessary (Garcia and Fonseca 2018; Gubaidulina and Kalmukova 2012; MartinOrtega et al. 2011; Rao et al. 2014). At the same time, a number of questions arise. The first question is the question of size, and level of damage and expenses. How to define economic damage from pollution? One option is to consider the sums necessary for the restoration of the produced environmental destruction (Mustafina et al. 2017). Another option is to consider the sums compensating decreased welfare of the population and revenues of other companies (Kundakchan and Gubaidulina 2014). In the first case, the damage will be defined very conditionally as far as not any destruction of the environment can be restored. At the second option, compensation is not related to the nature protection functions. In addition, there is often a problem of identifying responsibility for causing damage and calculating of its actual scale. For example, how to express quantitatively the damage caused to the population when using the polluted water or after volley discharge of pollutants into the atmosphere? Therefore, it is necessary to take into consideration the fact that there will be measurement errors when determining the valid damage (Levina et al. 2015). G. N. Kaigorodova (B) · G. K. Pyrkova · A. A. Mustafina · D. P. Alyakina Kazan Federal University, Kremlevskaya Str., 18, Kazan 420008, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_1

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G. N. Kaigorodova et al.

The second question is who should be responsible for compensation of actual or possible damage. It can be the manufacturer who is responsible for the damage or the consumer who demands “polluting” goods (Velichko et al. 2017). In the market economy, the manufacturer can shift regular environmental payments/taxes or onetime fines on the consumer of one’s products to the extent the market accept the increase in the price of this product (Gubaidulina et al. 2015; Lisitzina et al. 2015). On the other hand, the consumer demanding such product often has no alternative. This can happen for various reasons, for example, due to the low price of such products and low levels of their income. The manufacturing company can also be a local monopolist on a certain territory (Kokh et al. 2016). It is obvious that the damage should be compensated by those who are immediately responsible. This is the problem of internalizing external effects (Gubaidulina and Ivanova 2017). The harmful effects arising during the activity of an economic entity are called external damage. They are external because of their impact on other economic entities and/or the population. The internalization of external effects is to transfer the damage, which is external for the polluter enterprise, to internal one, i.e., to its costs. Various scientists examine in their works the financial possibilities of the state to minimize damage to the environment. Thus, Stoever and Weche examine the impact of the environmental state regulation on the performance of firms and investment behavior (Stoever and Weche 2018). In particular, the increase in the environmental tax, in the authors’ opinion, does not adversely affect the economic indicators of firms and their competitiveness. Marin et al. (2018) carry out an extensive study of the influence of emissions trading on a series of economic indicators of firms. The authors show that the firm transferred most of the expenditures and increased labor productivity to its customers. Andersen and Greaker note in their work that state governments are limited in their budget expenditures (Andersen and Greaker 2018). These restrictions are perceived especially acutely when the question concerns environmental costs. Therefore, the governments seek to replenish financial resources to finance the expenditures on eliminating damage caused to environment. In this case, one of the mechanisms is emissions trading. The work identifies what financial implications may arise when realizing emissions trading between countries. This is the relevant research in the study of the issues of environmental damage compensation on a global scale. There are studies on relationships between economic growth and environmental pollution (Shang et al. 2018). Meng and Huang conclude in their work that formation of the environmental policy of a state must take into account regional differences within the country (Meng and Huang 2018). Many authors support the idea that sustainable development along with environmental protection is possible and achievable only on a global scale (DanilovDanil’yan 2009). The protection of ecosystems and recovery of damages caused to environment are a necessary condition of humankind existence and a decisive factor for economic growth.

Regional and Country Aspects of Compensating for Environmental Damage

5

2 Methods Enterprises try to minimize their expenses and do not agree to increase the expenditures on the environmental protection. The state should legally and economically force them to do this (Fakhrutdinova et al. 2013). The state should perform the functions concerning environmental protection and compensation for environmental damage. From this perspective, the state needs to accumulate financial resources. Various kinds of taxes, fees, and regular payments for the use of natural resources are aimed at reproducing the resources consumed by society. If we are talking about environmental pollution, then the source of funding, in addition to the funds allocated by the state, can be payments for negative impact on the environment. Also it is necessary to systematize laws documents and bring them to the integrated concept of “environmental insurance” (Alyakina and Khisamova 2014). Within the framework of our research, we put forward a hypothesis: The drop in the rates of economic growth leads to the increasing level of environmental pollution. This occurs against a background of a decrease in the volume of capital investments in environmental protection. In our study, we have used the technique proposed by Professor M. R. Safiullin. We have used the conceptual foundations of his systemic functional model of a market economy (Sayfudinova et al. 2016). To determine the correlation between specific macroeconomic indicators and the indicators characterizing the contribution of society to protection of environment, the method of correlation analysis was used. The general economic–mathematical model of the graph depicted in the quadrant of Fig. 1 is as follows: Iret = F(GDPt ), where I t is investment in the fixed assets aimed at the protection of the environment and GDPt is the gross domestic product.

3 Results and Discussion At first, we studied the dynamics of environmental pollution cases in the period of economic recession of 2015–2017 in Russia. The largest number of the reported high and extreme cases of air and water pollution occurs in 2016, which is the most difficult year for the Russian economy (Fig. 1). This includes cases where the level of harmful substances contamination exceeds ten maximum permissible concentrations (MPC) (Official site Ministry of Natural Resources and Ecology of the Russian Federation 2017).

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G. N. Kaigorodova et al.

2115 625

2017

45 2 2342 636

2016

62 1 2117 597

2015

35 5

0

500

1000

1500

2000

2500

The number of incidences of water bodies pollution (high level) The number of incidences of water bodies pollution (extremely high level) The number of incidences of air pollution (high level) The number of incidences of air pollution (extremely high level)

Fig. 1 Dynamics of cases of harmful environmental pollution in Russia

The environmental factor limits national well-being: The health of the population worsens, and the number of genetic disorders increases (Kaigorodova and Mustafina 2014). For the period of 2013–2016, the morbidity of the circulatory system diseases increased by 8.5%; endocrine diseases and metabolic disorders—by 33.5%; infectious diseases—by 6.2%; diseases of the digestive system—by 3.4%. Congenital anomalies and chromosomal abnormalities increased by 5.7% in 2013–2016. The number of people with oncopathology is steadily increasing by 11.8%. In 2016, the number of newly diagnosed malignant neoplasms amounted to 409.0 per 100,000 people compared to 288.0 in 1996. The greatest value is the number of diagnosed malignant neoplasms in the mammary gland in women—87.1 per 100 thousand people (in 1996, the number was 50.0) (Federal Public Statistics Service 2017). In a number of factors affecting the health of the population, the air, water, and soils pollution, the accumulation of a significant amount of harmful substances in them plays a significant role (Ustinov et al. 2016). For example, there is a relationship between the air pollution with aerosols and the level of children’s overall morbidity, between the concentration of sulfur gas in the atmosphere and the mortality from cardiovascular diseases, between air pollution with benzapyrene and the incidence of lung cancer in the population (Kaigorodova et al. 2017). For benzapyrene, there were five cases of the MPC exceeding 28.4 (!) times in Novokuznetsk in 2017. In Magnitogorsk, there were seven cases of the MPC exceeding 27.7 times, in Chita there were seven cases where the MPC exceeding was 31.4 times. These are only the registered cases of extremely high pollution.

Regional and Country Aspects of Compensating for Environmental Damage 100

7

1,6

1,4 90 80 70 75,9

60

%

86,9 50 40 30 20

14,9

6,1

10 7,8

5,4

0 2015

Federal budget Russian Federation Own resources of organizations

2016

The Russian Federation subjects budgets and local budgets Other sources

Fig. 2 Distribution of investments in capital stock aimed at environmental protection and rational use of natural resources, in %

Compensation for environmental damages occurs through investments of environmental nature. The characteristics of the sources of financing investments in capital stock aimed at protecting the environment are shown in Fig. 2 (Federal Public Statistics Service 2017). The most significant share belongs to own funds of organizations with an increase of this share by 11.0% in 2016. This increase is accompanied by lower costs in federal budget and a significant costs decrease in the budgets of constituent entities of the Russian Federation and of local authorities. As it is known, advanced industrial countries spend about 1.5–3% of GDP to maintain the state of environment. Thus, according to Rosstat (Russian Federal State Statistics Service), in 2013–2014 the share of expenses on environmental protection in the Netherlands reached 1.6% of GDP, in Japan—1.3%, in Belgium—0.9%, and in Germany—0.6% (Federal Public Statistics Service 2017). According to the digest “Russia and the countries of the world. 2016”, the total costs for environmental protection in Russia in 2014–2015 reached 0.7% of GDP annually (Table 1). This figure refers to all the costs incurred by the state, the enterprises, and the population with the aim of protecting the environment. The table shows the costs for environmental protection in the Russian Federation and in the Republic of Tatarstan (region of the Russian Federation). At the same time, the emissions of harmful substances into the atmosphere per capita are very high in Russia (Table 2). The figures are provided for advanced industrial countries following the results of 2014, and for the RF and the CIS countries—following the results of 2015. The pollutant-emission level is either much higher or comparable to the emissions per capita in the developed countries. At that,

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G. N. Kaigorodova et al.

Table 1 Costs for environmental protection Indicators

2012

2013

2014

2015

2016

Costs for environmental protection in the Russian Federation, bln. RUB

445.8

479.4

535.9

562.4

584.2

0.7

0.7

0.7

0.7

0.7

19.4

22.0

19.8

19.3

17.9

1.3

1.4

1.2

1.0

0.9

The share of costs for environmental protection in the GDP of the Russian Federation, % Costs for environmental protection in the Republic of Tatarstan, bln. RUB Share of environmental protection costs in the gross regional product of the Republic of Tatarstan, %

Table 2 Emissions of pollutants into the atmosphere in terms of kg per capita

Countries

Sulfur dioxide

Nitrogen oxides

Russia

28.0

12.2

32.8

Germany

1.7

14.9

36.1

The Netherlands

1.7

12.8

33.6

Japan

5.4

10.0

18.0

Spain

2.8

17.3

43.0

USA

14.1

37.2

159.1

6.0

5.0

8.0

40.5

13.9

25.7

Belarus Kazakhstan

Carbonic oxide

Ukraine

19.4

5.5

17.9

Uzbekistan

10.0

2.2

2.4

the level of costs is lower. Considering that Russia occupies one-sixth of the land, these costs are extremely small. Among industrialized countries, it is worth allocating the USA by the number of emissions in the atmosphere. However, in the USA the costs of conservation make about 2% of GDP annually, which, considering the size of the USA budget, makes a wide margin in absolute figures (Yermoshenko and Trynchuk 2016). Total indicators characterize the impact of business activities on environment in Russia in Table 3. The data on the impact of business activities on environment in the Tatarstan Republic are given in Table 4. The strategy making for the regional development in the Tatarstan Republic, reflecting the promising indicators of its growth, is a very complicated process, taking into account a large number of constantly changing factors of the economy (Kvon et al. 2017). The implementation of the investment policy requires an analysis and assessment of the investment in environment state (Table 5).

Regional and Country Aspects of Compensating for Environmental Damage

9

Table 3 Main indicators characterizing the impact of business activities on environment Indicators Water withdrawal from natural water reservoirs for use, bln. cu. m

2000

2010

2014

2015

2016

75.9

69.7

63.2

60.8

61.3

Recycled and consistent use of water, bln. cu. m

133.5

140.7

136.6

138.8

137.9

Discharge of polluted sewage, bln. cu. m

20.3

16.5

14.8

14.4

14.7

Emission of pollutants into atmosphere, mln. tonnes:

32.3

32.4

31.2

31.3

31.6

– From stationary sources

18.8

19.1

17.5

17.3

17.3

– From mobile sources

13.5

13.2

13.8

14.0

14.3

Production and consumption wastes), mln. tonnes

127.5

3734.7

5168.3

5060.2

5441.3

– Including hazardous

127.5

114.4

124.3

110.1

98.3

Table 4 Main indicators characterizing the impact of business activities on environment in the Republic of Tatarstan Indicators

2014

2015

2016

Water withdrawal from natural water reservoirs for use, mln. cu. m

786.7

775.1

792.9

Share of total for the Russian Federation, %

1.24

1.27

1.29

Recycled and consistent use of water, mln. cu. m

4635

4794

5252

Share of total for the Russian Federation, %

3.39

3.45

3.81

Discharge of polluted sewage, mln. cu. m

439.4

382.5

325.2

Share of total for the Russian Federation, %

2.97

2.66

2.21

Emission of pollutants into atmosphere from stationary sources, thou. tonnes

293.6

293.6

338.3

Share of total for the Russian Federation, %

1.68

1.70

1.96

Emission of pollutants into atmosphere from mobile sources, thou. tonnes

323.9

325.0

328.7

Share of total for the Russian Federation, %

2.35

2.32

2.30

Atmospheric pollutants captured and neutralized, thou. tonnes

426.6

437.3

495.1

Share of total for the Russian Federation, %

1.20

1.19

1.17

Table 5. Costs of protecting the environment in the Republic of Tatarstan, bln. rub. Indicators Fixed capital investments for environmental protection and rational use of natural resources Current costs of environmental protection Capital repair costs of production of the basic funds for environmental protection

2013

2014

2015

2016

9.3

4.1

6.6

4.6

10.3

13.0

10.0

10.9

2.3

2.7

2.7

2.4

Other costs

0.1

0.1

0.1

0.1

Total costs

22.0

19.9

19.4

18.0

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G. N. Kaigorodova et al.

The maximum amount of investment aimed at protecting the environment in the Republic of Tatarstan in 2013 is 22.0 billion rubles. In 2014–2016, the costs of this nature are declining by 18.2%. We should also take into account the fact that the total amount of costs associated with maintaining the normal state of the environment includes, in addition to direct costs for nature, scientific activities in this field, educational activities, and the costs of maintaining the state apparatus dealing with environmental issues. Therefore, it is expedient, first, to base on the study of investment in the fixed assets aimed at protecting the environment in the Russian Federation (Table 6). The data on GDP, investments in the fixed assets, and investments in the fixed assets aimed at protecting the environment are given relative to the prices of 2011 with the account of statistical deflators. The maximum amount of investment in the fixed assets aimed at protecting the environment in 2014 is 128.3 billion rubles, or 0.2% of Russia’s GDP. The interrelation is also obvious—an increase in GDP in 2014 was accompanied by an increase in capital costs of this nature. In 2015–2016, the costs of this nature were declining notably at a faster rate than the decline in GDP. This leads even to a greater reduction in the share of investment in environmental protection. Table 6 Investments in environmental protection in the Russian Federation (in the prices of 2011) Criteria

2011

2012

2013

2014

2015

2016

GDP, bln. RUB.

60,282.5

62,478.4

63,600.2

64,067.1

62,457.6

62,358.7

95.7

106.8

107.7

128.3

113.7

101.1

Investments in the fixed capital aimed at environmental protection, bln. RUB. Share of investments in the environmental protection in GDP, % Investments in the fixed capital, bln. RUB. The share of investment in the fixed capital in GDP, % Share of investments in the environmental protection in the investment structure, %

0.159

11,035.7

18.31

0.867

0.171

11,536.3

18.46

0.926

0.169

11,696.8

18.39

0.921

0.200

11,246.2

17.55

1.141

0.182

10,409.1

16.67

1.092

0.162

10,625.5

17.04

0.951

Regional and Country Aspects of Compensating for Environmental Damage

11

Investment in fixed assets aimed at protecting the environment, bln. rub

Investments in the fixed assets in general also tend to decrease slightly. However, the share of investment in the fixed assets relative to Russia’s GDP after a small reduction in 2015 increased again in 2016. As part of investments in capital expenditures in general, capital costs of environmental protection are less than 1%. The correlation analysis showed a sufficiently high degree of correlation between investments in the fixed assets aimed at environmental protection and GDP. In the framework of our hypothesis, we have made the assumption that there is a high correlation between investments in the fixed assets in general and those related exclusively to environmental protection. It was assumed that the introduction of new technique, technology, modernization, and renewal of the fixed assets should be accompanied by investments in the environmental protection. However, the correlation analysis revealed a weak correlation between these indicators, and that we need a more detailed analysis, for which there are not enough statistical data. The regression analysis carried out on the statistical data analysis made it possible to obtain the results reflected in Fig. 3. For the period of 2011–2016, the regression equation of the relationship of capital investments in environmental protection has the form of Y = 0.0069x – 322.57. The linear equation shows that with GDP growth, the country’s capital investment in environmental protection will grow. However, GDP growth should be significant in order to have a positive effect. A certainty index of approximation is high enough (0.6389). 140

y = 0,0069x –322,57 2

120

R = 0,6389

100 80 60 40 20 0

60000 60500 61000 61500 62000 62500 63000 63500 64000 64500 65000

GDP, bln. rub Fig. 3 Interconnection of investments in fixed assets aimed at environmental protection and the GDP of the Russian Federation

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G. N. Kaigorodova et al.

According to the forecasts of the Bank of Russia, the GDP growth in 2018 will be about 1.7–2.2%. Expected inflation is from 3 to 5%. We assume that this will ensure the growth of the costs of a capital nature for the protection of the environment.

4 Conclusion The costs for compensating environmental damage should be imposed primarily on the enterprises. In our country, it is necessary to resolve the issue of internalization of external effects. During the crisis of the economy, there was an increase in environmental pollution. This is followed by a reduction in costs of environmental protection and compensation for environmental damage. Russia is characterized by a high level of environmental pollution while reducing the costs of maintaining nature in a normal state. The decrease in economic growth leads to a reduction in investment in capital stock related to environmental protection. However, the available statistics is not sufficient for a more complete analysis of these correlations. Thus, the further study is needed. It is advisable for statistical authorities to provide data on the volume and structure of the enterprises’ costs of environmental protection. The carried out work also shows that the reduction of environmental costs leads to an increase in the morbidity of the country’s population. It is necessary to specify the tools of the state environmental policy. There is no individual concept of environmental insurance, although certain federal laws regulate mandatory types of liability insurance, including for pollution of the environment. It is necessary to systematize these documents and bring them to the integrated concept of “environmental insurance.” Acknowledgements The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.

References Andersen, J. J., & Greaker, M. (2018). Emission trading with fiscal externalities: The case for a common carbon tax for the non-ETS emissions in the EU. Environmental and Resource Economics, 71(3), 803–823. Alyakina, D. P., & Khisamova, G. F. (2014). Methodology for rating of insurance portfolio. Mediterranean Journal of Social Sciences, 5(24), 137–140. Danilov-Danil’yan, V. I., Losev, K. S., & Reyf, I. E. (2009). Sustainable development and the limitation of growth (p. 262). Springer-Verlag Berlin Heidelberg. Fakhrutdinova, E., Safina, L., & Shigapova, D. (2013). Legislative provision of the quality of working life in Russia. World Applied Sciences Journal, 27(13), 92–96. Federal Public Statistics Service. (2017). Available via Rosstat. https://www.gks.ru. Cited 25 Dec 2017.

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Garcia, L. C., & Fonseca, A. (2018). The use of administrative sanctions to prevent environmental damage in impact assessment follow-ups. Journal of Environmental Management, 219, 46–55. Gubaidulina, T., & Ivanova, N. (2017). Theoretical and practical aspects of quality of life in the economic security system. Journal of Engineering and Applied Sciences, 12(19), 4990–4994. Gubaidulina, T. N., & Kalmukova, A. A. (2012). Investment provision of sustainable ecological and economic development of modern society. Kazan Sciences, 11, 80–83. Gubaidulina, T. N., Nugumanova, L. F., & Antonova, N. V. (2015). Development of a strategic management system based on realizing the potential of integrated entity. Mediterranean Journal of Social Sciences, 6(3), 746–750. Kaigorodova, G. N., Kosarenko, N. N., Shapovalov, D. A., Sayfutdinova, G. B., Sharonov, I. A., & Ignatov, S. B., et al. (2017) Integrative module technology of future engineers training in the field of ecological-economic safety. Eurasian Journal of Analytical Chemistry,12(7), 1079–1088. Kaigorodova, G. N., & Mustafina, A. A. (2014). The influence of forms of insurance coverage organization on population’s life quality. Mediterranean Journal of Social Sciences, 5(24), 118– 123. Kokh, I. A., Kaigorodova, G. N., & Mustafina, A. A. (2016). The research of conditions of insurance portfolio formation in the Russian practice. International Business Management, 10(23), 5657– 5662. Kundakchan, R. M., & Gubaidulina, T. N. (2014). Quality of life of the Russian population: Analysis of selected its components in modern conditions. Bulletin of the Kazan State Agrarian University, 9 (4), 40–43. Kvon, G. M., Faleeva, L. V., Pyrkova, G. K., Alyakina, D. P., Mustafina, A. A., & Kryukova, N. I., et al. (2017) Strategic priorities of regional investment activity. Eurasian Journal of Analytical Chemistry,12(7), 1099–1106 Levina, E. Y., Pyrkova, G. K., & Zakirova, C. S. (2015). Socio-economic systems strategic development managing. Journal of Sustainable Development, 8(6), 76–82. Lisitzina, T. B., Ibatullova, Y. T., Mustafina, A. A., Sadykova, E. R., Kozhanova, M. B., & Shaikhlislamov, A. K., et al. (2015). Principles of professionally-motivating training of students majoring in “tourism” and the rules for their implementation in practice. Mediterranean Journal of Social Sciences, 6(2), 15–21. Marin, G., Marino, M., & Pellegrin, C. (2018). The impact of the European emission trading scheme on multiple measures of economic performance. Environmental and Resource Economics, 71(2), 551–582. Martin-Ortega, J., Brouwer, R., & Aiking, H. (2011). Application of a value-based equivalency method to assess environmental damage compensation under the European Environmental Liability Directive. Journal of Environmental Management, 92, 1461–1470. Meng, L., & Huang, B. (2018). Shaping the relationship between economic development and carbon dioxide emissions at the local level: evidence from spatial econometric models. Environmental and Resource Economics, 71(1), 127–156. Mustafina, A. A., Kaigorodova, G. N., Pyrkova, G. K., Alyakina, D. P., & Syvorotkina, K. A. (2017). Sanatorium and resort treatment as a factor of economic development in the republic of Tatarstan. Astra Salvensis, 2, 267–276. Official site Ministry of Natural Resources and Ecology of the Russian Federation. (2017). Available at https://www.mnr.gov.ru. Cited 25 Dec 2017. Rao, H., Lin, Ch., Kong, H., Jin, D., & Peng, B. (2014). Ecological damage compensation for coastal sea area uses. Ecological Indicators, 38, 149–158. Sayfudinova, N. Z., Safiullin, M. R., Zafiullin, A. R., & Zainullina, M. R. (2016). Modeling of economic system of the development of the Russian Federation system. Journal of Economics and Economic Education Research, 17, 334–346. Shang, W., Gong, Y., Wang, Z. J., & Stewardson, M. J. (2018). Eco-compensation in China: Theory, practices and suggestions for the future. Journal of Environmental Management, 210, 162–170.

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Modeling of Transport and Logistical Processes at the Local Level: The Case of Tatarstan R. S. Nikolaev and D. O. Egorov

Abstract Transport flows at the local level (within the boundaries close to counties) are the basic links (elementary units), from which the formation of more powerful flows begins. This is one of the most complicated links of the logistics chain, which is the primary element of the “hinterland” formation for the transport and logistics nodes. The potential of the transport and logistics hub and the choice of mechanisms for optimizing transport and logistics processes at more global levels (meso- and macro-levels) will depend on the flows efficiency in the hinterland area. The work presents approaches to the study and modeling of transport-logistical processes at the local level, as well as the mechanisms for identifying the most acceptable space-time organization variants of transport and logistics processes. Modeling was carried out by the example of Yelabuga municipal district in the Republic of Tatarstan (Russia).

1 Introduction Transport and logistics processes are typical for all levels of the territorial hierarchy. In any case, the territories that do not generate commodity flows consume the product and require certain transportation expenditures. The spatial distribution and commodity specialization of physical flows in relation to the local environment where they take place remain largely unexplored (Ducruet and Itoh 2016). There are three main levels of transport and logistics systems: local, regional, and global (Rodrigue et al. 2017; Ortiz-Astorquizaa et al. 2018; Sheu and Lin 2012). The local level is a part of the regional one. Local transport-logistics processes arise in the counties (municipalities), as well as inside and between the settlements that make up its structure. A distinctive feature of this level is the low-flow intensity. As a rule, transport and logistics operations at this level are carried out directly by economic entities without the involvement of specialized transport and logistics agents. While R. S. Nikolaev (B) Perm State University, Perm, Russia D. O. Egorov Kazan Federal University, Kazan, Russia © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_2

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R. S. Nikolaev and D. O. Egorov

this level is often ignored, it can lead to appearance of social and economic isolation (Egorov and Nikolaev 2016), increasing inequality and violating the principles of sustainable development. One should note several properties of the local level, which makes it important in the analysis of transport and logistics processes: – The local level actively participates in the formation of gravity zones, attraction areas, and hinterlands for larger nodes; – The local level is the “last mile” logistics, considered as one of the most complicated (Aized and Srai 2013); – Single expenditures at the local level are almost invisible in the final value of goods, but due to multiplicity they are significant; – The local-level logistics is socially oriented and solves the problems of rural settlements and small towns, creating favorable conditions for their sustainable development. Transportation and mobility are central to sustainable development; they are means to improve social equity, health, resilience of cities, urban–rural linkages, and productivity of rural areas (Makarova et al. 2017; Cetinkaya et al. 2011; Gudmundsson et al. 2016; Litman 2014; McKinnon et al. 2015). Transport and logistics processes are operations in the field of movement, accumulation, (de)consolidation and distribution of goods, services, finance, information, and people. It covers the whole transport chain from production to consumption and includes inbound, outbound, internal, and external movements (Huber et al. 2015). Optimization measures at the local level are rather limited. Basic solutions for this level are multimodal terminals and operations, autonomous vehicles, an intelligent transportation system (ITS), infrastructure usage, economies of scale, and behavior/educational issues (Engström 2016).

2 Methodology The research is based on the microsimulation of economic agents’ behavior in the sale and supply. Multi-agent systems allow for the modeling of the local interactions of these actors (Ben-Akiva et al. 2013; Liedtke 2009). Having a microscopic modeling base for the transport of goods would be a significant improvement for transport forecasts and the assessment of policy measures (Liedtke and Schepperle 2004). Integrating the decisions and the behavior of logistics service providers into freight transport models is essential for accurate describing of future developments in freight transport systems (Rolko and Friedrich 2017). The basic model of economic agents’ behavior is the direction of sales flows to the nearest consumer able to use them (consume or transform). Business entities transport products to points with the greatest consumer potential and least transportation costs. For local territorial systems, the sales in the nearest zone are typical, but if there are necessary productions or transport-logistics infrastructure.

Modeling of Transport and Logistical Processes at the Local Level …

17

Fig. 1 Determining of the transport and logistics optimization appropriateness at the local level

Road transport is the most important for the “last mile” logistics. It is necessary to identify the hierarchical structure within the logistics buyer–supplier network and the distribution within the modal interaction of different territorial systems (Beckers et al. 2017). At the local level, as centers of attraction and distribution, it is advisable to consider nodes that meet criteria: population more than 5 thousand people; multimodality and (or) intermodality; availability of warehouse infrastructure. The study of transport and logistics processes at the local level and the definition of opportunities for their optimization are carried out in several stages (Fig. 1). Estimation of the generation and consumption potential of commodity flows was established through statistical and financial indicators that greatest extent tied to the territory: population; taxable incomes and social payments of the population; proceed from the activities of economic entities (taking into account the location of employees and the value of fixed assets); production and sale in physical terms; the number and carrying capacity of trucks. The volume of existing commodity flows was identified through generation and absorption capacity analysis.

3 Results and Discussion Approbation of the model was carried out by the example of Yelabuga County (municipal district) in the Republic of Tatarstan, which includes one city district and 16 rural settlements. The population of the county is 85.6 thousand people. The branches of specialization are agro-industrial production, manufacturing, and mining. Here is a special economic zone “Alabuga.”

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The external infrastructure of transport logistics is characterized by the access to the nodes. Logistics hubs hold a significant role today and this significance—especially regarding management and handling—is still increasing (Hesse and Rodrigue 2004). The external transport and logistics contour of the county is represented by six nodes—Naberezhnye Chelny, Mendeleevsk, Sosnovka, Mamadysh, Kizner, and Yelabuga (Fig. 2). At present, there are five automobile and two railways “inputs,” three monomodal, and two multimodal border connections. The internal transport-logistic framework is represented mainly by monomodal elements (Fig. 3). The only multimodal node in the county is Yelabuga city. The main frame-forming element is the M-7 highway, which determined the typological Fig. 2 External transport-logistic contour (framework)

Fig. 3 Internal transport-logistics network (framework)

Modeling of Transport and Logistical Processes at the Local Level …

19

features of the county’s transport system. The main flows pass along the multimodal meridional trunk (eastern part of the district) and the southern latitudinal corridor. Both of them have an extremely important transit position. For unimodal territorial systems, the availability of external transport-logistic nodes is important. These nodes have a greater degree of valence (counts of the transport direction), modality, and logistization, and provide a possibility to accumulate, consolidate, transship, and distribute different flows. More developed transportation and logistics nodes form a “hinterland” (a zone of “gravitation” or “attraction”) (Hoggart 2016). The attraction strength depends on: node power, degree of valence and modality, the level of development and configuration of the transport network in the hinterland zone, the availability of alternative nodes and transport options. Despite the proximity of the Mamadysh to the western part of the county, the greater capacity and modality of Yelabuga allow one to include all the district territories into the zone of its hinterland.

3.1 Transportation Volumes The pattern of freight flows at the local level is hierarchical and varies depending upon whether the flows are generated by wholesale trade activities or by manufacturing (Guerrero and Proulhac 2014). The potential of goods flow generated by settlement (Q gen ) is distributed through proportion of the property of economic entities and their revenues: Q gen = Q GEN · df · kr

(1)

Q GEN —volume of the goods flow in the county; df —share of productive assets in settlement i from all funds in the region; kr —correction coefficient for the volume of financial and economic activity. Absorption of the commodity flow (Q abs ) is carried out in the process of both final consumption by population and intermediate consumption by economic entities. The consumption volume by population was estimated through data on taxable incomes with calculation of social payments. Tonnage was calculated through the total weight of the annual commodity bundle consumed in the settlement. The fixed cost of the commodity bundle was adjusted for self-sufficiency of households. The volume of consumption by business entities was estimated through data of goods flow generating the intensity adjusted for the resource of the industry:   Q abs = Q gen · kabs + ((P · I ) · kw )

(2)

kabs —coefficient of resource intensity; P—population in the settlement i; I —average income per person in the settlement i; kw —weight coefficient (tons per unit of consumption expenditure).

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Fig. 4 Settlements of Yelabuga County by potential of commodity flows, Initial data: Rosstat Database Indicators of municipalities; Federal Tax Service Reporting

Fig. 5 Settlements of Yelabuga County by level of empty run

The volume of freight traffic (Q crg ) is formed by summing up the data on the generation (Q gen ) and absorption (Q abs ) of commodity flows. The potential volume of generated commodity flows in Yelabuga County is identified mainly through the agricultural complex. Among the rural settlements, the most intensive turnover is observed in Bol. Shurniak, St. Yurash, Morty, and Tanaika (Fig. 4). In the county, there are settlements that consume significantly more than they can offer for shipment that is reflected in the high degree of empty run of vehicles (Fig. 5). Also, some of the settlements in front generate more goods than they consume.

3.2 Internal Framework Optimization The linkage of settlements within the hinterland determines its stability, efficiency of the production, and inner transport. Accessibility is an important determinant of the attractiveness of counties for logistics activities (Van den Heuvel et al. 2014). The

Modeling of Transport and Logistical Processes at the Local Level …

21

distortion of transport networks is influenced by such factors as: natural physical and geographical barriers, administrative boundaries, features of the settlement system and major highways. The distortion degree of the transport network for a certain point (Ctln ) was calculated as the ratio of integrated real transport accessibility and integrated physical accessibility to all settlements. It is actually reflects the percentage of distances elongation due to network underdevelopment or the presence of any barriers in the settlement system: Ctln =



Dr −



Dp



Dp

(3)

 Dr —total minimum distance to all points in the network along the roads; Dp — total minimum physical distance to all points in the network. The level of braking in the internal network (Btln ) was calculated through the ratio of the existing and ideal (with a speed of 70–90 km/h) integrated time availability. It actually reflects the amount of lost time because of the inadequate state of roads and congestion on the roads per unit of distance (minutes per 100 km): 

 Btln =



Tr − Ti  ∗ 100 Dr

(4)

  Tr —total minimum time to reach all points in the network on existing roads; Ti —total time to reach all points in the network along the roads provided that the speed regime is 70–90 km/h. The ratio of the two indicators allowed identifying four groups of settlements (Fig. 6). The first group (I) includes settlements with minimal distortion of the transport network and the level of inhibition in it. For the second group (II), a high degree of network distortion is typical, but the speed mode is quite favorable. The third group (III) is characterized by an average degree of distortion of the network, but by lower speeds in it. For the fourth group (IV), transport and temporary availability should be improved.

Fig. 6 Settlements by the level of distortion and the level of braking in the internal transport network (Initial data: Yandex.Maps—physical distance and road distance, https://yandex.ru/maps)

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R. S. Nikolaev and D. O. Egorov

3.3 External Framework Optimization Interaction with the external framework reflects intramodal processes in which flows of one level move to another level through production or logistic operations. In the course of processing, the product is transformed. Then, it is distributed further or consumed. During logistic processing, the product is (de)consolidated, accumulated, and redistributed at the following levels. Thus, the external transportlogistics contour provides either consumption (processing) of the primary product, or its distribution through the network. In addition, there are vertical movements of the flows in the opposite direction: from the higher to lower levels (i.e., from the external to internal networks). Optimization is the same as for the internal framework, but relatively individual elements of the external structure are capable of absorbing and processing commodity flows. Optimization of the external framework is possible with respect to individual objects (the base or nearest element) or in combination with all contour elements (integral accessibility). Integral accessibility characterizes degree of alternative distribution and possible risks in the system. It is possible to reveal the volumetric level of inefficiencies of freight traffic (Ie ) relative to the nearest distribution element in the external framework (Fig. 7). It actually reflects the proportion of the overloaded cargo turnover or loss of time to the nearest element in the external contour due to the high degree of network distortion.    Ie = (Dr · qc )− Dp · qc /(Dr · qc ) · 100;

(5)

qc —volume of cargo transportation; Dp , Dr —physical and real distances. The main opportunities for optimization are associated with a reduction in transportation costs (Fig. 8). The “input–output” tables and the structure of gross output demonstrate that the main directions of rationalization and optimization are associated with a reduction in fuel costs (23%) and labor intensity (24%).

(а) Temporal

(b) Spatial

(c) Spatial-temporal

Fig. 7 Settlements by the inefficiency level of the transport network relative to the nearest element in the external contour

Modeling of Transport and Logistical Processes at the Local Level …

23

Fig. 8 Cognitive map of the impact of transport network optimization and improving the roads quality

Reducing costs along with increasing the average speed in the area (s ) is estimated by the following model: s =

      tr − t´ · n + (d · f ) − d · f´ · pf ;

(6)

tr , t´—time of transportation at the existing (real) and normative speed mode; n— wage rate; d—distance of carriage; f , f´—fuel consumption at existing and normative speed mode; pf —fuel price. Calculations for the Yelabuga County showed the possibility of reducing costs through improving the speed limit by about 0.75 million rubles per year in prices of 2018 (12 thousand US dollars at the average annual exchange rate). Speed optimization allows reducing up to 10% of costs. The reduction of costs in the optimization of the transport network (t ) is estimated according to the following model: t =



  dr · f · pf − dp*c · f´ · pf + (m · tr · n − m · ti · n) · ka

(7)

dr , dp*c —distance of the existing road and the physical distance with the curvature; m—number of trips; ka —coefficient of unaccounted expenses (depreciation, repairs, maintenance). It is quite clear that the restructuring of the entire network configuration in a short time is impossible and requires large investments, while in the long term the economic framework can significantly transform. Creating an ideal network model with minimum costs for the basic distribution center will save up to 4.5 million rubles (71.8 thousand US dollars) or about 40% of costs under current conditions for fuel, labor, and other expenses. The maximum saving potential from the transportation of 1 ton of cargo is characteristic of the northwestern part of the region (from 60 to 85 rubles per ton, which is equivalent to 1.0–1.5 US dollars per ton) (Fig. 9). But even this is a small amount. Savings from the

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Fig. 9 Settlements grouping by perspectives of network optimization

creation and operation of new routes with current costs for the roads construction and maintenance in Russia (from 10 to 50 million rubles (0.2–0.8 million US dollars) per 1 km of one lane1 ) make it profitable to optimize the network only on high-intensity routes with large traffic and cargo turnover. At the local level, other priorities are singled out: social problems, socioeconomic exclusion, and improvement of transport access. According to the simulation, savings (over 10 years) due to optimizing the most intensive freight routes only through local freight turnover can cover not more than 5% of building and operating costs. At the same time, there are a number of external and internal factors: – – – – –

additional economy during empty run; the population’s gain (temporary and cost); savings on intra-settlement transportation of goods (including technological); increasing production capacity and investment attractiveness; one separate optimized segment of the local level can become a transit element for more powerful directions (e.g., between large cities) (Fig. 10).

So, through optimizing the flows of meso- and macro-levels (regional, interregional, and federal), the payback of each particular element of the new network will be acceptable.

3.4 Distribution Center Distribution centers are the one of the most effective mechanisms for optimizing transport flows. At low hierarchical levels, it is also possible to create local accumulation and distribution points (LADP). The concept of “local logistics center” is rather vague. There are approaches when local logistics centers embrace 5–8 km and have limited logistics infrastructure (Grabara et al. 2012). If the existing network is unchanged, LADP placement must meet next criteria of minimizing distance: to the 1 Report

on the cost of construction, reconstruction, overhaul, repair and maintenance of 1 km of public roads of the Russian Federation. The Ministry of Transport of the Russian Federation.

Modeling of Transport and Logistical Processes at the Local Level …

25

Fig. 10 Interaction of different levels of transport and logistics systems in support of the rationale for optimizing solutions in the transport network

basic node in the external framework (Base), to all elements in external (Ext) and internal (Int) frameworks (with weighing) (Fig. 11). The existing network limits the distribution to all potential points. It is necessary to additionally determine the points suitable for inclusion in the system of local accumulation and distribution, in accordance with the condition:   db/l + dl/f ≥ db/f , Fig. 11 Model for LADP choosing

(8)

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db/l —distance from the basic distribution center (BLDC) to the LADP; dl/f —distance from the LADP to the final destination point (FDP); db/f —distance from the BLDC to the FDP.         (9) E b/f − Cl/f + E b/l + E l/f d = Cb/l + Cb/f + Total costs for moving the flow of goods:  Cb/f from BLDC to FDP by low-tonnage transport fleet. Cb/l from BLDC to LADP by medium-tonnage transport fleet. Cl/f from LADP to FDP by low-tonnage transport fleet. Losses from empty run:   E b/f from BLDC to FDP by low-tonnage transport fleet.  E b/l from BLDC to LADP by medium-tonnage transport fleet. E l/f from LADP to FDP by low-tonnage transport fleet. Modeling showed that with the existing network configuration, creating of the LADP in Yelabuga County (Fig. 12) will reduce the costs in the covered network by 25–30%, mainly by reducing the empty traffic. Consolidation and deconsolidation of cargo flows in the LADP allow using the vehicles with different carrying capacity. Accordingly, in the consolidated area it is more efficient to use vehicles with greater load-carrying capacity. Such differentiated approach allows a more rational distribution of fuel and labor costs. The large investment capacity of transport network transformation is reflected in the inertness of this process. It forces economic agents to use the existing (not always effective) transport network, or to seek other options for reducing transport costs. At present, there is a lack of interest in optimizing transport and logistics processes at the local level. Fig. 12 Functioning scheme of LADP

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Another approach to optimizing transport and logistics processes at the local level is the spatial and sectoral transformation of the economic framework, the shifting of the main production elements toward more successfully located zones in the transport network. In the Yelabuga County, there are also physical transformations of the economic framework.

4 Conclusions Transport-logistics processes at the local level have a number of distinctive features that limit the interest in them, both from logistics agents and from researchers. At the same time, processes at the lowest hierarchical levels are the basic elements that allow evaluating the potential of more global transport and logistics nodes, determining the effectiveness of their functioning and developing directions (mechanisms) for their optimization. The local level is distinguished by small volumes of cargo transportation. There is orientation to the external contour of economic centers and transport-logistics nodes, which provide consumption or processing of freight flows. In addition, at the local level there are quite intensive transport processes of intra-system significance. For systems more involved into the territorial division of labor, supply, and marketing, a higher degree of accessibility and development of the transport and logistics infrastructure should be envisaged. The main optimization directions of transport and logistics processes at the local level are the transport network transformation, rationalization of the transport fleet, and the creation of a storage or distribution infrastructure. Another rather extreme measure is the economic framework transformation. Usually, such transformation occurs naturally, but it lasts long enough. In addition, the recoupment and appropriateness of capital-intensive optimization measures cannot always be fully assessed through a system of cost indicators. Transport and logistics optimization allows reducing the social and economic exclusion, resources intensity, and pressure on the environment. All these aspects are in the plane of “sustainable development” and should be evaluated using other approaches. The algorithm for identifying the feasibility of optimization measures at the local level can consist of four major stages: (1) Analysis of the settlement and economic frameworks, identification of the spatial and branch structure of the flows. (2) Analysis of the existing transport and logistics complex (definition of nodes, networks and frameworks, gravitational fields). (3) Potential and feasibility assessment of the transport and logistics infrastructure optimizing: – transport network transformation; – transportation fleet modernization; – logistics elements creating.

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(4) Potential and feasibility assessment of optimization measures with the settlement and economic frameworks and (or) the spatial-assortment structure of flows. Acknowledgements The research was carried out at the expense of a grant from the Russian Science Foundation (project № 17-78-10066) “Optimization of the transport and logistics system of Russia and the regions as an instrument of sustainable development.” The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.

References Aized, T., & Srai, J. S. (2013). Hierarchical modelling of last-mile logistics distribution system. International Journal of Advantage Manufacturing Technology, 70(5–8), 1053–1060. Beckers, J., Vanhoof, M., & Verhetsel, A. (2017). Returning the particular: Understanding hierarchies in the Belgian logistics system. Journal of Transports (in Press). Access mode https://doi. org/10.1016/j.jtrangeo.2017.09.015. Ben-Akiva, M., Meersman, H., & Van de Voorde, E. (2013). Recent developments in freight transport modelling. Freight transport modelling. Cetinkaya, B., Cuthbertson, R., & Ewer, G., et al. (2011). Sustainable supply chain management: Practical ideas for moving towards best practice. Berlin Heidelberg: Springer-Verlag. Ducruet, C., & Itoh, H. (2016). Regions and material flows: Investigating the regional branching and industry relatedness of port traffics in a global perspective. Journal of Economic Geography, 16(4), 805–830. Egorov, D. O., & Nikolaev, R. S. (2016). Spatial and time remoteness as a subject of transport and logistics research (by the example of the republic of Tatarstan healthcare system). Journal of Economics and Economic Education Research, 17(2), 389–400. Engström, R. (2016). The roads’ role in the freight transport system. Transportation Research Procedia, 14, 1443–1452. Grabara, J., Dima, I. C., & Okwiet, B. B. (2012). Logistical centres and their roles in companies’ activities on the example of SME’s Enterprise X. Romanian Statistical Review, 45–53. Gudmundsson, H., Hall, R. P., Marsden, G., & Zietsman, J. (2016). Sustainable transportation—Indicators, frameworks, and performance management (Springer Texts in Business and Economics). Berlin Heidelberg: Springer-Verlag. Guerrero, D., & Proulhac, L. (2014). Freight flows and urban hierarchy. Research in Transportation Business and Management, 11, 105–115. Hesse, M., & Rodrigue, J. P. (2004). The transport geography of logistics and freight distribution. Journal of Transport Geography, 12, 171–184. Hoggart, K. (2016). The City’s Hinterland: Dynamism and divergence in Europe’s Peri-Urban territories. NY: Routledge London. Huber, S., Klauenberg, J., & Thaller, C. (2015). Consideration of transport logistics hubs in freight transport demand models. European Transport Research Review, 7, 32. Liedtke, G. (2009). Principles of micro-behavior commodity transport modeling. Transportation Research Part E, 45, 795–809. Liedtke, G., & Schepperle, H. (2004). Segmentation of the transportation market with regard to activity-based freight transport modelling. International Journal of Logistics Research and Applications, 7(3), 199–218. Litman, T. (2014). Well measured. Developing indicators for sustainable and livable transport planning. Victoria, BC, USA: Victoria Transport Policy Institute, U.S.

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Makarova, I., Shubenkova, K., & Gabsalikhova, L. (2017). Analysis of the city transport system’s development strategy design principles with account of risks and specific features of spatial development. Transport Problems, 12(1), 125–138. McKinnon, A., Browne, M., Whiteing, A., & Piecyk, M. (2015). Green logistics: Improving the environmental sustainability of logistics. Kogan Page Limited. Ortiz-Astorquizaa, C., Contrerasa, I., & Laporte, G. (2018). Multi-level facility location problems. European Journal of Operational Research, 267(3), 791–805. Rodrigue, J. P., Comtois, C., & Slack, B. (2017). The geography of transport systems (4th ed., p. 440). NY: Routledge. Rolko, K., & Friedrich, H. (2017). Locations of logistics service providers in Germany—The basis for a new freight transport generation model. Transportation Research Procedia, 25, 1061–1074. Sheu, J. B., & Lin, A.-Y. S. (2012). Hierarchical facility network planning model for global logistics network configuration. Applied Mathematical Modelling, 36(7), 3053–3066. Van den Heuvel, F., Rivera, L., Van Donselaar, K., De Jong, A., Sheffi, Y., & De Langen, P., et al. (2014). Relationship between freight accessibility and logistics employment in US counties. Transportation Research Part A: Policy and Practice, 59(1), 91–105.

Management of Investment Attractiveness of the Russian Federation Regions: Practical Analysis Irina S. Glebova, Svetlana S. Berman and Ruslan R. Galiakhmetov

Abstract There are various factors that can determine uneven economic development of the regions of the Russian Federation in terms of the possession of rich natural resources, government’s investment policy, region’s international recognition, and the presence of special conditions to facilitate innovation and human capital development. The incoming foreign capital enables industrial modernization, infrastructure development, and improvement of the living standards of the population in the regions. The chapter analyzes the effectiveness of the investment attractiveness management in fifteen regions of Russia and identifies the main determinants of the investment potential of the territory. The purpose of this work is to apply the authors’ methodology to assess the investment attractiveness of the territory using the statistical data on the regions located in different federal districts of the Russian Federation. The work provides organizational and management recommendations for regional authorities to overcome systemic mistakes in the development and implementation of regional investment policies.

1 Introduction Investors are more fastidious in selecting the destination for their investments when the demand for capital is growing. Their decision is determined not only by the economic efficiency of an investment project, but also by the quality of the environmental conditions of its implementation. Thus, the level of the investment attractiveness of the territory becomes an important determinant for domestic and foreign investors. I. S. Glebova (B) · S. S. Berman · R. R. Galiakhmetov Kazan Federal University, Kazan, Russian Federation e-mail: [email protected] S. S. Berman e-mail: [email protected] R. R. Galiakhmetov e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_3

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The volume of investments attracted to the economy of the region is one of the main criteria for assessing the effectiveness of regional authorities. The purpose of our study is to assess the investment attractiveness of the regions of the Russian Federation during the period from 2005 to 2016. Our research findings are based on the analysis of statistical data on 15 Russian regions. For the purpose of our research, we have selected the following regions: the Moscow Region, the Leningrad Region, the Krasnodar Region, the Rostov Region, the Republic of Bashkortostan, the Republic of Tatarstan, the Chuvash Republic, the Nizhny Novgorod Region, the Samara Region, the Sverdlovsk Region, the Chelyabinsk Region, the Krasnoyarsk Region, the Novosibirsk Region, Tomsk Region, and the Republic of Sakha (Yakutia). The choice of the regions is justified by the following reasons: The above-mentioned regions have repeatedly attracted special attention of rating agencies and were frequently mentioned in their reports; they are located in different federal districts allowing one to assess the overall situation in the country; and during the last 10 years, these regions have been rated as the most attractive for investors by the rating agencies, experts, and practitioners. Many scholars, such as Z. Bodie, P. Stanier, G. Fraser-Sampson, G. J. Alexander, W. Sharp, and others, have contributed to the development of the conceptual foundations of management of investment processes (Chakrabarty 2001; Sharpe et al. 1999; Fraser-Sampson 2014). The modern work puts emphasis on the assessment of investment attractiveness and the use of management practices at the regional and national levels. The conceptual bases for studying and managing investment were developed by a number of international scholars such as W. Sharpe, G. J. Alexander, J. W. Bailey, G. Fraser-Sampson, and others (Fraser-Sampson 2014). M. A. Jano-Ito and D. Crawford-Brown in the research work entitled “Investment decisions taking into account the economic, environmental and social factors: an actor’s perspective for the electricity sector of Mexico” consider the multi-purpose nature of investment and the behavioral aspect of decision making as one of the factors in the development of the electric power industry in Mexico (Jano-Ito and Crawford-Brown 2017). The research methods used by different authors include the statistical, computational, constructive, and comparative analysis methods. For example, O. A. Tsepelev and S. G. Serikov assess regional investment potential by institutional sectors: investment potential of financial and non-financial corporations, households, public administration (Tsepelev and Serikov 2016). Despite the existence of different methods for evaluating investment attractiveness, their principles are more or less uniform and contain similar stages of evaluation (Bodie et al. 2014). Significant differences in the research methods and results are associated with the preferences of different authors for particular indicators to be included in their analysis. An analysis of the available knowledge about the management of investment attractiveness reveals that the investments are more effective in the regions, which are able to establish more favorable conditions for investors (Jara 2017; Kautto 2002).

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2 Methods To analyze and compare the investment attractiveness of the regions, we applied the factor analysis method with the following stages: 1. The ranking method was applied to rate the regions in terms of change in their investment attractiveness for the period from 2005 to 2016. The evaluation was carried out using the correlation analysis. As the basic information source to build the investment attractiveness rating, we have used the statistical data on social and economic development of the regions of the Russian Federation. 2. At the first stage, 11 indicators reflecting investment risks and 13 indicators reflecting the investment potential were selected, substantiated, and analyzed for the period from 2005 to 2016. The following 11 indicators were selected to reflect the investment risk: • the amount of tax and fee liabilities of the region to the Russian federal budget (million rubles); • the amount of wage and salary arrears of the region (million rubles); • the amount of debt on loans in rubles provided by credit institutions to legal entities (million rubles); • the number of registered crimes per 100,000 inhabitants (units); • the number of road accidents per 100,000 inhabitants (units); • the average cost of developing 1 innovation (million rubles); • the number of pensioners per 1000 inhabitants (people); • the share of unprofitable organizations (in % of the total number of enterprises); • the level of depreciation of fixed assets (in %); • the amount of pollutants released into the atmosphere (thousand tons per year); • the share of the total area of dilapidated houses (in % of the total housing stock). The following 13 indicators were selected to reflect the investment potential: • • • • • • • • • • • • •

the amount of regional GDP per capita (rubles); the amount of per capita incomes of the population (rubles); the amount of per capita incomes of individual entrepreneurs (rubles); the volume of paid services per capita (rubles); the share of public roads with improved coverage (in %); the number of cars owned by citizens per 1,000 inhabitants (units); the size of population (units); the number of advanced technologies used (units); the number of graduated specialists with higher education (units); the amount of labor force (thousand people); the balanced financial result of organizations (million rubles); the industrial production index (in %); the share of captured and eliminated of pollutants from stationary sources (in %);

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• The amount of commissioned residential and non-residential facilities per capita (sq.m.). 3. At the second stage, in accordance with the statistical data, each region was given an intermediate ranking for each of the indicators above. For the both investment risk and investment potential, 15 was the maximum score and 1 was the minimum score. 4. At the third stage, each indicator was assigned a weight, calculated by the correlation method. For investment risk, the main indicator was the amount of direct foreign investment in the economy of the region. For the investment potential, the main indicator was the volume of fixed asset investments. 5. At the fourth stage, an integral estimate was calculated for each year, taking into account the weights of the indicators. 6. At the fifth stage, the obtained values were rated and given a corresponding number (rank). 7. At the sixth stage, the sums of integral estimates of investment risks and investment potential were summed up to determine an overall investment attractiveness, according to these two factors. Then, the obtained values were rated and given a corresponding number (rank). 8. At the final stage, the results obtained for each region were compared to assess the levels of investment attractiveness at different time periods.

3 Results As a result, we have determined the relative investment attractiveness of 15 Russian regions. The results are presented in a two-by-two matrix (Fig. 1). The two-by-two matrix contains four sectors, characterized by different levels of investment potential and investment risk. Sector I, characterized by low investment potential and high investment risk, contains four regions. Sector II has two regions that can be described as having high potential and high risks. Sector III, with the most favorable indicators—high investment potential and low risks—contains 4 regions. Finally, Sector IV comprises the other three Russian regions. It is also necessary to note that the Samara Region, which has high investment risks, has a medium level of investment potential, whereas the Republic of Bashkortostan with its high investment potential displays a medium level of investment risk. Our analysis suggests that the territorial factor, i.e., the region’s macro-regional affiliation, does not have any significant impact on the investment attractiveness score of the region. Different methods of assessing and determining the investment attractiveness undoubtedly lead to different ratings of the Russian regions. Table 1 compares the final rating stemming from our analysis with other existing Russian ratings of the regional investment attractiveness. For comparison, we included ratings suggested by two leading agencies of the Russian Federation: National Rating Agency and

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Fig. 1 Matrix of investment attractiveness of the regions of the Russian Federation Table 1 Ratings of the investment attractiveness of the regions of the Russian Federation Position

National rating agency

Expert rating agency

Resulting rating

1

Republic of Tatarstan

Moscow Region

Republic of Tatarstan

2

Moscow Region

Krasnodar Region

Krasnodar Region

3

Leningrad Region

Rostov Region

Moscow Region

4

Republic of Sakha

Republic of Bashkortostan

Sverdlovsk Region

5

Samara Region

Republic of Tatarstan

Leningrad Region

6

Sverdlovsk Region

Nizhny Novgorod Region

Rostov Region

7

Krasnodar Region

Samara Region

Republic of Bashkortostan

8

Tomsk Region

Leningrad Region

Samara Region

9

Republic of Bashkortostan

Sverdlovsk Region

Tomsk Region

10

Nizhny Novgorod Region

Chelyabinsk Region

Nizhny Novgorod Region

11

Novosibirsk Region

Krasnoyarsk Region

Chuvash Republic

12

Rostov Region

Novosibirsk Region

Chelyabinsk Region

13

Krasnoyarsk Region

Chuvash Republic

Republic of Sakha

14

Chelyabinsk Region

Tomsk Region

Krasnoyarsk Region

15

Chuvash Republic

Republic of Sakha

Novosibirsk Region

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Expert Rating Agency. They are most frequently used by the government authorities and investors in making financing decisions. As shown in Table 1, certain regions repeatedly rank high in the investment climate ratings: the Moscow Region, the Republic of Tatarstan, and the Krasnodar Region. Other regions continuously rank low in the ratings: the Republic of Sakha, the Chuvash Republic, and the Krasnoyarsk Region. It is necessary to highlight that the results of our rating are not significantly different from the results provided by the leading rating agencies. Such similarity is explained by several factors, one of the main reasons is the partial use of the same indicators in our analysis. The differences are primarily caused by the use of different calculation methods and the consideration of institutional factors by the rating agencies, as further discussed below. Finally, the differences in the results are explained by administrative barriers and the restricted access to some data. The investment attractiveness of the region is influenced by a large number of determinants, and therefore, it can be assessed using various methods. Earlier in the work, we have considered the use statistical indicators, and now we would like to focus on the institutional factors. In order to analyze the development of the institutional environment for attracting investments, we selected the following indicators: • the presence of an investment strategy and programs to support investment activities in the region, as well as the existence of a regional government agency responsible for investment activity management; • the presence of the special economic zones of industrial and/or technological type; • the number of technological and industrial parks; • the social and political stability rating; • the existence of the regional investment portal and the quality of its content; • the frequency of Google search queries with the keywords “investment,” “investment site,” etc. The results of our analysis are displayed on a two-by-two matrix (Fig. 2) with the institutional environment development index on the horizontal axis and the investment attractiveness on the vertical axis. Two regions are distributed into four sectors: • • • •

High attractiveness—low institutional development; High attractiveness—high institutional development; Low attractiveness—high institutional development; Low attractiveness—low institutional development.

The majority of regions fall into the Sectors II and VI. Sector III contains only one region (the Republic of Sakha), and Sector I is empty. The analysis suggests for the heterogeneity in the development of institutional factors in different regions. It is important to emphasize the positive relationship between institutional factors and the investment attractiveness of the regions. The investment attractiveness is a multidimensional concept, influenced by an enormous number of determinants, and only some of which can be directly measured.

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Fig. 2 Distribution of regions in terms of investment attractiveness and development of the institutional environment

Those regions of the Russian Federation that possess rich natural resources take an active position in the international arena, directly collaborate with foreign investors, organize and host international world-class events which are in a more favorable position compared to other regions.

4 Discussion Managing the investment attractiveness of the regions is an important aspect of the overall territorial development. The government authorities equipped with a well-thought-out investment policy are able to influence the investment climate of their regions. Our analysis suggests the following primary tools for increasing the investment attractiveness of the territory: • the development of territorial clusters and special economic zones with favorable conditions for investors; • the implementation of investment projects using the public–private partnership mechanisms; • the active participation of representatives of the region in communicative and networking events that allow investors to see that the territory is open for contacts; • the participation in national and international competitions to finance projects for the development of innovative businesses and innovation infrastructure;

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• the establishment of partnership relations with various organizations and foundations engaged in supporting the entrepreneurship development; • the development of the institutional environment that ensures the rights of investors and protects their property rights. It is worthwhile to note that under the economic and political crisis, the regions have already managed to find new solutions to support existing investors and attract new ones. For example, the Leningrad Region has developed effective mechanisms to financially support its car building enterprises. The Kaluga Region similarly supports its pharmaceutical industrial cluster. The Republic of Tatarstan pays enormous attention to international cooperation with investors from China and Turkey. The most important factor in the growth of the investment attractiveness of the Republic of Sakha is the development of new oil and gas fields and the construction of the “Power of Siberia” gas pipeline.

5 Conclusion Management of investment attractiveness is among the priorities for regional economic policy, because investments facilitate the transfer of advanced technologies, equipment, and innovations from abroad; enable the revival and growth of industrial production. To stimulate foreign investments, the Russian regions need to develop international relations and seek ways to overcome the sanction barriers, reduce bureaucracy and corruption. The introduction of anti-Russian sanctions has reduced the number of investment projects implemented in the Russian regions and has reduced the volume of foreign direct investments. One of the main challenges facing the regional authorities is the development of organizational initiatives to create favorable conditions for investments by creating new investment sites, special economic zones, and advanced development territories that provide preferential terms to investors. The regions of the Russian Federation differ in many respects: geographic location, population, level of socioeconomic development, federal funding opportunities, international recognition abroad, etc. However, the prospects of their further development largely depend on the ability of regional authorities to get on well with potential investors. Of course, the success of the regional policies is also affected by the overall socioeconomic and political situation in the country. The policies of the federal center, aimed at vertical centralization of financial resources, significantly limited the financial independence of the Russian regions and their investment opportunities. As suggested by our analysis, such situation forces the regional authorities to look for new ways to increase the investment attractiveness of their regions and to rely on their own resources and managerial capabilities.

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References Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments. McGraw-Hill. Chakrabarty, K. B. (2001). Urban management: Concepts, principles, techniques and education. Cities,18(5), 331–345. Fraser-Sampson, G. (2014). The pillars of finance: The misalignment of finance theory and investment practice (2014th ed.). Jano-Ito, M., & Crawford-Brown, D. (2017). Investment decisions considering economic, environmental and social factors: An actor’s perspective for the electricity sector of Mexico. Energy, 121, 92–106 (Elsevier). Jara, J. (2017). Determinants of country competitiveness in attracting mining investments: An empirical analysis. Resources Policy,52, 65–71. Kautto, M. (2002). Investing in services in West European welfare states. Journal of European Social Policy,12(1), 53–65. Sharpe, W., Gordon, J. A., & Bailey, J. W. (1999). Investments. Prentice-Hall. Tsepelev, O. A., & Serikov, S. G. (2016). Procedure for regional investment potential assessment by institutional sectors of economy. Indian Journal of Science and Technology, 9(14), 91523.

Infrastructure Projects and Transport System Financing in Russia Liliana R. Ikhsanova, Rezeda R. Shigapova, Joanna Koczar, Zarina I. Agliullina, Madina I. Agliullina and Maria E. Syslova

Abstract This work deals with the problem of the financing of projects of the transport systems development. Extrabudgetary funds are the main financing source that can be directed at the innovative development of the transport systems in Russia by 2030. The analysis of the Russian transport system funding proves that the significance of budget funding is gradually declining. At present, private and foreign investments and public-private partnership in infrastructure projects funding are becoming increasingly crucial. Special attention is paid to bank lending and financial leasing in the transport sector. With the help of econometric tools, three scenarios of GDP generated by the transport sector of Russia are constructed.

1 Introduction Transport systems constitute the necessary basis for providing the national economy with the opportunity of proper functioning and growth. The major direction aimed at promoting economic efficiency is the creation of the transport infrastructure, capable of ensuring homogeneous development of all the regions through quality transport services and public transport space design. Population growth, increase in the share of urban population and personal incomes, globalization and the development of international relations impose an additional burden on transport systems of many countries. Experts from the Organisation for Economic Cooperation and Development (OECD) expect a 2.5 time increase in passenger turnover provided that world GDP is going to double by 2030 (Ganelin and Vasin 2014). Meanwhile, cargo turnover is expected to increase three times and container cargo turnover—four times, respectively. The “McKinsey Global Institute” analysts believe that the minimum level of world infrastructure investments, required L. R. Ikhsanova (B) · R. R. Shigapova · Z. I. Agliullina · M. I. Agliullina · M. E. Syslova Kazan Federal University, Kazan, Russian Federation J. Koczar Wroclaw University of Economics, Wroclaw, Poland e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_4

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to meet the demands of business and the population, will reach 57–67 trillion dollars for the period up to 2030 (3.4–3.9 trillion dollars per year on average). The calculations of the level of required investments include the annual investments made in transport infrastructure at the level of 3.5–3.8% from GDP since 1998 (Ganelin and Vasin 2014). In Russia, the security degree of the developed transport systems is of immense importance. The novel technologies, international experience in the transport industry development, initiation of large-scale knowledge-based projects raise a demand for a review of existing financing and lending tools. The latter provide funding for the development of the transport sector in Russia.

2 Literature Review As a significant factor for the sustainable development of the regional economy, transport infrastructure and its financing have drawn attention of many researchers to the study of financing and lending tools for development of the sector. Russian and international authors are working jointly on various aspects of this problem. Some works over the issue highlight the role of the transport sector in promoting regional economic growth. Liu et al. (2008) analyze the influence of transport availability on regional economy. We know that transport availability or accessibility is one of the major reasons for spatial differences in regional economy. The authors suggest the Cobb–Douglas production function and its regression modeling for establishing the link between investments in transport availability and GDP (Liu et al. 2008). Ding (2013) concludes that improvement of the urban roads can entail an increase in the GDP share for both production and service-related areas. Nidziy (2017) justifies dependency of regional economic growth on efficiency of funding of transport infrastructure construction and emphasizes the infrastructure projects and public-private partnership as an effective means of financing. Tsvetkov et al. (2017) write about the need for creating a federal company responsible for funding and implementation of the projects aimed at developing the transport and transit potential of Russia, based on the principles of public–private partnership. Freidina (2017) analyzes international practical experience of financing infrastructure projects in the USA, Australia, Chile, China, India and Kazakhstan. The researcher notes that the most promising channels for Russia to attract investments in construction and renovation of transport infrastructure can be infrastructure bonds (Freidina 2017). In light of this discussion, Beck and Hensher (2015) claim that despite the private sector participation in some of innovative partnership projects of public-private partnership, it is time to consider a wider range of methods for infrastructure projects funding (Beck and Hensher 2015). Terenteva and Ikhsanova (2016) write that it is necessary to adopt actions, which can provide incentive funding of infrastructure projects from extrabudgetary sources. That will allow implementing development of the transport system of the Russian regions (Terenteva and Ikhsanova 2016). Agreeing with the scientific concepts proving the dependence of economic development on

Infrastructure Projects and Transport System Financing in Russia

43

the financing of the transport industry, the contribution of the transport sector to the GDP of Russia becomes relevant. The hypothesis of the importance of extrabudgetary sources such as bank lending and leasing will be considered in this work.

3 Practices in Transport Infrastructure Financing Applied in Russia The Russian transport infrastructure is one of the most diverse in the world; it combines roads, railways, airports, ports, pipelines, power engineering (including nuclear) and water supply (Tsamboulas et al. 2013). One of the most acute problems of the Russian infrastructure is a prominent level of transport infrastructure deterioration. As the majority of modern operated infrastructures were built in the middle of the XX century, they today require modernization. The core indicators of the transport system activity in the Russian Federation for the period from 2012 to 2017 are given in Table 1 (Basic Indicators of Transport in Russia 2018). The development plan of the Russian transport systems is considered in “The Transport Strategy of the Russian Federation till 2030” (The Transport Strategy of the Russian Federation till 2030 2018). To implement the innovative development of the transport systems by 2030, extrabudgetary funds must become the principle source of financing of most means of transportation (Fig. 1). Figure 2 shows the predicted structure of investments in the transport sector in terms of the mode of transport and areas of activity in the framework of innovative option of transport systems development for the period from 2016 to 2030. The extrabudgetary investments in the transport infrastructure consist of 60.80% of the organization’s own resources and 39.20% of attracted funds. It is obvious that only Table 1 Core indicators of the transport sector activity of Russia in 2012–2017 Indicators

2012

2013

2014

2015

2016

2017

Volume of transportation services rendered to the population, bn rubles

1068.9

1081.7

1049.3

1035.7

1036.9

1037.6

Volume of transportation services per capita, rubles/people

7474.8

7548.5

7302.2

7079.3

7077.8

7078.5

Value of fixed assets, bn rubles

34,649

36,179

40,301

42,494

44,582

45,863

Annual average of employees, thousand people

4339

4412

4322

4661

4647

4658

Investment in fixed assets, bn rubles

2829.6

2968.3

2629.7

2143.9

2345.3

2381.2

Labor productivity, rubles/people

381,357

389,251

405,210

403,225

415,967

416,203

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L. R. Ikhsanova et al. 22% Federal budget resources Budgets of constituent entities 14%

64%

Extrabudgetary funds

Fig. 1 Structure of the financial sources for the transport strategy to be implemented by 2030

Multi-aspect projects on transport system development

48.04

0.72

51.24 20.14

Underground

62.65

17.21 9.46

Land urban electric vehicles

90.54

0.00 Industrial railroad transport Motor vehicles 0.00 Air transport Inland waterway transport Sea transport

100.00

0.00 0.00

2.01

13.78

Extrabudgetary funds

86.27

Budgets of constituent entities

11.72 38.48

2.01 0.00

6.15 5.56

Federal budget resources 59.51 82.16

17.84 29.57 25.13

Motor road Railway transport

86.22

45.29 88.29

Fig. 2 Structure of investments in the transport sector of Russia in the framework of innovative development of transport systems from 2016 to 2030

large companies such as PJSC RZD—Russian Railway, PJSC Aeroflot—Russian Airlines, PJSC “Modern Commercial Fleet” can afford to finance large-scale projects at their own expenses. For example, PJSC RZD—Russian Railway accounts for 67% of extrabudgetary investments, using its own funds. Analysis of the funding sources for the development of transport systems proves that currently private investments in infrastructural projects financing are increasingly significant. The bonds are financial and lending tools, which enable the private investors to invest in long-term projects. It reduces the risks related to infrastructural projects. To develop the transport systems in Russia, it is necessary to secure the used bonds by guarantees from the Russian Federation and its constituent entities as well

Infrastructure Projects and Transport System Financing in Russia

45

as the bonds based on concession agreements. The bonds, secured by guarantees from the RF and its constituent entities, safeguard the investors’ interests and good credit rating. The procedure of issuing bonds is strictly regulated and thoroughly elaborated. However, to provide the bonds supported by government guarantees, it is necessary to follow high criteria of selection. The bonds, based on concession agreements, exclude debt burden on the budget. But concession bonds are characterized by certain limitations: a continuous process of arrangement of concession agreement including a private list of objects under concession agreement. In 2016, according to the annual report of the National Association of Concessionaires and Long-Term Infrastructure Investors (COLTI), 1836 concession tenders were issued. 94% of them were with total investments up to 100 mln rubles. All in all, 34 concession agreements on infrastructure projects have been concluded at value of more than 100 mln rubles. Most of the projects, proposed for concession tenders, are small. Russian practices show that concession often results in renewal of lease agreements or long-term contracts, accompanied by small investment commitments on the part of the private investor. Today, in Russia about 80 medium and large concession projects with the annual growth of 30–50 projects are being carried out. Volumes of investment commitments on medium projects range from 100 mln rubles to 1 bln, and on large projects—more than 1 bln rubles. The timeframe of such projects is from 10 to 40 years. The highest share of concessionaries’ bonds, represented at Moscow Exchange, was purchased by non-state pension funds. The total amount of investments according to the 2017 data was 83 bln rubles alongside the great growth potential (Concessionaires and long-term infrastructure investors National Association 2018). Infrastructure bonds form another promising debt tool of transport systems development. The infrastructure bond can be created by a concessionaire company. Interest and principal payments are provided by monetary flows, generated in the process of operation of the infrastructure object. Infrastructure bonds remain rather an underdeveloped source of financing in the Russian practices. According to World Bank data, between 2010 and 2014, in Russia the private sector had only 3% of infrastructure bonds (2017 Annual Update of the Private Participation in Infrastructure 2018). Infrastructure bonds are mainly purchased by commercial banks, non-state pension funds and other extrabudgetary funds, which adhere to a conservative approach to investment. At present, with the assistance of infrastructure bonds, investments in economy have reached about 50 bln rubles. According to forecasts of the Ministry of Transport of Russia, in the nearest future companies are expected to attract up to 500 bln rubles a year through infrastructure bonds. The aim is to implement the projects on transport systems development. Due to the government policy of attracting private investors to financing of the transport sector, many large projects are implemented with the support of foreign investors such as commercial banks, financial enterprises and construction companies. The example of such project is the reconstruction and modernization of Pulkovo Airport in Saint-Petersburg. In 2009, an agreement was concluded in accordance with BTLO1 between the administrative body of Saint-Petersburg and Fraport company,

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the project’s operator, for a period of 30 years. There is another example of Chinese investments when in 2014 there was joint financing of the railway bridge construction across the Amur River at the Russian-Chinese section of the border by the Russian Direct Investment Fund (RDIF) and the Chinese Sovereign Fund (RDIF Portfolio 2018). In May 2017, the Russian Direct Investment Fund, a leading Italian Motor Roads Company ANAS, together with the National “Avtodor” Company declared establishment of the consortium for road infrastructure development. As a joint pilot project, an integrated arrangement of the Federal M-4 “Don” road was chosen to be implemented by RDIF, regarding infrastructural partnership with the National “Avtodor” Company. According to the operator’s agreement, which will be in effect up to December 31, 2030, the accomplishment of the whole scope of reconstruction and complex improvement works is envisaged for the sum of 7 bln rubles (RDIF Portfolio 2018). According to international practices, financing of projects related to transport systems is effectuated at the expense of public budgets and funds (budgetary financing) including private and institutional investors (Infrastructure Finance Outlook–Standard & Poor’s Global Ratings 2017) (Table 2). In 2017, global investment figures allocated from public budgets and funds reached 1382 bln dollars, which made 63.70% of the total investments received in the transport infrastructure. 36.30% of the global transport infrastructure investments (787 bln dollars) in 2017 were provided through equity and debt financing from private and institutional investors. 19.7% were attracted by the revenues of the transport sector enterprise and equity placement. 79.3% of investments included debt financing, such as bank lending, loans from financial organizations and bond placement. Bank lending dominated over all the types of equity and debt financing due to the lack of well-developed capital markets. For the period of 1999–2010 in most developing countries, bank lending accounted for 90% of all investments, realized through debt financing. The economic crisis and tightening of norms, governing banking activity, had a negative effect on capital adequacy indicators of many large Table 2 Volume and structure of funding sources for transport systems in the world in 2017 Funding source

Investments, in USD, bln

Public budgets and funds

1382

63.70

196

9.04

Loans from international financial institutions

35

1.63

Companies’ share capital

38

1.75

117

5.39

Bank lending

Companies’ own financial assets corporate bonds Total

Investment structure, %

401

18.49

2169

100.00

Infrastructure Projects and Transport System Financing in Russia 1800000

47

1679563

1446737

1600000 1400000

1212741

1212073

1150599

1200000 1000000 800000

1220141 741121

600000 400000 200000 0 2011

2012

2013

2014

2015

2016

2017

Fig. 3 Dynamics of bank lending to transport sector enterprises for the period of 2011–2017, in mln RUB

banks, participated in providing loans to infrastructure projects in the transport sector. According to “Standard & Poor’s,” the bank share in debt financing of projects related to transport infrastructure had decreased from 30 to 8.9% by 2017 worldwide (Infrastructure Finance Outlook–Standard & Poor’s Global Ratings 2017). At present, the volume of enterprise lending to the transport sector is not adequate for supporting robust industry environment and qualitative transport services. Figure 3 shows the dynamic of bank lending to transport sector enterprises for the period of 2011–2017 (Volume of Loans in Rubles Granted to Resident Legal Entities and Individual Entrepreneurs by Economic Activity and Use of Funds. Bank of Russia 2018). For the period from 2011 to 2013, the crediting volume of the transport sector increased from 741 bln rubles to 1680 bln rubles. By the end of 2017, transport systems crediting amounted to 1447 bln rubles, which was 25.7% more than the figures of the previous year. Long payback periods of the invested capital, infrastructure construction and commissioning phases, dependence of company revenues on tariff rates, regulated by the government, determine the insufficient amount of loans, issued to implement transportation projects. Major lenders to transport infrastructure are large commercial banks, choosing to finance federal and regional projects and development banks. In Russia, in transport infrastructure funding, a financial and lending tool of leasing is significant. On the Russian market, the “State Transport Leasing Company” (STLC) specializing in leasing is one of the largest lessors. The Company cooperates closely with the State, implementing targeted programs and independent leasing projects together with the leading Russian banks. In Fig. 4, dynamics of financial leasing to enterprises of the transport sector for the period of 2011–2017 are shown. Therefore, analysis of financial and lending tools of the Russian transport systems development reveals that such mechanisms as infrastructure bonds and diverse types of public-private partnership projects are still poorly developed, though possess extraordinary high potential. In this regard, of special interest is the analysis of

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1000.0 800.0 600.0

855.9

741.4 536.3

499.8

517.6

627.8 330.0

400.0 200.0 0.0 2011

2012

2013

2014

2015

2016

2017

Fig. 4 Dynamics of financial leasing to enterprises of the transport sector for the period of 2011– 2017, in bln rubles

the link between volumes of gross domestic product in the transport sector and bank lending to transport sector enterprises, including financial leasing services rendered to transport sector enterprises.

4 Materials and Methods In the framework of public-private partnership in Russia, the following financial and credit instruments are used: funds of private and institutional investors; bonded loans, lending, financial leasing, project finance instruments; and grants provided by international financial institutions. In general, the choice of the used financial and credit instruments is influenced by key directions of development of the transport industry, the expected efficiency and profitability of the project, the ratio between risk and profitability of the project, the situation in the financial market and macroeconomic factors. Taking into account the mentioned trends in financing of the transport systems in Russia, the influence of such financial and credit instruments as bank lending and financial leasing on the industry development has been considered. The exclusion of one of these factors from the model during correlation–regression analysis of Russia’s GDP allows suggesting a greater efficiency of the financial and credit instrument that will be included in the model. That is, the development of this financial and credit instrument has great potential in achieving the goal of effective interaction between the state and business provided that the transport industry is financed. In view of high uncertainty in development of external conditions among the macroeconomic factors, the factor of devaluation/revaluation of the Russian ruble with respect to the US dollar and the key interest rate level of the Bank of Russia, influencing the inflation level and the cost of banking products for the real sector of the economy, are of importance. A direct relationship between the exchange rate and the volume of output due to a significant share of export of services is typical of the Russian transport industry. To establish the link among volumes of gross domestic product generated by the transport sector of Russia (in bln rubles)—Y, bank lending to transport sector enterprises (mln rubles)—X 1 , volumes of financial leasing to transport sector enterprises

Infrastructure Projects and Transport System Financing in Russia

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(bln rubles)—X 2 , the dollar/ruble rate—X 3 and CBR core rate (X 4 ), we have constructed an economic and mathematical model. It is based on multiple correlation and regression analysis. For the model construction, statistical data, covering the period of 2003–2017, have been used (2017 Annual Update of the Private Participation in Infrastructure 2018). In terms of the analysis, the “Data Analysis” tool by Microsoft Excel has been used. In the correlation and regression analysis process, there is a strong positive link between the Y performance feature with the X 1 factor feature: X 1 : r (Y; X 1 ) = 0.8375 with factor feature X 2 : r (Y; X 2 ) = 0.8708 and factor feature X 3 : r (Y; X 3 ) = 0.7787. There is a moderate link with factor feature X 4 : r (Y; X 4 ) = –0.5278, which allows removal of X 4 from the model. Within the model, factors X 1 and X 2 are closely interrelated. Since correlation coefficient between X 1 and Y is less than that of X 2 and Y, X 1 , the factor feature is subject to removal from the model. Thus, only factors X 2 and X 3 are incorporated into the regression model. For every factor feature, the elasticity coefficient is calculated. The volumes of financial leasing to enterprises in the transport sector increase by 1%. The GDP amount in the transport sector will increase by 0.367%, provided that the dollar/ruble exchange rates remain constant. If the dollar/ruble exchange rates increase by 1%, the GDP amount in the transport sector will increase by 0.546% provided that the volumes of financial leasing to enterprises of the transport sector are stable. Based on the model obtained, three forecast scenarios of GDP generated by the transport sector of Russia for the period of 2018–2020 are outlined (Table 3). We have constructed the economic and mathematical model, reflecting a direct link between GDP amounts in the transport sector, the volumes of financial leasing to transport sector enterprises and dollar/ruble exchange rates. It is interesting that financial leasing proves to show a stronger effect (correlation coefficient) on GDP amounts in the transport sector as compared to bank lending. Thus, it confirms the Table 3 Forecast values of the Russian Federation GDP, represented by the transport sector in 2018–2020 Forecast

Year

GDP generated by the transport sector, in bln rubles

Scenario 1*

2018

7318.141

2019

7668.928

Scenario 2**

Scenario 3***

2020

7955.329

2018

7258.863

2019

7618.974

2020

7921.138

2018

7377.420

2019

7718.883

2020

7989.521

*Based on linear trend (for X 2 factor), the Russian Ministry of Economic Development and Trade analytical forecast (for X 3 factor) **Increase in baseline forecast X 2 by 10%; decrease in baseline forecast X 3 by 10% ***Decrease in baseline forecast X 2 by 10%; increase in baseline forecast X 3 by 10%

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suggestion that the bank lending role in transport system financing is decreasing. Even with a limited scope of financial and lending tools under study, financial leasing is a more efficient option for facilitating infrastructure projects development as compared to bank lending. In accordance with the given forecast, a positive dynamics of the Russian Federation GDP volumes in the transport sector will remain in 2018–2020. This will happen in case of any of the three scenarios of changes in financial leasing to the sector, including changes in dollar/ruble exchange rates.

5 Conclusion Russia is in line with international trends, regarding the use of modern efficient mechanisms for transport systems and infrastructure projects financing. Infrastructure bonds and different structures of public-private partnership are obvious to realize a vast potential with a view to develop transport systems. Analysis, involving econometric tools, suggests significance of development of financial leasing in the transport sector of Russia. Among the factors which have an enormous influence on GDP amounts, falling to the transport sector, there is one for the Russian economy, the factor of changes in US dollar/Russian ruble exchange rates. The considered three scenarios of GDP amounts in the transport sector illustrate a positive trend till 2020. Perhaps, the obtained forecast values will be ensured by increasing attractiveness of infrastructure projects through facilitating access of private investors to large projects of transport infrastructure. Alongside the implementation of the mechanisms of the Russian government, it will provide an equitable collaboration of public and private partners.

References 2017 Annual Update of the Private Participation in Infrastructure (PPI). (2018). Access mode https:// ppi.worldbank.org/~/media/GIAWB/PPI/Documents/Global-Notes/PPI_2017_AnnualReport. pdf. Cited 15 May 2018. Basic indicator of Transport in Russia. (2018). Access mode https://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/en/figures/transport/. Beck, M., & Hensher, D. (2015). Finding long-term solutions to financing 21st century infrastructure needs—A think piece. Road and Transport Research, 24(3), 57–61. Concessionaires and long-term infrastructure investors National Association. (2018). Access mode https://investinfra.ru. Cited 10 May 2018. Ding, C. (2013). Transport development, regional concentration and economic growth. Urban Studies, 50(2), 312–328. Freidina, I. A. (2017). International experience of infrastructure projects financing. Ekonomicheskaya Politika, 12(4), 196–203.

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Ganelin, M., & Vasin, S. (2014). Infrastructure of Russia for the big ship—Great navigation. Market Overview Infrastructure Development. Access mode https://elitetrader.ru/index.php?newsid= 218004#sub. Infrastructure Finance Outlook—Standard & Poor’s Global Ratings. (2017). Access mode https:// www.spratings.com. Liu, H., Bao, A., Chen, X., Zhang, X., & Zhang, J. (2008). The effect of transport accessibility on regional economic performance. Acta Geog Sinica, 63(4), 428–436. Nidziy, E. (2017). Financing the construction of transport infrastructure as the basis for sustainable development of the regional economy. IOP Conference Series: Earth and Environmental Science, 90(1). RDIF Portfolio. (2018). Access mode https://rdif.ru/Eng_Portfolio/. Terenteva, K., & Ikhsanova, L. (2016). Structure of financing infrastructure project as a factor of its effectiveness. Journal of Economics and Economic Education Research, 17, 116–125. Transport Strategy of the Russian Federation to 2030. (2018). https://www.mintrans.ru/documents/ 7/2812. Tsamboulas, D., Verma, A., & Moraiti, P. (2013). Transport infrastructure provision and operations: Why should governments choose private-public partnership? Research in Transportation Economics, 38(1), 122–127. Tsvetkov, V. A., Zoidov, K. K., & Medkov, A. A. (2017). Public-private partnership as the core form of the implementation of Russia’s transport and transit potential. Economics of Regulation, 13(1), 1–12. Volume of Loans in Rubles Granted to Resident Legal Entities and Individual Entrepreneurs, by Economic Activity and Use of Funds. Bank of Russia. (2018). Access mode https://www.cbr.ru/ Eng/statistics/?PrtId=sors.

The Influence of the Russian Economic Crisis on the Regional Peculiarities of Investment Activity Marina N. Mironova, Uliana V. Mizerovskaya and Ludmila V. Shubtsova

Abstract This work analyses the influence of the current economic crisis on the transformation of investment flows in the economy of Russia, its regions and its causes. The volumes of investment in the capital stock as a whole in 2013–2016 are reduced by 12.2%. In the investment structure, there were changes in the forms of ownership, sources, directions and types. A decrease was in the state property share along with an increase in the private one. This led to an increase in the share of own funds and investments in mining industries and real estate. The tendency to increase the share of investment in buildings and structures while reducing it in machinery and equipment was revealed. The volumes of foreign direct investments (FDI) also decreased over this period. The geography of FDI inflows had more pronounced offshore nature. The estimation of the regional groups’ ratio with different investment volumes in the capital stock in 2013 and 2016 was indicative of the increasing concentration of investments in the economy of the most constituent entities of the Russian Federation. The analysis of regional features of the FDI inflow, outflow and balance dynamics also exacerbated their polarization. The comparison of the regional priorities of national and foreign investors identified the regions of the country with different levels of total investment activity.

1 Introduction The capital investments are a necessary element of the economic growth of any country. However, the possibility of their attraction is limited by the investment M. N. Mironova (B) · U. V. Mizerovskaya RUDN University, Moscow, Russia e-mail: [email protected] U. V. Mizerovskaya e-mail: [email protected] L. V. Shubtsova Financial University under the Government of the Russian Federation, Moscow, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_5

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environment which is formed under the influence of a number of factors: economic, political, social, environmental, etc. In recent years, the political factor has been one of the most significant and changeable in Russia. In 2014, when a number of Western countries introduced economic sanctions against Russia, its economy was forced to seek a new balance. The situation led to noticeable fluctuations in many economic indicators, including investment activity. This happened against the background of negative conjunctures and instability of the global hydrocarbon market (Mau 2015). All these processes resulted in the current long-term economic crisis in Russia. To reduce its consequences, the government measures were taken to regulate the development of the economy, for example, to systemically support important spheres. The result was a change in the investment situation in the country. Only in 2017, for the first time since 2013, an increase in domestic investment of about 4.4% in the capital stock was recorded. But despite the gradual overcoming of the investment recession, its impact on the economy of the country and individual regions is still great. The purpose of this work is to study the changes in the capital investment volumes (external and internal), the regional redistribution of their flows in the Russian Federation in 2013–2016 under the influence of the crisis, and the peculiarities of the regional economic development that were formed before.

2 Materials and Methods The theoretical and methodological basis of this work was the scientific works of domestic and foreign scientists who made significant contributions to the definition of regional inequality and the reasons for its formation (Porter 2003; Gubanova and Kleshch 2017), the current economic crisis in Russia (Mau 2015; Zubarevich 2015). The modern works on the identification of regional differentiation of investment activities in the Russian Federation are dedicated to practical issues (Bayev and Solovyeva 2014; Kuznetova 2016; Krasilnikova et al. 2017). The attention is also focused on the specifics and factors of the investment climate for determining the attractiveness of the Russian regions for domestic and foreign investors (Nesterova and Mariyev 2005; Mariyev et al. 2016; Nikitin 2016; Ershova 2017). The authors also relied on separate studies about the influence of political and institutional factors on the investment activity (Alvarez et al. 2018; Drobetz et al. 2018) and the definition of investment strategies of large transnational corporations (TNCs) due to the foreign policy because of the high state share in their ownership (Kalotay 2008). The information basis of the work is the data of the Federal State Statistics Service of the Russian Federation (Rosstat), the official sites of the Russian constituent entities and Expert RA rating. The authors use the economic-statistical method, comparative historical analysis of the dynamic series of indicators, typologies and system analysis methods. The time series analysis covers the period of 2013–2016, which allows making a comparison between indicators during the crisis and those of the pre-crisis time. To exclude the effect of inflation on the dynamics of investment volumes, the current

The Influence of the Russian Economic Crisis on the Regional …

55

prices in rubles are converted into constant prices using the Rosstat methodology. The quantity index numbers of investments in the capital stock are used as a deflator coefficient, including the average annual prices of 2016. The investment indicators are considered in comparable prices in terms of volumes and taking into account the structure of revenues from the viewpoint of sources and financing directions. Five groups of regions are identified in terms of investments in the capital stock based on an arithmetic scale for 2013 and 2016 and in comparable prices. The characteristics of the groups of the selected regions take into account the sectoral features of their economies, transport and business infrastructures, the potential sales market, the implementation of federal targeted development programs and eventbased state projects, etc. The result of the study is the author’s matrix summary table with grouping of the Russian Federation regions at the level of total investment activity. The interregional differences in the investment volumes in the capital stock determine the background investment situation, as well as the differences in the FDI balance in different regional groups of the country in 2016. This leads to the conclusion about the trends in the regional development of investment processes and the economy of Russia.

3 Results 3.1 The Dynamics of the Total Investment Volume and Structure in the Russian Federation in 2013–2016 The dynamics of investments in capital stock at current prices for the study period shows rather fluctuations than a noticeable increase or decrease in the investment flows. However, when evaluated at comparable prices, the overall reduction in investment by 12.2% is obvious. The most disastrous year was 2015 (Fig. 1). The investment volume in the capital stock in relation to Russian GDP, despite the crisis, declined slightly: in 2013—21.2%, in 2016—20.4% (the minimum for the period under review fell to 2015—19.6%). The index value was illustrative because of the lack of investment resources in the country, which was also observed in the pre-crisis period. The reduction in the investment actual volume in the capital stock was accompanied by a change in the structure of funding sources. If in 2013, the own funds of the enterprises accounted for only 45.2% of the total annual amount of the received funds, in 2016 they provided 50.9% of all the capital investments. The largest reduction in the structure of attracted funds was due to cuts in the budget revenues (19%—in 2013 and 16.5%—in 2016) primarily in terms of funding from the regional budgets. This was largely due to the redistribution of budgetary funds in accordance with the package of presidential decrees dated 07 May 2012. According to these decrees, the share of funds in the regions of the Russian Federation for various social development goals increased.

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200

20000

160

16000

120

12000

80

8000

40

4000

0

2013

2014

2015

2016

0

FCI volume (within the constant price rates of 2016) FCI volume (within the current price rates ) FDI inflows FDI outflows FDI balance

Fig. 1 Dynamics of total FDI inflows, outflows and balance (left axis, billion USD) and total FCI (right axis, billion Russian rubles within the current price rates and the constant price rates of 2016). Source compiled and calculated by the authors from: Russian regions. Socio-economic indicators 2017: statistics book. Rosstat, Moscow (2017)

The structure of investments in the capital stock in Russia according to the form of ownership over the years has also undergone a transformation. There was an increase in the share of private investments (from 53.9 to 56.3%) and a reduction in state and municipal investments (from 20.6 to 17.7%), as well as the mixed ownership (from 9.5 to 7.8%) at a constant share of foreign investments (about 7.5%). The main investors in the economy are the large business companies (such as Gazprom, Rosneft, Russian Railways, Rosatom, RAO UES, LUKOIL, Nornickel, RUSAL and Severstal). Therefore, the priority areas of investment in economic activity are related to their profile. In 2016, these were: real estate operations—21%, mining—20%, manufacturing industries—15%, transport and communications—16% (in 2013— 16%, 15%, 14%, 26%, respectively). Over the period of crisis, the focus on investing has shifted significantly from transport to real estate and mining industry. The investments in buildings and structures prevailed in the structure of investments in the economy (by 2016, their share in the total volume increased from 41.5 to 45.2%). New projects for oil and gas extraction and transportation (Nord Stream-2, TurkStream, Siberian oil pipelines), reconstruction of railways and highways, electric power facilities, bridges (Kerch, Volgograd), stadiums for the World Cup, etc. were most actively implemented. At the same time, the share of investments in machinery and equipment decreased from 38.8 to 30.6%. With a lack of investment, this led to an increase in the degree of wear and tear of production equipment from 46.3 to 50.2% in the country as a whole. This trend can lower the labour productivity. The FDI dynamics over the study period repeated the negative trends in domestic investment reaching the lowest possible values in 2015: the FDI balance in 2015 was only about $7 billion US dollars (compared with $69.2 billion US dollars in 2013 and even $32.5 billion US dollars in 2016). This drop was due not so much to the increase in FDI withdrawals from the Russian Federation (as compared with 2013, the figure grew by only 2%), but to a reduction in their inflows (by more

The Influence of the Russian Economic Crisis on the Regional …

57

than 30%). This reduction is a direct consequence of the introduction of sanctions against Russia. The sanctions policy increased the risk of uncertainty for potential investors and hindered the cooperation of Western and Russian companies in such competitive industries as the oil and gas and banking sectors, aircraft industry and the military-industrial industry. Since 2015, there has been a slow but steady recovery in FDI inflows (an increase of 2.8% in 2016 compared to the previous year, and an increase of 8.2% in 2017 according to the preliminary data). However, the FDI withdrawals dynamics is still unstable (in 2016, a decrease of 17.2% compared to the previous year, and in 2017 an increase of 15.1% again). Therefore, fluctuations in the FDI balance values are recorded; however, such strong decline as it was in 2015 is no longer observed. The geographic structure of FDI has been affected by the crisis in terms of the offshore aspect. The top-ten foreign investors in the Russian economy in 2016 were from such territories as Singapore, Bahamas, Bermudas, Virgin Islands, Jersey, as well as Austria, Switzerland, UK. They are very liberal in terms of foreign business. Attention is drawn to the paradoxical fact that the balance of FDI operations of the Russian Federation, as well as the share in the total volume of FDI of Russia and some countries initiating the sanctions for the period under review, has not decreased and even increased in some cases. Thus, in 2017 the balance of operations with the USA on FDI in Russia more than doubled.

3.2 Groups of the Russian Regions with Different Levels of Investment Activity in 2013 and 2016 In 2013–2016, the interregional differences in the volume of investment in fixed assets were caused not only by the traditional unevenness in socio-economic development and the scale of the economies of the constituent entities of the Russian Federation (Bayev and Solovyeva 2014). A noticeable influence was also exerted by the investment climate factors associated with changing socio-political conditions and government measures to regulate the economy (Krasilnikova et al. 2017). The country’s economy crisis which led to a reduction in revenues of the federal and regional budgets and the introduction of government measures that stimulate investment in import substitution industries (mainly agro-industrial and militaryindustrial complexes) have transformed the volumes and nature of investment in regional economies (Nikitin 2016; Zubarevich 2015). The economic crisis had a different impact on the state of industries and production. Their uneven placement in the country led to multidirectional dynamics of investment activity in the regions. During the crisis, in the overwhelming majority of Russian regions, the investment activity declined due to a decrease in the profits of investing enterprises and a decrease in the country’s budget revenues. At the same time, in 2016, in seven regions, the investment volumes decreased by more than 40%. They were the Republics of Mari El, Tyva and Buryatia, the Ryazan, Ivanovo and Kemerovo regions, the Krasnodar

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Krai (in the latter—by 60% due to the completion of financing of the state project on the Winter Olympics in Sochi). Only a quarter of the regions recorded an increase in the volume of capital investments in 2016 compared with 2013. In 40 regions, the investment growth was higher than the average for Russia (87.8%). They can be reckoned in the following region types (Investment Climate in Regions 2017; Zubarevich 2015): – Regions with a high share in the gross regional product (GRP) of developed agriculture, food industry and agricultural machinery: Tambov, Voronezh, Kursk, Oryol, Belgorod and other Central Black Earth regions. – Regions with a developed chemical complex, mainly fertilizer production: Tatarstan, Tula, Novgorod, Leningrad regions, etc. – Regions with a developed military-industrial complex (aircraft industry, small arms industry, etc.): Irkutsk, Rostov and Ulyanovsk regions, St. Petersburg, Udmurtia. – Regions of existing federal target programmes (Crimea and the city of Sevastopol, Kaliningrad and Sakhalin regions), public-private partnership projects for the construction of the Vostochny Cosmodrome (Amur Oblast), new oil pipelines [the Republic of Sakha (Yakutia)] and roads (Moscow Central Circle) including the Moscow Ring Road (in Moscow Oblast) and the preparations for the 2018 FIFA World Cup (Moscow, St. Petersburg, Volgograd and Rostov Oblasts, Tatarstan). – Export regions with preserved high foreign exchange earnings of resident companies (Khanty-Mansi Autonomous Okrug, Yamal-Nenets Autonomous Okrug (YANO), Vologda and Lipetsk Oblasts, Yakutia). – Regions with increased consumer demand for millionaire cities due to the adaptation to the crisis (e.g. except those mentioned, Bashkortostan). High investment growth was noted due to the effect of a low base in a number of regions such as the Kabardino-Balkar and Chechen Republics. All these regions are located mainly in the European part of the Central Federal District, North-Western Federal District and Volga Federal District which in 2016 concentrated 62% of the total capital investment of the country. The differences in the investment activity in the regional aspect as a whole have increased. The scale of asymmetry between the leading region (Moscow) and the outsider region (the Tyva Republic in 2016 and the Altai Republic in 2013) according to this index increased 199 times (in 2013—136 times). The difference in the investment volumes in dozens of leading regions and outsiders increased 48 times (in 2013—35 times). Almost the same situation can be observed in the leading regions with low investment activity in the rest of the regions that affect the current trend. This is confirmed by the growth trend in the concentration of investments in the most developed regions of the Russian Federation. The ratio of ten leading regions and outsiders in terms of investment in the economy is 48 and 1% of the total in the country, respectively (in 2013—46 and 1.3%). The composition of these regions is almost constant, except the Republic of Crimea and the city of Sevastopol, which have been taken into account in Russian statistics since 2014 (Table 1). A comparative analysis of the region groups of Russia with different amounts of investment in the capital stock revealed the regions with elevated and decreased

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59

Table 1 Top-ten lists of the leading and outsider regions based on FCI volume in 2016 and 2013 (as of constant price rates in 2016), million roubles and % of the total volume Regions

FCI volume

Regions

2013

%

1,663,211

10.0

Krasnodar Krai

1,044,852

Khanty-Mansi AO

875,852

Yamalo-Nenets AO

FCI volume 2016

%

Moscow

1,703,085

11.6

6.3

Yamalo-Nenets AO

1,097,131

7.5

5.3

Khanty-Mansi AO

804,103

5.5

864,754

5.2

Tatarstan Rep.

642,494

4.4

Moscow Oblast

702,416

4.2

Moscow Oblast

634,692

4.3

Tatarstan Rep.

642,494

3.9

Saint Petersburg

582,306

4.0

Saint Petersburg

591,697

3.5

Krasnodar Krai

428,972

2.9

Krasnoyarsk Krai

463,759

2.8

Krasnoyarsk Krai

419,060

2.9

Sverdlovsk Oblast

432,228

2.6

Bashkortostan Rep.

360,946

2.5

Nizhny Novgorod Obl.

360,453

2.2

Sverdlovsk Oblast

345,812

2.4

Ingushetia Rep.

32,498

0.2

Ivanovo Oblast

22,616

0.2

Kabardino-Balkar Rep.

28,436

0.2

Ingushetia Rep.

19,970

0.1

Kostroma Oblast

27,833

0.2

Karachay-Cherkess Rep.

19,899

0.1

Karachay-Cherkess Rep.

23,956

0.1

Adygea Rep.

15,391

0.1

Adygea Rep.

22,818

0.1

Kalmykia Rep.

13,510

0.1

Kalmykia Rep.

20,977

0.1

Jewish Aut. Oblast

12,859

0.1

Jewish Aut. Oblast

16,043

0.1

Altai Rep.

12,338

0.1

Tyva Rep.

15,681

0.1

Sevastopol

12,087

0.1

Chukotka AO

15,402

0.1

Chukotka AO

9,746

0.1

Altai Rep.

12,232

0.1

Tyva Rep.

8,556

0.1

Leaders Moscow

Outsiders

Source compiled and calculated by the authors from: Russian regions. Socio-economic indicators. 2017: statistics book. Rosstat, Moscow (2017)

levels of investment activity and also determined its condition in the whole country. Only 26% of the constituent entities of the Russian Federation are characterized by high volumes of investment in the economy (in 2013—30%), and 53% of regions with low amounts (in 2013—48%) which indicates a reduction in investment in most regions of Russia these years (Fig. 2). The group of regions of the Russian Federation with investments in the capital stock exceeding 200 billion rubles almost has not changed its composition. It is represented by export-oriented regions: oil and gas, except those listed in Table 1 in the Khanty-Mansi Autonomous Okrug, the Yamalo-Nenets Autonomous Okrug, the Republic of Tatarstan and Bashkortostan. These are Sakhalin and Tyumen Oblasts,

60

M. N. Mironova et al.

9% 27% 11% 17% 25% 19% 23% 26%

22% 21%

400 bln Roubles or more 200 – 399 bln Roubles 100 – 299 bln Roubles 50 – 99 bln Roubles less than 50 bln Roubles

Fig. 2 Proportion of groups of regions with different FCI volumes in 2016 (outer circle) and 2013 (inner circle), %. Source compiled and calculated by the authors from: Russian regions. Socio-economic indicators. 2017: statistics book. Rosstat, Moscow (2017)

the Republic of Komi and metallurgical in Krasnoyarsk Krai, Sverdlovsk and Irkutsk Oblasts, the Republic of Sakha (Yakutia). The investments in the resource regions are aimed at developing the resource base of leading companies in their industries. This group of regions also includes the capitals (Moscow and St. Petersburg), the neighbouring regions (Moscow and Leningrad Oblast) and economically strong regions with millionaire cities (Nizhny Novgorod, Rostov, Voronezh, Samara Oblasts, Perm Krai). The regions of this subgroup are distinguished, as a rule, by a stable diversified economy, a developed transport infrastructure, concentration and high potential of labour resources and consumer demand. The investments in them are used for the creation of enterprises for the production of finished products of the manufacturing industry (including machine-building and petrochemical ones on the basis of integration with the main export production), energy supply, transport and communications, housing construction, etc. (Rating of 600 biggest Russian companies 2017). Krasnodar Krai takes the leading position due to Sochi, the capital of the 2014 Winter Olympics and one of the locations of the 2018 World Cup. The group of the constituent entities of the Russian Federation with the investment volumes less than 50 billion rubles mainly includes the regions of the Northern Caucasus, Southern Siberia, Volga Region and Central Russia that are lagging behind in economic development. In addition to those listed in Table 1, these include: the Republics of Khakassia, Buryatia, North Ossetia, the Chechen Republic, Chuvashia and Mordovia, Kamchatka Krai, Kurgan, Kirov, Pskov, Oryol, Vladimir, Bryansk Oblasts, etc. Most of them have a significant share in investments represented by budget mainly federal funds. The large business is presented there very little or not at all as the increased financial risks, a low level of regional institutional development and personnel, poor transport connectivity with the centre determine the reduced investment activity (Nesterova and Mariyev 2005).

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3.3 Regional Features of the Dynamics and Location of FDI in the Russian Federation in 2013–2016 Since the main factor of the FDI dynamics is the development of large business, the interregional differences in the FDI distribution over the study period affected mainly the volume of the investment flows, rather than the regional priorities of the investors. Russia is characterized by the highest concentration of FDI in a limited number of Russian regions differing by a well-developed quaternary sector of the economy or its stable export orientation usually among the richest and most economically developed ones (Gubanova and Kleshch 2017). The main factors for FDI inflows are the investor’s country’s GDP, per capita GRP in the recipient region, the distance from the investor and the openness of the region, the economic situation in the region, its innovative potential and the FDI of the previous period (Mariyev et al. 2016). The first ten regions for attracting FDI in 2013 and in 2016 accounted for 85% of the total volume of received FDI. The leader is Moscow, although its share in 2016 compared to 2013 decreased from 55.8 to 47.4%. Other leaders that rank second are St. Petersburg, “near-capital” Moscow and Leningrad regions, oil and gas producing Sakhalin and Tyumen Oblasts, the Yamalo-Nenets Autonomous Okrug (in 2013 it ranked 11th), metallurgical Krasnoyarsk Krai, Vologda, Lipetsk and Belgorod Oblast (Table 2). All other regions beyond the top-ten leaders can be called outsiders in the attraction of FDI. The impact of the crisis increased the number of regions that attracted FDI of less than 1 million US dollars had no inflow. In 2013, there were only three such regions (the Jewish Autonomous Oblast, the Altai Republic and the Chechen Republic). In 2016, there were seven of them (the Altai, Ingushetia, Kalmykia, Chechen, Table 2 Top-ten regions based on the volume of the attracted FDI in 2013 and 2016, million USD and % of the total volume Regions

FDI volume

Regions

FDI volume

2013

%

Moscow

108,107

55.8

2016

%

Moscow

65,314

47.4

Saint Petersburg

13,058

6.7

Krasnoyarsk Krai

11,472

5.9

Sakhalin Oblast

8,295

6.0

Moscow Oblast

8,205

Tyumen Oblast

11,339

6.0

5.9

Saint Petersburg

7,631

5.5

Moscow Oblast Sakhalin Oblast

6,477

3.3

Vologda Oblast

7,343

5.3

4,421

2.3

Leningrad Oblast

5,776

4.2

Leningrad Oblast

3,762

1.9

Krasnoyarsk Krai

5,424

3.9

Vologda Oblast

1,951

1.0

Tyumen Oblast

4,402

3.2

Krasnodar Krai

1,925

1.0

Yamalo-Nenets AO

2,763

2.0

Belgorod Oblast

1,839

0.9

Lipetsk Oblast

2,023

1.5

Source compiled and calculated by the authors from Russian regions. Socio-economic indicators. (2017): statistics book. Rosstat, Moscow (2017)

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Karachay-Cherkess and Kabardino-Balkar Republics, the Nenets Autonomous Okrug). The FDI inflow volumes of in the leading regions are largely levelled out by the volume of withdrawals (Table 3). Therefore, we can conclude about the “cleansing” nature of FDI in Russia which is clearly reflected in the relatively low values of the FDI balance in terms of the country and the region. Due to the reduction in FDI inflows, the number of regions with a negative FDI balance increased: if in 2013 there were only 18 of them (Fig. 3), in 2016 there were already 26 (and this did not include the Republic of Crimea and Sevastopol). St. Petersburg was the leader in terms of negative balance in 2016 (−791 million US dollars): its share in terms of total FDI inflows in 2016 was only 5.5%, and in terms of withdrawal—8%. The real estate sector is the weakest point in the city: even before the crisis in 2014, the foreign investors negatively assessed its investment climate due to high management risks (after the change of the city governor in 2011, several large Table 3 Top-ten regions based on FDI outflow volume in 2013 and 2016, million USD and % of the total volume Regions

FDI volume

Regions

FDI volume

2013

%

Moscow

68,497

55.0

2016

%

Moscow

53,505

50.8

Krasnoyarsk Krai

10,491

8.4

Saint Petersburg

8,422

8.0 7.4

Saint Petersburg

6,639

5.3

Moscow Oblast

7,805

Moscow Oblast

5,611

4.5

Vologda Oblast

7,417

7.0

Leningrad Oblast

3,190

2.6

Leningrad Oblast

5,096

4.8 2.8

Rep. Sakha (Yakutia)

2,769

2.2

Tyumen Oblast

2,996

Sakhalin Oblast

2,639

2.1

Krasnodar Krai

1,509

1.4

Perm Krai

2,174

1.7

Yamalo-Nenets AO

1,270

1.2

Krasnodar Krai

2,008

1.6

Lipetsk Oblast

1,223

1.2

Yamalo-Nenets AO

1,750

1.4

Krasnoyarsk Krai

1,067

1.0

Source compiled and calculated by the authors from Russian regions. Socio-economic indicators. 2017: statistics book. Rosstat, Moscow (2017)

28%

2% 13% 5% 16% 19%

7% 7%

Regions with positive FDI balance (over 500 mln USD) Regions with positive FDI balance (less than 500 mln USD) Regions with net FDI balance Regions with negative FDI balance (less than - 500 mln USD)

53%

50%

Regions with negative FDI balance (over - 500 mln USD)

Fig. 3 Proportion of groups of regions with different FDI balance in 2016 (outer circle) and 2013 (inner circle), %. Source compiled and calculated by the authors from Russian regions. Socioeconomic indicators. 2017: statistics book. Rosstat, Moscow (2017)

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63

construction projects were cancelled at once). But when new risks were imposed on management risks, many investors began to gradually withdraw their assets from the St. Petersburg market. In general, the industrialized regions are leading in terms of the negative FDI balance, although their composition differs: in 2013 these regions were the Republic of Sakha (Yakutia), Perm Krai, Khanty-Mansi Autonomous Okrug, Yaroslavl and Sverdlovsk Oblasts, and in 2016—St. Petersburg, Belgorod and Samara Oblasts, Khanty-Mansi Autonomous Okrug, the Republic of Bashkortostan. This does not point to the fact that the current steady trend is the withdrawal of funds from certain regions, but to the fact that certain projects are stopped because of the crisis. The comparison of the groups of the Russian Federation regions with different investments volumes in capital stocks and FDI balance in 2016 made it possible to identify the leading regions and outsider regions of the country by the total investment activity (Table 4). Table 4 Typical regional groups based on FCI volume and FDI balance in 2016

Regions with positive FDI balance (over 500 million USD) Regions with positive FDI balance (less than 500 million USD)

Regions with net or negative FDI balance

Number of regions

Regions with large FCI Regions with medium FCI volume (from volume (over 200 100 to 199 billion billion roubles) roubles) Belgorod, Moscow, Lipetsk, St.Petersburg, Chelyabinsk, Krasnoyarsk Krai, Amur, and Tyumen, Sakhalin, Samara, and Leningrad Oblasts, Khabarovsk Krai Nizhny Novgorod Oblasts Yamalo-Nenets AO, Vologda, Tatarstan, Orenburg, Komi, and Kemerovo, Bashkortostan Novosibirsk, Republics, Tula, and Rostov, Voronezh, and Astrakhan Oblasts Irkutsk Oblasts Krasnodar Krai, Perm Krai, Khanty-Mansi AO, Sverdlovsk Oblast, Sakha (Yakutia) Republic

21

Saratov and Murmansk Oblasts, Primorsky Krai, Stavropol Krai

19

Regions with high total level of investment activity

Regions with small FCI volume (less than 99 billion roubles)

Number of regions

Magadan and Arkhangelsk Oblasts, Udmurt Republic 17

Volgograd, Kursk, Tver, Novgorod, Kaluga, Penza, Kirov and Bryansk Oblasts, Chuvash, Buryatia, Khakassia, Kalmykia and Tyva Republics, Zabaykalsky Krai Chechen, North Ossetia-Alania, Ingushetia, Karachay-Cherkessia, Crimea, Altai and Karelia Republics, Omsk, Vladimir, Ryazan, Ivanovo and Smolensk Oblasts 45

42

26

85

Regions with low total level of investment activity

Source compiled and calculated by the authors from Russian regions. Socio-economic indicators. 2017: statistics book. Rosstat, Moscow (2017)

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The highest total investment activity in the country’s economy was recorded in two capitals and “near-capital” territories, some foreign-oriented regions with a predominance of the oil and gas industry (YANO, Sakhalin and Tyumen Oblasts), ferrous and non-ferrous metallurgy (Belgorod, Lipetsk, Chelyabinsk, Irkutsk Oblasts), regions with diversified economy and millionaire cities (Nizhny Novgorod, Voronezh, Tatarstan, Bashkortostan, etc.). At the same time, an additional reason to attract foreign investors to individual regions is their cross-border nature (Orenburg, Rostov and Astrakhan Oblasts). Domestic and foreign investors were extremely reluctant to send money to the regions with a predominance of the primary economy sector. Regions that are characterized by political tensions (Chechen, Ingushetia, Altai, Kalmykia and Tyva Republics and Zabaykalsky Krai) and depressed regions nearby the capital (Vladimirskaya, Ryazan, Ivanovo, Smolensk regions, etc.) seem also unfavourable. A group of regions with low total investment activity (50% of constituent entities from different federal districts) is characterized by high depreciation of fixed assets, technological backwardness, expensive borrowed funds, etc. The situation there is characterized by a clear lack of investment resources, which will affect the rates of construction, level and quality of life, increase unemployment and migration outflow of the population. The fact that this group is the most numerous indicates that Russia as a whole has not yet come out of the crisis. These changes affect not only the ability of individual regions to withstand the still ongoing negative phenomena in the country’s economy, but can also determine their rate of recovery from the crisis. The analysis revealed the prerequisites for the prospects of the spatial development of the Russian economy. Capital investments are long-term; therefore, in regions with high investment activity, in relation to domestic and foreign investments in the economy the GRP, growth can be expected in the medium term (25% of the RF constituent entities), and in the regions with a low investment activity may continue declining. This will lead to increased regional inequality. Acknowledgements This work has been prepared with the support of the “RUDN University Programme 5-100”.

References Alvarez, R., Jara, M., & Pombo, C. (2018). Do institutional blockholders influence corporate investment? Evidence from emerging markets. Journal of Corporate Finance, 53, 38–64. Bayev, I., & Solovyeva, I. (2014). Empirical analysis of the relationship of investment and innovative activity of regions of Russia. Economy of Region, 1(1), 147–155. Drobetz, W., El Ghoul, S., Guedhami, O., & Janzen, M. (2018). Policy uncertainty, investment, and the cost of capital. Journal of Finance Stability, 39, 28–45. Ershova, N. (2017). Investment climate in Russia and challenges for foreign business: The case of Japanese companies. Journal of Eurasian Studies, 5, 151–160.

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Gubanova, E. S., & Kleshch, V. S. (2017). Methodological aspects in analyzing the level of nonuniformity of socioeconomic development of regions. Economic Social Changes: Facts, Trends, Forecast, 10(1), 58–75. Investment Climate in Regions 2017. JSC Expert RA. (2018). Access mode https://raexpert.ru/ ratings/regions/2017. Kalotay, K. (2008). Russian transnationals and international investment paradigms. Research in International Business and Finance, 22, 85–107. Krasilnikova, E. A., Mayorova, E. A., Nikishin, A. F., & Pankina, T. V. (2017). Investment activity in Russian regions. European Respiratory Statement Journal XX, 4B, 509–521. Kuznetsova, O. (2016). Differences in Russian regions attractiveness for foreign and domestic investors. Vopr Ek, 4, 86–102. Mariyev, O., Drapkin, I., Chukavina, K., & Rachinger, H. (2016). Determinants of FDI inflows: The case of Russian regions. Economy of Region, 1(4), 1244–1252. Mau, V. (2015). Economic crises in post-communist Russia. Economic Policy, 2, 7–19. Nesterova, D., & Mariyev, O. (2005). Factors of direct foreign investments involvement into Russian regions. Economic of Region, 1(4), 57–70. Nikitin, A. S. (2016). Investment rating as an instrument of effective govern management stimulation in Russian regions. Economic Policy, 6, 192–221. Porter, M. E. (2003). The economic performance of regions. Regional Studies, 37(6–7), 549–578. Rating of 600 biggest Russian companies following the results of 2016. JSC Expert RA. (2017). Access mode https://raexpert.ru/ratings/expert400/2017. Russian regions. Socio-economic indicators. 2017: statistics book. Rosstat, Moscow. (2017). Access mode: https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/ publications/catalog/doc_1138623506156. Zubarevich, N. V. (2015). Regional dimension of the new Russian crisis. Vopr Ek, 4, 37–52.

The Mechanism of Integration of Innovations in Technological Processes (by the example of the Construction Sector) Petr I. Ospishchev and Maja Andjelkovic

Abstract The features of the innovative development of the construction industry are considered in this work. The authors highlight the individual factors that influence the integration of innovation in modern processes. There are conditions of integration and adaptation of advanced production systems determined and evaluated in the construction industry. The work proposes a complex of measures aimed at ensuring the process of integration of innovations in the construction industry and their wide dissemination. The implementation of the research plan will allow forming a general idea of innovations in the construction sector and identifying its key aspects. The authors plan to form a structure of factors that have a negative impact on the area under consideration, as well as to determine the most optimal method of integration of innovations taking into account certain criteria.

1 Introduction Today, no one doubts the fact that the basis for the development of almost any enterprise as a subject of the economic environment is innovation. The innovation is able to provide a stable position of the enterprise market, to determine the vector of its development, to encourage further improvement of the business processes, to offer the market a better product, etc. The leadership of the country that supports this area by different programs and normative-legal acts determines the importance of innovation. Despite the importance of the innovation process, for both an individual enterprise and various sectors of the economy, recognized by all participants in market relations, there are still factors that, to some extent, have a negative impact on this

P. I. Ospishchev (B) Belgorod State Technological University named after V.G. Shukhov, Belgorod, Russia e-mail: [email protected]; [email protected] M. Andjelkovic University “Union-Nicola Tesla”, Belgrade, Serbia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_6

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process. One of these factors is the problem of integration of innovation results into existing technological processes. The problem of integration is more related not so much to the peculiarities of technological interface with the already functioning processes, but to the fact that organizations that play a decisive role in important sectors of the economy are not always focused on the use of innovative developments. The lack of interest in innovation and its integration is explained by the need to break the organizational, managerial and technological connections, formed in the process of core activities and significant for the enterprise. Enterprises create and develop some relationships over time, and abandoning them can lead to losses that are not comparable to the income gained from innovation. In addition, there are various mechanisms of control and certification of processes, developed at the stage of the formation of previous technological solutions and difficult to adjust and change without violating existing rules and certain regulations. As a result, a paradoxical situation is created. On the one hand, innovations are a necessary and important condition for the development of the enterprise and the industry. On the other hand, their implementation is not always desirable because of their destructive influence on various participants in the economic space. In addition, the situation is aggravated by the fact that innovations are the prerogative of large enterprises with financial or organizational and management capabilities to attract significant funds and narrow specialists to ensure innovation activity, rapid commercialization of the results. To a greater extent, they determine the vector of development of the industry and individual market segments. At the same time, it is not always possible to neutralize such dominance by smaller market participants. This problem is typical of the construction industry. This industry is rather poorly adapted to innovations in contrast to chemical, electronic, automotive and other industries, and their implementation can radically change the structure of partnerships between enterprises and the market entity. It raises a number of questions: 1. Is it possible to ensure the integration of various innovative production systems into existing processes without disrupting technological, organizational, economic and other connections? 2. How relevant and necessary is the external mechanism of integration of innovations in the production or other environment for the modern market?

2 Research Methods In our opinion, it is appropriate: • to consider the features of innovations in the construction sector and their conceptual understanding by modern researchers; • to identify the strengths and weaknesses of the main participants in the construction industry, as well as their impact on the innovation process;

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69

• to identify the factors that have a positive or negative impact on the innovative development of the construction industry; • to consider the types and methods of integration of innovations in the construction industry. The implementation of the research plan will allow forming a general idea of innovations in the construction sector and identifying its key aspects. It is planned to form a structure of factors that have a negative impact on the area under consideration, as well as to determine the most optimal method of integration of innovations taking into account certain criteria. The main method of research is a systematic approach. In a systematic approach, the researcher forms a holistic rather than simplified view of the problem. The system is bigger than its parts. The systems can be divided into soft and hard. In the literature, rigid systems are suitable for well-defined problems, while soft systems are suitable for vague and fuzzy ones. Rigid systems can be used to determine the relationship between the different sides of the innovation process in construction, while soft systems can be used to analyze the links between the different sides of the process. That is, rigid systems consider the relations between the parties of the process, while soft systems determine the content of these relations. The system approach has a number of significant characteristics. In particular, any system consists of components. In the process of innovative development of the construction industry, a system of relations between them can form. The components that form these relationships in a process can be related. By removing any of the components of the relationship system, the system can change the principle of operation. In this case, each component of the system can represent an independent system. The authors analyze the problem by studying the processed initial data presented in open sources. When using secondary information, two problems may arise: The first is the compatibility of previously collected and processed information, and the second is that one may collect the material for other purposes. In the framework of the study, the use of secondary material will not affect the quality of the study, but will allow a wider look at the problem and methods of its solution. In addition, the study will not use specific digital data, which does not require confirmation of the reliability of the results of previous studies.

3 Characteristics and Types of Innovations in the Construction Industry Innovations are the result of creative thinking manifested in a particular invention. With the help of innovations, the development of individual industries and spheres of economic activity is carried out either by large leaps or gradually. The modern literature presents a large number of definitions of innovations, the key difference of

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which is the field of implementation of these innovations. As a result, innovations in construction are an independent area defined by its own categories and definitions. Most of the works in this field consider the features of innovations and innovative development of the construction industry, identify some important aspects of the process, the degree of influence of various areas and business processes and so on. At the same time, there is not much research in Russian and foreign literature giving a comprehensive view of the problem of innovative development of the construction industry and offering real, practical, applicable mechanisms for the implementation and innovative development management. The study of the thematic literature shows that most authors have a similar understanding of the innovation process and its features, while focusing on different minor aspects. For example, M. Dulaimi defines innovations as “the pursuit of purposeful change in an economic or social enterprise.” In the definition, he emphasizes that innovations are primarily economic changes (Dulaimi 1995). C. Tatum defines innovations as “the process of opening or creating a new idea.” He specifies two aspects of the definition. The first aspect is “process innovation,” which refers to certain advances in technologies that allow for greater productivity. The second aspect is “product innovation,” i.e., the creation of quality new products. M. Dulaimi highlights that the innovations implemented in the technological process are focused on the production of a larger volume of products per standard unit of time. And product innovations are focused on the output of a qualitatively different product with a given amount and quality of incoming resources. Therefore, one should understand innovation as a fundamentally new idea or action, as well as the innovative use of existing technologies or processes to meet new requirements. C. Tatum pays attention to the fact that innovations based on two fundamental conditions: The first is a market demand, and the second is a scientific and technological process. In his opinion, these factors play a decisive role in the initiation of innovations leading to higher levels of efficiency and productivity (Tatum 1987). D. Arditi describes innovations as the transfer of knowledge to production, largely influenced by the level of talent of employees at all stages of development of the enterprise. Since innovation is a transfer process, a significant number of intangible factors influence this process (Arditi 1982). S. Ganesan states that innovations in construction are a competitive response to the restraints of the industry and the economy to ensure more efficient operation, production and completion of projects within the resource limitation (Ganesan et al. 1996). In his opinion, the construction industry and the economy are inextricably linked, where one of the key stages of the construction project is resources. However, the resources can be an obstacle to innovations. The construction industry is one of the most complex material-production systems of the economy. Its complexity is determined by the need to choose the construction site, which must meet such requirements as: geological and environmental conditions, the degree of exposure to man-made impacts, the state of the material and technical base, the degree of infrastructure development, energy capacity, etc. Determination of significant requirements allows starting the process of designing the object, making the decisions on the functional purpose of the object. Constructive and planning solution is determined and selected, the construction technology and

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the method of organization and management of construction are determined and the cost of the object is calculated. A constant interaction of participants of construction—investors, customers, contractors and designers, suppliers of material and technical resources, service organizations, supervisory bodies accompany the cycle of creation of construction products. These subjects of the construction industry can interact under different conditions of cooperation and integration. Some participants in the creation of construction products interact only in certain periods. Some of the selected participants directly influence the creation of the construction product. The resulting construction products are in demand in almost all sectors of the economy and regions. In addition, the products are in demand in the social environment. At the same time, the construction industry consumes material and technical resources of about 70 sectors of the economy. Therefore, the innovative development of the construction industry will be impossible without the participation of enterprises of these industries on mutually beneficial terms (Korol 2016). The paces of innovations in the industry of the construction equipment have increased over the past 30 years. This increase may be due to pressure caused by customer behavior and technological developments in the industry of the construction equipment as well as in other industries. Innovations in the construction equipment will benefit significantly construction companies because the use of advanced models increases their productivity and competitiveness (Arditi et al. 1997; Slaughter 1998; Tangkar and Arditi 2000). At the moment, there is a strong demand in the construction industry, which includes a demand for higher performance, demand for the reduction of construction costs, high volume requirements and quality of construction, the demand for speed of construction and early completion of projects, etc. Despite this, the demands for innovations in the construction are due to the latest market demands; the industry has unique characteristics that do not accelerate, but instead slow down the innovation process.

4 Factors Which Have a Negative Impact on the Innovative Development of the Construction Industry The implementation of innovations faces various constraints that exist in specific conditions and specific areas. The factors of hidden and obvious influence determine the direction and pace of innovative development. The research of the construction industry allows identifying a number of factors influencing its innovative development. 1. The structure of the construction industry. The structure of the construction industry, formed over a significant period of time, creates limitations for innovative development. It is possible to distinguish the following aspects, affecting to some

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extent this process: (a) Fragmented nature. The construction is an assembly process divided into two phases—design and construction. This separation is important in the traditional construction process, where two teams mainly handle two stages. In this process, one or more consultants assist an architect, who creates a design for the client. The drawings are detailed, the specifications are given and the contractor carries out the project, which is assisted by suppliers and subcontractors. Since each project is unique, the contractor has no reason to invest in innovations other than to optimize its own process. There are no economies of scale or learning effects (Pries and Janzen 1995). E. D. Love and M. Skidmore pay attention to the fragmented nature of the construction industry in their research on procurement strategies. They highlight the gap between design and construction, which in turn leads to serious behavioral, cultural and organizational differences between individuals and project teams. The researchers believe that the potential communication gap is one of the main shortcomings of the industry. In most cases, clients often receive less optimal design solutions because contractors are independent of the decision-making processes occurring in the project team (Love et al. 1998). 2. The dominance of the client. In the construction industry, in comparison with other industries, the client plays an important role. The importance of the client role consists in the fact that the client is the initiator of the project. Owners are often not just buyers of finished products, they are also participants in projects. The clients influence the formation of the mechanism which involves the parties in the project implementation, the process of communication and cooperation to make decisions on important technical issues during the execution of the project and sometimes share a significant part of risk. Thus, the owner’s willingness to innovate, to take risk and even one’s level of sensitivity and technical awareness becomes very important to achieve the best results in building innovation (Nam and Tatum 1997). In most cases, the client seeks to emphasize the need to reduce the level of risk because the products of the construction industry have a long service life. The client prefers to adhere to proven methods, focusing on strength and quality, avoiding radical changes. This greatly reduces the buyer’s ability to try out a new product. At the same time, depending on the willingness, commitment and formed relationship, the dominance of the client can have a positive impact. 3. Uniqueness of projects. Compared to the automobile industry or electronics in the construction industry, the opportunities for repetition are much smaller. In addition, the products essentially relate to a location and therefore subject to unique sets of constraints imposed by the location. They can be buildings, bridges, tunnels, roads, etc. In turn, all this becomes an obstacle as the application of a specific innovation for a large number of different types of products is decreasing. 4. Rigid dependence. As has been defined above, construction is an assembly industry. Various suppliers and subcontractors supply building materials, machinery, equipment and other materials. Many technical changes aimed at improving products or reducing costs are embodied in the materials, received from suppliers,

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which reflect their research and development (R&D). Unlike many other assembly industries, such as the automobile or aerospace industry, construction firms are relatively small and a number of suppliers are relatively large. Construction firms are highly dependent on technical developments originating from suppliers. Many construction firms do not have the technical expertise to cope with new technologies or conduct research and developments. Even large construction firms are the combination of small enterprises, where each enterprise should maximize profits and minimize risk in short term (Westling 1991). More than 80% of innovations in the sphere of products come from the supply industry. As a result, they are the ones who play the dominant role in the innovations of the construction industry. 5. External limitations. Environmental factors influence the innovative development, except certain factors of internal nature. 6. Government regulations. The construction industry is a state-regulated medium despite the high level of competition and the impact of various market processes. Thus, the state has a certain impact on the construction market through various mechanisms—financial, environmental, technological and other types of regulation. Technical regulations determine the quality level of most of the products of the construction industry. Environmental norms are also becoming increasingly important, and they seem influencing what is being done and what is done to ensure security for the consumer and for society as a whole. 7. Legislative base. The legislative and regulatory bases specify the features, principles and other standards of organization, provision and performance of the construction works, characteristics of the used materials. At the same time, the approbation of new materials and fixing them in normative documents takes place in a sufficiently long period of time.

5 Types and Kinds of Construction Innovations. Methodical Support of Integration of Innovations in the Building Industry The introduction of innovative equipment and innovative technological processes directly into the activities of individual organizations and groups of organizations allows improving the quality and various characteristics of the products, creating conditions for improving the methods of organizing production processes. As a rule, innovations can be introduced in the following areas: • development of new products and modernization of the products; • introduction into production of innovative technological processes, equipment, machines and materials; • introduction of new production methods and information technologies; • improvement and implementation of innovative methods of organization and management of production.

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There are five types of innovations for the construction industry—incremental, modular, architectural, systemic and radical. Incremental innovations are small changes based on current knowledge and experience. Modular innovations are a significant change in the concepts within the component, but other components and systems remain unchanged. Architectural innovations are associated with small changes in the component, without significant changes in the links with other components and systems. System innovations are characterized by integration of the components of several independent objects that must work together to perform new functions or to improve the performance of the whole object. Radical innovations (“shooting star”) are a breakthrough in science or technology, which often changes the nature of the industry. The basic principle of each feature bases on the degree of changes happening in the current state, associated with innovations and expected connections with other components and systems. The number and technological life of new models of construction equipment introduced every year can illustrate the complexity of these five models in the construction industry. In addition, other types of innovations characterize the construction industry: 1. Innovations in production technology. The introduction of these innovations is necessary to optimize production processes and reduce production costs. The development of high-tech and automated systems provides this type of innovations (Elistratkin et al. 2018). 2. Innovations in efficiency. The constant complication of building designs and increasing requirements for their efficiency determines the need for innovations of this type. Buildings are integrated with a variety of new subsystems, which allow expanding their efficiency in areas that were not previously perceived as part of the construction industry, but now are gradually merging with it. By integrating the technologies of microsystems into buildings, they not only become smarter, but also can be more personalized for the needs of residents. These changes can have a strong impact on all elements of the chain and can transform building structures, construction technologies and business models. 3. Innovations of wide consumption. It is possible to use existing production systems in a different context and in other areas if the innovative solution meets the requirements and challenges of the neighboring industry. 4. Innovations of transformations. This implies transformation and adaptation of the existing equipment to the changed conditions. 5. Innovations in the process of interaction with the consumer of goods and services. This process bases on the interaction of consumers and producers. In the course of the interaction, the producer finds out exactly what the consumer needs and what one expects from the product. The manufacturer takes into consideration the needs of the consumer and tries to develop the product that will fully meet the expectations (Bock et al. 2012). This is not a complete list of types of innovations implemented in the construction industry. However, at the same time, it shows the multifaceted nature of this process, as well as the complexity of its implementation. Despite the diversity of innovations in the construction industry, there are typical methods adopted in their implementation:

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1. Forced method. At the heart of this approach, one provides the introduction of innovations using administrative resources to overcome resistance from various market entities. This method is optimal in times of shortage. 2. Method of adaptive deviations. The fact shows that a manager does not control the process, but a special project team does. One can resolve conflict situations by compromise. This method is applicable when the danger is easy to foresee, and urgent decisions are not required. 3. Crisis management. This method is used when the administration is in a crisis situation. Changes in the external environment threaten its improvement, and it is in a rigid time frame. 4. Resistance control. If forced and adaptive methods are extreme measures of change, then the resistance control method is intermediate. With increasing urgency, this method is close to the forced method, and with decreasing urgency—to the adaptive method (Grishin 2010).

6 The Concept of the Integration Mechanism of Innovations in the Construction Sector As it has been mentioned above, the parties involved in construction activity do not always focus on innovative development. Moreover, because of their conservative thinking, they often try to avoid introducing innovations. The conservatism of consumers determines the conservatism of the participants of the construction industry. The conservatism of consumers and the conservatism of manufacturers depend on the level and number of partnerships, allow identifying the mechanism of integration of innovations in the construction sector and demonstrate its effect by the example of the integration of construction 3D printers, which print cement-based materials in existing technological and market processes. Before defining the features of integration of innovations in the construction sector, it is necessary to understand the definition of the “mechanism of integration of innovations.” The mechanism of integration of innovations is understood as a certain sequence of elements interfaced by a complex of links that provide integration of innovations by means of its transfer to the functioning production systems. Production systems imply understanding of individual enterprises, groups of enterprises, production complexes, clusters and industries. The study shows that it is necessary to fulfill the mandatory conditions in order to ensure the process of integration of innovations into functioning production systems and to create the conditions for their commercialization. There are such conditions as:

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1. availability of a production base which is capable to integrate the innovation into its own or third-party technological system. The technological base should imply the specific enterprises which strive or have the possibility for innovative integration; 2. the possibility of creating an “incubator base.” The incubator base implies actually existing production, organizational, economic, social, regulatory and other conditions which are capable to “bring” the innovation to the level of commercial use; 3. ensuring safety of the system of partnership relations between the key participants in the construction industry, as well as the possibility of their change and increment in order to provide the adopted standard technological process; 4. the distribution of risk between the subjects of the construction industry, which constitute the production and “incubator” base. Distributed (insured) risk allows one to be more actively engaged in marketing of new technology (product) of “grown” innovation; 5. regulatory legal certainty. This condition allows reducing the time of regulatory and legal formation of innovation in the construction industry, which will allow using it in mass production; 6. the imposition of innovations on consumers. As it has been defined above, both manufacturers and consumers of the products of the construction industry are quite conservative. As a result, new technology or new product must be imposed on the consumer in some circumstances in order to form the consumer and production basis and further research of innovations. Implementation of the number of conditions will reduce the time of integration of innovation and its commercialization. The mechanism of integration of innovations in the construction sector can be presented in a sequence of the following actions, which are decisive for the effective integration of innovations. 1. Formation of innovative solutions by the developer of the basic technological documentation and development of regulations of the main technological processes, which allow forming a common consumer view. 2. Providing the developer with the production base for testing innovative solutions and obtaining the first prototypes. Administrative methods (compulsory) and voluntarily (at the request of profile enterprises) can form the production base. An enterprise or a group of enterprises selected as a basis for testing innovation should assess its market and technological prospects, as well as analyze technological regulations. 3. Adoption of an innovative decision by the authorized federal agency to wide approbation and obtaining definite results from their practical use. In the case of obtaining satisfactory results, one ensures the timely certification of the invention. 4. Development of normative and technological documents defining the features and principles of the invention. 5. Provision by orders of the state of the supply of equipment or performance of the works in a particular area.

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The key point of the mechanism of integration of innovations in construction is the creation of a test production base. Enterprises that have a significant share of the relevant market, as well as smaller enterprises, should form this base. In case of refusal of the enterprise to be included in the production base, the decision must be taken by force with the imposition of certain obligations on the part of the state. The formation of the test base will solve a number of problems. First, this will create an innovative reserve of the enterprise. Second, the demand for a wide range of specialists will be ensured. Third, this will allow obtaining a comprehensive assessment of innovation by an independent entity. To ensure the effective functioning of the relevant departments of the enterprise and to reduce the likelihood of sabotage, it is necessary to fulfill a number of conditions. Firstly, the head of the implementation process and assessment of innovation is to be appointed by external authorized body. Secondly, investment support of the implementation process and certification of innovation is to be provided by the state at all stages, starting from the moment of adaptation of the regulations of the use of the technology. All this will end with the formation of the first orders for the products produced using the technology. Along with the creation of the production base, it is advisable to create a federal agency for the development of innovations, which will ensure the process of integration and adaptation of innovations. The creation of the federal agency will allow determining the most promising solutions in a timely manner, ensuring the effective spending of funds to support significant innovative solutions. In fact, the key task of this organization is to provide technological assistance to all participants in the construction industry.

7 Conclusion Innovations in construction create new opportunities for large construction companies and enterprises of the construction materials industry. With the increasing demand for more and more complex objects, traditional sources of construction materials and labor, most construction-related companies will pay attention to new technological solutions to improve their products and services and to reduce costs. Most of the subjects of the construction market focus on the introduction of innovations in order to technically improve the projects and improve the efficiency of the constructed facilities. The state bodies tend to support innovation as a means of improving the efficiency of the industry and its profitability. Acknowledgements The work was prepared during the implementation of Project No. 26.9642.2017/8.9 within the framework of the state task of the Ministry of Education and Science of Russia.

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References Arditi, D. (1982). Pub diffusion of network planning in construction. Journal of Construction Engineering and Management, 109(1), 1–13. Arditi, D., Kale, S., & Tangkar, M. (1997). Innovation in construction equipment and its flow into the construction industry. Journal of Construction Engineering and Management, 123(4), 371–378. Bock, T., Linner, T., Georgoulas, C., Mayr, M., & Meyer-Andreaus, J. (2012). Innovation deployment strategies in construction. In Proceedings of Creative Construction Conference (Vol. 3). Dulaimi, M. (1995). The Challenge of Innovation in Construction. Building Research and Information, 23, 106–109. Elistratkin, M. Y., Lesovik, V. S., Alfimova, N. I., & Glagolev, E. S. (2018). About developing of building press technologies. Bulletin of BSTU named after V.G Shukhov, 5, 11–19. Ganesan, S., Hall, G., & Chiang, Y. H. (1996). Construction in Hong Kong ± issues in lab our supply and technology transfer. UK: Avebury. Grishin, V. V. (2010). Innovational activity management in conditions of national economy modernization: Study guide (pp. 78–81). Moscow: Dashkov and Co. Korol, S. P. (2016). Innovative development of the construction industry as an economic category of the object of management. Regional Economy and Management: Electronic Scientific Journal, 1(45). Access mode: https://eee-region.ru/article/4501/. Love, P. E. D., Skitmore, M., & Earl, G. (1998). Selecting a suitable procurement method for a building project. Construction Management and Economics, 16(2), 221–233. Nam, C. H., & Tatum, C. B. (1997). Leaders and champions for construction innovation. Construction Management and Economics, 15(3), 259–270. Pries, F., & Janzen, F. (1995). Innovation in the construction industry: The dominant role of the environment. Construction Management and Economics, 13, 43–51. Slaughter, E. S. (1998). Models of construction innovation. Journal of Construction Engineering and Management, 124(3), 226–231. Tangkar, M., & Arditi, D. (2000). Innovation in the construction industry. Dimensi Teknik Sipil, 2(2), 96–103. Tatum, C. (1987). Balancing Engineering and Management in Construction Education. Journal of Construction Engineering and Management, 113, 264–272. Westling, H. (1991). Technology procurement: For innovation in Swedish construction. Stockholm: Swedish Council for Building Research.

Tax Capacity of the Russian Federation Constituent Entities: Problems of Assessment and Unequal Distribution N. M. Sabitova, Ch. M. Shavaleyeva, E. N. Lizunova, A. I. Khairullova and A. Zahariev

Abstract The Russian Federation (RF) is among the countries with a high degree of fiscal centralization. Centralization is connected with the tax assignment system by government levels. The Tax Code of the Russian Federation defines a closed list of federal, regional and local taxes. Meanwhile, federal taxes constitute the core of the country’s tax system, while regional and local taxes account for a small share in total tax revenues. Therefore, regional and local taxes cannot be the basis for the RF constituent entities’ budgets and local budgets. The RF constituent entities’ tax capacity determines the opportunities for their economic development. However, due to uneven economic development, there is a significant differentiation in the level of RF constituent entities’ fiscal capacity, which is associated with their insufficient tax capacity. The purpose of the study is to analyze tax revenues by federal districts and the RF constituent entities in order to identify the factors that affect them, the degree of these revenues differentiation, the system of tax revenues distribution by government level as a basis for assessing the tax capacity in budget planning.

1 Introduction The RF constituent entities’ tax capacity depends on a number of factors, including tax revenues, which are determined by the territory economic development, the permanently legislated assignment of taxes according to a budget system level, certain taxes prevalence in the RF various constituents entities revenues et al. In the RF, tax revenues dominate in the country’s budget system. Taxes are legislatively assigned according to the budget system levels. The Tax Code of the Russian Federation establishes a closed list of federal, regional and local taxes, while the Budget Code of the Russian Federation assigns certain federal taxes for the budgets of the RF constituent N. M. Sabitova (B) · Ch. M. Shavaleyeva · E. N. Lizunova · A. I. Khairullova Institute of Management, Economics and Finance, Kazan Federal University, Kazan, Russia e-mail: [email protected] A. Zahariev D. A. Tsenov Academy of Economics, Svishtov, Bulgaria © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_7

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and municipal entities with the only exception of the profit tax, which rates are distributed between the federal budget and the budgets of the RF constituent entities by the RF Tax Code. Moreover, one of the principles of the RF budget system is the unity principle, which presupposes a unified procedure for the budgets’ revenue generation and expenditures incurring of the RF budget system. In case of the unequal economic development of the RF constituent entities, the RF Ministry of Finance annually determines the RF constituent entities’ tax capacity when budgeting and determining the equalization transfers. Tax capacity is one of the key parameters significant for assessing the future revenues of the RF constituent and municipal entities budgets. The problem of unequal RF constituent entities’ tax capacity distribution is very relevant for the Russian Federation. Only a small percentage of the RF constituent entities can independently establish their budgets without financial assistance from the federal budget, for instance, in 2017 only 13 out of 85 could do it. They do not receive equalization transfers and this is a long-term trend.

2 Methods To study the budgets’ revenues of the RF budget system, the tax revenues and the tax capacity of the RF constituent entities, the authors employed the data from the official Web sites of the RF Ministry of Finance, the Federal Treasury and the Federal Tax Service. Data on tax revenues are published on the official website of the Federal Tax Service in the section “Data in statistical tax reporting format” (Data on forms of statistical tax reporting 2018). According to the statistical reporting formats, it is possible to explore the tax revenues structure by the types of taxes, by the levels of the budget system as well as to estimate the share of each RF constituent entity in the total tax revenues over a number of years. The authors employed the manual selection method for sampling the data on tax revenues in the Russian Federation as a whole, as well as the data broken down by the RF constituent entities. The exploration of the RF constituent entities’ tax revenues was conducted with the aim of determining their tax capacity differentiation, which is a consequence of uneven economic development. According to the data, such differentiation exists, and it is very significant. In the calculations of the RF Ministry of Finance, the assessment of the RF constituent entities’ tax capacity is implemented only within the limits of taxes that are credited to their budgets. Given there is a high degree of budgetary resources centralization, the variability of the budget legislation in terms of assigning taxes for the budget system levels, the authors of the work proceeded from the assumption that the tax capacity is the projected tax revenues expected to be mobilized on the territory of an RF constituent or municipal entity regardless of the tax assignment by levels. Therefore, this work analyzes all tax revenues broken down by the RF federal districts and constituent entities.

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3 Theory Taxes as the most significant component of revenues constitute the basis of budget revenues in any economy. The financial authorities’ objective is to provide an expected estimate of future revenues for the planning period. The budgeting depends on the degree of fiscal centralization or decentralization in the country, the development of fiscal federalism, the established system of inter-budgetary relations, determined by the legislation of each country. These issues are being investigated by both Russian and foreign researchers. In particular, the issues of fiscal federalism are presented in the works of Arcalean et al. (2010), Desai et al. (2005), centralization and decentralization are considered by Sacchi and Salotti (2014), Arzaghi and Henderson (2005), Vo (2010), Reayat et al. (2014), imbalance of budget systems is explored by Bird and Smart (2002), Sabitova et al. (2015), minimizing the economic inequality of the regions is studied by Kioko and Martell (2012), the tax potential of the regions is researched by Roschupkina (2013), Musaeva et al. (2015), inter-budgetary relations and equalization of the regions budget capacity is in the focus of Velenteichyk attention (2013). These issues are still relevant not only for the Russian Federation, but for many federal countries as well.

4 Result The uneven economic development of the RF constituent entities affects their tax capacity and tax revenues to the budget system, respectively. Analysis of the tax revenues’ structure for the federal districts in 2017 displays that the largest tax revenues are observed in the Central Federal District (28.5%), in the Urals Federal District (24%) and in the Volga Federal District 16% (Fig. 1). The smallest share of tax revenues to the RF budget system is provided by the North Caucasus Federal District (1%), the Far Eastern Federal District (4%) and the Southern Federal District (5%). In the course of the research, the authors selected five RF constituent entities, which provided the largest revenues to the country’s budget system in 2016–2017. They account for 47.2% of all tax revenues in the Russian Federation; therefore, their tax capacity is high. In their calculations presented in Table 1, the authors resorted to the officially published data. These entities include Moscow (18.4% in 2016 and 17.8% in 2017), the KhantyMansiysk Autonomous District-Yugra (11.8% in 2016 and 12.8% in 2017), St. Petersburg (6.3% in 2016 and 6.2% in 2017), the Yamalo-Nenets Autonomous District (5.6% in 2016 and 6.6% in 2017) and Moscow Region (5.1% in 2016 and 4.8% in 2017). However, the tax revenues’ structure for these entities is different: in Moscow, Moscow region and St. Petersburg, the profit tax, the value-added tax and the personal income tax contribute the largest share. In 2016, their share in Moscow accounted

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Fig. 1 Tax revenues distribution in the RF consolidated by federal districts in 2017

Table 1 Distribution of tax revenues to the RF consolidated budget for individual RF constituent entities in 2016–2017 RF constituent entity Total for the Russian Federation, including

2016

2017

Bln RUB

%

Bln RUB

%

14,387.7

100

17,197.0

100

Moscow

2,643.5

18.4

3061. 4

17.8

Khanty-Mansi Autonomous District-Yugra

1,700.6

12.8

11.8

2214.0

St. Petersburg

900.1

6.3

1074.1

6.2

The Yamalo-Nenets Autonomous District

810.4

5.6

1140.4

6.6

Moscow region Other RF constituent entities

733.5

5.1

830.7

4.8

7599.6

52.8

8876.4

51.8

for 88% of all tax revenues, in St. Petersburg—67.5%, in the Moscow region— 79.9%. By contrast in the Khanty-Mansiysk Autonomous District-Ugra and in the Yamalo-Nenets Autonomous District, the bulk of tax revenues is provided by the tax on the minerals extraction (72.5% and 66.3% correspondingly). These regions are oil and gas extracting. Meanwhile, the minimal tax capacity is observed in such entities as the Republic of Ingushetia (660 times less than in Moscow), the Republic of Kalmykia (550 times less than in Moscow) and the Republic of Tyva (530 times less than in Moscow).

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Tax revenues’ distribution by the budget system levels is an important issue. As it was noted above, in accordance with the RF legislation, taxes are subdivided into federal, regional and local ones. In addition, there are taxes envisioned by special tax regimes. In 2016, according to the Federal Tax Service, the share of federal taxes accounted for 88.8% of the total tax revenues, regional taxes—for 6.3%, local taxes— for 1.5% and taxes envisioned by special tax regimes—for 3.4%. It is clear that these taxes are not sufficient for the RF constituent and municipal entities’ budgeting. This is indicative of the imbalance envisaged in the RF budgetary system. However, this imbalance is reduced by transferring some of the federal taxes to the budgets of the RF constituent entities through the RF Budget Code. First of all, the percentage of profit tax and personal income tax is assigned to the RF constituent entities, which currently is the basis of their budgets. Thus, the share of the profit tax in the RF constituent entities’ budget revenues constituted for more than 30% in recent years, and the personal income tax—40% (Table 2). Moreover, until 2017, the income tax rate of 20% was distributed between the federation and the entities as 2–18%. However, in 2017 this ratio has changed in favor of the Federation and become 3% to 17% correspondingly. The personal income tax is completely transferred to RF constituent entities’ budgets and local budgets. The calculations demonstrate that the share of federal taxes (profit tax, personal income tax, excises and mineral extraction tax) in the tax revenues structure of the RF constituent entities’ consolidated budgets by years was as follows: in 2014— 80%, in 2015—79.1%, in 2016—79.8% and in 2017—79%. That is the proportion is approximately identical. The share of regional and local taxes is 20%, 20.9%, 20.2 and 21%, respectively. In the analysis of the federal taxes’ distribution structure by the budgetary system levels in accordance with their assignation in the RF Budget Code, the authors noticed that just over 50% is assigned to the federal budget and the trend is permanent. The Table 2 Structure of tax revenues to the consolidated budgets of the RF constituent entities (the calculations were made by the authors according to the officially published data, Report on the form 1-NOM as of 01/01/2018, 2018) as a percentage Types of taxes

2014

2015

2016

2017

Total tax revenues, including

100.0

100.0

100.0

100

Profit tax

30.4

30.5

30.2

30.9

Personal income tax

39.8

41.5

40.6

39.9

Excises

7.4

7.0

8.8

7.5

Personal property tax

0.4

0.4

0.5

0.6

Corporate property tax

9.8

10.3

10.1

10.5

Transport tax

1.8

2.0

1.8

1.9

Land tax

2.7

2.7

2.3

2.3

Mineral extraction tax

0.7

1.0

0.9

0.8

Other taxes and fees

5.2

5.4

5.5

5.7

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Fig. 2 Federal taxes distribution over the budgets of the RF budgetary system (the calculations were made by the authors according to the data presented in (Report on Form … 2018))

remaining federal taxes were credited to the budgets of the RF constituent entities. The share of federal taxes was credited to local budgets (Fig. 2). The analysis of 2014–2017 period demonstrates that there is a growing trend of the RF tax revenues and, on the whole, the structure of their distribution according to the levels of the budget system remained unchanged. Over the past three years, the ratio of the federal budget and of the RF constituent entities’ consolidated budgets revenues is slightly in favor of the budgets of the RF constituent entities of the Russian Federation (Table 3), although this ratio varies by federal districts and RF constituent entities. The share of revenues directed to the local budgets accounts for about 7% of the total tax revenues, meanwhile there is a slight downward trend. However, starting from 2017, this ratio will change in favor of the federal budget due to profit tax. Table 3 RF budget system tax revenues (the calculations were made by the authors according to the data presented in (Report on Form … 2018)) as a percentage Indicator

2014

2015

2016

2017

Total tax revenues, including

100

100

100

100

Received by the federal budget

48.8

49.7

47.5

52.4

Received by the RF constituent entities’ consolidated budgets, of which

51.2

50.3

52.5

47.6

– To the revenues of local budgets

7.5

7.1

7.0

6.3

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5 Conclusion The research of RF budget system tax revenues conducted by the authors made it possible to draw the following conclusions: 1. The existing practice of budget revenues planning within the RF budgetary system proceeds from the fact that under the RF constituent entities’ tax capacity the Ministry of Finance understands the expected tax revenues for the RF constituent entity in the form of regional taxes and a part of federal taxes assigned to the RF constituent entities on a permanent basis by the RF Budget Code. This is due to the need to calculate the fiscal capacity and equalization transfers for the RF constituent entities. However, this assignment is conditionally permanent, since the federal legislator makes changes to this assignment quite often. The authors of this research believe that for the RF constituent entities’ tax capacity it is more appropriate to consider all tax revenues of the territory, regardless of the assignment level. This is due to the fact that the system of assigning taxes for the levels of the budgetary system in the Russian Federation is not permanent. For instance, the last major change in legislation in terms of reducing the tax revenues of the RF constituent entities was implemented by Federal Law No. 401-FZ of November 30, 2016, when amendments to the RF Code were made to change the crediting rates to the federal budget and the budgets of the RF constituent entities for three years—from 2017 to 2020 (from January 1, 2017, the rate for crediting the profit tax to the budget revenues of the RF constituent entities’ budgets was reduced from 18 to 17%). 2. The overall distribution of tax revenues between the federal budget and the consolidated budgets of the RF constituent entities has lately been roughly equal, and however, starting from 2017 this ratio will be in favor of the federal budget, primarily due to the changes in the ratio of profit tax and other taxes that will reduce the tax capacity of the RF constituent entities. 3. Tax revenues are differentiated both for the federal districts and for the RF constituent entities, and this differentiation is very significant. The gap between the minimum (the Republic of Ingushetia) and the maximum revenue (Moscow) in 2016 is huge (more than 660 times). Therefore one could observe weak tax capacity of a number of RF constituent entities, and as a result—significant imbalances in the RF budget system. Consequently, RF constituent entities with low tax capacity experience problems in the budget generation, and therefore there is a need for their financial support from the federal budget. Thus, 72 RF constituent entities out of 85 are subsidized. 4. Federal taxes prevail in the tax revenues of the RF constituent entities (up to 80% over the last four years), the most significant are the corporate profit tax (30%) and the personal income tax (40–41%). However, in a number of the RF constituent entities, namely in the Khanty-Mansiysk Autonomous District-Ugra and in the Yamalo-Nenets Autonomous District, the bulk of tax revenues is provided by the tax on the minerals extraction (72.5% and 66.3% correspondingly). Regional and local taxes in the tax revenues of the RF constituent entities’ consolidated

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budgets do not allow to provide these budgets with sufficient funds, the basis for tax revenues is provided by the federal taxes assigned for the RF constituent entities’ budgets by the RF Budget Code. Their share in the total tax revenues is 20%. Accordingly, the share of federal taxes is high. Thus, the analysis of the RF constituent entities’ tax capacity in the framework of the existing legislation indicates its significant differentiation and the need for constant budgetary regulation of the fiscal capacity by the RF Ministry of Finance.

References Arcalean, C., Glomm, G., Schiopu, I., & Suedekum, J. (2010). Public budget composition, fiscal (de)centralization, and welfare. Canadian Journal of Economics, 43(3), 832–859. Arzaghi, M., & Henderson, J. V. (2005). Why countries are fiscally decentralizing. Journal of Public Economics, 89(7), 1157–1189. Bird, R. M., & Smart, M. (2002). Intergovernmental fiscal transfers: International lessons for developing countries. World Development, 30(6), 899–912. Data on forms of statistical tax reporting. (2018). Access mode: https://www.nalog.ru/rn77/related_ activities/statistics_and_analytics/forms. Desai, R. M., Freinkman, L., & Goldberg, I. (2005). Fiscal federalism in rentier regions: Evidence from Russia. Journal of Comparative Economics, 33(4), 814–834. Kioko, S. N., & Martell, C. R. (2012). Impact of state-Level tax and expenditure limits (TELs) on government revenues and aid to local 174 governments. Public Finance Review, 40(6), 736–766. Musaeva, K. M., Zaidullaevna, A. A., & Bashirovna, A. E. (2015). Problems and ways of improvement of tax instruments as a factor in the growth of tax opportunities territories. Asian Social Science, 11(19), 290–297. Reayat, N., Ahmad, I., Khalil, J., & Rahim, T. (2014). Fiscal decentralisation: What does the international experience suggests. Life Science Journal, 11(7), 1–10. Report on the form 1-NOM as of 01/01/2018. (2018). Access mode: https://www.nalog.ru/rn16/ related_activities/statistics_and_analytics/forms/6092076/. Report on Form # 1-NM for 2017. (2018). Access mode: https://www.nalog.ru/rn16/related_ activities/statistics_and_analytics/forms/6040192/. Roschupkina, V. V. (2013). Modern concepts of formation and development of tax capacity of the region. Life Science Journal, 10(12), 742–745. Sabitova, N. M., Shavaleyeva, C. M., & Nikonova, E. N. (2015). Horizontal imbalances of Russian Federation budget system. Asian Social Science, 11(11), 248–252. Sacchi, A., & Salotti, S. (2014). How regional inequality affects fiscal decentralisation: Accounting for the autonomy of subcentral governments. Environment and Planning C: Government and Policy, 32(1), 144–162. Velenteichyk, N. (2013). Congruence of interests of the center and regions in the course of intergovernmental relations regulation. Economic Annals-XXI, 5–6(2), 37–40. Vo, D. H. (2010). Economics of fiscal decentralization. Journal of Economic Surveys, 24(4), 657– 679.

Key Indicators of Regional Bank Activities: From Theory to Practice E. R. Tagirova

Abstract This work is devoted to the creation and implementation of the bank management system based on key performance indicators (KPIs). It describes the principles that are recommended to be adhered to in order to avoid the most common mistakes and increase the effectiveness of KPIs. Taking into account the complexity of linking the qualitative indicators to the sum values, the author presents a clear example of the establishment of KPIs for the project IT unit. The author emphasizes the connection between the strategic goals of the credit organization and the system of balanced indicators, and describes the methods and methodology of the process of building the chain. In this work, the most frequently emerging questions are sorted out and recommendations using an example from own practice are given. The work is of practical value and is aimed at increasing the efficiency of the regional bank activities.

1 Introduction Profit is one of the main goals of a commercial organization. A commercial bank is no exception. Gaining income from investing funds, ensuring economic efficiency and profitability of the credit organization’s activities are the goals of not only the shareholders of the bank but also its creditors and partners, and it is a challenging task (Gareeva and Grigoreva 2016). Let us consider the data on the indicators of the banking business profitability (Table 1). According to Table 1, top five banks have a rather high level of the indicators. At the same time, for other groups of credit organizations, the attractiveness of the banking business for investors, taking into account the capital profitability, has a disappointing trend. Given that the assets of existing banks are larger than their own funds, the return on assets has even lower values compared to those of the previous indicator. It should be stipulated that we are discussing average values, and E. R. Tagirova (B) Institute of Management, Economics and Finance, Kazan Federal University, Kazan, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_8

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Table 1 Profitability indicators of Russian credit organizations grouped by assets Groups of credit organizations

Asset profitability (%) 01.01.2015

01.01.2016

Capital profitability (%) 01.01.2017

01.01.2015

01.01.2016

01.01.2017

1–5

1

1

2

13

7

6–20

1

0

1

8

1

8

−2

−2

−2

−15

−25

−3

51–200

1

1

1

8

4

5

201–500

1

1

1

8

4

3

Over 501

3

1

0

9

4

0

21–50

18

Compiled by the author on the basis of the Central Bank of Russia data

there are banks with different levels of profitability within the groups. In this case, the author identifies the general state of the research field. Based on this fact, the issue of increasing of credit organization profitability is acute and requires maximum comprehensive consideration. It should be noted that according to the data of the Bank of Russia, there are credit organizations in each of these groups whose head offices are outside Moscow and the Moscow region (48.5%). Thus, it can be argued that the problem of low profitability indicators is especially relevant to regional banks. We note that the author takes into account the theme of the bank’s stability and believes that it will be unfair to discuss only the topic of profit. This work will consider one of the tools to increase the credit organization profitability in more detail, namely the development of key performance indicators (KPIs) (Keshavarz et al. 2014).

2 Definition and History of KPIs Turning to the history of the emergence of key performance indicators (KPIs—key performance indicators), we should mention the balanced scorecard (BSC) developed by D. Norton and R. Kaplan (Kaplan and Norton 1992, 1996; Parmenter 2007). BSC provides four projections, characterizing the effectiveness of the organization: finance, customers, processes and personnel. Thus, qualitative and quantitative estimations of the enterprise are made. The goals are set in each projection, the achievement of which will contribute to the fulfillment of the strategic tasks set by the shareholders. The achievement of the BSC is determined by the implementation of quantitative and qualitative indicators, in this case—KPIs.

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3 Regulation of KPIs and Review of Russian Practice The need to use KPIs to improve the processes efficiency was announced at the national level in February 2012. Then at the meeting of the Supervisory Board of the Agency for Strategic Initiatives, Vladimir Putin proposed the development of a key indicator system to assess officials’ effectiveness. Later in 2014, in a message to the Federal Assembly, Vladimir Putin declared the need to develop and implement key performance indicators in all companies where the state owns more than 50% of the shares. Regarding the banking sector, in today’s Russian practice a system of key performance indicators is used in large credit organizations, such as PJSC “Sberbank” and VTB Group. This method is also used in banks of the second and third responders. Nevertheless, there is the need for its testing before implementation in many regional credit organizations. The legislation that directly determines the level of remuneration in credit organizations, including the use of KPIs, does not exist yet. At the same time, the supervisor takes into consideration this issue. In particular, the purpose of the Russia Bank Instruction dated 17.06.2014, No154-I, is to “ensure the compliance of the credit organization’s payment system with the nature and scope of its operations, the results of its activities, the level and combination of risks they assume.” This instruction establishes the procedure for evaluation, adopted in the credit organization of the wage system. In addition, attention is paid to the non-fixed part of labor remuneration at various levels: – Banks in general; – Unit level; – Level of individual staff members. Furthermore, the annual monitoring pay system results of the bank are used in calculating the risk management indicators for material motivation of personnel (MI 7). MI 7 has a direct impact on the assessment of the bank management quality and the final result in determining the financial stability group to which it relates in accordance with the Bank of Russia Reference dated 03.04.2017 “assessment of the banks’ economic situation.” Therefore, the Central Bank of the Russian Federation makes an attempt to ensure a link between the level of labor payment of a credit organization and its financial stability.

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4 Basic Principles of Development and Implementation of KPIs We recommend adhering to the following principles when developing and implementing key performance indicators (Al-Hosaini and Sofian 2015; Safiullin and Shabanova 2016; Isaeva et al. 2013). The first principle is specificity. The wording of the indicators should be clear and exclude various interpretations. It is important to avoid the situation described in the anecdote: “in order to strengthen the fight against pests, the Government of China announced that 1 yuan will be paid for each delivered locust. Now all the peasants are breeding locusts.” Thus, KPI formulation should be specific and should exclude situations in which employees can misinterpret the tasks assigned to them. For instance, the use of the indicator “quality loan portfolio” is undesirable in view of the fact that there is no consensus on the criterion of “quality” of the loan. The quality of the loan is determined by the Bank of Russia, taking into account the quality of debt servicing and the financial condition of the borrower, determined in accordance with Bank of Russia Regulation dated June 28, 2017, N 590-P “On the Procedure for the Formation of Reserves by credit organizations for possible loans losses, loan and equivalent debt” (further 590-P). It should be noted that despite mandatory classification of the bank loan portfolio in accordance with the Bank of Russia requirements, commercial banks within the framework of internal reporting have the right to introduce additional indicators characterizing the quality of granted loans. For some credit organizations, this indicator can be defined as the level of overdue debt, calculated as the ratio of the volume of overdue loans to the total amount of the loan portfolio for the analyzed date. For other banks, the indicator can be given the wording “exposure to credit risk” and be calculated as the ratio of the number of formed reserves to the possible loan loss or to the total amount of the loan portfolio. Thus, the indicator “qualitative loan portfolio” is more suitable for formulating the strategic objectives of the regional bank. For key performance indicators, you can use: • The percentage of loans of 1–3 quality categories in the total volume of issued loans; • Level of overdue debts; • Level of credit risk; • Percentage of secured credit; • Others. In this case, individual above-mentioned indicators and those in aggregate can be used. It is recommended to give a decipherment of KPIs indicating the order of calculation. Thus, the indicator will take a specific form and eliminate duplication and ambiguity of interpretation. For example, to calculate the KPI “proportion of secured loans,” one of the following formulas (Formulas 1 and 2) can be used:

Key Indicators of Regional Bank Activities: From Theory …

 k=

 S1 + S2  , P

91

(1)

where S1 is the cost of providing the first quality category at the settlement date determined in accordance with 590-P; S2 is the cost of providing the second category of quality at the settlement date determined in accordance with 590-P; P is the total loan portfolio (debt principal) as of the settlement date.  S k= , P

(2)

where k is KPI “proportion of secured loans”; S is the cost of providing in the form of goods bail and/or property rights bail (claims) for immovable property, provided that the legal documentation with respect to the lien of the credit organization is framed in such a way that it does not contain conditions that prevent the realization of liens; P is the total loan portfolio (the balance of the debt principal) and the balance of the non-selected open credit lines, for which it is possible to obtain borrowed funds under the current loan agreements as of the settlement date. In this case, the determination of the procedure for calculating the indicator depends on strategic tasks, internal policy (financial, credit, collateral, etc.) and the degree of the bank automation. Achievability is the second principle which implies that the task of observing the set indicators should be achievable. It is necessary to assess the capabilities of the performer to determine the target value, namely technical, qualification, labor force, access to information, the ability to make decisions and the availability of management tools. In this case, values that are not average but achievable or almost achievable by the best unit (employee) should be established as planned. It is useful to explain what resources can assist in carrying out super-tasks set for the performers. Let us assume that the credit management is tasked to increase the volume of lending to small- and medium-sized businesses 5 times in the coming year. To achieve this goal, strategic initiatives may provide for a 20% increase in the number of unit personnel, automation of the lending process (calculation of the borrower’s rating, formation of orders, reports, credit and security agreements, etc.) by opening 2 operating and 6 additional offices. All these measures in aggregate can contribute to the achievement of the set goal of ambitious credit portfolio growth increasing the current labor productivity of credit management staff. At the same time, it is necessary to take into account the period during which the announced initiatives will be implemented. For instance, it is unreasonable to impute to credit management a 100% increase in the volume of loans issued from the first quarter if the stimulating factors in the form of process automation and the opening of new sales outlets are scheduled for the fourth quarter of the planned year. Time perspective is the next principle in which it is appropriate to describe a few points. First, the performance of KPIs can be limited in time without a hint of infinity. Second, a large task must be broken down into phased implementation in several reporting periods (e.g., monthly and quarterly). Third, when determining

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the variable part of the remuneration of employees in the case of integral KPIs, the calculation should be made for the actual hours worked (excluding vacation, sick day, etc.). Control is the principle of the organization of the procedure for monitoring the compliance (achievement) of established KPI values and analysis of the reasons for both non-fulfillment and significant overfulfillment. In practice, it is possible to meet regional banks that due to the limited labor resources impose this functional on the units (groups and employees) involved in accounting and work with cadres or accountants who pay a salary. However, this approach leads to some difficulties. First, in addition to the control and design function of KPIs, it is necessary to conduct a plan-factor analysis of the data. Second, information security is violated, which should limit the access of employees to data in all areas of the bank’s activities. The way out of this situation is to consolidate the control function of the unit (group and employee), whose duties are analysis and/or planning and/or reporting of the credit organization. Measurability is the next principle when key performance indicators should be accounting and measurable (in monetary terms, percentages, relative indicators, etc.). For instance, one of the key performance indicators can be “improving the quality of customer service” for operators, consultants and other banking employees interacting with customers. It is unacceptable to set the options to assess: • • • •

Customer service (friendly/unfriendly); Time of customer service (quick/slow); Average customer service time (in minutes); The presence of complaints and praise from customers (in pieces).

We should mention maintenance of indicator countability. The calculation process should be as much automated as possible and/or should exclude the counting labor intensity. Otherwise, it should be abandoned and replaced by other KPIs. Elimination of conflicting interest is especially topical principle when establishing KPIs for bank employees that combine executive and control functions (Golubev et al. 2017). A similar situation exists in many regional banks which are under conditions of rigid optimization of domestic expenses (including the labor remuneration fund) to provide competitive conditions for clients. In particular, the organization of work to attract customers, analysis of the potential borrowers’ creditworthiness, processing and maintaining credit transactions can be envisaged as the functional of the credit unit head. At the same time, “the growth of a quality loan portfolio” can be established as one of the targets. Thus, on the one hand, the efforts of its unit should be aimed at increasing the loan portfolio and, on the other hand, at reducing credit risks. In this case, the control function could be an obstacle to perform the executive function. A compromise version of a key performance indicator set of the credit unit head is presented in Table 2. Documentation is the principle of compulsory compliance because the fulfillment of key performance indicators is reflected in the remuneration of staff which requires documenting and familiarizing all employees of the credit organization with it (Golubev et al. 2017). In addition, it is necessary to describe clearly the rules of

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Table 2 An approximate version of the KPI set of the credit unit head No.

Task

Versions of indicators

Weight (%)

1

Growth of loan portfolio (k1)

Increase in the balance of credit accounts, increase in the average balance of credit accounts, increase in the balance of credit accounts, excluding arrears, credit issue growth (by turnover), credit issue growth in the number, etc.

35

2

Improving the loan portfolio quality (k2)

Compliance with the maximum limit for overdue debt on credit investments, the degree of the loan portfolio security (1, 2 categories of security management quality, determined in accordance with 590-P), share of loan growth in the 1–3 categories of quality, determined in accordance with 590-P, etc.

35

3

Other tasks (k3)

Ensuring the planned return of the loan portfolio, reduced time to process a loan application, etc.

30

Score K (rate of award)

K = k1 × 0.35 + k2 × 0.35 + k3 × 0.3

Compiled by the author

interaction between employees and units beginning from the process of developing KPIs ending with appropriate payments. Table 3 shows an exemplary list of documents which can fix the procedure for the development, calculation, control of key performance indicators as well as related charges and payments. It should be noted that the approval of documents takes place in accordance with the authority of the collegial body or person fixed in the regulatory legal acts and internal policy of the bank, and in the example given it is conditional and not exhaustive. Depending on the ability of the regional bank, it is advisable to provide for the separation of a subdivision, a group or responsible officer, whose functions will include the development, coordination, design, monitoring and analysis of the KPI performance across the entire credit organization. In this case, the listed functions can be direct or additional, focused on one performer or distributed among several performers. The limitation of indicator set is the principle which means that KPIs should be sufficiently significant and motivating for people to implement it. In particular, the weight of each indicator should be at least 10% but not more than 50% in the total volume of the integrated quantity. Thus, the number of KPIs attributable to one employee (of unit) can be from 2 to 5 titles. In addition to the number of indicators, the internal policy of the bank may set limits to the lower and upper levels of actual values involved in the calculation of weighted indicators. This restriction can be introduced, for example, if a pilot project is implemented, the result of which is ambiguous.

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Table 3 A sample list of documents containing information on KPIs Document titles

Appointment

Approving authority (person)

Remuneration orders

Establishes the principles, the general procedure and composition of workers’ remuneration (permanent part, compensation payments and incentive part)

Bank’s board of directors

Provisions on rewards

Establishes the principles and procedure of incentive payments (allowance, bonuses, awards)

Executive body (bank’s board)

Procedure for establishing incentive payments

Defines the categories of employees which key performance indicators, criteria, forms, periodicity of calculation, control and payments are established for

Executive body (bank’s board)

Regulation of interaction between units for the implementation of the procedure for incentive payments

Describes the necessary chain of actions, periods (dates) and responsible persons (of units)

Executive body (bank’s board)

Individual targets

The KPI list of the employee (of units), calculation period, minimum and maximum values, and calculation of the integral indicator for the calculation of payments are indicated

Executive body (bank’s board) and/or an authorized person (head of the unit)

Compiled by the author

Significant distortions are possible in such cases, allowing multiple excesses of the bonus part over the employee’s main earnings or, conversely, the demotivation of the staff due to the discrepancy between the extra efforts and the amount of “incentive” payments. Cascade implies decomposition of strategic goals for the top manager and the staff member. Following the reverse chain, through the implementation of plans by all employees of the unit, you can achieve the implementation of the plan of the unit and, ultimately, the entire bank. Command offset is the principle that provides for additional conditions or indicators, depending on the overall performance of the bank. For instance, regardless of the employees’ achievement degree of the established indicators, payment is made provided that the plan for the bank’s profit is fulfilled by at least 85%. Another example of the application of the principle “command offset” may be the presence of the top manager as personal indicators in the KPI list that are achievable with all

Key Indicators of Regional Bank Activities: From Theory …

95

available resources and powers at its disposal, and general ones. Their fulfillment is possible only through the efforts of the whole bank (asset profitability, size profits of the credit organization, etc.).

5 Development and Implementation of KPIs One of the main problems of increasing the organization profitability is to ensure the achievement of the planned indicators, since there is a gap between the approval of strategic goals and bringing them to performers. The task of the bank’s manager is to design the work and build optimal business processes and motivational part that contribute to the achievement of the set goals (Morphet 2015; Raut et al. 2017). For instance, the following targets for improving the efficiency of the credit organization are approved by bank’s board of directors: – Increase in profit by X%; – Increase of the share capital profitability by Y %; – Increase in asset utilization ratio by Z%. Such targets are clear for the bank’s owners and its top management, but are abstract for the main staff of the credit organization. Taking into account that the cumulative result of the bank’s activities is including personal contribution and effective (or ineffective) work of each unit (employee), there is a question on how to link the above-mentioned targets with the activities of ordinary performers (Prosvirkina 2014). The optimal solution for this situation is the decomposition that means the breakdown of strategic global goals into smaller ones in the form of KPIs. The indicators acquire a clear reflection of the tasks that the employee needs to perform in the course of his current and planned activities. Moreover, KPIs are reflected in a bank employee’s income, namely, in its variable part and can have the following form (Formula 3): VP = FP · K ,

(3)

where VP is the variable part of the employee’s income (e.g., bonuses); FP is the fixed part (e.g., the salary for the actually worked-out number of days in the calculation period); K is the final KPI implementation rate expressed as a percentage. It should be noted that the formula above is basic and may contain additional components. In particular, it is necessary to take into account the period of accrual of the variable part, the degree of implementation of the target strategic indicators for the bank as a whole, etc., depending on the financial policy adopted in the bank and system of remuneration. For example, a condition for the payment of premiums calculated using KPIs, taking into account the fulfillment of the plan for profit, may be established by the internal documents of the bank. A reservation may contain the following restrictions:

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– Implementation of the profit plan must be at least 80%. – The correction rate should not be higher than 100%. In this example, the personal motivation of a regional bank employee is in the same plane as the corporate goals of the credit organization, and the non-fulfillment of the profit plan (less than 100%) is broadcast as a general result of teamwork. Then, the formula can take the following form (Formula 4): VP = FP · K · P,

(4)

where P is the degree of the plan fulfillment for profit, in percent (e.g., from 81 to 99% if the rounding system to an integer is adopted by the relevant local documents of the bank). Thus, the unprofitable activity of a credit organization excludes payment of a variable part (bonuses and awards), which is justifiable from the point of view of the anti-crisis component, the reputation of the bank and the credibility of its clients. Let us consider the following simplified example. The target of achieving the profitability level of equity of 10% is set by the bank’s development strategy. The strategic map of the credit organization provides for key factors in the context of several projections (finances, clients, processes, personnel, IT). Let us suppose that the following tasks are approved in the “finance” projection: – Income increased by X%; – Costs reduced by Y %. Then, they are translated by decomposition into subtasks up to individual units and employees of the bank (Fig. 1). Strategic initiatives are being developed to achieve these financial indicators (Table 4). It is necessary to indicate the methods (initiatives) that contribute to their implementation in the context of each strategic task. At the same time, quite definite quantitative and qualitative indicators, terms of implementation, one or several responsible units whose functions include the fulfillment of the targets must be fixed.

profitability of bank's capital 10% increased income by Х% interest income growth

commis sion income growth

other income growth

reduced costs by Y% lower prices for the resources

decrease in administrati ve and management expenses

decrease in commis sion costs

Fig. 1 An example of a fragment of the strategic bank map in the “finance” projection developed by the author

Increase in the number of loan applications Increase in balances on customers’ checking accounts

Raising bank recognition and increasing the incoming customer flow by %

1. Review the product line: 1.1. “Overdraft 1”—for customers who have a checking account in a bank for a period of at least 6 months, without collateral under the guarantee of business owners in the amount of up to 40% of the “live” average monthly turnover 1.2. “Overdraft 2”—for new customers who do not have a checking account with the bank, in the amount of up to 20% of the average monthly turnover expected to be transferred

2. Adherence to a single standard of external and internal advertising groups (increasing the effectiveness of advertising)

Expansion of the credit market

Intended effect

Ways of achieving

Strategic initiatives

Table 4 Examples of strategic initiatives

First quarter of 2018

First 6 months of 2018

Realization term

Interest income growth by % Commission income growth by %

Growth in lending by % The growth of commission income from cash and cashier services by % Growth in the volume of “free” resources by %

Financial results from planned period

(continued)

Marketing and promotion department

Sales departments of corporate products of branches and additional offices Loan departments of legal entities of branches and additional offices Development department of banking products and services

Responsible unit (employee)

Key Indicators of Regional Bank Activities: From Theory … 97

Increasing the attractiveness and improving customer service by reducing the time for consideration of an application

2. Implementation of the credit factory

Compiled by the author

Reduction of the working time for processing the loan application by %

1. Automation of the credit process (calculation of a potential borrower solvency, the formation of credit support documentation, etc.)

Optimization of the lending process

Intended effect

Ways of achieving

Strategic initiatives

Table 4 (continued)

Second quarter of 2018

First quarter of 2018

Realization term

Reduction of administrative and management costs by %

Reduction of administrative and management costs by %

Financial results from planned period

Automation of banking operation department Optimization of business process department Development of banking products and services department

Automation of banking operation department Optimization of business process department Development of banking products and services department

Responsible unit (employee)

98 E. R. Tagirova

Key Indicators of Regional Bank Activities: From Theory …

99

Further, strategic initiatives can be used to develop KPIs in the context of each unit (employee) of the bank. It should be noted that the establishment of KPIs in relation to quantitative indicators is simpler than to qualitative ones. In particular, you can set the following indicators for employees of the corporate product sales department: (1) volume of loans issued; (2) number of open checking accounts; and (3) number of issued bank guarantees. It is not always possible to bring qualitative indicators to the sum values, but their impact on the final financial result of the organization is also important. One should not abandon the system implementation of balanced indicators in the accompanying banking business units. For example, for the designed IT units involved in the implementation of technical tasks for the automation of processes, the KPI set can take the following form (Table 5). It should be noted that in the given example, the economic feasibility of the technical assignment in the form of additional income and/or budget savings and/or acceleration of the information processing process is estimated at the initial stage before it is transferred for execution and is not included in the functional of the subdivision under review. Thus, it is not necessary to include these parameters as additional KPIs. Table 5 An approximate version of the KPI set for IT unit No.

Indicator

Options for assessment

Variants of proportion (%)

1

Respect the deadlines of technical requirement (k1)

Earlier than the timelines in percentage from the target date but not more than 110% Timely—100% Untimely—in percentage from the target date

35

2

Quality of performance (k2)

Assessment is after work completion: Absence of claims from outside customers—100% Complied with observations (satisfactorily)—from 51 to 99% Not implemented (unsatisfactorily)—up to 50%

35

3

Another indicator (k3)

…

30

Score K (rate of award)

K = k1 × 0.35 + k2 × 0.35 + k3 × 0.3

Compiled by the author

100

E. R. Tagirova

6 Conclusion An effective activity of any commercial enterprise consists not only in the presence of a strategy and development plan, but in the ability to implement them. The regional bank is no exception. Despite the difficulties that arise in view of the specifics of the banking business (the huge number of daily operations, the increased requirements for high manufacturability, etc.), the introduction of the BSC significantly improves the credit organization manageability. Thus, the achievement of strategic goals will be facilitated not only by the efforts of top managers, but also by the entire staff of the bank, including lower-level employees. Personal responsibility for the KPI implementation and the corresponding bonus program positively affects the effectiveness of regional banks.

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Development of Agroindustrial Companies: The Stakeholder Approach Maria Tyapkina, Maria Vinokurova, Sergey Vinokurov and Huizi Li

Abstract This work considers the problems of the development of agroindustrial companies. The necessity of the development of agroindustrial companies based on the stakeholder approach is demonstrated, which allows taking into account the interests of various groups of stakeholders and coordinating their interests among themselves to achieve common goals. The elements of the stakeholder approach for the strategic development of a company of the agroindustrial complex are considered. Based on an analysis of the level of interest and the power of stakeholders that influence the company, the main priorities of key players are identified. Directions for the strategic development of the company to deepen integration and expand diversification, taking into account the interests of shareholders and consumers, are proposed. A comprehensive assessment of the investment potential of agroindustrial companies is carried out, allowing them to assess their ability to simultaneously implement several investment projects in order to improve the activity efficiency and the competitiveness of their output.

M. Tyapkina (B) · S. Vinokurov Irkutsk State Agrarian University Named After A.A. Ezhevsky, Irkutsk, Russian Federation e-mail: [email protected] S. Vinokurov e-mail: [email protected] M. Vinokurova Baikal State University, Irkutsk, Russian Federation e-mail: [email protected] H. Li North China University of Science and Technology, Hebei, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_9

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1 Introduction The transition from the planned to market economy in Russia has led to the destruction of interfarm relations between enterprises of the agroindustrial complex. It identified such problems as a significant reduction in production volumes, crop areas, and livestock numbers; a high degree of accumulated depreciation; low workload of production facilities; dependence on supplies of foreign equipment and technologies; and lag in the technical, technological, financial, and equipment level in the field of scientific research. These all lead to the substitution of domestic products for import (seeds, breeds of animals and birds, fertilizers, food dyes, flavorings, equipment, machinery, etc.). To overcome the existing problems, companies are forced to independently ensure interrelations between the spheres of the agroindustrial complex. This is done in order to satisfy customers’ needs for food products, increasing efficiency of their activity owing to the use of integration and diversification strategies that contribute to the achievement of economic, synergistic, social, environmental, and other effects. The strategic approach allows developing an effective economic pattern taking into account the interests of shareholders, consumers, employees, and the state. It also allows changing the current negative trends in the industry, ensuring continuity in development by considering and coordinating the interests of stakeholders at the stage of identifying development projects and assessing, selecting economically significant investment projects by evaluating the cumulative effect.

2 Problem Statement Despite a significant number of existing general theoretical research and practical developments in the field of economics and management of the agroindustrial complex, some problems relating to the problem of development of agroindustrial companies and consideration of the interests of the main stakeholders remain insufficiently studied. In modern works of Russian and foreign authors, much attention is paid to such key stakeholders as owners (shareholders) and top managers of the company, whose interests are expressed in increasing the worth of the company. Interests of other stakeholders are not subject to in-depth analysis and are not sufficiently taken into account in the strategic development of the company. However, the company’s mission is to satisfy consumer needs, and therefore, in our opinion, it is necessary to take into account the interests of consumers when determining the strategic goals of company development. In addition, the state, which allocates targeted financing for projects aimed at the development of industries, satisfying the interests of which the company can develop at a higher rate, has shown interest in the development of the agroindustrial complex in recent years. In our opinion, there is a need to modernize the system of strategic management in companies of the agroindustrial complex by using strategies of integrated and diversified growth, taking into account the interests of key stakeholders.

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3 Research Questions In modern literature, development of agroindustrial enterprises is considered in the context of formation of various kinds of associations. Globalization leads to the fact that formation of clusters and business networks, integration into global chains of value creation (Carbajal et al. 2017; Kiseleva et al. 2016) become a strategic priority of firms. There are various approaches to manage a company: systemic, strategic, situational, stakeholder, stockholder, resource, project, and others. In the modern science and practice, various justifications for application of these approaches are applied (Fedotov 2016; Ogloblin and Sokolova 2018; Sokolova and Ogloblin 2018; Tambovtsev 2008). In the strategic approach, the main objectives are strategic objectives, formulation of which depends on the subjects of company management, being the basis of the stakeholder approach. The goal-setting phenomenon, as a rule, is not considered; it is thought that goals are set a priori. Without correct determination of goals, effective management, formation of the structure and the system of activity are impossible, but the question of where the goals originate from is not actually considered. We adhere to the viewpoint according to which the goals originate from the comprehension by the management subject of the state and the capabilities of the managed object to organize activities within the company and influence changes of the external environment. The subject of management is represented by stakeholders; therefore, a combination of strategic and stakeholder’s approaches seems necessary. The stakeholder approach began to form in the framework of the works by A. Burley and A. Dodd (Burley 1931; Dodd 1932), who made an assumption on the necessity to include in corporation’s goals ensuring safety of workplaces for employees to improve the product quality for consumers, increasing the contribution to the welfare of the local community. The stakeholder approach was first fully developed by Freeman (1984), who considers the company’s development strategy from the point of view of the interests of interested parties: organizations or individuals that the company influences and on whom the company depends. Donaldson and Preston (1995) supplemented the stakeholder approach with the requirement to attract simultaneous attention to the interests of all stakeholders during formation of the company structure and its general policy. Barkley and Holderness (1989) offered a cost estimate of benefits of the most influential stakeholders of the company possessing a large batch of shares as a characteristic of their private interests. A stakeholder is a subject or a group of subjects that influences the achievement by the organization of its goals (Hein et al. 2017). The stakeholder approach was fairly well presented and described, but with respect to the problem under consideration, it needs additional methodological developments and recommendations. The scientific proposals, formulated by the authors, are sufficiently substantiated and supported by the corresponding logical and analytical arguments. The theoretical and methodological basis was the works on the development strategy of the firm by Freeman (1984), Ramelt (2011), Hamel and Prahalada (2013),

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Kaplan and Norton (2004). In our study, different methods of strategic and stakeholder analysis were used, such as SWOT-analysis models by K. Andrews; a core competencies model by Hamel and Prahalada (2013); a system of balanced indicators by Norton and Kaplan (2004). They allowed combining the provisions of the strategic and stakeholder theory of the company in the framework of the development of a development strategy of an agroindustrial company, taking into account the interests, identified key stakeholders for the company. Further, we assessed the degree of interdependence of the company and stakeholders and identified key stakeholders. Then, the goals of key stakeholders were determined, taking into account new global trends (Svetnik 2011). On their basis, investment projects for the company development were proposed, which contributed to the most optimal level of vertical integration and diversification.

4 Purpose of the Study The work considers the elements of the stakeholder approach for the strategic development of a company of the agroindustrial complex of Russia. Taking into consideration all the advantages, the stakeholder approach to the development of economic patterns is limited in practical application by managers from the point of view of the time limits when determining the goals of stakeholders. In our opinion, goals should be formulated proceeding from ideas about the future state of the subject and the object of development. From this viewpoint, it is better to use it in addition to the company’s strategic development approach since it meets more fully the requirements of long-term development and formation of competitive advantages in the future.

5 Research Methods Development of the agroindustrial complex of Irkutsk region is subject to the same trends as those throughout Russia. One of the leading roles in the development of the agroindustrial complex of Irkutsk region belongs to the agricultural enterprise SH PAO “Belorechenskoye,” by the example of which strategic development benchmarks are defined based on identifying key stakeholders, their interests, and main goals. Identification of stakeholders in the company, their main goals, and available resources is carried out in the following sequence. – identification of the interdependence of stakeholders and the company; – identification of key stakeholders on the basis of determining the degree of influence of the stakeholder on the company and vice versa; – identification of top priorities of key stakeholders;

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Table 1 Determination of the degree of influence of stakeholders on development of SH PAO “Belorechenskoye” in Irkutsk region Stakeholder Level of power

Level of interest Low

High

Low

No influence

Some dependence

High

Lack of support

Key stakeholder

– coordination of interests by means of coordinating stakeholders’ interests in key areas of creating added value; – development of the company’s development strategy (Vinokurov and Tyapkina 2016). The main goals of the stakeholders are identified based on theoretical hypotheses and an expert opinion of the scientific community and the company’s management. Determination of the interdependence between the company and the stakeholders is made by expert judgment based on a matrix of interdependencies (Table 1). In transition to market management conditions, a huge role in the development of the leading agroindustrial companies of Irkutsk region belongs to their owners. The fact that companies have overcome all crises and challenges of the market economy and are now improving production is the main merit of the company’s owners and a team of competent managers. This statement also concerns SH PAO “Belorechenskoye,” where due to the competent management and effective interaction of managers and shareholders, the company is successfully developing. Shareholders actually carry out strategic and operational management of the company. The CEO and his deputies own a controlling block of shares; therefore, their interests directly depend on the company’s activities. High interdependence between the company and shareholders has been revealed. According to the matrix of interdependence of workers and the company, the dependence of the workers on the company is revealed, which is the power of the company. It is possible to change the interdependence by creation of a trade union of workers, which provides protection of the rights and interests of workers. The state (society), though slightly, depends on the company’s activity (taxes, social benefits, social prices, etc.), but this dependence is less compared to the company’s dependence on the state. The company depends on the state in the framework of participation in state programs, the system of taxation, and regulation of the company’s activity in the industry, but the state depends on the company in terms of the budget formation in taxes and levies and in providing employment for the population. According to experts’ assessment, it has been revealed that consumers of products made by SH PAO “Belorechenskoye” have a high level of interest and a high level of power. That is, consumers are interested in consuming the company’s products and, at the same time, the company is dependent on consumers’ interests. Therefore, consumers can be attributed to the group of key stakeholders of the company. As a result of the analysis of the level of interest and power of the stakeholders, the top priorities of the key players are revealed (Table 2).

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Table 2 Determination of top priorities of key stakeholders Stakeholder type

Interdependencies between firm and stakeholder

Interests of goal setting

Priorities

Consumers

Stakeholder power

Key players

Quality–price

Shareholders

High interdependence

Key players

Company cost increase

Workforce, including managers

Firm power

Some dependence

Growth of income

State

Stakeholder power

Key players

Recoupment of state subsidy

As a result of determining the degree of influence of the stakeholder on the company development, it has been revealed that the “key players” in the company are shareholders, the state, and consumers. Therefore, for the purposes of strategic development of the company, it is necessary to determine specific interests and expectations of these key players. Consumers’ interests usually boil down to the compliance of the company with the “quality–price” relationship. There is a ratio of quality features to the price of competitors’ analogues. The optimal price–quality ratio for the consumer is such offer of a product or service that corresponds to the maximum usefulness of this product for the consumer at the minimum cost (time, money, and efforts). To study the interests of consumers of companies’ products, as one of the most important key stakeholders, a direct questioning of consumers in the form of a sociological survey was conducted. 400 consumers from large cities and other settlements of Irkutsk region (Irkutsk, Angarsk, Usolye-Sibirskoye, Zima, Sayansk, etc.) took part in the survey. The purpose of this survey is to draw conclusions that characterize the preferences of consumers of basic foodstuffs of the company in terms of the “quality–price” ratio. The representativeness of the sample was provided by data obtained as a result of the survey of consumers from different settlements, differing by location, and consumers with different levels of income, taste preferences, and age groups. In this work, the company’s products were analyzed as compared to analogues of companies’ competitors, on the basis of which the main interests of consumers of the company’s products were identified, and then the company’s goals were formulated. For a visual demonstration of the quality and price ratio, as a method of evaluating a product or service, we calculated points for the indicators of quality and price using the comparison of a product with a similar product and the matrix construction. There is a compiled table to compare the goods and indicators. To determine the point of location of the selected product in terms of the “price” indicator, the most expensive product is taken as 100%. The price for analogue products refers to it. To assess the quality, the authors have developed a questionnaire in which questions, characterizing the quality indicators taking into account the specifics typical of a particular product,

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have been compiled. The scores marked in the questionnaire on quality indicators are summed up, and the arithmetical mean indicator is derived (for each product separately). One of the most important stakeholders in the development of agroindustrial economic patterns is the state representing the society as a whole. The relationship between stakeholders represented by the state and shareholders can be shown as the ratio of public benefit and costs of society: growth of society welfare due to the growth of incomes and employment in society; the amount of tax revenues to the budgets of all levels received from the economic pattern; and support for social initiatives (Kiseleva et al. 2016). When the range of the company’s activities increases and the number of stakeholders (workers or consumers) grows, the level of government interaction with such company rises. The costs of society can be presented as a monetary estimate of losses incurred as a result of the company’s activities (moral and physical harm in the form of performance loss, health deterioration due to ecology, etc.). But it is rather difficult to estimate them since losses are mostly characterized by qualitative characteristics having a conditional dependence. The carriers of the state’s interests are state bodies and persons who make decisions regarding the disposal of state property. Public authorities, being the guarantor for the population and the main agent of the economic system constituting the interests of the state and business, become incentives for effective economic activity aimed at meeting the needs of consumers of the social product, reproducible by the economic pattern. At the same time, these needs are expressed by objective social relations and acquire a specific character of satisfaction, depending on the evolutionary stage of society development (Zedgenizova and Ignatyeva 2017). With regard to the study, this is a characteristic of the interaction between the state and the company for the purpose of coordinated development that satisfies the basic interests of all stakeholders in the framework of the state program “Development of agriculture and regulation of markets for agricultural products, raw materials and food” for 2014–2020. The main target indicators of the state program are the volume of agricultural production in current prices per 1 ruble of the rendered state support; index of agricultural production in farms of all categories (in comparable prices); and creation of new jobs in agriculture. Based on the analysis of the list of departmental target programs and main activities of the State Program for Agriculture Development and Regulation of Agricultural Commodities Market, Raw Materials and Food Markets for 2013–2020, developed by the Ministry of Agriculture of Irkutsk region, experts suggest ideas for investment projects to deepen integration and/or expand diversification for an agroindustrial company. The Ministry of Agriculture of Irkutsk region is conducting a competitive selection of applications from agroindustrial companies aimed at development of agricultural sectors using a methodology that includes an integrated assessment of the following indicators:

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– – – –

output of products in physical terms; the number of jobs; average monthly salary; return of payments to the budget and extra-budgetary funds per 1 ruble of invested budgetary funds; – profitability of the investment project; – payback period; share of own funds. The existing methodology does not allow evaluating financial, technological, technical, and other possibilities, revealing agroindustrial companies that can implement several investment projects at the same time. These opportunities can be assessed using the investment potential index, adjusted by the coefficient of agroindustrial integration, with which it is proposed to supplement the existing methodology of the point assessment of economically significant projects of the agroindustrial economic patterns of the Ministry of Agriculture of Irkutsk region. The investment potential index consists of the sum of the aggregate indicators “Financial Investment Potential,” allows evaluating financial opportunities and “Technical and technological investment potential”—the technical and technological capabilities of the agroindustrial economic pattern. The coefficient of agroindustrial integration allows one to display the possible synergistic (financial, production, marketing, or personnel) effects from projects implementation. The use of these indicators makes it possible to reveal the possibilities of the agroindustrial economic patterns to simultaneously implement several economically significant projects. A comprehensive assessment of the investment potential is reflected in the works of many authors. As a result of the study, the authors propose taking as a basis the methodology for assessing the investment competitiveness of enterprises proposed by Mikhailov and Chaplyginoi (2011). The comprehensive assessment of investment potential using an integrated assessment methodology provides an opportunity to determine in one indicator a variety of factors different in content, units of measurement. This simplifies the assessment procedure. The investment potential represents the most important characteristic of the state and future use of resources capabilities and sources of commercial organization development. It is crucial in ensuring the economic growth of the organization and plays an important role in development of its other potential capabilities (production, financial, marketing, etc.) at the expense of investment activities (Nechaev et al. 2014; Tyapkina and Ilina 2018). Based on the specifics of organization’s activity, a system of indicators characterizing the performance of potentials of its functional activity is established. Using the methods of expert assessments, significant indicators of subsystems are selected, taking into account their importance. The indicators determined by the results of expert evaluation are entered into a table; a reference value is determined for each of them. The standard of comparison may be a normalized value of the studied indicator or the parameters of the most successful stage of the business activity of the enterprise or its competitor.

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The investment potential index is calculated by the following formula: Iip = (Ifip + Itip ) · Kai

(1)

Ifip = I1 · I2 · · · · · In

(2)

where n—number of indicators for calculation of financial investment potential. Itip = I1 · I2 · · · · · Im ,

(3)

where m—number of indicators for calculation of technical and technological investment potential. Kai = 1/5

(4)

where I—number of elements of the production cycle of an agroindustrial economic pattern. Based on the obtained indices, the aggregate indicator of investment potential is determined for each potential of functional activity. In 2016, there was a selection of economically significant investment projects aimed at development of the agriculture sector of Irkutsk region according to the criteria of integrated assessment of the effectiveness of investment projects by areas of activity using the methodology of the point assessment system. At the initial stage, enterprises were selected for participation in the competition. At the next stage, the enterprises presented their projects in accordance with the imposed requirements. After that, there was an assessment and selection of projects to obtain state support. The competitive selection of investment projects was carried out taking into account the companies’ rating, which was compiled using a number of criteria: – – – – – – –

the growth rate of agricultural production, the growth of average monthly wages, the number of newly created jobs, the payback period, the share of own funds, the production profitability, paying for taxes and levies per one ruble of invested budgetary funds.

At the same time, several organizations simultaneously participated in the selection of state support. Since the existing method does not allow assessing the ability of agroindustrial companies to realize several investment projects at the same time, it is proposed to supplement the methodology with indicators for estimating the investment potential index. This characterizes the investment possibilities of simultaneous implementation of several projects (Table 3). It should also be noted that the selection was made in 10 directions: production of milk, grain, cattle meat, vegetables, rape, pork, sheep and goat breeding, herd

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Table 3 Results of assessment of the investment potential of agroindustrial economic patterns of Irkutsk region Indicator

Standard

SH PAO “Belorechenskoye”

OAO “Iskra”

Value

Index

Value

Index

Absolute liquidity ratio

0.2

1.0

5.0

0.1

0.7

Current liquidity ratio

2.0

3.4

1.7

15.3

7.6

Financial stability index

0.6

0.8

1.3

0.9

1.6

Funding ratio

1.0

2.8

2.8

14.3

14.3

Equity ratio

0.5

0.7

1.5

0.9

1.9

Adequacy ratio of own liquid funds for realization of the project

1.0

9.4

9.4

0.0

0.0

Aggregate index “Financial investment potential”

IFIN

454.9

Capacity ratio

0.8

0.7

09

0.6

0.7

Profitability of outlay, %

10.0

10.6

1.1

0.1

0.0

Value of production per: 100 ha of agr. lands, thous. rub

6435

7723

1.2

5688

0.9

Per 1 worker, thous. rub

926

1899

2.1

575.5

0.6

Per 1 rub. of fixed asset, rub

0.9

1.8

2.0

0.4

0.5

Crop capacity, c/ha of grain crops,

16.1

–

–

–

–

Of vegetables

249

369.9

1.5

256.0

1.0

Of rape

9.6

17.5

1.8

–

–

Productivity: milk yield per 1 cow, kg

4472

6575

1.5

–

–

Weight gain, g

552

700

1.3

–

–

Aggregate index “Technical investment potential”

ITTIP

23.6

Agroindustrial integration index (KAI )

5 of 5

5 of 5

Investment potential index

IIP

478.6

5.5

0.001

1.0

2 of 5 2.2

0.4

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horse breeding, and creation of agricultural cooperation. Agroindustrial companies participated in several areas of providing state subsidies and, in the aggregate, were able to get more funding due to having the opportunities to invest their own funds in projects and, most importantly, in experience of projects implementation. It is important for the state to identify agroindustrial companies that are claiming to subsidize several economically significant projects from the standpoint of their ability to be implemented. The high index of the investment potential of SH PAO “Belorechenskoye” (I_IP = 473.53) confirmed the company’s ability to implement several investment projects simultaneously. The low index of the investment potential of OAO “Iskra” (I_IP = 2.2) revealed insufficient financial, technical, technological capabilities, and feasibility of rendering state support to only one project.

6 Conclusion As a result of assessment of the investment potential of agroindustrial companies, it has been revealed that SH PAO “Belorechenskoye” has a high index of investment potential for implementation of several investment projects simultaneously. OAO “Iskra” has insufficient financial, technical, and technological capabilities. Therefore, it can claim for state support for only one economically significant project. After selection, SH PAO “Belorechenskoye” submitted four projects to the Ministry of Agriculture of Irkutsk region, claiming for subsidies. As a result of the selection, the submitted projects received high total weighted average points and received state support in the form of subsidies. An important factor in the selection of projects was the fact that they were selected regardless of the size of the enterprise. The selection was carried out on the basis of indicators of the effectiveness of projects implementation from the viewpoint of state interests expressed in the state program “Development of agriculture and regulation of markets for agricultural products, raw materials and food for 2014–2020.” Thus, the application of the stakeholder approach to the strategy for the development of agroindustrial companies will allow taking into account the interests of various groups of stakeholders and coordinating their interests to achieve common goals. The result of it may be an increase in the activity efficiency of the company and the agroindustrial complex of the region. The application of the stakeholder approach to the development of companies in the agroindustrial complex will allow responding to the existing challenges and solving problems at the expense of available financial, personnel, technical, and other resources.

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Theoretical Aspects of the Optimization of the Regional Innovative Enterprises Sergey V. Zakharov, Evgeniya V. Bibaeva and Olga V. Nikitina

Abstract The chapter analyzes the problems of the theory and methodology of the formation of methodological approaches to improving the efficiency of small innovative enterprises. It also concerns creating the necessary conditions for effective functioning, improvement of the state, and competitive recovery of enterprises. The research is related to the identification of peculiarities and regularities of the development of enterprises in innovative small business in the conditions of global competition. It deals with the assessment of their status and interactions with external organizations, the determination of the ways of improving the state and conditions of development, selection of directions for improvement.

1 Introduction The easiness of production reorientation, the innovativeness of products, quick response to market needs, the possible achievement of maximum efficiency, etc., predetermine the main priority of the modern Russian economy. This concerns creation of conditions that ensure the fundamental development of small and medium-sized businesses. At the same time, in this economic sector, innovation and technological production activities of enterprises are the most relevant. To increase the efficiency of innovative small and medium-sized businesses, the government support and protection of the interests of small and medium-sized innovative enterprises providing competitive advantages are needed. This will allow ensuring the growth of potential, increasing the share of high-tech products, developing a system of support measures, trade-economic, organizational, financial, information, and consulting tools. In solving the problem of ensuring dynamically sustainable development of the enterprise, the primary role belongs to innovations and innovative activity. This activity is capable of providing the renewal of the production base, the introduction of modern innovative technologies in the management system. The innovation activity of a small S. V. Zakharov (B) · E. V. Bibaeva · O. V. Nikitina Irkutsk National Research Technical University, Lermontova Str., 81/21, 85, Irkutsk, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_10

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enterprise is determined by the objective need for development of science and technology, regulations of market relations connected with the orientation of economic entities toward demand and increasing market needs. This also includes the aggravation of competition and increasing complexity of economic ties. In this regard, the study of formation and development of competitive innovative small and mediumsized businesses is relevant because the understanding of the influence of certain elements of the innovation policy will develop the capacity of innovative small and medium-sized businesses. The insufficient development of theoretical and methodological foundations and problems connected with methodological approaches to improvement of the performance of small innovative enterprises predetermine the relevance of the chosen research topic.

2 Methods The research is based on the proceedings and works of foreign and Russian scientists devoted to the development and implementation of an innovative strategy at small-sized enterprises. They are the enterprises of Technopark of Irkutsk National Research Technical University. The development trends are described in the works of Nechaev, Konyukhov et al. (2014), Bovkuna (2012a, b) including the works written by the authors of this chapter. The authors used materials, documentation, and standards of Technopark of Irkutsk State Technical University (small innovative enterprises). Despite the significant number of studies related to various aspects of the work of small and medium-sized innovative businesses (Huang and Huo 2019; Saastamoinen et al. 2018; Sun and Jiang 2017; Zsuzsanna and Herman 2012), a number of theoretical and practical issues related to the problem are debatable and require further study. For example, elements of the formation of methodological approaches to improving the efficiency of small innovative enterprises have not been sufficiently developed. The scientific hypothesis consists in the idea of creating methodological approaches to improving the efficiency of small innovative enterprises, allowing justifying the priorities of the mentioned process and developing recommendations for its improvement. The object of the research is the methods and tools aimed at substantiation of the ways of improvement of the performance of small innovative enterprises. The subject of the study is a set of organizational and economic management relations in the process of forming methodological approaches to improving the efficiency of small innovative enterprises. The theoretical and methodological basis of the research is the modern experience in the functioning of small innovative enterprises. During the study, the methods of economic-statistical, systemic, comparative analysis, the method of structuring, logical methods, methods of classification, construction of the typology, and logical–theoretical analysis were used. The theoretical basis is the proceedings of Russian and foreign scientists devoted to the theory and practice of innovation activity. There are methods of evaluating the economic efficiency and sustainability of small innovative enterprises, the development of methodological approaches to improving the efficiency of management decisions

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and the work of small innovative enterprises (Andreeva and Nechaev 2013; Bovkuna 2012a, b; Nechaev and Antipina 2014, 2015). The theoretical basis also includes the provisions of labor and civil legislation, federal and regional regulatory documents on the regulation of the activity of small innovative enterprises. The empirical basis consists of the statistical materials of the Federal State Statistics Service of the Russian Federation, the reporting data from one of the University’s Technoparks. It includes the information materials published in periodical and economic publications of Russia and other countries, the data from Internet resources, the results of authors’ research and calculations (Bovkun 2012a, b; Konyukhov et al. 2014; Nechaev et al. 2015).

3 Results and Discussion The results were obtained during the analysis of the materials of statistical agencies consolidating statistical reports from enterprises in the region every year. The purpose is to summarize and develop the theoretical and methodological aspects of creating methodological approaches to improving the performance of small innovative enterprises. The following tasks have been predetermined for solution during the research: • To explore theoretical approaches to the essence of the concept, types and classification of small enterprises, and its innovation activities; • To take into consideration foreign experience in management and operation of small innovative enterprises; • To determine and justify the main ways and methods of management and support of small innovative enterprises; • To study the motivational aspects of the innovation activity of small business entities; • To offer a systematic approach to improving the performance of innovative small businesses; • To develop criteria and indicators of financial efficiency for evaluating the performance of small innovative enterprises; • To propose a cluster mechanism for improving the efficiency of small innovative enterprises; • To substantiate the reserves for improving the efficiency of innovative activities and their classification; • To recommend a methodology for quantitative assessment determination of reserves for improving the efficiency of innovation activity. The mechanisms of innovative development, innovative infrastructure, and features of using intellectual property are described in the works of Andreeva and Nechaev (2013), and Bovkun (2012a,b), and also Zakharov (2014a, b). Optimization and scientific novelty of the paper consists of the complex theoretical and methodological development of methodological aspects of creation of

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methodological approaches to improving the efficiency of small innovative enterprises and developing recommendations for their improvement. The most significant provisions, conclusions, results, and recommendations in creation of methodological approaches to improving the efficiency of work are the following. 1. It has been established that the objects of innovation activity are not only products/services and processes, but also technologies. Therefore, the following definition of innovation activity has been formulated. Innovation activity is a system of measures aimed at creating and implementing innovation activity objects. They are new or improved types of products/services, technology, processes to make a profit or to obtain other useful results, to achieve competitive advantages and sustainable enterprise development. 2. Main forms and methods of innovation activity management have been determined as a set of documentation and a specific list of quantitative indicators. The main documents are: a report on the status of the project; a report structure according to a standard project; a report on a complex project structure; a monthly project progress report; an information note on the project status; an employee’s weekly plan; a construction journal; a report of the employee on the work done during the month. 3. The identification of such criteria and assessment indicators are in the list of quantitative indicators of innovation management such as: the net cash flow of a small innovative enterprise (SIE); SIE cost; a capital structure; the structure of financial liabilities of SIE by liquidity. The list also includes a SIE assets structure; a current cost structure of SIE; the level of concentration of financial operations in high-risk areas, as well as relative indicators-constants calculated based on absolute criteria and evaluation indicators. 4. A systematic approach is to improving the performance of innovative small business enterprises has been proposed, including: • A system of indicators characterizing the efficiency of a small business and forming the first subsystem; • A risk system of conducting an innovative small business and forming the second subsystem; • Indicators of investment attractiveness of a small business and forming the third subsystem; • Elements included in the subsystems (indicators for assessing the efficiency and investment attractiveness and other components of this difficulty). 5. Criteria and indicators of financial performance evaluation of small innovative enterprises have been developed. They form a system of indicators, including profitability indicators, indicators of business activity, and indicators of financial stability. They can be calculated according to the balance sheet and report on financial results based on the reporting of small innovative enterprises. 6. Reserves for improvement of the efficiency of small innovative enterprises have been revealed and substantiated. They include the influence of factors associated with the reduction of expenditures and increase in profitability; leveling of various

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types of risk, forming a system of risk indicators in the set of factors, particular indicators of factors and methods. 7. Methodology for determining the quantitative assessment of reserves to improve the efficiency of innovation activity has been recommended in the mixture of several components and implemented sequentially in three stages. The first stage—calculation of the net cash income by the innovative solution to assess quantitative factors and reserves. The second stage—estimation of the reserves aimed at increasing efficiency during the implementation of an innovative solution taking into account factors of external and internal environment. The third stage—assessment of the quality of an innovative solution from the viewpoint of realization of reserves for increasing the efficiency of innovative activity of an enterprise. The authors have obtained the following results. 1.

2.

3.

Within a small business, an important component is the delimitation of small and medium-sized enterprises, including identification of micro-enterprises. This is possible using quantitative, qualitative, and combined approaches to the definition of small and medium enterprises, and they are used in the regulatory documents and in special literature. It has been revealed that the objects of innovation activity are not only products/services and processes, but also technologies. In this regard, the following definition of innovation activity has been formulated and proposed. Innovation activity is a system of measures aimed at creation and implementation of innovative activity objects. These are new or improved kinds of products/services, new or improved technology, and new or improved processes for the purpose of obtaining profit or another useful result, achieving competitive advantages and ensuring sustainable development of the enterprise. Accordingly, technological innovations include not only product and process innovations, but also innovations in the field of technologies. The foreign experience in the field of innovative small business entrepreneurship should be divided into some areas: • • • • •

4.

Experience in the field of government regulation; Experience in creating extra jobs; Experience in reducing administrative barriers; Experience in the use of universities; Experience in export orientation.

There are the following types of dependance of small enterprises on the criteria of division (the nature of the issue, the content of specific tasks (works), the kind of products, the place of creation, and the level of development): • Economic, social, and environmental; • Scientific and technical, scientific and production (technical), consulting, implementation, and intermediary;

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• Development and exploration of products for industrial purposes; • Production of consumer goods; • Development and mastering of intermediate products, semi-finished products, component parts, and products; • SIEs in the administrative structure of large research institutes and scientific development and production centers; • Shadow SIEs; • SIEs of a self-sustaining administrative unit; • High-capacity SIEs and free-floating SIEs; • SIEs at universities; • Life-sustaining and fast-growing. 5.

6.

7.

A set of documentation and a specific list of quantitative indicators can be used as ways and methods of managing innovation activity. The main documents are a report on the status of the project; the structure of the report on a complex project; a monthly report on the progress of works on the project. They also include an information note on the status of the project; an employee’s week plan; a construction journal; a report of the employee on the completed work during the month. It seems reasonable to single out the criteria and evaluation indicators. They are a net cash flow of SIE; SIE cost; the capital structure; the structure of financial liabilities of SIE on liquidity; the SIE assets structure; the current cost structure of SIE; the level of concentration of financial transactions in high-risk areas. The government regulation of the innovation policy represents a system that includes: taxation, crediting, depreciation policy, insurance, a system of federal and regional government orders, privatization, infrastructure, finance of commercial structures, etc. To increase the effectiveness of the system and mechanisms of state support for innovative small businesses, we propose: • Improving state support mechanisms; • Improving and harmonizing the legislative framework; • Improving the information support of the authorities and entrepreneurs.

8.

The motivations for encouraging innovation should be considered in two aspects: the motivational mechanism or the incentive mechanism of the enterprise as a whole and the motivational incentive programs for the staff. For the real stimulation and development of the managers’ motivation for introduction of innovations, it is necessary to use a whole range of compensation instruments such as wages and bonuses to encourage the effective activity of the enterprise. It is also efficient to use a stock option as an encouragement of the manager which grantees the right to buy the company shares at par. This is a significant part of the total income with a good market situation. The standard “motivation and staff incentives” should be developed at enterprises for successful implementation of innovation policy in small business. The standard “motivation and incentives for personnel of the ISU Technopark” has been proposed as an example.

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9.

Partnership of large and small enterprises for the realization of innovation projects and non-profit partnership is more feasible using a cluster approach. 10. A system approach to the problem of improving the efficiency of a small innovative business may include: • A system of indicators characterizing the efficiency of small business and forming the first subsystem; • A risk system of conducting innovative small business and forming the second subsystem; • Indicators of the investment attractiveness of small business, which form the third subsystem. • Elements included in the subsystems (indicators for assessing the effectiveness and investment attractiveness and other constituents of the component of this problem). 11. Analysis of innovative small business in Irkutsk region shows that practically detailed and sufficient analysis based on reporting has not been carried out. Only two indicators have been used for evaluation—the volume of work and investment. A system of indicators, which is the most correct and suitable for calculating the financial efficiency of small innovative enterprises, includes profitability indicators, indicators of business activity, and financial stability. They can be calculated using the balance sheet and income statement formed by the reporting of small business and innovative enterprises. 12. The identification and justification of reserves for improving the efficiency of small innovative enterprises are connected: • Firstly, with the influence of factors associated with the cost reduction and the increase in profitability; • Secondly, the leveling of various types of risk and reducing the impact of which can significantly improve efficiency. 13. The system of risk indicators should include factors, private indicators of factors, and methods. The private indicators in the mixture of factors may be individual for separate small enterprises or supplemented with specific indicators and methods of their calculation. There are more specific selection methods reflecting branch (subsectoral) and regional features, purpose, and sources of formation of innovative solutions. 14. Groups of indicators/factors revealing the reserves for increasing the efficiency of small innovative enterprises can be: scientific and technical; institutional; production and technological; financial and economic. There are also social; environmental; branch (regional); legal; organizational and informational; market (marketing) groups. For each group of factors, a specific set of private indicators is assumed. It can be different for individual small innovative enterprises depending on the specific features of the sectoral purpose and the profile of enterprises, goals of selecting innovative solutions, stages of their implementation, and sources of formation.

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15. The factors that form the innovation activity of enterprises are divided into internal (related to the enterprise) and external (independent of the enterprise). The reserves are determined due to these factors. In turn, external factors are divided into direct and indirect. For a general assessment of the influence of factors on an innovative solution due to its feasibility and the identification of reserves for improving efficiency, the magnitude of the points is calculated. In addition, we must take into account the weighting coefficients of significance. The assignment of points is carried out for each indicator based on expert evaluation. 16. The method of quantitative assessment of reserves for improving the efficiency of innovation activity can include the following components and is carried out in stages: First stage—calculation of the net cash income for the innovative solution to assess the quantitative factors and reserves; Second stage—assessment of the reserves for improving efficiency in the implementation of an innovative solution, taking into account external and internal environment factors; Third stage—assessment of reserves for improving efficiency in the implementation of an innovative solution, taking into account external and internal factors. The development of methods for using reserves to increase the efficiency of innovative activity in educational institutions and academic research enterprises consists in the creation of small enterprises at universities and scientific institutions. There is also the development of commercialization of solutions in these small enterprises. 17. A powerful tool for stimulating innovation policy and increasing its own sources of financing is a tax policy. To increase the effectiveness of innovative projects, the following tax policy measures should be taken: • In terms of VAT, to extend exemption from taxation (to enter a lower VAT rate) to all enterprises, implementing projects at the expense of various sources of financing; • The value of the investment tax credit during the implementation of innovation activity should be set in proportion to the absolute or incremental values of R&D; • Including the use of tax holidays for several years on profits gained from the implementation of innovative projects; • Other benefits for income tax.

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4 Conclusion The study is related to the identification of peculiarities and regularities of development of regional innovation-type enterprises. The conditions of global competition are assessment of their state and interactions with external organizations, identification of ways to improve the state and conditions of development. The choice of areas for improvement is generally vital and of great importance for the economy. In our work, we have demonstrated and investigated the problems of the theory and methodology of the formation of methodological approaches to improving the efficiency of such regional innovative enterprises. The solution of these problems will help create the necessary conditions for effective functioning, improvement of the condition, increase in competitiveness among regional innovative enterprises. We suppose that our methodological approaches can be applied in other countries.

References Andreeva, E. S., Nechaev, A. S. (2013). The mechanism of an innovative development of the industrial enterprise. World Applied Sciences Journal27(13 A), 21–23. Bovkun, A. S. (2012a). The innovative infrastructure of the University: Directions and ways of development. Bulletin of Belgorod State Technological University VG Shukhov,3, 126–129. Bovkun, A. S. (2012b). Formation of market of intellectual property for the innovation economy. Bulletin of Irkutsk State Technological University,8, 160–163. Huang, C.-H., & Hou, T.-T. (2019). Innovation, research and development, and firm profitability in Taiwan: Causality and determinants. International Review of Economics & Finance,59, 385–394. Konyukhov, V. Y., Nechaev, A. S., & Kychkin, A. A. (2014). Investment toolkit development for estimation of enterprises innovative activity efficiency. Actual Problems of Economics,162(12), 236–251. Nechaev, A., & Antipina, O. (2014). Taxation in Russia: Analysis and trends. Economic Annals XXI,1–2(1), 73–77. Nechaev, A. S., Antipina, O.V. (2015). Tax stimulation of innovation activities enterprises. Mediterranean Journal of Social Sciences 6(1S2), 42–47. Nechaev, A., Antipina, O., & Antipin, D. (2015). Efficiency estimation of innovative activity the enterprises. Journal of Mathematics and Statistics,10(4), 443–447. Saastamoinen, J., Reijonen, H., & Tammi, T. (2018). Should SMEs pursue public procurement to improve innovative performance? Technovation,69, 2–14. Sun, M., & Jiang, H. (2017). Innovating by combining: A process model. Procedia Engineering,174, 595–599. Zakharov, S. V. (2014a). The Algorithm of introduction of results of scientific and innovative activity of small innovative enterprises in the economic turnover of the region. Actual Problems of Economics,4, 198–203. Zakharov, S. V. (2014b). Foreign experience of development of small innovative business. Economics and Entrepreneurship,1, 654–658. Zsuzsanna, S. K., & Herman, E. (2012). Innovative entrepreneurship for economic development in EU. Procedia Economics and Finance, 3, 268–275.

Institutions in the Context of the Regional Development of Russia Darko Vukovi´c, Natalya Y. Vlasova and Olga T. Ergunova

Abstract This chapter analyzes the role of regional development institutions in the social and economic regional development of the Russian Federation. The essence and content of the notion “the institute of regional development” is specified. The main types of the regional development institutions existing in Russia are identified, including those that are at different territorial levels. The main functions of the regional development institutions are considered taking into account the specifics of the Russian Federation. The formation process of development institutions in the regions of the Russian Federation, which indicates their spatial concentration in several regions of the Russian Federation, is analyzed. The main problems of the effective functioning of regional development institutions, including those connected with the disagreement of legislatively assigned functions for different levels of government (regional and federal), are summarized. The directions for increasing the efficiency of their functioning have been developed on the basis of questionnaire survey conducted for the heads of development institutes of the constituent entities of the Russian Federation in terms of the scope of powers and degree of independence in support of investors and interaction with them, priorities in the choice of running projects for the development of investment and social infrastructure, as well as problematic issues that hamper their effective work.

D. Vukovi´c Belgrade Banking Academy and Geographical Institute “Jovan Cvijic” of Serbian Academy of Sciences and Arts, Belgrade, Serbia e-mail: [email protected] N. Y. Vlasova (B) Ural State University of Economics, Ekaterinburg, Russia e-mail: [email protected] O. T. Ergunova Ural Federal University, Ural State University of Economics, Ekaterinburg, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_11

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1 Introduction Nominally, regional development institutions are designed to ensure the diversification of the economy of the constituent entities of the Russian Federation and aimed at increasing the investment attractiveness of the regions, provide open access to necessary resources, promote the development of public and private partnership (PPP), including in small and medium-sized businesses. In the various constituent entities of the Russian Federation today, more than 200 organizations have been established, which, based on their functions, can be referred to regional development institutions (Vilensky et al. 2015). A statistical sample of 40 constituent entities of the Russian Federation confirms the political nature of the process and the Pareto principle: 20% of institutions were found over 80% of the time, and 80% of institutions, respectively, over 20% of the time. A contemporary view of the functioning of regional development institutions raises the question on the need for effective functioning of regional development institutions in the context of increasing transaction costs of spatial development. This is due to the acceleration of technological evolution, reduction of change cycle of technological modes, an increasing role in the economy of innovative industries, the development of which does not connect with “hard” location factors (such as natural resources). With a view to these postulates, it is interesting to trace the stages of development of regional development institutions and to identify those changes at the regional level that are likely to be in the post-industrial development of the economy of the constituent entities of the Russian Federation. In particular, special attention shall be given to how the business community assesses the work of leadership of regional development institutions.

2 Literature Review According to W. Hamilton, who first used the term “institutional economics,” institution is a verbal symbol, which for want of a better describes a cluster of social usages. The world of use and wont, to which imperfectly we accommodate our lives, is a tangled and unbroken web of institutions. According to T. Veblen, institutions, on the one hand “are settles habits of thought common to the generality of men”, and on the other hand they are “imposes those conventional standards, ideas, and canons of conduct that make up the community’s scheme of life” (Veblen 1919). Theoretical analysis of the problems of regulating the activity of development institutions and related issues is presented in the works of the representatives of modern school of institutional economics, representatives of European school. They are North (1990), Boettke et al. (2008), Osipov (2013), Kalyuzhnova and Osipov (2011), Tatarkin and Kotlyarova (2013), Khastieva and Fedorova (2015), Podstrigich (2012, 2013), Shedko (2013), Zaostrovtsev (2015), et al.

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Many researchers emphasize the importance of institutions for the regional development. Chang gives an overview of the concepts indicating the relationship between institutions and the level of economic development (Chang 2011). Rodriguez-Pose stresses that the effectiveness of institutions for regional development depends largely on whether regional characteristics have been taken into account (Rodríguez-Pose 2013). Meric proves the importance of the role of universities in the local economy, as well as the importance of creative regional strategies and the social integration policy. The work emphasizes that the local architecture of institutions can have a significant influence on innovation and investment activity in the region (Meric 2010; Hsu et al. 2013). Spruk and Keseljevic studied the relationship between economic freedom and economic growth by the example of 407 German districts. Six indicators of economic freedom were grouped into categories, reflecting tax rates and the scale of the government and public sector. The undertaken study allowed proving the existence of a stable relationship between the level of economic freedom and rates of economic growth. Moreover, an important factor is the level of inter-territorial differentiation in terms of economic freedom (Spruk and Keseljevic 2018). By the example of the North and South of the USA, the importance of political and legal institutions for regional development was proved. Kim, considering the spatial disparity in the income level between the northern and southern states, concludes that, although many factors contributed to industrialization of the North and stagnation of the South, differences in regional institutions were the most significant factor. In the north, the democratic institution boosted growth, while in the south, oligarchic institutions supported the status quo (Kim 2009). Several studies are devoted to the specifics of the regional development institutions in the transition economies, paying attention to the fact that economic transformation is a rather long process of institutional changes and the formation of new institutions that suit the conditions of capitalist economy (Redek and Sušjan 2005). Smallbone and Welter consider the relationship between institutional changes and enterprise development in countries where centralized planning was applied. The study is evidence of significant differences in relations between the state and entrepreneurs in different countries. The authors consider three groups of countries. Firstly, these are the former Soviet republics, where rather slow pace of institutional changes suppressed the entrepreneurship development. Secondly, these are Central European countries, which are currently a part of the European Union, and where institutionalized changes were largely related to the introduction of already tested institutions when these countries were entering the EU. Thirdly, this is China, which, by definition of the authors, represents a kind of a puzzle, since entrepreneurship is developing rapidly despite serious formal institutional shortcomings. Yang’s concept of double entrepreneurship is used to explain the so-called Chinese puzzle, where entrepreneurship acquires both socio-political and purely economic dimensions. Thus, the authors emphasize the complexity of institutional–business relations; however, they note that entrepreneurs can influence institutional changes even in a hostile institutional environment (Smallbone and Welter 2012).

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All in all, since the late 1990s, quite a lot of research has appeared that justify a relationship between the quality of institutions and the problems of economic development in developing countries. Thus, the researchers emphasize the importance of namely local institutions for the development of entrepreneurship, innovations and investments.

3 The Definition and the Concept of the Development Institutions In order to disclose the content of the regional development institutions, it is necessary to identify their role in the overall system of state management of the region’s economy (Alba 2016). In the conditions of the market, the economic functions of the state are mainly indirect in nature and are confined to the following principal areas: (1) maintenance of microeconomic stability and economic growth; (2) public services delivery, conditioned by the need for involvement in economic processes during economic decline (Boettke et al. 2008). In order for state activities to be effective and ensure the achievement of the necessary results, their internal structure must also be improved and their own potential must be adapted to new tasks (Cheng and Yan 2003). According to the analysis of the “strategic” backlash, that is, the gap between the desired actual level of the well-being of the population, it is necessary to develop the actions of public authorities that direct the economy toward an innovative type of development in accordance with established priorities (Albert 2003). We must also consider the building of capacity of public authorities and organizations of the public sector of economy, ensuring the necessary level of activities, as well as compliance of the delivered public services to the needs of the population and the established quality standards. Therefore, the regional development institutions cover the management subsystem and part of the system of public administration of the region’s economy, in part of the: (1) development of the capacity of the regional government to ensure the delivery of public services of the required volume and quality and to regulate economic processes in the region (Buchwald 2013); (2) work of public authorities and subordinate organizations of the public sector of the economy on the creation of favorable conditions and incentives for business to create new and modernize existing production facilities, introduce innovative technologies, increase the level of average wages of workers, etc. (Buchwald 2014).

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4 Types of Development Institutions The structures of all three types function on the federal, inter-regional, regional, inter-municipal and municipal levels, including centrally managed are legislative and executive authorities and local government authorities, offices of Presidential Plenipotentiary Envoy to federal districts, federal and regional development institutions, various Union of Industrialists and Entrepreneurs, chambers of commerce, development institutions and development corporations. They also include nonprofit corporations (NPC), religious organization, charitable trusts, etc. The decentralized (network) structures include partnerships and inter-regional associations of various levels (small town union, “Siberian Accord,” etc.), inter-regional cooperation programs (“Cooperation” for the Tyumen region and its autonomous regions), regional unions of mayor of cities and associations of inter-municipal cooperation. There are business associations and business partnerships of different levels: associations of NPC (partnership of local communities, Consumers’ Association, etc.) and social networks of various levels.

5 Functions of Regional Development Institutions Let us consider the functions of the regional development institution in terms of improving the capacity of public sector of the economy in order to increase the volume and quality of public services to the population (Alferova 2012). Currently, public services are delivered to population within the public sector of the economy. This includes state-owned enterprises and budgetary institutions of the relevant sectoral orientation, found for these purposes (Matushkina 2016). Current volumes and quality of delivered public services are ensured within the framework of certain branches of the public sector of the economy (Valentey et al. 2014). To increase them, it is necessary to build the capacity of the corresponding enterprises and state institutions, that is, the level of used equipment, technology, human resources, etc. (Cheng and Yan 2003). Taking into account the above-mentioned peculiarities and economic substance of the considered activity, the structure of functions necessary and sufficient to achieve the goal of the activities of regional development institutions is presented in Fig. 1.

6 Research of the RDI Efficiency Information on the concentration of development institutions in the regions of Russia is specified in Fig. 2.

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Development and creation of Regional development institution (1) development and planning of the implementation of the functioning standard for Regional Development Institution (2) arrangement of work on creation within the regional system of public management of the economy of organizations responsible for performing the functions stipulated by the standard of activity of public authorities for interaction with business; (3) control over the implementation of the standard of the activities of the Regional Development Institution by the experts of the Agency for Strategic Initiatives (ASI)

Measures aimed at functioning of Regional development institution Functions of control subsystem of Regional development institution (1) determination of activities included in the public sector of the economy on the delivery of public services to population and regulation of the economy; (2) determination of priorities and volumes of investment of certain types of activities included in the public sector of the region’s economy; (3) determination of the volume of assets of the Regional Investment Fund and distribution of assets; (4) development of PPP projects in the public sector aimed at the attracting private investment in the public sector of the economy (5) generation of sectoral development strategies (development programs by types of services to the population); (6) organizing the activities to provide public services to population or business, measures to support business (7) allocation of resources within the established budget between the subordinate organizations of the public sector of the economy; (8) control over the progress in the implementation of development programs and fulfillment of state tasks in terms of volume and quality of services delivered to population.

Functions of principle activities of Regional development institution (1) attraction of investors and presentation of opportunities and investment potential of the region for the business community (2) expertise and selection of investment projects to provide support measures in accordance with the criteria for achieving the objectives of the Social and Economic Development Strategy for the region (industry affiliation,) (3) delivery of services and support measures: construction of engineering and transport infra structure; selection of land plots on the territory of region, assistance in attracting financing of banks, strategic investors and federal development corporations, etc.

Fig. 1 Structure of regional development institution functions (made by authors)

Despite the creation of regional development institutions for investor interaction, regions face difficulties related to: (1) granting according to a special procedure of land plots for the creation of industrial parks. When creating state industrial parks, regions face two problems: First, it is necessary to provide a site for the construction of an industrial park infrastructure to a specialized organization, and at the second stage have the opportunity to alienate the plots to residents. Without bidding, it is possible to do this only on a lease basis in the manner provided for by subparagraph 3 of paragraph 2 of Article 39.6 of the Land Code of the Russian Federation. It is based on an order of the highest official of the RF subject of the Federation for implementing large-scale investment projects. Without bidding, this is possible

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Fig. 2 Development institutions share in GRP and per head made according to analytic data of “Expert RA” based on Rosstat data and G1 Consulting (“G1 Consulting” https://g1cb.ru/)

to do only on a lease basis according to the procedure set forth in subparagraph 3 of paragraph 2 of Article 39.6 of the Land Code of the Russian Federation. This is the order of the highest official of the RF constituent entity for the implementation of large-scale investment projects. However, for this it is necessary to have a corresponding law in the region and subject to the compliance of the project for the development of an industrial park with the criteria established in the law. The following granting of land plots to future residents of an industrial park is probably possible only by the transfer of rights for lease (Ergunova et al. 2017). (2) performing the administrative procedures in one contact (Argüelles et al. 2014). Most of the administrative procedures in the field of urban development are related to the level of federal or municipal authorities, and time frames of their implementation are regulated by law. RDI has no legal basis to participate in them. One-contact mode works well in federal cities, where regional powers in the sphere of attracting investments are harmoniously supplemented by the powers in the sphere of urban development. (3) taxation in the implementation of infrastructure projects. Development corporations or companies managing industrial parks, implementing state projects for the creation of industrial infrastructure, are forced to bear the burden of paying land tax and corporate property tax. As a result, there are permanent losses at the stage of creation and operation of the industrial park and consumption of the registered capital. In such conditions, many management companies of industrial parks until the last moment do not transfer land from the category of agricultural land to the residential or industrial areas (this allows for a time to reduce the land tax base). For the budget, the effect of such state of business is not understandable, since the authorities, allocating funding for the establishment of an industrial park from one “pocket,” withdraw a significant portion of these funds in another “pocket” in the

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form of taxes (Markelova 2012). Meanwhile, institutions and authorities that build and operate other objects of the public infrastructure are simply exempt from these taxes. The solution of the above-mentioned problems is seen in the proper regulation of RIR activities in federal legislation.

7 Conclusions The solution of the task of improving the spatial organization of Russia dictates the need to switch to a more flexible system of planning and management and contribute to the creation of new management schemes and mechanisms aimed at the developing regional economic system. Such structures should become a spatial form of application of the project approach in the management of social and economic development. They should be created for specific (mainly economic) tasks and have a built-in transformation mechanism depending on the current economic situation or the stage of achieving the desired goal. However, it is important to clearly define and limit their powers and terms of operation by solving design tasks. Temporary project structures should be created for specific current tasks without legislative consolidation in the administrative-territorial division. Thus, the administrative-territorial division will not be associated with economic zoning. At the same time, the possibility of changing the boundaries of such spatial formation as the economic situation as transformed is certainly implied, and the mutual overlap of their boundaries is also permissible. However, the territory of the same subject of the Russian Federation may be included in different design structures. When forming inter-regional organizational structures, it is reasonable to take into account the experience of the formation of Euroregions. The size and scale of the economy are often comparable to the macro-regions of Russia. There is also a historical example—the Tennessee River Valley Administration, created for a number of anti-crisis measures during the Great Depression in the USA. The territories of advanced development, special economic zones could also be a possible form of implementation of such management mechanisms in Russia. Formation of inter-municipal management structures (such as inter-municipal associations of economic development) is of paramount importance for urban agglomerations, usually covering the territories of several municipal settlements and requiring coordination in the development of transport, medicine, education and other areas of the social sphere, processes of waste disposal and recycling and other. Thus, it is necessary to create a normative and institutional framework for ensuring flexible interaction between the various structures of different substructures of the country’s institutional territorial structure. These are the areas of responsibility of state and regional authorities, the zones of activity of commercial firms, nonprofit organizations and other civil society institutions. This will require a radical amplification of the entire territorial institutional structure of the country. However,

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such turn becomes urgent and is an adequate response to the challenges of acceleration of socio-economic development. Flexible coordination of various substructures of the country’s institutional territorial structure will become a spatial form of the implementation of the social treaty as an effective interaction of the state, business community and civil society.

References Alba, S. A. (2016). Comparative analysis of approaches to the concepts of special economic zones and offshore zones. Finances, 3, 119–123. Albert, G. S. (2003). Special economic zones and quotas on on-line goods: A policy proposal. Oxford Economic Papers, 55(4), 696–715. Alferova, T. V. (2012). The concept of “institute” in the context of managing sustainable economic development. Journal of Russian Entrepreneurship, 10, 30–33. Argüelles, M., Benavides, C., & Fernández, I. (2014). A new approach to the identification of regional clusters: Hierarchical clustering on principal components. Applied Economics, 46(21), 2511–2519. Boettke, P. J., Coyne, C. J., & Leeson, P. T. (2008). Institutional stickiness and the new development economics. American Journal of Economics and Sociology, 67(2), 331–358. Buchwald, E. M. (2013) Development of strategic planning and program-targeted management at the regional level. In Economic and legal institutions for regulating regional development of the Russian Federation (pp. 187–210). Buchwald, E. M. (2014). Institutes of development and new priorities of regional policy in Russia. Theory and Practice of Social Development, 6, 108–114. Chang, H. (2011). Institutions and economic development: Theory, policy and history. Journal of Institutional Economics, 7(4), 473498. Cheng, H., & Yan, S. (2003). Foreign direct investment and economic growth—The importance of institutions and urbanization. Economic Development and Cultural Change, 51, 883–896. Ergunova, O. T., Efimova, E. G., & Zhigalovskaia, O. V. (2017). The analysis of the processes of the formation of the territories of the advanced socio-economic formations. Economy and Entrepreneurship, 4(1), 297–300. Hsu, M.-S., Lai, Y.-L., & Lin, F.-J. (2013). Effects of industry clusters on company competitiveness: Special economic zones in Taiwan. Review of Pacific Basin Financial Markets and Policies, 16(3), 1350017. Kalyuzhnova, N. Ya., & Osipov, M. A. (2011). Evaluation of the influence of regional development institutions. Bulletin of the Irkutsk State Technical University, 4, 145–153. Khastieva, D. V., & Fedorova, L. P. (2015). The role of development institutions in regional governance in conditions of institutional transformations. Modern Problems of Science and Education, 2(2). Access mode: https://science-education.ru/en/article/view?id=22765. Kim, S. (2009). Institutions and US regional development: A study of Massachusetts and Virginia. Journal of Institutional Economics, 5(2), 181–205. Markelova, A. S. (2012). Innovative development of Russia and development institutions as its tool. Proceedings of the Institute of Control System of SSEU, 1–2(4–5), 138–141. Matushkina, N. (2016). Experience in implementing the mechanisms of territorial development in the regions of Russia. Regional Economy, 12, 4–17. Meric, S. G. (2010). Rules of the game: The place of institutions in regional economic change. Regional Studies, 44(1), 1–15. North, D. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press.

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Osipov, M. A. (2013). State and effectiveness of the institute for small business development in Russia. Fundamental Research, 10(13), 2916–2920. Podstrigich, A. V. (2012). Institutional aspects of the modernization of the regional link of the Russian economy. Economic Theory: Analytical Practice, 5, 68–77. Podstrigich, A. V. (2013). Institutes of development in the system of regional strategic planning. Bulletin of the Institute of Economics of the Russian Academy of Sciences, 3, 88–96. Redek, T., & Sušjan, A. (2005). The impact of institutions on economic growth: The case of transition economies. Journal of Economic Issues, 39(4), 995–1027. Rodríguez-Pose, A. (2013). Do institutions matter for regional development? Regional Studies, 47(7), 1034–1047. Scientific Report of the Federal State Budgetary Institution of Science of the Institute of Economics of the Russian Academy of Sciences. (2015). Moscow: Institute of Economics, Russian Academy of Sciences. Access mode: https://www.inecon.org/docs/Vilensky_paper_20151222.pdf#1. Shedko, Yu. N. (2013). Institutional-synergetic approach to the sustainability of the development of the region. In XXI Kondratiev Readings “World Economy of the Near Future: Where to Wait for an Innovative Breakthrough?”. Abstracts of the Participants of the Readings (pp. 428–431). Access mode: https://www.ikf2010.ru/members/login.php. Smallbone, D., & Welter, F. (2012). Entrepreneurship and institutional change in transition economies: The commonwealth of independent states, Central and Eastern Europe and China compared. Entrepreneurship & Regional Development, 24(3–4), 215–233. Spruk, R., & Keseljevic, A. (2018). Economic freedom and growth across German districts. Journal of Institutional Economics, 14(4), 739–765. Tatarkin, A. I., & Kotlyarova, S. N. (2013). Regional development institutions as factors of economic growth. The Economy of the Region, 3, 9–18. Valentey, S. D., Lykova, L. N., Bakhtizin, A. R., & Kolchugina, A. V. (2014). Typification of subjects of the Russian Federation. Economic Policy, 2, 677–709. Veblen, T. (1919). In B. W. Huebsch (Ed.), The place of science in modem civilization and other essays (pp. 239, 243). New York. Zaostrovtsev, A. (2015). Modern Austrian school on institutes, problems of development and the role of economist. Voprosy Economiki, 7, 74.

The Problems of Contemporary Regional Policy

The Formation of the Single Stock Market by Russia and Kazakhstan in the Conditions of Development of the Eurasian Economic Union Tatiana K. Blokhina, Elena Y. Goryunova and Dmitry V. Kovalevich

Abstract This chapter explores the relationship between stock market capitalization and macroeconomic factors (inflation rate, refinancing rate, US dollar/national currency rate, nominal GDP) based on the time series data complied in Russia and Kazakhstan from 2010 to 2017. Due to the multiple linear regression model, there is a strong correlation between stock market capitalization and the US dollar/national currency rate and the insignificant correlation with other indicators. Such correlation is connected with the monetary policy of currency appreciation and fixing of its exchange rate in the considered countries. This paper also considers the main problems, which should be solved for the provision of a single stock market by these countries: asymmetry of the development level of the stock markets, current inconsistencies in legislative and methodological support, the development level of the market infrastructure. There is also an organizational activity of professional participants from these countries, the lack of effective cooperation and information exchange among professional participants of the stock markets in partner countries.

1 Introduction The stock market has become an essential market playing a vital role in economic prosperity, which fosters capital formation and sustains economic growth. Stock markets are more than places to trade securities. They operate as a facilitator between savers and users of capital by means of pooling the funds, sharing risk, and transferring wealth. T. K. Blokhina · E. Y. Goryunova (B) · D. V. Kovalevich Department of Finance and Credit, Economic Faculty, Peoples’ Friendship University of Russia, Moscow, Russian Federation e-mail: [email protected] T. K. Blokhina e-mail: [email protected] D. V. Kovalevich e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_12

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In particular, a higher level of market development provides more investment inflows, thus creating greater opportunities for the progressive development of the economy. However, the investment capital flow in the global financial system goes in the direction of major financial centers, which provides investors with the necessary conditions for investing at minimal risks. The development of national stock market consolidation in the world has been encouraged. In Russia, this is reflected in a merger of the largest stock markets—Moscow Interbank Currency Exchange (MICEX) and the Russian Trading System (RTS) into Moscow Exchange (MOEX). Besides, Russia seeks to achieve a larger stock market that will attract other countries. Kazakhstan is the closest one to the Russian stock market among post-Soviet countries by the level of development. This circumstance under certain conditions can create a single basis for cross-country circulation of securities. To form the single stock market, the following issues should be addressed: (1) (2) (3) (4)

gradual deepening of economic integration; harmonizing financial market regulations; ensuring non-discriminatory access to the financial markets for countries; creating conditions for the mutual recognition of licenses in key sectors of the financial market: stock, banking, and insurance; (5) providing conditions for the mutual recognition of licenses in the stock market sector; (6) establishing a comparable procedure for the supervision of the activities of financial market participants. The level of the stock market development is defined by its volume of capitalization, contribution to the GDP, the dependence degree of national currency fluctuations and reliability from inflation dependence and refinancing rates. Each of these factors, to varying degrees, defines constituent elements of the potential of stock market development. Accordingly, these indicators are formed as the basis for the analysis of the single stock market formation by Russia and Kazakhstan.

2 Literature Review In the economic literature, there are differing views concerning the influence of different factors on the stock market development. It is generally assumed that the emerging markets are less efficient than the developed markets. Since its implementation by developed countries, financial liberalization has set as its main objective the strengthening of financial integration in order to reap its benefits (risk diversification, reduction of cost of capital, informational efficiency). These benefits will help to strengthen economic growth (Chari and Henry 2004; McKinnon 1973, 1993). The implementation of such policy in emerging markets leads to several consequences. Several previous studies have shown that, for example, financial liberalization has an important role in improving the financial situation of emerging markets and,

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consequently, their economic growth. However, despite its many advantages, everybody is aware that in the short term, financial liberalization is often accompanied by a wave of financial crises, many of which have taken a systemic extent and hit, in particular, the newly liberalized economies. More recently, with the multiplicity of financial crises in emerging economies, the financial literature has concentrated on studying the volatility transmission in times of crises (contagion). Some studies show the strengthening of financial integration as the main objective of financial liberalization. It has been obtained through the progressive abolition of various barriers to international investment, as well as the elimination of capital mobility restrictions, essentially responsible of emerging markets financial turbulences (see among others, Dell’Ariccia et al. 2005; Eichengreen and Arteta 2000; Kaminsky and Reinhart 1999; Ranciere et al. 2006). According to these studies, the success of this goal depends heavily on each country’s economic conditions when opening its market. Some studies show that the strengthening of financial integration following the financial liberalization process, which has been mostly characterized by phasing out various barriers to international investment, was particularly responsible for several financial turbulences. Bekaert and Harvey (1995), Phylaktis and Ravazzolo (2002) and Carrieri et al. (2007) argue that financial liberalization has made financial markets more integrated into global international financial movements, and therefore more sensitive to external shocks. The financial sector as one of the principal sectors of an economy plays a key role in the integration process of economies in case of the positive impact of financial development on economic development (A widespread movement toward financial development and the economic growth nexus emerged notably with the early work of Goldsmith 1969 and Shaw 1973 in the 1970s). Since then, there have been various studies investigating the relationship between financial development and economic growth. A large body of research concentrating on this relationship has shown that a well-functioning and market-oriented financial sector contributes to improved economic outcomes (King and Levine 1993; Levine and Zervos 1996; Beck and Levine 2004; Seven and Yetkiner 2016). Therefore, the creation of a single market for financial services has been a key aspect of European integration. The link between financial stock market and economic growth becomes the field of research that is more and more explored. Financial stock market influences economic growth. Levine found a positive relation between financial stock market and economic growth by issuing new financial resources to the firms. Filer et al. (1999) examined stock market-growth nexus and exhibited a positive casual correlation between stock market development and economic activity. Spears (1991) reported that at the early stages of development, financial intermediation induced economic growth. Luintel and Khan (1999) explored the bi-directional causality test between financial development and economic growth. They found that the financial stock market affected economic growth. Their research on the relationship between the stock market development and economic growth provided empirical evidence on

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the major theoretical debates about the linkages between stock markets and longrun economic growth using data on 41 countries from 1976 to 1993. Their result showed that stock market liquidity was positively and significantly correlated with current and future rates of economic growth, capital accumulation, and productivity improvements, even after controlling economic, political and other factors. According to Siegel (2002), in developed markets, economic growth and stock market returns are negatively correlated in the long run, and Ritter (2012) argues that the correlation is negative in both developed and emerging markets. In contrast, Estrada (2012) has not found any significant relationship between economic growth and stock returns and between the fundamental condition of a company and the rate of return on its stocks. The most significant results are obtained by Dike (2016) in the study of integration processes in the stock markets of the African countries, where he proves using regression analysis that there is a positive and significant relationship between stock market development and economic growth, both in the short run and in the long run. The results provide a strong and significant relationship between stock market development and economic growth. Numerous studies have been analyzing the role of stock markets in economic growth; most of them have focused on individual stock market, and some—on merged market. Thus, the methodological approaches in the economic literature allow considering the influence of the factors on the stock market development as assessment parameters of the potential of the single stock market formed by Russia and Kazakhstan.

3 Formation of the Single Stock Market: Conditions and Difficulties At the present day, Russia and Kazakhstan attach particular importance to the issue of the future single stock market. Firstly, stock markets operate on the basis of electronic trading systems, which in the near future would be integral parts of the global financial system. Secondly, stock market integration implies business expansion of stock exchange market, increases volume of the market and income basis stock exchanges. Thirdly, index values of stock exchange activity are indicators of the national economy’s state. Fourthly, the situation on the stock market reflects an economic environment and sends a message to investors for allocating their capital. Russia and Kazakhstan, as parties of the Eurasian Economic Union (EEU), set a target to integrate their key economic segments, including national stock markets, to provide financial market development of these countries and formation of the single exchange area. It is a system of international treaties that establish the rules and procedures of access to the exchange EEU. State operators of exchange trading of all types of financial instruments, including securities and cash, also formed a unified system of regulation and supervision of the participants of exchange trade EEU.

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There are economic factors of the financial integration which provide favorable conditions for capital flows between Russia and Kazakhstan: common information space, lack of language and cultural barriers, and others. Despite the proximity and socioeconomic similarity of partner countries, imbalances in the development level of their financial systems and legal restrictions cause a number of complications for the formation of the integrated stock market. Proof of that is accumulated experience of the Russian and Kazakhstan stock market integration, in which there is an insignificant investment cooperation of the financial markets of Russia and Kazakhstan. The primary cause of this situation is the existence of inhibiting factors, which impede effective cooperation of these countries’ financial markets: – asymmetry in the development level of the stock markets; – current inconsistencies in legislative and methodological support, development level of the market infrastructure and organizational activity of professional participants from these countries; – lack of effective cooperation and information exchange among professional participants of stock markets of the partner countries. Such problems are connected with differences in the level of the Russian and Kazakhstan stock market development. According to the data in Table 1, the Russian key financial indicators are significantly higher than similar indicators in Kazakhstan. So, the total volume of the Kazakhstan stock market accounts for 3.17% of the Russian market. However, it is Kazakhstan that is the closest to the Russian stock market by the development level among other countries of the Eurasian area. Table 1 Trading volumes on the stock markets, $ mln, 2017 Market segment

Russia (MICEX–RTS)

Kazakhstan (KASE)

Government securities

150,686

6092

Corporate debt security

298,941

3539

Equity market

157,464

830

Forward market

1,448,509

68

Market segment

Russia (MICEX–RTS)

Kazakhstan (KASE)

Exchange market (excluding REPO deals)

5,960,046

227,607

REPO deals

6,648,931

226,380

Stock market

14,664,578

464,515

Source Access mode: https://www.eurasiancommission.org/ru/act/integr_i_makroec/dep_stat/ finstat/Documents/Express-information/Express_Stock_2017_1q.pdf

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4 Methodology and Data Analysis The analysis of statistical data shows that there are significant differences in the aspect of the formation of the single stock market by Russia and Kazakhstan. To find ways to overcome these differences, we conduct the econometric analysis of key factors which influence the stock market capitalization in these countries. It should be noted that the process of stock market integration and a single stock market formation includes not only legal harmonization of the integrated countries, but convergence in market sizes, the number of participants on the finance market, etc. In our opinion, these processes should be considered using such economic term as “potential”—the opportunity for Russia and Kazakhstan to create a single stock market under the valuation of stock market capitalization. The statistical data for the calculations (from 2010 to 2017) are annually published by the official statistical agencies of these countries. We check the hypothesis, under which such factors as inflation rate, nominal GDP, refinancing rate and US dollar/national currency rate equally influence the stock market development level. Taking into account the identified factors, we create a regression model which reflects their correlation with stock market capitalization in Russia and Kazakhstan. The multiple linear regression model for Russia and Kazakhstan: Y = a0 + a1 x 1 + a2 x 2 + a3 x 3 + a4 x 4 + an x n

(1)

where Y —stock market capitalization; a1 , a2 , a3 , a4 , an —regression coefficients; x 1 —inflation rate; x 2 —average weighted refinancing rate; x 3 —US dollar/national currency rate; x 4 —nominal GDP. The statistical data for regression analysis are shown in Table 3. These statistical data show a significant difference in stock market capitalization level of Russia and Kazakhstan. However, other correlations and indicators suggest that the Kazakhstan stock market is the closest to the Russian among other countries of the Eurasian area. The results of parameter estimates are summarized in Table 3. In Russia, the multiple regression for the model is r = 0.9552. This indicator is close to 1. So we can talk about the strong relationship between variables. Estimation of statistical significance of regression’s parameters is carried out using t-statistic and calculation of the confidential interval of each of these indicators. T table = 2.447 is for numbers of degrees of freedom df = n − 2 = 8 − 2 = 6 and α = 0.5. The obtained actual values of t-statistics are t 1 = −2.3715; t 2 = 0.4141; t 3 = 3.9284; and t 4 = −2.5892. If T table > t 1 . T table > t 2 . T table > t 4 , these parameters are statistically insignificant. If T table < t 3 , there are statistically significant differences between stock market capitalization and the US dollar/national currency rate. R Square is 0.9124. It means that the model is very good for explaining the connection of the factors and it should not be improved by the introduction of new independent variables.

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Table 2 Main financial indicators of the Russian and Kazakhstan economies Year

Stock market capitalization, billion USD (Y )

Inflation rate, % (X 1 )

Average weighted refinancing rate, % (X 2 )

US dollar/national currency rate (X 3 )

Nominal GDP, billion USD (X 4 )

Russia 2009

761.74

8.8

9.97

0.0315

1222.64

2010

951.30

8.8

7.91

0.0329

1524.92

2011

783.55

6.1

7.96

0.0340

2051.66

2012

825.34

6.6

8.25

0.0324

2210.26

2013

770.66

6.5

8.25

0.0314

2297.13

2014

385.93

11.4

8.25

0.0261

2063.66

2015

393.24

12.9

8.25

0.0164

1368.40

2016

622.05

5.4

10.26

0.0149

1284.73

2017

623.42

2.5

8.87

0.0171

1577.52

124.89

100.00

79.34

104.44

124.72

Growth rate, % 2010 2011

82.37

69.32

100.63

103.34

134.54

2012

105.33

108.20

103.64

95.29

107.73

2013

93.37

98.48

100.00

96.91

103.93

2014

50.08

175.38

100.00

83.12

89.84

2015

101.89

113.16

100.00

62.84

66.31

2016

159.19

41.86

124.36

90.85

93.89

2017

100.22

46.30

86.45

114.77

122.79

2009

27.93

6.2

8.35

0.0068

115.31

2010

26.67

7.8

7.00

0.0068

148.05

2011

22.54

7.4

7.41

0.0068

192.63

2012

23.54

6.0

6.06

0.0067

208.00

2013

26.23

4.8

5.50

0.0066

236.63

2014

22.97

7.4

5.50

0.0056

221.42

2015

34.89

13.6

5.50

0.0045

184.39

2016

40.16

8.5

5.50

0.0029

137.28

2017

45.56

7.1

8.27

0.0031

159.41

2010

95.50

125.81

83.83

100.00

128.39

2011

84.50

94.87

105.86

100.00

130.11

2012

104.46

81.08

81.78

98.53

107.98

2013

111.41

80.00

90.76

98.51

Kazakhstan

Growth rate, %

113.77 (continued)

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Table 2 (continued) Year

Stock market capitalization, billion USD (Y )

Inflation rate, % (X 1 )

Average weighted refinancing rate, % (X 2 )

US dollar/national currency rate (X 3 )

Nominal GDP, billion USD (X 4 )

2014

87.59

154.17

100.00

84.85

93.57

2015

151.88

183.78

100.00

80.36

83.28

2016

115.10

62.50

100.00

64.44

74.45

2017

113.44

83.53

150.36

106.90

116.12

Source Access mode: World Bank. World development indicators. Washington, DC: World Bank, various issues (website https://data.worldbank.org/), International Monetary Fund. International Financial Statistics. Washington, DC: International Monetary Fund, various issues (website https:// www.imf.org/), Eurasian Economic Commission. Financial Policy Department. Russia, Moscow (Office of the Eurasian Economic Commission): various issues

Table 3 Obtained indicators of the regression model Russia

Kazakhstan

Multiple R

0.9552

0.9540

R square

0.9124

0.9101

Adjusted R square

0.7957

0.7902

F

7.8154

7.5908

Significance F

0.0614

0.0638

t-stat Stock market capitalization Y

P-value

t-stat

P-value

0.4709

0.6698

2.3141

0.1036

−2.3715

0.0984

0.0035

0.9974

Refinancing rate X 2

0.4141

0.7067

0.9373

0.4177

US dollar/national currency rate X 3

3.9284

0.0294

−3.9710

0.0285

−2.5892

0.0811

−0.3037

0.7812

Inflation rate X 1

Nominal GDP X 4

Source Calculated by the authors in Microsoft Excel

The analysis of indicators of the linear multiple regression model (Kazakhstan) is the following. Multiple regression is (r) = 0.9540. This indicator is close to 1. So we can deal with a rather strong relationship between variables. T table = 2.447 is for numbers of degrees of freedom df = n − 2 = 8 − 2 = 6 and α = 0.5. t1 = 0.0035; t2 = 0.9373; t3 = −3.9710; t4 = −0.3037. T table > t 1 . T table > t 2 . T table > t 3 . T table > t 4 , so these parameters are statistically insignificant. R Square is 0.9101. It indicates that the model explains all the variability of the response data around its mean value.

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Table 4 Correlation coefficients for both countries Stock market capitalization

Inflation rate

Refinancing rate

US dollar/national currency rate

Nominal GDP

−0.4820

−0.2318

0.6803

0.2539

0.3336

0.2714

−0.9265

−0.6493

Russia 1 Kazakhstan 1

Source Calculated by the authors in Microsoft Excel

For defining the relationship between factors, it is necessary to analyze the correlation between stock market capitalization and the following macroeconomic factors (Table 4). Due to the analysis of calculated coefficients of correlation, we can make a conclusion that in Russia and Kazakhstan, the US dollar/national currency rate has the strongest correlation with stock market capitalization. It can be explained as follows. Russia and Kazakhstan had been pursuing the monetary policy of currency appreciation and fixing its exchange rate. At the same time, most of the export earnings went to the reserves of the Central Bank and the inflation rate growth was observed. It is obvious that with this increase in the US dollar exchange rate, the economic growth rate decreases significantly, and business growth on the Russian and Kazakhstan stock exchanges slows down accordingly. But from 2010 to 2017, in Russia there was another situation. The US dollar exchange rate had been falling, but the stock market capitalization had been increasing. The possible causes of this situation can be the following: high volatility of the Russian stock market, growth of exchange rate of USD/RUB, increase in the number of investors who exercised a large number of warrants, which could also increase the amount of shares on the market and could lead to the growth of the stock market capitalization of the companies, etc. Consequently, in Russia and Kazakhstan there is an insignificant influence of considered indicators on the stock market capitalization. As a result, these factors do not significantly influence the potential of formation of the single stock market by Russia and Kazakhstan. Thus, it can be argued that the opportunities of the formation of the single stock market of considered countries can be realized in the direction of asymmetry overhauling of the development level of their financial system and elimination of law restrictions of the stock market development.

5 Conclusion Based on the conducted analysis, we can conclude that the potential of the formation of the single stock market by Russia and Kazakhstan can be estimated as positive despite the fact that there is an asymmetry in the development level of the Russian and

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Kazakhstan stock markets. The proof of that is the check of the hypothesis according to which such factors as the inflation rate, refinancing rate and US dollar/national currency rate influence equally the level of stock market capitalization. The conclusion that can be made from the above-mentioned calculations is that these factors influence weakly the stock market capitalization. Financial market prospects for integration can be improved without including these factors, besides the US dollar/national currency rate. Consequently, the potential of the creation of a single stock market by Russia and Kazakhstan should take into consideration such factors which are aimed at overcoming the development asymmetry of the national stock markets.

References Ake, B. (2010). The role of stock market development in economic growth: Evidence from some Euronext countries. International Journal of Financial Research, 1(1). Beck, T., & Levine, T. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking and Finance, 28(3), 423–442. Bekaert, G., & Harvey, C. R. (1995). Time-varying world market integration. Journal of Finance, 50, 403–444. Carrieri, F., Errunza, V., & Hogan, K. (2007). Characterizing world market integration through time. Journal of Financial and Quantitative Analysis, 42, 915–940. Chari, A., & Henry, P. B. (2004). Risk sharing and asset prices: Evidence from a natural experiment. Journal of Finance, 59, 1295–1324. Christopher, A. (2013). A Eurasian (or a Soviet) Union? Consequences of further economic integration in the commonwealth of independent states. Hartwell Business Horizons, 56, 411–420. Dell’Ariccia, G., Detragiache, E., & Raghuram, G. R. (2005). The real effect of banking crises. Journal of Financial Intermediation, 17, 89–112. Dike, C. (2016). Stock market efficiency promotes economic development: Empirical evidence from Africa. International Journal of Economics and Financial Issues, 6(3), 1287–1298. Eichengreen, B., & Arteta, C. (2000). Banking crises in emerging markets: Presumptions and evidence. Working paper c100-115. Center for International and Development Economic Research, University of California Berkeley. Estrada, J. (2012). Blinded by growth. Journal of Applied Corporate Finance, 24(3). Eurasian Economic Commission. Russia, Moscow: Financial Policy Department (Office of the Eurasian Economic Commission) various issues. Filer, R. K., Hanousek, J., & Campos, N. F. (1999). Do stock markets promote economic growth? Working paper no. 267. Goldsmith, R. W. (1969). Financial structure and development. New Haven, CT: Yale University Press. International Monetary Fund. International financial statistics. Washington, DC: International Monetary Fund, various issues (website https://www.imf.org/). Kaminsky, G., & Reinhart, C. (1999). The twin crises: The causes of banking and balance of payments problems. American Economic Review, 89, 473–500. Kılınç, D., Seven, Ü., & Yetkiner, H. (2017). Financial development convergence: New evidence for the EU. Central Bank Review, 17, 47–54. King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. The Quarterly Journal of Economics, 108(3), 717–737. Levine, R., Loayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46, 31–77.

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Levine, R., & Zervos, S. (1996). Stock market development and long-run growth. World Bank Economic Review, 10, 323–339. Levine, R., & Zervos, A. (1998). Stock markets, banks and economic growth. American Economic Review, 88(3), 537–558. Libman, A., & Vinokurov, E. (2010). Regional integration and economic convergence in the postSoviet space: Experience of the decade of growth (MPRA Paper No. 21594). Retrieved November 9, 2012, from https://mpra.ub.uni-muenchen.de/21594/. Luintel, K., & Khan, M. (1999). A quantitative reassessment of the finance-growth nexus: Evidence from a multivariate VAR. Journal of Development Economics, 60, 381–405. Masoud, N. (2013). The impact of stock market performance upon economic growth. International Journal of Economics and Financial Issues, 3(4), 788–798. McKinnon, R. I. (1973). Money and capital in economic development. Washington, D.C.: Brooking Institutions Press. McKinnon, R. I. (1993). The order of economic liberalization: Financial control in the transition to market economy. Baltimore: Johns Hopkins University Press. Mohtadi, H., & Agarwal, S. (2004). Stock market development and economic growth: Evidence from developing countries. Access mode: https://faculty.apec.umn.edu/mohta001/PA1-4-01.pdf. Office of the Eurasian Economic Commission. (2017). Financial statistics. The results of trading on stock and commodity exchanges. Access mode: https://www.eurasiancommission.org/ru/act/ integr_i_makroec/dep_stat/finstat/Documents/Express-information/Express_Stock_2017_1q. pdf. Phylaktis, K., & Ravazzolo, F. (2002). Measuring financial and economic integration with equity prices in emerging markets. Journal of International Money and Finance, 21, 879–903. Ranciere, R., Tornell, A., & Westermann, F. (2006). Decomposing the effects of financial liberalization: Crises versus growth. Journal of Banking and Finance, 30, 3331–3348. Rejeb, A. B., & Boughrara, A. (2015). Financial integration in emerging market economies: Effects on volatility transmission and contagion. Borsa Istanbul Review, 15(3), 161–179. Ritter, J. (2012). Is economic growth good for investors? Journal of Applied Corporate Finance, 24(3). Seven, U., & Yetkiner, H. (2016). Financial intermediation and economic growth: Does income matter? Economic Systems, 40(1), 39–58. Shaw, E. S. (1973). Financial deepening in economic development. Oxford: Oxford University Press. Siegel, J. (2002). Stocks for the long run (4th ed.). New York: McGraw-Hill. Sindhu, M. I., Bukhari, S. M. H., & Hussain, A. (2014). Macroeconomic factors do influencing stock price: A case study on Karachi stock exchange. Journal of Economics and Sustainable Development, 5(7). Spears, A. (1991). Financial development and economic growth-causality tests. Atlantic Economic Journal, 19, 66–74. World Bank. World development indicators. Washington, DC: World Bank, various issues (website https://data.worldbank.org/).

Financial Engineering Tools for Stress Testing in Banks Vadim L. Babur, Adel A. Daryakin, Nailya F. Yalalova, Aliya A. Ahmadullina and F. A. Feras

Abstract Under the conditions of economic instability, stress tests are being actively introduced to assess banking risks. Instability in local markets primarily creates problems for small- and medium-sized banks. It results in the outflow of funds from deposits and changes in the temporary structure of assets and liabilities. This work presents the main methodological approaches applied to stress testing of market risk and assessing the liquidity of banks. The authors also conclude that simple models based on the time value of money, in particular, the GAP, DGAP analysis models, could be successfully applied for identifying critical problems with liquidity, market risk, and taking urgent measures to eliminate them. As an information basis for calculations, the data of one of the regional banks are used. As a result of simulation of stress scenarios based on GAP and DGAP analysis, some risks of the bank were identified, and the likely response to this scenario was predicted. The analysis concluded that, despite the strong correlation between liquidity indicators and DGAP, they are not interchangeable. They complement each other and therefore, in the models of stress testing, one indicator cannot be replaced by another. Besides, a weak correlation between overdue loans and DGAP was revealed. Apparently, this can be explained by the fact that within a one-time interval these indicators are relatively independent. We believe that they should be analyzed in dynamics. The results of the study suggest that that the models despite their simplicity remain adequate and relevant. The undoubted advantage of such models is the ability to apply them independently or as a separate element within more complex, integrated stress testing models.

1 Introduction The development of the financial market causes the need to minimize the associated risks with the help of various tools. One such tool actively used by the banking system is stress testing. V. L. Babur (B) · A. A. Daryakin · N. F. Yalalova · A. A. Ahmadullina · F. A. Feras Kazan Federal University, Kazan, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_13

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When developing models of stress testing, it should be borne in mind that under conditions of high uncertainty and volatility, changes in macroeconomic factors, complex econometric models based on the use of time series do not always prove to be adequate. Meanwhile, relatively simple models based on the principles of time value of money, in particular the GAP model, DGAP analysis, are quite suitable for determining the key problems of the bank and taking urgent measures to eliminate them. When forming a model based on the current macroeconomic situation, it is necessary to take into account the relevance of the initial data, and the time horizon, over which the model is considered actual. If possible, when building a stress model, a combination of risk assessment methods should be used and the results obtained should be finally processed using the statistical analysis tool. In the principles for sound stress testing practices and supervision, the Basel Committee on Banking Supervision (BCBI) prescribes supervisory bodies to supervise the internal assessment of the bank’s capital and liquidity risk management, analyze the procedures for stress testing of the bank, and require management to take into account the results of stress tests in the process of making managerial decisions. Banks are obliged to carry out systematic work to improve stress methods, which involves a wider use of various methods of financial engineering. Stress testing is an assessment of the potential impact on the bank’s financial position of a set of changes in risk factors that correspond to exceptional but probable events. Any stress test should give an estimate of the scale of potential losses in a hypothetical change in stress factors. When developing the methodology and directly organizing stress testing, it is necessary to solve a number of issues: (1) to select specific types of risk and models to be used for stress tests; (2) to identify the advantages, disadvantages, limitations of the application of different models, the range of methods, and tools of financial engineering required for the functioning of the model. (3) to determine the form for stress tests results presentation and how to use them in the process of making managerial decisions. The results of stress testing can also be used within the strategic management system of the bank when planning the need for sources of capital and liquidity. The issues of improving the methodology of stress testing were reflected in the documents of the Basel Committee, the regulations of the national regulators of the financial market and, in particular, the Bank of Russia, as well as in the works of Russian and foreign authors. In this study, the following tasks were set: – to analyze the efficiency of the use of fairly simple in terms of financial engineering models of GAP, DGAP analysis when carrying out the stress testing of a small regional bank;

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– to confirm the capabilities of this simulation model to assess the impact of various events on the bank’s liquidity and capital, the bank’s behavior and reaction to specified “shock” scenarios; – to analyze the possibilities of this model for the purposes of forecasting the bank’s activities for the short and medium term.

2 Methods As the object of the study, the work considered a small regional bank, its performance indicators, the time structure of its assets and liabilities sensitive to interest rate changes. For the purposes of constructing the model, the methods recommended by the Basel Committee and described in the Letter of the Bank of Russia, No. 15-1-36/3995, “On International Approaches (Standards) of the Organization of Interest Rate Risk Management” were used (Klaas and Daryakin 2016). These include, in particular, methods based on the time value of money and, in particular, the GAP analysis method and the duration (DGAP) method. GAP is the difference between the sum of long and short positions in financial instruments sensitive to changes in interest rates determined for each time interval. It is this approach that underlies the model developed by the regulator to assess market risk, which is subsequently included in the calculation of the capital adequacy ratio: NII = (GAP) · (I )

(1)

where NII—expected changes in net interest income; I—expected changes in the level of interest rates. The change in net interest income depends on changes in rates and the GAP between assets and liabilities. With the growth of rates, a positive GAP will lead to the expected increase in net income in the form of interest. If the rates go down, the negative GAP will also lead to an increase in the expected net income in the form of interest. When calculating interest rate risk, the calculations are based on the known discount formula: PV =

T  t=1

CFt (1 + Rt )t/360

(2)

where CFt —set cash flow per day t; t—day on which the cash flow is calculated; Rt —discount rate for incoming (outgoing) payments for day t. For the purpose of developing stress scenarios on market risk, credit institutions prefer to use approaches based on the duration method. The duration is the weighted

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average period up to maturity of the instrument and is calculated using the present value of cash flows for the relevant asset or liability. Duration D is determined by the formula:  t CFt 1 × × D= , PV t=1 360 (1 + R)t/360 T

(3)

where CFt —incoming (outgoing) cash flow for day t; t—day on which the cash flow is calculated (1 < t < T ); R—discount rate for incoming (outgoing) payments; PV—current value of the flow of incoming (outgoing) payments. The duration and current value of the payment flow are linked by a correlation: 1 dPV = −D × × PV, dR (1 + R)

(4)

where R—discount rate. From the last correlation within infinitesimal increments, it follows that: PV R ≈ −D × . PV 1+ R

(5)

Duration is the elasticity of the price of the instrument (the current value of the flow of payments) relative to the interest rate level and serves as a measure of the risk of a change in the price of the instrument when the interest rate changes. DGAP = DA − u × DP,

(6)

where DA—duration of assets, DP—duration of liabilities, u—ratio of total liabilities to total assets. To construct the model, a temporary structure of the bank’s assets and liabilities sensitive to interest rate changes was recreated, and a “bottom-up” approach was applied. In constructing the model, macroeconomic indicators characterizing the current situation in the Russian and regional financial markets were used: rates on the credit and deposit market, current performance indicators of the bank. The baselines are: – the current level of profitability on the bank’s active operations was taken equal to 14%, which corresponds to the level of the bank’s return on capital and generally reflects the level of average market rates; – the level of rates for passive operations, represented mainly by deposits of legal entities and individuals, is set at 7%. In the first scenario (Tables 1 and 2), the rates for active operations are expected to grow by 2% (200 bp). The scenario can be considered probable, given there is a possibility of additional sanctions against Russia. In particular, they may affect the circulation of Russian bonds on foreign markets. This will increase the cost of

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Table 1 Characteristics of the bank’s assets portfolio Duration (days)

Up to 90

91–180

181–1 year

1–3 years

More than 3 years

Total

Duration in calculations

0.1233

0.3726

0.7397

1.5

4.5

Original build factor

1.0163

1.0500

1.1018

1.2172

1.8033

x

The build-up ration for scenario 1

1.0185

1.0569

1.1160

1.2494

1.9501

x

PV of assets basic

1,903,150

492,543

2,843,781

2,303,404

2,631,275

10,174,153

Including bonds

50,036

109,800

340,893

304,434

229,267

1,034,430

PV of assets scenario 1

1,899,074

489,362

2,807,430

2,244,091

2,433,196

9,873,152

Including bonds scenario 1

49,929

109,091

336,535

296,595

212,008

1,004,158

Portfolio cost depreciation

−4076

−3181

−36,351

−59,313

−198,079

−301,001

Including bonds

−107

−709

−4358

−7839

−17,259

−30,272

PV of assets scenario 2

2,560,031

489,362

2,470,538

1,974,800

2,141,213

9,635,944

government borrowings in the domestic market. We note a low share of liquid bonds in the bank’s assets portfolio—2.6%. This instrument can be considered as a liquidity reserve. When the scenario is implemented, the bank’s assets will be depreciated by 2%, which is uncritical given that long-term loans make the largest contribution to the depreciation. The current duration of assets is calculated by the formula (2) DA = 1.7513 years. The implementation of the first scenario will slightly reduce the duration of assets to 1.7025 years. In terms of the liabilities portfolio, the scenario assumed a 2% cut (200 pp), down to 5%, which is also quite realistic, given the reduction in the Bank of Russia’s key rate to 7.25%. As a result of this scenario, the value of the liabilities portfolio will increase by 2.56% and will be 1.359 years. Thus, the potential losses of the bank as a result of changes in the rates are 305.262 million rubles or 14% of the bank’s capital. It should be noted that these losses do not affect the capital and are not reflected in the form of a loss in the bank’s accounts. This amount primarily describes the change in the estimated value of assets

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Table 2 Characteristics of the liabilities portfolio of the bank Duration (days)

Up to 90

91–180

181–1 year

1–3 years

More than 3 years

Total

Duration in calculations

0.1233

0.3726

0.7397

1.5

4.5

PV of liabilities basic

2,732,911

122,297

1,306,926

5,350,120

1,131,711

10,643,965

PV of liabilities scenario 1

2,734,794

123,160

1,325,295

5,503,706

1,232,000

10,918,955

PV of liabilities scenario 2

2,857,953

0

1,325,295

5,503,706

1,232,000

10,918,955

Increase in the value of liabilities 1

1883

863

18,369

153,586

100,289

274,990

Increase in the value of liabilities 2

125,042

−122,297

18,369

153,586

100,289

274,990

and liabilities of the bank. It will be reflected in the statements and capital only in case of early sale of assets and assignment of obligations. The second, more severe scenario of the development of events follows logically from the first scenario. As possible events of the second scenario, we believe that the rates will remain at the level of the first scenario, but the financial condition of 5% of borrowers will worsen, and they will not be able to pay off on time in loans. In turn, this will lead to a deterioration in the quality of loans and increase the amount of reserve funds for possible losses. Reducing deposit rates and maximizing the level of inflation make deposits less attractive. Therefore, the bank may face an outflow of deposits. This will shift the time structure of the deposits. Investments with a short term may be more attractive. To take into account this factor, we assume that depositors will not close long deposits for a period of six months or more, since they are subject to an increased rate. The bank can solve the problem of imbalance of liquidity on short terms by selling collateral for problem loans. According to the bank, all loans for 79% are secured by a pledge that the bank can realize with a discount of 80%. The proceeds will increase the volume of liquid funds, as indicated by the figures in Table 3. Sale of assets at a discount will bring the bank direct losses in the amount of 60.800 rubles, which will be reflected in the statements and directly affect the bank’s capital. In all cases, the DGAP indicator is positive and varies slightly. This suggests that in the event of a further increase in the interest rate, the bank will incur additional losses from the depreciation of the current value of the portfolio of assets, and in the event of a decrease—its additional revenues.

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Table 3 Performance indicators of the model under different stress scenarios Index

Up to 90 days

91–180 days

181–1 year

1–3 years

More than 3 years

GAP base (thousand rubles)

−829,761

370,246

1,536,855

−3,046,716

1,499,564

GAP scenario 1 (thousand rubles)

−835,720

366,202

1,482,134

−3,259,615

1,201,196

GAP scenario 2 (thousand rubles)

−958,880

489,362

1,145,243

−3,528,906

909,213

GAP scenario 2 reaction (thousand rubles)

−297,922

489,362

1,145,243

−3,528,906

909,213

Assets/liabilities basic

0.6964

4.0274

2.1759

0.4305

2.3250

Assets/liabilities scenario 1

0.6944

3.9734

2.1183

0.4077

1.9750

Assets/liabilities scenario 2

0.8958

x

1.8641

0.3588

1.7380

DGAP basic (years)

0.3921

x

DGAP scenario 1 (years)

0.3138

DGAP scenario 2 reaction (years)

0.1628

Let us summarize the interim results of the analysis. When implementing the stress scenarios presented, the bank’s losses will be uncritical, and the liquidity problems that have arisen can be resolved. The model, despite its simplicity, allows one to identify the key problems of the bank in the field of liquidity, market risk, and it can be considered adequate. The advantage of such models is the possibility of their expansion. Working with the model allows you to accurately predict liquidity and plan activities to manage it. For example, given the availability of data on the portfolio structure over shorter time intervals, the data in Table 3 will make it possible to draw more substantiated conclusions about the risks of insufficient and excess liquidity. To improve the quality of assessing the risk of unbalanced liquidity, you can additionally use the information on the turnover of repayment of loans and the return of deposits. Accordingly, the model can be expanded, considering the change in turnover rate, as another risk factor can be varied within stress scenarios.

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The effectiveness of the model increases with the availability of detailed information on the status and changes in the loan portfolio of the bank (Klaas and Daryakin 2016). This makes it possible to more accurately predict changes in the cash flows that each specific loan generates. It should be borne in mind that the portfolio of assets and liabilities react differently to changes in rates and determine the underlying risk. This approach is used in assessing interest rate risk using the “standardized gap” method. The standardized gap is calculated taking into account the terms and rates for groups of assets and liabilities allocated over the relevant time periods and weighted by the corresponding sensitivity to rate changes. Accordingly, for the total portfolio of assets and the total portfolio of liabilities, the equation of the correlation between the change in their value and the rate change has a different form. For example, the correlation can look like this: Y (loans) = 6.1 + 0.72x Y (deposits) = 3.08 + 0.37x, where x—interest rate. In the example presented, the sensitivity of the loan portfolio rate is 0.72 and, if the balance position is 100, the loan portfolio weighed by the sensitivity will be 72. With interest rate sensitive assets of 100 and their sensitivity of 0.72 and liabilities of interest rate sensitive in the amount of 120 and the corresponding sensitivity of 0.37, the simple gap is 100 − 120 = −20 and the standardized one is 0.72 · 100 − 0.37 · 120 = 27.6. Variations in the rate i affect the change in the return on assets: 0.72i and the change in the liabilities payable are 0.37i, respectively. The final change in the interest margin will be determined by the expression: M = (standardized gap) · i.

(7)

Accordingly, the time structure of interest rates for portfolios of assets and liabilities allocated by maturity can vary from bank to bank, and therefore, the models presented can be calculated for each bank, taking into account the structure of rates. The initial data are the data in Tables 4 and 5. As a result, the following correlations are obtained: Y (assets) = 102,081 + 8,948,848x; Y (liabilities) = −1E + 0.6x 2 + 1E + 0.7x 2 − 3E + 0.7x + 2E + 0.7. From these equations, you can directly calculate the change in the value of subportfolios of assets and liabilities as a result of a change in rates for the analyzed bank. An unconditional advantage of the model is its dynamism, which allows one to predict the most likely response of the bank’s management to the change in stress parameters. At each iteration, external events and the “response” of the bank are modeled.

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Table 4 Distribution of assets and liabilities by maturity and rates Duration (days)

Up to 90

91–180

181–1 year

1–3 years

More than 3 years

Total

Term in calculation

0.1233

0.3726

0.7397

1.5

4.5

PV assets basic

1,903,150

492,543

2,843,781

2,303,404

2,631,275

10,174,153

Return on assets

17.30%

18.42%

20.63%

20.40%

21.50%

x

Interest income

40,592

33,805

433,977

704,842

2,545,758

3,758,973

PV obligations basic

2,732,911

122,297

1,306,926

5,350,120

1,131,711

10,643,965

Interest rates on liabilities

6.20%

6.40%

6.42%

8.40%

8.74%

x

Interest expenses

20,890

2916

62,066

674,115

445,102

1,205,090

Table 5 The level of correlation between the stress test indicators DGAP

H2

H2

Overdue loans

DGAP

Overdue loans

X

Y

Y

Z

X

Z

0.4424

71.43

71.4300

62,082

0.4424

62,082

0.3921

69.7

69.7

61,932

0.3921

61,932

0.3138

48.6

48.6

446,944

0.3138

446,944

0.1628

69.8

69.8

5250

0.1628

5250

−0.5412

84.0

85.2

4000

−0.5412

4000

0.2111

62.0

62.0

4000

0.2000

4000

rxy =

−0.6432

ryz =

−0.7818

rxz =

0.3065

The sensitivity analysis allows assessing the potential exposure of banks to a particular type of risk, but at the same time provides a static assessment (stress acts simultaneously, that is, all risks are realized simultaneously with the bank’s balance sheet at the reporting date, the factors compensating losses such as profit, liquid support of the Bank of Russia, are not taken into account). Forecasting the response of the system, it is possible to predict further changes in cash flows and introduce new stress scenarios that actually restart the original model with new incoming parameters (Daryakin and Ahmadullina 2017). The accuracy of the forecast can be increased by introducing into the model an additional module characterizing the impact of changes in each stress parameter, as

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well as the response of the system to a change in the capital adequacy ratio of the credit institution. When the model is expanded, the analytical value of the applied performance indicators should be taken into account, as well as the relation between these factors. As a result of the analysis of the correlation of the dynamics of three forecast indicators, DGAP, liquidity ratio H2, and the level of overdue loans, a number of noteworthy results were revealed. A significant negative relation between the DGAP indicators and the H2 liquidity indicator was revealed. This is quite expected, but initially, it was assumed that they are in a closer relationship. Another assumed and quite expected result was the fact that the growth in the volume of overdue loan debts significantly worsens the bank’s liquidity indicators-the correlation coefficient is − 0.7818. At the same time, the relation between the DGAP indicators and the level of overdue loan debt was insignificant. And this despite the fact that DGAP and liquidity were quite strongly related to each other, we believe that this result needs additional verification using a larger sample size, based on historical data, with the construction of longer time series based on the analyzed indicators. In the meantime, we can say that despite the methodological similarity of the liquidity indicators and DGAP, they are complementary indicators, and therefore, one indicator cannot be replaced by another one in the models of stress testing. If we talk about the weak connection between overdue loan debt and DGAP, this result needs additional thinking. Apparently, this can be explained by the fact that within one-time interval these indicators are relatively independent. But at the next iteration, to correct the situation, management decisions are aimed to change these indicators. We believe that these indicators should be analyzed not within the same time cut, but taking into account their dynamics. The methodological error of these models is the difficulty in determining the objective probability of stress factors signs. To minimize this, it is necessary to avoid using stress scenarios at the stage of scenario creation, which is unlikely to occur, as this discredits the very idea of stress testing.

3 Result Models based on the principles of the time value of money can be used successfully in the process of stress testing of banks. The obtained results show that the use of the GAP, DGAP analysis models gives quite good results when assessing certain types of risk. At the same time, the accuracy of the forecast significantly increases if we use these models with other models that allow us to assess adjacent risks. In this case, the bank’s cash flows are more accurately predicted and the correction of the indicators of the basic model is in progress. The unconditional advantage of the model is its simplicity and the possibility of using even with insufficient information. It should be noted that it is easy to integrate with other models of stress testing.

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Stress tests require a high detalization and standardization of methodological approaches to the organization of stress testing. The disadvantage of the model is a certain subjectivity in determining the scenarios of stress tests and the need for frequent adjustments to the input parameters of the model in the case of the high volatility of macroeconomic indicators. The results obtained in the analysis of the model do not give an unambiguous idea of the impact of changes in stress parameters on financial results and capital. This is also a drawback of the model.

4 Conclusion Models using basic financial engineering tools have not lost their relevance to date. The use of time-tested models of financial engineering in most cases gives a good result. The use of simple standardized approaches could be considered as a step in the direction of using more complex advanced models. The potential of standard models is not exhausted, and they can be expanded or built-in as individual elements in advanced models.

References Daryakin, A. A., & Ahmadullina, A. A. (2017). Economic and mathematical modeling and forecasting of key performance indicators of Pjsc “Sberbank”. Astra Salvensis, 5(10), 387–398. Klaas, J. A., & Daryakin, A. A. (2016). The indicative model of financial stability management of the banking sector. Academy of Strategic Management Journal, 15(2), 43–49.

The Public–Private Partnership as a Part of High Technologies of Modern Russia Yury A. Doroshenko, Lyudmila A. Minayeva, Irina O. Malykhina, Zhanna N. Avilova and Zivota Radosavljevic

Abstract The meaning of a public–private partnership, its relevance for the development of the Russian economy, and the specifics of the Russian public–private partnership are examined. The characteristic of the modern market of high-tech industries is given; the main reasons of failures in the implementation of the research and production potential are distinguished. The specific features of the concession, life cycle contract, as well as their application in high-tech industries are considered. The following public–private partnership tools were highlighted: crowd funding and crowd investing. Particular attention is paid to the reasons for the slow development of progressive instruments of public–private partnership. The proposals for the formation of a favorable investment climate for the development of public–private partnership are given.

1 Introduction Public–private partnership takes one of the first places in the implementation of social policy at the present stage of development of the Russian economy. The implementation of social projects which is under public–private partnership is delayed due Y. A. Doroshenko (B) · L. A. Minayeva · I. O. Malykhina · Z. N. Avilova Belgorod State Technological University named after V.G. Shukhov, Belgorod, Russia e-mail: [email protected]; [email protected] L. A. Minayeva e-mail: [email protected] I. O. Malykhina e-mail: [email protected] Z. N. Avilova e-mail: [email protected] Z. Radosavljevic University “Union-Nicola Tesla”, Belgrade, Serbia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_14

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to the lack of a well-developed mechanism of interaction between the government and private owners. Lengthening the implementation period increases the cost of projects. Parties of the partnership, pursuing their own goals, have different motivations. We know that the main goal of the state is to solve the economic and a social problem of the region, country, but the goal of each business is to increase its own profits. At the same time, both parties of the partnership are interested in the successful implementation of the project. Nowadays, effective communication of business and government is considered as the most promising investment tool for attracting investment in many socially significant sectors and as a rational use of state property. Projects of government and business communities provide a long-term perspective of stable economic growth of the territory through increasing in employment, trade development, an increase in demand for personal services, etc. Features of the development of countries in the global economy are determined by the attention that they pay to high-tech industries using high technologies. There is an industrial development of the “sixth technological order, the core of which contains nanoelectronics, genetic engineering, multimedia interactive information systems, high-temperature superconductivity, space technology, fine chemistry” (Samsonov 2009). According to analysts, 1/5 of all enterprises operate on the basis of public–private partnership in the USA. In developing countries, in more than 50% of cases, the public–private partnership mechanism does not work. The main reason for failure is a high degree of risk to the business and lack of profitability in the short term. Often the state does not act as an equal partner when we set the parameters of public–private partnership projects. There are a number of problems in Russia related to the fact that “in many respects the behavior in relation to business depends on the specific interests of society and not on the needs of economic development” (Kolyaskin 2009). A state that pays attention to big business has nothing to do with the economy, and generator of innovative ideas and technologies. It is necessary to provide a careful analysis of the world and historical Russian experience, the legislative formulation of the policy of public–private partnership for the successful development of the domestic system of public–private partnership. Firstly, because in the country in the second half of the XIX century, separate elements of public–private partnership were used, and they showed their effectiveness. Secondly, the relevance of public–private partnership is increasing due to the need to actively develop the high-tech segment, which is characterized by a high degree of riskiness and the duration of the return on investment. Thirdly, there are potential opportunities for attracting foreign capital; with the help of public–private partnership, it is possible to implement large-scale, high-tech projects. Usually, public–private partnerships are mainly focused on infrastructure projects, which require, as a rule, large-scale concentration of investment resources for a long-term period. Recently, analysts and practitioners have taken steps to more

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actively interact with the state and entrepreneurship in high-tech industries: the creation of investment-oriented state-owned corporations, technology-innovative and industrial–industrial economic zones, etc. Meanwhile, the principles, rules and, mechanisms which are governing the development of the institution of public–private partnerships have not yet received full legislative and regulatory support. “Public–private partnership projects proposed for investment practically do not concern the high-tech sector of the economy and do not arouse interest from private capital. There is an acute shortage of innovative projects suitable for financing while deepening the technological gap between Russia and the industrialized countries” (Pyankova and Kosvintsev 2010). It shows the urgency of the problem of the development of public–private partnership in the high-tech segment. The level of study of this issue is insufficient not only in Russian but foreign editions as well (Caperchione et al. 2017; Cui et al. 2017; Pechlaner et al. 2009).

2 Methods The methodological substantiation of the feasibility of a public–private partnership shows that in conditions of lack of budget funds to support socially important sectors of the economy, the inability or rejection of increasing the tax burden of business entities to replenish the country’s budget, the implementation of projects on the principles of public–private partnership “show a higher return from the use of capital in the private sector and increased market competition while reducing budget expenditures” (Kolyaskin 2009). Some elements and evolutionary aspects of public–private entrepreneurship were studied by R. Smith, T. Sanford, S. Myers, X. Brown, E. Bredley, R. Tewels, etc. The connection between the country’s competitiveness and the development of public– private partnership is described by V. G. Varnavski, P. Vyrupaev, Yu. S. Emelyanov, A. V. Kievich, D. A. Koipash, K. V. Pyankova, and others. Important methodological aspects of cooperation between the state and private business and the mechanisms of public–private partnership for solving infrastructure projects were studied by some experts: D. M. Amunz, T. Atkinson, J. Bertram, C. Werner, J. Hamilton, J. Delmon, P. Fitzherald, and others. The problems of the development of public–private partnership in Russia are studied by I. E. Kolyaskin, S. Yu. Malnev, D. Yu. Samsonov, Z. R. Khasanov, A. A. Chernov, and T. I. Sherstobitova et al. Such researchers as Emelyanov (2012), Kolyaskin (2009), Mottayeva (2012), and Samsonov (2009) et al. defended dissertations devoted to problems of public–private partnership. Despite a wide range of research in the field of public–private partnerships, the problems of developing high-tech based on such partnerships are insufficiently explored. It is a real challenge of nowadays. The modern meaning of public–private partnership is “an institutional and organizational alliance between government and business in order to implement national and international, large-scale and local, but always socially significant projects in a

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wide range of activities. This happens from the development of strategically important industries and research design work (R&D) to the provision of public services” (Mottayeva 2012). That means there are two approaches are combined in this formulation. On the one hand, such partnership is a system of relations between the state and private entrepreneurship, “which is used as a tool for national, international, regional, urban, municipal economic and social development and planning” (Varnavsky 2009). On the other hand, these are specific projects which are implemented by government organizations and businesses at state and municipal property. The Ministry of Economic Development of the Russian Federation provides the following definition: “a public–private partnership is legally defined for a specific period and based on pooling of resources, risk sharing cooperation of a public partner, on the one hand, and a private partner, on the other hand, carried out on the basis of a public–private partnership agreement private partnership, in order to attract in the economy of private investment, ensuring the availability and improvement of the quality of goods, works, services, which consumers due to the powers of state and local authorities” (Ministry of the Economy of the Russian Federation 2018). According to V. G. Varnavsky, “even developed countries with their powerful institutional base of partnership relations, public–private partnership is often used for the realization of primarily private interest. Such negative manifestations and distortions of the essence of partnerships lead to the emergence of defamations in economic policy, a violation of the conditions of competition, an increase in distrust towards the very phenomenon of partnership between the state and the private sector.” (Varnavsky 2009). We may say that contradictory partnership is due to the fact that public–private partnership is an alternative to traditional methods of public procurement and is a way to ensure the provision of social services to the population, creating the necessary infrastructure facilities. Also, public–private partnership is not a quick solution to problems and it is not a panacea for all ills. So we may say that the state should intervene in the market, if there are segments that play an important role in the economy, as well as market failures, structural imbalances, overt or covert monopoly. Such components of the economy as the scientific and technical field, the role of the state in which has always been strong and grounded, are clearly traced.

3 Discussion The current regulatory legal framework of a public–private partnership is a complex of laws and bylaws, including Federal Law No. 115-FL from July 21, 2005 (as amended on December 28, 2013) “On Concession Agreements,” Federal Law of April 5, 2013 No. 44-FL (as amended on 04.06.2014) “On the contractual system in the field of procurement of goods, works, services to meet state and municipal needs,” Federal Law No. 116-FL of July 22, 2005 (as amended on July 23, 2013, as amended on June 23, 2014) “On Special Economic Zones in the Russian Federation” and others.

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The Ministry of Economic Development of the Russian Federation had recommendations on the preparation and implementation of public–private partnership projects in the regions of the Russian Federation. The essence of the partnership in the recommendations is stated as: “the interaction of the public partner, on the one hand, and the private partner, on the other hand, carried out on the basis of a public–private partnership agreement concluded as a result of competitive procedures, aimed at improving the quality and accessibility of services provided to the public and also on attraction of private investments in economy” (Report of the Ministry of the Economic Development of the RF). The modern market of high-tech industries is based on the development and implementation of macrotechnologies, which include elements that contribute to the creation of final products that increase the economic potential of the country and allow for political independence. Comparison of states on the possibility of implementing macrotechnologies has shown that “the maximum number (16) can be mastered by the USA, Australia, Germany, Israel, Canada, Japan, South Korea; from 10 to 12— Russia, China, India, Poland; from 6 to 9—Indonesia, Mexico, Turkey, South Africa, Brazil; from 1 to 5—African countries” (Muzhzhavleva 2014). Despite the fact that the Russian Federation occupies a middle position in this grade, the indicator characterizing the degree of the introduction of macrotechnologies is insignificant—0.89, whereas in the USA it is 5.03, Japan—3.08, and Germany, Canada, Taiwan—within 2. Russia has reserves to enhance the technological efficiency of the economy. Firstly of all, it is the education system, which is capable of preparing highly qualified personnel. Secondly, the integration of science and production through the creation of technology parks. Thirdly, the presence of the institution of registration of intellectual property. Fourthly, the proven in practice excess of the level of profitability of hightech industries over raw materials and resource processing. Failures in the implementation of research and production potential in recent years are due to the lack of significant investment projects and insufficient development of the system in financing innovation activities. Comparing countries with developed economies, in Russia “the system of public– private partnership in the implementation of innovative projects is not sufficiently developed—the share of organizations receiving funding from the budget for these purposes is 0.8% (in Germany—8.8%, in Belgium—12.7%). It is necessary to expand the legislative base, providing for the creation of partnership on the basis of not only a concession agreement, but also other contractual forms, for example, life cycle contracts, private financial initiatives, etc.” (Sedash and Setchenkova 2013). Since the time of Peter the Great, Russia has experience in implementing concession agreements and developments in the creation of domestic concession law. The Federal Law “On Concession Agreements” was approved; it is constantly being amended in accordance with the requirements of the time. However, most of the amendments focus on the process of regulating relations of concession law but not on the process of preparing the draft. We see “first of all, the mechanism of attracting private investments in creating public infrastructure facilities, ensuring effective management of state and municipal

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property, as well as improving the quality of socially important services provided to the population” in the concession (Pyatkov and Mikhalyuk 2015). That means specific feature of this form of public–private partnership is the keeping ownership of the object of the agreement. Concession for high-tech industries as a tool to attract investments can be used for many reasons: – partnership is long term, governed by federal law; – possibility of flexible structuring of concession fees for the possession and use of the object; – participation of the investor’s financial resources in the creation or reconstruction of the concession object; – private partner has only the rights to own and use the object of the concession, but not the right of ownership; – rights of the concessionaire are supported by state guarantees. According to some authors, “a life cycle contract is an ideal mechanism for public– private partnership in conditions of shortage, when the authorities cannot participate in projects involving budgetary investments” (Tkachuk and Timchuk 2012). The main point of the life-cycle contract is that the contractor of the project, using its own materials, builds (modernizes) the infrastructure facility, exploits it for the entire estimated period (life cycle), serves the facility, the state (or municipal unit)—grantor pays for the expense of the budget of the appropriate level of services for the provision of the object in use. A specific feature of this kind of partnership is that the state on a competitive basis concludes an agreement with private business for the design, construction, and operation of the facility for the life of the facility and makes payment for the project in equal shares after the facility is commissioned. Payment will be provided if the subject will be in condition that the private partner will maintain the necessary requirements (compliance of the object with quality standards). The decision on implementation of a project is usually taken in the form of a legal act on the realization of a public–private partnership project. The following projects can be singled out as the main examples of implementation of projects: construction of the Northern double of Kutuzovsky Prospekt in Moscow, creation of transfer hubs in the capital, purchase of railway cars under the terms of the life-cycle contract for the needs of the underground, as well as low-floor trams and buses, etc. In Russia, at the end of 2016, about 900 projects were implemented; the volume of investments exceeded 680 billion rubles. The main share of projects fell to the municipal level (86.7%) (Yuryeva 2016). The majority of the projects carried out on the basis of public–private partnership are implemented in the field of transport, power engineering, housing, and communal services. About 16% fall to the social sphere, the main part of it being concentrated in health care. Recently, in regional markets, projects appear in such segments as railway and public transport for general use, in the control system of traffic safety, etc. It should be pointed out that public–private partnership projects are unevenly distributed throughout the regions of the country. They are relatively actively realized

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in Moscow, St. Petersburg, Samara region, etc. In such regions as the Chukotka Autonomous Region, the Karachay-Cherkess Republic, the Republic of North Ossetia, there is a lack of such projects or their number is minimal. looseness-1Crowd funding (from English crowd funding: crowd—“crowd”, funding—“financing”) is a relatively new kind of public–private partnership for Russia. Crowd funding is actively developing in the USA and Western Europe. We noticed that the process of using this type of public–private partnership is slow in Russia. The main reasons are the peculiarities of the Russian mentality, the low level of wealth and the involvement of people on the Internet, as well as the low degree of confidence in financial projects. The idea of crowd funding is collective funding of projects in which “money to create a new product comes from users who receive in return any goods or services, including the final product” (Gorovaya 2016). For the first time in Russia, crowd funding was used by Planeta.ru Web site. Then, by analogy with the Western Kick starter, the Bum starter Web site was created. By 2015 “The Planeta.ru website had more than 527 thousand registered users, more than 700 thousand visits per month, 2189 successful projects, 509,569,804 collected funds” (Planeta-Russian crowd-funding platform 2016). In addition to these two large companies, “Russian crowd funding in the market there are many others, and some of them also collected impressive sums. For example, ‘Start man’ collected about 32 million rubles. Fortunately, such services are becoming more and more” (Malnev and Hasanov 2015). Crowd funding is attractive for business, inventors, small business owners, and creative people for many reasons: – it is not necessary to prepare a large number of documents; – amount of the limit is determined by the author of the idea; – only the author of the idea may changes to the project, i.e., independence allows you to implement creative plans. The main task for crowd funding is to seek funding, because it should satisfy the needs of community groups and individuals. In this regard, it is necessary to attract those who can directly participate in the project: major sponsors, Maecenas, and investors. Unfortunately, today there are few projects related to high-tech business, while crowd platforms are a global trend (Sherstobitova and Chernov 2017). The final result of a public–private partnership is influenced not only by the financing instruments. It is also influenced by the cost characteristics that contribute to the growth of the many-sided nature of the relations and connections that appear in the financing process. One of the types of financing in the form of crowd funding is the collective participation in investing, the so-called—crowd investing. Crowd investing is a “process of attracting available financial resources of a wide range of individuals (individuals, micro-investors), guided by their own interests, for financing, mainly through the global Internet, commercial projects with the subsequent receipt of financial rewards by investors” (Kievich and Koipash 2016).

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The main feature of crowd investing is precisely the financial part of investor remuneration. Financing schemes may be different. Shares in the amount of project financing can be distributed as between a venture capital fund, a private venture investor, and crowd investing. Funds of the project budget may also include funds from individuals. The main forms of crowd investing: royalties, public lending, and equity crowd funding are different ways of financial reward. Royalty form of crowd investing— it is implied that the investor will receive financial reward from participation in the project in the form of a certain share of income (profit), which is not clearly indicated at the financing stage. That is why the investor is motivated by the financial success of the project, its own involvement at all stages of the project. This approach is successfully implemented within the framework of the video game development site such as LookAtMyGame (Kievich and Koipash 2016). Crowd lending involves the financing by individuals of legal entities and the public. Legal relations are established between the investor and the borrower. Crowd lending, as an innovative form of public–private partnership, is attractive for the following reasons: – speed and efficiency of obtaining financial resources by the borrower in the case when there is interest from the investor; – amount of financing for the borrower can be more attractive than on the traditional financial market; – attraction of those activities which are due to the specifics impossible to use a bank loan; – clear documentation of the material terms of the transaction related to the debt repayment schedule, the timing of the project, and the amount of financial reward. As a financial model of an investment project of high-tech business, crowd investing has the following advantages: – helps to attract a wide range of investors aimed at obtaining additional, exceeding the traditional forms of savings, income; – activates competition in the market; – promotes employment growth and attracting highly qualified personnel to hightech industries; – ensures the growth of technological and innovative development of the country; – implies a broad independent examination of the relevance and success of the investment project; – allows a business to receive financing on terms acceptable to it; – eliminates or reduces the number of intermediaries in investment activities. Usually, crowd funding and crowd investing processes are implemented through an Internet platform with information about the project and the borrower. The Internet platform will be an intermediary and a guarantor of the transaction. Analysts point out three problems hindering the development of crowd funding and crowd investing: technical difficulties, the lack of ideologically correct projects, and the riskiness of these financial instruments.

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Technical difficulties are primarily associated with poor elaboration of legislative issues. That is why major participants of the investment market afraid to be a part of it. Because there is no ideologically correct projects means that, according to mentality, Russians can support only those projects that are of value to them and simple and they can understand. Also those who invested in such projects say what “people are not really waiting for a return on investment, it’s more important for them that they have become part of something important and significant today” (Vyrupaev 2014). Crowd funding and crowd investing are extremely risky tools for the middle class of Russians because of the duration of the projects and the inability to get out of them.

4 Conclusion The level of development of the country and the industry is chosen to attract investment through public–private partnership cooperation with each other. The policies of developed countries with a high level of social protection are priority oriented to health care and education. In the developing countries, on the contrary, these sectors are not priorities. They paid attention to investing transport infrastructure through public–private partnerships (construction and reconstruction of roads, railways, ports, etc.). We suppose that the following opportunities will be provided in Russia: – projects of regional and interregional importance and located within the framework of the modern infrastructure of the national innovation system (technology parks, business incubators, technology transfer centers); – projects aimed at improving the competitiveness of basic industries (energy, oil and gas industry, chemical production); – projects focused on the development of the mineral crystal base and the rational use of resources, including the creation of high-tech enterprises for the extraction and processing of raw materials; – projects for the development of the agro-industrial sector, involving of high-tech industries in agriculture, etc. Favorable investment climate should be in the country for the high growth of public–private partnership. Regarding the development of high-tech business, public funds should not be the basis and source of its development, but a kind of catalyst for private investment. “By investing budgetary funds in the economy, the state should only ‘lend up’ where the risks for private investors are still too high” (Pyankova and Kosvintsev 2010; Vyrupaev 2014).

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As measures of state support for enterprises of high-tech industries should be offered: – introduction of preferential taxation for the development of innovative products: from a reduction in the taxable base to the differentiation of VAT rates depending on the value added value share in the cost of finished products; – improvement of legislation in the field of support for innovative entrepreneurship, providing for direct additional state funding for support when high-tech enterprises enter foreign markets; – changing in customs fees, the priority task should be protectionism in relation to domestic manufacturers. Acknowledgements The work was prepared during the implementation of project No. 26.9642.2017/8.9 within the framework of the state task of the Ministry of Education and Science of Russia.

References Caperchione, E., Demirag, I., & Grossi, G. (2017). Public sector reforms and public private partnerships: Overview and research agenda. Accounting Forum, 41(1), 1–7. Cui, C., Liu, Y., Hope, A., & Wang, J. (2017). Review of studies on the public–private partnerships (PPP) for infrastructure projects. International Journal of Project Management, 36(5), 773–794. Emelyanov, Yu. S. (2012). Public-private partnership in the innovation development of the economics of Russia (Master’s thesis). Access mode: https://dissers.ru/avtoreferati-dissertatsiiekonomika/a243.php (Cited 07 May 2018) Gorovaya, V. V. (2016). Practical manual on crowd-funding. Moscow. Kievich, A. V., & Koipash, D. A. (2016). Crowd-investing as an alternative model of financing an investment project. Economy and Banks, 1, 58–65. Kolyaskin, I. E. (2009). Development of public-private partnership in high-technologies (Dissertation). Access mode: https://www.dissercat.com/content/razvitie-gosudarstvenno-chastnogopartnerstva-v-sfere-vysokikh-tekhnologii#ixzz5EsV1aE1b (Cited 10 May 2018) Malnev, S. Yu., & Hasanov, Z. R. (2015). Crowd-funding in Russia and its development. In: Economics, Ecology and Society of Russia in the 21st Century. Collection of Scientific Works of the 17th International Science and Practice Conference, pp. 85–87. Ministry of the Economy of the Russian Federation. Access mode: https://economy.gov.ru/minec/ activity/sections/privgovpartnerdev (Cited 11 May 2018) Mottayeva, A. B. (2012). Methods of the spatial distribution of entrepreneurial structures of the region on the basis of the development of transport infrastructure (Doctoral dissertation). Muzhzhavleva, T. V. (2014). Role of the state support of high-tech enterprises in provision of the economic security of the country (experience of Russia and of foreign countries). Bulletin of the Chuvash University, 1, 230–234. On development of public-private partnership: Recommendation on preparation and realization of PPP in the subjects of the RF; legal aspects of PPP. Report of the Ministry of the Economic Development of the RF Pechlaner, H., von Holzschuher, W., & Bachinger, M. (Ed.) (2009). Unternehmertum und public private partnership. Berlin: Springer.

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Planeta-Russian Crowd-Funding Platform. (2016). Access mode: https://planeta.ru/about#faqquestion-7 of subordinate document (Cited 11 October 2016) Pyankova, K. V., & Kosvintsev, N. N. (2010). Public-private partnership: Advantages and disadvantages of the state and business. Vestnik of the Permsky University. Economics, 4(7), 12–20. Pyatkov, T. V., & Mikhalyuk, O. N. (2015). Concession agreement as the most popular type of public-private partnership. Bulletin of the Ugra State University, 4(39), 67–72. Samsonov, D. Yu. (2009). The role of private-public partnership in the development of high technologies (Dissertation). Access mode: https://www.dissercat.com/content/razvitie-gosudarstvennochastnogo-partnerstva-v-sfere-vysokikh-tekhnologii#ixzz5EsV1aE1b (Cited 12 May 2018) Sedash, T. N., & Setchenkova, L. A. (2013). Development of mechanisms of financing of the innovation processes in the world and Russian economy. Investment Policy, 1(139), 16–25. Sherstobitova, T. I., & Chernov, A. A. (2017). Crowd-funding: Distinctive features and features of development in Russia. International Scientific Journal “Innovation Science”, 02(1), 249–251. Tkachuk, L. T., & Timchuk, O. G. (2012). Life cycle contracts as a new mechanism of public-private partnership at the regional and municipal levels. In: Economics and economic sciences. Bulletin of the Irkutsk State Technical University, 3(62), 265–269. Varnavsky, V. G. (2009). Public-private partnership. MIEMO RAN, 1, 312–321. Vyrupaev, P. (2014). Three reasons why crowd-investing does not work in Russia. Access mode: https://firrma.ru/data/s_opinion/2417/ (Cited 12 May 2018). Yuryeva, T. V. (2016). Projects of public-private partnership in Russia and foreign countries. Regional Economy and Management: Online Scientific Magazine, 4(48). Access mode: https:// eee-region.ru/article/4833/.

Food Stamps as a Method of the Parallel Government Support Andrey Nechaev, Elena Ilina and Miaomiao Li

Abstract Agricultural production is a risky activity since it depends on both economic factors and natural and climatic conditions. On the other hand, it is in this industry that vital food products are created. In such conditions, to ensure food security, the state needs to develop the support measures that will stimulate the production of agricultural products and ensure the availability of these products to low-income segments of the population. The work proposes using ration cards to support not only the population, but also regional agricultural producers. This will facilitate developing enterprises to find a permanent market. The selling price for the producer will contribute to the growth of profitability of the main activity and indirectly stimulate the introduction of innovations. The proposed tool of support is promising in the condition of the budgeted deficit since the funds allocated for the population increase the availability of foodstuff and stimulate agricultural production in parallel.

1 Introduction Providing the population with foodstuffs is a primary task facing the government of any country. One of the obvious solutions is to support own commodity producers to minimize natural and environmental risks, ensure sales markets. On the other hand, the output must be available to buyers. The need for state support is justified in the works of many research economists and is unquestionable.

A. Nechaev Irkutsk National Research Technical University, Irkutsk, Russian Federation e-mail: [email protected] E. Ilina (B) Irkutsk State Agrarian University named after A. A. Ezhevsky, Irkutsk, Russian Federation e-mail: [email protected] M. Li Henan University of Animal Husbandry and Economy, Huiji Qu, Zhengzhou Shi, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_15

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It should be noted that there is no consensus of the expediency of using a specific method of state support for agricultural producers. Some authors consider direct methods to be the most promising; others believe in indirect methods. When developing and implementing the agrarian policy, the state is in conditions of limited available funds, and this determines the need for the development of new methods of support to increase the effectiveness of management decisions. On the other hand, macroeconomic conditions may adversely affect the level of purchasing power of the population, which also requires support from the state (Garasky et al. 2006). Thus, measures of state support should, on the one hand, stimulate the production of agricultural products and, on the other hand, support the consumption of these products. In such conditions, in our opinion, the most promising is parallel state support, when funds are received by low-income segments of the population for purchasing food products from regional producers.

2 Methods and Results Within the framework of realization of the concept for domestic food aid in the Russian Federation and solving one of the tasks outlined in the Doctrine of Food Security of the Russian Federation, it becomes necessary to achieve and maintain physical and economic accessibility of safe food products for every citizen of the country in volumes and assortment. This corresponds to the established rational consumption rates of food products vital for an active and healthy lifestyle. The government, represented by the Minister of Industry and Trade of the Russian Federation, Denis Manturov, proposes the introduction of targeted food aid for low-income population strata by issuing ration cards in the form of electronic certificates. At the same time, low-income citizens are citizens with income below the living wage established in the region. It is assumed that the monthly payment will be 1400 rubles. Recipients will be able to spend it on essential products (meat, milk, fruits, vegetables, seeds, and seedlings), as that card funds cannot be used to buy cereals, canned food, household chemicals, alcohol, and tobacco. If the recipient does not use the money in time and for its intended purpose, they will be written off from the card. It will be impossible to withdraw funds from the card, but, according to the Ministry of Industry and Trade, it can be replenished. It is planned that when crediting funds to a card, its owner will receive from 30 to 50% of the amount deposited as a state bonus, but the list of goods for which it will be possible to spend the amount deposited will not change. Any trade enterprises connected to the central processing system of the program, which will be administered by one of the largest banks in the Russian Federation, will be able to take part in the program. It should be noted that ration cards were widespread after the end of the Second World War. In the Czech Republic, the rationing system for basic foodstuffs was introduced in 1939; it existed until 1953 with changes and additions when it was

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canceled during the monetary reform. In Japan, the rationing system was canceled in 1949. In Israel, the rationing system was introduced in 1949–1952 (Dwyer 1982). Currently, the rationing system is present in such developing countries as Venezuela and Cuba. The rationing system in Cuba was introduced in 1962. The government of this country spends more than a billion dollars on food subsidies, which are distributed by means of cards among the population. The Cubans pay only 12% of the real cost of food. Venezuela, which has been in a state of crisis for several years, also has a food stamp system; at that, the rationing system was introduced in 2014. The rationing system of supporting low-income population strata has been operating in the USA for over fifty years. The average monthly maintenance amount per person is $125.5. Cardholders can spend these funds on purchasing any necessary products. The monthly income of the claimant to maintenance should not exceed 130% of the official poverty threshold. For example, any family of four with a total income of no more than $2500 per month is eligible for food stamps. A single American should receive no more than $1.200 per month to become a member of the program (Chang et al.2013; Kreider et al. 2011). In this regard, the number of recipients of benefits increases in the years of crisis and decreases in the years of growth. In 2013, the historic record was set: stamps for an overall amount of 76.1 billion dollars were given to 47.6 million Americans. In the 2016 financial year, 44 million Americans received food aid in the amount of 66.6 billion dollars (Fig. 1). In Russia, the amount of living wage is established quarterly; nowadays, its level is 10,444 rubles. Figure 2 shows the dynamics of the number of people having an income below the living wage. On average, over the last decade, 17.8 million people were on the breadline. In the periods of economic crisis (2008, 2015–2016), their number increased. The most favorable year was 2012, when the number of people with an income level below the living wage level made a record low value of 15.4 million people. By the end

Fig. 1 Number of people using ration cards in the USA in 2008–2017 (https://www.fns.usda.gov/ pd/supplemental-nutrition-assistance-program-snap)

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Fig. 2 Number of people with an income below the living wage in the Russian Federation in 2008– 2017 (https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/population/level/#)

of the period under consideration, the share of the population with an income below the living wage was 12.3%. In Irkutsk Region, the living wage level for the able-bodied population since 2015 has exceeded 10,000 rubles. At the same time, in the region over the past 12 years, the share of the population with cash incomes below the subsistence minimum has not fallen below 16.8%, and since 2012 has had a steady growth trend (Fig. 3). Distribution of the population by the level of per capita money income over the past two years is presented in Table 1.

Fig. 3 Share of the population with cash incomes below the living wage level in Irkutsk region over the period of 2005–2016 (https://irkutskstat.gks.ru/wps/wcm/connect/rosstat_ts/irkutskstat/ru/ statistics/standards_of_life/)

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Table 1 Distribution of population by per capita money income in Irkutsk region over the period of 2016–2017 (https://irkutskstat.gks.ru/wps/wcm/connect/rosstat_ts/irkutskstat/ru/ statistics/standards_of_life/) Indicators

Total population

2016

2017

Thousand people

Specific weight, %

Thousand people

Specific weight, %

2412.8

100.0

2408.9

100.0

Including with per capita money income per month rubles Up to 7000.0

211.2

8.7

204.5

8.5

From 7000.1 to 10,000.0

277.2

11.5

273.0

11.3

From 10,000.1 to 14,000.0

397.3

16.5

394.8

16.4

From 14,000.1 to 19,000.0

424.7

17.6

424.9

17.6

From 19,000.1 to 27,000.0

459.5

19.0

462.1

19.2

From 27,000.1 to 45,000.0

438.0

18.2

442.3

18.4

From 45,000.1 to 60,000.0

117.2

4.9

118.6

4.9

Over 60,000.0

87.7

3.6

88.7

3.7

Over the period under study, the population of the region reduces by four thousand people. In the first two groups (per capita income up to 7000 rubles and up to 10,000 rubles), the relative density declined by 0.2 percentage points in each; however, the combined value remains quite high. Thus, more than 20% of the population of the region or over 477.5 thousand people will become potential participants of the program under discussion. Thus, at present, on average in Russia, the share of the population with a lowincome level is more than 12%, but in the regions, this indicator is much higher; for example, in Irkutsk Region it was 20%. This means that there is an objective necessity to support the population, namely to introduce ration cards for purchasing food products. Similar measures, firstly, will have a beneficial influence on the standard of living of the population, as well as contribute to the achievement of goals outlined in the Doctrine of Food Security of the Russian Federation. Secondly, the allocated money can support agricultural enterprises in parallel by stimulating demand for their products. In our opinion, such program of domestic food aid (DFA) should be oriented not only to support the population with incomes below the living wage level, but

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Agricultural enterprises

Trade organisations

Low-income consumers

Production of essential foodstuffs

Trade increment to essential products produced by regional enterprises must not exceed 10%

Purchasing essential quality products

Movement of goods

Movement of funds

Fig. 4 Interaction of agricultural enterprises, trade organizations, and low-income consumers when rendering domestic food aid

also to act as an instrument of state support for regional agricultural producers by stimulating the demand for their products. In this regard, we propose creation of a “food chain,” which will unite regional producers of goods indicated in the program, trade organizations and low-income population. The scheme of interaction of the participants is presented in Fig. 4. Thus, the relationship between producers and consumers of food products will be organized through trade organizations that are not entitled to establish a trade increment above 10% for essential products mentioned in the program and for those produced in the given region or this federal district for the Far North regions and areas equivalent to them. For trade organizations, such interaction is promising since participation in this program will increase the number of customers who, when they come to the store to buy essential products using ration cards, will purchase other goods. It is assumed that the regional Ministry of Agriculture will approve the list of enterprises or peasant (farmer) farms participating in the support program. As a criterion indicator, it is possible to establish the conformity of products with GOST, which will allow providing the population with high-quality foodstuff. The advantages of such interaction for agricultural enterprises consist of creating a permanent channel for sales of products. The enterprises set the price independently, considering the fact that the buyer will purchase products with a 10% surcharge. Regulation of the trading margin will increase the selling price of producers, which will have a favorable effect on the profitability level. The return on costs for some types of products on average for Irkutsk Region is presented in Table 2. Over the period under consideration, the return on costs of agricultural enterprises of the region has not exceeded 12%, while for introduction of extended reproduction this indicator must exceed 20% (Tyapkina and Ilina 2018). It should be noted that the report includes all enterprises of the region, including the largest agricultural complexes. They are:

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Table 2 Return (payback) on costs of agricultural enterprises of Irkutsk region over 2015–2016, % Type of products

2015

2016

2017

Deviation (+; −) −0.77

Entire activity

11.15

9.48

10.38

Milk

12.68

12.52

11.94

−0.74

(81.07)

(80.49)

(87.41)

+6.34

Cattle meat Pork

9.47

4.04

7.67

−1.80

Poultry

8.11

13.80

6.40

−1.71

12.24

13.97

13.73

+1.49

Eggs Grain

9.52

18.36

11.43

Potatoes

(70.41)

(88.65)

3.30

Vegetables

(83.21)

(79.61)

(84.01)

+1.91 – +0.80

Calculated by the authors according to the annual report on activities of agricultural enterprises of Irkutsk region

– the Agricultural Russian Joint Stock Company “Belorechensk” (SHRAO “Belorechenskoe”); – the Agricultural Industrial Complex “Usolsk pig-breeding farm” (SHPK, “Usolski svinokompleks”); – the Limited Liability Company “Sayansk broiler” (OOO “Sayanski broiler”); – the Closed Joint Stock Company “Angarsk poultry farm” (ZAO “Angarskaya ptitsefabrika”); – the Agricultural Production Cooperative “Okinsk” (SPK “Okinski”); – the Joint Stock Company “Railroader” (AO “Zheleznodorozhnik”); – the Limited Liability Company “Bratsk poultry farm” (OOO “Bratskaya ptitsefabrika”); – the Joint Stock Company “Bolsheelansk” (AO “Bolsheelanskoe”); – the Joint Stock Company “Farming firm Angara” (AO “Agrofirma Angara”); – the Russian Joint Stock Company “Kuytunsk cornfield” (PAO “Kuytunskaya Niva”). Their profitability is above 22%. In the first place, the support program should be focused on developing enterprises that have problems with product sales since large-scale enterprises have their own retail chains (Akmarov and Berykina 2008). Let us consider the impact of the proposed program by the example of the open joint-stock company “Barki” (OAO “Barki”) in Irkutsk Region (Table 3). Based on the materials of the enterprises of Irkutsk Region, the investment attractiveness of 107 enterprises engaged in milk production was assessed. According to the results of the assessment, the enterprises were divided into the following groups. The first includes investment-attractive enterprises (one organization), potentially attractive enterprises in a good financial condition, but having low competitiveness (62 enterprises). The second includes potentially attractive, competitive enterprises

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Table 3 Characteristics of production and sale of milk in the open joint-stock company “Barki” of Irkutsk region over the period of 2016–2017 Indicators

2016

2017

Milk production volume, c

21,100

24,150

Selling price of milk, rubles/l

21.06

21.29

Sales proceeds of milk, thousand rubles

44,442

51,421

Total cost of milk production, thousand rubles

54,492

52,871

Sales loss of milk, thousand rubles

−10,050

−1450

Recoupment of expenses, %

81.56

97.26

in an unsatisfactory financial condition (44 enterprises), investment-unattractive enterprises (Tyapkina and Ilina 2013, 2016). Thus, developing enterprises that have problems with production distribution belong to the largest group. The selected criteria are: use of resource, production, market potentials; the characteristic of solvency, financial sustainability, payback, business activity; the occupied market share; the price level in comparison with the industry average one; application of innovative technologies; and selective and pedigree work (Tyapkina and Ilina 2016). A “typical” enterprise is the open joint-stock company “Barki”. Therefore, let us consider the influence of the proposed program by the example of this enterprise (Table 3). Despite the fact that the cost recovery is growing, the activity of the open jointstock company “Barki” in production and sale of milk remains unprofitable. The enterprise is forced to “cover” this loss at the expense of other types of products. Attention should be paid to a low level of the selling price of one liter of milk. The average market price in the region for this type of product is 47.11 rubles and 50.49 rubles per liter in 2016 and 2017, respectively. When implementing the proposed measures, the open joint-stock company “Barki” will be able to sell milk at a price of 40 rubles, which, taking into account the trade increment, will amount to 44 rubles. Consequently, the products will be competitive in price available for the end consumer. And it will also increase the return on costs up to the level of more than 20%, which will encourage the company to undertake activities in terms of expanded reproduction. An opportunity to introduce innovations will appear (Nechaev et al. 2014). Such a result will be typical of all sixty-two enterprises of the identified group. Currently, 1/5 of the population of the region is on the breadline. Therefore, there is an objective need for implementing the program of domestic food aid by means of introduction of ration cards. According to the data of Irkutskstat in 2016, the amount of welfare payments to the population was 35.381 million rubles, the state support of agriculture—3.889 million rubles. To implement the proposed measures in 2016, 8.205 million rubles would be required (at the rate of 1400 rubles per person a month). 7384 million rubles (minus the trade increment) will be given to specific agricultural organizations for producing the products demanded by the market. In other words, implementation of the proposed program does not require the outflow of a significant sum of budget funds from the budget, since a part of the

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allocated funds will be purposefully spent. That is, the funds will not be used to pay loans, housing and public utilities or buy new gadgets. Instead, they will be used to purchase high-quality food products that are produced in this region. On the other hand, an increase in the profitability of agricultural enterprises will lead to an increase in revenues because of the single agricultural tax to the budgets of municipal corporations. Realization of this program will influence favorably the health state of the population. In case of the state, the benefits consist in the absence of necessity to allocate additional funds in conditions of budget deficit, and the allocated funds support the population and agricultural enterprises concurrently.

3 Discussion Thus, the measure proposed by the government of the Russian Federation to support the population with an income below the living wage level by introducing ration cards can contribute to achieving the goals of the Doctrine of Food Security of the Russian Federation. It may act as a tool of support of regional agricultural producers. In other words, using these ration cards people can purchase essential foodstuffs, produced by local producers, reduces the trade increment for these goods up to 10% The increase of the share of agricultural enterprises in the final price of sold products will contribute to the growth of their profitability. In addition to direct support, as an indirect method of rendering aid to regional agricultural producers and ensuring the stability of food supply to the market, it is possible to consider support for effectual demand for products on the part of disadvantaged social groups. In parallel, the problem of the economic availability of foodstuff for disadvantaged social groups will be solved. In addition, the regional government will have an opportunity to control the quality of the supplied products by restricting the access to participation in this program for legal entities whose products do not comply with GOST. We also believe that the proposed instrument for parallel state support is interesting in conditions of bureaucracy since the funds allocated for supporting the poor are finally used to support regional agricultural producers. The problems of realization of the proposed support program for the population and agricultural enterprises, first of all, include organizational issues, drawing up a list of agricultural enterprises and products participating in the support program. It also concerns organization of product deliveries to commercial enterprises, especially to geographically remote villages, etc. The solution of the mentioned problems consists in drawing a clear plan for implementing the program along with assigning responsibility centers. To solve the problem of ensuring the products delivery to commercial enterprises, a differentiated approach to areas with different densities of the population is required, which depends on the presence of agricultural enterprises in the area. One must note that the difficult macroeconomic conditions that Russia experiences now make the problems of development of new instruments of state support for

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agriculture relevant since this industry is able to ensure the food security of the whole country.

References Akmarov, P. B., & Berykina, K. F. (2008). Features and problems of integration of Russian agriculture into the world economy. Agrarian Herald of the Urals, 7(49), 13. Chang, Y., Chatterjee, S., & Kim, J. (2013). Household finance and food insecurity. Journal of Family and Economic Issues, 35(4). Dwyer, J. T. (1982). Food for thought on food stamps. American Journal of Public Health, 72(8), 786–787. Garasky, S., Morton, L. W., & Creder, K. (2006). Effect of the local food environment and social support on rural food insecurity. Journal of Hunger & Environmental Nutrition, 1(1), 83–103. Kreider, B., Gundersen, C., & Pepper, J. (2011). Economics of food insecurity in the United States. Applied Economic Perspectives and Policy, 33(3), 281–303. Nechaev, A. S., Antipin, D. A., & Antipina, O. V. (2014). Efficiency estimation of innovative activity the enterprises. Journal of Mathematics and Statistics, 10(4), 443–447. Official web page Federal Service of State Statistics of the Russian Federation. (2008). Access mode: https://www.gks.ru/. Official web page Territorial organ Federal Service of State Statistics for Irkutsk Region. (2008). Access mode: https://irkutskstat.gks.ru. Official web page United States Department of Agriculture. (2008). Access mode: https://www. usda.gov/. Tyapkina, M. F., & Ilina, E. A. (2013). Differentiated approach to increasing the investment attractiveness of enterprises. News of the Irkutsk State Economic Academy, 4. Tyapkina, M. F., & Ilina, E. A. (2016). Enterprise investment attractiveness evaluation method on the base of qualimetry. Journal of Applied Economic Sciences, 2(40), 289–293. Tyapkina, M. F., & Ilina, E. A. (2018). Assessment of the reproduction process of agricultural enterprises. International Journal of Ecological Economics and Statistics, 39(1), 171–179.

Efficiency Factors of the Innovative Activity in High-Tech Industries Olga I. Koloskova, Irina V. Somina and Milan Radosavljevic

Abstract The chapter shows the role of economic and mathematical methods in the study of the innovation effectiveness. The authors have found that the assessment of the effectiveness of innovation enterprises is impossible without a quantitative measurement of the influence of various factors. We have calculated the difficulties that remain in the impact factor’s measurement. We have systematized and supplemented various factors influencing the above phenomenon and confirmed the feasibility of applying the main factors to research the effectiveness of innovation activities in the Russian high-tech sector. These factors are market demand, investment and technological, organizational and economic factors.

1 Introduction Nowadays, the production potential decreases, which hinders the increase in manufacturing and competitiveness of products. Under these circumstances, the development and effective implementation of innovations and advanced technologies are central to the economic development of domestic enterprises. In addition, in recent years there has been a decrease in the efficiency of innovation in many countries of the world, including in Russia (Glisin and Kalyuzhny 2015). As a result, increased attention is paid to assessing the effectiveness of innovation, finding reasons for its decline and developing measures to ensure efficiency. Research in this area is becoming particularly popular. In this case, a special role is given to high-tech industries, the effectiveness of innovative activities, to which this research is devoted. O. I. Koloskova · I. V. Somina (B) Belgorod State Technological University named after V.G. Shukhov, Belgorod, Russia e-mail: [email protected]; [email protected] O. I. Koloskova e-mail: [email protected] M. Radosavljevic University “Union-Nicola Tesla”, Belgrade, Serbia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_16

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We can analyze the results of innovative activity and the reasons that determine them through the application of economic and mathematical methods, in particular, the factor analysis. The idea of factor analysis belongs to K. Pearson, who proposed the method of principal components in 1901. This is the most frequently used method for solving problems of factor analysis. This method was developed by G. Hotteling in 1933. Since the second half of the twentieth century, factor analysis has been recognized as a universal method of compact representation of large arrays of statistical and experimental data. Despite the development of the problem of using factor analysis in relation to the study of economic systems, the issues of studying the effectiveness of innovation using this method are insufficiently researched. This circumstance determines the relevance of this work to improve the theory of innovative development of the enterprise and the practical aspects of management. We have analyzed the high-tech sector of the Russian economy (Kostromitskaya), the materials of the Federal State Statistics Service (Federal State Statistics Service 2018) and the research of Russian economists (Togushchakova 2018; Zudin 2015). We will identify and quantify the influence of the main factors on the effectiveness of innovation in high-tech industries of Russia.

2 Materials and Methods The construction of a mathematical model of any object or phenomenon is associated with the restoration of its condition by observing its reactions to various input effects. At the same time, the number of significant (independent) factors is less than the number of measured signs, between which correlation dependence is observed. The essence of factor analysis is the transition from a system description with a large set of indirect indicators to the description of the same system by a smaller number of maximum informative factors. An important distinctive feature of factor analysis is the possibility of the simultaneous study of an arbitrarily large number of interdependent variables, which is especially significant when describing socio-economic phenomena (Blizorukov 2008). The concept of “efficiency of innovation” does not have generally accepted interpretation. There are at least two of these interpretations—in the broad and narrow sense of the word. On the one hand, the effectiveness of innovation activity represents the successful commercialization of innovation and in most cases reflects the ratio of the result of innovation activity and the costs that have caused it. On the other hand, the effectiveness of innovation involves the definition of the complex of its main specific results. The starting point in assessing the effectiveness of innovation is the interpretation of factors contributing to its intensification and inhibition. This contributes to obtaining quantitative characteristics of the significance of these factors for the innovation activity of the enterprise. It allows a better understanding of the nature of

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the phenomenon being studied. And this, in turn, influences the identified factors, intervenes in the relevant economic process in order to obtain the desired results. A more in-depth analysis of factors affecting the efficiency of innovation activity is possible using methods of factor analysis. When we use this method, it allows evaluating effectiveness of using available resources and also making scientifically based conclusions about the adequacy of the structures, methods and management tools used to resolve specific production situations. Also, it helps to determine the direction of development of the enterprise management mechanism, to identify the reserves of its components and to evaluate the functioning effectiveness. Innovation includes all aspects of the functioning of industrial enterprises (from new ideas to the manufacture of new products and their implementation). That is why in practice it is quite difficult to conduct a factor analysis of its effectiveness. Among other things, the industry is a collection of heterogeneous industries and enterprises whose innovative activity has its own characteristics. As a result, a certain factor has a different degree of influence on each level of technological effectiveness. At the same time, it is necessary to analyze external changes that alter the intensity and the vector of influence of the main factors that greatly affect the efficiency of innovation activity. While using factor analysis for calculating the effectiveness of innovations, the factors that have been formed can be interpreted as specific areas of the enterprise’s innovative development. Its innovative potential and the values of factors, indicating the importance of each in implementing and shaping the trajectory of innovative development, can be used. When we measure the factor’s impact on the efficiency of innovation, we face a number of difficulties arising from the specifics of innovation activity and affecting the effectiveness evaluation. 1. Uncertainty in the structure of the effectiveness of innovation. 2. Low predictability of innovation results and a wide range of risks. 3. Often the lack of access to officially confirmed data and, as a result, a low degree of reliability of the obtained analytical results. There are many classification factors proceeding from the meaning of innovation, difficulties in its study that have an impact on it. In particular, they can be applied to the effectiveness of innovation. So, Glagolev (2009) classifies factors according to such characteristics as belonging to the enterprise’s environment (factors of external (direct and indirect impact) and internal environment), nature (economic and non-economic) and possibility of forecasting (predictable and unpredictable), duration of action (acting once, periodically and constantly) and others. For example, Chayran and Belyakova (2014) systematically systematized the factors influencing the innovative development of enterprises, grouping and highlighting them according to the following aspects: technological, educational, regulatory, organizational, institutional, production, cooperation.

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After analyzing the existing division of factors that affect the efficiency of innovation activities of industrial enterprises, let us present their generalized and augmented classification (Fig. 1). We note that for an objective assessment of the effectiveness of innovative activities of industrial enterprises, it is necessary to identify the key factors that contribute to innovative breakthroughs and impede innovative development. It will make it possible to build an effective strategy aimed at the growth of innovative activity based on analysis and elimination of bottlenecks. The changes in the demand for products and

Factors influencing the efficiency of innovative activity in industry

According to belonging to the enterprise environment External Direct influence (factors of mesic environment) Indirect influence (factors of macroenvironment)

According to the nature

Economic

According to the content

Technical and technological factors

Financial Business factors Commercial

Non-economic

Internal

Scientific and research factors Conjuncture forming factors

Organizational factors

Regulatory factors

Technological factors

Investment factors

Economic factors

Financial factors According to the duration of influence

Staff factors, etc.

Acting once According to the forecasting capability

According to the control and management Acting periodically

Predictable factors

Controllable factors Uncontrollable factors

Acting constantly

Unpredictable factors

Fig. 1 Classification of the factors affecting the efficiency of innovative activity in industry

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competition, especially in the face of declining imports, are important. Definitely, all the above factors influence the efficiency of innovation in industry. However, the study of the influence of each of them is a long and very difficult process. The solution to this problem is to apply a factor analysis, the main goals of which are to reduce the number of variables (data reduction) and to determine the structure of the relationships between variables. One of the methods of factor analysis is the method of main factors, which allows measuring the influence of the entire set of factors affecting the object under study, and determining the shares of the influence of the main factors. The method of main factors allows determining: – a group of factors that are major for the object under study and measure the comparative degree of impact of each of them; – the influence of the identified factors on each parameter of the functioning of the object separately and the entire set of studied parameters of the factor analysis. This means that as a result of computational procedures made for a single model, a matrix of estimates of the factorial effect on all parameters of the factor analysis is formed. Despite the difficulties of applying the method of main factors, its advantages in conducting predictive and analytical studies of the functioning of industries include the following: 1. The possibility of evaluating the multifactor influence on the object of study when using the information base with the number of observations insufficient for the application of other methods of research of multifactor models. 2. The possibility of carrying out calculations simultaneously for a large number of indicators, which allows you to identify several factors that have the main impact on the object of study. 3. Obtaining an estimate of the cumulative effect of factors that form the specificity of the object of study. 4. The ability to predict the factorial impact on the dynamics of production and economic indicators of the object of study. Let us consider in detail the method of applying main factors in order to identify and quantify the factor impact. Factor analysis using the method of main factors is built on the transformation of reduced correlation matrices reflecting the correlation between the considered parameters of matrices. Those elements are the correlation coefficients between the parameters and the main factors. In terms of the factor analysis, the factor loading is the correlation coefficient between the factor and the parameter. The main model of the factor analysis is written in the following form: zj = aj1 F1 + aj2 F2 + · · · + ajp Fp + · · · + ajm Fm + dj Uj (j = 1, 2, . . . , n),

(1)

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where zj —the parameter of the factor analysis; {aj1 , aj2 , …, ajm }—the coefficients of the factor loadings according to common factors; {F 1 , F 2 , …, F m }—common factors; d j —the coefficient of the factor loading by specific factor; U j —specific factor. The variance of the parameter zj is a contribution of common factors and a specific factor: 2 2 2 + aj2 + · · · + ajm + dj2 . Sj2 = 1 = aj1

(2)

Hence, the communality (factor variance) being the contribution of common factors to the variance of the parameter zj is the sum of squares of the factor loadings: 2 2 2 2 + aj2 + · · · + ajp + · · · + ajm . h2j = aj1

(3)

And the distinctness is the contribution of the specific factor dj2 . To conduct the factor economic analysis, we have to determine the set of variables (parameters of the factor analysis) that characterize the innovative activity of the object under study. The composition of the variables being studied should provide an opportunity to identify factors and determine the nature of the factor influence on the generation of the main indicators of innovative activity efficiency of the object. The factor analysis algorithm includes the following procedures: – The transformation of the formed database of observations into the factor monitoring tables, containing numerical values of the coefficients of the factor loadings.   2 and – The placement of the coefficient of the factor loadings by their squares ajp the calculation of commonalities according to each parameter. The squares of the factor loadings in the first column of the table will correspond to the relevance of the first factor. The indicators of the influence of the second factor will be in the second column of the table, etc. The evaluation of the quality of the factor analysis results is made according to the following criteria: (a) the predominance of the importance of the first factor; (b) the share of the first two factors in the structure of the communality should not be less than 80%; (c) the index of the completeness of factorization, showing the share of the communality in the variance (the share of variance explained by the common factors), should not be less than 90%. The index of the completeness of factorization is: If =

m  n  p=1 j=1

2 ajp /n

(4)

Efficiency Factors of the Innovative Activity in High-Tech … Fig. 2 Logical diagram of the estimation procedure of the factor impact

Main model of the factor analysis zj = aj1F1 + aj2F2 + … + ajpFp + … + ajmFm + djUj ( j = 1, 2, …, n)

Parameters of the factor analysis

Assessment of the results of the factor analysis

187

Factor a11a12 … a1m (n – the number of the parameters) Monitoring a21a22 … a2m (m – the number of the factors) table an1an2 … anm

Interpretation of the main factors

The structure of the communality +…+ +…+ + =

Assessment of the relevance of the factor impact / p = 100

where (j = 1, 2, …, n)—the number of the parameter of the factor analysis; (p = 1, 2, …, m)—the number of the common factors. Then, analytical processing of tables of factor mapping assesses the quality of the results of factor analysis in accordance with conditions (1), (2), (4). If the conditions are not met, then the set of parameters of the factor analysis is adjusted and the statistical base is refined. To obtain results that satisfy the requirements of factor analysis, we may take several iterations to refine the parameters and input data (Fig. 2). The bases for the interpretation of the main factors are the factor map tables. Interpreting the results of factor analysis by comparing the values of factor loadings allows you to identify the main factors. Thus, we determine which phenomenon has a factorial effect on the parameter. The main factors are the factors that have the most significant impact on the object of study and are common to all parameters included in the factor analysis. The number of identified main (general) factors depends on the specifics of the phenomenon being studied, the capabilities of the available statistical base of observations and the complex of parameters of the factor analysis. The calculation of the main factors stops when a numerical value is not determined by any of the parameters. According to the results of the factor analysis, at least two common factors should be identified.

3 Results Let us investigate the impact on the effectiveness of innovation through factor analysis using the package Statistica 13. When forming the complex of parameters of the factor analysis of the effectiveness of innovation activity in high-tech industries, the values of indicators for the period from 2012 to 2016 were taken (Table 1). Let us calculate the pairwise correlation coefficients between the parameters under consideration (Table 2). The data of the above matrix confirm a sufficiently high degree of the dependence between the parameters. This, in its turn, indicates that it is possible to move to the

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Table 1 Indicators for the factor analysis of the innovation effectiveness in high-tech industries for 2012–2016 The parameter of the factor analysis

Unit

Symbol

2012

2014

2015

The ratio of the volume of shipped innovative goods, works, services and the costs for technological innovations

Shares

X1

1.7

1.6

2

1.9

1.7

The share of innovative goods, works, services in the total volume of shipped goods, works and services

%

X2

14.3

16.9

17.7

18.6

18.2

The share of internal costs for researches and developments in priority directions in the total amount of the costs for research and developments

%

X3

61.3

66.3

60.6

76.7

76.4

The share of the organizations implementing technological innovations in the total number of organizations

%

X4

30.1

29.3

30.6

30.3

29.4

Index of investment in fixed capital aimed at the reconstruction and modernization

%

X5

151.9

86.2

178.3

97.1

99.5

Degree of depreciation of fixed assets

%

X6

50.2

48.1

47.1

44

45.6

X7

113.1

109.3

117.4

100.7

96.8

X8

107.1

101.2

100.9

90

98.1

104.8

Index of production The ratio of the index of production and the index of changes in the number of employed

Shares

Index of labor productivity

2013

2016

X9

105.6

102.2

102.5

97.1

The share of high-tech goods in total exports

%

X10

10

10.2

10

12.8

14.5

The share of high-tech goods in total imports

%

X11

63

62.4

61.2

58.7

61.3

Source The data of the Rosstat (Federal State Statistics Service 2018)

description of the phenomenon on the basis of a smaller number of factors rather than on the basis of the total number of indicators. Table 3 shows that received results of the factor analysis correspond to the necessary criteria: The importance of the first factor prevails, the share of the first two factors exceeds 80% and the index of the completeness of factorization is 90%. According to the data of Table 3, the high level of communality was determined for all parameters of the factor analysis. This means that the influence of the three

−0.93

−0.52

−0.82

−0.64

0.60

−0.76

0.24

−0.38

−0.13

−0.04

−0.59

X7

X8

X9

X10

X11

−0.64

0.88

−0.62

−0.77

−0.79

X6

−0.36

−0.32

0.22

−0.11

0.47

−0.08

0.71

−0.72

−0.36

−0.89

0.55

−0.42

X5

1.00

−0.33

0.01

0.01

0.85

X4

0.65

0.85

X4

−0.33

0.65

X3

−0.08

1.00

1.00

0.41

−0.08

X3

0.41

X2

X2

1.00

X1

X1

0.25

−0.53

0.21

0.49

0.74

0.42

1.00

0.71

0.88

−0.71

0.60

0.92

0.66

1.00

0.42

−0.08

−0.79

−0.89

−0.72

−0.36

X6 −0.42

0.55

X5

0.48

−0.90

0.64

0.62

1.00

0.66

0.74

0.47

−0.93

−0.52

0.24

X7

Table 2 Correlation matrix of the indicators characterizing the efficiency of innovative activity X8

0.91

−0.60

0.40

1.00

0.62

0.92

0.49

−0.11

−0.77

−0.82

−0.38

X9

0.34

−0.82

1.00

0.40

0.64

0.60

0.21

0.22

−0.62

−0.64

−0.13

X10

−0.51

1.00

−0.82

−0.60

−0.90

−0.71

−0.53

−0.32

0.88

0.60

−0.04

X11

1.00

−0.51

0.34

0.91

0.48

0.88

0.25

−0.36

−0.64

−0.76

−0.59

Efficiency Factors of the Innovative Activity in High-Tech … 189

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Table 3 Results of the factor analysis of innovative activity efficiency in high-tech industries The parameter of the factor analysis

Factor loading I

II

III

Communality

Distinctness

The share of the first two main factors in the communality, %

X1

−0.15

−0.96

X2

−0.83

−0.28

−0.11

0.95

0.05

98.73

−0.03

0.78

0.22

99.89

X3

−0.94

X4

0.19

0.22

0.05

0.92

0.08

99.73

−0.92

0.02

0.88

0.12

X5

0.62

99.97

−0.62

−0.41

0.93

0.07

82.33

X6 X7

0.92

0.30

−0.06

0.95

0.05

99.66

0.86

−0.39

0.06

0.90

0.10

X8

99.66

0.88

0.30

−0.31

0.95

0.05

90.03

0.70

−0.35

0.64

0.90

0.10

80.70

X10

−0.87

0.21

−0.32

0.91

0.09

88.63

X11

0.78

0.52

−0.25

0.94

0.06

93.23

X9

The index of the completeness of factorization, %

90.9

main factors on most parameters almost excludes the influence of the factors caused by the specificity of the parameter. As a result of the calculations, three main factors were identified (Table 4). The first factor—the factor of market demand—is the determining factor for the formation of most model parameters. They are significantly influenced by the prevailing economic situation. The share of internal costs is for research and developments in priority directions. There is also a share of innovative goods, works, services in the total volume, the index of production and the shares of high-tech goods in total exports and imports. The next factor is interpreted as an investment and technological factor in view of the high factor loading under the parameters characterizing the use of technological innovations and investing in fixed capital. Due to the maximum factor loading under the parameters characterizing the level of organization of labor and production, we have been able to interpret the third factor as a business factor. As we have noted earlier, the influence of the first factor practically on all parameters is significant. The significant influence of the second factor on parameters X1, X4, X5 is explained by the fact that in high-tech industries, as a rule, the technological “shifts and breakthroughs” and the introduction of new technologies sharply increase the competitiveness of an enterprise. They also lead to maximization of profit in the long-term period. Moreover, high-tech enterprises invest more in R&D and innovations compared to enterprises of lower levels. A sufficiently high degree

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Table 4 Assessments of the influence of the main factors in high-tech industries in 2012–2016 The parameter of the factor influence

Symbol

The structure of the factor influence, % The factor of market demand

Investment and technological factor

Business factor

The ratio of the volume of shipped innovative goods, works, services and the costs for technological innovations

X1

2.33

96.40

1.27

The share of innovative goods, works, services in the total volume of shipped goods, works and services

X2

89.60

10.29

0.11

The share of internal costs for research and developments in priority directions in the total amount of the costs for research and developments

X3

94.71

5.01

0.27

The share of the organizations implementing technological innovations in the total number of organizations

X4

4.05

95.92

0.03

Index of investment in fixed capital aimed at the reconstruction and modernization

X5

41.02

41.31

17.67

Degree of depreciation of fixed assets

X6

90.38

9.27

0.34

Index of production

X7

82.75

16.91

0.34

The ratio of the index of production and the index of changes in the number of employed

X8

80.84

9.19

9.97

Index of labor productivity

X9

54.58

0.30

45.13

The share of high-tech goods in total exports

X10

83.72

4.90

11.37

The share of high-tech goods in total imports

X11

64.22

29.01

6.77

62.56

28.96

8.48

Intensity of factor influence

of influence of the investment and technological factor on the efficiency of innovative activity reflects the level of innovation activity in the branch of industry. Thus, we can state that in the period under review, the efficiency of innovative activity was largely determined by the market demand. In addition, the influence of the investment and technological factor was quite strong. It is difficult to consider the impact of the business factor as significant. However, the domestic producers managed to compensate for the limitation of the import of foreign products in a rather short time: The investment, technological and business factors came to the fore, which is confirmed by the increase in the factor loading.

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This circumstance indicates that the sanctions should not be regarded solely as the negative factor of the influence on innovative activity in the Russian industry. Undoubtedly, the sanctions restrictions have radically changed the competitive environment, which has a key influence on the choice of the business development direction. In the current context, the competition has become a powerful motivation for increasing innovative activity in order to improve the quality of products and reduce costs.

4 Discussion and Conclusion The variety of previously considered factors complicates the procedure of quantitative measurement of their impact on the effectiveness of innovation. This problem can be solved by resorting to the use of factor analysis, the essence of which consists in the transition from a system description with a large set of indirect signs to the description of the same system with a smaller number of maximally informative factors. This study allows concluding that the identification of factors and the quantitative measurement of their influence on the efficiency of innovative activity can be used as a preliminary stage for developing a methodology for assessing the efficiency of innovative activity. The result of the study is the confirmation of the fact that in order to study the efficiency of innovative activity in the high-tech sector and its underlying causes, it is possible to use the method of the main factors. This allows measuring the influence of the entire set of factors affecting the object under study and determining the shares of influence of the main factors. In the end, three main factors were identified: the factor of market demand, the investment and technological factor and the business factor. It was also found out that the most significant impact on the efficiency of innovative activity belonged to the first factor, especially at the first stage of implementing the import substitution policy. Moreover, under the conditions of import limitation and, as a consequence, the necessity of the prompt replacement of goods, domestic producers started to use the import substitution reserves and all available resources. They changed the speciality of existing enterprises and organized new import-substituting industries and so on. All this shows the strengthening of the influence of the investment and technological and business factors on innovative activity efficiency. Acknowledgements The work was prepared during implementation of Project No. 26.9642.2017/8.9 within the framework of the state task of the Ministry of Education and Science of Russia.

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References Blizorukov, M. G. (2008). Statistical methods of market analysis: Study guide (p. 75). Ekaterinburg: Institute of Management and Business of the Ural State University. Chayran, Y. A., & Belyakova, G. Y. (2014). Factors influencing the development of innovative activity. Creative Economy, 11, 162–170. Federal State Statistics Service (Rosstat). Access mode: https://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/science_and_innovations/science/#. Glagolev, S. N. (2009). Factors affecting the possibility of the adaptation of industrial enterprises to current conditions. Economic Analysis: Theory and Practice, 25, 63–66. Glisin, F. F., & Kalyuzhny, V. V. (2015). Assessment of the efficiency of scientific and innovative activities in foreign countries and in Russia. Innovations, 6, 32–36. Nadjafova, N. (2018). Essence and approaches to determining the effectiveness of innovation. Innov: Electronic Scientific Journal, 3(36), 7. Togushchakova, I. V. (2018). The choice of the innovative project of quantitative method. Bulletin of BSTU Named After VG Shukhov, 28, 116–119. Zudin, N. N. (2015). Interrelationship of the technological level of the sector with the characteristics of companies and government support. Innovations, 6, 61–70.

Establishing the Innovative Economic System of Russia Through Gaining Foreign Experience T. A. Sibgatullin, M. F. Musallyamova and C. Schnöller

Abstract This chapter examines the research results of the problematic aspect of introducing foreign innovation policy and the possible ways of creating a future innovative economic system of Russia. The main aim is to consider the possibility of establishing the innovative economic system of Russia through gaining foreign experience. Specifically, we discuss the peculiarities of the US innovative economic system and the key features of the Russian economy as well as innovation processes that take place in the Russian economy. The objects of analysis are the innovation systems of the USA and Russia. The main method of analyzing these two systems is the comparison of individual qualitative and quantitative indicators which could show the effectiveness of an innovation system and compare the strengths and weaknesses of the existing Russian innovation system.

1 Introduction In the era of innovative technologies, each country seeks to bring the national economy under the criterion of an “innovative economy” in various ways. Some countries attract innovative companies to their territory with favorable taxation conditions in order to create a special climate for the commercialization of research and development (R&D). For instance, the large city of Bangalore, India, positions itself as a scientific and innovative center. Other countries create an institutional basis for organizing their own scientific and technical developments by updating and improving their own legislation. Also, support measures of developing countries are oriented to maintaining innovation activity. T. A. Sibgatullin (B) · M. F. Musallyamova Institute of Management, Economics and Finance, Kazan Federal University, Kazan, Russia M. F. Musallyamova e-mail: [email protected] C. Schnöller AREA 47 Betriebs GmbH, Ötztaler Achstraße 1, 6430 Ötztal-Bahnhof, Austria e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_17

195

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In the scientific community, the concept of “innovation” is defined as follows: a completely new or newly introduced or modernized product that is the result of intellectual work, the basis of economic growth and national competitiveness of the country. The creation and implementation of innovative technologies, the encouragement of technological initiatives at the level of small and medium-sized enterprises, etc., have a positive impact on the socioeconomic development of the country. It also favorably influences its competitiveness in the world market and in the system of the international division of labor. In the innovation studies, such innovation, being the result of “new combinations” (Fagerberg 2003) of existing knowledge, capabilities and resources, is regarded as a major source of change in all economic activities. It is true for poor and rich countries (Edler and Fagerberg 2017), for low tech and high tech (von Tunzelmann and Acha 2004), for services (Gallouj and Djellal 2011), manufacturing in the public (Osborne and Brown 2013) and private sectors, and so on. For Schumpeter, a main reason for his distinction between invention and innovation was the realization of what matters economically and societally. This is not the idea itself, but its exploitation in the economic and social system. The economic historian and innovation scholar, Nathan Rosenberg, emphasized the importance of the exploitation phase. He pointed out that most important innovations go through drastic changes in their lifetimes. These changes may and often do totally transform their economic significance. Rosenberg pointed out that many of these improvements occur in the diffusion phase through interaction with various involved parties such as customers and suppliers. Hence, according to this view, innovation policy needs to focus on the creation of new solutions and their exploitation and diffusion, including many feedbacks back and forth that occur between the various phases of the innovation process. On the basis of these distinctions, three main types of innovation policy may be distinguished. Mission-oriented policies (Ergas 1986) are aimed at providing new solutions, which work in practice, at specific challenges that are on the political agenda. Invention-oriented policies have a narrower focus in the sense that they concentrate on the R&D/invention phase, and leave the possible exploitation and diffusion of the invention to the market. System-oriented policies are of more recent origin and focus on system-level features such as the degree of interaction between different parts of the system. The extent of some vital component of the system is in need of improvement or the capabilities of the actors that take part. A system-oriented approach will be considered in this scientific article by the example of the US innovation policy and the introduction of this approach in the Russian economy (Edler and Fagerberg 2017). Like any economic processes, innovations and the country’s innovative activity have certain impacts on the country’s economic development. 1. Innovative activities directly facilitate the development and introduction of new technologies in the economy, improving the production and management process (Pérez-Luño et al. 2011).

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2. Innovation gives a boost to new small and medium-sized innovative enterprises and to the emergence of new industries. So, the number of jobs increases that indirectly affects the economy of the country. At the same time, enterprises should have confidence in the fact that innovation is in demand and the state supports such initiatives. 3. The most important advantage of innovative activity is the development of new technologies. They in turn stimulate the inflow of capital (investments) into the domestic economy, while stimulating the investment activity of private companies. The relevance of this issue is that there is no universal way to turn the country’s economy into an innovative one. Currently, Russia can be attributed to the transitional type of economy. The Russian economy is capable of assimilating the achievements of other countries in the field of innovation after an adoption of particular measures that will eliminate inequality between the economies. For a comprehensive assessment of the results of innovation policy and an analysis of indicators of innovation activity, innovative policies of countries such as the USA and Russia were examined.

2 The Development of Innovation Processes in the US Economy The main prerequisite for the development of the innovative potential of the US economy was the fact that the World War II “bypassed” this country due to the remoteness of the continent from the main focus of military operations. They did not damage the economy of the country in comparison with the losses of other countries in Europe. This advantage has allowed the USA to take a leading position in the field of innovation. It should be noted that initially the level of fundamental science in the USA was not high enough. However, the “world leadership policy,” especially in the postwar years, allowed for unification of large corporations that accumulated capital from military orders and the public sector to achieve a common goal (David 1996). The world leadership policy of the USA, firstly, was aimed at: • Achieving leadership in all areas of development and maximizing personal benefits of the participants in the process; • Awareness of the need for scientific and technological progress at all levels of government; • Close cooperation between the state and corporate sectors in the regulation and implementation of major projects. The national innovation system of the USA began to emerge from the 1945s to 1950s shortly after the end of World War II. The beginning of the arms race in the Cold War with the Union of Soviet Socialist Republics (USSR) has also contributed to the growth of innovation activity. The US government played a big role in this process,

198 Fig. 1 Distribution of the US federal budget funds throughout the economic sector, including the research and development programs

T. A. Sibgatullin et al.

Distribution of R &D financing by each sector

Public sector 12% Universities 14% Industry 74%

providing the bulk of research and development funding not only for government laboratories and research institutes, but also for universities and the private sector (Fig. 1). According to the OECD report prepared for the National Innovation Ecosystems meeting, 12.2% of R&D programs were funded by the public sector, 13.2% by universities and 71.6% by industry as of 1980 (Statistical Abstract of the USA 1988). Also, the increase in the importance of innovative processes is evidenced by data which show the increase in the expenditure percentage on the development of science-intensive sectors of the US economy in the postwar years, including years during the arms race in the period from 1953 to 1987. In connection with the above reasons and assumptions, according to official statistics, the share of government funding of science development has constantly increased (David 1996). Such cooperation of the three institutional units has made it possible to achieve significant results in the construction of an innovative economic system. The system of creating and supporting R&D has three subsystems: • Universities; • National laboratories; • Innovative clusters. The working mechanism may be described as follows: 1. Universities train specialists in different fields and simultaneously take part in technological development and their own research. 2. National laboratories fulfill government orders. 3. Innovative clusters are engaged in high-tech production and research (Statistical Abstract of the USA 1988) (Fig. 2). The Research and Development Center financed from the federal budget (FFRDC), known in America as the Federal Contract Center until 1967, is a state research institute. It also carries out activities in the field of technical support. This

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Ratio of U.S. R&D to gross domestic product, by roles of federal, business, and other nonfederal funding for R&D: 1953–2015 3.00 Total

Percent to GDP

2.50 Federally funded

2.00 1.50

Business funded

1.00

Other nonfederal

0.50

1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

0.00

Year

Fig. 2 Expenditure on science development in the USA in the pre- and post-Cold War periods

institution is also known as an institution owned by the state/contractor (GOCO), which means that it is “owned by the government,” but “managed by non-state contractors.” According to the US Department of Defense data, the specific objectives of this organization are: 1. To support long-term competence in those areas of technological development in which the government cannot rely on the capabilities of the domestic or private sectors; 2. To develop and transfer important new technologies to the private sector so that the government can benefit from a broader knowledge base; 3. To participate in research programs that emphasize the evolution and demonstration of advanced concepts and technologies, as well as the transfer of technology. According to the data of Harvard Business School for 2013, the leading branch of innovative development is occupied by “information technologies,” whose share of approved patents is 33.5% of all approved patents in 2013 (namely 37,060). The second and third places in the number of approved patents are held by the branches “communication equipment and services” and “aerospace equipment and defense.” The progress of other industries is shown in Fig. 3. The size of bubbles illustrates an amount of awarded patents in each cluster. In conclusion, we would like to stress that the USA was able to build a perfect innovative economic system for 100 years. It still generates new ideas, technologies and products that are subsequently used by different groups of consumers. In turn, a flexible multi-level innovation system effectively supports the innovation activity of

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United States Innovation by Traded Cluster, 2013

Information Technology and Analytical Instruments

Share of U.S. Utility Patents Awarded, 2013, percent

40.00 35.00

Communications Equipment and Services

30.00

Aerospace Vehicles and Defense

25.00

Production Technology and Heavy Machinery

20.00

Biopharmaceuticals

15.00

Plastics Medical Devices

10.00

Automotive 5.00

-5.00

0.00 0.00

Lighting and Electrical Equipment 5.00

10.00

15.00

-5.00

Downstream Chemical Products Upstream Chemical Products

Changes in Share of U.S. Utility Patents Awarded, 1998-2013, percent

Fig. 3 Number of patents awarded in each cluster, 2013

small, medium and large enterprises at different levels (Information and statistical material “Statistics of Science and Education” 2016).

3 The Possibility of Introducing the US Experience to Create an Innovative Economic System in Russia Analyzing the experience in creating innovations and supporting them in other countries, we assume that it is sufficient to apply the same foreign practice to achieve the goal of creating an economy. In it, medium and large companies can easily engage in the creation of R&D and subsequently be commercialized. In practice, such decisions are made but often do not justify hopes due to different features between national economies. The process of implementation of a foreign experience into a national economy is very similar to the process of implantation of a donor organ from a healthy person into the body of a patient suffering from a particular ailment. The donor organ ideally works in the body of the donor, but the body of the patient may not receive the organ and will reject it due to various genetic characteristics of the organism. The same thing happens at the level of national economies. They are not

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201

always able to accept foreign experience in its pure form, and therefore they adapt measures and reforms to make them applicable in certain conditions. If we describe the possibility of using the experience of the USA, it is necessary to give a brief description of the development of the Russian economy during the twentieth century: • The Russian/Soviet economy belonged to the command-administrative type. The main components of the war-time economical command economy were surplus appropriation and the nationalization of industrial enterprises. • World War II caused significant damage to the Soviet economy, but the Cold War forced the USSR to join the arms race. This led to an increased level of industrial production, but in the 1970s–1980s the Soviet economic system entered a phase of degradation, resulting in increased worker expropriation. • The qualitative backlog of the Soviet economy from the developed countries of the West grew. The shadow economy flourished (due to worldwide energy prices decreasing in the 1980s). The Soviet economy was in a state of crisis with its dissolution in 1992 being the final step undermining the economy, destroying its basic principles, sharply accelerating the course of market reforms. This in turn greatly jeopardized the country’s innovative development. Therefore, there is an imbalance in the costs of internal research and development in Russia. This happens because the state comprises the largest financial source. Small and medium businesses are not able to bear the risks associated with spending on R&D. Moreover, the government tries to maintain the level of innovation activity at a certain level, while in developed countries (Japan, Germany, Sweden, Switzerland, the UK and the USA) the majority of the R&D expenditure burden belongs to the source business sector (Information and statistical material “Statistics Of Science And Education” 2016) (Fig. 4). However, despite this trend, according to the Federal State Statistics Service from 2013 to 2016, there was a generally positive trend toward an increase in spending on civilian science from the federal budget, where scientific research funding has the largest share. Taking into an account the fact that there was no advanced innovation system, it is worth considering the attempt to create an innovative system with the creation of the Skolkovo innovation complex. It would help improve the existing innovation system. On September 21, 2010, the State Duma adopted Federal Law No. 244 of September 28, 2010, “About the Skolkovo Innovation Center.” It was approved by the Federation Council on September 22, 2010. The work of Skolkovo is based on a cluster-type innovation complex, which is an example of borrowing a foreign approach to the system of creating and supporting innovative technologies. The creation of this innovative cluster was the beginning in the development of the innovative sector of Russian economy.

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Structure of internal costs for research and development in Russia and leading countries by sources of funding: 2014 Sources of the government sector

Sources of the business sector

Other national sources

Funds of foreign sources

Japan Countries

Sweden Switzerland USA Germany United Kingdom Russia 0%

20%

40% Share,%

60%

80%

100%

Fig. 4 Structure of internal costs for R&D according to sources of funding in Russia and other leading countries in 2014

Currently, there are five clusters in Skolkovo: 1. 2. 3. 4. 5.

Information technology; Energy-efficient technologies; Nuclear technologies; Biomedical technology; Space technology and telecommunications.

According to the 2016 annual report, the total revenue of the fund’s participants from the results of research activities was estimated at 50 billion rubles. Additionally, Skolkovo created more than 22,000 jobs and the volume of private investment grew by 30%, exceeding the value of 19 billion rubles. Investments in the Skolkovo ecosystem have doubled during the last 2 years. In 2017, Skolkovo companies began to populate the Skolkovo Technopark, where 140 company exhibitions are located. Forty centers of collective use were accredited in the Technopark. In the territory of Innograd, there were 17 R&D centers of the fund’s partners, including a unique training and research center Boeing aircraft. The chairman of the board paid special attention to the services of the Skolkovo Intellectual Property Center. In 2016, 656 applications for registration of intellectual property objects and 120 international applications were submitted. Eighty percentage of the total number of applications were filed with the assistance of the Skolkovo Intellectual Property Center.

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However, despite the advantages of this complex, its following shortcomings are highlighted (due to the complex ignoring foreign experience): 1. Critics point out that the organization of the Skolkovo’s work is not combined with international experience. The scientists are not members of the Foundation Council, and they have created a separate “Scientific Advisory Council.” As a result, it can be concluded that the harmonious cooperation of fundamental academic science and applied R&D is not planned. 2. Lack of work results that may be seen on the mass market. 3. The experience of the Malaysian “city of the future” Cyberjaya created in the late 1990s is ignored. According to some sources, this city has been desolated for 10 years after its foundation. No high-tech production has been established in this territory. Cyberjaya was expected to become a “Silicon Valley of Malaysia.” In the mid-1990s, the development of this project was accompanied with success. However due to the Asian financial crisis of 1997, the government bought out a controlling stake of the project’s developers. The authorities decided to control the process of building an innovative city themselves, but initially they refused this approach. Presently, there are more than 500 companies in the city, including the Indian Wipro, Ericsson, Motorola, BMW and IBM. According to the plan, the project should have moved to a new level. But due to the 2008 world financial crisis, 15,000 Malaysians engaged in the production of industrial and consumer electronics in Cyberjaya were made redundant. The conclusion is that deterrent state regulation and the decrease in demand for electronics in the USA, Japan and EU countries seriously damaged the high-tech sector, providing up to 40% of Malaysia’s export earnings. 4. The experience of the Indian city of Bangalore is ignored. The innovative processes taking place in this city do not relate to economic problems in the country and their resolution. Large corporations use a highly skilled, but low-cost labor force, solving problems using their own research programs, transferring resources to outsourcing. In Bangalore, Western companies use a well-educated but lowwage workforce, the initial salary of an IT specialist being approximately $3600 per year. But in the USA, it is approximately more than $20,000. The “Brain Drain” is not surprising as for many Indian programmers moved from Bangalore to America. Today in Bangalore, there are 66 foreign companies in the IT field (Pérez-Luño et al. 2011). However, there is also a positive example of the successful creation of an innovative infrastructure in the field of information technology in the Republic of Tatarstan, being the city of Innopolis (Table 1). It is assumed that Tatarstan will become a testing ground, where it will be possible to evaluate the project of an “advanced” environment for the development of IT. If expectations do not coincide with reality, then such projects will be attempted in the territories of other regions of the Russian Federation. It should be noted that such a policy is aimed at developing only IT. Therefore, it is necessary to support the initiatives of small and medium-sized innovative enterprises to create conditions for

21,2 million rubles 382,5 million rubles

Expenditures

441 million rubles

Other income

392,2 million rubles 211,8 million rubles

462,6 million rubles

Income

2014

Revenue received from sponsors

2013

Year

Table 1 Annual budget of the Innopolis project

643,2 million rubles

218,6 million rubles

535,9 million rubles

7545 million rubles

2015

4390 million rubles

4390 million rubles

2016

4509 million rubles

4509 million rubles

2017

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maintaining and increasing innovative activity both in the Republic of Tatarstan and in other regions of Russia. In our opinion, it is necessary to create an innovative complex through which it will be possible to effectively commercialize innovative ideas in any field. The mission of the innovation complex is obtaining the status of an “innovative economy.” The main goals of the innovation complex are to: 1. Create an effective innovative infrastructure in the country; 2. Produce an inflow of investments to the Republic of Tatarstan and increase the investment appeal of the region; 3. Create broadband platforms for the implementation and commercialization of innovative projects and ideas; 4. Provide comprehensive support for the innovation initiative (regulatory, financial, tax benefits, informational and expert support, educational programs, grant property on a preferential and non-reimbursable basis); 5. Target and rationalize the use of expenditure items of the federal and regional budgets to promote the innovation potential of the region; 6. Create a favorable economic climate for the revival of small and medium-sized innovative enterprises. As a result, a self-sufficient, self-developing national innovation economic system should materialize. Therefore, it is necessary to organize the following system tasks and subtasks for implementation of this project (Fig. 5).

Fig. 5 System for implementing the tasks and subtasks presented for the creation of an innovative economic system in Russia

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Table 2 SWOT analysis of the Russian innovative economic system Strengths The presence of rich mineral deposits; availability of a qualified and cheap labor force (in comparison with European countries); rapid economic growth and recovery from the financial and economic crisis of 2008 and overcoming the sanctions imposed in 2014; modernization of the industrial sector; development of management in different spheres; information and technological resources

Weaknesses The degree of support for small innovative companies (the question remains open—at what ratio will support be appropriate, and is it not better to direct resources to support technology lending from large companies); low level of innovative business activity and immunity to innovation; weakly developed innovation culture; lack of high domestic demand for innovation; low level of cooperation between the state, private sector and universities; obsolete technological structure due to fixed capital; monopolization of the market; undeveloped market of venture investment

Opportunities Technological development, increase in the number of technological centers; increasing competition through reform of some industries; entering new international markets by improving products and encouraging national innovation initiatives

Threats Technological backwardness; reduction of incentives for entrepreneurial activity due to state interference and increased tax burden; strengthened protectionism; innovative activity of other countries, which will prevent entry into new markets

Based on all of the above, we can summarize illustrating the main points of the study in a SWOT analysis table of the national innovation system (Table 2). Based on the compiled matrix, we made an analysis, comparing strengths and weaknesses with opportunities and threats. 1. The field of strengths and opportunities is the availability of qualified labor force and rapid economic growth, combined with the reform of new industries. This fact can become a driving force for improving the innovative and investment climate in the country. 2. The field of strengths and threats is that reforming and modernizing the industrial sector can minimize existing threats, which are represented by technical backwardness. 3. The field of weaknesses and opportunities: 4. The negative sides are associated with a poorly developed innovative culture, but technological development and increase of technology centers can eliminate this weakness. 5. The field of weaknesses and threats is an ineffective support mechanism of small and medium-sized innovative enterprises at the regional level leading to a decrease in innovative business activity. This affects the innovation potential of the economy of the regions and the country as a whole.

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4 Conclusion It can be concluded that Russia’s innovation policy has moved toward long-term planning, which would bring many advantages that many countries have. However, there are no signs of an innovative economy in the elements of government policy. The Russian model of innovation development is aimed at borrowing experience, but borrowing should not consist of an unsystematic copying of experience. It is necessary to take into account the framework conditions to identify the strengths and weaknesses of the national innovation system, taking into account the regional features of innovation processes. It is necessary to conduct a structural analysis of the economic structure, regulatory and legal framework and its formation, the chosen development strategy, and only then decide on the possibility of adapting the model under study to the national economy.

References David, C. (1996). The US national innovation system: Recent developments in structure and knowledge flows (p. 1). Edler, J., & Fagerberg, J. (2017). Innovation policy: What, why, and how. Oxford Review of Economic Policy, 33(1), 2–23. Ergas, H. (1986). Does technology policy matter? CEPS Papers No. 29, Brussels, Centre for European Studies. Fagerberg, J. (2003). Schumpeter and the revival of evolutionary economics: An appraisal of the literature. Journal of Evolutionary Economics, 13(2), 125–159. Gallouj, F., & Djellal, F. (2011). The handbook of innovation and services: A multi-disciplinary perspective. Cheltenham: Edward Elgar. Information and statistical material “Statistics of science and education”. (2016). Access mode: https://www.csrs.ru/archive/stat_2016_finance/finance_2016.pdf. Cited March 21, 2018. Osborne, S. P., & Brown, L. (2013). Handbook of innovation in public services. Cheltenham: Edward Elgar. Pérez-Luño, A., Wiklund, J., & Valle, R. (2011). The Dual nature of innovative activity: How entrepreneurial orientation influences innovation generation and adoption. Journal of Business Venturing, 26, 555–557. Statistical Abstract of the United States. (1988). Wash. (p. 557). von Tunzelmann, N., & Acha, V. (2004). Innovation in “Low-tech” industries. In J. Fagerberg, D. Mowery, & R. Nelson (Eds.), The Oxford handbook of innovation (pp. 407–432). Oxford: Oxford University Press.

Inter-budgetary Transfers from the Standpoint of Russian Federalism Irina I. Zedgenizova and Elena L. Vlasova

Abstract This work analyzes inter-budgetary transfers from the point of view of Russian federalism. Federalism as the basis of the constitutional system of Russia is expressed in the budgetary activity of the state. It has a significant impact on all financial relations. The economic role of inter-budgetary transfers in providing a balance of the regional budget by the example of Irkutsk region is considered.

1 Federalism is a Basis of the Constitutional System of Russia Many works were devoted to the topic of inter-budgetary transfers, including those by professor Khimicheva (1979), Bugarin and Marciniuk (2017). Problems of financial interrelations between the Federal Government and regions are relevant for federative states. Let us consider this topic from the point of view of Russian federalism. Federalism is one of the foundations of the constitutional system of Russia. “The Russian Federation—Russia, according to the article of the first Constitution of the Russian Federation (Constitution of the Russian Federation 2012), is a democratic federative legal state with a republican form of government.” The term “federation” is derived from Latin “federate”—meaning an association, union (Dictionary of foreign words 2014). However, federation is not a union of separate states, but a single state that can be allied if it is based on a union agreement or it does not have the quality of an allied state. But in any case, the federation is a state exercising public authority along with those states (or other entities) that are part of it. In our opinion, the Russian Federation is virtually a federation of treatyconstitutional type based on the Federal Treaty signed on March, 31, 1992. The main

I. I. Zedgenizova (B) · E. L. Vlasova National Research Irkutsk State Technical University (Zedgenizova I.I); Russian State University of Justice (Vlasova E.L.), Irkutsk, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_18

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provisions of the contract are reflected in the Constitution of the Russian Federation of 1993, which is an act of higher legal force. The process of the highest legal consolidation of the structure of Russia on federal principles means elevation of federalism to the level of the fundamental principle of its constitutional structure (Mikhaleva 2010). Unfortunately, the Constitution of the Russian Federation has ambiguously enshrined the model of federalism in force, proclaiming, on the one hand, the equality of all subjects, and on the other—recognizing an opportunity of mutual delegation of authorities of the Federation and its subjects on a contractual basis. Analysis of the Constitution of the Russian Federation (hereinafter RF) allows singling out a number of legal signs characterizing the federal structure of Russia, the implementation of which will contribute to the successful development of the budgetary activity of the state and municipal entities. The most important elements of the constitutional and legal status of Russia as a federative state are: • exercising state power based on a constitutional or contractual (constitutional– contractual) distribution of powers among federal bodies of state power and the authorities of the Federation subjects; • independence of subjects of federative relations in exercising their powers, the right of a participant of federative relations to bilateral regulation of state authorities; • equality of subjects of the Federation; • the right to choose by the subjects of the Federation (within the limits defined by the RF Constitution or the contract) the form of their political organization; • the system-forming unity and inextricable interrelation of the subjects of the Federation; • the right of the Federation solely to join larger state political unions (Krokhina 13). In our opinion, a significant element of the legal status of the Russian Federation is the unity of financial policy and budgetary activities at all levels of the state (federal, regional, and local), taking into account the peculiarities and traditions of municipal entities. Thus, from a legal point of view, the Federation, as a form of statehood of Russia, is a single state that unites in itself the territories of subjects of the Russian Federation. They represent independent state entities possessing public authority in respect of those issues that are not within the exclusive competence of the federal public authority. Federalism is a political and legal concept determining the form of statehood. In each federative state, in addition to other relations, there are also budgetary relations among the center, subjects, and municipal entities. Therefore, it is necessary to fully agree with the conclusion of L. K. Karapetyan about the possibility of “organizing and regulating these relations in accordance with the principles of federalism” (Karapetyan 2006).

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2 The Principle of Federalism in the Budget Law In modern Russia, the principle of federalism has become universal. It began to determine the relationships between the center and the constituent parts of the Russian state—the subjects of the Federation. Thus, the basis of federalism is the relationships between the center and the regions. And it is safe to say that the budget relations refer to the most important aspects of federalism (Elasar 1977). Finding its reflection in the budget law, the principle of federalism transfers its essence to the budget activity of the state. At the same time, the constituent elements of the principle of federalism are interpreted and acquire characteristic features. They are in the sphere of differentiation of budget revenues and expenses, budgetary competence of the Russian Federation and its subjects, inter-budgetary relations, rendering financial assistance from the federal budget to the budgets of RF entities, etc. In addition, the principle of federalism in the budget law exerts a significant influence on the budgetary activity of municipal entities. Thus, the principle of federalism in the budget law as the initial onset of the state budget activity is expressed in the combination of national financial interests and the interests of RF subjects. It consists in distribution of budget revenues and expenses, as well as in the delimitation of budgetary competence between the Russian Federation and its subjects.

2.1 Budgetary Activities of the State Budgetary activities are fundamental for development of federative relations in the state and formation of institutions of local self-government. By means of mobilization, distribution, and use of centralized funds, material security of authorities of the Russian Federation, its subjects and municipal entities are provided. In the field of budgetary activity, the state and state-territorial entities have their own jurisdiction, established by the RF Constitution and other regulatory and legal acts. A characteristic peculiarity of the budgetary activity in conditions of the federative state should be considered the area of joint jurisdiction of the Russian Federation and its subjects. The content of the state budgetary activity is manifested in its functions, that is, the objective, internal purpose of this kind of activity. Analysis of the legislation in force and the practice of its application allow distinguishing the following functions of the state budget activity: 1. accumulating function—in the course of budgetary activity, the state provides the revenue part of the budget, for which the methods of voluntary and obligatory payments are used; 2. equalization function of incomes in the budget system of the state—in the course of implementing budgetary activity, the redistribution of national income and part of national wealth are carried out through the tax system and transfer mechanisms.

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These functions of budgetary activity have a common scope of application and are interdependent. An integral element in each of them includes a control function, which is called the budget in accordance with the content of entire activity (Gracheva 2010). Thus, the budgetary activity of the state is interdisciplinary, complex, and functional. It is important that the subjects of the budget law are carriers of budget rights and obligations in the field of education, distribution, and use of the centralized monetary fund of a certain territory. Then, potential and real participants of budget activity of the state will be the Russian Federation as a whole and its subjects (Khimicheva 1979). The objects of budgetary activity of the state are the corresponding centralized monetary funds and the budgetary structure of the state. Therefore, it can be stated that the very existence of budgetary activity is determined by objective necessity of having budgets and systematization of the latter at the disposal of state authorities. One should pay particular attention to the fact that it is the objects of budgetary activity that most vividly show the manifestation of the principle of federalism in budget law. Budgetary activity is carried out by representative and executive authorities of the Russian Federation, its subjects and local governments. This is conditioned by distribution of functions, content and scale of education, distribution, and use of relevant budgetary funds. In budgetary activity, the role of state bodies and local self-government bodies is manifested not only as carriers of power, but also as owners of business entities. In the economic turnover, state and local government bodies act as ordinary subjects, creating enterprises, concluding various kinds of transactions. They are aimed not at making profit, but at solving problems of the population of the territory (Martemnov 1994). It should be noted that the subjects of the Russian Federation and municipal entities realize budgetary activity not in isolation from the federal center. The scope of budgetary activity belongs to the jurisdiction of both federal agencies and subjects of the Federation, as well as local governments. In addition, there is an area of joint jurisdiction of the Federation and its subjects in the form of establishing general principles of budgetary activity of municipal entities. This follows from Art. 72 of the Constitution of the Russian Federation. Therefore, in many situations, the success of budget activity depends on the interaction of all territorial subjects of budget law. Each subject of the federation, administrative-territorial, and municipal formations have their own budgets, the funds of which are intended to provide tasks and functions related to the objects of their jurisdiction. An aggregate of federal budgets, budgets of the subjects of the Russian Federation, local budgets, and budgets of state extra-budgetary funds (hereinafter referred to as the State Extra-budgetary Fund— SEF) make up the budget system of RF. However, often, when forming their budgets, the authorities of a lower territorial level do not have enough funds from their own revenues. This happens for reasons beyond control to ensure the minimum necessary expenses in accordance with the functions and authorities assigned to them. Therefore, all budgets included in the budget system of the country are interconnected in the framework of inter-budgetary relations (Polyak 2000).

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2.2 Inter-Budgetary Relations The budget code of the Russian Federation defines inter-budgetary relations as “the relationships between public legal entities concerning the issues of regulation of budget legal relations, organization and implementation of the budget process” (Budget Code of the Russian Federation 2017). Intergovernmental relations are relations that exist in any state having administrative-territorial division, but they are structured on different principles. To begin with, in the unitary states, there is a two-level budget system. A high level of centralization of budgetary funds, absence or an insignificant amount of budgetary rights of subordinate authorities corresponds to it. Federal budget systems, on the contrary, are characterized by a high degree of independence of territorial budgets, while respecting the unity of state interests. Federal budget systems are three-level. The principles on the basis of which federal budgetary systems operate are the principles of budgetary federalism. Budgetary (financial) federalism is a system for managing public finances that is based on distinguishing between different levels of authority of budgetary rights and authorities in the area of formation and expenditure of budgetary funds. This happens while combining the interests of the budget process participants at all levels of the country’s budget system and interests of society as a whole. The fact that some authors included the budgets of municipal entities in the system of budgetary federalism is questionable, since the presence of the latter is a reflection of the state structure of Russia, but not the main condition for existence of the federation. Municipal entities do not enter into genuinely federative relations directly with the Russian Federation and its subjects. Moreover, in accordance with the RF Constitution, local governments are not included in the system of government bodies (Art. 12). Consequently, from a legal point of view, municipal entities are part of the subjects of budget legal relations, but are not subjects of budget legal relations at the federal level. This discrepancy of the subject composition also determines the different scope of concepts “budget legal relations” and “budget legal relations of a federal state” (fiscal federalism): budget legal relations are broader in content. However, because of the importance of subjects and financial resources circulating as a result of their budgetary activity, budget legal relations of federative character represent the main element of budget legal relations. It should be noted that some authors do not include the relations, arising in the process of financial activity of local governments (including budget ones), into the subject of financial law at all (Polyak 2000). N. I. Khimicheva rightly criticized this viewpoint noting that supporters of the mentioned opinion do not take into account the public nature of financial activity of local governments and the principle of unity of the financial system of the Russian Federation, expressed, in particular, in the unity of its legal base. The latter is clearly manifested in the RF budget code, which contains legal norms relating to the budget system of the Russian Federation as a whole with budgets of all levels included into it (Dadashev and Chernik 1997).

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In order to clarify the nature and legal peculiarities of manifestation of the principle of federalism in budgetary legal relations, it is necessary to classify the latter on various grounds: 1. The original ground is a three-tier structure of the budgetary system of the Russian Federation, which determines the division of budgetary legal relations into budgetary legal relations relatively the federal budget, budgetary legal relations concerning the budgets of subjects of the Russian Federation, budgetary legal relations concerning the budgets of municipal entities. Each of them is independent and has its own specificity of legal regulation, conditioned by the legal status of the territory within which it develops. 2. The federal structure of the Russian state is a criterion for subdivision of budget legal relations into vertical and horizontal. The former mediates relations among the Federation as a whole, its subjects and municipal entities, as a rule, on the basis of subordination. The latter reflects the relations among territories equal in legal status: subjects of the Federation relative to each other or municipal entities, respectively. In conditions of the federal state, vertical budget relations are predominant. It is also important in the scientific and practical terms to distinguish vertical budget legal relations by the constitutional status of the participants. Relations in the budget sphere between state entities, as well as between state and municipal territorial entities, are delimited in this case. In our opinion, it is expedient to call the mentioned groups of budgetary legal relations as inter-budgetary legal relations. Inter-budgetary legal relations are different from the entire set of budgetary legal relations by a narrower circle of participants—only territorial subjects of budget law are involved in inter-budgetary relations: the Russian Federation, its subjects and municipal formations. The federal-legal nature of budgetary activity covers only state budgetary relations, since it is the Russian Federation as a whole and its subjects that are the necessary attributes of the federal state. The presence of local government emphasizes rather a peculiarity of the state structure of the Russian Federation. 3. Based on the quantitative composition of the participants, there are bilateral and multilateral budgetary legal relations. The undertaken classification allows identifying certain features of budgetary legal relations conditioned by the state structure of the Russian Federation: (a) they arise in connection with the formation, distribution and use of a centralized, state or municipal monetary fund of the relevant territory; (b) the rights and obligations of subjects of legal relations are determined by formation and execution of the budget as the main financial plan of the state, state or municipal formation; (c) the state, state or municipal formation or the relevant state and local government bodies, representing their interests, participate necessarily in the budgetary legal relations. (Alymurzaev 1998)

The peculiarity of budgetary legal relations of a federative state should be constant presence of a certain circle of participants: a state (Russian Federation), on

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the one hand, and a state-territorial entity (represented by the subject of the Russian Federation)—on the other. The fact that the state itself enters into legal relations not only as a single whole, but also as a system of subjects of the Federation should be considered as the specificity of the parties of budgetary legal relations of a federal state.

3 Grants, Subventions and Subsidies—Forms of Inter-Budgetary Transfers A distinctive feature of budgetary legal relations of federative nature is a more limited object of legal regulation—centralized monetary funds (budgets) of the federal and regional levels, their external legal implementation, and legal forms of budgetary regulation. Consequently, in the entire mass of budgetary legal relations, the relations between the Russian Federation as a whole and its subjects are singled out, that is, budgetary legal relations of federal nature. Thus, the principle of federalism in the budgetary activity of the state is realized through budgetary legal relations of the federal level. The main task of budgetary federalism consists in selection of the most effective model of budgetary relations in specific economic and political conditions. The selection of the model is made using inter-budgetary transfers. Inter-budgetary transfers are irrevocable and gratuitous financial aid provided by one level of the budget system for another level of the RF budget system. In this case, the transfer of funds can be both free of charge (in the form of grants, subsidies, and subventions) and returnable (in the form of budget loans). The goal of inter-budgetary transfers is to equalize the level of fiscal capacity for the purpose of co-financing, financial support for expenditure commitments, and other purposes. It should be noted that subsidies, subventions, and subsidies as methods of providing financial resources to territorial budgets are imperfect. In our opinion, they lack stimulating properties and create dependency attitudes among local administrations. It is clear that, depending on the territorial location, infrastructure development, climatic, resource, socio-economic, and other factors, the RF subjects are not always in the same financial position, which is the basis for a policy of equalization and balance of budgets. In other words, there are “rich” RF subjects, for example, Moscow, which does not receive financial aid from the budget, and “poor” ones, for example, such as the Tyva Republic. Nevertheless, it seems that the practice of intergovernmental transfers does not contribute to the development of economic initiatives of local administrations, reduces their impact on economic processes occurring in the territory, and on this basis, it reduces the possibility of over-fulfillment of their revenues, weakens financial control. It is absolutely impossible to refuse them at present. With the instability of sources of revenues, their situation can only worsen. It would be more expedient

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Table 1 Budget of Irkutsk region

Budget of 2017 in Irkutsk region

Budget of 2018 in Irkutsk region

Revenues

126.2 billion RUB

126.8 billion RUB

Expenditures

134.6 billion RUB

133.6 billion RUB

Deficit

8.4 billion RUB

6.8 billion RUB

Inter-budgetary transfers

20.9 billion RUB

21.9 billion RUB

to give the rights and resources to the regions and cities, which themselves, taking into account the various local conditions, would deal with the tasks relevant to them. The centralization of all financial resources at the federal budget level has the following consequences: (1) with the increasing concentration of financial resources in the central budget, the state of territorial finances is deteriorating. Thus, the public debt of Irkutsk region as of July, 1, 2018, reduced to 12.7 billion. RUB. Since the beginning of the year, the decline amounted to 1.1 billion. RUB. However, the upper limit of the state domestic debt of Irkutsk region as of January, 1, 2019, was set in the amount of 32.1 billion. RUB. Irkutsk region is still a subsidized region (Khimicheva 2012). As the comparative table shows, Irkutsk region still cannot overcome the budget deficit. The size of inter-budgetary transfers is growing, and this implies the inertia of regional authorities and their incapability or unwillingness to balance the budget of the region (Table 1). (2) a decrease in the share of territorial finances entails worsening of the economic and social situation in the regions. Thus, in 2017 subsidies in Irkutsk region amounted to 7.17 billion RUB. In 2017, the region received 962 billion RUB for salaries of public sector employees and 255 million as budget loans with approved budget revenues for 2016–2017 (Law of Irkutsk region as of July 5, 2017 No. 53-OZ (2017) “On Amendments to the Law of Irkutsk Region on the Regional Budget for 2017 and for the Planning Period of 2018 and 2019” 2017). The region is not coping with its financial problems; (3) a decrease in the parameters of economic and social development of regions leads to deterioration of these indicators as a whole throughout the country. Thus, the deficit of the 2018 federal budget is almost 1.27 trillion RUB. The RF Budget Code distinguishes between direct transfers (from superior to subordinate budget), Art. 131, 132, 132.1, 133, 137–139, 139.1, 140, 142.1, 142.4 of the RF Budgetary Code and reverse transfers (from subordinate to superior budget) Art. 1338.1, 142.2, 142.3, 142.5 of the RF Budgetary Code.

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Despite the mentioned negative consequences of inter-budgetary transfers, one should note several positive goals that are achieved with their help, namely: (1) budget leveling is carried out, and, thus, citizens are provided with equal provision of state and municipal services throughout the RF territory; (2) direct financial support is provided to those public entities that, due to economic, social, and other reasons, cannot be provided with sufficient by volume tax and non-tax revenues; (3) a balance of all budgets is achieved. Earlier, we identified three main forms of inter-budgetary transfers: grants for equalization of fiscal security, subsidies, and subventions.

3.1 Grants Grants as a type of inter-budgetary transfers are used exclusively for budget equalization of fiscal capacity of RF subjects and municipal formations (hereinafter MF) with insufficiency of own tax and non-tax revenues of budgets to resolve issues related to their competence. There are grants for equalization of fiscal capacity of RF subjects (Art. 131 of the RF Budgetary Code), grants for equalization of fiscal capacity of settlements and grants for equalization of fiscal capacity of municipal regions (urban districts) (Art. 137, 138, 142, 142.1 of the RF Budgetary Code). The volume, conditions, and procedure for provision of grants depend on the level of fiscal capacity of subjects of RF and MF. The equalization mechanisms are based on the minimum required estimated level of fiscal capacity per inhabitant. Within them, a set of budget services should be provided according to the list of issues of local importance for each type of MF. This level is annually established by the law of the RF subject, taking into account the real possibilities of its fiscal capacity and may differ for urban districts, settlements, and municipal regions, taking into account the scope of their functions. MF, the level of budgetary security of which at the expense of revenues collected on their territories is lower than the estimated one, receive grants (par. 4 of Art. 131 of the RF Budgetary Code). Based on the level of fiscal capacity of RF subjects and MF, there are: – heavily subsidized RF subjects, in which the share of the federal budget funds exceeds 60% of the volume of own revenues during two of the last three reporting financial years; – medium-subsidized RF subjects, whose share of federal budget funds ranges from 20 to 60% of the volume of own revenues; – low-subsidized RF subjects receiving funds from the federal budget, not exceeding 20% of their revenue. As for the subsidies for equalization of fiscal capacity of settlements, it is provided to all settlements except for those whose estimated tax revenues exceed the level of

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fiscal capacity established by the law of the RF subject (par. 13 of Art. 377 of the RF Budget Code). This level serves as a criterion with respect to which the budget equalization of all MF on the territory of a certain RF subject is carried out. However, Art. 137 of the RF Budget Code establishes two exceptions to the general rule. Thus, Clause 4 of Art. 137 of the RF Budget Code provides that grants for equalization of fiscal capacity for settlement may be replaced with additional standards of deductions from personal income tax. In this case, MF accepts the “risk” of insufficient tax revenue, received for itself, and the income received additionally is not subject to withdrawal. The purpose of this provision is to stimulate the economic initiative of local governments. The second exception is related to the fact that municipal districts may be vested with state authorities in calculation and provision of grants to the budgets of settlements (par. 5 of Art. 137 of the RF Budget Code). In this case, grants are provided not from the regional budget, but from the budget of the municipal district, based on the number of residents in MF and other necessary indicators. The condition for obtaining grants is compliance with the current tax and budget legislation (par. 1 of Art. 136 of the RF Budget Code). In case of their non-observance, the provision of inter-budgetary transfers may be suspended (par. 5 of Art. 136 of the RF Budget Code). Receiving grants provides for a number of restrictions on the rights of MF (Clause 2–4 of Art. 136 of the RF Budget Code). They are bans on exceeding the standards for expenditures on wages of deputies, established by the state bodies of the RF subject, and the maintenance of local self-government; the inadmissibility of the establishment and execution of expenditure commitments not related to the authorities of local governments; mandatory signing of agreements with the financial body of the RF subject. Thus, grants as an object of federative inter-budgetary relations are budget funds. They are not stipulated by the target area in a firmly expressed amount provided based on the legal act from the federal budget by the decision of the Federal Assembly to the budgets of the RF subjects. It is done with a view to equalize their minimum fiscal capacity in case of insufficiency of own and control revenues.

3.2 Subventions Subvention is intended to solve a different task—to provide funds for transferred state authorities from RF to the RF subjects and from the RF subjects to MO (Art. 133 and 140 of the RF Budgetary Code). The purpose of granting subventions is the financial support of expenditure commitments arising during exercising the authorities of a higher public legal formation, transferred for the execution to a lower public legal formation, that is, delegated authorities. Within the framework of the authorities transfer, a number of important social issues are resolved.

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1. the organization of the activity of commissions for the juvenile justice and protection of rights; 2. organization and implementation of activity in guardianship and custody; 3. social support for orphans and children left without parental care; 4. implementation of comprehensive educational programs in municipal educational organizations and others (Government of Irkutsk region 2018). It is typical that the provision of subsidies from the federal budget for exercising state authorities is not bound by any terms and does not entail any restrictions in authorities. This is explained by the fact that entrusting the RF subjects with the duties of federal bodies must be accompanied by appropriate financial support, the reduction of which for reasons not related to the delegated authorities themselves is unacceptable (Tsindeliani 2017). The distribution of subventions among local budgets is carried out according to the same methodology for all MF and is reflected in the law of the RF subject on the budget for each MF and subvention type. Thus, subventions have a number of specific features that distinguish them from other types of intergovernmental transfers, namely: (1) (2) (3) (4)

purpose of the allocated funds; compensatory nature that provides funding for delegated authorities; funds are transferred from a higher budget to a subordinate budget; transfer of authorities is executed by a regulatory legal act—agreement on the transfer of a part of authorities; (5) based on the results, the recipient of the subvention reports to the higher budget.

3.3 Subsidies Subsidies are another form of intergovernmental transfers, used when the Russian Federation or a RF subject participates in co-financing of expenditure obligations arising from exercising the authorities of the state bodies of the RF subjects and local governments on issues of local importance (Art. 132, 139 of the RF Budgetary Code). A distinctive feature of the subsidy is the term of its provision—co-financing, that is, additional financing of the expenditure obligations of the RF subjects related to their own expenditure commitments and financed from their own revenues. The granting of subsidies is provided for the fulfillment of the authorities of the state bodies of the RF subjects of social nature, for example, the provision of targeted subsidies to citizens for payment of housing and utilities, social support for certain categories of citizens. Subsidies are also used in case of necessity of financial support at the expense of the federal budget for the construction of regional property objects (Art. 79.1 of the RF Budget Code), projects of long-term programs (par. 4 of Art. 1779 of the RF Budget Code). The properties of subsidies are manifested most vividly in comparison with other forms of inter-budgetary transfers. There are

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two significant differences between a subsidy and a subvention. First, a subvention implies full coverage of the costs of a public entity exercising the delegated authority, and the subsidy is only partial (shared) financing. Second, a subvention is used to finance own, but transferred authorities, and the subsidy is used to finance (cofinance) “alien” authorities, but precisely those in which the subject providing the subsidy is interested. The common feature for a subsidy and a subvention is presence of a specific purpose for which these funds can be used, as well as the possibility of their return (clause 5 of Art. 2242 of the RF Budget Code). The presence of the purpose implies control over the targeted use and does not exclude coercive measures, including liability, in the event of violations. The equalization grant is distinguished by the lack of a single goal, the use of budget funds in this case covers the need to finance the entire set of authorities related to the recipient level. Summarizing all the above-mentioned, one can conclude that a sufficiently long existence of norms in the RF Budget Code, allowing for inter-budgetary transfers, makes one think that the legislator is looking for other frameworks that take into account the specifics of obtaining budgetary funds or specific goals of such transfer, but it is ineffective nowadays. One of the ways to improve budget legislation should be the desire for its further unification. To reduce the administrative burden on local governments and minimize budgetary expenditures, it seems that the most optimal way would be the introduction of the institute of state monitoring of the exercising by local governments of individual state authorities, as well as the provision of a single subvention to local budgets (Popov and Olenin 2015). The single subvention is a documentarily prepared list (registry) of all transferred state authorities and, accordingly, the amount of subventions provided in the context of MF. At the same time, expenses should be planned for the next fiscal year and planning period. The introduction of the registry allows automating the activity of calculating subventions and of necessary property provided to local authorities, as well as facilitates monitoring of their use and drawing up the report on exercising authorities. The development of local economic initiative, attracting investors, issuing municipal bonds—all these measures should be actively applied by local governments in order to reduce financial burdens on the federal budget, reduce the level of allocated grants, subsidies, and subventions. Thus, the principle of federalism is manifested in various areas of the financial activity of the state, but it is namely in the fiscal sector that it is expressed as the sum of all the components that constitute this principle of elements. Federalism as the basis of the constitutional system of Russia, finding its concentrated expression in the budgetary activity of the state and owing to particularly important significance of the state budget, has a significant impact on all financial relations. The scope of activity of the federalism principle in budget law is budgetary activity of the state, which can be defined as an aggregate of functions based on legal norms and realized by subjects of the budget law concerning planned accumulation, distribution, and use of the centralized monetary fund in public interests. Spheres of

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state budgetary activity include the federal budget, the budgets of the RF subjects, the budget structure of the Russian Federation, and the budget structure of the RF subjects. One of the aspects of the manifestation of the federalism principle in budget law is the presence of the Russian Federation and its subjects of budget rights (competencies) regarding the budgetary activity of municipal entities. The budgetary activity of the local level is largely conditioned by the political and social role of local selfgovernment and in conditions of the federal state is carried out in strict accordance with the division of budgetary rights between the Russian Federation, the subjects of the Federation and municipal formations.

References Alymurzaev, G. (1998). Local self-government and local finance: The model of “municipal community. Russian Economic Journal, 5(28). Budget Code of the Russian Federation. (2017). Moscow: Prospect. Bugarin, M., & Marciniuk, M. (2017). Strategic partisan transfers in a fiscal federation: Evidence from a new Brazilian database. Journal of Applied Economics, 20(2), 211–239. Constitution of the Russian Federation. (2012). Moscow: Eksmo. Dadashev, A. Z., & Chernik, D. G. (1997). Financial system of Russia. Moscow: Study Guide. Dictionary of Foreign Words. (2014). 14th ed. Adelant. Elasar, D. (1977). Governing peoples and territories. Philadelphia, 1983; Mathews, R. Federalism in Australia. Current Trends Publius 7(3). Government of Irkutsk Region. (2018). Access mode: https://irkobl.ru. Cited: September 18, 2018. Gracheva, E. Y. (2010). Problems of legal regulation of state financial control (p. 58). Moscow. Karapetyan, L. K. (2006). On the issue of “models” of federalism. State and Law, 12(57). Khimicheva, N. I. (1979). Subjects of the Soviet budget law. Saratov. Khimicheva, N. I. (2012). Financial law: Textbook. Moscow: INFRA. Krokhina, Y. A. (2011). Financial law of Russia: Textbook (4th ed., revis. and suppl.). Moscow: INFRA-M. Law of Irkutsk region as of July 5, 2017 No. 53-OZ. (2017). On amendments to the law of Irkutsk region on the regional budget for 2017 and for the planning period of 2018 and 2019. Access mode: https://www.garant.ru/hotlaw/irkutsk/1123087. Cited: September 18, 2018. Martemnov, V. S. (1994). Business law: Course of lectures (p. 205). Moscow. Mikhaleva, N. A. (2010). Legal aspects of contemporary Russian Federalism. Federal structure of Russia: History and modernity (pp. 86–87). Polyak, G. B. (2000). The budget system of Russia. Moscow: Unity-Dana. Popov, D. A., & Olenin, D. S. (2015). State administrative control over local governments: Problems and solutions. State Power and Local Self-Government, 2, 22–27. Singh, N., & Vasishta, G. (2004). Patterns in centre-state fiscal transfers: An illustrative analysis. Economic and Political Weekly, 39(45), 4897–4903. Tsindeliani, I. A. (2017). Financial right. Moscow: Prospect.

Leasing as a Tool for Financing of Innovative Projects Y. N. Barykina, E. I. Gavrikova and M. L. Tang

Abstract The Russian leasing market as a source of financing innovative projects is studied. The authors analyze the problems and prospects for the development of financial support of innovative projects. A comparative analysis of the conclusion of financial leasing contracts of organizations engaged in financial leasing in the Russian Federation is carried out. A survey of the leasing industry made at the end of 2017 is considered. The surveyed companies participate in at least 75% of the financial leasing market.

1 Introduction Use of the leasing form of financing in the world practice allows accelerating the growth of industrial production owing to an increase in the turnover of objects of fixed assets. This happens by means of the accelerated depreciation policy applied in the calculation of depreciation. The analysis of the leasing form of financing, conducted by the authors, showed the correctness of the above-mentioned statement. A study of the development of investment leasing in Russia, including the formation of an innovative leasing market, is currently relevant. The proportion of obsolete equipment is significant, its efficiency is low. There is no spare parts supply. The solution to the problem can be, first of all, modernization and improvement. The cost of leasing agreements, under the terms of which the leasing property is on the balance sheet of the lessor, is taken into account according to the lessor’s data, using subsequent distribution of costs at the location of the leased property. The lessors as part of investments in fixed assets also reflect the value of acquired property (Nechaev et al. 2015a, b). Within a few years since the establishment of the first leasing companies, financial leases have grown in the territory of the Russian Federation and have taken their

Y. N. Barykina (B) · E. I. Gavrikova · M. L. Tang Irkutsk National Research Technical University, Irkutsk, Russian Federation e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_19

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rightful place among other financial instruments. Leasing today is a direct means of financing using tax preferences. Despite the large number (over a thousand) of companies in the Russian Federation, which formally can be engaged in financial leasing operations, 75% of companies are stable in the leasing market. At the same time, the entire volume of the financial leasing market is only 2.5% of the size of the banking sector. At the same time, companies that are directly or indirectly owned by the state occupy the dominant market share. In their leasing portfolios, a significant share belongs to a few clients. Such high concentration of lessees or industries is a source of a credit claim, since the default of one large client or a decline in certain market segments may affect the financial stability of leasing companies. Large and small companies that do not have strong equity holders in the face of banks remain most at risk of losing financial stability. Their own shareholders can still have additional capitalization, while profitability is often worse, and credit risks are higher. Today, there is the Government Decree of 03. 05. 2017, No. 518 “On the provision of subsidies from the federal budget for the reimbursement of losses in the incomes of Russian leasing organizations when providing a lessee with a discount on the payment of an advance payment under construction and road and municipal engineering leasing contracts concluded in 2017.” It provides for several goals. The first is an increase in sales of equipment, including Russian-made, which is for the most part the main goal. The second is an increase in leasing. The third is ensuring the loading of enterprises—manufacturers of equipment. The fourth is change of the park of outdated equipment, whose service life has exceeded the amortization period.

2 Research Topic According to the results of the data of the federal state statistics service, the authors made a survey of the business activities of organizations carried out in 2013–2017 (Tables 1 and 2; Fig. 1). Based on data of these tables, the authors plotted the dynamics of growth in the total value of financial leasing contracts concluded by organizations operating in the field of financial leasing in the Russian Federation in billions of rubles and as a percentage (Fig. 2). The total value of financial leasing contracts concluded in 2013 amounted to 894.0 billion rubles. The amount of 265 billion rubles was leased for buildings (except residential ones) and structures, machinery, and equipment. This is 29.7%. For vehicles (motor vehicles, including buses and trolley buses, ships, railway vehicles, flying aerial vehicles, working, productive and breed animals), this is 627.8 billion rubles, being 70.3% of the total investment in the active part of fixed assets. There is a significant increase in the total value of financial leasing contracts concluded by organizations engaged in financial leasing in the Russian Federation in 2017. In this case, the total value of the conclusion was 1.1407 billion rubles (Molchanova 2013).

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Table 1 Total value of financial leasing contracts concluded by organizations operating in the field of financial leasing in the Russian Federation (billion rubles) 2013

2014

2015

2016

2017

893.0

1028.3

502.5

743.5

1140.7

10.4

26.9

15.5

7.5

6.0

253.1

257.9

155.5

217.6

270.5

1.5

7.3

2.3

3.0

11.1

627.8

741.4

330.0

517.6

855.9

252.8

299.3

211.6

270.7

402.6

18.8

10.8

9.9

47.2

50.3

Railway transport means

220.5

348.9

49.0

150.3

299.2

Air transport

133.9

75.0

32.5

38.3

123.5

1.7

2.1

1.5

0.7

1.7

Total value of financial leasing contracts Including Buildings (in addition to residential ones) and constructions Machinery and equipment Among them computers and computer networks Transport means Among them Automobiles Vessels

Working, production, and breeding animals

Table 2 Total value of financial leasing contracts concluded by organizations operating in the field of financial leasing in the Russian Federation (in %) Total value of financial leasing contracts

2013

2014

2015

2016

2017

100

100

100

100

100

1.2

2.6

3.1

1.0

0.5

Including Buildings (in addition to residential ones) and constructions Machinery and equipment

28.3

25.1

31.0

29.3

23.7

Among them computers and computer networks

0.2

0.7

0.5

0.4

1.0

Transport means

70.3

72.1

65.6

69.6

75.0

28.3

29.1

42.1

36.4

35.3

Among them Automobiles Vessels

2.1

1.0

2.0

6.3

4.4

Railway transport means

24.7

33.9

9.8

20.2

20.1

Air transport

15.0

7.3

6.5

5.2

10.8

Working, productive, and breeding animals

0.2

0.8

5.2

1.5

4.4

Financial leasing is most actively developed in Moscow, St. Petersburg; Moscow, Yaroslavl, Sverdlovsk, Tyumen, Kemerovo regions, and the Republic of Tatarstan. Leasing companies located in these regions of the Russian Federation account for about 87.7% of the total value of contracts concluded in 2017.

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Fig. 1 Dynamics of growth and reduction of the total value of financial leasing contracts concluded by organizations operating in the field of financial leasing in the Russian Federation (billion rubles)

Fig. 2 Dynamics of growth and reduction of the total value of leasing contracts operating in the field of financial leasing in the Russian Federation (in %)

In 2017, the best dynamics of air leasing was observed (20% growth). It seems that demand is influenced by Russian Airlines according to Rosstat, in particular by the Aeroflot group, which received 56 new airplanes for use. A significant part of this was acquired on lease, which has a positive impact on the segment according to the authors of this work. The car leasing segment, represented mainly by passenger car leasing, grew by more than 10% in 2017.

Leasing as a Tool for Financing of Innovative Projects Fig. 3 Results of leasing companies in 2017

70 60

227

58

50 40 30

21

20 5

10 0 Leasing companies

New business

High-tech equipment leasing

For the first 9 months of 2017, the volume of leasing business grew by 58% and amounted to 710 billion rubles. The main drivers of the market using the implementation of state programs are the transport segments. At the same time, thanks to railway and aircraft technology transactions, the share of operating leasing in the new business reached 21% (Fig. 3). Against the background of active growth of transport segments, leasing of hightech equipment, the need for which is experienced by large industrial enterprises, is still less than 5%. The development of this segment would speed up the process of modernization of the economy and reduce the sectoral concentration of the leasing market and the volume. This sum by the end of 2017 will exceed one trillion rubles. The trend of development of innovative leasing in Russia is the development of sectors from the point of view of investors. There is a possible action to mitigate the risks. This allows suppliers and consumers to correctly navigate in the current market conditions and to influence risks in certain circumstances (Ries 2011).

3 Conclusion Innovative leasing is an important source of funding for many organizations. The leasing mechanism allows large and small enterprises—lessees—to acquire assets and increase their production capacity on favorable financial terms. Therefore, leasing can be considered as an effective tool for optimizing expenditures while developing the material and technical base of the enterprise. There are direct and indirect methods of financing. 1. Direct financing is wide-spread sources of direct financing that are systematically set out in collective work. 2. Indirect methods of financing provide innovative projects with the necessary material, technical, labor, and information resources.

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Leasing activity in a new form is largely determined by the leasing of the innovation infrastructure, representing a sphere of leasing. It includes market innovations, market capital (investments), and market for pure competition of innovations (Basova and Nechaev 2013). It is also worth noting that banking subsidiaries most often engaged in the leasing business under the brand-house of the parent banks also have a very high share in the sector—about 70%. However, for banking groups themselves, which include black-leasing companies, this business remains insignificant. The lack of regulation of the leasing industry allows the company to operate with minimal capital, which is especially typical of state-owned companies. It allows showing good profitability, on the one hand and, and on the other – to avoid deductions from the capital of parent banks. Thus, innovative leasing is a system of interaction between innovators, investors and manufacturers, and a developed investment and leasing infrastructure.

References Basova, A. V., & Nechaev, A. S. (2013). Taxation as an instrument of stimulation of innovation-active business entities. World Applied Sciences Journal, 22, 1544–1549. Boobyer, C. (2003). Leasing and asset finance: The comprehensive guide for practitioners, 86, 35–48. Izyumov, D. B., & Mironova, D. S. (2015). Analysis of the current state of innovative activity of the state and business: Foreign experience and Russian realities. Innovation and Expertise, 1, 40–49. Kihara, E. (2013). Private sector welcomes government leasing route. Access mode: https://www. capitalfm.co.ke/business/2013/06/private-sectorwelcomes-govt-leasing-route. Krishnan, V. S., & Moyer, R. C. (2004). Bankruptcy costs and the financial leasing decision. Journal of Financial Management, 23, 31–42. Liang, Z., Wang, W., & Li, S. (2012). Decomposition valuation of complex real options embedded in creative financial leases. Economic Modelling, 29, 2627–2631. Molchanova, O. P. (2013). Innovative management: A textbook for universities. Moscow: Vita-Press. Nechaev, A. S., & Antipina, O. V. (2015a). Tax stimulation of innovation activities enterprises. Mediterranean Journal of Social Sciences, 5. Nechaev, A. S., & Antipina, O. V. (2015b). Technique of tax rates and customs duties updating as the tool of enterprises innovative activity stimulation. Modern Applied Science, 9, 88–96. Nechaev, A. S., Barykina, Y. N., & Puchkova, N. V. (2017). Analysis of articles of fixed assets renewal of Russian business enterprises. In Advances in economics, business and management research 38. Nuryani, N., Heng, T. T., & Juliesta, N. (2015). Capitalization of operating lease a nd its impact on firm’s financial ratios. Procedia-Social and Behavioral Sciences, 211, 268–276. Official Site of the FSSS. (2017). Access mode: https://www.gks.ru/wps/wcm/connect/rosstat_ main/rosstat/ru/statistics/science_and_innovations/science. Ries, E. (2011). The lean startup: How constant innovation creates radically successful businesses. London: Penguin Group. Sergeev, V. A., Kipcharskaya, E. V., & Podymalo, D. K. (2014). The fundamentals of innovative design: The manual. Ulyanovsk: UlSTU. United Nations Organization for Culture and Science. (2017). Access mode: https://www. unesco.org/new/ru/mediaservices/singleview/news/how_much_do_countries_invest_in_rd_ new_unesco_data_tool_re.

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World Intellectual Property Organization. (2017). Access mode: https://www.wipo.int/pressroom/ ru/articles/2017/article_0006.html. Wakelin, O., Otheno, O., & Kinyua, K. (2003). Leasing equipment for business: A handbook for Kenya. Access mode: https://practicalaction.org/microleasing/leasing.htm.

Structural Changes in the Regional Economy

The Effectiveness of the Regional Healthcare System: The Evidence from the Republic of Tatarstan (Russia) Irina A. Kabasheva, Irina A. Rudaleva, Alexandr V. Gorbatov and Olga A. Krioshina

Abstract Relevant management tools and innovative principles of organizing the national health protection must be fully consistent with the economic trends in the development of the modern healthcare system. The chapter assesses the factors influencing the increase in the efficiency of expenditures of the regions of the Russian Federation (by the example of the Republic of Tatarstan) allocated for the healthcare system. In constructing the econometric model, indicators of the health system supply including labor, material and technical resources were used. The level of mortality of the population at working age (the number of deaths per 100 thousand people of the corresponding age) was chosen as an indicator characterizing the efficiency of the healthcare system. As a result of the analysis, the authors came to the following conclusion. The number of medical and nursing personnel (direct connection), the presence of beds in hospital organizations (direct dependence) and the incidence of 1000 people population (inverse relationship) influence the death rate of the population at working age.

1 Introduction The current trends in the development of the national health system must fully match organizing the protection of the nation’s health using topical management tools, financing systems and innovative principles. At the same time, the existence of a significant diversity of national health systems using different models of their financing leads to a multiplicity of criteria for their classification. And this, in turn, significantly complicates the assessment of the effectiveness of their functioning.

I. A. Kabasheva (B) · I. A. Rudaleva Kazan Federal University, Kazan, Russia A. V. Gorbatov · O. A. Krioshina Russian Presidential Academy of National Economy and Public Administration, Kaluga, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_20

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In accordance with the differences in the socio-political structure of society, M. G. Field distinguishes the following types of national health systems: classical, pluralistic, insurance, national and socialist (Field 1980). The classical system was characteristic of the industrialized countries of the nineteenth century, the pluralistic one for the US in the twentieth century. The insurance system dominates in the industrialized countries of Europe these days. The National Health Service was formed after the Second World War in the UK, Scandinavian countries and Australia. The socialist system was implemented in the Soviet model of health care. The level of social development of the society by M. Fotaki is close to this classification criterion (Fotaki 1999). In accordance with it, the following health systems are distinguished. Universalist (Great Britain, Ireland and partly Denmark) is financed mainly due to general taxation. The system of social insurance or continental (Germany, Austria, France, etc.), ¾ of the total expenditure on health is carried out through deductions from the wages of workers and social funds. The “Southern model” (Spain, Portugal and Greece) of health financing is mainly provided by contributions related to employment. The institutional or social democratic “Scandinavian model” (Sweden, Finland and Denmark) is funded from income tax, with the benefits paid and tied to the earnings. Without a detailed analysis of other existing national healthcare models, we would like to note that the specific model was formed by evolutionary way under the influence of a large number of factors determining the level of socio-economic, political and demographic development of the country. In a large-scale study of Rogozin A. V., Kravchenko N. A. and Rozanova V. B., the most significant factors influencing the effectiveness of the healthcare system were identified (Ragozin et al. 2013). Among these, the following were defined: the level of production; population density; size, development and uniformity of the population; state of the transport system; effectiveness of the city network; investment attractiveness and conditions for doing business; level of the tax system centralization; activity of antimonopoly regulation in the market of medical services; development of the consumer society and culture of social services consumption. In view of these factors, scientists have attempted to compare the effectiveness of national health systems using different approaches to its financing: insurance—market or budget-social models. Taking, for example, the national model of Russia, the economists came to the following conclusion. An attempt to use insurance funding in the absence of the necessary objective conditions leads to a drop in the effectiveness of health care, even in the condition of a constant increase in the cost of medical care. This conclusion allowed them to raise the question of the correctness of the Russian healthcare reform vector for the period until 2020. Simultaneously, the direction of improving the efficiency of the healthcare system in the Russian economy (Rogozin et al. 2013) is associated with its return to the budgetary (social) model of financing. It uses modern economic incentives and innovative tools (contract, standards, tariffs, single-channel financing of healthcare providers, etc.), motivating medical personnel to achieve high health indicators. The current stage of socio-economic development is characterized by accelerated penetration of information technologies in various areas and, in particular, in health care. The concept of “e-health” was introduced in 2000 to designate a new format of

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the medical field, characterized by the availability and ease of obtaining information. However, up until now there has been no generally accepted definition of the term “e-health.” At the same time, the development of the e-health system in developed countries is part of the e-government development program (Kurnosov 2004). The penetration of information technologies into e-health, the tendency to blurring borders between states and increasing the mobility of the world’s population require providing constant, prompt access to information about the health status of every citizen, regardless of his location (Karpenko 2016). Foreign experience in the development of e-health infrastructure shows that this sphere of health care is the central element of the future system of safe, effective and high-quality health care (European health report 2009). Foreign countries have accumulated quite a lot of experience in this field. The e-health system should integrate all its areas: the provision of health services, labor, financial management and logistics of the industry to properly manage resources. It should ensure no duplication of medical services that full information about each patient is available through the introduction of unified data standards and the provision of a continuous and simple information transfer system (Fotaki 1999). The introduction of the e-health system is carried out with the introduction of electronic health passports, the construction of an information infrastructure and the organization of the exchange of electronic documents among interested participants. According to experts, “the program of informatization of health provides: – Creation of an electronic patient card integrated into all systems in the field of medicine and social protection; – Providing access to accurate information from all points of the health system; – Management of medical information necessary to ensure patient safety and quality of services; – Improvement of the organization of the provision of medical services, human resources management and the health administration; – Ensuring control over chronic diseases; – Providing access to medical services; – Improvement of financial control and tariffication of medical services; – Creation of a world-class health system for keeping money inside the country or exporting medical services; – Satisfaction of the population’s requests in the field of medical services at the state level” (Ragozin et al. 2013). However, despite the priority of e-health development, some generally accepted uniform standards for its implementation have not yet developed. Each country has its own national types of e-health type. Identification of characteristics and factors of effectiveness of national e-health systems has been given considerable attention in scientific research. Thus, scientists Shuaib W., Suarez J. M., Romero J. D., Pierrecharles S. B. and Sanchez L. R. with the help of correlation-regression analysis revealed the effectiveness of the healthcare system directly correlates with the improvement of the use of electronic medical records (Shuaib et al. 2016).

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Similar results were reached by Escobar-Pérez B., Escobar-Rodríguez T. and Bartual-Sopena L. They state that the transition of medical organizations to the use of new technologies allows improving internal processes and increasing the efficiency of using hospital resources (Escobar-Pérez et al. 2016). The use of digitized medical records is a means of reducing hospital costs. Studies have shown the advantages of e-medicine in the accessibility of information from several places. “… Increasing access to knowledge in the field of health and telemedicine in the world, in particular in the field of global cooperation for emergency response, aims to develop links between health professionals in order to improve the quality of life and environmental conditions” (Program for the Information Society 2005). So, Korean scientists Kim, K., Seok, H., Proctor, R. link e-health development with improving the quality and safety of medical care (Kim et al. 2016). After conducting a comparative analysis of national medical electronic assistance in the USA and South Korea, the authors concluded that the South Korean model was more effective because of the highly developed telecommunications infrastructure. A feature of the American model of e-health is that it operates based on an isolated health system, since medicine in the USA is basically an insurance, although the state can also pay taxes. In this regard, the state cannot determine the general rules for the functioning of e-health, which certainly reduces the effectiveness of its implementation. Technological methods of providing medical information in the USA, primarily related to medicines, a healthy lifestyle and diabetes are not well thought out. Moreover, the researchers of Beckwith N., Jean-Baptiste M.-L., Katz A. note the existence in the USA of significant infrastructure and technological barriers in the provision of primary health care (Beckwith et al. 2016). In addition to the development of e-health, innovative methods include a crosssectoral approach to health protection, state responsibility for population demographic security, public interest and responsibility in strengthening one’s health. These give priority to primary health care and preventive interventions, developing science in medicine and stimulation of its effectiveness (Escobar-Pérez et al. 2016). Carrying out an assessment of the quality of the German healthcare system, the scientists Lauerer M., Emmert, M. and Schöffski O. associate its effectiveness with a high level of health financing, a well-developed infrastructure and high availability of personal and material resources. However, the prospective improvement of this system is associated with the development of electronic aid systems and information management (Lauerer et al. 2013). Comparative characteristics of the quality and effectiveness of German and Japanese medical care systems were undertaken by scientists Rump A. and Schöffski O. As a result of the study, the Japanese system, in terms of average medical expenditure per capita, proved to be more effective than German. Drawing attention to the significant volume of investments in the health care of these countries, scientists identify the lack of a healthcare system in Germany associated with obvious problems of coordination between the outpatient and the inpatient sector (Rump and Schöffski 2016).

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2 Method While striving to increase the efficiency of the subjects’ expenditures for health care, it is important to take into account the results shown by the system in the previous period, as well as the factors which determined the dynamics of its development. Basing on the obtained results, it is possible to formulate the new tasks and to correct the programs and subprograms. When determining the volume of various programs and subprograms financing, we can apply the econometrics modeling, for example, the least square method (LS method). It allows one to find the extent of influence of various factors on the healthcare efficiency in the region. We chose the death rate of the able-bodied population (the number of deaths by 100 thousand people of the given age) as an indicator of the healthcare system efficiency. First of all, this indicator is included into the list approved by the Decree of the Russian government of November 3, 2012, No. 1142 (Decree of the Government … 2009; Decree of the President … 2007). Second, we believe that the preventive measures and revealing the disease at the early stage help to increase the treatment efficiency, life span and improve the quality of life of the population.

3 Result When building the model, we took into account the indicators of provision of the healthcare system with labor and material and technical resources (Table 1). Basing on the analysis of the initial data (Table 2), we build the correlation matrix (Table 3). Apparently, there is close correlation between the resulting indicator and the factor features. Table 4 shows the resulting parameters of the model. Let us note that factors X 5 − X 6 are not included as their presence did not allow one to build the quality model. Testing for excess variables has led to their exclusion from the model. Table 1 Indicators of the factors of the healthcare system efficiency Indicator

Variable

Death rate of the able-bodied population (number of deaths by 100 thousand people of the given age)

Y

Morbidity rate by 1 thousand people

X1

Patient capacity (including day hospitals) (by 10 thousand people)

X2

Number of doctors (by 10 thousand people)

X3

Number of paramedical staff (by 10 thousand people)

X4

Capacity of outpatient clinics, visits per shift (by 10 thousand people)

X5

Death rate of the able-bodied population (number of deaths by 100 thousand people of the given age)

13.60

13.80

13.10

13.00

13.00

12.70

13.10

12.40

12.20

12.10

12.20

Years

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

841.1

831.6

845.2

849.8

846.2

866.6

819.7

825.4

835

804.5

760.8

Morbidity rate by 1 thousand people

85.1

88.5

91.2

85.7

87

88.5

90

99.5

101.4

107.1

109

Patient capacity (including day hospitals) (by 10 thousand people)

42.1

41.4

41.9

44.2

44.2

44

43.1

44.8

45.3

44.9

44.9

Number of doctors (by 10 thousand people)

106.4

106.7

107

107.2

106.5

106.9

107.3

112.1

113.1

114.2

116.6

Number of paramedical staff (by 10 thousand people)

Table 2 Initial data for the econometric model of the healthcare system efficiency in the Republic of Tatarstan

264.7

243.5

248.3

241.6

223.9

219.9

224.2

224

226

227

224

Capacity of outpatient clinics, visits per shift (by 10 thousand people)

331.1

323.2

311.9

310

316.6

328.2

327.3

363.5

367.7

368.2

372.7

Number of persons receiving health care in ambulatories and at home (by 1 thousand people)

238 I. A. Kabasheva et al.

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Table 3 Correlation matrix Y

X1

1.0000

−0.6571

X2 0.8073

X3 0.8073

X4 0.7974

1.0000

−0.7917

−0.3227

1.0000

0.6419 1.0000

X5

X6

−0.7352

0.7519

−0.8000

0.2557

−0.6906

X1

0.9794

−0.4838

0.9176

X2

0.7105

−0.7264

0.6670

X3

1.0000

−0.4609

0.9366

X4

1.0000

−0.4395

X5

1.0000

X6

Y

Table 4 Estimated variables of the model (monitoring in 2004–2014) Dependent variables

Coefficient

Statistical error

t-statistics

P-value

Const.

34.0237

7.51559

X1

−0.0148253

0.00371360

X2

0.124379

0.0313335

X3

0.441499

0.0704139

6.270

0.0008***

X4

−0.364221

0.0910538

−4.000

0.0071***

4.527

0.0040***

−3.992

0.0072***

3.970

0.0074***

Let us pay attention to P-value in the model; student’s test shows that all factors significantly influence the death rate of the able-bodied population with the 99% probability. Table 5 shows the indicators characterizing the tests and results of the model quality. Table 5 Summary table of the final results and tests

Test indicators of the model

Value

Mean dependent variable

12.83636

Sum of squared errors R-square F (4.6) Logarithmic likelihood Schwartz criterion

0.149868 0.954105 31.18340 8.019118 −4.048759

Standard deviation of the dependent variable

0.571442

Standard error of the model

0.158044

Corrected R-square

0.923509

P-value (F)

0.000373

Akaike criterion

−6.038236

Hannan-Quinn criterion

−7.292322

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The built model is a quality one as the determination coefficient is 0.954105. The factors taken into account in the model explain the death rate of the able-bodied population (number of deaths by 100 thousand people of the given age) by 95.41%, while 4.59% is explained by the factors unaccounted in the model. In the model the P-value is 0.000055 thus, the differences at a high degree of statistical significance are detected. The model reliability can be determined by comparing the standard error and the determination coefficient. The standard error in this model is 0.158044, which is less than the determination coefficient (0.954105). Thus, the model is reliable according to this criterion. The analysis showed that the model is not heteroscedastic. We also tested the normal distribution of errors. The errors are distributed according to normal law. Regression analysis allowed one to obtain the linear equation of the following form: HSE = 34.0237 − 0.0148X 1 + 0.1244X 2 + 0.4415X 3 − 0.3642X 4 .

(1)

4 Conclusion As one can see from the equation, the largest influence on the death rate of the ablebodied population as an indicator of the healthcare system efficiency is made by the number of doctors (direct dependence), the number of paramedical staff (inverse dependence), patient capacity (including day hospitals) (direct dependence) and morbidity rate per 1 thousand people (inverse dependence). The obtained results of modeling can be used for determining the extent of influence of other factors on the healthcare system efficiency.

References Beckwith, N., Jean-Baptiste, M.-L., & Katz, A. (2016). Waiting room education in a community health system: Provider perceptions and suggestions. Journal of Community Health, 41(6), 1196– 1203. Decree of the Government of the Russian Federation No. 322. (2009). On measures to implement the Presidential Decree No. 825 of June 28, 2007. On assessing the effectiveness of the executive bodies of the constituent entities of the Russian Federation. Decree of the President of the Russian Federation No. 825. (2007). On the evaluation of the effectiveness of the executive authorities of the subjects of the Russian Federation. Escobar-Pérez, B., Escobar-Rodríguez, T., & Bartual-Sopena, L. (2016). Integration of healthcare and financial information: Evaluation in a public hospital using a comprehensive approach. Health Informatics Journal, 22(4), 878–896. European Health Report. (2009). Health and health systems [Electronic resource]. Access mode: https://www.euro.who.int/__data/assets/pdf_file/0006/117186/E93103E.pdf.

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Field, M. G. (1980). The health system and the polity: A contemporary American dialectic. Social Science & Medicine. Part A: Medical Psychology & Medical Sociology, 14(5), 397–413. Fotaki, M. (1999). The impact of market oriented reforms on choice and information: A case study of cataract surgery in outer London and Stockholm. Social Science & Medicine, 48(10), 1415–1432. Kabasheva, I. A., & Rudaleva, I. A. (2014). Factor analysis of the interest in the work of socially important institution. Mediterranean Journal of Social Sciences, 5(2), 317–320. Karpenko, A. M. (2016). Electronic health (e-health) as a part of the global health system. Progress in Modern Economics, 10, 156–162. Kim, K., Seok, H., & Proctor, R. (2016). Comparison of national e-health in the United States and South Korea. Human Factors and Ergonomics in Manufacturing, 26, 692–699. Kurnosov, I. N. (2004). Implementation of the concept of e-government: A new stage 6, 18–26. Access mode: https://emag.iis.ru/arc/infosoc/emag.nsf/BPA/ 890b2440d66b70fcc32571780046f577. Lauerer, M., Emmert, M., & Schöffski, O. (2013). The quality of the German health care system. Gesundheitswesen, 75(8–9), 483–491. Nurmuhametov, I. M., & Smirnov, A. A. (2016). Ways to solve the fundamental problems in the effective implementation of the program of import substitution. International Journal of Economic Perspectives, 10, 221–227. Program for the Information Society, Tunis. (2005). Access mode: https://www.itu.int/wsis/ outcome/booklet/tunis-agenda_Dru.html. Ragozin, A. V., Kravchenko, N. A., & Rozanov, V. B. (2013). Efficiency of the national health system: Is the health financing model used. The Objective Conditions of the Country, 11, 18–24. Rump, A., & Schöffski, O. (2016). The German and Japanese health care systems: An international comparison using an input–output model. Public Health, 141, 63–73. Scott, P. J., Curley, P. J., Williams, P. B., Linehan, I. P., & Shaha, S. H. (2016). Measuring the operational impact of digitized hospital records. BMC Medical Informatics and Decision Making, 16(1), 1–13. Shuaib, W., Suarez, J. M., Romero, J. D., Pierrecharles, S. B., & Sanchez, L. R. (2016). Transforming patient care by introducing electronic medical records in a developing country. Health Informatics Journal, 22, 975–983.

A Transition to the Innovative Model of the Oil and Gas Industry Development as an Integral Part of Environmental Safety E. A. Potapova, E. I. Bulatova, A. N. Kiryushkina and T. V. Polteva

Abstract The entire world community is facing the issue of environmental safety. The rate of environmental pollution depends on the level of technology development. Therefore, innovative activities are the determining factors of the ecological state of the territories. In the Russian Federation, the activities related to the mining of mineral resources, based on the oil and gas industry, are particularly damaging to the environment. This work suggests a methodical approach to the assessment of the influence of various factors on the amount of production and consumption waste in the sphere of mining. The basis of the simulation in the study is the correlation-regression analysis. The conclusion is made that there are insufficient state support and the need to finance technological innovations in the oil and gas industry by the companies themselves. However, in the modern economy, the innovation activity of enterprises is limited. Most of the implemented innovative projects are aimed primarily at reducing costs. At the same time, the aspect of improving environmental safety is neglected. The work defines other mechanisms for financing such innovations and identifies factors that affect their level.

1 Introduction and Relevance The fact that without ensuring harmony of all components of the ecosystem and solving environmental problems, long-term sustainable development is impossible and is now recognized at the international level. E. I. Bulatova Kazan Federal Universuty, Kazan, Russia E. A. Potapova (B) · A. N. Kiryushkina · T. V. Polteva Togliatti State University, Belorusskaya, 14, Togliatti, Russia A. N. Kiryushkina e-mail: [email protected] T. V. Polteva e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_21

243

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The current pace of economic development has exacerbated the problem of limited natural resources. So, there is a need to take into account environmental requirements for the economy. It should be emphasized that economic development is internally contradictory. On the one hand, it gives rise to a number of acute environmental problems. On the other hand, in the economic development, there is the foundation for eliminating these contradictions. Most developed countries have already faced an environmental crisis, the way out of which costs them a lot. The change in the situation concerning the provision of natural resources due to their quantitative depletion, the manifestation of the consequences of ignoring the indicators of environmental damage, leading to inefficient territorial organization of production, sharply pose the problem of further socio-economic development and environmental security of the Russian Federation. One of the ways to solve this problem is to find ways to increase the innovativeness of the economy, both in the sphere of environmental management and in the production sector, capable of not only improving the applied technologies, increasing their effectiveness, but also changing the relationship between business and the state on environmental issues, which determines the relevance of this study. The situation is complicated by the fact that the leading role in the Russian economy is played by the oil and gas industry. It has the strongest negative impact on the environment in comparison with other industries. There is a significant longrun equilibrium relationship among economic growth, oil consumption, financial development, industrialization, trade openness, higher levels of economic, social and political globalization and CO2 emissions (Alam and Paramati 2015; Bu et al. 2016). The activities of enterprises in the oil and gas industry lead to pollution of the air, water bodies and accumulation of harmful production waste. In the structure of the complex, the most significant impact on the environment is provided by such kind of economic activity as “mining of minerals.” The specific gravity of emissions in the atmosphere of this type of activity in 2016 was 29.3% (Table 1). Within the framework of the fuel and energy complex, in connection with the foregoing, the authors consider it expedient to consider precisely the sphere of mining. The aim of the study is to analyze and assess the impact of innovative development of the oil and gas industry on the country’s environmental security and to find ways to ensure investment in this development.

2 Current State and Problems The oil and gas industry of Russia today faces the need to change the technological development of the complex, which is associated with negative trends in oil production in the traditional supplier regions, which are explained by a number of factors. The first negative factor is a decrease in the share of active and increase in the share of hard-to-recover oil and gas reserves. The second is the completion of the era of giant

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Table 1 Influence on ecology of various kinds of economic activities (Federal State Statistics Service 2018) Type of economic activity

Specific weight of pollutant emissions in the atmosphere, %

Share of production and consumption waste generation, %

2016

2015

2014

2015

2014

2013

Agriculture, hunting and forestry

1.31

1.19

1.10

0.91

0.84

0.80

Extraction of minerals

29.55

28.58

29.30

92.85

93.80

93.59

Production of food

0.94

0.88

0.85

0.39

0.37

0.41

Wood processing and production of wood products

0.55

0.54

0.53

0.09

0.10

0.11

Pulp and paper production

0.72

0.69

0.70

0.14

0.12

0.18

Production of petcoke and petroleum products

3.62

3.66

3.72

0.03

0.04

0.03

Chemical production

2.26

2.22

2.14

0.30

0.25

0.33

Production of other non-metallic mineral products

2.13

2.42

2.31

0.27

0.37

0.36

23.00

24.01

23.43

4.29

3.28

3.44

Manufacture of vehicles and equipment

0.46

0.45

0.47

0.04

0.05

0.06

Production and distribution of electricity, gas and water

21.93

22.07

22.29

0.53

0.55

0.48

Transport and communications

11.11

11.33

11.45

0.06

0.08

0.09

2.42

1.96

1.7

0.10

0.15

0.12

Metallurgical production

Provision of other services

246

E. A. Potapova et al.

deposits with unique oil and gas reserves and the concentration of oil production in fields with highly productive reserves. The third factor is related to the depletion of oil and gas reserves at depths of up to 3 km. One of the most serious problems for the environmental situation in Russia is the large volume of accumulated industrial waste. The share of the extractive industry accounts for over 90% of the total volume of industrial wastes. Quite a large proportion of waste is of the first class of danger. At the same time, the impact on the environment is exacerbated by the fact that the waste has a regional concentration, which leads to the destruction of the ecosystem of producing regions. If we look at the dynamics of the general level of environmental pollution, we can note a slight decrease in it in recent years. However, this trend is caused by the general economic recession, tightening of legislative requirements in the field of subsoil use and restrictions on the use of non-environmentally friendly fuels. Enterprises of the oil and gas industry, facing the new economic reality of low oil prices, are forced to look for ways to move to new models of functioning. Competitive success in energy markets requires access to new technologies for exploration and production of oil and gas and development of a broader portfolio of supply from sustainable sources such as biofuels and wind. The architecture of innovation has evolved from a vertically integrated model with bilateral procurement contracts to a virtual enterprise model that requires openness to new technology developments (Weil et al. 2014). Among them there is increased efficiency of field development and exploration, intensification of well construction, increased productivity of wells and production of cleaner fuels. But the production of cleaner fuels requires deeper oil refining, which leads to an increase in the number of industrial wastes and a worsening of the economic situation in the regions where oil is extracted and processed. Currently, despite a large number of developments in the field of waste processing, there are no optimal ways to solve them at the present time. Reducing the amount of solid waste invariably leads to an increase in atmospheric emissions and water pollution and vice versa. More than half a trillion tonnes of carbon has been dumped in the atmosphere since the Industrial Revolution (Paramati et al. 2017a, b). Recently, a number of international organizations have started putting more pressure on both developed and developing countries to reduce their CO2 emissions (Paramati et al. 2017a, b). In the opinion of the authors, the main task of the transition to an innovative and ecological model for the development of the extractive industry is to find technological and methodological solutions to the problem of processing and utilization of waste in an economically optimal way.

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3 Simulation Results The principles of Russian OGI operation in the modern economic conditions may and should provide for a use of sets of organizational and technical, economic solutions targeted on the adoption and implementation of environmental and nature conservation-related technologies (Cherepovitsyn and Ilinova 2016). Traditionally, the investment factor plays a decisive role in the emergence of new technologies. Without adequate funding, a scientific and technological breakthrough is impossible. Moreover, the initiative of such financing should come not from the state, but from the private sector, since it is the main producer, customer and consumer of new technologies. The transition to an innovative development model becomes a key factor in the growth of competitiveness in the international market. A positive impact is also connected with an increase in the reputation of companies due to environmentally responsible behavior. In connection with the above, the authors consider it expedient to evaluate the influence of various factors on the amount of production and consumption waste in the sphere of mining. For this, a correlation-regression analysis was used. The official data of the Federal State Statistics Service were used as the modeling base. Correlation-regression analysis and data processing were performed using the GNU Regression software, Econometrics and Time-series Library. As a dependent variable, the formation of production and consumption wastes in million rubles was chosen. According to the authors, the growth of environmental innovations that are being implemented should significantly affect the reduction of industrial waste. At the same time, the increase in costs for technological innovation and for research and development can also have a positive impact on the resultant indicator. It is interesting to see an assessment of the impact of the general innovation activity of the sector’s enterprises on the environmental performance of activities. With the help of the software, the least-squares data were estimated using the results of the following model equation: Y = − 29, 260.6 − 763.944 × X 1 − 0.00269943 × X 2 + 3381.93 × X 3 + 28.3062 × X 4

(1)

where Y is the formation of production and consumption wastes, million tons. X 1 —the proportion of organizations that carried out environmental innovations in the reporting year, %; X 2 —the cost of technological innovation, million rubles; X 3 —innovative activity of organizations, %; X 4 —internal costs for research and development, million rubles. The coefficient of determination of the model obtained was 0.99; the Fisher test (737.84) exceeded the tabulated value, which indicated the statistical significance of the regression equation and the possibility of its use for analysis and forecasting of economic processes. The standard error of the model was 24.77. Checking the model for heteroscedasticity with the help of built-in software tests showed its absence, which positively characterized the obtained model, since the

248

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presence of heteroscedasticity leads to the fact that standard errors of the model are underestimated. According to the modeling results, the following practical-oriented conclusions were made: (1) The amount of production and consumption waste produced by the type of economic activity “extraction of minerals” is inversely proportional to the number of organizations that carried out environmental innovations in the reporting year and the costs of technological innovation of enterprises in this sector of the economy. (2) At present, the innovative activity of organizations in the mining sector and their internal costs for research and development do not lead to a reduction in production and consumption waste. This conclusion may seem paradoxical at first glance, but it is due to the current trends in the development of the extractive industry. In conditions of instability of prices for energy resources, the innovative activity of enterprises is limited. Most of the implemented innovative projects are aimed primarily at reducing costs. At the same time, the aspect of improving environmental safety is neglected. It should be noted that the costs of technological innovation influence positively the environmental indicators.

4 Ways of Leveling Problems A number of problems in the field of environmental safety in the oil sector can be avoided by means of the use of modern techniques and technologies aimed at minimizing environmental pollution. Companies need to invest in the development of research works that in the near future will allow saving considerable funds on payments for emissions and discharges (Nikolaichuk and Tsvetkov 2016). The development and implementation of technological innovations in the oil and gas industry will significantly improve the environmental safety of the producing regions and the country as a whole. At present, the state of the environment in Russia can be called as crisis. The need to introduce technological innovations is associated with the already emerging and possible consequences of such crisis. They will affect the environment even more if modernization of oil and gas production is not carried out. The biggest blow will be in the Siberian Federal District. The main source of funding for technological issues for ecological innovations in the developed countries remains state support. However, state revenue policies typically do not address environmental externalities associated with oil and gas development such as potentially harmful air emissions, risks of water contamination and climate change (Newell and Raimi 2018a, b). The industry typically receives special treatment expressed in state tax codes, and the revenues it generates can have major fiscal effects at state and local levels (Newell et al. 2018a, b). But, for a radical change in the environmental picture of the impact of the oil and gas industry,

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public funds are not enough. Companies themselves must invest in the development of innovation. But in modern economic and political conditions, they prefer to save on costs, including environmental protection costs, and not to seek the best technological solutions. The most likely scenario is that under the circumstances, a small number of companies will modernize production of oil and gas. In this connection, the anthropogenic impact on the environment will only increase. The unwillingness of oil and gas companies to invest in the development of innovations is also associated with the existing architecture of the country’s financial system. One of the problems of this system is the lack of “long money.” Banks tend not to issue loans for a long period (more than a year). In such circumstances, environmental protection and the costs of it are not in the forefront of the interests of companies. Thus, it is necessary to use other financing mechanisms, for example, through mutual investment funds (mutual funds), which are a property complex based on trust management of the fund’s property by a specialized management company. The financial and investment market can always offer the services of many different mutual funds, each of which meets certain requirements in terms of risk, profitability, timing and types of investments. Mutual funds are an example of collective investment. Another new mechanism for financing innovations can be syndicated and bonded loans. The former is a type of loans that are presented to the borrower by several banks at the same time. This form of borrowing is used when allocating a loan in a particularly large amount. A bond loan is also a form of credit, in which borrowers issue, place and distribute bonds. Bond placement is a type of debt financing that can be used to securitize a debt. We note that the bond loan is both a commercial and state financing instrument. The above-mentioned financing mechanisms have a number of advantages. The first advantage is “cheapness” regarding the classical form of commercial lending. The second advantage is their investment attractiveness. They are preferable for investor financing through the use of new technologies, the operation of international reporting systems, openness, the effect of participation, and also due to ensuring environmental safety of production and production. Other factors influence the development of innovations in the oil and gas industry. Exhaustion of oil and gas reserves at a depth of up to 3 km is also a factor in the development of innovative activity. Unknown quantities of coal, gas and oil reserves are buried deep in the ground or under the ocean. Therefore, the identification of new sources is becoming increasingly difficult and expensive and the exploitation is very dangerous (Pacesila et al. 2016). Russian oil companies are increasingly looking more closely at fields that require innovative approaches when developing them. These deposits can include hydrocarbon deposits in low-permeability shale rocks, which are difficult to drill. The reserves of shale hydrocarbons are virtually limitless—they will last not 20–30 years, like gas and oil in traditionally developed fields, but 200–300 years. The desire of companies to independently invest in innovation is affected by a number of indirect factors. For example, economic and political sanctions against the

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Russian Federation. It would seem that this should only have a negative impact on the environmental safety of the oil sector. After all, the leading Russian companies in the industry lost access to international capital markets and necessary equipment. This equipment is used in deep-sea drilling, production of shale oil and extraction in the Arctic. This situation sharply raised the question of the need to reduce dependence on the countries of the west. This is what stimulated innovation activity in the oil and gas industry for the development of technology. This is also due to the tightening of Russian legislation in the field of environmental protection. Thus, one of the ways to reduce dependence on the western countries in technological aspects is the development and implementation of targeted programs to improve production efficiency, work on the creation and implementation of new technologies that oil and gas companies should implement. However, only a small number of such companies, one way or another, currently finance innovation. A positive example of the company investing in innovations is PJSC NK Rosneft. This company invests in research conducted on the basis of leading domestic profile enterprises and universities that have the necessary scientific and technical potential. There is an integration of the company’s innovation activities in terms of its own developments and the forces of third-party developers.

5 Conclusion A significant impact on the environmental safety of the Russian Federation is provided by such type of economic activity as the extraction of minerals, which is determined by the oil and gas industry. Therefore, the development of technological innovations in this complex becomes particularly important. The work assesses the influence of various factors on the amount of production and consumption waste in the mining industry based on correlation-regression analysis using the least-squares model. According to the simulation, the amount of production and consumption waste in the mining sector is inversely proportional to the number of organizations that carried out environmental innovations, and the innovative activity of companies in this sector. Their internal costs for research and development do not lead to a reduction in production and consumption waste. Ultimately, the environmental scenario of the development of the oil and gas industry through the introduction and use of innovative technologies has every chance of implementation. It depends not only on state regulation and financing, but also on the investment and strategic development plan of the companies themselves in the context of technological innovation. However, we should not forget that the formation of an effective environmental and economic mechanism for the development of the oil and gas complex depends on a number of indirectly influencing factors related to the political situation, economic sanctions, the exchange rate and other conditions necessary for economic modernization.

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References Alam, M. S., & Paramati, S. R. (2015). Do oil consumption and economic growth intensify environmental degradation? Evidence from developing economies. Applied Economics, 47(48), 5186–5203. Bu, M., Lin, C.-T., & Zhang, B. (2016). Globalization and climate change: new empirical panel data evidence. Journal of Economic Surveys, 30(3), 577–595. Cherepovitsyn, A., & Ilinova, A. (2016). Ecological, economic and social issues of implementing carbon dioxide sequestration technologies in the oil and gas industry in Russia. Journal of Ecological Engineering, 17(2), 19–23. Federal State Statistics Service. (2018). Access mode: https://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/science_and_innovations/science/#. Newell, R. G., & Raimi, D. (2018a). US state and local oil and gas revenue sources and uses. Energy Policy, 112, 12–18. Newell, R. G., & Raimi, D. (2018b). Fiscal impacts of increased U.S. oil and gas development on local governments. Energy Policy, 117, 14–24. Nikolaichuk, L. A., & Tsvetkov, P. S. (2016). Prospects of ecological technologies development in the Russian oil industry. International Journal of Applied Engineering Research, 11(7), 5271– 5276. Pacesila, M., Burcea, S. G., & Colesca, S. E. (2016). Analysis of renewable energies in European Union. Renewable and Sustainable Energy Reviews, 56, 156–170. Paramati, S. R., Apergis, N., & Ummalla, M. (2017a). Financing clean energy projects through domestic and foreign capital: The role of political cooperation among the EU, the G20 and OECD countries. Energy Economics, 61, 62–71. Paramati, S. R., Mo, D., & Gupta, R. (2017b). Effects of stock market growth and renewable energy use on CO2 emissions: Evidence from G20 countries. Energy Economics, 66, 360–371. Weil, H. B., Sabhlok, V. P., Cooney, C. L. (2014). The dynamics of innovation ecosystems: A case study of the US biofuel market. Energy Strategy Reviews, 3(C), 88–99

World Manufacturing Industries in the Post-industrial Society: Tendencies and Regional Shifts Irina Rodionova, Tatiana Kokuytseva and Cezary M˛adry

Abstract Different paces and directions of the development of countries and world regions reflect the transformation of the spatial configuration of the world manufacturing industry. The key trend in the development and transformation of the modern spatial structure of the world manufacturing industry is a rapid growth of high-tech production. The purpose of this research is to reveal regional shifts in the world manufacturing production, including high-tech production, which is a driving force behind the economic growth and development of countries in the post-industrial era. The authors used the statistical base of UNIDO, the World Bank, the US Science Foundation, and many other sources to collect data for the research. The basis for the calculations was the database of Science and Engineering Indicators. The work analyzes the dynamics of manufacturing industry production, as well as production and export (import) of high-technology products in regions and countries in 2001– 2016. The work shows that there is a shift toward Asia in the location of production capacities in the world during the analyzed period.

1 Introduction The research of the driving forces and shifts in the sectoral structure and spatial organization of the world manufacturing industry, the analysis of trends in the development of industries are the traditional tasks for the scientific community. It is actualized by the significantly accelerated dynamics of changes in the globalization process. In recent years, many new leaders have appeared in the global industry—regional and even global. The ranking of countries in terms of manufacturing production I. Rodionova (B) · T. Kokuytseva People’s Friendship University of Russia, Moscow, Russia e-mail: [email protected] C. M˛adry Institute of Socio-Economic Geography and Spatial Management, Adam Mickiewicz University in Pozna´n, Pozna´n, Poland e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_22

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(by manufacturing value added) is now headed by China (over 24% of the world manufacturing production, 2016), ahead of the USA (16.0%), then followed by Japan (less than 9%) and Germany (6%). The fifth and the sixth places among the leaders in the world manufacturing industry are occupied by India and the Republic of Korea, ahead of Italy and France (the seventh and the eighth positions). The ninth position is occupied by Brazil (ahead of the UK). Indonesia is on the 11th place, Mexico is on the 12th, and Russia is on the 13th (ahead of Canada and Spain) (Industrial Statistics Database 2017). It is important to note that the share of the 15 leading countries accounts for almost 80% of the world’s manufacturing production. Currently, many developing countries are becoming increasingly integrated into transnational value-added chains, which provide them with an increased level of competition in virtually all global markets. It is of great interest to analyze not only the countries’ positions in the world manufacturing industry, but also economic groups of them (the EU, NAFTA, APEC, BRICS) (Bandi 2015; Giera´nczyk 2010; Marino 2014). The processes of modernization of industrial production, various aspects of development and cooperation of countries in this sphere in the context of the transnationalization of the world manufacturing industry are analyzed (Luckhurst 2013; Rodionova et al. 2016). It is also interesting to analyze the issues of innovative development and innovative and technological cooperation (Crescenzi and Rodriguez-Pose 2011; Dominiak and Rachwał 2016). The purpose of this research is to reveal regional shifts in the world manufacturing production, including high-tech production, which is a driving force behind the economic growth and development of countries in the post-industrial era.

2 Methodology To obtain the necessary data for this research, we have investigated the following sources: UNIDO, the World Bank, the National Science Foundation of the USA, and other sources, including Industrial Development Report (UNIDO); International Yearbook of Industrial Statistics (UNIDO); and UNESCO Science Report. Toward 2030, Science and Engineering Indicators—2018; World Investment Report—2017; INDSTAT 4—2017 (Science and Engineering Indicators 2018; World Development Indicators 2018). Different paces and directions of the development of countries and regions logically reflect the transformation of the spatial configuration of the world industry that can be easily proved by the methods of statistics of the volumes of manufacturing industry production in dynamics. The stages of the research are the following: (1) to collect statistical data on the manufacturing industry production; (2) to select the indicators and to create a matrix of data them reflecting the level of industrial development; (3) to calculate the share of the regions in the world manufacturing industrial production in dynamics since the beginning of the XXI century; and (4) to analyze the positions of regions

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and countries and to identify the changes in the spatial organization in the world manufacturing industry. The requirements of the statistical base are the following: completeness, objectivity, reliability, modernity, and consistency. The US Science Foundation database of the world industry was selected as a main one (Science and Engineering Indicators 2018). The research compared the positions of seven large regions (North America, Central and South America, Europe, Asia, Africa, Australia, and Oceania) by manufacturing production in general, by high-tech production and trade (export/import). But initially, it is worth characterizing the development trends of the world economy in recent decades.

3 Transformational Shifts in the World Economy Globalization and innovative development led to considerable changes in the sectoral structure of the world economy. There was a “servisation” of the economy. At present, the share of the tertiary sector in the world GDP is 62.4% (the share of the secondary sector, i.e., industry, is 31.1%, agriculture—6.5% in 2017). However, despite the dominance of the tertiary, i.e., service, sector in the world GDP, the source of the civilization progress and its measuring instrument is the improvement of the forms and methods of manufacturing production. From the perspective of a reduction in the industry employment, “deindustrialization” does not mean “uselessness” for the industry in most of the world’s countries, including the most highly developed at the post-industrial stage of development (Rodionova 2014). At present, high-tech production is a decisive factor for achieving high productivity and efficiency of a whole economic mechanism. And this is largely the prerogative of economically developed countries which have a strong industrial base. At the same time, at least two important aspects should be noted. Firstly, employment in the secondary (industrial) sector grows fast since the beginning of the twenty-first century in most developing countries (and primarily in China, India, and Brazil). For example, it has already exceeded 30% of economically active population in China, in India and Brazil—20% (a labor force in China is 800 million, in India—500 million, in Brazil—about 110 million people). Secondly, it is significant that many leading countries of the world implement their strategies in the direction of re-industrialization after a global financial and economic crisis of 2008–2009. They seem to restore the role and importance of industrial production in the economy. But it is clear that re-industrialization will occur on a new technological wave in developed countries. It will be a high-tech industry of the post-industrial era. Compared with developing countries, the developed ones are now less competitive by traditional industries (especially mass production), although these sectors are still in their economies. Nevertheless, the developed countries are still among the world’s leaders in terms of many industry production sectors (Gorkin 2012).

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4 Changes in the Manufacturing Industry Structure Manufacturing industry is currently a powerful driver for the world economy development. At the same time, as before, many of its innovations and technologies are successfully used in various sectors of the economy (in agriculture and services), strengthening the multiplier effect. Due to the introduction of information technologies and new materials, the improvement of the skills, labor productivity in the manufacturing industry is growing much faster than in other economy sectors (extractive industry, agriculture, and services). Over the past decades, the role of labor and resource-intensive industries has steadily declined in the world industry structure. At the same time, the importance of the group of “global innovative and technological industries” (computer and office, electronics and semiconductor, production of medical instruments and optics) is growing rapidly. The production of communications equipment (radio, television and communication equipment) was developing at an extremely high rate. At the same time, the trajectories of the industry production development have been geographically very different in the last decades. In the most developed countries, its changes before the beginning of a financial and economic crisis of 2008–2009 were a process of a gradual adjustment of the economy through the introduction of the scientific and technical progress achievements in the transition to the postindustrial stage of development and because of the increase in the income level of the population. Priority industries were science-intensive ones. In the CIS countries and in many other “transition economies,” since the early 1990s, the changes in the industrial structure have been determined by the transformation of the entire economic mechanism and the structural restructuring of the economy (they reflect the general economic problems of this type of countries). For the developing countries, the industrial structural changes are expressed in the process of changing the organization and modes of production due to their deeper involvement in the international division of labor (in globalization and transnationalization). To a large extent, this process is influenced by the location of production facilities by the largest TNCs. It is shown up, for example, in the industry structure change in Asian countries of new industrialization and export-oriented economies. However, in the most backward countries of the world (Asian and African), there are still no significant changes in the industrial structure. Many of them are still at the initial stage of the process of industrialization.

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5 The Main Changes in the Territorial Structure of the World Manufacturing Industry At a global level, the differences are still between “North” and “South” (developed countries vs developing ones). This reflects well one of the most important indicators of the level of industrial development—value added of manufacturing industries. However, the role and importance of developing countries, especially the leading ones—China, Brazil, India, Indonesia, Mexico, and the Republic of Korea—by manufacturing industry production and export is growing steadily. Moreover, a growing factual argument is that the narrowing of the gap in the levels of industrial and overall economic development between the Center and the world Periphery in the broad sense of the word becomes a prerequisite for the further progressive development of the Center itself, on the one hand, and the basis for deepening the globalization processes, on the other hand. At a regional level, the shifts in the global industry are directed in general from the West to the East. Former leaders—North America and Europe—are giving up their positions (Table 1). Thus, the share of North America in the structure of value added of all manufacturing industries decreased in 2001–2016, including the share of Western Europe. Now, the rating of the most large-scale world “industrial” regions is headed by Asia (about 50%). Its share in the structure of the world manufacturing industry in the analyzed period has grown. But it is even higher than the total index of the following regions of Europe and North America (Table 1). Such progress is undoubtedly connected with the economic phenomenon of China and India (as well as with the ongoing development of Asian NIS—the Republic of Korea, Singapore, Taiwan, Malaysia, Thailand, Indonesia, etc.). China is the first in the ranking by the manufacturing production and it has left behind all the industrial giants—the US, Japan, and Germany. Comparing with Asia, the rest of the world (especially Africa) is an outsider. Table 1 Share of regions in the world manufacturing industry production in 2001–2016, % Regions

2001

2004

2008

2012

2016

North America

33.4

28.6

22.9

21.1

22.5

Central and South America

4.1

3.8

6.0

6.2

4.0

Europe

29.3

33.1

32.6

24.4

22.5

Africa

0.9

1.0

1.1

1.2

1.1

Asia

31.4

32.2

36.3

46.0

49.1

Australia and Oceania

0.9

1.3

1.1

1.1

0.9

World

100

100

100

100

100

a Calculated

by Science and Engineering Indicators (2018)

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6 Trends of High-Tech Production in Regions and Countries As it is noted above, the growth in high technology production is a key trend in the transformation of the spatial structure of the world manufacturing industry. Only for the analyzed 15 years (from 2001 to 2016) value added of HT manufacturing industries increased more than twice—from 778 to 1681 billion dollars at current prices. This kind of dynamics is inherent only in selected segments of the world economy. North America is still a global-centering link. High-tech manufacturing production is growing in this region slowly, but stably (mainly due to the USA). In 2001– 2016, it increased in this region from $321 billion to $528 billion (at current prices), and from $288 billion to $495 billion in the USA. The share of North America in the world high-tech manufacturing production is now almost 33% (although it was 41% in 2001). Europe is also a stable region (about 20% of the world high-tech manufacturing production–$322 billion in 2016). But because of the impact of the global financial and economic crisis of 2008–2009, the positions of the EU countries have changed significantly. There was a decline in production during the crisis and immediately after it in some countries. But in Slovakia, the Czech Republic, Romania, Poland, and Bulgaria, high-tech manufacturing production increased significantly over the past 15 years. It is closely connected with the transfer of production capacities of TNCs into these countries from the most developed countries of Europe. This allowed one to increase the production in the EU more than one-and-a-half times (from $140 to $263 billion). The rest of the world (except Asia, as it will be shown below) forms the opposite, although a “dynamically positive,” side. However, if the two–three-fold increase of production in Africa, Central and South America, Australia, and Oceania should be viewed as a “zero base” effect, the situation is different in Asia. The scale and growth rates of high-tech manufacturing production for the analyzed 15 years are very large and impressive there—from 280 to 730 billion dollars—even despite the reduction in absolute volumes of production in Japan. At the same time, the highest growth rates of production (in current prices) over the past 15 years were typical for Vietnam (20 times, the highest growth recorded in the last 5 years), China (7 times), Indonesia (3 times), India (3 times), Singapore, Taiwan and even Bangladesh and Pakistan. So, a dynamic progress in the world ranking of leaders—both at the level of regions and countries—by high-tech production is logical. The world region-leader is Asia (45%), primarily due to the increase in the share of China (the growth is from 6 to 23.5%), while the share of North America decreased from 41 to 33%, followed by Europe, which almost retained its positions with an insignificant reduction of the share down to 20% (Table 2).

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Table 2 Share of regions in the world high-tech manufacturing industry production in 2001–2016, % Regions

2001

2004

2008

2012

2016

North America

41.2

36.7

33.4

31.9

32.6

Central and South America

1.8

1.4

2.1

2.1

1.3

Europe

20.7

24.7

25.8

21.2

19.9

Africa

0.2

0.3

0.3

0.4

0.4

Asia

35.7

36.5

37.9

43.6

45.1

Australia and Oceania

0.3

0.3

0.5

0.7

0.4

World

100

100

100

100

100

a Calculated

by Science and Engineering Indicators (2018)

It is important to note that the countries-giants are constantly increasing their influence in the control on the world market of high-tech manufacturing products, including due to the development of processes of re-industrialization and neoindustrialization (Industry 4.0). One of the recent obvious vectors within the global community is the strengthening of monopolization tendencies. It is suffice to say that the total share of three largest economic groupings (NAFTA, EU and BRICS) in the world high-tech production in 2001–2016 grew from 2/3 to 4/5. It represents a partial “limited” global markets and non-competitiveness of all other participants in the market of high-tech products. The leaders in the world high-tech manufacturing production are the USA (30% of the world production), China (25%), Japan, Germany, Taiwan, the Republic of Korea, Switzerland, Britain, Ireland, France, etc.

7 Export Volumes of High-Tech Manufacturing Products: Regional Features Over the past 15 years, the world exports of high-tech products has tripled and amounted to 2.55 trillion dollars (in current prices). High-tech products are represented by various means of telecommunications, computer and office equipment, computer components, pharmaceutical products, scientific equipment, and aerospace products. It is possible to illustrate the change of the share of regions by the high-tech export based on the analysis of the statistics of the US Science Foundation for the period 2001–2016. Asia took the lead, accounting for 63% of all high-tech (HT) exports (Table 3) (Science and Engineering Indicators 2018). We emphasize the main thing, both in production and in international trade in manufactured products. The importance of developing countries is constantly growing.

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Table 3 Share of regions in the world export of high-technology products in 2001–2016, % Regions

2001

2004

2008

2012

2016

North America

19.3

13.5

14.6

13.3

12.9

Central and South America

1.1

1.2

1.3

1.8

1.1

Europe

22.7

22.6

23.6

22.9

21.8

Africa

0.2

0.3

0.4

0.3

0.4

Asia

55.3

68.6

59.2

60.7

62.7

Australia and Oceania

1.1

0.6

0.7

0.7

0.9

World

100

100

100

100

100

a Calculated

by Science and Engineering Indicators (2018)

At the same time, a significant share of exports of high-tech products are components and materials imported from China, Mexico, and other developing countries for final assembly in developed countries within TNCs. China is now the largest exporter of high-tech products among the developing countries and one of the main suppliers of this type of product to the world market (its share is 24% in 2016). The dynamics of high-tech exports in China has grown rapidly (over the past 15 years, this figure has increased in current prices almost 7 times). In addition, the largest world exporters of this type of products are still the EU countries (total about 16%, 2016) and the USA (12%). The share of North America declined. The US share in the world export of high-technology manufacturing products in 2001–2016 declined (from 18 to 12%), although the export increased almost twofold. The growth in high-tech exports was mainly due to the growth in production and export of such products as pharmaceutical and aerospace. As for the Asian countries—Japan, the Republic of Korea, and Taiwan—they showed different trends. Japan’s share in the world exports of high-tech products is declining. Many Japanese companies, faced with financial difficulties, transferred their production to Taiwan, China, and other Asian countries to reduce their costs. The share of other regions and countries in the world is insignificant, although high-tech exports of developing countries are growing faster than the developed ones. As for Russia, it exports a very small amount of high-tech products, while its imports are quite large. At the same time, considering the dynamics of Russia’s exports and imports, we can note that the share of high-tech exports is growing, the share of high-tech imports is falling, caused by the economic sanctions of Western countries and plans for import substitution. Returning to the characteristics of the largest exporters of high-tech products, it should be noted that the leaders are China, the USA, Taiwan, the Republic of Korea, Singapore, Japan, Switzerland, Germany, Malaysia, France, and Vietnam (the volume of this country grew 120 times over the analyzed period).

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8 Import of High-Tech Products: Regional Features Import of high-technology manufacturing products, like export, is mainly carried out by Asian countries, which account for more than 50% of total exports of HT products. China is the largest importer of high- technology products. Its share in the world imports of HT products accounts for 20% (this is more than the total imports of all EU countries). Over the past fifteen years, the dynamics of HT imports of China has grown steadily, having increased six fold. The second largest importer of high-tech products is Europe (over 20%). North America (the USA, Canada, and Mexico) is also one of the largest importers of HT products in the world. But the share of this region in the world import of hightechnology products has steadily declined over the past 15 years; nevertheless, in 2016, it was 18% (the share of the US is 14%). At the same time, the Asia’s import of HT products during the analyzed period increased (from 42% in 2001 to 52% in 2016). The share of Europe (including the share of the EU countries) has either increased or decreased somewhat, depending on the situation on the world market. The share of the rest of the world is small, although the import of HT products increased (Table 4). The largest importers in the market of high-technology products are China, the USA, Japan, the Republic of Korea, Singapore, Taiwan, Mexico, France, Great Britain, Malaysia, Switzerland, and Vietnam (which increased its imports of high-technology products 28 times). In other words, there are both developed and developing countries among the leaders. The share of imports of high-technology products in Russia over the past 15 years has tended to increase, yet its share in the world in 2016 was only 1%. Table 4 Share of regions in the world import of high-technology products in 2001–2016, % Regions

2001

2004

2008

2012

2016

North America

25.0

22.6

19.1

18.4

18.2

Central and South America

3.6

2.8

4.7

5.0

3.8

Europe

25.3

25.5

26.8

23.6

21.5

Africa

2.0

2.0

2.6

2.1

2.3

Asia

42.1

44.7

44.5

48.8

52.4

Australia and Oceania

2.1

2.3

2.3

2.1

1.9

World

100

100

100

100

100

a Calculated

by Science and Engineering Indicators (2018)

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I. Rodionova et al.

9 Discussion What are the prospects for the development of the world industry and high-tech industries? In recent years, there has been a steady trend toward the formation of a single national scientific and innovation–technological space in developed countries that will unite the public and private sectors of national economies on the principles of partnership and cooperation. At this stage, for all highly developed countries, the state of the national innovation system is an important component of scientific and technical policy. The main factor that determines the difference between “rich” and “poor” countries is the level of development of science and the position of countries in the world market of high-tech products and technologies. Today, science is a priority branch of the economy of highly developed and some developing countries. This is determined by the cost of used material and technical equipment and the size of the resulting economic effect from the use of various kinds of scientific discoveries or established technologies in terms of the expenditure on R&D. In recent years, the globalization of the world economy has been growing. It is accompanied by the formation of a knowledge-based economy. Information and knowledge are becoming increasingly available. There is a rapid exchange of technology and rapid internationalization of the economy in the field of innovation. This leads to intensive development of international cooperation in the field of development of scientific research and development. The activities of TNCs are such example. Competition accelerates all processes in the modern world. Every year an increasing number of countries are involved in the process of internationalization. Internationalization includes a wide variety of research activities. The development of the Internet makes it possible to apply it not only in your country, but also to have access to knowledge centers around the world. In recent years, due to the internationalization of the economy, TNCs are also changing the ways of innovation. A network of research units is created all over the world. The internationalization of production, as well as the creation of global value chains, takes place in the framework of international production, which in turn, leads to the most active placement research structures in various parts of the world. The purpose of this technological activity is to gain access to local sources of knowledge and new technologies. All the above mentioned leads us to the need to pay attention to a very important aspect of the studied problem of allocation of high-tech production in the world. Today, the global economy is characterized by the presence of global production systems, in which trade of intermediate goods and services serves fragmented and separated between different countries production processes.

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The production of goods and services is increasingly organized into complex network organizational forms. Now, the world reproduction process is built in the form of global value-added chains (GVCs). Different intermediate components are produced in different countries on different continents. Prevailing now in the world industry of reproductive interdependence of national economies and the capacity of TNCs with their affiliates turned them into constituent elements of a global economic organism.

10 Conclusion The modern structure of the world industry has been formed by the influence of globalization, the development of information technologies, and new network organizational structures in industrial production. Globalization and innovative development have led to significant changes in the sectoral and territorial structure of the world economy and world industry, in particular. At the same time, while the process of industrialization is just beginning in many developing countries, industrialization in some of them (China, Asian NIS, etc.) is taking place at such a rapid pace that these processes lead to a spatial changes not only at a regional but also global level. In recent years, the share of developing countries in world production, consumption, and the exchange of industrial output has been increasing. Global centers are still North America and Europe, where the industrial production (including high-tech manufacturing industries) has grown slowly but stably, mainly due to the USA. The “dynamically positive” side of high-technology manufacturing production is the rest of the world. However, while the two–three-fold increase in Africa, Central and South America, Australia, and Oceania can be viewed as a “zero base” effect, the situation in Asia is different. The scale and rates of growth in high-tech manufacturing production in the analyzed period in Asia are enormous (despite the reduction in production in Japan). The highest growth rates of manufacturing production at current prices over the past 15 years were in Vietnam, China, Indonesia, India, Singapore, and Taiwan. In other words, the Asian trend is fixed in the world economic, including manufacturing industrial development. In other words, the position of the region-leader was taken by Asia, whose share in the world manufacturing production for the last 15 years continued to grow, primarily due to the increase in the share of China. The North America has moved to the second position because of a decrease in its share in the world manufacturing production. Europe is on the third place. However, it is important to note that there are also very serious changes that affect the strengthening of their positions in the world economy and world industry in the last decades in developed countries. These are processes of re-industrialization and neoindustrialization.

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The work shows that during the analyzed period, the location of production capacities in the world has changed at a regional level in favor of Asian countries. However, a simple comparison of statistics of manufacturing industrial production of countries and regions (even as in our case, by value added of manufacturing industries) can somewhat distort the actual picture, or rather its completeness. Therefore, we came to the conviction that it is expedient to study the location of production not only from the position of the international division of labor, but also from the position of the transnational division of labor, as well as the study of global chains of value added. Acknowledgements This research was carried out within the framework of the state task of the Ministry of Science and Higher Education of the Russian Federation No. 26.13445.2019/13.1 “Scientific and methodological, analytical and regulatory support for the implementation of the Set of Measures for 2018–2020 for the implementation of the Interstate Program for Innovative Cooperation of the CIS States until 2020.”

References Bandi, R. P. (2015). BRICS and the global economy. Financial technologies knowledge management. Access mode: https://www.bis.org/review/r160720c.htm Crescenzi, R., & Rodriguez-Pose, A. (2011). Innovation and regional growth in the European Union. Advances in spatial science. Berlin, Heidelberg: Springer-Verlag. Dominiak, J., & Rachwał, T. (2016). Chief development tendencies, structural changes and innovativeness of the industrial and service sectors in Poland. Quaestiones Geographicae, 35(4), 49–69. Giera´nczyk, W. (2010). Development of high technologies as an indicator of modern industry in the EU. Bulletin of Geography. Socio-economic Series, 14, 23–35. Gorkin, A. (2012). Geography of postindustrial (methodology and research results, 1973–2012). Oykumena: Smolensk. Industrial Statistics Database. (2017). Available via: https://www.unido.org/data1/IndStatBrief/ World_Leading_MVA.cfm?print=no&ttype=W6&Country=&Group=. Luckhurst, J. (2013). Building cooperation between the BRICS and leading industrialized states. Latin American Policy. Access mode: https://onlinelibrary.wiley.com/doi/10.1111/lamp.12018/ full. Marino, R. (2014). The future BRICS. A synergistic economic alliance or business as usual? New York: Palgrave Macmillan. Access mode: https://find.lib.uts.edu.au/search?N=4294232357. Rodionova, I. (2014). World industry in post-industrial society: tendencies and regional shifts. Miscellanea Geographica, Regional Studies on Development, 18(1), 31–36. Rodionova, I., Kokuytseva, T., & Semenov, A. (2016). Features of migration processes in different world industries in the second half of the XX century. Journal of Applied Economics Science XI, 8(46), 1769–1780. Science and Engineering Indicators. (2018). Appendix (tables 6). Two volumes. Arlington, VA: National Science Foundation, USA. Access mode: https://nsf.gov. The World Factbook. (2018). Central intelligence agency, USA. Access mode: https://www.cia.gov/ library/publications/the-world-factbook/geos/xx.html. World Development Indicators: Science and technology. (2018). Access mode: https://wdi. worldbank.org/table/5.13#.

Mathematical and Cartographic Modeling and Demographic Analysis of Rural Settlements V. A. Rubtzov, N. K. Gabdrakhmanov, N. M. Biktimirov, M. R. Mustafin and R. R. Nurmieva

Abstract This chapter presents mathematical and cartographic methods used in the demographic research. Particular attention is paid to consider the issues of integrated presentation of spatially coordinated information on the population. This work discusses the possibility of organic integration of mathematical and cartographic models and the inexpediency of opposing them. It substantiates how modern methods and solutions in demographic analysis are influenced by time. As it turns out, modern cartography and geo-informatics have a unique method for presenting and analyzing information at all levels. That allows not only considering the current situation comprehensively, but also elaborating scenarios for its development. When conducting the research at the regional level, the geographical and cartographic components quite often become the major ones. The cartographic interpretation of mathematical calculations brings them to the form suitable for optimal use, which also serves for performing multilateral analysis of the mathematical modeling results. This work substantiates that any map is a strictly defined mathematically formalized model, the construction of which is carried out according to the canons of mathematical cartography. The study determines that a formalized cartographic image is well suited for mathematical analysis. It turns out that many areas of mathematics are applicable for processing and analyzing a cartographic image. Special attention is paid to the use of the main function of information theory, i.e., entropy. Comparison of maps of different subjects and different times allows making forecasts based on the identified relationships and trends in the development of a phenomenon. The analysis reveals that cartographic extrapolations are not universal. This work identifies the factors, on which the reliability of forecast maps depends.

V. A. Rubtzov · N. M. Biktimirov (B) · M. R. Mustafin · R. R. Nurmieva Kazan Federal University, Kazan, Russia e-mail: [email protected] N. K. Gabdrakhmanov Ural State University of Economics, Ekaterinburg, Russia © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_23

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1 Introduction Demographic analysis should be considered as an integral part of studying population reproduction, associated with the development of principles for application of statistical, mathematical, sociological, geo-informational, and other methods. As a rule, demographic analysis means any analysis concerning the population, carried out using sociological, statistical, mathematical methods. It is not just a systematization of qualitative and quantitative methods used to study demographic patterns. Demographic analysis means studying the process of people generations change and its factors (Berlyant 1986). Correspondingly, the demographic analysis (Borisov 2005; Serbenyuk 1990) combines mathematical and statistical methods, using which people study the reproduction of the population. That is, changes in the number of population are an interaction result of the main indicators characterizing the natural and mechanical movement of the population. As a result, a special “demographic” view of the population and the factors that determine its dynamics are formed through the prism of methods of analysis and modeling of reality, building various scenarios of demographic development (Tian et al. 2012). Thus, demographic analysis binds theoretical and empirical knowledge in the study of the population. The development logic of the theory and practice of demographic analysis can be characterized as follows: – transition from random borrowing and adaptation of statistical-mathematical and other methods to the targeted development of the actual demographic toolkit, based on language and conceptual apparatus; – transition from theoretical constructs and models describing particular cases of interrelations of demographic variables to generalized ideas and models of reproduction of the population and its processes on a new qualitative basis of GIS-technologies and spatial databases. Modern methods and solutions in demographic analysis, as a rule, are influenced by time and new problems of studying the changing demographic reality. At the same time, demographic analysis is increasingly relying on its own methodological developments and conceptual constructions. The methods used in demographic analysis are divided into the following groups: (1) statistical; (2) graphic-analytical; (3) mathematical; (4) sociological; (5) actually demographic; and (6) cartographic. Borisov (2005) and Medkov (2008) analyzed the methods of demographic analysis.

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2 Methods and Results The development of the theory of the demographic analysis was always connected with the study problems of demographic reality at various stages of its evolution and the necessity of the generalization of empirically observed regularities. The modern cartography and geo-informatics have a unique method for representation and the analysis of the information at all levels. It allows considering the current situation and developing the scenarios of its development. Natural or social and economic systems including the population, the geographical and cartographical components quite often become basic during the research of the regional level. As a result, the production of a new level has appeared, which is known as atlases. Today, it is impossible to get along without analytical electronic atlases (GIS-atlases) and the satin information systems (SIS). Fundamentally, any card represents a strict certain formalized model mathematically which is created by the canons of the mathematical cartography. However, there is a modeled reality on the card as well as in the mathematical model. It is transferred in a conditional sign form, but the card has a property which differs from its mathematical and any other models. The zone visualizes territorial concreteness (Bugaevskii 1998; Tikunov 1997). As for the cartographical models, many valuable properties important for geographical research of territorial systems are characteristic: geometrical similarity, geographical compliance, abstractness, selectivity, synthetical character, scale, presentation, and logicality (Berlyant 1986, 1988). Consequently, these properties make maps as a very effective means of receiving information about natural, social, and economic systems, thereby turning cards into the reliable tool aimed at knowing existential regularities of surrounding reality. But, the card is not only an abstract sign, but also an analog model of reality. The card is a fine form of representation of mathematical calculations at intermediate and final stages of mathematical and cartographical modeling. It is easy to determine advantages and shortcomings of the used mathematical models by cards, to estimate information applied in the course of modeling from the point of view of its compliance with the purposes and research problems. The applied models allow noticing the shortcomings and errors caused by imperfection of an information support or shortcomings of mathematical models (Serbenyuk 1990). It allows speaking about the possibility of an organic integration of mathematical and cartographical models and inexpediency of their opposition. The formalized cartographical image is well adapted for the mathematical analysis. It turns out that many sections of mathematics are applicable for processing and the analysis of the cartographical image. A certain problem of the following character allows picking up the mathematical model and giving a reliable substantial interpretation of results of modeling. It is necessary to recognize that the cartographical analysis included some sections of the numerical analysis, multidimensional statistics, probability theory, and the theory of information.

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In mathematics, there is an understanding of an approach (replacement) to difficult or unknown functions including other, simpler functions, in which properties are already known as approximations. The computer modeling allows carrying out similar approximations for surfaces of any complexity, calculating the equations of a high order supporting sometimes several dozens of members of decomposition. Approximations are used for the analytical description of surfaces (fields) represented on cards and for a performance of various actions with them: summation, subtraction, integration, and differentiation. For example, the significant direction of using approximations is the decomposition of surfaces on components that allows allocating and analyzing normal and abnormal components in development and spatial placement of the phenomena. The other standard research purpose is an assessment of the interrelation between the phenomena. For this purpose, the theory of correlation is well approved in statistics. The device of the correlation theory varies; there are indicators in it which are convenient for the analysis of interrelations according to cards of areas (where the phenomena are characterized only by two states “yes” and “no”), according to cards of a qualitative background (where each phenomenon has many states, but it is not characterized quantitatively). There are coefficients for calculation of curvilinear dependences and connections among three phenomena (the coefficients of multiple correlations), etc. The other standard research task is interrelation assessment between the phenomena. For this purpose, let us address this problem using the theory of correlation, which is well approved in statistics. The device of the theory of correlation is rather various. In it there are indicators convenient for the analysis of interrelations according to cards of areas (where the phenomena are characterized only by two states “is” and “no”), according to cards of a qualitative background (where each phenomenon has many states, but it is not characterized quantitatively). There are coefficients for calculation of curvilinear dependences and communications among three phenomena (coefficients of multiple correlations), etc. Thus, the calculation of correlations provides for more difficult types of the analysis: regression, factorial, dispersive, etc. Often during research, the major factors showing development and placement of this or that phenomenon are set as an allocation task. These problems are successfully solved using the multidimensional factorial analysis. It allows minimizing (to three–four main factors) big sets of the initial indicators characterizing the difficult phenomenon. For example, such calculations are especially convenient when comparing isolinear cards. The mathematical statistics represent the group of methods of mathematical and cartographical modeling intended for studying by means of cards of spatial and temporary statistical sets, the statistical surfaces formed by them. The statistical analysis of the cartographical image pursues several tasks: – to study characteristics and functions of the phenomenon distribution; – to study the form and narrowness of connections between the phenomena; – to assess the extent of influence of separate factors on the studied phenomenon and allocation of decisive factors.

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The methods of the information theory are used in those cases when there is a need to assess the uniformity degree and mutual compliance of the phenomena studied according to cards. In these cases, one uses the main function of the theory information—entropy. In the cartographical analysis, this function is quite convenient for assessment of a uniformity degree or heterogeneity of a so-called variety of the cartographical image. Due to this property, the entropy function allows directly and quantitatively characterizing the heterogeneity of the cartographical image (Berlyant 2003) understood as a variety of contours and unevenness of their distribution throughout the area (distinction of sizes). Besides, the information functions are used to assess the extent of mutual coincidence of contours on different cards. In this case, they act as the corresponding indicators of interrelation of the phenomena like correlation coefficients. The cartographical and mathematical modeling in geography is referred not only to the universal number (Gabdrakhmanov 2014b; Tikunov 1997). At the same time, the system approach to study of the population demands a new approach—generalization of different types of modeling for the purpose of creation of a more flexible system method to research demographic processes. They have a complex structure with various interrelations of their elements, showing dynamism and being bulky on the scales. Use of the mathematical and cartographical models can be various and expressed in simple forms and as a difficult multistage process. If one considers it in the form of a scheme, then such model is expressed as follows: data + mathematical model = result of modeling (Tikunov 1997). Consequently, the mathematical and cartographical model synthesizes mathematical and cartographical elements together. A very wide complex of mathematical models is applied in mathematical and cartographical modeling of social and economic systems. Their choice is dictated by properties of these models, the features of initial information and properties of the modeled territorial systems, and character of the created cards. At the same time, it is possible to allocate three types of models. They are models of the structure of the phenomena, for example, models of the network of settlements, gender and the age structure of the population, the national structure of the population, etc. The second group of models reflects processes of interrelations of the phenomena. For the best understanding, it is possible to give example models of dependence of population on natural and mechanical motion of the population, dependence of birth rate on the level and quality of life, influence of an ecological component on the population, etc. A wide distribution of data from demographic and geodemographic research was obtained using models of development dynamics of the phenomena (a model of dynamics of spatial and temporary distribution of the phenomena and a model of dynamics of substantial development of the phenomena). The different predictive models can be a striking example (number, birth rate, mortality of the population, marriage and divorce rate, migration flows, etc.). The mathematical and cartographical modeling of demographic processes is a difficult process including a number of stages, which assumes an application of a set of various receptions and methods united by the principles of system methodology

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(Hohlova 2006). It is customary to allocate four groups of the mathematical models thus creating any of the cards: correlation, regression, factorial and taxonomical. The correlation models are created to reveal statistical dependences between two or several sizes. The regression models also show statistical dependences, but unlike the correlation analysis in this causality, one phenomenon is always accepted for a reason and the other for the research. The examples of the correlation and regression models can be: – creation of the capacity models in the field of resettlement, a uniformity of the location of settlements, approximations of statistical surfaces; – models of coherence of contours of objects among themselves, correlations of the spatial variation of characteristics of two phenomena; – models of population shifts (dynamics model) cannot be created without consideration during mathematical formalization of the spatial aspect and without attraction of the spatial coordinates fixing the provision of the phenomena. On the other hand, in the multidimensional group of territorial units, there is a complex of demographic indicators in the uniform groups (a model of the structure). There is a task to analyze the structure, interrelation of the phenomena of any territorial unit in comparison with other units regardless of their location. Sometimes, the obtained data of the mathematical modeling of substantial characteristics of the phenomena are plotted that allows giving spatial understanding which contributes to the analysis of the received results on the relation in the space. It provides additional benefits over other forms of representation of modeling results, lists, and tables what can also often be seen in demography. Using a possibility of a combination of separate links, elementary models in the course of stage-by-stage modeling, we can solve problems of big complexity. The cartographical interpretation of the mathematical calculations allows an optimum use and serves to make the multilateral analysis of the results of mathematical modeling. The research on the cards offers great opportunities: – for studying the location and the existential structure of the phenomena and processes, their mutual ratios and relations; – for defining tendencies of development and dynamics; – for obtaining various quantitative characteristics and estimates, dividing into districts and classifications, forecasting changes in time and space. Thus, research on the cards is always a more or less formalized procedure, at all stages of the substantial analysis of the obtained results. Their correlation with a real situation has to accompany the analysis and if there is a need to update the procedure of the research. Consequently, the structure of the phenomena and processes are identification and the analysis of their elements, locations in space, a configuration, an order, and hierarchy. The total research objective always consists of the knowledge of the spatial organization of systems, their origin, and identifying the mechanism of functioning.

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One of the most informative options of the structure research is the analysis of a configuration of cartographical images, i.e., studying the geometrical drawing of the image. It is possible to judge on an object of its morphology, origin, and the factors which have created this or that object. The cartographical method allows revealing effectively spatial regularities and anomalies, i.e., characteristic, steady, widespread structures, and deviations from them. The cards are specially intended for identification of the general regularities of various levels. It is useful to separate the main components from minor ones in the course of studying the structure of the phenomena. The study of the structure of the phenomena quite often demands transformation of the cartographical image for the purpose of creation of derivative cards and receiving new data on them. It is necessary to distinguish several types of transformations. The analysis and quantitative assessment of internal and external relations and interdependence between systems, their subsystems, and separate components are the main objectives of the mathematical and cartographical modeling. According to these data, it is convenient to estimate variability of relations in space, to mark out the main and minor dependences, to carry out indicator research. To study interrelations, a wide arsenal of techniques is used. For example, the simplest among them are the visual analysis and the description of interrelations. Consideration of the system is a major moment implying the tracing of change in interrelations across the territory, an identification of zones where connections are strong and where they are weakened or even absolutely are absent. To realize it, special cards of interrelations reflecting their spatial variation are created. There are cards of different types: – zoning maps by the degrees of mutual compliance with graphic overlay and mapping of regions; – the cartograms of interrelations where indicators of correlation are calculated by units of territorial division, usually administrative regions; – cards of entropy of contours by which mutual compliance of the phenomena is estimated using the entropy indicator for each separate contour, area, and landscape.

3 Discussion The more detailed variation of interrelations shows a more interesting map of the dimensional analysis. To study dynamics of the phenomena and processes, i.e. their emergence, development, changes in time, and movements in space, the research resorts to using different-time cards. The feature of these cards is an opportunity to represent the same objects in different timepoints. But the different time cards are made and published in different years, at the same time fixing different timepoints include the reconstruction cards. As a result of the card comparison, by which the phenomena are presented in different timepoints, it is possible to reveal the changes which happened in different time periods. It is necessary to estimate the increments

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of distances, areas, and volumes. For example, according to the different-time cards, we established not only sizes of changes, but also their directions estimated by vectors and average speed. The changes of different types are studied according to the different-time cards: slow changes, fast changes, periodic and cyclic changes, incidental and catastrophic changes. The other way of display of dynamics is drawing up cards under different conditions of the phenomenon for different dates. Thus, an increase in the population of districts is often shown in this way. The most widespread way of representation of results of the source analysis is drawing up cards of the phenomena change. It is made by the graphic overlay, i.e., a combination of two cards (the previous and current state) on a general basis. The legend of the areal card of changes is given in a matrix form; it characterizes the change in a condition of the studied process. The card of comparison of different subjects and times allows forecasting the development of the phenomena. The forecast for cards is considered as a study of the phenomena and processes not available to the modern direct studying. It makes it limited when forecasting hypotheses of development of the phenomena or processes in the future. Consequently, it is possible to predict the modern and unknown phenomena.

4 Summary Forecasting is based on cartographic extrapolations, which should be understood in a broad sense as the distribution of patterns obtained in the course of a cartographic analysis of a phenomenon. This concerns the unexplored part of this phenomenon, another territory. At the same time, we note that cartographic extrapolations, like any other (mathematical, logical), are not universal. Their advantage is that they are well adapted to predict both spatial and temporal patterns. It is widely known that the practice of forecasting using maps often involves the application of analogy methods, indications, expert estimates, calculation of statistical regressions, etc. There are basically three types of forecasting on maps: – a forecast over time, based on an extrapolation of the dynamic trends identified on multi-temporal maps; – a spatial forecast based on interrelations and analogies identified on maps of various subjects; – a spatial–temporal forecast, combining the two forecast types mentioned above and allowing predicting the main trends in the development and evolution of a phenomenon in the prediction space. When making cartographic extrapolations, background surface maps are of particular importance. These maps enable predictions of the main, defining, background features of any phenomenon without going into detail and possible random deviations. It turns out that the maps of background surfaces are equally suitable for forecasting in time and space.

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5 Conclusion Thus, the accuracy of forecast maps depends on the forecast lead time and extrapolation range, on the nature of the phenomenon itself, its stability, mobility, repeating pattern, on the reliability and completeness of the source maps. This also includes the stability of the identified trends, the closeness of interrelationships, which is to a great extent determined by the applied forecasting method. Depending on the degree of their reliability, forecast maps are normally subdivided into preliminary forecast, probable forecast and very probable forecast maps, as well as prospective calculation maps. Acknowledgements The research was conducted with the financial support of the Russian Federal Property Fund and the Republic of Tatarstan, project No. 17–12-16005 “The forecast of Social and Economic Development of the Rural Settlements of the Republic of Tatarstan.”

References Berlyant, A. M. (1986). Image space: Map and information. Moscow: Think. Berlyant, A. M. (1988). Cartographic research method. Moscow: MGU. Berlyant, A. M. (2003). Cartography. Moscow: Aspekt Press. Borisov, V. A. (2005). Demography. Moscow Bugaevskii, L. M. (1998). Mathematical cartography. Moscow Gabdrakhmanov, N. K. (2014b). Positioning of Volga Federal district regions by demographic situation index. World Applied Sciences Journal, 30(6), 792–795. Hohlova, O. A. (2006). Methodology of a static study of the regional economy. Monograph. Irkutsk: BGEUP. Medkov, V. M. (2008). Demography. Moscow: INFRA-M. Serbenyuk, S. N. (1990). Cartography and geoinformatics—their interaction. Moscow: MGU. Tian, G., Qiao, Z., & Zhang, Y. (2012). The investigation of relationship between rural settlement density, size, spatial distribution and its geophysical parameters of China using Landsat TM images. Ecological Modelling, 231, 25–36. Tikunov, V. C. (1997). Modeling in cartography. Moscow: MGU.

Development of the Project Management Mechanism Based on Renewable Energy Sources for the Northern Regions of Russia Xiang Li, Aleksandr S. Bovkun, Galina M. Beregova, Aleksandr F. Schupletsov and Yullia A. Skorobogatova Abstract This work analyzes the development and functioning of power supply systems in the northern regions of Russia. The authors identify different peculiarities leading to a low level of reliability of energy supply for consumers in the northern territories of the country. This work reveals that the increase in the energy efficiency of the closed territories is considered using non-traditional renewable energy sources. The use of renewable energy sources in underdeveloped territories has become a priority direction of the development of economically developed countries of the world. The authors developed a project management mechanism based on renewable energy sources. The proposed mechanism for introduction of energy-efficient projects based on renewable energy sources includes a sequence of seven stages, which allows covering the entire list of works in the northern region and organizing effective management of the implementation of projects.

X. Li Chinese Geological University, Beijing, China A. S. Bovkun (B) · G. M. Beregova Institute of Economics, Management and Law, Irkutsk National Research Technical University, Irkutsk, Russia e-mail: [email protected] G. M. Beregova e-mail: [email protected] A. F. Schupletsov · Y. A. Skorobogatova Enterprise Economics and Entrepreneurship Department, Baikal State University, Irkutsk, Russia e-mail: [email protected] Y. A. Skorobogatova e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0_24

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1 Introduction Russia is a country with significant reserves of almost all types of fuel-power resources, especially fossil fuels such as oil, coal, and gas. It is generally assumed that in the presence of large stocks of organic fuels, the issues of energy efficiency, energy saving and introduction of fuel-free technologies based on renewable energy sources are not the most relevant. However, improvement of environmental safety of power production and introduction of local and renewable resources increase not only environmental sustainability, since both fossil and renewable energy resources are unevenly distributed throughout the country. An important feature of the existing energy system of Russia is a high degree of centralization. About 90% of the total amount of electric power is produced by large power plants, which distribute electric power throughout an extensive electrical network (Voropai 2018). At the same time, centralization is typical of densely populated regions of the European part of the country and some regions of Siberia. Most of the Russian territory (about 60% and 10 million of population) is not connected to centralized energy systems. They receive electric power mainly from low-power, autonomous diesel generators. The negative side of such centralization is expenditures for transportation and significant energy losses during transportation and transmission over long distances. These circumstances make renewable energy sources (RES) one of the most promising types of resources. RES, being local distributed energy resources, can be effectively used for such decentralized energy supply. The introduction of RES provides an opportunity to increase energy security in the regions of Russia and to increase the coefficient of self-sufficiency. Currently, renewable energy sources (RES) are the fastest growing in percentage terms kind of energy in the world. The confidence that RES can enable resolving energy security problems and reducing environmental impact has forced many OECD governments to provide benefits and other types of support for RES, thus causing prices to fall and markets to grow. As a result, renewable energy in the most suitable conditions turns out to be quite competitive in price with energy obtained from traditional sources, especially when considering its environmental advantages. Now, in Russia, despite the enormous potential reserves, renewable energy sources are used very insignificantly. Causes of insufficient use of RES in Russia proceed from a complex set of factors (Suslov 2017; Voropai and Stennikov 2012). First, general public, business circles are experiencing a lack of reliable information about availability and economic opportunities of sources and systems of the renewable energy sector. In the absence of such information, renewable energy in Russia is usually too expensive. Second, the abundance of reserves of combustible fossils along with excessive generating capacity in the power industry is often indicated as other constraints to the renewable energy development in Russia. Russia is the largest producer and exporter of fossil fuels in the world, and the widespread view is that it does not need to use its vast reserves of renewable energy. Nevertheless, development of the market of renewable energy sources does not contradict to the use of stocks of traditional fuels, but complements it.

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Third, use of renewable energy sources is perceived by many as an expensive project, and large financial investments are needed. Fourth, development of the renewable energy sector is hindered by an unfavourable Russian investment climate. A series of obscure laws and regulations, a weak financial sector, a lack of transparency and violations of shareholders’ rights— they are all among the factors that restrain investment in all sectors of the Russian economy. Artificially underestimated domestic energy prices, in particular, are a fundamental obstacle to attracting investment in the energy sector. The development of power conversion technologies of renewable sources has a highly intelligent, knowledge-intensive and innovative nature, ensuring a reduction in material consumption and cost in power production (Good et al. 2015). Economic well-being of Russia essentially depends on the development of vast, but sparsely populated and inaccessible territories of the Far North with harsh climatic conditions (Kiushkina 2016). In the northern territories, there is a shortage of power and its expensiveness. The cost of the annual northern delivery of fuel to the regions of the Far North and similar areas in 2018 may exceed 100 bln rubles. With the cost of diesel fuel for consumers in the central part of Russia, equal to about 46 thous. rubles/ton, it turns out that the price of diesel fuel for many isolated territories is 70–90 thous. rubles/ton (Voropai et al. 2018). In many cases, expenditures for transporting fuel is covered by budgetary subsidies, making energy economically more affordable. The energy shortage and its high price restrain the development of the local economy and limit the possibilities of providing comfortable living and, therefore, the attractiveness of the northern territories. The extreme North is characterized by special conditions: • economic closure of territories; • limited transport accessibility; seasonality of navigation; complex, multi-link transport schemes for fuel delivery (up to 7000 thousand km) with numerous transshipments. They include expenditures for rent, security, loading, reloading, maintaining winter roads, and delivery of fuel sometimes only in the second year after the moment of its dispatch from an initial delivery point due to changes in the water content of the northern rivers and ice conditions; • for this reason, in some cases, the need to have a two-year supply of fuel; • long heating season (9–11 months), polar night, snowstorms, low temperatures, and high wind loads; • threat of permafrost degradation under the influence of climate change; • relatively small unit electrical and thermal loads of consumers of the Far North (Sosnina et al. 2014). A lot of papers (Arai et al. 2009; Aizenberg and Stashkevich 2015; Mochalin and Chuvikova 2011; Surzhikova 2012; and Suslov 2015, 2017) are devoted to the technical and economic assessment of power supply to closed areas. An increase in energy efficiency of closed areas is considered at the expense of using non-traditional renewable energy sources. Application of renewable energy sources (RES) in less developed closed areas has become a priority for the development of economically developed countries of the world (Gerasimov and Ukolova 2016).

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2 Results and Discussion Despite the fact that Russia has enormous resources of wind, geothermal, solar energy, biomass energy, and hydropower resources, currently renewable energy sources (apart from large hydropower facilities) are used very little in the country. Our country is seriously lagging behind both in input volumes and in technologies for conversion of various types of renewable energy. The present-day indicator is less than 1% of the total volume (Voropai et al. 2017). To implement projects based on using RES in the region, we have developed a mechanism for introduction of energy-efficient projects based on renewable energy sources. The proposed mechanism for the introduction of energy-efficient projects based on renewable energy sources (Fig. 1) includes a sequence of seven stages, allows covering the entire list of works in the region, as well as organizing effective management of realizing RES projects in the region. To prove this position, let us analyze in detail each stage of the mechanism. 1. The first stage “Forecasting and planning of RES introduction in the region.” The first step of this stage is the development of a strategy and determination of priority directions for RES in the region. The strategic priorities of generation development based on renewable energy sources will be: I. Ensuring energy security based on: (1) partial diversification of electric energy production based on non-fuel energy; (2) reduction of energy losses for transportation and energy distribution due to introduction of distributed generation based on RES and approximation of production and energy consumption facilities; (3) increase in the level of energy security and reliability of energy supply at the expense of increasing its decentralization level. II. Provision of energy efficiency of the economy by: (1) reducing the growth rate of consumption of fuel and energy resources; (2) involvement of additional fuel and energy resources in the fuel and energy balance. The next step will be coordination of priority directions for RES development with the legislative and executive authorities of the region. 2. The second stage “Substantiation of needs of municipalities for electric and thermal energy, satisfaction of which is possible at the expense of RES.” is necessary to identify the settlements that are in desperate need of implementation of RES projects. For that, a database of settlements not connected to public networks, using delivered fuel for heating and having unstable power supply, is compiled. In Russia, as a rule, this is a large part of the northern territories of the country. 3. The third stage “Analysis of existing plants, equipment and technologies of RES in the region.” is carried out to analyze the already existing reserve for RES introduction in the region. It is necessary to explore the strengths and weaknesses of already existing and ongoing projects. 4. The fourth stage is “Selection of the list of projects for realization of the program of RES introduction.” For this purpose, a project feasibility study

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Fig. 1 Mechanism of introduction of power-efficient projects based on renewable energy resources

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is developed for each project. At this stage, it is necessary to determine which type of renewable energy will be the most attractive for commercialization in a particular area. For each project, it is necessary to assess the effectiveness and financial feasibility to determine the most commercialized projects. 5. The fifth stage “Search for an investor and resources for project implementation.” is the main stage of introduction of energy-efficient projects based on RES, because the effectiveness of the entire activity of the region depends on it. 6. The sixth stage is “Project support.” If interested companies are found, the region realizes the legislative and regulatory implementation of the project, as well as scientific, technical, and personnel support. At the present time, a number of laws and by-laws have been adopted for RES development. The main regulatory document is Federal Law of March 26, 2003, No. 35-FZ (as amended by Federal Law No. 250-FZ of November 4, 2007) “On Electric-Power Industry.” For the purpose of improving the legal framework in the field of RES, decree No. 1839-r of the Russian Federation Government of 04. 10. 2012 approved a set of measures to promote the use of RES. Resolution No. 116 of February 17, 2014, of the Russian Federation Government approved changes in the qualification procedure for a generating facility operating based on RES. It was approved by the resolution of the Russian Federation Government, No. 426, of June 3, 2008, “On Qualification of a Generating Facility Operating Based on Using RES.” The resolution of the Russian Federation Government of February 17, 2014, No. 117, approved the rules for maintaining the register of issue and cancelation of certificates, confirming the volume of electric power production at generating facilities operating based on RES. On the wholesale market of electric energy and power, the Federal Law “On Electric Power Industry” provides for using the mechanism for selling capacity of generating facilities functioning based on RES under contracts of power supply to the wholesale market (PSC RES) at a price and in the manner established by the Russian Federation Government. The mechanism of support for RES consists in conducting a competitive selection of investment projects for construction of generating facilities based on RES and a conclusion of PSC RES regarding selected projects. The resolution of the Russian Federation Government of May 28, 2013, No. 449, approved the rules for determining the price of capacity for such generating facilities. The price for the capacity of the RES facility is determined proceeding from the compensation condition for the share of expenses made, determined proceeding from the method for determining the share of expenses, according to which the use of a coefficient reflecting fulfillment of the target indicator of the localization degree of generating equipment is used. Target indicators of the localization degree and input volumes for each type of generating facility of RES for the period up to 2020 are established by the Russian Federation Government in the decree of the Russian Federation Government of May 28, 2013, No. 861-r.

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Today, it is absolutely necessary to develop a legislative and organizational base for development of the renewable energy sector, including adoption of the federal law “On Renewable Energy Sources.” These documents should foresee measures to stimulate the use of RES, as well as to solve a number of organizational and legislative problems. There is a need to adopt regional legislative and regulatory acts, taking into account peculiarities of the region. Meeting the needs of autonomous consumers can be considered as a promising growth point for development of the renewable energy sector in the Russian regions. In a situation where only 40% of the Russian territory is provided with centralized energy, RES can be considered a serious alternative to the “northern delivery”—transportation of energy resources to remote areas. For autonomous economies, being distant from the centralized energy networks of RES, this is a triple benefit: the introduction of the energy alternative is also justified from the social, economic, and environmental points of view. 7. The seventh stage is “Analysis of the activity for the reporting period.” At this stage, analysis of the effectiveness of measures on implementation of RES projects is made; a report is drawn up for the reporting period, which is subsequently presented and discussed at a meeting of the regional legislative and regulatory authorities. This stage ends with an assessment and adoption of recommendations on realization of further activities in the subsequent reporting period.

3 Conclusion The renewable energy sector in Russia is underestimated from the point of view of political, economic, and social importance. Today, the raw material model of development prevails in Russia, and propaganda is being spread against the use of RES because of the allegedly high cost and technical imperfection of this type of power industry. At the same time, renewable energy industry may perfectly and will probably actively develop from below as the most appropriate alternative to hydrocarbon resources at the moment. Thus, using the mechanism of introduction of energy-efficient projects based on renewable energy sources allows creating tools that ensure efficient management of the continuous process of projects introduction according to a preplanned scenario, thereby contributing to improving the life quality among population, efficient organization of administrative work, and also allows minimizing the costs associated with expenses on their management. In our opinion, the widespread use of renewable energy sources will contribute to achievement of the following goals: • achievement of target indicators in capacity and production of electric power; • attraction of investments, joint projects with foreign partners in construction of new capacities, and assessment of multiplicative effects from RES development;

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• replacement of fossil fuels burned during production of electric power; • decrease in average prices in the wholesale market at the expense of replacement of high-margin stations with new generation of RES; • reducing the expenses on measures on ecology and protection of public health; • creation of new jobs in the industries producing generating and auxiliary equipment for enterprises of the renewable energy sector; • additional tax revenues; • formation of a high-technology branches of scientific developments in the field of RES and the power machine building branch; • growth of the share of products of high redistributions in the structure of industrial production in Russia.

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Conclusions Niyaz Kamilevich Gabdrakhmanov and Lenar Nailevich Safiullin

The most important results of this volume include new approaches to the development of different sectors of the economy and individual markets and improvement of the efficiency of entrepreneurship in general. By combining the efforts of the scientists, it has become possible to analyze the concept, meaning and directions of the socio-economic development of the regional subjects in the Russian Federation and other countries. The scientific studies that are collected in this volume make a significant contribution to the development of entrepreneurship, regional nature management, rationalization and optimization of resource use, state territorial administration, sustainable economic growth in the regions and the transport infrastructure. We hope that such fruitful cooperation of our authors and this book will become the basis for stimulating interest in new research and inventions in this area. In conclusion, we must mention that further prospects of the fundamental and applied research in the field of sustainable economic growth are associated with a number of factors. They are the practical implementation and approbation of the developed methodological approaches, recommendations of the authors and economic models in the activities of modern socio-economic systems aimed at verifying their effectiveness, specification and improvement.

N. K. Gabdrakhmanov National Research University Higher School of Economics, Moscow, Russia e-mail: [email protected] L. N. Safiullin Kazan Federal University, Kazan, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 N. K. Gabdrakhmanov and L. N. Safiullin (eds.), Regional Economic Development in Russia, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-39859-0

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