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УДК 332.1
DOI: 10.18413/2409-1634-2022-8-1-0-4
Сюй Чжэньпэн
ОБЗОР КЛЮЧЕВЫХ ИННОВАЦИОННЫХ ПОКАЗАТЕЛЕЙ РОССИЙСКИХ РЕГИОНОВ: АНАЛИТИЧЕСКИЙ ОБЗОР ИННОВАЦИОННО-АКТИВНЫХ ТЕРРИТОРИЙ ЦЕНТРАЛЬНОГО ФЕДЕРАЛЬНОГО ОКРУГА_
Провинция Ляонин, г. Далянь, Китай e-mail: xuzhenpeng@yandex.ru
Аннотация.
В статье представлены результаты исследования инновационного потенциала российских регионов. В качестве субъектов исследования были выбраны три региона Центрального федерального округа, лидирующие в 2020 году по уровню инновационной активности организаций среди субъектов Российской Федерации (Тульская область, Белгородская область, Ивановская область). Был проведен обзор инновационной инфраструктуры и ключевых стратегических приоритетов инновационного развития в отдельных регионах. Изучена взаимосвязь между параметрами "затраты на инновационную деятельность организаций региона" и "уровень инновационной активности организаций региона". Составлены корреляционно-регрессионные модели, выражающие взаимосвязь между временными характеристиками уровня инновационной активности и затратами на инновационную деятельность.
Ключевые слова: innovation; regions; Central Federal District; innovation activity; costs of innovation activity; correlation-regression models
Информация для цитирования: Сюй Чжэньпэн Обзор ключевых инновационных показателей российских регионов: аналитический обзор инновационно-активных территорий Центрального федерального округа // Научный результат. Экономические исследования. 2022. Т. 8. № 2. С. 34-44. DOI: 10.18413/2409-1634-2022-8-1 -0-4
Xu Zhenpeng
OVERVIEW OF KEY INNOVATION INDICATORS OF RUSSIAN REGIONS: ANALYTICAL PROFILE OF INNOVATION-ACTIVE TERRITORIES OF THE CENTRAL FEDERAL DISTRICT
Province Liaoning, city Dalian, China e-mail: xuzhenpeng@yandex.ru
Abstract.
The article presents the results of the study on the innovation potential of Russian regions. Three regions of the Central Federal District have been selected as research subjects, leading in 2020 in the level of innovation activity of organizations among the subjects of the Russian Federation (Tula Region, Belgorod Region, Ivanovo Region). A review of innovation infrastructure and key strategic priorities of innovative
development in selected regions has been undertaken. The correlation between the parameters "costs of innovation activity of organizations in the region" and "level of innovation activity of organizations in the region" has been studied. Correlation-regression models have been compiled that express the relationship between the temporal characteristics of the level of innovation activity and the costs of innovation activity.
Key words: innovation, regions, Central Federal District, innovation activity, costs of innovation activity, correlation-regression models.
Information for citation: Xu Zhenpeng "Overview of Key Innovation Indicators of Russian Regions: An Analytical Profile of Innovation Active Territories of the Central Federal District", Research Result. Economic Research, 8(1), 34-44, DOI: 10.18413/2409-1634-2022-8-1 -0-4
Introduction
Innovation is a global trend that has become a key strategy for many countries to seek competitive advantage. Innovation processes are the result of a large number of interactions between various participants and institutions that form the national innovation system as a whole.
The innovation system consists of relationships and exchanges with enterprises, governments and academia on science and technology development.
The theory and practical aspects of innovative development have been a relevant area for researchers for many years. Almost all research on innovation was based on results previously obtained by J. Schumpeter (Theory of Dynamic Economics), F. Hayek (Diffused Knowledge Concept), D. North (Institutional Theory), R. Solow (role of scientific and technological progress in economic growth), P. Romer and R. Lucas (Endogenous Growth Theory).
In the 21st century, the role of innovation in the economy has significantly increased, and the scientific and innovation potential of countries has become a vital aspect of competitiveness, both within countries and within regions.
Research objective is to study the innovation activity of the Russian regions of the Central Federal District, in particular Belgorod Region, Ivanovo Region, and Tula Re-
gion. The regions in question are currently characterized by a high level of innovation activity among the subjects of the Russian Federation.
The methodological basis for studying the innovation activity of regions is the system approach that allows generalization and synthesis of the studied material.
The analysis of a number of indicators of innovation activity in the Russian regions makes it possible to underscore the vector of their innovative development.
Main part
In 2020, there were 4,175 scientific research and development organizations in the Russian Federation. Most of them was concentrated in the Central Federal District (1579 organizations or 37.8%) and the Volga Federal District (690 organizations or 16.5%). Compared to 2019, the number of relevant organizations increased by 3.1%. 11,386 organizations in the Russian Federation carried out innovation activities in 2020, which is 15.7% more over 2019[ On Innovation Activities..., 2009].
To assess the success of the implementation of certain aspects of innovative development in the Russian Federation, we will conduct a comparative analysis of the state of the innovation sphere in a number of regions (limited to the territory of the Central Federal District).
Таблица 1
Уровень инновационной активности организаций по субъектам Российской Федерации, % [Лидеры по показателям..., 2020; Федорова Л.А., 202 0]
Table 1
Level of innovation activity of organizations, by subjects of the Russian Federation, % [ Leaders in terms..., 2020; Fedorova, L.A., 2020]
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
according to the Oslo Manual (3rd ed.) criteria according to the Oslo Manual (4"' ed.) criteria
Russian Federation 9.5 10.4 10.3 10.1 9.9 9.3 8.4 S.5 14.6 12.8 9.1 10.8
Central Federal District 8.6 10.2 10.9 10.7 10.9 10.9 10.3 9.9 18.5 16.2 10.8 12.5
Belgorod Region 10.9 12.2 9.2 9.6 11.5 12.7 14.1 14.8 19.8 18.2 15.1 18.0
Brvansk Region 8.8 9.6 8.9 7.8 8.2 7.7 6.8 6.2 9.5 8.2 10.1 10.9
Vladimir Region 9.5 10.S 12.8 10.7 12.6 11.2 10.4 9.0 15.3 13.0 10.5 12.6
Voronezh Region S.6 9.2 9.0 10.0 10.3 11.0 11.6 11.7 1S.6 17.1 13.4 15.9
Ivanovo Region 5.8 5.1 8.5 8.4 6.3 4.4 3.2 4.2 9.0 8.1 10.0 16.2
Kaluga Region 8.3 7.9 10.6 10.9 9.7 10.9 8.5 9.0 17.5 16.6 11.5 12.1
Kostroma Region 8.5 9.1 6.0 7.0 6.0 8.2 8.6 2.8 6.5 5.8 4.6 5.6
Kcrsk Region 7.1 13.7 13.0 10.7 9.9 7.3 6.5 5.0 10.6 8.9 5.4 7.6
Lipetsk Region S.9 10.0 14.1 17.5 1S.6 20.0 19.2 IS.5 29.1 23.6 11.1 11.5
Moscow Region 6.7 S.l 8.5 8.4 8.7 8.0 8.5 8.9 18.8 14.1 8.6 10.8
OryoL Region 11.5 10.7 10.1 8.4 8.4 9.6 7.4 6.8 11.0 8.6 10.4 13.7
Ryazan Region 7.0 S.4 11.0 11.4 13.1 12.7 12.3 12.1 17.5 16.4 11.8 10.9
Smolensk Region 5.5 6.6 6.7 6.6 6.6 7.3 6.9 6.5 11.7 10.8 8.4 7.1
Tambov Region 8.2 5.9 8.5 8.8 9.1 9.6 10.6 11.0 12.3 11.0 10.2 12.5
Kgt. Region 5.1 7.S 9.3 9.2 S.O 1.9 7.9 S.7 16.3 15.6 12.1 12.0
Tula Region 10.5 11.0 13.1 12.9 13.4 12.9 10.9 9.2 16.9 15.4 11.7 20.2
Yaroslavl Region 10.0 12.0 12.3 11.0 10.3 8.7 7.1 8.3 16.3 14.2 10.6 10.7
Moscow 13.3 18.6 18.6 18.3 18.8 19.7 16.1 14.3 32.4 33.8 12.1 13.0
Some regions of the Central Federal District have drawn our attention, namely, those with the most significant results in this sphere: Belgorod Region, Ivanovo Region, Tula Region. These regions are the leaders (TOP-3) in 2020 in the level of innovation activity of organizations among the subjects of the Russian Federation.
It should be noted that, in general, this district has results above the average. This clearly indicates that the regions of the Central Federal District are most successful in implementing the main objectives of the innovative development strategy.
Let us analyze the innovation potential of the selected regions.
Belgorod Region. To date, the region is actively working to formulate an updated Strategy for the Socio-Economic Development of Belgorod Region up to 2030. The key direction of development in the strategy being developed is "Innovative Belgorod Region". The goal of this priority area is to ensure a scientific and technological breakthrough in key sectors of the Belgorod and Russian
economy, and to increase the share of innovative products in the shipment due to comprehensive support of researchers and developers.
The main strategic objectives of the innovative Belgorod Region up to 2030 are:
- developing the region as a point of attraction for researchers;
- introducing Belgorod technologies to the Russian and world markets;
- creating a closed-loop economy;
- reducing ecological footprint;
-increasing cooperation between science and business.
On the territory of Belgorod Region, the Law of Belgorod Region dated October 1, 2009 No. 296 "On Innovation Activities and Innovation Policy in the Belgorod Region" is in force [On Innovation..., 2009].
With regard to innovation infrastructure, institutions include:
-Department of Investment and Innovation (part of the Ministry of Economic Development and Industry of Belgorod Region), which ensures the implementation of public
innovation policies at the regional level, providing a maximum enabling environment for the implementation of the innovative scenario for the development of the region;
-Belgorod Regional Resource Innovation Center (Business Incubator), which is the regional representative of the Foundation for Assistance to Small Innovative Enterprises in Science and Technology (Innovation Assistance Fund) since 2016. The Center implements innovation development programs aimed at creating new and developing existing high-tech companies, as well as commercializing the results of scientific and technical activities;
-World-class Scientific and Educational Center "Innovative Solutions in the Agro-Industrial Complex", which is considered a connecting element for the development of innovations in Belgorod Region and through which trending scientific technologies are transferred to real business. The Center was established in 2019 as part of the implementation of the "Science" National Project. The activities of the Center are aimed at actively participating in ensuring the competitiveness of the Russian Federation as a world scientific power and leader in the field of agrarian science; strengthening food security and the independence of the country; expanding Russia's presence in world food markets; improving the well-being and quality of life of citizens of Russia and Belgorod Region.
Also, the regional innovation infrastructure of Belgorod Region includes Belgorod Regional Fund for the Support of Small and Medium Enterprises, Belgorod Intellectual and Innovation System Development Fund, "Severny" Industrial Park, Patent and Information Center of the Belgorod State Universal Scientific Library, Regional Center for Intellectual Property at Belgorod State National Research University (NRU BelSU), Technological Park "High-Tech" of NRU BelSU, Center of High Technologies of Shu-khov Belgorod State Technological University (BSTU), Business incubator of Shukhov BSTU, Innovation and Technology Center of Shukhov BSTU, Educational and Scientific
Innovation Center "Agrotechnopark" of Gorin Belgorod State Agrarian University, Biryuch Innovation Center, Centers for Youth Innovative Creativity "CLUSTER" and "Metamorphosis".
Ivanovo Region. Decree of the Government of Ivanovo Region dated April 27, 2021 approved the Strategy for Social and Economic Development of Ivanovo Region until 2024, within which the block "Innovation Development Strategy of the Region" was identified. The objective of the innovation strategy of development of the region is "formation of an innovative-oriented structure of the economy, a comfortable economic environment and institutional conditions for enhancing the innovative development of the region".
Among the priorities for innovative development in the region are:
-technological upgrading and modernization of industrial enterprises;
-development of science and innovation activity.
The main areas of innovation and scientific activity of Ivanovo Region, which generally determine the specialization of the region, are developments in the textile industry, in the sphere of manufacturing of industrial machinery and equipment, special and construction equipment, in energy-saving technologies, as well as fashion and design.
From 2014 to 2024, the Ivanovo Region State Program "Economic Development and Innovative Economy of Ivanovo Region" (with changes of 01/20/2022) is being implemented. Under the State Program, such subprograms as the development of small and medium-sized enterprises, fundamental scientific research, human resources for the innovative economy, and the creation of a favorable investment environment operate.
The region's innovation infrastructure is quite extensive. It includes Industrial Park "Rodniki", Educational and Scientific Center for Technological and Ecological Safety in Energy, Center for Design and Improvement of Reliability of Electrical Equipment, Center for Energy Efficient Technologies, Research
Center and Experimental Biomedical Clinic with Vivarium, Research Institute for Macro-Heterocyclic Compounds, Regional Center for Nano Industry, etc [Lukasheva, N.A., 2015].
Tula Region. According to the Program for Social and Economic Development of Tula Region (2021-2026), one of the strategic objectives of the region (there are 7 in total) is "building a highly efficient and sustainable scientific ecosystem of the region with the volume of 100 billion rubles per year of shipped innovative domestically produced goods by 2030" [Program of Social., 2022].
In general, the priority direction of the region's development is its innovation activity ("Knowledge: Innovation Activity"), focused on improvement and development of higher education, general innovation development and creation of breakthrough technologies.
Regional priority projects within the strategic direction of development "Knowledge: Innovation Activity" by 2026 are: active operation of the Scientific and educational center of the world level "Tu-laTECH"; formation of research and innovation infrastructure of the innovative scientific and technological center "Composite Valley"; increased share of new advanced high-tech products; formation of the Center of Technological Excellence of the Tula Region; creation of an ecosystem of personnel training at the request of enterprises for their research and production activities; implementation of new approaches in the design, production and testing of prototypes; enhancement of technological and digital maturity of industrial production [On Approval., 2021]; transformation of educational space in the field of general, secondary vocational and higher education, and formation of a single educational ecosystem of the region, including development of student technological entrepreneur-ship.
The region has a Strategy for the Development of Science, Technology and Innovation in Tula Region until 2030 (approved by Decree of the Government of Tula Region No. 539 dated August 27, 2021).
The existence of this strategic document is a certain competitive advantage of Tula Region in innovation activity.
The Strategy contains an assessment of regional innovation policies, including the innovation activity of organizations, the performance of higher education organizations, activities of regional authorities to develop innovative infrastructure and measures to support innovation projects.
As for the innovation infrastructure of Tula Region, it includes the Committee of Tula Region on Science and Innovation (which forms and implements the regional policy in the field of science and innovation in the Tula Region); Autonomous Non-Profit Organization "Scientific and educational center of the world level "TulaTECH" (association of universities, scientific organizations and organizations operating in the economic sector); innovative scientific and technological center "Composite Valley" on the basis of Tula State University.
In 2022, eleven scientific laboratories began their operation in the region at Tula State University, Tolstoy Tula State Pedagogical University (TSPU), and Novomoskovsk Institute of the Mendeleev Russian Chemical-Technological University. The main activities of the new laboratories are developments in the areas of defense and weapons, the creation of new materials and technologies for their production, environmental safety, and medicine.
After analyzing the current state of innovation activity in the selected regions, we address to recent statistics that illustrate the increase in innovation capacity in the above-mentioned regions (Table 2).
Our main indicator for evaluating success has been the level of innovation activity, which is defined in the guidelines as the ratio of the number of organizations that have implemented technological, organizational, or marketing innovations to the total number of organizations surveyed in the country, industry, and region over time.
Таблица 2
Уровень инновационной активности организаций по отдельным субъектам Центрального федерального округа, %
Table 2
Level of innovation activity of organizations, for selected subjects of the Central Federal District, %
2018 2019 2020 Deviation +/-, %
2019 to 2018 2020 to 2019
Russian Federation 12.8 9.1 10.8 -28.9 18.7
Central Federal District 16.2 10.8 12.5 -33.3 15.7
Belgorod Region 18.2 15.1 18.0 -17.0 19.2
Ivanovo Region 8.1 10.0 16,2 23,5 62.0
Tula Region 15.4 11.7 20.2 -24.0 72.6
Compiled by author
Although these regions have shown the most successful performance in recent years, the data in Table 1 indicate a decline in innovation activity in 2019 compared to 2018 in the following regions: Belgorod Region by 17% and Tula Region by 24%. In 2020, there was no negative dynamics, a-nd the largest increase was in the Ivanovo Region by 62%.
This indicator can be considered as the resulting value (final outcome) that correlates with the costs of innovation activity of organizations in the region. The dynamics of costs for innovation activity in the analyzed territories is presented in Table 3.
Таблица 3
Затраты на инновационную деятельность организаций по отдельным субъектам Центрального федерального округа, млн руб.
Table 3
Costs of innovation activity of organizations, for selected subjects of the Central Federal District, million rubles
2018 2019 2020 Deviation +/-, %
2019 k 2018 2020 k 2019
Russian Federation 1472822.3 1954133.3 2134038.4 32.7 9.2
Central Federal District 494893.3 844271.4 890687.9 70.6 5.5
Belgorod Region 20703.2 30653.0 21540.9 48.1 -29.7
Ivanovo Region 154.7 195.3 1402.4 26.2 7 times more
Tula Region 18717.7 56822.0 26638.5 203.6 -53.1
Compiled by author
As can be seen from Table 3, the dynamics of the costs of innovation activity is different. In 2020, the decline was in Belgorod Region (by 29.7%) and Tula Region (by 53.1%). Against this background, in Ivanovo Region in 2020 there was a significant increase in the costs of innovation activity of
organizations, the figure in 2020 exceeded the level of 2019 by more than 7 times. This explains the significant increase in innovation activity.
To objectively substantiate the correlation between the level of innovation activity and the costs of innovative activity, a correla-
tion and regression analysis based on the growth rates of these indicators is conducted. We have to use relative indicators, since in 2017 some adjustments were made to the cal-
culation of the level of innovation activity (Oslo Manual). The growth rates allows for a convergence of the series and a review of the period from 2015 to 2020 (Table 4).
Данные для корреляционно-регрессионного анализа Data for correlation regression analysis
Таблица 4 Table 4
Growth rate 2016 to 2017 to 2018 to 2019 to 2020 to
2015 2016 2017 2018 2019
Russian Federation level of innovation activity, % 90,3 101.2 87,7 71,1 118.7
costs of innovation activity, % 106.7 109.4 104.8 132.7 109.2
Central Federal level of innovation activity 94,5 96,1 87,6 66,7 115.7
District costs of innovation activity 128.4 86,6 108.2 170.6 105.5
Belgorod Region level of innovation activity 111.0 105.0 91,9 83.0 119.2
costs of innovation activity 850.0 117.3 86,8 148,1 70.3
Ivanovo Region level of innovation activity 72,7 131.3 90,0 123.5 162.0
costs of innovation activity 127.7 70,2 61,1 126.2 718,1
Tula Region costs of innovation activity 84,5 84,4 91,1 76,0 172.6
costs of innovation activity 151.2 92,6 116.1 303.6 46.9
Moreover, by constructing histograms for the growth rates of the selected indicators
we present their comparative analysis in the territorial comparison of data (Fig. 1).
Рис. 1. Гистограммы временных характеристик уровня инноваций
и затрат на инновации Fig. 1. Histograms of the temporal characteristics of the level of innovation
and the costs of innovation
A graphic representation of the temporal characteristics of the level of innovation activity and the costs of innovation activity (Fig. 1) does not show a clear correlation between the growth rates of the indicators con-
sidered. This can be explained by the lack of synchrony in the influence of factor magnitude and the responsiveness of the result. The results of the construction of the regression equations are presented in Fig. 2 and Fig. 3.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0,617419
R Square 0,381207
Adjusted R Square 0Д74Э42
Standard Error 15,93965
Observations 5
df ss MS F Significance F
Regression 1 472,5135 472,5135 1,848147 0,267161
Residual 3 767,0065 255,6638
Total a 1239,52
Upper
Coefficients Standard Error tstat P-va!ue Lower 95% 95% Lower 95,0% Upper 95,0%
Y-intercept 200,948 79,13993 2,539146 0,084743 -50,9107 452,8067 -50,9107 452,3067
Variable XI -0,95192 0,700216 -1,35947 0,267161 -3,18032 1,276479 -3,18032 1,276479
а) Российская Федерация a) Russian Federation
SUMMARY OUTPUT
Regression Statistics
Multiple R 0,747664
R Square 0,559002
Adjusted R Square 0,412002
Standard Error 13,52354
Observations 5
df SS MS F Significance F
Regression 1 695,4695 695,4695 3,802745 0,146265
Residual 3 548,6585 182,8862
Total 4 1244,123
Upper
Coefficients Standard Error tStoi P-value Lower 95% 95% Lower 95,0% Upper 95,0%
Y-intercept 141,5101 26,03953 5,434435 0,012231 58,64072 224,3795 53,64072 224,3795
Variable X 1 -0,41207 0,211309 -1,95006 0,146265 -1,08454 0,260413 -1,08454 0,260413
б) Центральный федеральный округ b) Central Federal District
Рис. 2. Результаты регрессионного анализа для Российской Федерации и Центрального федерального округа Fig. 2. Results of the regression analysis for the Russian Federation and the Central Federal District
Compiled by author
SUMMARY OUTPUT
fiegreisron Statu tics
Multiple R R Square
Adjusted R Square Standard Error Observations
0,282156 0,079612 -0,22718 16,13763 5
df SS MS F Significance F
Regression 1 67,57365 67,57865 0,259496 0,645573
Residual 3 781,2694 260,4231
Total 4 848,848
Upper
Coefficients Standard Error T Stat P-va/Lie Lower 95% 95% Lower 95,0% Upper 95,0%
Y-intercept 98,89012 9,478147 10,43349 0,001879 68,72643 129,0538 68,72643 129,0538
VariableX 1 0,012298 0,024142 0,509407 0,645573 -0,06453 0,089129 -0,06453 0,089129
а) Белгородская область a) Belgorod Region
б) Ивановская область b) Ivanovo Region
SUMMARY OUTPUT
Regression Statistics
Multiple R R Square
Adjusted R Square Standard Error Observations
0,633726 0,401608 0,202144 35,71456 5
df SS MS F Significance F
Regression 1 2563,199 2568,199 2,013438 0,250964
Residua 3 3826,589 1275,53
Total 4 6394,788
Upper
Coefficients Standard Error t-Stat P-value Lower 95% 95% Lower 95,0% Upper 95,0%
Y-intercept 138,4855 30,43756 4,549822 0,019892 41,61957 235,3513 41,61957 235,3513
Variable XI -0,25877 0,182364 -1,41896 0,250964 -0,83913 0,321596 -0,83913 0,321596
в) Тульская область c) Tula Region Рис. 3. Результаты регрессионного анализа для отдельных регионов Центральный федеральный округ Fig. 3. Results of the regression analysis for selected regions of Central Federal District
Compiled by author
As we can see, the dependence under study is not strongly related, as evidenced by factor and residual dispersion values. For some regions, the value of the factor dispersion is less than the residual. Therefore, the resulting regression equations do not have practical value, but they can be applied as theoretical models.
Thus, the tested hypothesis of a direct correlation between the observed temporal indicators is confirmed in the models of Belgorod Region and Ivanovo Region, where the coefficient for the variable X1 is positive. In practice, these are the subjects that most successfully implement the strategic tasks in the innovation sphere facing the regions. In other models, the coefficients are negative. However, if we take into account the lag in the dynamics of changes in factor and output variables, then in all models the coefficient will be positive.
Conclusion
In summary, it is possible to say that the impact of innovation policies can be assessed by the timeliness of their implementation in economic activity. In other words, the longer the pace at which innovation activity lags from the cost of innovation, the less effectively the strategy of innovative development of the region is implemented.
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авторы не имеют конфликта интересов для декларации.
Conflicts of Interest: the author has no conflict of interest to declare.
Сюй Чжэньпэн, научный исследователь, (Провинция Ляонин, г. Далянь, Китай)
Xu Zhenpeng, Scientific Researcher, (Province Liaoning, city Dalian, China)
