Methodology for comprehensive assessment of regional innovative development

evapolina.job@gmail.com ABSTRACT Innovative development of territories is strategically important for the pros-perity of any country. This article aims at describing original methodology for comprehensive assessment of innovative development of Russian regions. The proposed model takes into account specific features of innovative activity of regions and identifies growth potential and resources of territories, taking into account not only the innovation environment, but also areas of innovative activity. The study relies on the statistical data provided by the Central Statistical Database and the Unified Interdepartmental Information and Statistical System. In the course of processing and analyzing data, the index method, the mul-tidimensional average method, factor-index analysis and other statistical data processing methods are used. The research involves ranking Russian regions according to their levels of innovative development and further dividing them into groups of powerful, strong, medium and weak innovators. We also ana-lyzed the dynamics of innovation in the regions by looking at the changes in their ranking positions. The research findings brought to light the uneven development of Russian regions. The proposed assessment toolkit can be further used for drawing individual profiles for regions and formulating recommendations and guidelines for these regions’ development by taking into consideration their strengths and weaknesses. The results of this study have theoretical and practical significance and can be used as a tool for management of innovative activities both at the level of individual territories and at the national

regions and identifies growth potential and resources of territories, taking into account not only the innovation environment, but also areas of innovative activity. The study relies on the statistical data provided by the Central Statistical Database and the Unified Interdepartmental Information and Statistical System. In the course of processing and analyzing data, the index method, the multidimensional average method, factor-index analysis and other statistical data processing methods are used. The research involves ranking Russian regions according to their levels of innovative development and further dividing them into groups of powerful, strong, medium and weak innovators. We also analyzed the dynamics of innovation in the regions by looking at the changes in their ranking positions. The research findings brought to light the uneven development of Russian regions. The proposed assessment toolkit can be further used for drawing individual profiles for regions and formulating recommendations and guidelines for these regions' development by taking into consideration their strengths and weaknesses. The results of this study have theoretical and practical significance and can be used as a tool for management of innovative activities both at the level of individual territories and at the national level. KEYWORDS innovative climate, innovative potential, innovation, regional development, Russian regions Практические аспекты оценки и анализа инновационного развития регионов

Introduction
Innovation is an important indicator of regional development and development of the country as a whole. Modernization has a significant impact on the country's economic stability and competitiveness on the international arena.
In our study, innovative development is understood as the process of continuous development of science, technology, methods of production, technological processes as well as the creation of conditions to stimulate innovation 1 . Innovative development is a complex process which has two main objectives: to realize innovative projects (sustainable innovative activity) and to develop innovative potential. Innovative activity comprises a complex system of interconnected elements and there is a perceived lack of comprehensive methodologies for assessing innovative activity since the vast majority of the existing tools focus only on individual aspects. Therefore, our research is aimed at designing a tool for integrated assessment and analysis of innovative development in Russian regions by taking into account the shortcomings of the existing assessment methods.
Review of theoretical and methodological approaches to assessment of innovative development In Russia, methods for assessing innovative potential, innovative activity and the state of innovative environment are developed by such researchers as L. V. Shabaltina [1], S. A. Novikov [2], S. E. Tikhonova [3], T. N. Kosheleva [4], I. V. Shlyakht [5], Yu. P. Anisimov [6], E. A. Lapteva [7] In our previous studies, we systematized and classified the approaches to assessment of innovative development proposed by Russian researchers [8]. We found that there is currently no agreement among Russian researchers as to how define different categories of innovative development and assess them. The main drawback of these assessment methodologies is that they use a large number of qualitative indicators and, therefore, expert and score assessments (for a more detailed analysis of these approaches see [9]).
International studies are aimed at assessing innovative development of countries (world economies) and individual territories (states or regions). To assess innovation, these studies use specialized competitiveness indices developed by the World Economic Forum (see Table 1).
The above-described indices are used by rankings of world economies and innovation territories. Our analysis has shown, however, that foreign indices are either not applicable for Russia (complex and specialized indices) or require substantial adaptation (specialized indices of innovative development). Thus, while the methodological toolkit proposed by Russian researchers is based on the conceptual apparatus and the interrelationship between the main innovative development categories, in international methodologies, assessment of innovative development of territories is mainly based on the results of innovation implementation and the effects of their use in related areas and industries.
Our research is aimed at developing a methodology for integrated assessment of regional innovative development, which will allow us to take into account the interrelation between the main categories of the innovation environment [8] and the internal and external conditions for innovation [11; 12]. Moreover, such methodology should enable us to assess the impact of 4 Website of the Association of Innovative Regions of Russia. Retrieved from: http://www.i-regions.org/materials/regional-research/2304 (Accessed 27 January 2019) 5 Website of the National Association of Innovation and Information Technology Development. Retrieved from: http:// www.nair-it.ru/news (Accessed 27 January 2019) 6  To calculate the integral index, indicators divided into indices that have the same weight are used: the technology creation index, the distribution index of modern innovations, the distribution index of old innovations, the human ability index. There is no specific set of indicators, because it is impossible to cover the whole range of technologies The index characterizes the close correlation between economic well-being and the development of innovation. It is calculated by using 53 parameters divided into 3 main groups: the presence of conditions, readiness for use and the level of use of ICT. The basis of calculation is the statistical data of the United Nations, the International Telecommunication Union, the World Bank and surveys of top executives 2

Specialized competitiveness indices
Innovation Capacity Index It characterizes the innovation infrastructure (ability of the national economy to develop and commercialize the flow of new technologies). To calculate this index, it is necessary to select indicators, determine the scores and calculate the integral indicator [10] 3. Specialized competitiveness indices (regional level) Regional Innovation Scoreboard, RIS (European Union) 3 Portfolio Innovation Index, PII (USA) 5 Index evaluates innovative activity by 11 indicators divided into 3 blocks: factors of innovative development, data on performance of companies, effectiveness of innovative activities of companies 4 The composite index of innovation development includes 20-30 indicators divided into 4 blocks with different weight factors: human capital (30%), economic dynamics (30%), productivity and employment (30%), and well-being (10%) 6 Indices combine resources and results of innovative activity regional innovation on the country's overall economic development. This tool can be also used for designing strategies of regional innovative development.
Methodology for assessing regional innovative development The point of departure for our study is the assumption that innovative development is a complex and continuous process of improving the conditions of innovative environment [13][14][15]. Therefore, we need to design an assessment model that will allow us to take into account a complex system of factors. Our methodology is based on a qualitatively new approach, involving the assess-ment of the innovation component in certain categories (innovative climate, innovative potential, innovative activity) in the context of the main areas of innovation activity.
We identified the following areas of innovation: -socio-economic (social and economic indicators of the region's development); -production and technology; -investment (innovation financing, funding of reconstruction and modernization); -R&D (development of science and strategies for innovative development in Russian regions); -human resources for R&D; -R&D funding.
www.r-economy.ru Our model for the assessment of regional innovative development is shown in Figure 1 and reflects the matrix structure of the main innovation categories used for assessment [9]:  Assessment results are represented in the form of a ranking. For our model, we combined two methodologies: the methodology proposed by the Higher School of Economics and Management, which evaluates indicators according to areas of innovation implementation, and the methodology of the Association of Innovative Regions of Russia and the Ministry of Economic Development, which evaluates indicators according to innovation categories. The algorithm for calculating the integral index of innovative development is a complex multi-step process.
The stages of assessment reflect the analysis procedure which involves selected indicators and calculation of the integral index used to rank regions according to their level of innovative development.
These steps are as follows:

Data normalization
We have managed to achieve homogeneity and comparability of indicators with the help of transition from absolute to weighted values. We propose to normalize the indicators for a mini-max formula (1). This method of rationing the source data is optimal, since it allows to fill a range of values tihtly and evenly. The range of values is, determined by the empirical magnitude of the data from 0 to 1.
where ij X  is the transformed value of the i th indicator in the j th region; x ij is the initial value of the i th indicator in the j th region; xmin i is the minimum value of the i th indicator among Russian regions; xmax i is the maximum value of the i th indicator among Russian regions.

Significance of factors and calculation of region-specific indices
In order to assess innovative development of regions, we first need to decide whether our methodology should take into account certain factors or not. The role of these factors can be determined by using expert assessments. To calculate partial indices, it is proposed to use the multidimensional average formula (2). This is a generalized characteristic of a certain phenomenon built on the basis of converging its individual characteristics into a single indicator, which is calculated from the interrelation of attribute values for a unit of aggregate to average values of these attributes.
where PIs is the region-specific index of the region by quantity by the block of indicators (area of implementation // innovation category); i = 1 ... m is reduced partial indicators; m is the number of reducible indicators; ij X  is the numerical value of the ith indicator for the jth region in each block of indicators (area of implementation // innovation category); iaver X  is the average value of the i th indicator among all regions in the block of indicators.

Calculation of the Integral Index
We applied three-factor and six-factor models of factor analysis to calculate the final indices for the areas of implementation and innovation categories and to calculate the integral index of innovative development.
Due to the fact that the assessment model has a matrix structure (Figure 1), we need to solve the problem of classifying the indicators which must simultaneously belong to one of the six implementation areas and characterize one of the three categories of innovation environment. Thus, innovation climate characterizes external conditions of the region's environment, that is, how favourable are the existing scientific, technological, industrial and socio-economic conditions for innovation in the region [8].
In its turn, innovative potential characterizes the conditions and reflects the dynamics of inwww.r-economy.ru Online ISSN 2412-0731 ternal factors of the region's innovative environment -a set of financial, human, scientific and technical, organizational and managerial, informational, methodological and marketing resources that make the region capable of fulfilling a set of innovative tasks [8].
Innovative activity characterizes effectiveness of innovation. The level of innovative activity is an indicator of economic development.
The assessment model uses 54 indicators, which are divided into 3 innovative categories and 6 areas of innovative activity, thus forming 18 region-specific indices. Indicators are taken from such sections of the state statistics as population; labor market, employment and wages; science, innovation and information society; macroeconomic indicators. The proposed approach allows us to calculate not only the integral index, but also to determine development factors, growth driv-ers, and bottlenecks of innovation activity in regions and specific territories.
For example, in the socio-economic block, the indicators that form the region-specific index by climate category include the index of physical volume of GRP; production and technology includes the coefficient of renewal of fixed assets; the investment activity block, the volume of investment in fixed capital per capita and the growth rate of investment in fixed assets in GRP; R&D, the coefficient of inventive activity; the block of human resources for R&D, the proportion of doctoral candidates and candidates in the total economically active population and the growth rate of researchers' average salary; and, finally, for R&D funding, we used such indicators as the share of domestic expenses on R&D in the expenses of the consolidated budgets of Russian regions and the growth rate of organizations' expenses on technological innovation. The integral index of innovative development, calculated with the help of the above-described indicators, allows us to assess continuous development of economy, science, technology, production as well as the development of conditions necessary for innovation. Innovative development is a complex process which has two main objectives: realization of innovation projects (ensuring sustainable innovative activity) and development of innovative potential in the existing innovative environment [8].

Results
The assessment method we propose is a multifunctional tool that has several levels of possible practical results.
At the first level, this method allows us to form a ranking of Russian regions by the value of the integral index of innovative development (ID index) (see Table 2).
At the second level, the regions are divided into four groups according to the value of the integral index of innovative development (ID) (see Table 3).
Regions of the first group -"alfa regions"have the highest value of the integral index of innovative development: more than 100. This group of powerful innovators includes regions with the highest level of innovation development of the territory.
Regions of the second group -"beta regions" -have the value of the index from 10 to 100 and are called strong innovators.
Regions of the third group -"gamma regions" -have the value of the index from 1 to 10 and are called medium innovators.
Regions of the fourth group -"delta regions" -are weak innovators with an index value from 0 to 1.
In addition, at this level, our methodology allows us to analyze the distribution of regions by the value of the integral index of ID and to analyze  Division of regions into groups according to the value of the index of ID can be represented in the form of fields or matrices. We compiled distribution fields of the four groups of regions for the period 2014-2016. Thus, in the study we received 12 distribution fields, reflecting the full range of values of the index. The distribution fields of regions by groups for the whole period under review look similar to the field of group I distribution in 2016. This field is presented in Figure  4 (the first group of regions) as an example. The regional distribution matrixes compiled by the fields complete this level of results. is possible to explain such a high proportion of regions in the group (caused by the decrease in the calculated indicators) by the economic crisis of 2014, its causes and consequences. Stagnation, slowdown, and then sharp weakening of the national currency due to the significant decline in oil prices and economic sanctions led to a rise in inflation, a decline in real incomes of the population and a change in consumer behavior. These factors affected all aspects of regional performance including innovative development, which is illustrated by the changes in regions' ranking positions.
The analysis of the structural shifts focuses on regions' positions in the rankings. For example, sometimes regions remained within one group, moved to an upper group or to a lower group (see Table 4).   The matrix of the distribution of regions for 2016 shows the regions included into selected groups: group I -"alpha regions" or powerful innovators; group II -"beta regions" or strong innovators; group III -'gamma regions' or medium innovators; and group IV -"delta regions" or weak innovators (see Table 5). At this level, it is also possible to analyze the dynamics of average values of the integral index of ID by focusing on specific groups of regions.
The dynamics of average values of the integral index reveals the factors that have the greatest influence on innovative development in different groups of regions. For example, let us consider Figure 5, which reflects the dynamics of the average value of the index in regions of the first group.
The high average value of the index in group I in 2015 was almost 6 times higher than in 2014 and 4.5 times higher than in 2016. The rise in 2015 was caused by a significant increase in indicators At the third level, we are going to analyze the dynamics of the rankings positions of the regions.
The regional rankings based on the innovative development index for 2014, 2015 and 2016 allow us to draw a number of conclusions, tracking how the region's position changed and how they either moved up or dropped in the ranking depending on their levels of innovative development.  For example, in 2014, Novosibirsk region ranked 9 th and was a part of the third group with the index value of 5.935, which almost twice exceeded the average index value of this group in the give period. In 2015, the region dropped in the ranking by 11 positions and moved to group IV, "delta regions", with the index value below the average of this group. By 2016, Novosibirsk region regained its position in the group of "gamma regions", after having improved its position by 5 points compared with the previous period. At the same time, it still lagged behind the average indicator level for the group 1.6 times.
Thus, we can rank the regions according to the intensity of changes in their ranking positions for the period 2014-2016. Regions that improved their positions in 2016 compared to 2015 are presented in Table 6, regions that moved down the rankings in 2016 compared to 2015 are presented in Table 7.
Compared with 2015, fifteen regions improved their positions in 2016 by more than 10 points. As it can be seen from Table 5, five regions moved to the group with a higher value of the index of ID, from "delta regions" to "gamma regions". The Republic of Sakha (Yakutia) rose in the ranking by 37 points: in 2016 it ranked 11 th and in the previous year, 48 th by achieving significant improvements in all areas: socio-economic area, production and technology, investment, R&D, human resources for R&D, and funding. The transition of Kursk region to the group of medium innovators is explained by a significant increase in indicators characterizing production and technology, investment and funding for R&D as well as the factors shaping the innovative potential and innovative activity of the region. Tyumen region closes the top three leading regions in 2016 ranking. It moved to the 8 th position from the 34 th in the previous period due to its improved performance in R&D, human resources for R&D and funding, which is directly related to the growth in indicators of innovative environment -innovative potential and innovative activity. Table 6 shows 14 regions and the change in their ranking positions in 2016 compared to 2015, which was more than 10 points. Sakhalin region did not leave the group of "delta regions" in 2016 and led the top three outsider regions with a sig-nificant loss of 65 positions. From the 19 th place in the 2015 ranking, in 2016, it dropped to the 83 rd place due to a very low level of innovative activity and low rates of research and investment activity. Penza region closes the top three, it dropped from the 36 th place to the 52 nd in 2016. The decline in production and technology, investment activity and R&D funding, affected the indicators of innovative activity in the region, which resulted in the region's joining the group of weak innovators.
At the fourth level, we are going to divide regions according to the values of the final indices in innovative categories and areas of implementation. This level allows us to analyze the relationship between innovative development and the factors of external and internal environment as well as the relationship between the areas of implementation of innovation activities.
Thus, the fields reflect the direct relationship between innovative development and innovative potential in 2016 ( Figure 6) and between innovative development and funding for R&D in 2016 (Figure 7).  At the fifth level, we are going to draw a profile of a region depending on its innovative development. The innovative profile relies on the results of region-specific assessment, which allow us not only to reveal the change in the integral index of ID, but also the impact of final indices. The dynamics of changes in the final indices according to innovative categories and implementation areas is presented in Figures 8-9.
The dynamics of the values of region-specific indices, which form the final indices according to implementation areas and innovative categories, are shown in Figure 10 ("Socio-economic area") and Figure 11 ("Innovative climate").
A similar presentation has the dynamics of region-specific indices in the following areas of implementation: production and technology, investment activity, R&D, human resources and funding for R&D.
A similar presentation has the dynamics of region-specific indices in the categories "potential" and "activity". 5  Analysis of the results at this level allows us to rank regions according to the final indices -innovative climate, innovative potential and innovative activity in the regions. Similarly, it is possible to rank regions according to the level of development of the implementation areas of innovation activities. Thus, we can make an overall assessment of regions' innovative development. The integral index of ID is calculated on the basis of final indices of innovative categories and implementation areas, and final indices are calculated on the basis of region-specific ones. The latter can be also used to create a profile for each region. The innovative profile of a region reflects the results of the analysis of its innovative environment and show the region's strengths and weaknesses, growth drivers and resources. Therefore, profiling can be useful to devise recommendations and guidelines for further innovative development of the region.

Conclusion
In this research, we were trying to address the problem of the lack of a generally accepted conceptual and terminological apparatus for studying innovation as well as a toolkit for a comprehensive assessment of innovative development.
The proposed methodology is suitable for assessment of innovative development of terri- A limitation of this study is the use of official statistics in calculations: these data are published with a time lag, which may affect the picture we get when assessing the regions' innovative development.
The results described in this article may be further used for studying innovation potential of Russian regions and devising strategies and policies for enhancing innovation in these regions and in the whole country. Further research in this area may involve creation of profiles of innovative development for specific regions, highlighting their strengths and weaknesses. Moreover, the proposed assessment toolkit may be applied in the context of other countries.