On diversification taking into account innovation activity and resource availability of regions

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Introduction
Currently, one of the priorities of state policy is the transition of the economy to an innovative path of development.The researchers note that today the economy should not only provide a platform for the implementation of entrepreneurial initiatives, but also demonstrate a receptivity to innovation and encourage innovative initiatives, research and development.
As well as the economic potential of the region, the potential of innovation activity is a factor in the diversification of the regional economy.The theory of diversification and empirical estimates are presented in [1][2][3].According to this theory, companies benefit from facing a heterogeneous environment consisting of different industries, as new ideas come from the external environment.In this paper, the process of diversification of the regional economy is associated with the emergence of new developed sectors in it.
Assessment of the impact of innovation activity in the region on the development of the economic sector.To assess the impact of the region's innovation activity on the development of the economic sector, the components of the economic basis are used, including characteristics of regional differentiation and indices of innovation activity.The description of the economic basis {L, te, s 1 , s 2 } and the methodology of its application for assessing socio-economic development at the regional level are presented in [4,5].The description of the innovation activity indices used below, based on the stochastic boundary concept, is given in [6].An expanded economic basis {L, te, s 1 , s 2 , INN} is being formed, including the INN innovation activity index.This basis reflects not only the economic structure of the regional economy, but also the specifics of the innovation activity of the regions, focused on a specific result of innovation activity.If the index of innovation activity statistically depends on some components of the economic basis, then to prevent the effect of multicollinearity, it is advisable to use a modification of the index, cleared of the influence of these components.A regression analysis of the production volumes of each sector of the economy is carried out using an expanded economic basis.Let's build regressions of the form ln   =   + 1    + 2    + 3   1  + 4   2  + 5    +  , (1) Here   -the volume of production of sector i in region j;   -the scale of the economy of region j (the Rosstat indicator "number of economically active population" is used as a characteristic of the scale of the economy);   -assessment of the technical efficiency of regional production [7];  1   -the index of industry specialization (the first main component of the GRP structure);  2   -the index of industrialization (the second main component of the GRP structure).When constructing the main components, the author's methodology and Rosstat indicators for the industry structure of GRP were used [8].INN is an index of innovation activity (one of the author's indexes is used here, built on the basis of the stochastic boundary concept based on data on international patent applications (TEMPZ), patent applications (TEPZ), granted patents (TEVP), newly developed production technologies (TETTCH) [6]. , -regression error.From the set of sectors, those for which the parameter score is 5  positive and significant at 95% level are distinguished.The volume of production of each of these sectors depends on the level of innovation activity of the regions, determined by the INN index.
The structure of strong sectors.To describe the structure of strong sectors of the economy, regional data on production volumes in a fairly wide range of sectors are used.To begin with, we will determine the indicator cp RCA of the identified comparative advantages [9]: where cp y is the volume of production of sector p of the economy of the region c.
The indicator cp RCA is the ratio of the share of production from sector p in the total volume of production from all sectors of the economy of region c to the share of production of sector p for all regions in the volume of production from all sectors of the economy of all regions.In accordance with [10], to identify comparative advantages in economies, an indicator cp RCA is used for which a condition of the type of restriction from below is checked.Namely, if the value cp RCA exceeds one, it is assumed that the economy of region c has identified comparative advantages in the output of sector p. Otherwise, it is assumed that the identified comparative advantages do not exist.More formally: ( ) The matrix ( ) , cp a contains data on the sectors of the economy that are developed in different regions at the level of the identified comparative advantages determined using the expression (2).The rows of this matrix correspond to regions, the columns correspond to sectors of the economy.Next , we will call the vector ( ) Resource availability of the sector in the region.The assessment of the level of resource availability of the sector in the region is determined by the level of compliance of the actual volume of production of the sector with the expected, due to the characteristics of differentiation of the region.The regions in which the expected output of the sector is higher than the actual one are identified.In such regions, the transformation of the sector into a strong one is possible due to the unfulfilled potential of economic development.This applies to all sectors.Including those whose production volume does not depend on the level of innovation activity of the region.
If the actual output of the sector is higher than expected, then the sector, having already realized the growth potential in the region, still has not become strong.In this case, the development of the sector to a strong level can be based on the growth of innovation activity in the region.This applies to sectors whose production volume depends on the level of innovation activity of the region.Different metrics can be used to compare the actual output of the sector with the expected output in a particular region and to assess resource availability.For example, the resource availability of the sector   in the region   can be assessed based on the concept of identified comparative advantages.With sufficient resources, the indicator _     of the identified comparative advantages corresponding to the expected output of the sector   in the region   should be at least 1 in order for the sector to become strong.This means that the inequality must be satisfied (_     /((_     + ∑   ))/(∑   / ∑  ) ≥ 1 ;  ≠  (3) where _     =  {     −  , } where , and      is determined by the formula (2).
Note that the right side of inequality (4) is a negative value.This follows from the inequality      < 1which is true since the sector  is not strong in the region   .Let's denote it  * , .Thus, if the regression error  , (1) is less than a negative value * , , then the sector   has sufficient resource provision in the region   in the sense that with the expected volume of production it will become strong.Otherwise, we believe that the resource provision of the region   is not enough to turn the sector   into a strong one.
The choice of a priority direction for the diversification of the region's economy is associated with the choice of a sector for its development in the region to the level of a strong one.The rationale for the choice may be the solution of a multi-criteria optimization problem taking into account a number of characteristics for each sector from a set of sectors that are not strong in the region.Including assessments of the impact of the region's innovation activity on the development of the sector and the resource availability of the sector in the region.

Research results
Table 1 shows the correlation matrix of the components of the economic basis and the indices of innovation activity.The correlation analysis of the four components of the economic basis and the four indices of innovation activity shows: all components of the economic basis can be considered mutually independent; the indices of innovation activity can be considered mutually independent (with the exception of the TEPZ and TEVP indices, the dependence of which is due to their specifics); each index of innovation activity is independent or weakly dependent on the economic basis.  2 presents the results of a regression analysis of production volumes by sector on the characteristics of the economic basis, expanded, as an example, due to the TEMPZ innovation activity index, based on data on international patent applications.As estimates of production volumes, data on tax revenues by economic sectors can be used (Data on tax revenues by economic sectors https://www.nalog.ru/rn77/related_activities/statistics_and_analytics/forms/8826515/), which makes it possible to characterize the structures of regional economies, including sectors focused on both external and internal markets.  2 shows the names of sectors whose production volumes in the regions depend on the value of the TEMPZ innovation activity index.Column (2) shows the estimate of the constant in the regression, in parentheses t-statistics.Column (3) shows an estimate of the regression coefficient for the logarithm of the economically active population and t-statistics.Column (4) shows an estimate of the regression coefficient for the index of technical efficiency of regional production and t-statistics.Column (5) shows an estimate of the regression coefficient for the first main component of the GRP structure and t-statistics.Column (6) shows an estimate of the regression coefficient for the second main component of the GRP structure and t-statistics.Column (7) shows an estimate of the regression coefficient for the TEMPZ innovation activity index and t-statistics.The coefficient of determination for each of the 20 sectors of the economy is quite high.This means that the SHS Web of Conferences 141, 01004 (2022) MTDE 2022 https://doi.org/10.1051/shsconf/202214101004basis used for the characteristics of regional differentiation, expanded by the indexTEMPZ  , explains quite well the specifics of the production volumes of the sectors.
At this stage of the study, 20 sectors of the economy have been identified, the development of which depends on the innovative activity of the region when creating international patent applications.Regions, forming international patent applications and demonstrating activity in this area, influence the development of each of these 20 sectors.It follows from the simulation results that the economic development potential of each of these sectors is associated with the growth of the scale of the regional economy, specialization or industrialization of the region, and increased technical efficiency.Depending on which components of the economic basis there are significant estimates of the coefficients in the regression (1).Another way is connected with the realization of the potential of innovation activity.If we replace the TEMPZ index with another index of innovation activity, we will get a list of sectors whose output volumes depend on the innovation activity of the region when creating the corresponding result of innovation activity.
Column (2) of Table 3 shows the number of strong sectors in the structure of the region's economy, that is, an assessment of economic diversification.The most diversified (with more than 35 strong sectors) economies of the regions are: Tver Region -42; Chuvash Republic -40; Moscow Region -39; Novosibirsk Region -39; Vladimir Region -37; Lipetsk region -36.The least diversified (with the number of strong sectors less than 10) economies of the regions: Astrakhan region -9; Tyumen region -8; Orenburg region -6. Figure 1 shows the distribution of strong sectors by region.On the abscissa axis -the number of the region, on the ordinate axis -the number of strong sectors, ordered in descending order.As an example, column (5) of Table 3 provides estimates of the identified comparative advantages of the Construction sector for various regions.It is indicated in which regions the Construction sector has identified comparative advantages and is strong (value 1), and in which it is not (value 0).The estimates obtained indicate that the Construction sector, according to 2019 data, is strong in the economy of 33 In these regions, the growth of the output of this sector will no longer lead to diversification of the structure of strong sectors of the economies.The Construction sector is not strong for 47 regions.For these regions, economic diversification is possible due to the growth of the output of this sector and its transformation into a strong sector.According to the data of 2019, the Construction sector is one of the 20 sectors whose production volume depends on the innovation activity of the region.

Conclusion
An approach to the formation of recommendations for the development of regions and sectors of the economy, taking into account innovation activity, is proposed.The approach is based on the regression analysis method using an extended economic basis.The approbation of the approach confirmed the possibility of identifying a set of economic sectors whose production volume in the region depends on innovation activity.According to the data of 2019, 20 sectors have been identified, the volume of production of which depends on the innovative activity of the regions aimed at creating international patent applications.
A condition has been formalized under which the sector, having provided the expected volume of production corresponding to the characteristics of the differentiation of the region, becomes strong.It is shown that for each sector of the regional economy, regions can be identified that have sufficient resources to turn the sector into a strong sector based on the realization of the potential for economic growth.For the Construction sector according to 2019 data, 11 such regions have been identified.
The work was supported by the Russian Foundation for Basic Research (RFBR project 20-010-00223)

1 ,
region 's economy c .Note that for any c there exists a p for which 1 cp RCA 

Fig. 1 .
Fig. 1.Distribution of the number of strong sectors by region.

Table 1 .
Correlation matrix of the components of the basis and indices of innovation activity according to 2019 data.

Table 2 .
Sectors whose development depends on the innovation activity of the regions according to 2019 data.