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INNOVATIONS AND REGIONAL ECONOMIC DEVELOPMENT IN RUSSIA

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INNOVATIONS AND REGIONAL ECONOMIC DEVELOPMENT IN RUSSIA

151.Elizaveta Kolchinskaya

Abstract

To be competitive in the global economy Russia needs in production innovative products. According to the Government of the Russian Federation the innovative socially oriented model of development is the main method of long-term development of the country. The data for research were taken from Russians Federal State Statistics Service and portal of the State support of innovative development of business. The main method of this research is a production function which was constructed for the Russian manufacturing. Labour, capital, infrastructure indices (included innovations) were evaluated. I investigate 9 years (from 2005 to 2014) and almost all Russian regions. The coefficients for the selected function were obtained using the correlation-regression analysis approach. The result is that the coefficient of innovation in logarithmic production function is 0,1 and it’s significant on the 1% level. One of the main factor except labor and capital is innovations that means that to increase manufacturing production in Russia the government should improve innovation infrastructure. And also two groups of regions were compared. The result is that regions with innovative clusters on their territory have a better economic indexes than regions without clusters.

Key words: manufacturing, innovations, production function
JEL Code: R11, L11, O14

152.1 Introduction

The Russian economy transition to an innovative socially oriented model of development is the main method of long-term development of the country according to the Government of the Russian Federation. This idea is reflected in the Strategy of Innovative Development of the Russian Federation by 2020 approved by the Government Order No. 2227-р dated December 8, 2011.

This Strategy mentions a range of measures of State support for domestic economy modernization processes. These measures include stimulation of creation and development of innovation clusters as well as efforts to refine innovative and market infrastructure in regions. The mentioned two measures are not an exhaustive list, however, this study will consider their influence exactly on the economy of the Russian regions, because significant efforts are aimed at their implementation.

Innovative development is equally important not for all sectors of the national economy (Crépon, Duguet, & Mairesse, J. 1998). In terms of the spheres to which attention of the State authorities is given in relation to this issue at most, then it is processing industry, medicine and education. All they are very different and it does not seem appropriate to study them in terms of one paper. Therefore the object of this paper is processing industry.

Innovative development of processing industry according to a number of researchers (Srithanpong, 2014) and public officials may become a locomotive of upsurge of the country economy and crisis recovery. It is related to the fact that a traditional problem of the Russian economy is its specialization in extractive industry. At present the lion's share of the domestic export is raw products of extractive industry. In 2014 this share was 69.5 % according with official Russian statistic data (Regions of Russia, 2015). Respectively, only development of innovative processing sectors will help to overcome this scenario.

Thus, the purpose of this paper consists in determination of cause and effect relations between the measures taken by the government in the sphere of development of innovative economy of Russia and the effect obtained from the economy. this framework two tasks will be completed. The first task consists in comparative analysis of economic results of the regions of two groups – regions with innovation clusters and regions without these clusters. The second task is in study of influence of factors of innovative and information infrastructure on development of processing industry.

The verifiable hypothesis is the statement that at large the taken measures have positive effect on development of the Russian economy, however, not all of them to the same extent. Besides, it shall be verified which important factors are left out of account at preparation of the programs of support for innovative development.

153.2 Role of innovation clusters in economical indicators of the Russian regions

A center of formation of an innovation cluster is, as a rule, research institutions. Around this core the other cluster participants are concentrated — innovative companies and equipment suppliers. A distinguishing feature of the innovation cluster is application of radically new technologies, products or services, being in demand or capable to be in demand in the world or domestic markets. The very idea of creation of an innovation cluster consists in the fact that a new product inside it can pass through cyclical turnaround – starting from an idea to final output.

The innovation cluster is considered as an integral system of new products and technologies interconnected and focused on a certain time period and in a certain economic area (Rastvortseva, 2014). Thus, as defined above, the innovation cluster is a basis and complex of innovation derivatives which make it impossible to expand the economy in conventional lines.

The USA experience (Silicon Valley phenomenon) shows that innovative (industrial) clusters can be formed at the regional level where concentration of interrelated industry sectors is high. A distinctive aspect of the Russian clusters consists in the fact that the state, as a rule, participates in their creation and operation. Clusters are not created on the spur of the moment, but they are also not absolutely artificial and therefore the region specialization in the corresponding industry sectors is equally important for the Russian cluster policy too.

Thereunder, in 2012 the State authorities selected possible variants of support and fixed the attention on the most advanced territorial clusters from their point of view. Selection was carried out by the experts according to the following criteria⁰:

research-and-engineering and educational capacity of the cluster.
Production capacity of the cluster.
Quality of life and development level of transport, energy, engineering and housing infrastructure of the territory of the cluster location.
The level of organization development of the cluster.

According to the results of the competition the development programs of 25 innovative territorial clusters received the highest appraisal of the experts which were included into the list of the innovative territorial clusters were selected. I can distinguish 6 lines of technological specialization of the selected clusters (refer to Table 1).
Tab. 1: Lines of specialization of the clusters supported by the Government of the Russian Federation

Lines of technological specialization	The regions within which territory clusters are situated
Nuclear and radiation technologies	the Moscow Region, the Nizhny Novgorod Region and the Ulyanovsk Region, the Krasnoyarsk Territory
Production of aircrafts and space crafts, shipbuilding	the Ulyanovsk Region, the Samara Region, the Arkhangelsk Region and the Tomsk Region, the Perm Territory and the Khabarovsk Territory,
Pharmacy, bioengineering and medical industry	the Moscow Region, the Kaluga Region, the Novosibirsk Region and the Leningrad Region, the Altai Territory, Saint Petersburg
New materials	the Sverdlovsk Region, Moscow
Chemistry and petroleum chemistry	the Nizhny Novgorod Region and the Kemerovo Region, the Republic of Tatarstan and the Republic of Bashkortostan
Information technology and electronics	Moscow, the Moscow Region, the Republic of Mordovia

Source: Own elaboration

For the selected clusters comprehensive measures of stimulation of development at the federal, regional and municipal level are carried out. Also, the mechanisms of public private partnership are applied in terms of implementation of development projects of territorial clusters.

Within this framework it appears to be interesting to assess availability of any remarkable positive differences of the regions selected for this program the other regions by the development level. To carry out such assessment the regions were divided into two groups. The first group included the regions which were not selected for the above mentioned program of cluster support or just had no innovation clusters within their territory. The second group included the regions listed in Table 1. The Tumen Region and the Sakhalin Region, the Chukotka Autonomous District and Moscow were excluded from consideration. The first three among the mentioned regions have specialization in extraction of commercial minerals and due to this have GDP exceeding the national average GDP several times, therefore their presence in the selection distorts the overall picture. Traditionally, Moscow is the richest region of Russia, therefore the same assumptions are true for it as well.

The left part of Figure 1 shows the results of distribution of the regions of these two groups by the scales of the GDP quantum index of the region for 2013 and average regional GDP quantum index for 2010 – 2013. Such limits of the analysis period are determined by the fact that till 2010 the Russian economy passed through the consequences of the world economic crisis of 2008, and after 2013 the indices reflected influence of new economic crisis started in Russia in 2014.

Fig. 1: Difference of volume and rate of GDP change

Source: Own elaboration

The data given in the figure show that the regions with the innovation clusters within their territory have at large significantly high values of the regional GDP indices per capita than the regions of the second group. Also, difference in the indices of GDP growth is observed.

The right part of the figure shows the similar data but for the indices of population income per capita. This distribution also displays the higher absolute indices of the regions with the innovation clusters on average.

However, it is difficult to determine exactly whether presence of the clusters within the region territory is a stimulus for GDP growth and population income or such results can be explained by the fact that already successful regions were selected for participation in the program. Therefore for more detailed analysis, assessment of influence of the innovative development indices has been carried out, the procedure and results of which are described in the following paper section.

154.3 Innovation infrastructure as a factor of development of processing industry

Idea of such research may consists in plotting of production function including individual indices of development level of innovation and information infrastructure in the region (Crespi & Zuniga, 2012). To the end that the model will be full, it was also added with the labor indices (employment in processing industry to the regional population ratio) and capital indices (volume of investment to the capital assets of the processing industry companies) (Brown & Guzmán, 2014), as well as the indices of the other types of the infrastructure.

On the basis of the theoretical provisions and available statistical information 27 factor indices of the infrastructure development in the region were selected. All monetary indicators were brought to the prices of 2013. In addition, all indices were brought to the commensurable values, where it was required. After check of the selected indices for multicollinearity 7 from them were excluded from the subsequent analysis as they were correlated with the other indices.

To set up regression equations the panel data models were used. The Cobb–Douglas production function was taken as a basis (Brown & Guzmán, 2014). The indices for all Russian regions were collected for the period from 2005 to 2012 and used then as a panel data. All indices were taken from official Russian statistic data (Regions of Russia, 2006 - 2015). However, the data for the partial period were available for three regions therefore they were excluded from selection. More 25 observations were deleted after check for emissions. The index "Volume of shipped home-produced goods and works and services carried out without subcontracting" was taken as a dependent variable. As per this procedure 20 indices given in Table 2 were selected.

It is important to note that division of the indices by infrastructure types is conventional to a certain degree in this case. For example, such indices of social infrastructure as population of students and educational establishments can also be related to the innovation and information infrastructure. However, in this case such distribution was made for analysis of the role of social factors in comparison with innovative ones as the former ones are given with relatively insignificant attention in the programs on development of the Russia's innovative economy.

Tab. 2: Indices specifying the development level of regional infrastructure

Item No.	Infrastructure	Index description	Units of measurement
1	innovation and information	number of personal computers	pieces per 100 employees
		share of companies using special software	%
		volume of communication services rendered to population	rubles per a resident
		employment in research and development to the region population ratio	%
2	social	population of students trained as per undergraduate program, specialist program and Master's program	persons per thousand residents
		number of higher educational establishments	pieces for beginning of the school year per thousand residents
		population of medical advisers of all specialties	persons per thousand residents
		population of low-grade medical workers per capita	persons
3	transport	density of public railway tracks	km of tracks per 10 thous. sq. m of territory
		density of public motor roads	km of tracks per 10 thous. sq. m of territory
		truck shipment	thous. t per thous. persons of population
		number of public buses,	units per 100 thous. persons of population
4	engineering	length of street sewerage networks	km per 10 thous. sq. km of territory
		length of heat supply and steam networks	km per 10 thous. sq. km of territory
		length of street water supply networks	km per 10 thous. sq. km of territory
		number of heating plants	thous. pieces
5	market	number of credit organizations	pieces per thous. region residents
		return on total assets of organizations by economic activities	%
		retail turnover	mln. rubles per thous. residents,
		wholesale retail	mln. rubles per thous. residents

Source: Own elaboration

First of linear models of panel data ware set up. This collection is heteroscedastic: the F-statistic for the Goldfeld–Quandt test is equal 249.66. To improve this situation I used the logarithmic models. For them the F-statistic for the Goldfeld–Quandt test is equal 0.499. This is to say that for these models heteroscedastic is not significant. The results for 3 types models with panel data (between, fixed and random) are submitted in the table 3.

Tab. 3: Results of regression analysis

Independent variables	Model
Independent variables	Random	Fixed	Between
Log of capital	0.312*** (0.07)	0.286*** (0.032)	0.356*** (0.105)
Log of labor	0.494*** (0.042)	0.445*** (0.482)	0.608*** (0.119)
Log of number of personal computers	-0.018 (0.020)	-0.007 (0.013)	-0.036 (0.062)
Log of share of companies using special software	-0.0001 (0.002)	-0.0002 (0.000)	0.0007 (0.001)
Log of volume of communication services rendered to population	0,0003* (0.159)	-0.075 (0.150)	1.721 (1.102)
Log of employment in research and development to the region population ratio	0.092*** (0.039)	0,030** (0,015)	0,355*** (0,048)
Log of population of students trained as per undergraduate program, specialist program and Master's program	0.025 (0.030)	0.380 (0.23)	0.162 (0.160)
Log of number of higher educational establishments	0.656* (0.325)	0.423 (0.233)	0.241 (1.133)
Log of population of medical advisers of all specialties	0.012* (0.005)	-0.004 (0.002)	-0.002 (0.003)
Log of population of low-grade medical workers per capita	0.015* (0.004)	-0.000 (0.002)	0.003 (0.002)
Log of density of public railway tracks	0.002** (0.001)	0.001 (0.003)	0.001 (0.001)
Log of density of public motor roads	0.0005 (0.000)	-0.000 (0.000)	-0.000 (0.000)
Log of truck shipment	0.024 (0.027)	0.000 (0.000)	0.000 (0.000)
Log of number of public buses	0.003*** (0.001)	0.003*** (0.000)	-0.0007 (0.003)
Log of length of street sewerage networks	-0.0008* (0.000)	-0.001 (0.002)	-0.002 (0.003)
Log of length of heat supply and steam networks	-0.081 (0.044)	-0.142 (0.155)	-0.389 (0.042)
Log of length of street water supply networks	0.127* (0.059)	0.001 (0.418)	0.131 (0.077)
Log of number of heating plants	-0.021 (0.015)	0.022 (0.041)	0.278 (0.23)
Log of number of credit organizations	-0.0003 (0.0001)	0.271 (0.231)	0.126 (0.152)
Log of return on total assets of organizations by economic activities	0.00026 (0.001)	0.443 (0.223)	0.244 (1.153)
Log of retail turnover	0.228 (0.237)	-0.008 (0.003)	-0.005 (0.001)
Log of wholesale retail	0.109 (0.367)	0.035 (0.033)	0.377 (0.213)
Constant	-2.18	-1.11	-5.133
Number of obs	546	546	546
Number of groups = 79	76	76	79
R²	0.9463	0.6992	0.9166
	Prob > chi2 = 0.0000	Prob > F = 0.0000	Prob > F = 0.0000
	Wald chi2(5) = 2543.52	F(5,462) = 214.78	F(5,73) = 309.28

Significance: *10%; **5%; ***1%. Numbers in parentheses are robust standard errors

Source: Own elaboration

It can be seen that more significant coefficients are in the random model then in the others. Thus, upon completion of regression analysis of influence of separate innovation and infrastructure factors on industrial development of region I can conclude that among the analyzed factors only employment in research and development to the region population ratio is significant for development. Thereunder, the attention paid to development of this factor in the programs of the Government of the Russian Federation is fully justified.

Alongside with that, the fact that relationship with the factors of transport, engineering, information and social infrastructures is traced comes under notice. At that, it is interesting that among all considered factors of the transport infrastructure the most weighty is the index of availability of public buses in the region. Taking into consideration the fact that the social infrastructure indices have positive effect on development of the regional industry: number of higher educational establishments, population of medical advisers and low-grade medical workers (with relatively high values of coefficients, especially, of the education, but with ten per cent level of significance), it can be assumed that the conditions created for the residents (company employees) are important for development of industry in the region. That makes sense as people of the most active working age want to move on there where the conditions not only personally for them but for comfortable living of their families are created. That is the objects of the social infrastructure which at first sight are not immediately related with innovations and development of industry, are of paramount importance for this process. They are schools, nursery schools, medical centers and hospitals. Therefore it can be assumed that development of social programs in the region will be encourage growth of industry rather than only more certain enterprises. However, development of this line is not given separate attention in the programs of innovative development support.

155.4 Discussion

I can state that the verifiable hypothesis on positive influence of the innovative development support measures taken by the Government on development of the Russian economy was confirmed. As mentioned in the part 2 of this article, the regions the territory of which locates the clusters supported by the governmental programs show significantly better results of economic development than other regions. They have higher absolute indices and coefficient of GDP growth and per capita income (Figure 1).

This brings us to the conclusion that the policy pursuing in this line is effective enough. However, drawing such conclusion it is necessary to consider that the part of the success effect of the regions with innovation clusters is reached due to the fact that initially the effective regions were selected for support. Therefore, it seems appropriate to suggest the innovative development programs for the other regions for equalization of living standard in different parts of Russia. It is possible that artificial formation of clusters within those territories where there are no prerequisites for this will not lead to good results, that is why it is not recommended to extend the list of the supported clusters. However, it seems feasible to stimulate implementation of innovative processes to extractive and other industry sectors, which are not enough involved in this process.

Besides it is determined which factors of innovation and information infrastructures have higher influence on development of one of the most sensitive to innovation sphere of processing productions. Among the considered indices only the employment in research and development to the region population ratio is significant in the regression model.

Acknowledgment

The article was supported by the Russian Foundation for Basic Research grant 20 16-06-00566\16

156.References

Brown, F., & Guzmán, A. (2014). The Introduction of the Cobb-Douglas Regression. Journal of Technology Management & Innovation, 9(4), 36-52.

Crépon, B., Duguet, E., & Mairesse, J. (1998). Research, Innovation and Productivity: An Econometric Analysis at the Firm Level. Economics of Innovation and New Technology, 7(2), 115-158.

Crespi,, G., & Zuniga, P. (2012). Innovation and Productivity: Evidence from Six Latin American Countries. World Development, 40(2), 273-290.

Griffth, R., Huergo, E., Mairesse, J., & Peters, B. (2006). Innovation and Productivity Across Four European Countries. Oxford Review of Economic Policy, 2(4), 483-498.

Hajkova, D., & Hornik, J. (2007). Cobb-Douglas production function: The case of a converging economy. Czech Journal of Economics and Finance, 9(10), 48-68.

Oliner, D. & Sichil D. (2000). The Resurgence of Growth in the Late 1990s: Is Information Technology the Story? The Journal of Economic Perspectives, 14, 3-22.

Regions of Russia. Social and Economic Indicators. Moskow: Rosstat, 2006-2015. Print.

Song L. & Geenhuizen M. (2014). Port infrastructure investment and regional economic growth in China: Panel evidence in port regions and provinces. Transport Policy, 36, 173-183.

Srithanpong, T. (2014). Innovation, R&D and Productivity: Evidence from Thai Manufacturing. International Journal of Economic Sciences & Applied Research. 7 (3), 103-132.

Sгvoiu G. & Юaicu M. (2014). Foreign Direct Investment Models, based on Country Risk for Some Post-socialist Central and Eastern European Economie. Procedia Economics and Finance, 10, 249-260.

Rastvortseva S. (2014). Assessment of the regional economic potential for the industrial clusters development. SGEM2014 conference on political sciences, law, finance, economics and tourism, Conference Proceedings, 1(9), 75-82.
Contact

Elizaveta Kolchinskaya

National Research University Higher School of Economics, ICSER Leontief Center

16 Ulitsa Soyuza Pechatnikov, St.Petersburg, Russia 190008

ekolchinskaya@hse.ru

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