УДК 330.34

СПОСОБ ОЦЕНКИ ФУНКЦИОНИРОВАНИЯ ИННОВАЦИОННОЙ СИСТЕМЫ СТРАНЫ

Прошкина Оксана Николаевна
Санкт-Петербургский Государственный Экономический Университет
Магистрант

Аннотация
Развитие инновационной системы играет значимую роль в жизни каждой страны. Статья предлагает один из способов оценки функционирования региональной инновационной системы. Методология состоит из пяти различных этапов, которые позволяют установить как сильные стороны системы, так и слабые.

EVALUATION OF INNOVATIVE SYSTEM OF A COUNTRY

Proshkina Oksana Nikolaevna
St. Petersburg State University of Economics
undergraduate student

Abstract
The development of innovation system is very important in the life of each country. The article offers the way to value the regional innovation system of a country. The methodology consists of five distinct stages and it helps us in identifying strengths and weaknesses of national innovative system.

Keywords: innovations, national innovation system


Библиографическая ссылка на статью:
Прошкина О.Н. Evaluation of innovative system of a country // Экономика и менеджмент инновационных технологий. 2015. № 9 [Электронный ресурс]. URL: http://ekonomika.snauka.ru/2015/09/9693 (дата обращения: 30.09.2017).

In this work I suggest one method of analyzing how different factors influence ratio of regional per capita gross domestic product in country’s regions (GDP). For achieving this aim I ought to make five steps. First step is refers to collecting data for GDP and variables from the OECD Regional Database. It has been decided to take the period from January 2005 to January 2015. These variables can be seen in the first table.

Table 1 Variables

Aspects

Variables

Institutes which generate knowledge and encourage its dissemination The number of organizations engaged in R&D
The number of personnel in enterprises connected with R&D (per 1 million of people in the region)
Postgraduate students (per 1 million of people in the region)
Expenditure on R&D (as% of GRP )
Innovative business activity Innovative performance (as% of total amount of goods, works and services)
The level of innovative business activity
Innovation Infrastructure Number of institutions of scientific and technological capabilities in the region
Integration elements of RIS Export of innovative products
Import of innovative products

According to the information included in the first table nine variables are picked up for all four issues which are proposed for further research.

Secondly, it is necessary to conduct a descriptive analysis of the sample for a preliminary assessment of its quality. For analyzing quantitative data of samples I calculate such statistical values as the mean, median, minimum, maximum, lower quartile, upper quartile, and the coefficient of variation. Than after generating of a box-plot and a histogram for GDP It is possible to identify whether there are any ejection or not. In the first case ejection should be eliminated for approximation to the normal distribution. In the second case this procedure is ought to be missed. For the next move it is reasonable to use several formal tests on normal distribution: Shapiro-Wilk W test for normal data; Skewness/Kurtosis tests for Normality. The results of three tests for normality allow us to accept the null hypothesis of normality GDP, or reject it. Now the relationship between quantitative variables should be analyzed. After conductions I construct scatter diagrams and the correlation matrix. Based on these results it is possible to make an assumption about the most important factors and the type of the relationships.

The third step is to build a basic linear model and diagnose it. Diagnosis involves finding significant and insignificant factors and the analysis of the coefficient of determination. Insignificant factors should be eliminated and the analysis is to be made again. Also there is a need to conduct formal tests for functional form and heteroscedasticity: Breusch-Pagan-Godfrey; White; Glejser. The next stage is to assess the basic model’s multicollinearity and to create the correlation matrix. If this matrix includes factors which have a strong linear relationship, I get rid of them and repeat this step again.

For the fourth step I evaluate alternative model specifications and to diagnose them according to the previous step. Than receiving new model specifications should be compared with each other. For this move I use comparative analysis of the next three values: Adjusted R-squared; kurtosis; asymmetry. The final stage of the fourth step is selecting the best model. This decision is based on picking up the pattern with the highest R-squared and without heteroscedasticity.

As a result we have a certain method, which is described above. The mythology of the research includes next six steps:

  1. to collect data from the OECD Regional Database;
  2. to conduct a descriptive analysis of the sample for a preliminary assessment of its quality;
  3. to build a basic linear model and diagnose it;
  4. to evaluate alternative model specifications and to diagnose them;
  5. to assess the impact of indicators on GDP.

Interpreting multiple logistic regression coefficients: the regression coefficient at regressor expresses the elasticity of the dependent variable on this factor at a constant other variables. As it has been mentioned before model proposes that the regressor is regional per capita gross domestic product in country’s regions. On the on hand, if some factors have a weak degree of influence on the GDP, I make the assumption that there are problems in some of considered areas. On the other hand, in the case where factors have a strong influence relevant areas have a favorable environment in the region. Thus we can identify weaknesses and strengths of country’s Innovative system.


References
  1. Carvalho N., Carvalho L., Nunes S. A methodology to measure innovation in European Union through the national innovation system //International Journal of Innovation and Regional Development. – 2015. – Т. 6. – №. 2. – С. 159-180.
  2. Fomby T. B., Hill R. C., Johnson S. R. Advanced econometric methods. – Springer Science & Business Media, 2012.
  3. Yongze Y. Innovation Cluster, Government Support and the Technological Innovation Efficiency: Based on Spatial Econometrics of Panel Data with Provincial Data [J] //Economic Review. – 2011. – Т. 2. – С. 012.
  4. Usai S. The geography of inventive activity in OECD regions //Regional Studies. – 2011. – Т. 45. – №. 6. – С. 711-731.


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