“Economic growth of the country and national intellectual capital (evidence from the post-socialist countries of the central and eastern Europe)”

The purpose of the article is to study the innovation levers of developing the intel- lectual background for economic growth in two groups of post-socialist Central and Eastern European countries (middle-income and lower-middle-income coun- tries). To achieve that, the quantitative effect of the national intellectual capital components (human capital, market capital, structural capital and capital of re- newal and development) on the dynamics of the countries’ economic growth was determined. For both groups, multiple regressions have been constructed that reflect the quan- titative relationship between the economic growth rates (in the regressions – the indicator of real gross domestic product per capita) and the components of nation- al intellectual capital in 2010–2018. It has been established that the key innovative indicator of the economic growth of middle-income countries is the national capi- tal of renewal and development, which in general corresponds to the pan-Europe-an model of innovation and investment development. Education is the main factor that provides the basis for the economic growth of lower-middle-income countries. Recommendations on improvement of national innovation policy are offered. innovation projects, etc.). As a result, not only the sectoral structure of national economies changes, but also factors influencing the dynamics of their economic growth. The pro-cesses of creating and using new knowledge, ideas and information are key factors ensuring economic stability, sustainable economic development and technological competitiveness of modern macroeconomic systems. Highly skilled and educated workers with their knowledge, skills and abilities become the main driving force of social progress and provide the formation of a new, innovative type of economic systems.


INTRODUCTION
Currently, a gradual transition to a new technology, i.e., an information-innovation economy, or an economy based on knowledge, information and innovation, is taking place in the world. Its characteristic features are: creation of innovations, introduction of resource and energy saving technologies, state support for science-intensive industries and protection of intellectual property (patents, licenses, know-how, innovation projects, etc.). As a result, not only the sectoral structure of national economies changes, but also factors influencing the dynamics of their economic growth. The processes of creating and using new knowledge, ideas and information are key factors ensuring economic stability, sustainable economic development and technological competitiveness of modern macroeconomic systems. Highly skilled and educated workers with their knowledge, skills and abilities become the main driving force of social progress and provide the formation of a new, innovative type of economic systems.
The research of innovation levers of the formation of the intellectual background for the economic growth of modern macro systems, as well as the definition of tools, methods and organizational and economic mechanisms for optimizing the relationship between the indicators characterizing these processes, is an urgent task. Awareness of this requires developing a new paradigm of knowledge about shaping the economic growth intellectual basis, which in the long run will allow to improve the state policy of innovation development based on increasing the efficient management of the national intellectual capital components.

LITERATURE REVIEW
The introduction of scientific and technological progress, the focus on the production of science-intensive products, which belongs to the fifth and sixth technological paradigms, paying particular attention to education, science, culture, healthcare, which form qualitative human capital, have provided the world's major economies with due basis for achieving economic stability and competitive advantages on world commodity markets. Therefore, it is not surprising that economies based on knowledge, information and innovation are the most successful and economically developed ones (see Table 1).
In the economic literature, identifying innovation levers of the country's economic growth can be considered at least in the context of the two groups of methodological approaches. The first group should include work in which the linear production functions are used to study the innovation levers of economic growth of the country. This allows determining the quantitative relationship between the volume of investment in intangible assets (utility models, inventions, software products, databases, trademarks, brands, etc.) and the level of social productivity of labor. This group includes the scientific works by Corrado, Hulten, and Sichel (CHS model) (2005,2009), Barnes and McClure (2009), Castells and Himanen (2002), Ferreira and Hamilton (2010), and Edquist (2011) who calculated the quantitative effect of aggregate investment in non-physical capital (intellectual property objects and new technologies) on the growth rate of social productivity in the European Union countries and have concluded that such a connection is more noticeable in the leading European economies (Great Britain, Germany, France, Austria) and less noticeable in other countries (Sweden, Italy, Poland, Czech Republic).
Taking the basic provisions of the CHS model as a basis and using comparative statistics and indices for the Southern European countries, Corrado (2005), Roth and Thum (2010), and  found that the main tool of supporting economic growth in this region is an innovation lever, which requires increased spending on raising the educational and qualification level of employees, implementation of scientific and technical works, software development and organizational and marketing innovations. Corrado (2005), Roth and Thum (2010) confirmed their findings by the following statistical data: in 2005-2007, an increase in the cost of conducting fundamental research by 15% provided Italy, Spain and Portugal with steady economic growth of 3-5%.  Representatives of another group of scientific approaches to the analysis of economic growth are mainly using methods of economic and mathematical modeling, in particular regression, factor and discriminatory analysis. Thus, Bontis (2004) used a regression analysis as a tool for research on innovative levers of economic growth in the Middle Eastern countries (Egypt, Kuwait, Qatar, Tunisia, and the United Arab Emirates). He proved that the main innovation lever that has ensured the steady positive dynamics of the economic growth of countries located in the region is human capital represented as knowledge, skills and motivation of employees that bring them income in the form of labor rent. Based on Bontis' calculation results, Uziene (2014) has constructed a regression model to analyze innovation levers of economic growth in the Baltic economies (Latvia, Lithuania and Estonia), which are at the stage of transition to information and innovation drivers. Uziene (2014) has established that the global index of intellectual capital and the index of human development have the most significant impact on the level of national competitiveness of the Baltic countries.
While developing the idea of using the methods of economic and mathematical modeling to determine the innovation levers of the country's economic growth, P. Stahle

METHODS
The study uses a method of regression analysis, which will determine the quantitative relationship between the economic growth rates of the country and the components of national intellectual capital. Regression models take into account four components of national intellectual capital, namely human capital, market capital, structural capital, and capital of renewal and development. The economic value of the factors (regressors) consists in the fact that they show how much (in percentage terms) the dependent factor will change (in the models of the current study, the amount of real GDP per capita), if independent factors (indicators characterizing the national intellectual capital components) change by one percent.

RESULTS
The research of innovation levers of developing the intellectual basis for economic growth in post-socialist countries involves several stages (see Figure 1).  As a regress, the amount of real GDP per capita is determined, and regressors (the most significant factors) are indicators that characterize the national intellectual capital components (see Table 2).
Hereafter, an input matrix is formed that characterizes the process of forming the intellectual basis for economic growth in post-socialist countries during 2010-2018. As a research object, 20 countries of Central and Eastern Europe were selected, which, depending on the per capita GDP values, were divided into two subgroups (clusters): middle-income countries (> 15 thousand dollars) and lower-middle-income countries (< 15 thousand dollars) (see Table 3).

National intellectual capital components Indicators
National human capital The number of teachers per 1,000 people (X 1 ), the number of teachers in higher educational institutions per 1,000 people (X 2 ), the share of people with higher education in the total population (Х 3 ), the number of health care workers per 1,000 people (X 4 ), the expected lifespan (  Statistical information was processed in the Stat Graphic Centurion software environment (module -Regression Analysis) and tested for accuracy, homogeneity and compliance with the normal distribution law. The research has shown that in the regressions obtained, there is a clear tendency of grouping the input data close to the center. Positive and negative deviations from the center are equally probable, with the frequency of deviations decreasing rapidly in the event of a significant increase in deviations from the center. Accordingly, it was concluded that the distribution of the feature investigated, namely the volume of real GDP per capita, is close to normal with a more acute peak of distribution.
The statistical significance of the regressions obtained is confirmed by many indicators. First of all, the parameters of t-statistics (T-Stat) were calculated. Thus, it is proved that it is necessary to exclude those indicators from the regression equations whose values exceed the maximum acceptable norms. In this regard, the following indicators were excluded from the regressions that determine the relationship between economic growth rates and the national intellectual capital components in the middle-income countries: the number of telephone lines per person (X 11 ), the number of Internet providers per person (X 12 ) and the number of patents issued by the United States Patent and Trademark Office (X 18 ). And from re-gressions determining the relationship between economic growth rates and the components of national intellectual capital in the Central and Eastern European post-socialist countries with lower than middle income of the population, the following indicators were excluded -the expected lifespan (X 5 ), the share of venture capital enterprises in the total number of economic entities (X 17 ), the number of patents issued by the United States Patent and Trademark Office (X 18 ). After that, the variation coefficient was calculated. This made it possible to conclude that the balance of intellectual property purchase and sale transactions (X 6 ) should be excluded from both clusters of postsocialist countries, and the number of employees who upgraded their qualifications during the year, per 1,000 people (X 19 ) should be excluded from the cluster of lower-middle-income countries. In addition, interconnected factors should be excluded from the models. For this purpose, the matrix of pair coefficients was constructed, which indicated that all factors included in the model (X 1 , X 2 , X 3 , X 4 , X 5 , X 7 , X 8 , X 10 , X 13 , X 14 , X 16 , X 19 -in the first the cluster of countries, X 1 , X 2 , X 3 , X 7 , X 9 , X 10 , X 11 , X 12 , X 13 , X 15 , X 16 -in the second cluster) are not very closely interconnected (R ˂ 0.85). This provision was also confirmed by calculations of the criterion of statistical significance of the estimated correlation (P-value ˃ 0.05). Also, the Durbin-Watson (DW) statistics were used. In particular, the boundaries of DW statistics for the studied countries were de-  Table 4).

DISCUSSION
Formation use and reproduction of human capital is the main lever of developing the intellectual basis for economic growth of the countries under investigation. This is confirmed by the quantitative effect of the following indicators: the number of teachers per 1,000 people (0.7428Х 1 ), the number of teachers at higher educational institutions per 1,000 people (0.3482Х 2 ), the number of health care workers per 1,000 people (0.1911Х 4 ).
Another lever for the innovation and investment model of development is the national capital of renewal and development, that is, the national innovation capital, which in the regressions received is characterized by indicators such as the amount of internal expenses for scientific and innovation activity (0.7186Х 14 ), the number of employed in high-tech sectors of the national economy per 1,000 people (0,1234Х 16 ) and the number of employees who upgraded their qualifications during the year, per 1,000 people (0.6589Х 19 ). It has also been established that financing of innovation infrastructure objects (design bureaus, research institutes, technoparks, technopoles, technoecopoles) (0.7186Х 14 , 0.7934Х 15 ) contributes to the implementation of innovative projects, the production of high-tech industrial products, the development of knowledge-intensive business, and hence, and to an increase of the technological competitiveness of countries belonging to this cluster.
At the same time, there are some differences between the multiple regression equations obtained for the two groups of post-socialist countries: middle-income countries and lower-middle-income countries. In addition, the national human capital has a significant positive impact on the dynamics of economic growth in the Central and Eastern European post-socialist middle-income countries. This is manifested in the quantitative effect of the following indicators: the number of teachers per 1,000 people (0.7122Х 1 ), the number of teachers of higher educational institutions per 1,000 people (0.6431Х 2 ), the share of people with higher education in the general structure of the country's population (0.0345Х 3 ), the number of health care workers per 1,000 people (0.2526Х 4 ), the expected lifespan (0.1988Х 5 ). These results are explained by the fact that the governments of this group of countries recognize the knowledge, skills, and motivation of people as the main productive force of the national economy, and proper financing of the branches of education, culture and healthcare is an integral part of the national innovation policy of these countries. Therefore, in countries with "fast-growing markets" (Poland, Slovakia and Hungary), significant budget funds are allocated to the industries that form human capital, thus providing training for highly skilled professionals capable of producing new ideas, developing and implementing innovative products, which belong to the fifth and sixth technological patterns and are competitive on world commodity markets. The dynamics of the economic growth of the Central and Eastern European post-socialist middle-income countries is significantly influenced by in-dicators that characterize the national structural capital, namely the number of educational institutions per 1,000 people (0.3226Х 9 ) and the number of libraries per 1,000 people (0.2539Х 10 ) In addition, the multiple regression equation obtained for middle-income countries points to the important role of information and communication technologies, the latest electronic communications, as well as global, regional and local systems in providing structural and innovation transformations and the formation of the intellectual basis of economic growth, as they promote the interest of domestic investors in the Central and Eastern European markets (0.6911Х 14 ), the inflow of foreign direct investment and accelerating the pace of technological progress and economic growth.
Instead, the regression equations determining the relationship between the dynamics of economic growth and the national intellectual capital components of the Central and Eastern European post-socialist lower-middle-income countries differ significantly from the previous ones. First of all, the obtained equations confirm and deepen the preliminary conclusions (Cherkashyna, 2016(Cherkashyna, , 2017) that, despite the fact that Albania, Bosnia and Herzegovina, Macedonia, Moldova, Ukraine and Montenegro, in terms of state financing of education, science, culture, and health care are still significantly behind more developed post-socialist countries of the Central and Eastern Europe (Poland, Slovakia, Slovenia, Hungary, etc.), it is in lower-middle-income countries that an innovative lever of economic growth such as education has the greatest positive impact on the dynamics of economic growth. This makes it possible to recognize the knowledge, intelligence, erudition, emotions, creativity and system thinking of people as key factors in shaping the basis for the successful economic development and social progress of this group of countries.
Considerable attention is also paid to the fact that national market capital does not play a key role in ensuring economic growth of the Central and Eastern European countries with lower than middle incomes. That is these countries do not actively participate in international scientific and technological exchanges. This is evidenced by the follow-

CONCLUSION
National capital of renewal and development (that is the national innovation capital) is the main lever of shaping the intellectual basis for the economic growth of middle-income countries (Belarus, Bulgaria, Estonia, Latvia, Lithuania, Poland, Russia, Romania, Slovakia, Slovenia, Hungary, Croatia, Czech Republic). Therefore, the strategic task of the state policy in these countries is shaping an innovation-investment model of "catchup" economic growth dominated by science-intensive industries in the structure of national economy.
Equally important for middle-income countries is the formation and efficient use of high-quality human capital. In view of this, the main priorities of governments should be, on the one hand, economic and social motivation of every citizen to be healthy, educated, highly moral and building on this basis an innovation and information society of highly educated and creative people. On the other hand, the state should ensure legal, economic, organizational and infrastructural conditions for following the appropriate way of life. As a result, these countries will be able to solve the main task, i.e. developing effective innovative systems and achieving high rates of economic growth based on the unity and balance of public policy in the fields of education, culture and health care.
Human capital is also the main driver of the economic growth in the Central and Eastern European post-socialist countries with lower than middle incomes. It is necessary to improve the quality and competitiveness of national innovation systems and thus solve two pressing problems: to lower the drain of highly skilled scientific and technical personnel, which is characteristic for lower-middle-income countries; and to increase the efficiency of the system for transferring new knowledge and technologies from the scientific sector to the manufacturing sector and accelerate the process of convergence. The development of national structural capital through the spread of global computer networks, Internet technologies, electronic communications, mass media, social networks and artificial intelligence systems is important for shaping the intellectual basis for economic growth.
In the long run, the implementation of the identified measures in the economic policy of the post-socialist countries under investigation will make it possible to significantly improve the efficiency of national