Competitiveness of the Regions of the European Union in a Sustainable Knowledge-Based Economy

Abstract

The aim of the article is to analyze the level of the knowledge-based economy (KBE) in the European Union countries in terms of sustainable development. The added value of the work is the presentation of research results at different levels of data aggregation (EU countries, EU macro-regions, EU regions). This type of approach was used for the first time in this study. The research assumes that knowledge and skills are one of the basic factors in implementing the concept of sustainable development. Currently, there are very large disproportions at the level of KBE in the countries, macro-regions, and regions of the EU. It also translates into their socio-economic situation and thus into competitiveness and innovation. The highest level of KBE is in north-western and central Europe countries, while the lowest is in the countries of eastern and south-eastern Europe. This regularity also applies to macro-regions and regions located in these countries.

1. Introduction

The regional development in the European Union can be considered in three main areas: the regional policy of the entire Union, the policy of individual Member States towards regions, and the policy implemented by regions themselves. The concept of balanced and sustainable development, referred to in the literature as sustainable development, is important for all the identified dimensions. Sustainable development is a process where actions are integrated [1] in the economic, social, and environmental spheres [2,3,4,5] in order to improve the level and the quality of life of both contemporary and future community [6,7]. It has become a requirement for implementation on all levels, from the local to the global scale. It is the basis of international and national activities. It applies to the entire economy as well as its individual elements.

Nowadays, the idea of sustainable development manifests itself in almost all areas of the economy, also in its knowledge-based model. The framework of this idea involves a process of continuous improvement of the human being, the economy, and the environment. A sustainable knowledge-based economy is conducive to efficient knowledge management and the introduction and dissemination of the effects of innovative activities. Its development is one of the most significant challenges faced by public authorities, entrepreneurs, R & D entities, and business support institutions.

The development of the knowledge-based economy is mainly linked to information and communication technology (ICT), technological progress, and innovation. According to the World Bank Institute [8], four conditions must be met for a country to participate fully in the knowledge-based economy:

1.

an appropriate regulatory and economic environment that enables the free flow of knowledge supports investment in information and communication technologies, and encourages entrepreneurship,

2.

an appropriate level of education and training, because an educated and skilled population is needed to share and use knowledge,

3.

a dynamic information infrastructure enabling effective communication, dissemination, and processing of information,

4.

having a network of research centres, universities, private enterprises, and community groups capable of harnessing the growing knowledge resources, adapting them to local needs, and generating new knowledge.

A characteristic feature of highly developed countries is investing not only in new technologies but also in human and social capital. The foundation of human capital is education, experience, and skills. At an appropriate level, human capital as an element of intellectual capital enables a better adaptation of societies to the changing economic conditions. It positively influences the implementation of the concept of sustainable development [9]. Education should be included among the basic factors, thanks to which it is possible to implement the idea of sustainable development with care for future generations. It also accelerates the opportunity to eliminate developmental disparities between regions. An important problem related to the knowledge-based economy is also monitoring the level of expenditure on financing research and development (R & D) activities, which is the driving force behind the growth of innovation and competitiveness of the economy on a micro and macro scale.

Research collaboration is an integral part of the knowledge economy [10,11]. Teirlinck and Spithoven [12] define it as an interaction between people and entities with different interests in order to undertake research and use its results to deepen knowledge in a scientific field or innovation. Cunningham and Link [13] draw attention to the cooperation between universities and industry in research and development (R & D), as well as cooperation between business and universities, which by increasing the efficiency of industrial investments, become important factors of economic growth and development. The effect of the appropriate use of knowledge in enterprises is their technical, technological, and organizational innovation, which in turn increases their competitiveness and provides an effective basis for further development. Innovative solutions developed at the level of enterprises may constitute a basis for improving the competitiveness of countries and their regions. It is possible, among others, thanks to investments in human capital, to increased spending on research and development as well as new technologies. The elements mentioned above operate at a specific time and space and are dependent on socio-economic factors.

The above considerations prompted the authors to seek answers to the following questions: what the differentiation of the level of a knowledge-based economy in the European Union countries is, in terms of sustainable development, and to what extent it is related to the level of the knowledge-based economy at lower levels of aggregation. The research assumes that knowledge and skills are one of the basic factors in implementing the concept of sustainable development. Individual regions in the EU struggle with many development problems, but thanks to the inhabitants’ adequate intellectual capital, knowledge, and skills, they can more effectively to adapt to changing economic conditions. Hence, the aim of the article is to analyze the level of the knowledge-based economy (KBE) in the European Union countries in the aspect of sustainable development. The article is of a research nature and focuses on the identification of spatial connections of the knowledge-based economy. The added value of the work is the presentation of research results at different levels of data aggregation (EU countries, EU macro-regions, EU regions). This type of approach was used for the first time in this study. As a rule, such studies are limited to large territorial units, i.e., individual member states.

The structure of this article includes an introduction, which presents the main purpose of the paper and explains the authors’ main motivations for conducting research on the competitiveness of European Union regions in a sustainable knowledge-based economy. Furthermore, a literature review on the knowledge-based economy is included. The following section discusses the statistical data used in the article and describes the research procedure. Finally, the research findings, discussion, and conclusions of the study are presented.

2. Determinants of Regional Development concerning Their Competitiveness, Sustainable Development and Knowledge-Based Economy—A Literature Review

Regional development is a complex process and depends on many conditions. On the one hand, there is an emphasis on the need to increase the competitiveness of regions and, on the other hand, on their sustainable development. Some researchers believe that the drive to improve competitiveness precludes sustainable development, while others argue against this view [14]. Some researchers have used the similarities between sustainable development and competitiveness at the regional level to combine these two concepts into the idea of sustainable competitiveness [2,15,16]. The main features common to competitiveness and sustainable development are the pursuit of improved quality of life and economic development, the introduction of eco-innovation, and the prominent role of local and regional governments.

Sustainability assessment methods have mainly national coverage. However, many authors believe that modelling sustainability in smaller spatial units such as a region, for example, is essential to understanding and achieving a global level of sustainability. Hence, regions are becoming the focus of sustainable development researchers, natural resource managers and strategic planners working on developing and implementing sustainable development goals [17,18,19,20]. Regional development is usually defined as the integral development of a community (social, economic, environmental and health, technological, cultural, and recreational) in a specific territory. Graymore et al. [19] argue that the regional level provides the greatest opportunity for local governments to collaborate with their communities on sustainable development. According to Czyż [21], it is a place to form a competitive advantage in a globalizing economy and a means to achieve socio-economic development.

Both in the case of sustainable development and competitiveness, resources are an essential element in the region [14]. It applies to both natural resources, man-made resources (e.g., infrastructure), human resources, and social capital. Their proper combination is one of the factors determining the socio-economic development of regional competitiveness. They are also crucial in the case of sustainable development—this applies, for example, to the level of education, the adopted system of values and ethical norms (including environmental preservation as a value), creativity (which in turn determines innovation), entrepreneurship, and ability to work. Similar factors of regional development are mentioned by Šabić and Vujadinović [22], while emphasizing that regional development is closely related to internal resources and the specificity of regions, and only their proper use can contribute to an increase in the competitiveness and attractiveness of the region.

One of the basic factors determining competitiveness and economic growth, especially in highly developed economies, is creating innovative solutions [23,24,25,26]. On the other hand, R & D remains the focal point of any innovation process to create new knowledge resources. The development of the economy of a given territory (country, region) depends not only on the effectiveness of enterprises but also on how they interact with each other and with entities from different sectors, including the scientific and institutional ones, in the process of creating and disseminating knowledge.

Porter [27] admits that much of the research on competitiveness has focused on the national scale, failing to recognize the internal regional differences that exist in all countries. In his opinion, the main components determining economic results are regional components, such as specialized outlays, infrastructure, education of the workforce, institutions that will foster the agglomeration of companies in the form of clusters. According to Malecki [28], innovation and knowledge are characteristic features of the knowledge-based economy and are the basis of competitiveness. Global innovation networks that enable creativity and innovation emerge in some places and are attracted to other sites.

While there are many dimensions of competitiveness, the primary dimension is innovation, consisting of local, global, and virtual networks and innovation systems. Competitiveness will also be fostered by social capital, defined as norms and values that determine relationships between people [29]. Among the factors influencing the shape of social capital, an important role is played by institutional conditions, such as cultural potential and creative potential of an organization/community/society as a whole. The influence of cultural capital and creative potential on the formation of social capital is bilateral. Culture and creativity of individuals and societies facilitate the formation and strengthening of social capital, which in turn influences the development of culture, innovation, and increases the level of tolerance. Thus, social capital has the opportunity to sustainably develop and strengthen its position as a resource in societies characterized by a high level of cultural and creative capital.

Due to its nature, cultural capital is closely related to social and material status [30]. On the one hand, it enhances social stratification [31,32], but it is also a factor that binds society together as a whole [33]. It is considered to be an important determinant of socio-economic development due to the fact that it has a direct impact on the behavior of both individuals and entire societies [34]. This is especially important in relation to sustainable development [35,36], including by emphasizing its importance during the education process [33] as well as changes in consumption behavior [37].

Creative capital influences many economic and social phenomena and processes. Its role—both in practice and in theoretical models—is constantly growing, and it is considered an important resource and even a driving force of the modern regional economy [38,39,40,41,42]. It is recognized as a key factor in the socio-economic development and shaping of the knowledge-based society and economy. In the literature on the subject, creative capital is mainly associated with such terms as creative class, creative economy, creative sector, and creative industry. The first of these concepts, i.e., the creative class, refers to people who perform creative, innovative work, have higher education, and are involved in research and development activities [43]. It includes scientists, teachers, IT specialists, managers, lawyers, etc. According to Veselá and Klimová [44], “Creative industry in mutual synergy with a knowledge-based economy creates conditions for a strong and sustainable creative economy”.

According to Fratesi [45], the regional competitiveness is a dynamic process, constantly changing and adapting through the learning process and various types of innovation. It requires constant renewal, and this feature gives it a significant advantage over static aspects of production, such as the level of costs or the advantage of natural resources. Annoni and Dijkstra [46] argue that there are many approaches to measuring regional competitiveness due to the diversity of interpretations of the current conceptual framework of this approach. Although there is no uniform manner of measuring competitiveness, it is possible to identify groups of factors that significantly impact this particular aspect. Factors such as innovation, transport and digital infrastructure, and health and human capital measures are often considered. Increasingly, within regional development theory, the view that the traditional factors of development—land and mineral resources, labor and capital—are giving way to knowledge has become popular. The part of the economy that is predominantly influenced by science is called the knowledge-based economy (KBE). According to Czyż [21], it may be an important area of competitive advantage. The competitiveness of the region is then considered not only concerning the potential factors of KBE development, but also in terms of their practical use, leading to specific benefits in economic and social activity, which in turn lead to the advantage of the studied region over other regions.

The importance of knowledge and human capital for long-term economic growth has long been known. As early as the 1920s, Young [47] pointed out that the productivity gap between American and British industries at that time could be explained by different levels of inventive activity and better organizational structures of American industry. According to Caspari et al. [48], Young’s opinion can be considered an early description of the importance of human capital and technological changes for economic growth.

According to the OECD [49], a knowledge-based economy is an economy that is directly based on the production, distribution, and application of knowledge and information. Regarding the national and regional scale, a knowledge-based economy operates on a macro-economic scale, characterized by the rapid development of economic areas related to information processing and the development of science, mainly the branches classified as high technology. On the other hand, KBE bases its competitive advantages on knowledge-intensive enterprises on the microeconomic scale.

The pace of change in the economy means that existing knowledge and skills change rapidly. It forces the necessity to improve qualifications and continuous learning, which is conducive to creating and using knowledge in practice. The knowledge-based economy is inextricably linked with the concept of intellectual capital (IC). An extensive discussion of this concept was provided by Choong [50]. Its quality is determined by society’s readiness to raise professional qualifications throughout the entire period of professional activity. In the long term, intellectual capital contributes to the development of new solutions (innovations), which may become the basis for increasing the competitiveness of the entire economy of a country or region. Increasing the level of innovation, apart from improving the quality of intellectual capital, is also possible thanks to increasing expenditure on research and development. Knowledge measurement and knowledge-based activities are of increasing interest to economists, society, and policymakers. However, there are still not many papers showing the development of KBE in individual countries and even less in regions.

The construction and development of knowledge-based economies in the EU countries are of key importance for their further socio-economic development. To this end, an appropriate economic policy must be pursued. Orłowski [51] recommends the following solutions:

• an appropriate macro-economic and structural policy aimed at lowering the level of corporate taxation, increasing the openness of the economy, effective privatization, and restructuring,
• creating a competitive R & D market and increasing the efficiency of using research funds,
• appropriate education policy aimed at increasing the number of students, improving the quality of education, and adjusting the system to the market needs.

The Europe 2020 Strategy outlines three interrelated priorities relating to the knowledge economy:

• intelligent development—the development of the economy based on knowledge and innovations,
• sustainable development by supporting a resource-efficient, more environment-friendly and highly competitive economy,
• inclusive growth by fostering a high-employment economy delivering social and territorial cohesion.

This approach responds to the challenges of sustainable development and is consistent with an increasingly important approach to innovation policy, and thus the competitiveness of territorial units (countries, regions).

The spread of the knowledge-based economy means that the existing competition between countries and regions for material resources is replaced by competition for intangible resources, primarily through knowledge related to the latest achievements of modern science. As a result, the development opportunities of countries and regions are increasingly determined by the intellectual potential of people, science, and the sphere of research and development, which provide innovative solutions, thus “driving” a new type of economy [52].

3. Materials and Methods

3.1. The Characteristics of the Research Material

The following indicators (diagnostic features) were used to characterize various aspects of the knowledge-based economy—from the education level of the general population to the higher of R & D personnel in total employment, to R & D expenditures and the higher added value from R & D in total added value:

• X₁—the share of people with higher education in the population aged 25–64%,
• X₂—the share of people participating in courses and training in the population aged 25–64%,
• X₃—the share of gross value added from scientific, technical, and administrative activities in total gross value added (%),
• X₄—gross expenditure on research and development per capita (Euro/person),
• X₅—the share of the employed in the knowledge and technology sector in total employment (%),
• X₆—the share of R & D personnel employed in the enterprise sector in total FTE employment (%),
• X₇—the share of R & D personnel employed in the government sector in total FTE employment (%),
• X₈—the share of R & D personnel employed in the higher education sector in total FTE employment (%),
• X₉—the share of people with higher education employed in science and technology sectors in the population aged 15–74%,
• X₁₀—the share of the number of scientists and engineers in the population aged 15–74%,
• X₁₁—the rate of growth of employment in scientific, technical, and administrative and service activities (%),
• X₁₂—the share of gross fixed capital formation in scientific, technical, and administrative and service activities in total gross fixed capital formation (%).

Data at various aggregation levels (NUTS 0, NUTS 1, and NUTS 2) were taken from the Eurostat database [53].

The choice of such indicators was determined by the availability of data at all levels of aggregation (country, macro-region, region). Most of the information was from 2019, and only in three cases, the data from the previous year was chosen due to the lack of data. Due to the method used in the study (TOPSIS), the nature of the diagnostic features is significant as their values are the basis for determining the synthetic measure. Therefore, it should be noted that all these indicators are stimulants; that is, they are diagnostic features that positively affect the level of the knowledge-based economy in a given area.

The study included 25 EU countries (except France and Sweden—no statistical data for many indicators), 75 macro-regions, and 207 regions located in these countries.

Table 1. The values of selected descriptive characteristics of the analyzed diagnostic features.

Features	Countries (NUTS 0)			Macroregions (NUTS 1)			Regions (NUTS 2)
	$\overline{\mathit{x}}$	Vs (%)	As	$\overline{\mathit{x}}$	Vs (%)	As	$\overline{\mathit{x}}$	Vs (%)	As
X1	33.976	24.881	−0.040	31.341	28.208	0.063	30.197	31.586	0.574
X2	10.496	67.413	1.093	9.123	63.895	1.355	9.076	65.718	1.375
X3	9.883	27.959	1.077	9.339	37.907	0.371	8.526	45.266	0.735
X4	556.468	89.096	0.850	598.491	94.608	1.248	595.456	109.736	2.216
X5	4.436	28.090	0.986	3.845	45.149	0.617	3.673	54.860	1.144
X6	0.724	56.990	0.184	0.680	71.897	0.975	0.682	80.460	1.673
X7	0.169	60.090	0.596	0.192	84.603	1.497	0.166	115.654	2.455
X8	0.422	39.027	0.210	0.379	47.600	0.735	0.387	62.373	1.374
X9	15.212	26.120	0.844	14.112	29.716	0.465	13.475	34.905	1.022
X10	5.056	27.769	0.128	4.787	32.945	0.127	4.579	40.823	0.642
X11	5.184	106.271	−0.409	5.473	94.745	1.023	7.426	472.436	9.467
X12	6.901	32.549	0.388	6.972	57.106	1.638	6.695	74.878	2.406

presents the basic parameters characterizing the average level, the degree of differentiation, and the asymmetry of indicator distributions at individual levels of aggregation.

The analysis of the parameter values reveals the following regularities:

• for seven indicators (except X₄, X₇, X₈, X₁₁, X₁₂), their average level decreases with the transition to a lower level of aggregation, which is related primarily to an increase in the number of examined objects at particular levels of aggregation;
• the variation in the values of all indicators at different levels of aggregation is above 20% and increases with the transition to a lower level of aggregation, except X₂ and X₁₁; countries, macro-regions, and regions are characterized by the lowest variation in terms of the share of people with higher education in the population aged 25–64 (X₁), and the highest—in terms of the rate of growth of employment in scientific, technical, and administrative and service activities (X₁₁);
• the distribution of all indicators at the level of macro-regions and regions is characterized by strong or very strong right-hand asymmetry, which is unfavorable from the point of view of the phenomenon under study, as this means the predominance of objects in which the level of indicators is below average;
• for countries, in the case of most indicators, a right-hand asymmetry of lower strength can also be observed, except for the X₁₁ indicator, for which there is a moderate left-sided asymmetry, which means that for most countries, the growth rate of employment in scientific, technical, and administrative and service activities is above average; also noteworthy is the X₁ indicator (the share of people with higher education in the population aged 25–64 years) characterized by a distribution close to symmetrical.

3.2. Research Method

In the study of the level of the knowledge-based economy at different levels of aggregation (countries, macro-regions, and EU regions), the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) developed by Hwang and Yoon [54] was used. It derives from the family of multi-criteria decision-making methods (MCDA) [55,56,57,58,59], but it can be successfully used for the linear ordering of multi-chain objects [60,61,62].

The TOPSIS method consists of determining a synthetic measure based on which it is possible to order the studied objects from the point of view of the level of the studied phenomenon. For this purpose, a pattern (positive ideal object) and an anti-pattern (a negative ideal object) are determined for each diagnostic feature. The optimal object should be characterized by the smallest possible distance from the pattern and the greatest possible distance from the anti-pattern.

Due to the comparison of the value of diagnostic feature values with the pattern and anti-pattern, it is classified as a compensatory method [63] that allows for trade-offs between criteria (features), where a poor score for one criterion can be compensated by a good score in another criterion [64].

The classic computational procedure involves four stages [54,65,66]:

1.

Bringing a set of diagnostic features to comparability using a normalization procedure (see [67]):

$$r_{ij}=\frac{x_{ij} }{\sqrt{{\sum }_{i=1}^n{x}_{ij}^2}},\hspace{1em}\hspace{1em}\hspace{1em}\left(i=1,\dots ,n\right)\left(j=1,\dots ,k\right)$$

(1)

2.

Determination of coordinates:

• A pattern (a positive ideal solution):

$$v_j^{+}=\left\{\begin{array}{c}\begin{array}{cc}\underset{i}{\max }{r}_{ij}& \mathrm{for} \mathrm{stimulants}\end{array}\\ \begin{array}{cc}\underset{i}{\min }{r}_{ij}& \mathrm{for} \mathrm{destimulants}\end{array}\end{array}\right.$$

(2)

• An anti-pattern (a negative ideal solution):

$$v_j^{-}=\left\{\begin{array}{c}\begin{array}{cc}\underset{i}{\min }{r}_{ij}& \mathrm{for} \mathrm{stimulants}\end{array}\\ \begin{array}{cc}\underset{i}{\max }{r}_{ij}& \mathrm{for} \mathrm{destimulants}\end{array}\end{array}\right.$$

(3)

where:

$v_j^{+}$—j-th coordinate of the pattern (a positive ideal solution),

$v_j^{-}$—j-th coordinate of the anti-pattern (a negative ideal solution).

3.

Determination of the distance of the i-th object from the pattern and anti-pattern. Euclidean distance is most commonly used for this purpose:

$$D_i^{+}=\sqrt{{\displaystyle \sum }_{j=1}^k{\left(r_{ij}-v_j^{+}\right)}^2}$$

(4)

$$D_i^{-}=\sqrt{{\displaystyle \sum }_{j=1}^k{\left(r_{ij}-v_j^{-}\right)}^2}$$

(5)

where:

$D_i^{+}$—the distance of the i-th object from the common pattern (a positive ideal solution),

$D_i^{-}$—the distance of the i-th object from the common anti-pattern (a negative ideal solution).

4.

Calculation of the value of the synthetic measure for each object analyzed:

$$C_i=\frac{D_i^{-}}{D_i^{-}+D_i^{+}}$$

(6)

Synthetic measure ($C_i$) is normalised in the interval [0, 1], and the closer its value is to 1, the higher the level of the knowledge-based economy. Based on the value of the measure, objects can be classified into four typological groups according to the formulas [62]:

$$\mathrm{group} 1: C_i\ge \overline{C}+S\left(C\right)$$

(7)

$$\mathrm{group} 2: \overline{C}\le C_i<\overline{C}+S\left(C\right)$$

(8)

$$\mathrm{group} 3: \overline{C}-S\left(C\right)\le C_i<\overline{C}$$

(9)

$$\mathrm{group} 4: C_i<\overline{C}-S\left(C\right)$$

(10)

where: $\overline{C}$—arithmetic mean of taxonomic measure; $S\left(C\right)$—standard deviation of taxonomic measure.

The first typological group (the best) includes objects (countries, macro-regions, regions) with the highest values of the synthetic measure, while the fourth group (the worst) has the lowest values.

4. Results

Table 2. Countries (NUTS 0), Macroregions (NUTS 1), and regions (NUTS 2) by typological groups.

Countries	NUTS0	Ci	Typological Group	Macroregions Typological Group				Regions Typological Group
				1	2	3	4	1	2	3	4
Luxembourg	LU	0.676	1	1	0	0	0	1	0	0	0
Finland	FI	0.646	1	1	1	0	0	2	3	0	0
Denmark	DK	0.609	1	1	0	0	0	2	3	0	0
Belgium	BE	0.596	1	2	1	0	0	5	6	0	0
Netherlands	NL	0.572	1	1	2	1	0	4	8	0	0
Ireland	IE	0.555	2	1	0	0	0	1	2	0	0
Austria	AT	0.519	2	1	2	0	0	1	5	3	0
Germany	DE	0.513	2	4	5	6	1	9	12	17	0
Malta	MT	0.485	2	0	1	0	0	0	1	0	0
Estonia	EE	0.476	2	0	1	0	0	0	1	0	0
Slovenia	SI	0.469	2	0	1	0	0	1	0	1	0
Spain	ES	0.430	3	1	2	4	0	1	3	15	0
Portugal	PT	0.407	3	0	0	2	1	1	0	5	1
Greece	EL	0.401	3	0	1	3	0	0	2	7	4
Lithuania	LT	0.401	3	0	0	1	0	1	0	1	0
Hungary	HU	0.392	3	1	0	1	1	1	0	6	1
Czechia	CZ	0.378	3	0	0	1	0	1	1	5	1
Cyprus	CY	0.367	3	0	0	1	0	0	0	1	0
Poland	PL	0.353	3	1	0	5	1	2	2	9	4
Latvia	LV	0.346	3	0	0	1	0	0	0	1	0
Slovakia	SK	0.345	3	0	0	1	0	1	1	1	1
Italy	IT	0.341	3	0	0	3	2	0	2	15	4
Bulgaria	BG	0.303	4	0	0	1	1	0	1	0	5
Croatia	HR	0.253	4	0	0	0	1	0	0	4	0
Romania	RO	0.226	4	0	0	1	3	1	0	1	6
Total	-	-	-	15	17	32	11	35	53	92	27

provides the results of ordering and grouping the studied EU countries, macro-regions, and regions by the level of the knowledge-based economy.

Table 2 shows that the highest level of the knowledge-based economy is in the Benelux countries Denmark and Finland, namely countries that have been in the EU almost since its formation. These are highly developed countries which are at the forefront of EU countries in terms of GDP per capita. In the case of Luxembourg, which topped the ranking, all the indicators analyzed were above average. Two of them—the share of R & D personnel employed in the government sector (X₇) and the share of people with higher education employed in the science and technology sectors (X₉)—were at the highest level among the countries surveyed. Most of the analyzed indicators in the other countries in this group were above average (even at the highest level among all the investigated countries). However, there were also individual indicators with values much lower than the average.

Table 3. Strengths and weaknesses of the knowledge economy in Finland, Denmark, Belgium, and the Netherlands.

Country	Strengths of the Knowledge-Based Economy	Weaknesses of the Knowledge-Based Economy
Finland	the share of people participating in courses and training in the population aged 25–64 (${\mathrm{X}}_2=29\%$). the share of the number of scientists and engineers in the population aged 15–74 years (${\mathrm{X}}_{10}=7.4\%),$ the rate of employment growth in scientific, technical, and administrative and service activities (${\mathrm{X}}_{11}=18.5\%)$.	the share of gross fixed capital formation in scientific, technical, and administrative and service activities in total gross fixed capital formation (${\mathrm{X}}_{12}=3.8\%$ at the average 6.8%).
Denmark	gross R & D expenditure per capita(${\mathrm{X}}_4=1568.6$ Euro/person), the share of R & D personnel employed in the higher education sector in total employment (${\mathrm{X}}_8=0.8\%)$.	the share of R & D personnel employed in the government sector in total employment (${\mathrm{X}}_7=0.079\%$ at an average of 0.158%), the rate of employment growth in scientific, technical, and administrative and service activities (${\mathrm{X}}_{11}=4,3\%$ at the average 5, 64%).
Belgium	the share of gross fixed capital formation in scientific, technical, and administrative and service activities in total gross fixed capital formation (${\mathrm{X}}_{12}=12.2\%)$.	the rate of employment growth in scientific, technical, and administrative and service activities (${\mathrm{X}}_{11}=2,4\%$ at the average 5.64%), the share of people participating in courses and training in the population aged 25–64 years (${\mathrm{X}}_2=8.2\%$ at an average of 10.5%)
Netherlands	the share of the number of scientists and engineers in the population aged 15–74 years (${\mathrm{X}}_{10}=7.4\%$).	the share of R & D personnel employed in the government sector in total employment (${\mathrm{X}}_7=0.107\%$ at an average of 0.158%) the rate of employment growth in scientific, technical, and administrative and service activities (${\mathrm{X}}_{11}=4,1\%$ at the average 5.64%)

presents indicators whose level has identified the strengths and weaknesses of the knowledge-based economy in these countries. Strengths are represented by indicators with the highest values among the examined countries, and weaknesses—indicators with values significantly below average. It should be emphasized that in all countries from this group, the strength of the knowledge-based economy was the share of people with higher education in the population aged 25–64 (X₁), which exceeded 40%, with the average among the surveyed countries at the level of 34.3%.

The level of the knowledge-based economy in the second typological group is above the average of the countries studied, but slightly lower than in the first group. The top place in this group is occupied by Ireland, whose advantage is the highest percentage of people with higher education among the surveyed countries (X₁ = 47.3%) and the largest share of employees in the knowledge and technology sector in total employment (X₅ = 8.1%). By contrast, the weakest side of the knowledge-based economy in Ireland is the very low share of R & D staff employed in the government sector in total employment (X₇ = 0.053% at an average of 0.158%). In the case of Austria and Germany, the strengths of the KBE are mainly the high share of gross fixed capital formation in scientific, technical, and administrative and service activities in total gross fixed capital formation (X₁₂ above 10% with an average of 6.54%) and the high gross R&D expenditure per capita (X₄ above 1300 Euro/person with an average of 461 Euro/person). In contrast, the share of people with tertiary education, both in the population aged 25–64 (X₁) and in the science and technology sectors (X₉), and the growth rate of employment in scientific, technical, administrative, and service activities (X₁₁) are below average in these countries. Malta, on the other hand, was in this group due to the highest share of gross value added from scientific, technical, and administrative and service activities in total gross value added among the countries surveyed (X₃ = 17.2%) and a very high rate of employment growth in scientific, technical, and administrative and service activities (X₁₁ = 16.6% with an average of 5.64%). The weakest side of Malta’s economy is the lowest share of R & D staff employed in the government sector in total employment (X₇ = 0.072%) among the countries surveyed. The strength of the Estonian economy is the very high share of people with higher education in the population aged 25–64 (X₁ = 41.4%). At the same time, the weakness is the very low growth rate of employment growth in scientific, technical, and administrative and service activities (X₁₁ = 1.1% with an average of 5.64%). On the other hand, Slovenia was included in this group, as most of the analyzed indicators were above or close to the average among the examined countries. Only two indicators (X₈—the share of R & D staff employed in the higher education sector in total employment, X₁₁—the rate of employment growth in scientific, technical, and administrative and service activities) were clearly below average. A characteristic feature of the four countries from this group (Germany, Austria, Estonia, Slovenia), and at the same time the weakness of their knowledge-based economy, is the very low growth rate of employment in scientific, technical, and administrative and service activities (X₁₁ is in the range from 1.1% to 4.4%).

The third most numerous group includes not only the countries admitted to the EU after 2004, but also the Southern European countries admitted to the EU much earlier (Greece, Italy, Spain, Portugal), and it is three of these countries that occupy the top positions in this group. The level of most of the analyzed indicators in the countries in this group was below average, but there were indicators whose level distinguished some of these countries. An example of such an indicator is the growth rate of employment in scientific, technical, and administrative and service activities (X₁₁), which in as many as seven countries was well above the average, and in four of them was even above 7% (Greece 9.8%, Cyprus 8.7%, Poland 8%, Spain 7.3%). Additionally, the share of people with higher education in the population aged 25–64 (X₁) in two countries exceeded 40% (Cyprus, Lithuania), and in Spain, it was at the level of 38.6%. In four countries, the share of gross fixed capital formation in scientific, technical, and administrative and service activities (X₁₂) was well above average—in Portugal and Spain, it exceeded 8%, and in Lithuania and Italy, it was 7.94% and 7.79%, respectively. Three countries in this group (Italy, the Czech Republic, Hungary) had a high share of R & D personnel employed in the enterprise sector in total employment (X₆), Greece had a high share of R & D personnel employed in the government sector in total employment (X₇). Italy’s last place in this group may be puzzling, but unfortunately, as many as eight indicators in this country were clearly below average.

The last group with the lowest level of the knowledge-based economy included three countries from south-eastern Europe (Bulgaria, Romania, Croatia), which joined the EU at the latest, i.e., in 2007 and 2013. In these countries, almost all indicators were well below average. In Romania (the last country in the ranking), most of the indicators were at the lowest level among the countries surveyed—for example, the share of people with higher education in the population aged 25–64 (X₁) was 18.4% (average in the surveyed countries 34.8%), and gross expenditure on research and development activities per capita (X₄) was 55 Euro/person, while the average in the surveyed countries was at the level of 641 Euro/person. In Bulgaria, only the share of R & D staff employed in the government sector in total employment (X₇) was above average. On the other hand, in Croatia, two indicators were slightly above the average (X₇—the share of research and development personnel employed in the government sector in total employment, X₁₂—the share of gross fixed capital formation in scientific, technical, and administrative and service activities in the total gross fixed capital formation), but in this country, the most significant decline in employment in scientific, technical, and administrative and service activities was recorded—the growth rate of employment in scientific, technical, as well as administration and service activities (X₁₁) amounted to −12.3%.

The spatial distribution of countries due to the level of KBE is presented in Figure 1.

When analyzing the knowledge-based economy at lower levels of aggregation (macro-regions, regions), due to a large number of studied objects, the focus was primarily on indicating the strengths and weaknesses of the extreme typological groups (first and fourth).

Seventy-five macro-regions were analyzed in the study (Table 2). Their number in individual countries varies from 16 (Germany) to 1—in the case of as many as 12 countries surveyed. The highest levels of the synthetic measure, i.e., a high level of the knowledge-based economy (group 1), were recorded for 15 macro-regions from eight countries: Belgium, Germany, Finland, Spain, Luxembourg, Hungary, Denmark, Austria, Poland, and Ireland. The countries admitted to the EU before 2004 prevail. Apart from three macro-regions covering entire countries (Luxembourg, Denmark, Ireland), as many as eight of them cover capital cities (Belgium, Germany, Finland, Spain, Hungary, Netherlands, Austria, Poland). It stems from the fact that due to their specific character, they concentrate in a relatively small area, both a large number of economic entities and universities and research and development units. It is worth noting that two macro-regions in Belgium and four areas in Germany are classified in Group 1. Group 1 macro-regions are primarily characterized by high values of four human capital indicators (group averages are in parentheses): X₁—share of people with higher education in the population aged 25–64 (average 42.5%); X₅—share of the employed in the knowledge and technology sector in total employment (average 6.2%); X₉—share of people with higher education employed in science and technology sectors (average 20.5%); and X₁₀—the share of the number of scientists and engineers in the population aged 15–74 years (average 6.9%). It indicates their high potential in this area.

In the second typological group, 17 macro-regions belonging to ten countries are found with values of most of the examined indicators above the average, with the largest number of macro-regions located in Germany (5), Netherlands (2), Austria (2) and Spain (2). On the other hand, the third most numerous typological group included 32 macro-regions, located mainly in Germany, Poland, and Spain.

The group of macro-regions with the lowest level of the knowledge-based economy (group 4) includes 11 macro-regions from eight countries: Germany, Hungary, Italy, Portugal, Poland, Croatia, Romania, and Bulgaria. Against this background, Romania stands out with three macro-regions in this group out of four and Italy with two macro-regions out of five. Most of the macro-regions in the analyzed group are characterized by a low value of GDP per capita, constituting about 61% of the EU average. The analysis of the values of diagnostic features for this group shows that their levels were clearly lower than in other typological groups for all indicators. Three of them, in particular, are prominent (group averages are given in parentheses): X₁—the share of people with higher education in the population aged 25–64 (average 18.2%); X₉—the share of people with higher education employed in the science and technology sectors (average 8.6%), and X₁₀—the share of the number of scientists and engineers in the population aged 15–74 (average 2.9%). They indicate that the main limitation in developing knowledge-based economies of these macro-regions is the low potential of human capital.

Figure 2 shows the spatial distribution of typological groups of the analyzed macro-regions regarding the level of the knowledge-based economy.

Moving to the lowest level of aggregation (NUTS2), out of the 207 regions analyzed, 35 (about 17% of the surveyed regions) were classified into the group with the highest level of the knowledge-based economy. They are located in 17 countries, with Germany (9), Belgium (5), and the Netherlands (4) being the most prominent. It should be noted that 29 of them belong to the macro-region classified into group 1 (see Table 2), among which the country capitals are located.

Very high scores for the synthetic measure in this group were recorded for regions accepted after 2004, including those from Poland, the Czech Republic, Hungary, and Slovakia. The analysis of the value of diagnostic features shows that the high position of two regions from Poland is a consequence of the very high growth rate of employment in scientific, technical, and administrative and service activities (X₁₁), which is several times higher than in other regions. In the case of the Czech Republic and Hungary, the highest indicators in this group are noteworthy: X₅ (the share of knowledge and technology employees in total employment), X₆ (the share of R & D personnel employed in the enterprise sector in total employment), X₇ (the share of R & D personnel employed in the government sector in total employment). The high position of the region from Slovakia is a consequence of the highest value of X₈ (the share of research and development personnel employed in the higher sector in total employment) and a very high rate of employment growth in scientific, technical, and administrative and service activities (X₁₁). In the regions of North-West and Central Europe (Austria, Germany, Belgium, Denmark, the Netherlands), there are high gross expenditures on research and development activities per capita (X₄), significantly exceeding the average value of the surveyed regions. A similar situation (except for Denmark) concerns the X₁₂ ratio (the share of gross fixed capital formation in scientific, technical, and administrative and service activities in general gross fixed capital formation).

Regions with the lowest KBE (group 4) belong to 9 countries, and most of them were admitted to the EU after 2004. The exceptions here are regions from Italy (4), Greece (3), and Portugal (1), but it should be remembered that these countries are struggling with both economic and social problems. Most of the regions from the analyzed group come from Romania (6) and Bulgaria (5), i.e., from the poorest countries of the community. The values of the diagnostic features of the analyzed group of regions reached particularly low levels in the case of features: X₂—the share of people participating in courses and training in the population aged 25–64 (2.8%); X₄—gross expenditure on research and development activities per capita (60 Euro); X₆—the share of research and development personnel employed in the enterprise sector in tin total FTE employment (0.18%); and X₉—the share of people with higher education employed in the science and technology sectors (8.3%). It means that in the case of these regions, the problem is not only the low potential of human capital but also the underinvestment of sectors of the knowledge-based economy, which is particularly unfavorable because the lack of an adequate “material base” causes a slowdown in the development of the knowledge economy, especially with a low level of human capital.

The spatial distribution of typological groups of the analyzed regions in terms of the level of knowledge-based economy is shown in Figure 3.

The conducted analysis clearly shows the spatial connections of the level of knowledge-based economy between individual levels of aggregation. In the EU countries which are at the forefront of the ranking due to KBE, most macroregions and regions also took the leading positions in the rankings (see Table 2 and Figure 1, Figure 2 and Figure 3). A similar situation applies to EU countries with the lowest KBE levels—in these countries, the majority of areas (macroregions, regions) have low values of the synthetic measure, which consequently assigns them to the fourth (or third) typological group. It follows that the national KBE level is the product of its regional levels.

5. Discussion

The knowledge-based economy is a gradual transition from a material-intensive economy to one that exploits the potential of science and information. Intangible resources are gaining importance, especially human capital, knowledge, and new technologies. According to Kukuła [52], the KBE can be considered a strategy for economic development in the 21st century. However, this does not mean that it is or will be available in the future to all countries interested in this development path. Opportunities for the development of the KBE are mainly for developed countries and some developing countries. The operation and development of the KBE require a specific technical and human potential (infrastructure, high technology, highly-skilled society, developed R & D).

The findings in this study also confirm that. The highest level of the knowledge-based economy is found in the Benelux countries and Denmark and Finland, i.e., in highly developed countries, which in terms of GDP per capita are among the top EU countries. The lowest are in the economically less-developed countries of south-eastern Europe (Bulgaria, Romania, Croatia).

Some interesting thoughts on the relationship between the knowledge economy and economic growth can be found in the paper by Barkhordari et al. [68]. The authors, con-ducting research in the countries of the MENA region (the Middle East and North Africa), identified the most important pillars of the KBE, including institutions and research, human capital, and innovation. In their opinion, education, including higher education, as well as research and development, is important for achieving a high rate of economic growth. The KBE needs an educated and skilled population to create and use knowledge [69,70]. The links between human capital and economic growth were also analyzed by [71,72,73,74,75,76,77].

Qadri and Waheed [78] and Runiewicz-Wardyn [79] demonstrated the effect of education level on KBE. Such a relationship was also identified in the present study. It turned out that in countries, macro-regions, and regions with the highest level of the knowledge-based economy, the share of people with higher education is at a very high level and in many cases exceeds 40%, while in countries, macro-regions, and regions with a low level of the KBE, this share is rarely above 20%. The appropriate level of education of the society translates into highly qualified staff, which is an essential factor necessary for the proper development of the KBE both at the state level [80,81] and their regions. It was also confirmed by the present study, as it was found that the high growth rate of R & D employment, among other things, resulted in two regions from Poland and one region from Slovakia being ranked in the first typological group. The high position of the Czech and Hungarian regions is also a consequence of the relatively high share of research and development personnel employed in the enterprise and government sectors. These regions are the capitals of these countries and one metropolitan area. These areas [82], acting as a magnet, attract human resources [83,84,85,86], including highly skilled ones seeking better living conditions [87,88]. Unfortunately, this contributes to a drain on the labor force from other regions, which lose a vital part of their potential [89]. This phenomenon is noticeable both for regions from the countries of the former Eastern Bloc that joined the EU after 2004, as well as in the so-called “old Union”, e.g., in Portugal, Spain, or Germany—this is particularly visible between regions from the western and eastern parts of Germany (see [90,91]).

According to the literature review [92,93], as well as the research conducted in this study, it can be concluded that the level of the knowledge economy is essentially a consequence of high gross expenditures on research and development activities per capita (Denmark, Germany, Austria) and high gross fixed capital formation in scientific, technical, and administrative and service activities in total gross fixed capital formation (Belgium, Germany, Austria). The high level of these two indicators was also observed in the regions of North-Western and Central European countries. These countries and their regions were included in the first typological group, i.e., the group with the highest KBE level. Thus, we can see that the appropriate regional policy of countries in the field of financing research and development activities translates into a high level of the KBE at lower levels of aggregation. The important factors influencing the level of KBE in the regions include the interconnection of enterprises, the scientific environment, and the institutional business environment, which should be actively supported by local authorities and supported by the state’s regional policy [11,94,95,96]. According to the research on the level of the KBE in EU countries, it is clear that very high and high levels of the KBE dominate in north-western and central European countries. The economies of these countries are much more based on services [97] than on industry or agriculture. In the case of Luxembourg, Finland, and Denmark, i.e., the countries with the highest level of the synthetic measure, the services sector in 2019 generated approximately 79.2%, 60%, and 64.8% of the gross value added of the entire economy, respectively [98].

Another important issue related to the KBE is its impact on competitiveness. Sum and Jessop [99] believe that knowledge-based economies create an environment where competition is crucial. Facing the increasing competition forces the creation and introduction of innovations on the market, which requires the society to raise the level of education continuously, have appropriate competencies and create and absorb new knowledge [10,100]. It is crucial for sectors related to new technologies (high-tech). In order to test how the level of the KBE translates into competitiveness and innovation, the strength and direction of the relationship between these phenomena was examined by comparing the values of the synthetic measure at the regional level with the values of the Regional Competitiveness Index (RCI) [101] and the Regional Innovation Index (RII) [102], and at the country level with the Global Competitiveness Index (GCI) [103]. The values of the calculated Pearson linear correlation coefficients (r), which for the regions amounted to r(C_i, RCI) = r(C_i, RII) = 0.70, and for countries r(C_i, GCI) = 0.83, indicate a strong positive correlation between the studied phenomena, so the higher the KBE level, the higher the competitiveness and innovation. Similar conclusions concerning competitiveness are found in Dima et al. [104] and Skórska [105], among others, and in relation to innovation in the report by Bramanti and Tarantola [106]. The results on the spatial distribution of regions by the KBE level and competitiveness level obtained in this study were confirmed by the research conducted by Aria et al. [107]. Their research shows a clear difference in competitiveness level between the countries of northern Europe and the countries of southern and eastern Europe.

The analyses show that the level of the KBE in the studied EU countries, and thus its impact on the social, economic, and political life of societies, is highly geographically diversified. Meusburger [108] is of a similar opinion, arguing that knowledge, technological possibilities, and skills are never evenly distributed in space. It turns out that the spatial differentiation of regions resulting from the research on the level of the knowledge-based economy translates into their similar differentiation due to the socio-economic situation. The results obtained by del Campo et al. [109], who through the methods of multivariate statistical analysis, distinguished the following four groups of EU regions differing in their socio-economic situation: the regions of north-central Europe, regions including national capitals, the regions of southern Europe, and the regions of eastern Europe. Comparing these results with the results obtained in this research, it can be noticed that most regions with a high KBE level had a very good or good socio-economic situation, and the regions with a low KBE level had a much worse situation. In light of the presented results, it is worth noting that in the case of Germany, Belgium, and the Netherlands, there is greater dispersion in terms of the level of KBE development among the regions and macro-regions studied. On the one hand, it should be interpreted as the effect of activities carried out as part of sustainable regional development [20], both at the central and regional level of countries and the entire EU [110,111]. On the other hand, the observed phenomenon should be classified as the next stage in developing the KBE economies, amounting to an easier and more efficient allocation of available resources [112].

6. Conclusions

Based on the conducted research, it can be concluded that there is a pronounced variation in the level of knowledge economy at different levels of aggregation in the European Union. It is highest in north-western and central European countries and lowest in eastern and south-eastern European countries. This regularity also applies to macro-regions and regions. In the countries with the highest level of the KBE, all macro-regions and regions belong to at least the second typological group (except the Netherlands—one macro-region in group III). On the other hand, in countries with the lowest KBE level, all macro-regions and regions are in the third group at most, except for one region in Romania (the national capital), which was included in the first typological group. For countries in the other typological groups (II and III), the distribution of the number of macro-regions and regions by the level of the KBE is not so explicit.

A good example is Germany, with a high KBE level, where 6 macro-regions and 17 regions belong to the third typological group, i.e., with a low KBE level and one macro-region (DEE—Sachsen-Anhalt) is even in group IV. These administrative units are located mainly in the eastern part of Germany, which belonged to the GDR before October 1990. Another regularity is worth noting—if in the countries of the third or fourth typological group there is a region from group I (with a very high level of the KBE), it is the region with the capital of the country. According to the authors, the analyses of spatial connections at different levels of aggregation are very valuable, especially in the case of countries with a large number of macroregions and regions, as they enable the identification of those areas where there are clear differences in the level of the phenomenon under study. Thanks to this approach, it is easier to identify and analyze the causes of disproportions in the studied area. This, in turn, may translate into actions taken by national and regional authorities aimed at reducing these disproportions, which is in line with the idea of sustainable development.

Another important conclusion that can be drawn based on the conducted research is the fact that a very high and high level of the KBE was observed in countries that have been in the EU almost since the beginning. In contrast, the countries that joined the EU in 2004 and later, unfortunately, have a low or even very low level of the KBE. However, in some countries in this group, some symptoms may indicate that they are trying to improve this level. For example, in Lithuania, the share of people with higher education in the population aged 25–64 and the share of gross fixed capital formation in scientific, technical, administrative, and service activities is significantly above the average for the analyzed countries. In Poland, a high rate of employment growth was observed in scientific, technical, and administrative and service activities. In contrast, in the Czech Republic and Hungary, a high share of research and development personnel employed in the enterprise sector in total employment was recorded.

In summary, it should be stated that the identified substantial disparities in the level of the KBE both at the level of countries, as well as macro-regions and regions of the EU are also translated into their socio-economic situation, and thus into competitiveness and innovation, not only at the Community level but also globally. In order to balance these disparities, it is necessary, first and foremost, to increase spending on research and development activities and invest in human capital and new technologies. On the one hand, these are activities that are part of the sustainable development strategy of the entire European Union. On the other hand, they should be coordinated and supported at the level of individual countries to use their potential effectively.

An important contribution of this article, due to the fact of the study covering various degrees of aggregation, is also the indication of the main pillars shaping the level of the KBE, such as:

• the appropriate level of education of the society and creation of conditions for lifelong learning,
• ensuring the highest possible expenditure on research and development,
• creating conditions to facilitate transfers of educated workers to work in R & D activities.

The analyses conducted in the paper, especially at lower levels of aggregation (in regions), revealed the need for additional empirical research to obtain detailed information at the level of enterprises in terms of their innovative activities and their role in creating the KBE. Although this is a crucial task from the point of view of the studied phenomenon, it is not easy to achieve, if only due to the availability of substantive and comparable statistical data. The authors are aware of this limitation, which is also a barrier to answering the questions that have already arisen during the analysis of the results: what are the similarities of individual countries and regions in terms of the structure of the economy and how does this structure affect the level of the KBE? An attempt to answer these questions is the next stage of the research conducted by the authors of this paper. In the context of future research, it is also vital to search for new indicators that may affect the knowledge-based economy level and combine quantitative analysis based on existing data with qualitative research conducted among public institutions (e.g., universities, research centers, innovative enterprises). This type of approach will significantly complement research related to the KBE.