The Cooperativity and Spatial Network Relationship Between Regional Economic Quality Development and Higher Education Scale in China

Abstract

The sustainable development of regional higher education is closely related to the level of regional economic development. There is a close interdependence between higher education and economic development. Based on data from 31 provinces in China in 2022, this study uses the entropy method to construct an evaluation index system to explore the coupling and coordination relationship between the regional economy and the development of higher education, as well as the social network effects presented by various regional cities. The results indicate the following: (1) there is a trend of a more coordinated relationship between economic development and the development of higher education in the eastern region compared to the western region, exhibiting a pattern of “higher in the east and lower in the west”; and (2) the economic development and the scale of higher education in East China and Central China are coordinated, and some provinces have played a role in bridging and internal and external linkages in the spatial network effect, economic development, and the scale of higher education in some provinces in Northwest and Southwest China to moderate the imbalance and weak internal and external linkages in the spatial network. Exploring the compatibility between the scale and structure of higher education and economic development is not only of guiding significance for promoting the regional layout and development of higher education in China, but also has an important reference value for economic structural adjustment and transformation.

1. Introduction

The coordinated development of regional economic quality and the scale of higher education is the inherent requirement for the integration of national education and technological talents. The unbalanced development of higher education is a “social fact” in a static sense and a dynamic process of historical generation. In the 1960s, Schultz proposed the concept of human capital [1]. He believed that the quality of human capital is the product of investment, and the accumulation of human capital is the source of economic growth. All levels of formal education, vocational and technical education, medical and health care, etc., are important contents of human capital investment. As a comprehensive indicator to measure the size, structure, and quality of the population, human capital determines the speed and quality of economic growth in a country and region. Higher education can effectively promote technological innovation and high-quality economic and social development by promoting the accumulation of human capital. As early as 1983, Burton Clark proposed a classic “triangular coordination model” of higher education system analysis in his book Higher Education System—A Cross-border Study of Academic Organizations [2], in which market regulation as an invisible hand has its internal laws. The imbalance between supply and demand and the relationship with competition are the fundamental driving forces of economic development. The sustainable development of the economy in the market model has played a role in promoting the development of higher education. The New Economic Growth Theory further reveals the coordinated relationship between higher education and economic growth, where the coordinated relationship refers to the correlation between elements, which is the foundation for stable and sustainable systems. It reflects the degree of positive correlation within systems and indicates the quality of the coordination situation [3].

In the 1970s, American educational sociologist Martin Trow proposed a famous theory regarding the popularization of higher education whereby the development process of national higher education can be divided into three stages: the elite stage (with a gross enrollment rate below 15%), the massification stage (with a gross enrollment rate between 15% and 50%), and the universal stage (with a gross enrollment rate above 50%) [4]. In 1999, the Chinese government issued the “Decision on Deepening Education Reform and Comprehensively Promoting Quality Education”, making significant deployments to expand the admissions of universities. By 2002, the number of students increased by 3.2 million, and the enrollment rate of higher education in our country reached 15%. Higher education entered the massification stage ahead of schedule [5]. In 2019, the total number of students in general colleges of Chinese higher education exceeded 9 million, and the gross enrollment rate had surpassed 50% [6]. Since then, China’s higher education has entered the universal stage from the massification stage, the education level of the people has been dramatically improved, and the talent reserve has become increasingly abundant, which has extensively promoted the development of the economy and society. However, there are also various imbalances in higher education. (1) Due to the geographical location, the northwest and southwest regions have vast landforms, steep terrain, a harsh environment, a sparse population, and a relatively weak economic growth foundation, and there are serious deficiencies in funds and infrastructure in western universities [7] (Figure 1). (2) The underdeveloped regions have backward economic development models and industrial structures, and the disconnect between talent cultivation and market demand has led to insufficient driving force for the development of higher education, which has affected the sustainable and sustainable development of higher education [8]. (3) High-quality teachers and students often gather in economically developed areas and universities, while underdeveloped areas have limited capacity to attract higher education talents and face long-term problems such as teacher shortages and difficulties in talent introduction [9]. These have led to the coexistence of opportunities and crises in China’s higher education and economy.

For a long time, in China, the largest developing country in the world, higher education and regional economic development have had a long-standing uncoordinated relationship. Either the economy takes precedence over education or education takes precedence over the economy. Internationally, this phenomenon seems to exist in every country [10,11]. In addition, there are still many problems in coordinating the regional economy and the development of higher education. On the one hand, the maximum quantity and quality of human capital that each region can sustain during a specific period affects the quality of the economy and the development of higher education. On the other hand, increasing regional talent capital and enhancing regional technological innovation capabilities through higher education activities can promote economic growth. According to statistics from China’s education department, there is a big gap in the distribution of education resources in various regions of China. This is evidenced by a polarization in funding, university infrastructure, and faculty development. Moreover, the local employment rate for college graduates in the eastern region exceeds 90%, whereas in the western and central regions, these rates are significantly lower at 80% and 73.1%, respectively [12].

The economy is an important indicator to measure the development level of a region, and the level of higher education determines the development potential of a region. Social and economic development has a decisive effect on the development of education, and there are inextricable links between the economy and the scale of higher education. Some researchers used co-integration analysis and the Granger test [13], a time series perspective to establish the error correction mode [14], the stepwise regression method [15], the non-linear causality test [16], and other methods to verify that economic development has an impact on higher education. This includes the impact of changes in the scale of education and the causal relationship between the two; the economic factors that affect the scale of higher education in various regions; and the coordinate analysis of the relationship between the scale of higher education and economic development. There is a relatively balanced relationship between the scale of higher education and economic development, but due to differences in regional development, the degree of this balanced relationship is different. Although these methods have a certain scientific validity in their use, they have not yet further revealed the coupling and coordination relationship between economic quality and higher education development in various regions of China, as well as the regional distribution characteristics. The core of new economic geography is to explain the spatial distribution and agglomeration of economic activities, and to further promote the changing of the unbalanced economic geography pattern of each region [17]. It is evident that the coordinated development of regional economic quality and the scale of higher education is a strategic demand for the integrated development of China’s national education, science, and technology, as well as talent. On the one hand, this study reveals the disparities and issues across China’s regions, offering insights to meet the needs of regional economic and industrial development. This aims to reduce regional disparities and foster sustainable social development. On the other hand, it can make colleges and universities more accurately grasp the needs of regional economic and social development, reasonably plan the distribution of higher education institutions, disciplines and specialties, promote the matching of education supply and industrial demand, and realize the benign interaction and coordinated development of regional economy and higher education, so as to improve the social recognition and influence of higher education.

Considering the relationship between economic development and the scale of higher education, this study can provide specific research value for the sustainable development of higher education. Thus, this study aims to systematically establish an evaluation index system for the development of regional economies and higher education based on existing research. This research will select the entropy method and coupling coordination coefficient as evaluation methodologies and will employ social network analysis to aid in the examination of regional development. This study will focus on exploring the following questions: (1) Is there a coordinated relationship between the economic quality development of various regions in China and the existing scale of higher education?; and (2) What are the characteristics of various regions in China under the dual influence of economic quality development and the higher education scale?

2. Literature Review

Higher education and economic development promote each other, they are restricted by socio-political and economic development, and they must serve socio-political and economic development.

2.1. The Impact of Economic Development on Higher Education

The development of modern society is centered on the economy. Economic development provides a solid material foundation and financial guarantee for the expansion of the higher education scale, while, conversely, the expansion of the higher education scale provides intellectual support and talent guarantee for economic development. Mr. Pan Maoyuan proposed the law of internal and external relations of higher education, emphasizing that “education should be adapted to the development of people” and “education should be adapted to the development of society”. This also highlights the internal subsystems of the education system, the education system itself, and other systems, reflecting the group’s dialectical symbiosis and overall synergy [18]. Both the economy and higher education play an essential role in social development. But today, the misconception that the so-called “development” in higher education is still largely equated with “growth” still exists. In higher education, “quality, such as ‘freedom’ or ‘fairness’”, is an elusive concept [19]. The relationship between higher education institutions and the regional economic development process is analyzed from the perspective of historical development and evolution [20]. In the universal view of higher education, underdeveloped areas have relatively backward cultural, talent, and educational concepts. Enrollment expansion has significantly improved higher education entrance opportunities, but it has not promoted the fair development of education and the distribution of educational opportunities [21,22]. Higher education and the development of economic quality are both a real problem, and the unfairness of higher education caused by this is also a considerable challenge. On the one hand, there is indeed an imbalance between education and economic development [23,24], and the development of higher education will encounter a crisis. On the other hand, an important reason for the lack of coordination between higher education and regional economic growth is that the professional settings at universities are disconnected from market demands, lacking an effective market mechanism [25,26]. If each region gains increasing autonomy in addressing its social and economic issues, formulating regional strategies, and assessing the university’s capacity to respond to regional development challenges [27,28], the imbalance in the development of higher education may be alleviated.

Some scholars have studied the impact of economic development on the development of higher education. From the perspective of sustainable economic development, the GDP index can describe the current state of economic development and has a good prediction function for regional economic development [29]. Sachs (2017) found that the technological revolution transferred the national income of the United States from labor to capital (material, human, and intellectual capital), and brought about the inequality of the return of production factors [30]. The unbalanced employment structure has a significant negative impact on regional economic development [31]. Fang’s research results show that human capital, technology level, and foreign investment have a significant impact on export technology complexity [32]. In particular, the quality structure of the population, innovation ability, and pension service industry have a positive effect on the regional industrial development and layout content [33].

2.2. The Impact of Higher Education on Economic Development

The long-term development of higher education in any university is deeply influenced by historical and cultural accumulation. The differences in the natural environment, economic population, industrial technology, and other aspects caused by geographical differences in China have resulted in imbalances in higher education. Human capital is considered to be one of the main determinants of economic growth [34]. The expansion of the scale of higher education, especially the rapidly expanding group of college students, has provided a huge impetus for the accumulation of high-quality human capital in China, which is an important factor for China’s economy to take off [35]. Teachers have played an important role in promoting the development and application of new technologies and new products, promoting high-quality economic developers, forming a talent agglomeration effect, and promoting regional scientific and technological innovation and industrial upgrading [36]. Whether it is the number of enrollments or employment services, the development of higher education in the northwest and southwest regions has not matched overall growth. It is inferior to the development of the eastern coastal and central areas. Higher education has different effects in regions with other economic development levels. The scale of the development of higher education institutions has a positive and significant impact on regional economic growth [37], and education expenditures and education policies are the guarantee of economic growth and support economic development, for which they lay a solid foundation [38]. In particular, Chinese research universities play an active role in developing the urban circular economy. The positive significance of this depends on the scale of the university’s R&D funding and the degree of academic support to promote regional economic development [39]. In areas with high levels of economic development, the spillover effect of technological innovation on economic growth can be more present. At the same time, in higher education, vocational education has also played an active role in promoting financial employment. Vocational education can meet the needs of regional development and promote regional economic development [40], provided that the internal market needs are fully considered in the changes [41].

3. Research Methodology

3.1. Data Source and Index Selection

The primary data for the study comes from the public data related to the economic development and the scale of higher education in 31 provinces in 2022 from the National Bureau of Statistics of China, the Ministry of Education of China, and the website of China Statistical Yearbook.

The research divides the economic development indicators into five significant aspects: monetary aggregate, labor employment, urban structure, foreign economy, and social welfare. The scale indicators of higher education are mainly divided into the number of higher education institutions, the number of students in higher education institutions, and the number of faculty members and staff in higher education institutions. The primary measurement variables are shown in

Table 1. Regional economic quality development and higher education development index system.

First Level Index	Secondary Indicators	Three-Level Indicators	Direction
Economic development	Economic Income	X1: Gross Regional Product (100 million yuan)	+
		X2: General budget revenue of local finance (total) (ten thousand yuan)	+
		X3: Total industrial profit (100 million yuan)	+
		X4: Total profit of construction industry (ten thousand yuan)	+
		X5: Total retail sales of consumer goods (100 million yuan)	+
		X6: Foreign exchange income from international tourism (millions of dollars)	+
	Social employment	X7: Number of employed persons in urban areas (10,000 people)	+
		X8: Urban registered unemployed persons (10,000 people)	-
	Regional population and flow	X9: Total population at the end of the year (10,000 people)	+
		X10: Urban population density (person/km2)	+
		X11: Passenger volume of transportation, post and telecommunications (10,000 people)	+
		X12: Number of inbound tourists received (10,000 person-times)	+
	Social Welfare	X13: Number of people participating in basic pension insurance at the end of the year (10,000 people)	+
		X14: Number of health institutions (a)	+
		X15: Total number of people participating in urban basic medical insurance at the end of the year (10,000 people)	-
Higher education scale	Number of higher education institutions	Y1: Number of ordinary colleges and universities	+
		Y2: Number of adult higher education institutions in the central sector	+
		Y3: Number of other private higher education institutions	+
	Number of higher education students	Y4: Number of graduate students enrolled in institutions of higher learning (persons)	+
		Y5: Number of postgraduate students in institutions of higher learning (persons)	+
		Y6: Number of students enrolled in colleges (institutions) for undergraduates and junior colleges (persons)	+
		Y7: Number of undergraduate and junior college students (persons) in colleges and universities (institutions)	+
	Number of faculty members and staff in higher education	Y8: Number of faculty members and staff in higher education institutions (institutions)	+
		Y9: Total number of teachers hired outside school (persons)	+

. The first-level indicators are mainly economic development and the scale of higher education. The second-level and third-level indicators are classified as follows. The positive arrows “+” indicate that the elements promote each other with economic development, and the negative indicators ”-” are the opposite result of high-quality economic development.

3.2. Model Building

3.2.1. The Model for Measuring the Degree of Coupling and Coordination Between Economic Development and the Scale of Higher Education

(1)

Data standardization

Since the economic index data obtained in this study and the higher education index data have different units and numerical values, the overall data need to be processed in a dimensionless manner. First, the information needs to be standardized, and the range method used in the research is to standardize the data.

The positive index is calculated by:

$$Y_{ij}={\displaystyle \frac{X_{ij}-\min \left(X_i\right)}{\max \left(X_i\right)-\min \left(X_i\right)}},X_i=x_1,x_2,\cdots ,x_i$$

(1)

The negative index is calculated by:

$$Y_{ij}={\displaystyle \frac{\max \left(X_i\right)-X_{ij}}{\max \left(X_i\right)-\min \left(X_i\right)}},X_i=x_1,x_2,\cdots ,x_i$$

(2)

where Xi represents the index value of the i index, Xij represents the index value of i index in the j area, and Yij represents the standardized result of the Xij index value.

(2)

Determine the weight of each index based on the entropy weight method

$$Wi={\displaystyle \frac{1-Ei}{{\displaystyle \sum _{i=1}^n(1-Ei)}}}$$

(3)

$$E_i=-\ln {\left(n\right)}^{-1}{\displaystyle \sum _{i=1}^n{p}_{ij} \ln p_{ij}}$$

(4)

$$Pij=Yij/{\displaystyle \sum _{i=1}^{n}Yij}$$

(5)

Pij represents the sample index weight, Ei represents the information entropy of the i index, n represents the number of samples, and Wi represents the weight of different samples obtained by the entropy weight method.

(3)

Coupling coordination degree calculation

$$C=2{\displaystyle \frac{\sqrt{f(x)\times g(y)}}{f(x)+g(y)}}$$

(6)

where f(x) represents the coupling coefficient of economic development, and g(x) represents the coupling coefficient of the higher education scale. The larger the value of C, the higher the degree of coupling between the economic quality development and the scale of higher education. Furthermore, to better reflect the systematicity and coordination among the indicators, we constructed the coupling and coordination coefficient Di of economic development and the scale of higher education as follows:

$$Di=\sqrt{Ci\times Ti}, Ti=\alpha f(x)+\beta g(y)$$

(7)

α and β, respectively, represent the weight indicators of economic development and the scale of higher education. Taking into account the interaction and influence between the two, we set α = β = 0.5.

3.2.2. Economic Development and Higher Education Scale Network Spatial Effect Model

(1)

Calculation of the contribution rate in the coupling and coordination of regional cities

$$Ri={\displaystyle \frac{Di}{Di+Dj}}$$

(8)

where Di represents the degree of coupling and coordination between the urban economic quality development of province i and the scale of higher education, and Dj represents the degree of coupling and coordination between the urban economic quality development of province j and the scale of higher education. Ri represents the contribution rate of the coupling coordination between province i and province j.

(2)

Calculation of cyberspace effects

$$Si={\displaystyle \frac{Ri}{\sqrt{GDij}}}$$

(9)

where Ri represents the contribution rate of the coupling coordination in region i, and GDij represents the distance between the two provincial capital cities in regions i and j. Si represents the degree of radiation in cyberspace.

(3)

Constructing an incidence matrix

Table 2. Example of Incidence Matrix.

	City 1	City 2	…	City 30	City 31
City 1	1	0.035	…	0.018	0.021
City 2	0.035	1	…	…	…
……	…	…	…	…	…
City 30	0.003	0.017	…	1	0.015
City 31	0	0.016	…	0.015	1

shows the incidence matrix among cities.

(4)

Social network analysis spatial network effect

The social network analysis method comprises a set of norms and techniques for examining the structure and attributes of social relationships [42]. Its unique theoretical foundation and analytical metrics enable it to probe the intricate relationships among research subjects, and it has been extensively utilized across various disciplines, offering a novel research paradigm for scholarly inquiry. Given that economic growth and the expansion of higher education have direct impacts on the economic benefits and talent development of different regions, as well as spatial spillover effects relationships on other areas [43], this study employs social network analysis primarily to visually depict the correlation and influence of economic development across various Chinese regions on the scale of higher education in different provinces.

In the specific application of this study, the process of social network analysis encompasses the acquisition of social network data, the construction of social network relationships, the analysis of network indicators, and the interpretation of the analysis. Firstly, based on the aforementioned model, we calculated the coupling and coordination index between the economic development of various regions in China and the scale of higher education. Secondly, we constructed a social network relationship matrix between different regions, dividing the indicators in the social network into overall network indicators for regional economic development and higher education development (actual connections, distance between connections, etc.) and individual network indicators (degree centrality, betweenness centrality, and closeness centrality). Finally, by calculating and analyzing the results, a network relationship diagram is further drawn to present the characteristics and relationships between the economic development of various regions in China and the scale of higher education.

4. Results

4.1. Research on the Coordinated Relationship Between Higher Education and the Development of Economic Quality

Based on the calculation results of the above model, the study divides the results into six levels according to the evaluation criteria of the degree of coupling and coordination: quality coordination (0.8 < di ≤ 1), moderate coordination (0.6 < di ≤ 0.8), primary coordination (0.5 < di ≤ 0.6), basic disorder (0.4 < di ≤ 0.5), moderate disorder (0.2 < di ≤ 0.4), and severe disorder (di ≤ 0.2).

Table 3. Coefficient of coupling and coordination between regional economic development and the scale of higher education in 2022.

Number	Province	Coordination Coefficient	Grade
1	Guangdong	0.806	High-quality coordination
2	Jiangsu	0.731	Moderate coordination
3	Shandong	0.700
4	Beijing	0.664
5	Shanghai	0.658
6	Henan	0.642
7	Sichuan	0.613
8	Hubei	0.609
9	Zhejiang	0.598	Primary coordination
10	Hunan	0.572
11	Liaoning	0.560
12	Hebei	0.550
13	Shaanxi	0.547
14	Anhui	0.519
15	Jiangxi	0.497	Basic disorder
16	Heilongjiang	0.493
17	Fujian	0.491
18	Shanxi	0.465
19	Chongqing	0.463
20	Guangxi	0.459
21	Jilin	0.447
22	Yunnan	0.439
23	Tianjin	0.428
24	Gansu	0.410
25	Guizhou	0.400
26	Xinjiang	0.366	Moderate Disorder
27	Inner Mongolia	0.357
28	Hainan	0.250
29	Ningxia	0.223
30	Qinghai	0.208
31	Xizang	<0.200	Severe Disorder

presents the coupling degree and coupling coordination coefficient between regional economic development and the scale of higher education in 2022. From the perspective of the coupling degree of economic development, the higher the coupling degree coefficient, the stronger the degree of influence on economic development. The main representative cities are Guangdong, Jiangsu, Shandong, Zhejiang, etc.; from the scale of higher education coupling degree, the higher the coupling coefficient, the stronger the degree of influence on the scale development of higher education. The main representative cities are Beijing, Shanghai, Guangdong, and Jiangsu.

From the analysis of the coupling coordination degree coefficient, Table 3 presents the evaluation results of the coupling coordination degree of different regional economic development and the scale of higher education. Guangdong Province shows high-quality coordination, and Beijing, Shanghai, Jiangsu Province, Shandong Province, Henan Province, Hubei Province, and Sichuan Province are moderately coordinated. Inner Mongolia, Hainan Province, Qinghai Province, Ningxia Hui Autonomous Region, and Xinjiang are somewhat unbalanced. Xizang is severely disordered. Areas with a moderate imbalance include the Inner Mongolia Autonomous Region, Hainan Province, Qinghai Province, the Ningxia Hui Autonomous Region, and the Xinjiang Uygur Autonomous Region. At present, only the Xizang Autonomous Region has a severe imbalance. The coordination relationship between “economic development and the scale of higher education” in each region is quite different.

4.2. The Network Effect of Higher Education and Regional Economy

Results of Visualization Analysis

The relationship data in the social network data have a particular form of expression; that is, the relationship characteristics and structure are reflected through certain variables or values. Firstly, based on the calculation of the coordination index between the economic development of the various regions in China and the scale of higher education, the network spatial effect model is used to make further calculations. Secondly, using the above calculation results, a network relationship matrix between the various regions in China is constructed. Thirdly, the social network analysis software UCINET 6 is used for the calculation and presentation of the network results. Figure 2 shows the use of the social network analysis tool UCINET, based on the degree of centrality, according to the total economic volume, labor employment, urban structure, foreign economy, and social welfare, and the number of higher education institutions, the number of higher education students, the number of higher education faculty members, etc. After data processing, the spatial effect network diagram of the coupling and coordination between regional economic development and the scale of higher education is drawn. According to the differences in geographic location and natural and human geography, the 31 provinces in China are divided into seven regions according to their geographic areas. Each part is represented by a different color, as shown in Figure 2.

From the perspective of the overall network, there are no isolated points between the regional cities, showing a typical network structure. At the same time, the spatial network of the coupling and coordination of regional economic development and the scale of higher education presents a “scale-free feature”. In network theory, the scale-free feature of the network is manifested in that most of the nodes in the network are connected to only a few points, and a very small number of nodes and a very large number of nodes reflect the characteristics of the connection. Most cities have few connection points, and a few cities have more. The spatial network has apparent heterogeneity. According to network relationship analysis, the distance-based cohesion (“cohesion”) is 0.553 (cohesion ranges from 0 to 1) in the network composed of 31 provinces; the more significant the cohesion index of the network, the closer the network relationship between provinces. From an overall point of view, the network has shown a trend of “high in the east and low in the west”, indicating that the network relationship between economic development in the eastern region and the scale of higher education is more coordinated than the overall coordination in the western region.

Regarding the analysis of individual network characteristics, the seven major regions are Jiangsu Province (East China), Guangdong Province (South China), Hubei Province (Central China), Beijing (North China), Gansu Province (Northwest), Sichuan Province (Southwest China), and Heilongjiang Province (Northeast). They have more connections with the other provinces in the overall network, so they play an essential role in connecting the internal and external links.

4.3. Results of Mathematical Analysis

Table 4. The characteristics of the overall network structure of economic quality development and the scale of higher education.

Number	Province	Degree Centrality	Closeness Centrality	Betweenness Centrality
1	Beijing	50	40.541	2.485
2	Tianjin	56.667	41.667	3.284
3	Hebei	56.667	41.667	1.925
4	Shanxi	60	42.254	1.723
5	Inner Mongolia	50	40.541	0.549
6	Liaoning	30	34.884	0.453
7	Jilin	20	32.609	0.046
8	Heilongjiang	16.667	32.258	0
9	Shanghai	53.333	41.096	0.943
10	Jiangsu	66.667	43.478	2.453
11	Zhejiang	53.333	41.096	1.05
12	Anhui	63.333	42.857	1.901
13	Fujian	30	35.714	0.046
14	Jiangxi	53.333	41.096	1.446
15	Shandong	66.667	43.478	5.474
16	Henan	66.667	43.478	2.171
17	Hubei	76.667	45.455	5.552
18	Hunan	56.667	41.667	1.719
19	Guangdong	43.333	38.462	1.171
20	Guangxi	30	35.714	0.306
21	Hainan	50	38.462	1.908
22	Chongqing	40	37.975	0.884
23	Sichuan	33.333	37.037	1.166
24	Guizhou	40	37.5	0.692
25	Yunnan	23.333	35.294	0.126
26	Xizang	/	/	/
27	Shaanxi	56.667	41.667	1.286
28	Gansu	40	38.961	0.348
29	Qinghai	76.667	45.455	11.943
30	Ningxia	70	44.118	3.731
31	Xinjiang	3.333	31.915	0

Note: Due to the sparse population and complex terrain in Xizang, it has been difficult for the National Bureau of Statistics to obtain data for a long time, so this will not be considered temporarily.

presents the characteristics of the overall network structure of high-quality economic development and the scale of higher education. The features of the spatial network effect between the two are mainly analyzed through three indicators: degree centrality, relative centrality, and betweenness centrality.

The number of degree centers represents an index used to measure the number of resources occupied by nodes in the network. The average number of degree centers in the 31 provinces (regions) in the table is 46.23, and 18 provinces have parameter values higher than the average, indicating that such cities have more relationships with other cities in the spatial network of coupling and coordination of economic quality development and the scale of higher education. These 18 cities are distributed in four regions, mainly in Central and East China; the remaining 13 cities below the average number of degree centers are mainly distributed, i.e., Liaoning, Jilin, Heilongjiang, Fujian, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, Gansu, and Xinjiang, indicating that such regions have relatively few relationships with other regions in the coupled coordination spatial network.

Betweenness centrality measures the extent to which an actor can control other actors, and such actors act as a bridge of communication. The average centrality of the 31 provinces (regions) in the table is 1.83, and 11 provinces (regions) have parameter values higher than the average, indicating that such cities control the economy among other cities in the spatial network of economic quality development and higher education scale coordination. The ability of ecological coupling to coordinate space is vital. Among the cities, the centrality of Tianjin, Shandong, Hubei, Qinghai, and Ningxia scores more than three, which is much higher than other cities, indicating that these provinces (regions) are not only in a central position in the spatial correlation network of economic quality development and the scale of higher education but also play the role of intermediary and bridge between them. In addition, the cities with a median centrality lower than the average value (<0.5) are mainly distributed in regions such as Liaoning, Jilin, Fujian, Guangxi, Yunnan, Gansu, etc., and play a lesser role in the domination and control of the spatial network.

Closeness centrality means the centrality measured according to the tightness or distance between nodes in a network. The shorter the total distance measured, the higher the tightness of the network and the more likely it is to be in the center. The 31 provinces (regions) in the table have an average closeness centrality of 38.33, and 20 provinces have parameter values higher than the average, indicating that this type of city can better interact with other cities in the coordinated spatial network of economic quality development and higher education scale. The inner connection plays the role of the central actor in the spatial network. When considering the distance attenuation effect (that is, the closer the relationship between the elements of the network is), the distance attenuation effect of adjacent areas is more obvious (that is, the closer the relationship between the neighboring areas is).

It is worth noting that the 20 cities mentioned above with a closeness centrality higher than the average are similar to the spatial distribution of the central degree, mainly concentrated in Central China and East China, playing a role of internal and external linkage. The remaining 11 cities that have a lower than average value of centrality are mainly distributed, i.e., Liaoning, Jilin, Heilongjiang, Fujian, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, and Xinjiang. The internal and external linkage is relatively poor.

5. Conclusions and Recommendations

The practical logic behind the uneven development of regional higher education in China does not adhere to the “principle of equality”, “principle of ability”, or “principle of freedom”, but rather is influenced by the decisive intervention of “national will”. Drawing on Keynes’s disequilibrium theory, the balanced development of the regional economy is an exceptional case within economic growth, with disequilibrium being the norm in economic development [44]. The economy serves as a key indicator for gauging the level of development, and the quality of higher education determines a region’s potential for growth. Investigating the relationship between these two aspects holds academic and practical value for establishing a fair evaluation system for higher education. In this study, we systematically explore the use of social network analysis to examine the correlation between regional economic development and the advancement of higher education. The interactions among cities have significantly propelled the development of higher education and are crucial to China’s economic transformation and upgrading. The main conclusions are as follows:

(1)

There are significant differences in the coordination between economic development and higher education scale in different regions of China.

The East China, South China, and Central China regions are basically coordinated, but the economic quality development and higher education scale in the northwest, southwest, and northeast regions are moderately imbalanced, and the development of the regional economy restricts the development of the higher education scale. It is recommended that the role of public finance in promoting the development of higher education in regions with imbalanced development be fully utilized, with increased investment in higher education. Regional disparities should be narrowed through macroeconomic policies and financial support.

(2)

The various regions in China exhibit a stepped geographical distribution pattern of “high in the east and low in the west” in terms of cyberspace effects.

Certain cities in East, North, and Central China exhibit robust internal and external connectivity within their spatial networks, whereas the northwest, southwest, and northeast regions demonstrate weaker internal and external linkages in their spatial networks. It is recommended to utilize the intermediary and bridging roles of the more developed areas to extend their influence and stimulate the advancement of neighboring provinces and cities. Additionally, efforts should be made to cultivate region-specific educational resources, further advancing the development of higher education in the northeast and western regions, and ensuring the seamless progression of higher education in these areas.

The limitations of this study in its implementation need further clarification. The current research is based on the latest publicly available data from the National Bureau of Statistics of China, i.e., from 2022, and theoretically more data research may be needed to explain the relationship between and impact of China’s regional economic development and the scale of higher education. However, considering that since 2019, a large-scale outbreak of novel coronavirus disease 2019 (COVID-19) has occurred in the world, and China’s economy has stagnated from 2019 to 2021, the data analysis during this period is not representative and interpretable. Although it is impossible to list all influencing factors, the application of this method can provide a standard research paradigm for building a fairer higher education system in China. Therefore, applying this research method has two implications for building a fair higher education system in China. On the one hand, the entropy method can calculate the weight between economic development and higher education development indicators in various regions and then analyze the coupling and coordination relationship between the products. On the other hand, it has specific educational value to provide data support for constructing a fair higher education evaluation system.

Based on official data from China, some scholars have predicted the size and resource allocation of the school-age population in higher education from 2024 to 2040, and found that by 2040, the demand for full-time teachers in ordinary Chinese universities will decrease by 837,500 people, the total investment in higher education will decrease by CNY 646.134 billion, the demand for the building area of university buildings will decrease by 536.52 million square meters, and the demand for teaching and research instruments and equipment in universities will decrease by CNY 87.756 billion [45]. In order to better optimize the future economic development and allocation of higher education resources in various regions of China, it is necessary to further collect data from other dimensions such as policies and industrial development in subsequent research to more accurately analyze the impact of changes in the scale of higher education on regional economic development. At the same time, we should continue to explore the impact and series of effects of future changes in the supply of higher education resources on regional economic development.