Analysis of synergies between vocational education and regional economic development

Vocational education is one of the four major types of education parallel to the status of basic education, higher education and adult education, and is an important part of China’s education system [30].

The current scholars’ understanding of the concept of vocational education is divided into the broad and narrow sense. Vocational education in the broad sense refers to education that focuses on teaching specialized knowledge, training vocational skills and cultivating professional ethics. In terms of extension, it includes all levels and types of vocational school education and all forms of vocational training, including both technical education and technical training. In terms of the level of vocational schooling, there are three levels: primary, secondary and higher vocational schooling. Vocational training, on the other hand, includes pre-employment training, on-the-job training, job-transfer training and other vocational training, and can be categorized into primary, intermediate and advanced vocational training according to the actual situation. Vocational education in the narrow sense refers to the pre-vocational education and post-entry training of knowledge, technology, attitude and so on for the educated who are in need of various occupations and positions in the society, which is also known as skilled labor education or technical education.

In this study, we adopt the concept of vocational education in the broad sense, i.e., education that allows the educated to acquire the vocational knowledge, skills and professional ethics required for a certain occupation or productive labor, and that aims to cultivate applied human resources with a certain level of literacy and professional knowledge and skills.

Regional economic development refers to the vigorous development of the overall economy of the region within a specific geographic area by enhancing the employment rate, increasing the per capita gross national product and improving the living standards of the residents [31]. The purpose of regional economic development is to satisfy the economic and living needs of local residents by realizing the transformation and upgrading of the regional economic structure and the sustainable growth of the regional economy, and ultimately to enhance the competitiveness of the entire region. Regional economic development strategies usually focus on combining the economic development characteristics, resource endowment advantages, industry types and industrial layout of a specific region to formulate targeted development plans to achieve high-speed economic development. Regional economic development should also be tailored to local conditions to formulate different development plans, according to the different ways of investment attraction, industrial cultivation mode, direction of technological innovation, infrastructure construction profile and quality of education and training institutions in different regions, to formulate different strategic plans to achieve regional economic growth. Regional economic development will lead to the diversification of regional industries, create more employment opportunities, improve infrastructure and public service levels, and ultimately enhance the quality of life of local residents.

In addition, regional economic development can also enhance the attractiveness and competitiveness of the local economy, attract the inflow of capital, promote the acceleration of regional economic development, and realize the sustainable development of the economy. In the specific implementation process, the realization of regional economic development needs to be targeted to formulate the entire region’s regional development planning, and combined with the corresponding policies and projects to implement regional economic development. Therefore, the government has a very important role in the process of regional economic development, the government through the provision of policy support and effective supervision, etc., to promote the rational allocation of resources. At the same time, the Government can also invite State-owned enterprises, private enterprises and other social organizations to participate in the formulation of economic development plans in order to jointly promote regional economic growth and progress.

The synergistic development of vocational education and regional economy refers to a development mode in which vocational education and regional economy complement and promote each other.

First, talent training and industrial demand docking. The main task of vocational education is to cultivate high-quality technical and skilled talents who are adapted to the needs of regional industrial and economic development. Synergistic development means that vocational education should be closely integrated with industrial demand, adjusting the professional settings, curriculum system and teaching content, so that the cultivated talents can meet the actual needs of regional industries.

The second is the integration of industry and education. The integration of industry and education is the core of the synergistic development of vocational education and regional economy. It includes cooperation between schools and enterprises in talent training, building internship and training bases, and carrying out scientific research projects together. Through the integration of industry and education, students are able to learn and practice in real industrial environments and improve their vocational skills and employment competitiveness.

Third, scientific and technological innovation and achievement transformation. The synergistic development of vocational education and regional economy encourages cooperation in scientific and technological innovation between schools and enterprises, joint scientific research projects, and the transformation and application of scientific and technological achievements. This helps to enhance the technological level and innovation capacity of industries and promote industrial upgrading and transformation.

Fourth, social services and regional development. Vocational education also undertakes the task of providing social services such as technical training and consulting services for the society. Synergistic development requires that vocational education and the regional economy work together to serve regional development and contribute to local economic and social development.

Figure 1 shows the symbiosis framework of vocational education and regional economic development, and its core components can be carefully divided into three core components, namely, symbiosis unit, symbiosis environment and symbiosis mechanism. The symbiosis unit, as the basic building block of the symbiosis relationship, bears the important responsibility of energy creation and exchange, and is the indispensable material cornerstone and prerequisite for the construction of the symbiosis relationship. The symbiotic environment, on the other hand, is an inclusive external system, which includes all the influencing factors except the symbiotic unit, and builds an intricate external ecological network. The symbiotic mechanism analyzes the specific patterns of interactions between symbiotic units and their intensity, revealing not only the deep-seated intrinsic connections between the units, but also how these connections shape the symbiotic environment in a far-reaching way.

Figure 1.

Vocational education and regional economic development

In this symbiotic framework, vocational education and the regional economy, as the core symbiotic unit, realize the efficient flow of material, energy and information through close and seamless interaction. The tightness of this interaction process is profoundly influenced by external environmental factors such as policy system and social and cultural atmosphere. The symbiotic environment is also embedded with the key element of symbiotic interface, which not only serves as a bridge for the communication and interaction of symbiotic units, but also exists in diverse forms, such as tangible industrial parks and innovation platforms, as well as intangible policy and regulatory frameworks, such as the policy of school-enterprise cooperation and vocational education regulations. Compared with other components in the symbiotic environment, the symbiotic interface shows a more direct and significant role in optimizing the symbiosis mode and promoting efficient collaboration between units.

As vocational education is an ongoing, long-term process, it includes the determination of target audiences and the use of funds as well as subsequent evaluation and supervision. Therefore, this paper draws on the traditional vocational education evaluation index construction ideas, on the basis of which it emphasizes the criticality of the main stakeholders, such as students, teachers, schools, enterprises and the government, and combines the help of vocational education policies to implement the utilization of talents and the effectiveness of vocational education, and to analyze and evaluate it in a more comprehensive and in-depth manner. Table 1 shows the evaluation index system of the development level of vocational education, which is mainly evaluated in three dimensions: education guarantee, cultivation quality and social effectiveness.

For the level of high-quality development of the regional economy, this paper constructs 18 indicators to characterize the comprehensive evaluation index system of the level of high-quality development of the regional economy based on the five dimensions of innovation, coordination, green, openness and sharing of the new development concept, the specific content of which is shown in Table 2.

A region is a territorial space that contains natural, human and economic features, and whose natural and human environments are significantly different from those of the surrounding areas. At present, the division of regions in China is mostly based on the economic function as the general direction, based on the similar interests, thus forming the economic functional area with strong internal connection and able to radiate the surrounding areas. Different scholars have different criteria for determining the indicators for the division of regions. Some use GDP, some use GDP per capita, and some build a comprehensive indicator system based on regional economic structure and market level. This paper draws on the existing relevant studies and adopts GDP as the main and GDP per capita as the supplement to distinguish the scope of the study area.

According to the criteria for selecting indicators in this paper, based on the GDP and GDP per capita of 14 prefecture-level cities in Hunan Province in 2022, and based on the results of the 10th Party Congress of Hunan Province, the 14 prefecture-level cities in Hunan Province will be divided into four regions, namely ChangZhuTan, Xiangnan, Xiangxi and Dongting Lake regions. The ChangZhuTan region refers to the cities of Changsha, Zhuzhou and Xiangtan; the Xiangnan region refers to the cities of Hengyang, Chenzhou and Yongzhou; the Xiangxi region refers to the five cities (states) of Shaoyang, Zhangjiajie, Huaihua, Loudi and Xiangxi Autonomous Prefecture; and the Dongting Lake region refers to the cities of Yueyang, Changde and Yiyang.

In this paper, the year 2012 is selected as the starting point of time when studying the synergistic effect between vocational education and regional economic development. Given the availability of data, the research period of this paper is from 2012 to 2023. Relevant data were obtained from China Education Expenditure Statistical Yearbook, China Education Statistical Yearbook, Hunan Statistical Yearbook, official website of the Ministry of Education, and official website of Hunan Provincial Department of Education. Among them, the number of national model higher vocational colleges and universities consists of national model higher vocational colleges and universities, national backbone higher vocational colleges and universities, and “double-high” program construction units. In addition, since 2021, the distribution of schools at all levels has been specialized in the statistics of vocational colleges and universities at the specialized level. Therefore, it is possible to directly check the faculty and staff of specialized-level vocational colleges and universities in Hunan Province from 2020 to 2023, while the data on the faculty and staff of specialized-level vocational colleges and universities in Hunan Province from 2012 to 2019 are approximated based on the proportion of faculty and staff of higher vocational colleges and universities in the country among the faculty and staff of ordinary higher education institutions.

The principle of entropy weighting method is to use the ratio of a certain indicator of each program system to the sum of the values of the same indicator. As an objective assignment method, this method avoids the subjective bias caused by human factors [32]. Therefore, the entropy weight method model is used here to determine the weight of each indicator in the two subsystems of vocational education and regional economic development, and to obtain the comprehensive development index of each of the two subsystems, which is calculated as follows: 1)

The raw data are dimensionless as follows:

The positive indicator can be expressed as: (1)ui=xij−min(xij)max(xij)−min(xij)${u_i} = \frac{{{x_{ij}} - \min ({x_{ij}})}}{{\max ({x_{ij}}) - \min ({x_{ij}})}}$

Negative indicators can be expressed as: (2)ui=max(xij)−xijmax(xij)−min(xij)${u_i} = \frac{{\max }}{{({x_{ij}})}} - {x_{ij}}\max ({x_{ij}}) - \min ({x_{ij}})$

Where u_i(0 ≤ u_i ≤ 1) is the data obtained after eliminating the effect of the scale of the original data x_ij, indicating the value of the jth indicator of the city i. When u_ij is closer to 1, it means the greater the contribution of the indicator to the system, max(x_ij) indicates the maximum value of the indicator, and min(x_ij) indicates the minimum value of the indicator. 2)

Non-negativization of data

After the standardization of the indicators to get the matrix A = (u_ij)_n×m, and entropy value method needs to use the logarithmic form, in order to avoid the data meaningless, to ensure the reasonableness of the data, we need to shift the matrix processing, after the shift of the matrix is (a_ij)_n×m, the formula is as follows: (3)Ai=(uij+0.0001)n×m=(aij)n×m${A_i} = {({u_{ij}} + 0.0001)_{n \times m}} = {({a_{ij}})_{n \times m}}$

where k is a constant, generally made k=1ln(n);0≤ej≤1$k = 1 \big/ {{\ln (n)};0 \leq {e_j} \leq 1}$. 5)

Calculate the coefficient of variation for the jrd indicator, i.e.: (6)gj=1−ej${g_j} = 1 - {e_j}$

The smaller the entropy value e_j and the larger the value of the coefficient of variation g_j, the greater the impact produced by program evaluation and the more important the indicator. 6)

Determine the weights of the indicators, i.e: (7)Wj=gj∑j=1mgj(j=1,2,⋯,m)${W_j} = \frac{{{g_j}}}{{\sum\limits_{j = 1}^m {{g_j}} }}(j = 1,2, \cdots ,m)$

U₁, U₂ denotes the comprehensive development level of the two subsystems of vocational education and regional economic development respectively, where u_1j, u_2j denotes the size of the contribution of the jth indicator to the two subsystems respectively, and w_1j, w_2j denotes the weight of each indicator of the two systems respectively.

Based on the evaluation index system of the development level of vocational education given in the previous section, the entropy weight method is used to measure the development level of vocational education in Hunan Province from 2012 to 2023, and its specific results are shown in Table 3, and Figure 2 shows the distribution pattern of the regional development level of vocational education in Hunan Province.

Based on the data distribution of the chart, the following conclusions can be drawn: 1)

The overall development level of vocational education in Hunan Province

The development level of vocational education in Hunan Province has increased from 0.0467 in 2012 to 0.1533 in 2023, with a growth rate of 228.27%, and the overall average value between 2012 and 2023 is 0.0895, indicating that the development level of vocational education in Hunan Province has shown a year-on-year increasing trend, but the overall level is relatively low. The reason is that in recent years, the gradual improvement of the conditions for higher vocational education, the continuous improvement of the legal system of vocational education, and the continuous improvement of the personnel training mode have contributed to the steady improvement of the development level of higher vocational education. However, at present, the investment in higher vocational education still lags behind the changes in industrial structure, the cooperation between industry, academia and research is not in-depth, and the effectiveness of the implementation of the integration of industry and education is insufficient, which leads to the relatively low level of development of higher vocational education.

Figure 2.

Occupational education regional development level

Combined with the evaluation index system of regional economic development level constructed in the previous section, the regional economic development level of Hunan Province from 2012 to 2023 is measured by entropy weight method, and its specific results are shown in Table 4, and Fig. 3 shows the distribution pattern of the regional economic development level of Hunan Province.

Figure 3.

Regional economic regional development level

On the whole, the regional economic development level of Hunan Province increases from 0.1741 in 2012 to 0.2418 in 2023, and the overall growth rate reaches 82.08%, indicating that the regional economic development of Hunan Province shows an upward trend as a whole. From the regional level, the high-quality economic development level of the four major regions in Hunan Province grows year by year, and the average values of regional economic development water in ChangZhuTan, Xiangnan, Xiangxi and Dongting Lake regions are 0.3284, 0.2216, 0.2093, 0.2080 respectively from 2012 to 2023, of which the high-quality development level of the economy of ChangZhuTan region is much higher than that of the other regions, and the regional economic development level reaches 0.2418 by 2023, indicating that the regional economic development as a whole shows an upward trend. In 2023, the regional economic development level reached 0.4594, but there is still a large space for development, and its economic quality, industrial structure, environmental protection and other aspects of coordinated development need to be further improved. Dongting Lake region and western Hunan region development trend is basically the same, in 2017 before the level of high-quality development of the economy are in a low stage, since 2017 put forward the concept of high-quality development of the regional economy, the Hunan provincial government followed by the introduction of a number of policies, the level of development of the four major regions have been significantly improved since then. At the municipal level, Changsha City, as the capital city of Hunan Province, is still ahead of the other cities in terms of its level of high-quality economic development. Changsha City’s regional economic high-quality development level has grown from 0.3924 in 2012 to 0.7735 in 2023, which is at a high level, but there is still room for further improvement. As of 2023 Zhuzhou, Hengyang and Chenzhou are in the second tier of the regional economic high-quality development level above 0.31, and there is still much room for development. The level of high-quality development of the regional economy of other cities is only about 0.2 in a lower state of development, and there is still a lot of room for catching up from realizing the high-quality development of the regional economy.

This paper adopts the common method in academia to construct the coupling degree model of vocational education-regional economic development system to calculate the coupling degree, and the measurement steps are as follows: (10)C=U1×U2(U1+U2)2$C = \sqrt {\frac{{{U_1} \times {U_2}}}{{{{({U_1} + {U_2})}^2}}}}$

Where, C is the system coupling degree, which takes values between 0 and 1. When C = 1, it indicates that the system is in an optimal coupling state. When C = 0, it indicates that the elements within the system are not related to each other, and the system develops toward disorder. U₁, U₂ represents the comprehensive score of the two systems of vocational education and regional economic development, respectively [33].

The coupling degree, as a measure, is mainly used to describe how closely different subsystems interact with each other. However, although the coupling degree can tell us whether there are interactions among the subsystems, it cannot reflect the level of coordination, let alone tell us whether these systems are moving forward on the track of benign interaction and healthy development. Therefore, in order to better assess the coordination and development status between systems, we need to introduce a more comprehensive and in-depth indicator - coupling coordination degree. This indicator not only considers the degree of interaction between subsystems, but also pays attention to whether the systems maintain a benign interactive relationship of mutual promotion and common development. By calculating the degree of coupling coordination, we can more accurately judge whether the systems have really realized coordinated development and whether they are developing in a healthy and sustainable direction, and the steps are as follows: (11)D=C×T$D = \sqrt {C \times T}$ (12)T=αU1+βU2$T = \alpha {U_1} + \beta {U_2}$

Where D is the degree of coupling coordination, C is the degree of coupling, T is the comprehensive evaluation index of the binary system, α, β represents the coefficient to be determined, considering that vocational education is equally important as the regional economic development, so it takes α = β = 0.5.

Referring to the existing literature on the coupling coordination degree division standard, combined with this paper’s measurement of the data distribution is more intensive and other factors, to get the coupling coordination degree level as shown in Table 5, mainly divided into dysfunctional decline type and coordinated development type.

In this paper, regional economic development differences and their sources are analyzed using the Dagum Gini coefficient and its decomposition method, which can be expressed as the definition of the Dagum Gini coefficient: (13)G=∑j=1k∑h=1k∑i=1nj∑r=1nh|yji−yhr|2n2y¯$G = \frac{{\sum\limits_{j = 1}^k {\sum\limits_{h = 1}^k {\sum\limits_{i = 1}^{nj} {\sum\limits_{r = 1}^{nh} {|{y_{ji}} - {y_{hr}}|} } } } }}{{2{n^2}\bar y}}$

Where G represents the overall Gini coefficient, the larger the value of G, the greater the regional differences. y¯$\bar y$ denotes the overall level of regional economic development, j denotes the level of regional economic development of i cities within the region, and h denotes the level of regional economic development of r cities within the region. Denotes the number of cities within i region and h region respectively, n denotes the total number of cities, k denotes the number of regions, and the means of the target variables for each group are ranked according to the following equation. i.e: (14)y¯h≤⋯≤y¯j≤⋯≤y¯k${\bar y_h} \leq \cdots \leq {\bar y_j} \leq \cdots \leq {\bar y_k}$

Dagum’s Gini coefficient and its decomposition method can decompose the overall Gini coefficient G into three components: the contribution of intra-group disparity G_w, the contribution of inter-group disparity G_nb and the contribution of hypervariance density G_t [34]. To wit: (15)G=Gw+Gnb+Gt$G = {G_w} + {G_{nb}} + {G_t}$

The intra-group gap contribution G_w is the difference in the distribution of the level of regional economic development between the cities included in each region, which is calculated with the following expression: (16)Gw=∑j=1kGjjpjsj${G_w} = \sum\limits_{j = 1}^k {{G_{jj}}} {p_j}{s_j}$ (17)Gjj=12y¯j∑i=1nj∑r=1nj|yji−yjr|nj2${G_{jj}} = \frac{{\frac{1}{{2{{\bar y}_j}}}\sum\limits_{i = 1}^{nj} {\sum\limits_{r = 1}^{nj} {|{y_{ji}} - {y_{jr}}|} } }}{{n_j^2}}$ (18)sj=njy¯jny¯${s_j} = \frac{{{n_j}{{\bar y}_j}}}{{n\bar y}}$

G_jj represents the Gini coefficient of region j, p_j is the ratio of the number of cities to the total number of cities within j the region, y¯j${\bar y_j}$ is the overall level of regional economic development within j the region, and y_jr represents the level of regional economic development of r cities within the j region.

The contribution of the inter-group gap G_nb is the difference in the distribution of the level of regional economic development between regions, then: (19)Gnb=∑j=2k∑hj−1Gjh(pjsh+phsj)Djh${G_{nb}} = \sum\limits_{j = 2}^k {\sum\limits_h^{j - 1} {{G_{jh}}} } (\:{p_j}{s_h} + {p_h}{s_j}){D_{jh}}$ (20)Gjh=∑i=1nj∑r=1nh|yji−yjr|njnh(y¯j+y¯h)${G_{jh}} = \frac{{\sum\limits_{i = 1}^{nj} {\sum\limits_{r = 1}^{nh} {|{y_{ji}} - {y_{jr}}|} } }}{{{n_j}{n_h}({{\bar y}_j} + {{\bar y}_h})}}$ (21)sh=nhy¯hny¯${s_h} = \frac{{{n_h}{{\bar y}_h}}}{{n\bar y}}$

G_jh represents the Gini coefficient between regions j and h, p_h is the ratio of the number of cities to the total number of cities in the h region, and y¯h${\bar y_h}$ is the overall level of regional economic development in the h region. Where D_jh represents the relative impact of the level of regional economic development between region j and region h, then: (22)Djh=djh−pjhdjh+pjh${D_{jh}} = \frac{{{d_{jh}} - {p_{jh}}}}{{{d_{jh}} + {p_{jh}}}}$

where d_jh denotes the difference in the level of regional economic development between regions j and h, and p_jh is defined as the hypervariable first-order moment, computed as: (23)djh=∫0∞dFj(y)∫0y(y−x)dFh(x)${d_{jh}} = \int_0^\infty d {F_j}(y)\int_0^y {(y - x)} d{F_h}(x)$ (24)pjh=∫0∞dFh(y)∫0y(y−x)dFj(x)${p_{jh}} = \int_0^\infty d {F_h}(y)\int_0^y {(y - x)} d{F_j}(x)$

where F_j and F_h represent the cumulative density distribution function for regions j and h, respectively.

The hypervariable density contribution G_t represents a Gini coefficient residual of the cross-influence of the level of regional economic development between regions, calculated as follows: (25)Gt=∑j=2k∑h=1j=1Gjh(pjsh+phsj)(1−Djh)${G_t} = \sum\limits_{j = 2}^k {\sum\limits_{h = 1}^{j = 1} {{G_{jh}}} } ({p_j}{s_h} + {p_h}{s_j})(1 - {D_{jh}})$

Based on the score of the level of vocational education and regional economic development in Hunan Province obtained in the previous section, and combined with the formula on the coupling degree in the principle step of the coupling coordination degree model, the trend of the coupling degree of vocational education and regional economic development subsystems in Hunan Province from 2012 to 2023 can be calculated as shown in Figure 4.

Figure 4.

The coupling of the subsystem

It can be seen from the figure that the coupling degree of vocational education and regional economic development in the four major regions of Hunan Province during 2012~2023 is between 0.33~0.50, and from 2012 to 2023, the coupling degree of vocational education and regional economic development in Changsha-Zhuzhou-Zhuzhou-Tan area has been around 0.35 and fluctuated and increased, which is in a low-level coupling stage, indicating that the economic development system and vocational education system in Changsha-Zhuzhou-Tan area are in a state of low coupling and coordination all year round. However, the coupling degree of Changzhutan region was in a declining trend in 2014, but the decline was relatively small, and then returned to an upward trend. The coupling degree between vocational education and regional economic development in the southern and western Hunan regions has an average value of about 0.45 in 2012-2023, which is in the medium coupling stage, but the coupling degree in the southern Hunan region has been fluctuating and decreasing from 2014 to 2017, with a decline of 10.69%, indicating that the coupling relationship between the economic system and the vocational education system of the southern Hunan region in this stage has experienced a huge decline. This situation lasted until the beginning of 2018 before it improved and turned into an upward trend. The western Hunan region has been in an overall upward trend, although there was a brief decline between 2014 and 2015. The coupling relationship between the regional economic development system and the vocational education system in the Dongting Lake region shows a more obvious fluctuating trend between 2012 and 2023, which also reflects the unbalanced distribution of the Dongting Lake region’s own location disadvantages and vocational education resources.

Based on the coupling coordination degree model given in the previous section, combined with the coupling degree and the level of vocational education and regional economic development, the coupling coordination degree of the two systems of vocational education and regional economic development in Hunan Province from 2012 to 2023 is calculated as shown in Figure 5, and Table 6 shows the coupling and coordination relationship of the two systems between 2012 and 2023.

Figure 5.

Coupling coordination of two systems

During the measurement period, the coupling and coordination degree of the two systems of vocational education and regional economic development in ChangZhuTan region was slightly improved in 2012-2016, and substantially improved in 2017-2023, and overall the fluctuation of the coupling and coordination degree is large, and the coupling is in the antagonistic development period. Combined with the changes in the overall score of the two systems, it can be found that the antagonistic development is manifested in the development of vocational education on the regional economic growth has a certain inhibition, which may be due to the fact that vocational education is a high-input and high-output but has a certain time lag in the activities of vocational education, vocational education brought about by the relative lag in the transformation of economic benefits, which is reflected in the reality of the data measured that the vocational education is in a high-speed development stage, the vocational education capacity to enhance significantly higher than the regional economic growth. During the period of 2012-2023, the two systems of vocational education and regional economic development in ChangZhuTan region transitioned from moderate dysfunctionality in 2012 to mild dysfunctionality in 2019, and combined with the changes in the coupling grade of the two systems and the comprehensive score, the coordination grade of the two systems realized moderate dysfunctionality to mild dysfunctionality in 2019, even though it was in a state of antagonistic development from 2012 to 2023, and the coordination grade of the two systems realized moderate dysfunctionality to mild dysfunctionality. Dissonance, the degree and level of coordination in the benign coupling between the two gradually deepens, and it is the first among the four regions to enter the mildly dysfunctional state. In the measurement results of Xiangnan region, although the coordination level of the two systems during the period of 2012-2023 realizes the evolution from moderate to mild dysfunction with 2019 as the turning point, and the degree and level of coordination in the benign coupling is improved, the two systems are always in the state of dysfunction. During the measurement period, the coupling and coordination degree of the two systems of vocational education and regional economy in western Hunan shows a continuous upward trend, transitioning from moderate dysfunctions in 2012 to mild dysfunctions in 2021, and the degree and level of coordination in the benign coupling have been greatly improved. The coupling coordination degree of the two systems of vocational education and regional economic development in Dongting Lake region has been lower than 0.3 from 2012 to 2023, and its coupling and coordination relationship has always been in the state of moderate dysfunction. Combined with the changes in the composite score of the two systems, it can be found that the antagonistic development is manifested as the development of vocational education has a certain inhibition on the regional economic growth, which may be due to the fact that the high input of vocational education is quite a challenge compared to the regional economic strength of Dongting Lake region itself, and at the same time, the transformation of the economic benefits brought about by the vocational education is relatively lagging behind, and the enhancement of the regional economy may not be obvious in the short period of time, which is reflected in the real data. This is reflected in the real data, which shows that the improvement of vocational education capacity is significantly higher than the regional economic growth.

This section adopts the Dagum Gini coefficient decomposition method to reveal the relative regional differences and the sources of differences by analyzing the overall differences, intra-regional differences, inter-regional differences and hypervariance density of the coupled coordination degree of vocational education and regional economic development in Hunan Province. Figure 6 shows the evolution of the Gini coefficient of coupled coordination degree, and Table 7 shows the inter-group differences of coupled coordination degree. In the table, CZT-XN, CZT-XX, CZT-DTH, XN-XX, XN-DTH, and XX-DTH denote ChangZhuTan-Xiangnan, ChangZhuTan-Xiangxi, ChangZhuTan-Dongting Lake, Xiangnan-Xiangxi, Xiangnan-Dongting Lake, and Xiangxi-Dongting Lake, respectively.

Figure 6.

The Gini coefficient of coupling coordination

At the level of Hunan province, the overall difference in the degree of coupling and coordination between vocational education and regional economic development shows a steady decline over the sample period, with the Gini coefficient decreasing from 0.2241 in 2012 to 0.2206 in 2023, and the overall difference is reduced. This may be due to the fact that regional synergistic development has gradually become the focus of attention of governments around the world, and the accelerated implementation of the regional coordinated development strategy has strengthened inter-provincial cooperation and promoted regional coordinated development. In terms of sub-regions, the intra-regional differences in the coupling coordination degree of ChangZhuTan region and western Hunan region are the most significant, with the mean values of their Gini coefficients of 0.1967 and 0.2008, respectively, followed by southern Hunan region, and the intra-regional differences in the coupling coordination degree of Dongting Lake region are the smallest, with a value of the Gini coefficient of 0.0972, and the Gini coefficients of the intra-regions have not exceeded that of the province as a whole, which indicates that the degree of imbalance within each region is relatively small, and inter-regional differences are the main source of overall differences in the sample data. In addition, the Gini coefficients of the coupling and coordination of vocational education and regional economic development in each region have different trends. The intra-regional differences in the coupling coordination degree of the ChangZhuTan region show an overall downward trend, and the intra-regional differences in the coupling coordination degree of the Xiangnan region show more frequent fluctuations, with a small rebound in 2016 and 2019 followed by a gradual fall. The intra-regional difference in the coupling coordination degree of western Hunan region shows wave-like ups and downs, with small but decreasing overall changes, and the intra-regional difference in the coupling coordination degree of Dongting Lake region shows the evolution of “rising-declining-rising-declining”, with an overall narrowing trend. In short, the intra-regional differences in the degree of coupling and coordination between vocational education and regional economic development in each region show a decreasing trend of different degrees. The reason for this may be due to the increased awareness of synergistic development in each region and the adoption of corresponding measures in the process of development, such as the introduction of relevant policies to promote the formation of synergistic innovation mechanisms, so that the intra-regional differences continue to decrease, and promote the coordinated development of the cities and towns in the region.

In addition, the inter-regional differences in the coupling and coordination of vocational education and regional economic development in each region can be divided into four periods, 2012~2014 is a rising period, the inter-regional Gini coefficient have risen, and the inter-regional disparity continues to decrease. 2015~2016 is a declining period, except for the Gini coefficient of the ChangZhuTan-DongTing Lake inter-regional Gini coefficient, the Gini coefficient of the interregional Gini coefficient of the 2015 compared to the In the period of differential development from 2017 to 2023, the Gini coefficients between the ChangZhuTan-Xiangxi, Xiangnan-Dongting Lake, and Xiangxi-Dongting Lake regions show significant decreases, while the gap in the coupling harmonization degree between the ChangZhuTan-Dongting Lake region shows a widening trend, and the Gini coefficients between the ChangZhuTan-Xiangnan, and Xiangnan-Xiangxi regions fluctuate a lot, but decrease overall.

In this paper, panel data at the provincial level of Hunan is selected for analysis from 2012 to 2023, and the measurement software chosen is EVIEWS and STATA. On the basis of data selection and descriptive statistics, the smoothness of the data was tested by EVIEWS, and then the selected data were analyzed econometrically with the help of STATA. In order to determine the type of model, firstly, F test and Hausman test are carried out on the data of the whole province, ChangZhuTan, XiangNan, XiangXi and DongTingLake region, and then the time trend term is examined, and finally, the two-way fixed effect model is selected for empirical analysis.

In order to study the influence mechanism of vocational education on regional economic development, this paper chooses regional economic development level (REDL) as the explanatory variable and vocational education development level (VEDL) as the explanatory variable. The selection of key variables and measurement methods are explained as follows: 1)

Regional economic development level. There are many measures of regional economic development level, such as GDP growth value, GDP growth rate, per capita GDP and per capita real GDP. In this paper, the comprehensive score obtained by solving the regional economic development level evaluation index system in the previous article is used as the evaluation source, and the larger its value is, the higher the economic development level is.

In addition, based on the research on vocational education and regional economic development of high quality, this paper selects the level of Internet technology (Net), cultural level (Cul), medical level (Med), and environmental level (ER) as the control variables to explore the degree of influence of vocational education on regional economic development.

In order to test the synergistic relationship between vocational education and regional economic development, and further analyze the influencing factors that promote the high-quality development of the regional economy, this paper establishes the following baseline econometric model: (26)REDLit=α0+α1VEDLit+α2∑Controlit+εit$RED{L_{it}} = {\alpha_0} + {\alpha_1}VED{L_{it}} + {\alpha_2}\sum {Contro{l_{it}}} + {\varepsilon_{it}}$

Where i represents the region, t represents time, the explanatory variable REDL_it represents the level of high-quality development of regional economy, α₀ is the constant term, and α₁ ~ α₂ is the regression coefficient of each variable. VEDL_it represents the core explanatory variable vocational education development level, ε_it is the random error term, and Control_it is the control variables, including the level of Internet technology, cultural level, medical level, and environmental level.

Based on the data selected in the previous section, the benchmark econometric model constructed is used as a way to test whether the development of vocational education can promote the high-quality development of the regional economy. Table 8 shows the results of the benchmark regression of the impact of the development level of vocational education on regional economic development, in which model (1) is the regression result without adding control variables, and models (2) to (5) are the regression results after gradually adding control variables. The t-statistics corresponding to the estimated coefficients are shown in parentheses, and ** and *** indicate that the estimated coefficients are significant at the 5% and 1% levels, respectively, and the same as later.

Based on the data in the table, it can be seen that there is a positive link between vocational education and the high-quality development of the regional economy, regardless of whether it is affected by the control factors or not, which suggests that vocational education can provide a strong impetus for the high-quality development of the regional economy.

In model (1), without adding control variables and considering individual fixed effects and time fixed effects, the correlation coefficient between vocational education and the level of high-quality development of regional economy is 0.315, and the two are significantly positively correlated at the 1% level. That is, for every 1 percentage point increase in the level of vocational education development, the level of regional economic development will increase by 0.315 percentage points. After gradually adding control variables, the coefficient of influence of vocational education on the level of regional economic development gradually decreases, and after adding all control variables, the correlation coefficient between vocational education and the level of high-quality development of regional economy is 0.227, which is significantly positively correlated at the level of 5%. Therefore, the positive influence effect of vocational education on the high-quality development of regional economy is verified. In the case of control variables, the level of Internet, culture and medical care also has a significant positive correlation with the high-quality development of the regional economy, probably because the improvement of the level of the Internet promotes the dissemination of technology, reduces the transaction cost of information, etc., and then promotes the high-quality development of the regional economy. The improvement of cultural level and medical level promotes the physical and mental quality of labor force, which in turn improves the high quality development of regional economy. The relationship between environmental regulation and the level of high-quality development of the regional economy is positive, but not yet significant, probably because the impact of environmental regulation on the high-quality development of the regional economy is formed over time and does not present itself in the early stages of high-quality development.

In order to verify the robustness of the benchmark regression results obtained in this paper, this paper conducts the robustness test by three methods: lagging one period, replacing the measure of the explanatory variables, and shrinking the tail of the variables. Table 9 shows the results of the robustness test, in which models (1) to (3) are the test results of lagging one period, replacing the measurement method of the explanatory variables and variable shrinkage treatment, respectively. 1)

Lagged one-period sample. Considering that the development of vocational education is generally shown through the accumulation of time, and the sample data with one period lag can obviously obtain the educational input and educational effectiveness of vocational education, which may cause regression bias and make the results distorted. Therefore, this paper selects the sample with one period of lag in this section, and uses the model to conduct regression analysis again, and the regression results show that the reliability of this research is high.

In summary, the econometric model constructed in this paper can accurately reflect the synergistic effect of the development level of vocational education on the regional economic development, and the full enhancement of the level of vocational education can help to strengthen the level of regional economic development and promote the high-quality development of the regional economy.

Benchmark regression analysis from the overall level of Hunan Province demonstrates that the level of vocational education has a positive effect on regional economic development, but there are obvious differences in the foundation of economic development and the level of vocational education in various regions of Hunan Province, resulting in greater spatial heterogeneity in the level of development of vocational education in Hunan Province, and there are large differences in the degree of contribution to regional economic development. In view of this, this paper respectively according to ChangZhuTan, XiangNan, XiangXi and DongTing Lake four major regions of the sample group regression, in order to explore the different regional dimensions of vocational education in Hunan Province on the regional economic development of the impact of the effect and regional differences. Table 10 shows the benchmark regression results by region.

The contribution effect of vocational education development level on the economic development of ChangZhuTan region is larger than that of Xiangnan region, with contribution coefficients of 0.186 and 0.075 respectively, both significant at 1% statistical level, and it shows a non-significant positive promotion effect on the economic development of western Hunan and Dongting Lake regions, with contribution coefficients of 0.036 and 0.137 respectively. This is mainly due to the uneven spatial distribution of vocational education resources, and the fact that advanced vocational education is more centrally distributed in the four regions of Hunan. Advanced vocational education is more concentrated in economically developed areas, these areas have a good foundation for development, and the higher level of vocational education development drives the rapid development of the region’s economy, absorbing more factors of production under the siphon effect, and realizing the sustained improvement of the level of economic development under the effect of the cyclic cumulative causality, so vocational education has a big role in promoting the economic development of ChangZhuTan and Xiangnan areas in Hunan Province. Therefore, vocational education contributes greatly to the economic development of ChangZhuTan and Xiangnan areas in Hunan Province. On the other hand, the level of vocational education in western Hunan and Dongting Lake region is relatively low, which makes it difficult to integrate with the regional economy, and the level of regional industrialization is low, which contributes little to the regional economic development. The level of development of vocational education restricts the further development of digital technology and real industry, which results in the insignificant empowering effect of vocational education on the economic development of western Hunan and Dongting Lake region. In conclusion, there is obvious regional heterogeneity in the role of vocational education development level on the regional economic development of Hunan Province.