Introduction

The integration of industry and education has long attracted much attention from scholars and policymakers. Western developed countries’ educational practices and post-war newly industrialized countries have shown a benign interactive relationship between higher education and industrial economy (Carnoy, 1997; Barro and Lee, 2013; Gruber and Kosack, 2014; Sun, 2020). According to data released by the International Monetary Fund, China’s GDP per capita reached $12,514 in 2023, ranking 69th among the world’s countries and regions. As a representative of developing countries, China emphasized the importance of building a high-quality education system and a modern industrial system and promoting the integration of industry and education in the report of the 20th Party Congress. Theoretically, Higher education can promote industrial economic development by upgrading the human capital level (Wu et al., 2023), increasing labor productivity (Simeunovic et al., 2022), and promoting technological innovation (Wu and Liu, 2021). In turn, industrial economic development can enhance the quality and effectiveness of higher education by increasing government tax revenue, providing jobs, and supporting university-enterprise cooperation (Drucker, 2015). However, unlike developed countries, developing countries, including China, face a series of competitive mechanisms in the development of their higher education due to their relatively backward economic development, including the allocation of educational funds, that lead to differences across regions and institutions, thus constraining higher education from serving the industrial economy. This problem also exists in Chongqing, which epitomizes China’s economic development, with a GDP per capita of $13,308 in 2023, similar to the national average. Given this, this paper takes Chongqing as a research sample, which is of great practical significance for revealing the coupling coordination development of higher education and industrial economy in developing regions and promoting the benign interaction between the two.

Early literature focused on the relationship between higher education and overall economic development. Early economists believed that economic growth came from capital accumulation and that material factors were the only source of economic growth. However, with the development of the theory of economic growth, the neoclassical school of economics represented by Marshall proposed that education was also a form of investment, thus linking education to economic development. Schultz (1962), after formally proposing the human capital theory, further stimulated academic research on the impact of education on economic development, laying the foundation for the new economic growth theory. Most studies suggest that higher education should be compatible with economic development (Cico et al., 2021). For example, Bell (1996) and Shin (2012) tested the impact of higher education on economic development using samples from developed countries such as Canada and South Korea, respectively. Wang and Liu (2011) and Zhu et al. (2018) conducted the same test using samples from developing countries such as Nigeria and China, respectively. Although early studies analyzed the relationship between higher education and economic development, such studies were more macro and did not focus on the industrial-economic level.

In recent years, some studies have delved further into the industrial economy to analyze its coordinated development with higher education. Johnes (1997), Drucker (2015), and Bertoletti et al. (2022) examined the impact of higher education on the industrial economy, using samples from developed countries such as the United Kingdom, the United States, and the European Union. Only a few scholars have explored the impact of higher education on the industrial economy in developing countries (Jumakulov et al., 2019). In China-specific studies, most of the literature (Wu and Liu, 2021; Ren and Zhou, 2022; Geng et al., 2023) tends to focus on China as a whole or the Yangtze River Economic Belt. However, the existing literature has the following limitations: first, the existing literature mainly focuses on the coupling coordination development of higher education and industrial economy in developed countries such as the United Kingdom and the United States (Johnes, 1997; Drucker, 2015), and little literature focuses on developing countries. Second, when the existing literature studies the coordinated relationship between higher education and industrial economy, it mostly starts the analysis from the qualitative research level (Jumakulov et al., 2019) or adopts macro data such as provinces and cities (Ren and Zhou, 2022), which limits the breadth and depth of the analysis to a certain extent. Third, the existing literature mainly explores the mutual influence between higher education and industrial economy (Geng et al., 2023), but does not deeply analyze the coupling coordination mechanism between the two.

In summary, this paper takes the data of 63 higher education institutions in Chongqing from 2013 to 2021 as the observation samples, matches them with the corresponding district and county data, and prudently measures and profoundly analyzes the coupling coordination degree and coupling mechanism between higher education and industrial economy in Chongqing. The possible contributions of this paper are reflected in the following three areas. First, unlike the existing literature that mainly focuses on developed countries or regions, this paper shifts the research perspective to developing areas, taking the data of Chongqing, China, as a sample to analyze the current situation and the influence mechanism of the coupling coordination between its higher education and industrial economy. Second, unlike the existing literature that explores the level of higher education development mainly based on macro data, this paper estimates the level of higher education development in Chongqing as a whole, in different regions and at various school levels based on microdata from 63 higher education schools in Chongqing. Its coupling coordination degree with industrial economy is further analyzed on this basis. Finally, this paper empirically examines the coupling coordination mechanism between higher education and industrial economy, which is easily neglected in the existing literature.

Research design

Analysis of the coupling mechanism between higher education and industrial economy

The level of higher education development is the output of the functioning of the higher education system in a region, which can be reflected by the conditions, scale, quality, and internationalization of higher education (Wu and Liu, 2021). The industrial economy covers all sectors of the national economy, which is mainly reflected by industrial scale, industrial structure, and industrial efficiency (Wang et al., 2022). Higher education and industrial economy mutually reinforce coupling relationships (see Fig. 1).

Fig. 1
figure 1

Coupling relationship between higher education and industrial economy.

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Higher education for industrial economic development

Higher education can expand the scale, optimize the structure, and enhance the efficiency of the industrial economy. First, higher education expands the industrial scale by directly promoting the development of the tertiary industry. Higher education improves the scientific and cultural level of educated people, thus promoting the development of scientific research, technical services, and high-tech industries (Liu et al., 2024; Zheng et al., 2024). Second, higher education promotes the upgrading of industrial structure by enhancing human capital (Teixeira and Queirós, 2016; Bai et al., 2020). The development of higher education can cultivate more professional talents and optimize the structure of the labor force so that the industrial sector can obtain higher comprehensive quality and promote the upgrading of the industrial structure (Mukhuty et al., 2022). Third, higher education supports industrial efficiency by improving technological innovation (Kong et al., 2022; Zheng et al., 2024). Higher education fosters technological innovation capacity and provides innovation factors for the industrial economy, which is a crucial driver for improving the efficiency of industrial innovation output.

Industrial economy for higher education development

Industrial economic development provides support for higher education. First, industrial economic development offers employment opportunities for higher education by expanding industry scale. Industrial economic development leads to the expansion of industrial scale, enhances the labor demand, and provides high-quality and sufficient jobs for college graduates to optimize the labor market structure (Voumik et al., 2023). Second, industrial economic development leads to optimizing the educational structure by adjusting the industrial structure (AlMalki and Durugbo, 2023). The existing higher education structure makes it difficult for the talents cultivated by colleges and universities to meet the needs of the development of local emerging industries, and the paradoxical phenomenon of “difficult employment” and “labor shortage” coexist. Therefore, to better serve the development of local economies, higher education must scientifically and reasonably adjust its hierarchical, regional, and disciplinary structures. The educational structure will be gradually optimized in this process of adaptation. Third, industrial economic development provides financial support for higher education by improving industrial efficiency (Bertoletti et al., 2022). Industrial economic development will optimize the allocation of resources and enhance economic efficiency, thus increasing local financial income and solving the problem of insufficient funding for education in backward areas.

Measurement of higher education and industrial economy

Indicator system for higher education and industrial economy

Indicator system of higher education (h). Based on the principles of scientificity, hierarchy, representativeness, and operability, combined with the actual situation of the development of higher education in Chongqing, this paper establishes the indicator system of higher education based on the conditions, scale, quality, and internationalization of higher education, respectively (see Table 1).

Table 1 Indicator system of higher education and industrial economy.
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Industrial economy indicator system (w). In this paper, the industrial economy is divided into three subsystems: industrial scale (w1), industrial structure (w2), and industrial efficiency (w3) (see Table 1). The specific measurements are as follows.

(1) Industrial scale is measured by the value added by each industry.

(2) Industrial structure includes industrial structure rationalization and industrial structure upgrading. The industrial structure rationalization is measured by the inverse of the Theil index, which is calculated as:

$$is{r}_{it}=1/T{L}_{it}=1/\left[\mathop{\sum }\limits_{j=1}^{3}({Y}_{itj}/{Y}_{it})\mathrm{ln}\left(\frac{{Y}_{itj}}{{Y}_{it}}\times \frac{{L}_{it}}{{L}_{itj}}\right)\right]$$
(1)

where i is the province; t is the year; isr is the industrial structure rationalization; TL is the Theil index; Y denotes the regional GDP; Yj denotes the value added of industry j; L denotes total employment; and Lj denotes the employment in industry j.

The formula for the industrial structure upgrading is as follows:

$$is{u}_{it}=\mathop{\sum }\limits_{j=1}^{3}\frac{{Y}_{itj}}{{Y}_{it}}\times j$$
(2)

where isu denotes the industrial structure upgrading, Yit and Yitj as above.

(3) Industrial efficiency is calculated using the DEA-Malmquist productivity index method (Chen et al., 2020). The outputs are measured by the value added by the three industries, respectively; the labor input is measured by the year-end employment of the three sectors; and the capital input is estimated by the perpetual inventory method. It should be noted that estimating the capital input of the three industries requires access to the current year’s investment series, depreciation rate, base period capital stock, and fixed asset investment price index. The specific measurements are as follows. ① Current year’s investment series. It is measured by the current year’s fixed asset investment in the three industries. ② Depreciation rate. By weighting the proportion of construction, equipment, and other costs in fixed asset investment from 2013 to 2022, the service life of fixed assets in Chongqing is set at 34 years. Then, assuming a residual value rate of 4%, the average depreciation rate of fixed assets in Chongqing is estimated to be 9.03% (Guo et al., 2023). ③ Base period capital stock. Referring to Young (2003), the formula \({K}_{0}={I}_{0}/(\delta +g)\) is applied. According to the sample period selected in this paper, the base year is determined as 2013, K0 is the base period capital stock, I0 is the comparable fixed asset investment in the base period, δ is the average depreciation rate of capital, and g is the average growth rate of fixed asset investment in Chongqing during the sample period. ④ Fixed asset investment price index. Since the fixed asset investment price index for each industry in Chongqing has not been released, Chongqing’s fixed asset investment price index is used as a substitute.

Methodology for measuring higher education and industrial economy

Based on the indicator system in Table 1, the entropy value method is applied to measure higher education and industrial economy levels, respectively. The steps are as follows.

Since all the indicators are positive, Eq. (3) is used to standardize the data, where max (Xij) and min (Xij) represent the maximum and minimum of variables, i and j represent regions and indicators.

$${x}_{ij}=[{X}_{ij}-\,\min ({X}_{ij})]/[\max ({X}_{ij})-\,\min ({X}_{ij})]$$
(3)

Equation (4) calculates the information entropy of each indicator in the composite index system of high-quality economic growth, where n represents the number of regions.

$${d}_{j}=-\,\mathrm{ln}\,\frac{1}{n}\mathop{\sum }\limits_{i=1}^{n}\left[\left({x}_{ij}/\mathop{\sum }\limits_{i=1}^{n}{x}_{ij}\right)\mathrm{ln}\left({x}_{ij}/\mathop{\sum }\limits_{i=1}^{n}{x}_{ij}\right)\right]$$
(4)

Equation (5) is used to determine the weight of each indicator, where m represents the number of indicators.

$${w}_{j}=(1-{d}_{j})/\mathop{\sum }\limits_{j=1}^{m}(1-{d}_{j})$$
(5)

The higher education (h) and industrial economy (w) levels are calculated respectively.

$${h}_{j}({w}_{j})=\sum _{j}{w}_{j}\left({x}_{ij}/\mathop{\sum }\limits_{i=1}^{n}{x}_{ij}\right)$$
(6)

Coupling coordination degree evaluation model

The measurement steps of the coupling coordination degree model are as follows:

Step 1 measures the coupling degree between higher education and industrial economy. Coupling is a principle of physics that refers to the dynamic association of two or more systems that are interdependent, coordinated, and facilitated. The coupling degree records the degree of interaction between system elements. According to the concept of coupling and coefficient model in physics, the coupling degree between higher education and industrial economy (C) is obtained, where 0 ≤ C ≤ 1. When C tends to 0, the systems are detuned. When C tends to 1, the systems are well coupled.

$$C=2\sqrt{h\times {\rm{w}}}(h+{\rm{w}})$$
(7)

Step 2, the coupling coordination degree between higher education and industrial economy (D) is measured, better identifying the coordination degree of the interaction coupling between two systems in different regions. T is the comprehensive coordination index, reflecting the synergistic effect between the two systems. α and β are the undetermined coefficients, and set α = β = 0.5.

$$D=\sqrt{C\times T},T=\alpha \times h+\beta \times w$$
(8)

The value of D ranges from [0,1], where 0~0.3 is low coordination, 0.3~0.5 is medium coordination, 0.5~0.8 is high coordination, and 0.8~1.0 is extreme coordination.

Empirical model of the coupling mechanism

To test the mutual influence of higher education and the industrial economy, this paper constructs the following model:

$${{\rm{h}}}_{i{\rm{j}}t}={\alpha }_{1}+{\beta }_{1}{{\rm{w}}}_{i{\rm{j}}t}+\mathop{\sum }\limits_{n=1}^{7}{\theta }_{{\rm{n}}}{C}_{{\rm{ij}}t}+{\mu }_{i{\rm{j}}}+{\eta }_{t}+{\varepsilon }_{i{\rm{jt}}}$$
(9)
$${{\rm{w}}}_{i{\rm{j}}t}={\alpha }_{2}+{\beta }_{2}{{\rm{h}}}_{i{\rm{j}}t}+\mathop{\sum }\limits_{n=1}^{7}{\theta }_{{\rm{n}}}{C}_{{\rm{ij}}t}+{\mu }_{i{\rm{j}}}+{\eta }_{t}+{\varepsilon }_{i{\rm{jt}}}$$
(10)

where h is the higher education level; w is the industrial economy level; β denotes the coefficient of the core explanatory variable on the dependent variable, and θ denotes the coefficient of the control variables on the dependent variable; α is a constant term; i is the higher education institution, j is the district and county in which the higher education institution is located, and t is the year; μ and η denote the individual and time effects, respectively, and ε denotes a random error term. C is a vector of control variables, including the economic development (ed), measured by GDP per capita; government expenditure (ge), measured by the share of general budget expenditure of the local government in GDP; the residents’ consumption (rc), measured by the per capita living consumption expenditure of residents divided by GDP per capita; the financial development (fd), measured by the proportion of deposit and loan balances of financial institutions to GDP; the openness degree (od), measured by the proportion of total imports and exports to GDP; the people’s livelihoods (pl), measured by the per capita disposable income of the residents divided by the per capita GDP; and the public cultural services (pc), measured by the number of books per capita in public libraries.

To examine the impact mechanism of the coupling coordination degree between higher education and industrial economy, this paper further constructs the following empirical model:

$${{\rm{D}}}_{{\rm{xij}}t}={\alpha }_{3}+{\beta }_{3}{{\rm{h}}}_{i{\rm{j}}t}+{\beta }_{3}{w}_{{\rm{xij}}t}+{\beta }_{3}{{\rm{h}}}_{i{\rm{j}}t}\times {w}_{{\rm{xij}}t}+\mathop{\sum }\limits_{{\rm{n}}=1}^{7}{\theta }_{{\rm{n}}}{C}_{{\rm{ij}}t}+{\mu }_{i{\rm{j}}}+{\eta }_{t}+{\varepsilon }_{i{\rm{jt}}}$$
(11)

where Dx represents the coupling coordination degree, which is D, D1, D2, D3 in different models, respectively, indicating the coupling coordination degree between h and w, w1, w2, w3; wx is the independent variable corresponding to the dependent variable Dx, which is w, w1, w2, w3 in different models, respectively, indicating the industrial economy, industrial scale, industrial structure, and industrial efficiency; other symbols are consistent with the above equation. Before the empirical analysis, the data of Chongqing districts and counties from 2013 to 2022 were matched with the data of 63 colleges and universities, with 630 observations. The descriptive statistics of each variable are shown in Table 2.

Table 2 Descriptive statistics.
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Data sources

Considering data availability, this paper selects 2013~2022 as the period span. The data for the higher education indicators in Table 1 come from the data related to the 63 colleges and universities of Chongqing in the Chongqing Education Yearbook. The data for the industrial economy indicators in Table 1 is sourced from the Chongqing Statistical Yearbook. The data of control variables in Section 2.4 are from China Statistical Yearbook (County-level) and local statistical yearbooks of Chongqing districts and counties. It should be noted that the missing values are supplemented by linear interpolation, and all variables involving price factors are deflated with 2013 as the base period.

Measurement analysis of higher education and industrial economy

Analysis of higher education

According to the indicator system in Table 1, this paper adopts the entropy value method to measure the education level of 63 higher education institutions in Chongqing from 2013 to 2022.

First, from the perspective of different types of higher education institutions, the 63 higher education institutions are divided into public undergraduate, private undergraduate, public specialized, and private specialized, with the results reported in Fig. 2. Figure 2 shows that, on the one hand, the average higher education level in Chongqing shows a trend of “rapid growth - essential stabilization—slight decline” during the sample period. This is manifested in rapid growth in 2013–2016, essential stabilization in 2016–2019, and a slight decline in 2019–2022. Higher education is closely related to the country’s economic development (Crawford and Cifuentes-Faura, 2022). In 2013–2016, China’s economy grew at a high rate, and the government increased the investment in human capital, leading to the booming development of higher education in Chongqing. In 2017, China’s economy shifted from the stage of rapid growth to the stage of high-quality development (Wang et al., 2022), and the economic growth rate slowed down, resulting in higher education development in Chongqing remaining stable. In 2019, the epidemic has not significantly improved the level of higher education in Chongqing. On the other hand, the higher education level of public undergraduate in Chongqing is much higher than that of other types of higher education institutions. Because public institutions in developing countries often have access to government policy guidance and financial support, this investment in education can enhance the regional competitiveness of public institutions. Meanwhile, undergraduate education is generally better than specialized education in terms of personnel training, scientific research, and students’ comprehensive quality.

Fig. 2
figure 2

Temporal trend of high education level in Chongqing and different types of higher education institutions from 2013 to 2022.

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Second, according to the planning of “one area and two clusters” Footnote 1 in Chongqing, all higher education institutions are categorized into the central urban areaFootnote 2, new area of the city properFootnote 3, city cluster of Three Gorges Reservoir area in northeast ChongqingFootnote 4 (referred to as the “northeast Chongqing”), and city cluster of Wuling mountain area in southeast ChongqingFootnote 5 (referred to as the “southeast Chongqing”), to analyze the higher education level in different regions of Chongqing. The specific results are depicted in Fig. 3. Figure 3 shows that the development of higher education levels in Chongqing is unbalanced, characterized by “central urban area>average level>new area of the city proper>northeast Chongqing>southeast Chongqing.” Due to the limited educational resources, developing countries usually adopt an unbalanced development approach in the early stages of academic development, which leads to unequal distribution of educational resources across regions. The central urban area has the most undergraduate institutions in Chongqing, with a solid comprehensive strength. In contrast, most higher education institutions in the other three regions are specialized institutions. There are no undergraduate institutions in southeast Chongqing, so its higher education level has always been the lowest.

Fig. 3
figure 3

Temporal trend of higher education level in Chongqing and different regions from 2013 to 2022.

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Finally, this paper categorizes the 63 higher education institutions by region in Chongqing to calculate the higher education level in the different areas (see Fig. 4). Figure 4 shows that, first, in terms of the central urban area, Beibei and Yuzhong are at the forefront of higher education level, Yubei, Nanan, and Shapingba are in second place, and Banan, Jiulongpo, and Jiangbei are relatively backward. Among them, public undergraduate education is exceptionally high in Beibei and Yuzhong, followed by Yubei, Nanan, Shapingba, and Banan. Second, in terms of the new area of the city proper, Jiangjin, Yongchuan, and Fuling have higher levels of higher education. At the same time, Bishan, Changshou, Hechuan, Qijiang, Dazu, and Tongliang are relatively lower. Among them, Jiangjin has an extremely high level of public specialties, followed by Yongchuan and Fuling. Third, higher education in northeastern Chongqing relies only on Wanzhou, and southeastern Chongqing relies only on Qianjiang. Meanwhile, both Wanzhou and Qianjiang have lower levels of higher education, with the former slightly higher than the latter. Compared with Qianjiang, Wanzhou’s public undergraduate level is higher.

Fig. 4
figure 4

Average level of higher education in districts and counties of Chongqing.

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Analysis of industrial economy

Based on the indicator system in Table 1, this paper measures the level of industrial economy and its three subsystems in Chongqing from 2013 to 2022 (see Fig. 5). Figure 5 shows that, on the one hand, the industrial economy level of Chongqing has developed rapidly, rising from 0.145 to 0.950, with a high average annual growth rate of 23.23%. On the other hand, the levels of the three subsystems of the industrial economy also show an upward trend, with the current level of industrial efficiency being the highest, followed by that of industrial scale and industrial structure.

Fig. 5
figure 5

Temporal trends of the level of the industrial economy and its three subsystems in Chongqing from 2013 to 2022.

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Specifically, the industrial scale index has been rising steadily, which is related to the fact that the value added of Chongqing’s primary, secondary, and tertiary industries has grown steadily over the sample period. The industrial efficiency index has been rising amidst fluctuations related to Chongqing’s ongoing commitment to improving industries’ quality and efficiency. Although lower than the other two subsystems, the industrial structure index also shows an upward trend, indicating that the transformation and upgrading of traditional industries have achieved significant results (Ran et al., 2023). However, the transformation and upgrading of the industrial structure is a long-term process that cannot be accomplished overnight, so the value is lower than the other two subsystems.

Analysis of the coupling coordination degree between higher education and industrial economy

Perspectives from the whole region and different types of higher education institutions

Perspectives from the whole region

Table 3 shows the coupling coordination degree between higher education and industrial economy of the whole region and different types of higher education institutions in Chongqing from 2013 to 2022. It can be found that from 2013 to 2022, the average value of the coupling coordination degree between higher education and industrial economy in Chongqing is 0~0.5, which is at the medium level of coordinated development. This indicates that the interaction between the two has not entered a benign coordination stage. However, the mean value of the overall coupling coordination degree increases steadily during the sample period, indicating that the coupling coordination degree between higher education and industrial economy in Chongqing is relatively stable, and the coupling coordination degree between the two is gradually developing in a good direction.

Table 3 Coupling coordination degree between higher education and industrial economy of different types of higher education institutions in Chongqing.
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Perspectives from different types of higher education institutions

Table 3 shows that, on the one hand, the coupling coordination degree between higher education and industrial economy of the public undergraduate in Chongqing is significantly higher than that of private undergraduate, public specialized, and private specialized. This indicates that the interactive development between higher education and industrial economy of public undergraduate in Chongqing has stepped into the stage of benign coordination. In contrast, private undergraduate, public specialized, and private specialized have not. On the other hand, the mean values of the coupling coordination degree of different types of higher education institutions show a steady increase in the sample period, indicating that the coupling coordination degree of both public undergraduate and the other three types of higher education institutions is steadily improving.

Perspectives from different regions and different subsystems

Perspectives from different regions

Table 4 shows that: ① Although the coupling coordination degree of each region has been increasing over time, there is the phenomenon of “the high is always high, and the low is always low.” On the one hand, the coupling coordination degree of all regions and counties shows an increasing trend. On the other hand, the 19 districts and counties in Chongqing after 2016 are relatively fixed at the medium and high levels of coordinated development. ② The coupling coordination degree of each region shows an olive-shaped distribution with fewer areas at the two ends and more in the middle. Most districts and counties have switched from low coordination to medium coordination after 2016, and there are currently no districts and counties with high coordination and low coordination. ③ The coupling coordination degree in Chongqing’s “one area” is generally higher than in the “two clusters”. Specifically, the six districts with a high coordination degree in 2022 are all in the central urban area. Yongchuan, Dazu, Qijiang, Hechuan, and northeast Chongqing switched from low to medium coordination after 2014. Southeast Chongqing converted from low to medium coordination after 2016. The industrial economy of Chongqing is developing faster. However, the higher education level in most districts and counties lags, leading to the inability of higher education in these districts and counties to satisfy the demand for high-level talent in the industrial economy (Ren and Zhou, 2022), thus resulting in a low coupling coordination degree.

Table 4 Coupling coordination degree between the higher education and industrial economy in different regions of Chongqing.
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Perspectives from different subsystems

Table 5 shows that ① the coupling coordination degree between higher education and industrial scale, industrial structure, and industrial efficiency in Chongqing shows a growing trend, and the distribution structure shows an olive-shaped distribution with fewer areas at the two ends and more areas in the middle. However, the coupling coordination degree of the three subsystems has differences, with the mean value of h and w3 being the largest, the mean value of h and w1 being the second largest, and the mean value of h and w2 being the smallest. ② The path-locking effect of the coupling coordination distribution varies according to industry scale, industry structure, and industry efficiency. Specifically, the stability of the coupling coordination distribution of h and w1 in each region is the highest, with only five districts spanning two different coupling coordination regions; the stability of the coupling coordination distribution of h and w3 is the lowest, except Changshou, the rest of the districts and counties spanning two different coupling coordination regions; and the stability of the coupling coordination distribution of h and w2 is in-between the former two. ③ The distribution pattern of the coupling coordination degree between higher education and industrial efficiency is similar to the overall coupling coordination degree. Comparing Fig. 6a, d, it can be found that the distribution of the coupling coordination degree between h and w3 is similar to that of h and w. ④ The distribution pattern of the coupling coordination degree between higher education and industrial scale is identical to that of higher education and industrial structure. Comparing Fig. 6b, c, it can be found that the coupling coordination degree distribution area of h and w1 is similar to that of h and w2, and only Yuzhong and Banan have differences. ⑤ The coupling coordination degree between higher education and industrial efficiency is significantly higher than the other two subsystems. Figure 6 shows that compared to the coupling coordination degree between h and w3, the coupling coordination degrees of h and w1 or w2 in most districts and counties are low. ⑥ The coupling coordination degree of three subsystems in the central urban area has a leading role. Table 5 and Fig. 6 show that Yuzhong has a relatively high degree of coupling coordination among the three subsystems, followed by Beibei, Yubei, Nanan, and Shapingba. The possible explanation is that the higher education level in the central urban area is significantly higher than in other regions, consistent with the pace of industrial economic development in Chongqing.

Table 5 Coupling coordination degree between higher education and three subsystems of industrial economy in different regions of Chongqing from 2013 to 2022.
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Fig. 6: Spatial distribution of the coupling coordination degree between higher education and industrial economy and its three subsystems in Chongqing in 2022.
figure 6

Notes: h is the higher education level; w is the industrial economy; w1 is the industrial scale; w2 is the industrial structure; w3 is the industrial efficiency.

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Coupling mechanism between higher education and industrial economy

Based on the panel data of 63 higher education institutions in Chongqing from 2013 to 2022, this paper regresses Eqs. (3) to (5), respectively, and the estimation results are reported in Table 6. Columns [1] and [2] correspond to Eqs. (3) and (4), and columns [3] to [6] correspond to Eq. (5).

Table 6 Coupling interaction mechanism and coupling coordination degree impact mechanism of higher education and industrial economy in Chongqing.
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Table 6 shows that ① the mutual promoting effect between higher education and industrial economy in Chongqing is significant. The impact coefficient of w on h in column [1] is 0.105, and the impact coefficient of h on w in column [2] is 0.254, both of which are positive at the 1% significance level. ② The development of higher education, industrial economy, industrial scale, industrial structure, and industrial efficiency in Chongqing can all enhance their respective coupling coordination. In columns [3]~[6], the coefficients of h and wx on Dx are significantly positive in their respective models. ③ Higher education and industrial economy, industrial structure, and industrial efficiency in Chongqing have significant positive moderating effects on their respective coupling coordination degrees. In contrast, the positive moderating effect of industrial scale is not significant. In columns [3]~[6], except for the insignificant effect of the interaction term between h and w1 on D1, the interaction terms between h and w2 and between h and w3 have significantly positive effects on their respective coupling coordination degrees, with marginal effects of 2.446 and 0.673, respectively. ④ Economic development, government expenditure, openness degree, and people’s livelihood are conducive to the overall coupling coordination development and the coupling coordination development of higher education and industrial scale. Government expenditure and financial development are essential factors in promoting the degree of coupling coordination between higher education and industrial structure. Economic development, financial development, and openness degree are crucial factors in promoting the coupling coordination degree between higher education and industrial efficiency (Fang et al., 2021; Geng et al., 2023).

This paper further carries out a sub-sample regression based on different types of higher education institutions and regions to test the heterogeneity of the impact mechanism of the coupling coordination degree between h and w, and the estimation results are reported in Table 7. Table 7 shows that the promoting effect of private higher education on the coupling coordination degree in Chongqing is higher than that of public higher education, and the promoting effect of the industrial economy on the coupling coordination degree is higher in undergraduate education. Columns [7]~[10] show that the impact coefficients of higher education and industrial economy on the overall coupling coordination degree are significantly positive in different types of higher education institutions. Among them, the impact coefficient of higher education reaches 2.299 and 2.303 in private undergraduate and private specialized education, while it is 1.222 and 0.414 in public specialized education and public undergraduate education. Meanwhile, the impact coefficient of the industrial economy reaches 0.136 and 0.103 in public and private undergraduate education, while it is 0.094 and 0.084 in private and public specialized education. The possible explanation is that, on the one hand, just as production factors satisfy the law of diminishing marginal returns (Mwananziche et al., 2023), since the private higher education level in Chongqing is lower than the public higher education level, private higher education contributes more to the coupling coordination degree when the same amount upgrades both. On the other hand, the new economic growth theory suggests that the enhancement of human capital improves the skill and efficiency of laborers so that they can better serve the development of local industries. Compared with specialized education, undergraduate education reflects a higher level of human capital, so the promoting effect of the industrial economy on the coupling coordination degree is more significant in undergraduate education.

Table 7 Heterogeneity test of the impact mechanism of the coupling coordination degree between higher education and industrial economy in Chongqing.
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Compared with the central city, the higher education and industrial economy in the two clusters and the new area of the city proper of Chongqing have a promoting effect on the overall coupling coordination degree. According to columns [11] to [14], the impact coefficients of higher education and industrial economy on the overall coupling coordination degree are significantly positive in different regions. Among them, the promoting effect of higher education and industrial economy is the largest in southeast Chongqing, followed by northeast Chongqing, the new area of the city proper, and the central urban area. The possible explanation is that, compared with the central urban area of Chongqing, the higher education and industrial economy in the new area of the city proper and the two clusters are relatively backward, so once the higher education and the industrial economy have been improved, it can give play to its latecomer’s advantage, which can enhance the coupling coordination degree between h and w to a greater extent.

Main conclusions and policy recommendations

Main conclusions

This paper matches the data of 63 higher education institutions with the corresponding data of districts and counties in Chongqing from 2013 to 2022, prudently measures and analyzes the level and coupling coordination degree between higher education and industrial economy (including the three subsystems) in Chongqing, and carries out theoretical deduction and empirical test on the coupling coordination mechanism of higher education and the industrial economy to draw the following conclusions:

  1. (1)

    The level of higher education in Chongqing shows a trend of “rapid growth-essential stabilization-slight decline” during the sample period, and the higher education level of public undergraduate is much higher than that of other types of higher education institutions. In addition, the development of higher education levels in Chongqing is unbalanced, characterized by “central urban area>average level>new area of the city proper>northeast Chongqing>southeast Chongqing.” The level of the industrial economy and its three subsystems in Chongqing shows a clear upward trend during the sample period, with the current level of industrial efficiency being the highest, followed by that of industrial scale and industrial structure.

  2. (2)

    Although the coupling coordination degree between higher education and industrial economy in Chongqing is at a medium level, it is gradually developing in a good direction. Specifically, first, in terms of different types of higher education institutions, the coupling coordination degree between public undergraduate education and the industrial economy has entered a benign coordination stage, and private undergraduate, public specialized, and private specialized education have not entered a benign coordination stage, but are steadily getting better. Second, in terms of different regions, the coupling coordination degree of each area has been improving over time. Still, there exists the phenomenon of “high constant high, low constant low,” and it is characterized by an olive-shaped structure with fewer areas at the two ends and more areas in the middle for the quantity distribution and “one area” higher than “two clusters” for the geospatial distribution. It should be emphasized that the main reason for the low degree of coupling coordination is that the industrial economy is ahead of the higher education of most districts and counties in Chongqing. Finally, in terms of different subsystems, the coupling coordination degree between the higher education and three subsystems of the industrial economy in Chongqing shows a growth trend, and the distribution shows an olive-shaped structure. The degree of coupling coordination between higher education and industrial efficiency is significantly higher than that of industrial scale and structure. In addition, the coupling coordination degree of the three subsystems in the central urban area has a leading role.

  3. (3)

    Theoretically, higher education and industrial economy can realize benign coupling interaction. Further empirical tests found that ① Higher education and industrial economy in Chongqing have a significant mutual promotion effect. ② The development of higher education, industrial economy, industrial scale, industrial structure, and industrial efficiency in Chongqing can enhance their coupling coordination degree. ③ Higher education and industrial economy, industrial structure, and industrial efficiency in Chongqing have significant positive moderating effects on their respective coupling coordination degrees. ④ Economic development, government expenditure, openness degree, and people’s livelihood are conducive to the overall coupling coordination development and the coupling coordination development of higher education and industrial scale. Government expenditure and financial development are essential factors in promoting the degree of coupling coordination between higher education and industrial structure. Economic development, financial development, and openness degree are crucial factors in promoting the coupling coordination degree between higher education and industrial efficiency. ⑤ The promoting effect of private higher education on the coupling coordination degree in Chongqing is higher than that of public higher education, and the promoting effect of industrial economy on the coupling coordination degree is higher in undergraduate education. Compared with the central city, the higher education and industrial economy in the two clusters and the new area of the city proper of Chongqing have a promoting effect on the overall coupling coordination degree.

Policy recommendations

In the process of promoting education and economic development in developing countries, the experience of coupling coordination of higher education and industrial economy in Chongqing provides us with significant reference value:

  1. (1)

    Balanced development is fundamental to the coupling coordination of higher education and industrial economy. Developed regions and relatively developed countries should make full use of their financial resources, openness degree, policy environment and livelihood conditions, and other advantages, not only fully developing the industrial economy to increase the demand for higher education but also increasing the construction of higher education to provide sufficient human capital for the development of the industrial economy. At the same time, in addition to the need for new and expanding higher education institutions, less developed regions, and less developed countries need to focus more on coordinating the discipline construction of existing colleges and universities with significant industries.

  2. (2)

    The coupling coordination between higher education and industrial structure in Chongqing is relatively low, resulting in a low coupling coordination between higher education and industrial economy. Therefore, the implication for developing countries is that industrial economic development should focus on improving industrial structure. Specifically, it is necessary to strengthen the deep integration of government, industry, academia, research, and application, better connect innovation with the market, scientific research with industry, scientists with entrepreneurs, and promote the upgrading of industrial structure.

  3. (3)

    The coupling coordination development of higher education and industrial economy is a long-term and systematic project. Developing countries can refer to Chongqing’s experience and start from various aspects, such as economic development, government expenditure, financial development, openness degree, and people’s livelihood, to accelerate the coupling coordination development of higher education and industrial economy.

  4. (4)

    Higher education and industrial economy in Chongqing have not yet reached a benign coordination state, which is ultimately due to the private undergraduate, the public specialized, the private specialized, the new area of the city proper, and the two clusters have not entered the benign coordination stage. Therefore, developing countries should also pay attention to the uncoordinated development of the industrial economy and private and specialized education, as well as education in peripheral areas. On the one hand, colleges and universities need to strengthen private and specialized education through the construction of faculty, the implementation of preferential policies, the standardization of enrollment management, and the protection of equal rights and interests. On the other hand, the Government should not only increase the tilting of quality resources to peripheral areas through policy support, resource sharing, and infrastructure investment but also enhance the independent development capacity of peripheral regions in terms of economy, finance, and livelihood.