Introduction

China has experienced a significant transformation in recent decades, shifting from a mainly agricultural society to a leading global economic force. The nation’s unwavering dedication to developing a highly skilled and educated workforce has been crucial in this transformation (Wang et al., 2018). China’s higher education sector has seen remarkable growth and expansion, demonstrating a strong commitment to development. Enrollment rates in higher education institutions have increased significantly from 2% in 1978 to 48.5% in 2021, as reported by the World Bank (Abaidoo, 2021). The rapid growth illustrates the significant investment and focus on higher education in China, where more than 44 million students are enrolled in universities and colleges nationwide (Yang, 2018).

China’s higher education system is now officially the largest in the world due to the recent increase in enrollment. The growth of higher education has not only boosted access to educational opportunities but has also been crucial in advancing socio-economic development and fostering innovation in the nation (Xu et al., 2020). China’s expanding higher education sector demonstrates its dedication to developing human capital and acknowledging its crucial role in shaping the nation’s future as it asserts its global influence (Xu & Montgomery, 2019). The growth of China’s higher education system has unquestionably impacted the country’s human capital development. China has experienced a notable improvement in its economic competitiveness and technological strength due to its growing number of skilled professionals (Liu et al., 2020a, 2020b). Research conducted by esteemed organizations such as the Organisation for Economic Cooperation and Development (OECD) highlights China’s rise as a leading performer in important areas like science, technology, engineering, and mathematics (STEM) education (Jarvis & Mok, 2019). China’s significant investment in higher education has enhanced its workforce’s skills and stimulated innovation, leading to advancements in multiple sectors and establishing China as a strong competitor on the global stage (Bodolica & Spraggon, 2021).

Despite notable accomplishments, the changing dynamics of China’s higher education system and its complex connections to human capital development pose intricate challenges that require further investigation. Although current research has clarified some aspects of this intricate relationship, significant gaps remain in our comprehension (Mei & Symaco, 2022). The gaps include the effectiveness of certain policy interventions, the socio-economic implications of expanding higher education, and the changing demands of the labor market due to technological advancements. A thorough research is needed to explore these complexities and provide insights for policy decisions and institutional strategies to support China’s human capital development trajectory (Anlimachie & Avoada, 2020; Cooke et al., 2021). Contemporary research mainly emphasizes the numerical aspects of China’s higher education growth, frequently neglecting the qualitative aspects of this change. There is a lack of research on how changes in curriculum, teaching methods, and student demographics affect the skills and employability of graduate students (Zhou & Luo, 2018). The complex interplay among higher education policies, market needs, and individual career goals in the Chinese context has not been thoroughly researched.

This study aims to fill knowledge gaps by thoroughly analyzing China’s higher education system and its diverse impact on human capital development. This study aims to thoroughly investigate China’s higher education system and its impact on human capital development. We will analyze the development of China’s higher education system by examining important policy changes, curriculum reforms, and pedagogical shifts that have influenced its progress. We aim to evaluate how higher education in China influences graduates’ skills, job prospects, and career paths by examining the connection between academic achievements and professional results. Our research investigates how individual aspirations, market demands, and higher education policies interact to impact human capital development in China. Finally, we aim to create suggestions to enhance China’s higher education system, specifically by developing a workforce that can effectively adapt to the challenges of a rapidly evolving global environment. We seek to contribute to the current discussion on reforming higher education and developing human capital in China. We aim to provide valuable perspectives for policymakers, educators, and stakeholders interested in the country’s socio-economic advancement. The paper includes a literature review, methodology, findings, discussion, and conclusion in its structure. The literature review provides context for China’s higher education system, while the methodology describes the research approaches. Analyze empirical data to derive findings and discuss the implications for stakeholders. The conclusion briefly overviews important discoveries and suggests actions for upcoming research and policy measures.

Literature Review

Theoretical Underpinning

Investments in education pay off in the long run, both for people and for society as a whole, according to human capital theory, which is a foundational framework for comprehending the connection between education, skill development, and economic prosperity (Xu & Li, 2020). This theory gives a robust framework for examining China’s economic growth and competitiveness path, especially in light of its efforts to expand its higher education system. Human capital theory emphasizes the importance of higher education in preparing individuals to succeed in a world that is becoming more globalized and technologically advanced by viewing it as an investment in human capital (Marginson, 2019). The growth of China’s university system aligns with the tenets of human capital theory because it shows that the country values education highly for its contribution to innovation and economic progress. China aspires to improve the quantity and quality of its human capital by expanding access to higher education (Guo et al., 2019). This will help the country achieve sustained economic growth and competitiveness. Greater workforce participation from people with more education and training means more productivity increases and more opportunities for innovation in all parts of the economy (Surya et al., 2021). In addition, the theory of human capital emphasizes the significance of ongoing education and skill development, pointing out that human capital accumulation is inherently dynamic. The capacity to learn and adapt is crucial in today’s fast-paced Chinese economy, where new technologies and changing consumer preferences are driving forces behind economic growth (Shen et al., 2020). In addition to making more formal education available, China’s higher education expansion promotes a culture of lifelong learning, which helps people adapt to a dynamic job market. The human capital theory also highlights how education has a multiplier impact on societal and economic development (Ali et al., 2021). Investments in higher education have good externalities that benefit society overall, in addition to the obvious personal advantages. For China, increasing highly educated workers is good for the country’s innovation ecosystem, promoting entrepreneurship, driving technological progress, and helping the economy diversify (Gao & Mu, 2021). A highly educated workforce also leads to greater civic engagement, social cohesiveness, and general social welfare.

A compelling framework for comprehending the revolutionary effect of China’s expansion of higher education on economic growth and competitiveness is provided by human capital theory. China is enhancing its human capital and setting the stage for long-term growth and prosperity through its investments in education and skill development (Zhou et al., 2018). Leveraging the principles of human capital theory will be essential for China to maximize the dividends of its investment in higher education and realize its vision of becoming a global leader in innovation and economic dynamism, as education remains a strategic imperative for the country (Tao et al., 2022). China is placing a premium on developing a larger workforce with strong technical and scientific backgrounds, and the skill-based competition theory offers a useful framework for comprehending this trend (Liu et al., 2020a, 2020b). According to this school of thought, technological prowess and the ability to put that knowledge to use are the two most important differentiating factors in today’s global marketplace. China’s goal of preparing its workforce to compete successfully in the global economy is closely related to this theory, which explains why it is making strategic investments in higher education (Alsharari, 2018). Aiming to enhance the country’s global competitiveness, China is making a concerted effort to expand its higher education system and prioritize STEM education. China aspires to be a global leader in innovation, technology, and industrial development by fostering a group of highly competent individuals in vital fields like engineering, IT, and scientific research (Kjellgren & Richter, 2021). The skill-based competition theory emphasizes the significance of these investments in developing a country’s comparative advantage and strengthening its ability to succeed in a globally integrated and competitive market (Harrison et al., 2018). The ability to adapt one’s skill set and apply it in the face of changing market conditions and technology developments is a central tenet of the skill-based competition theory (Li & Li, 2021). Practical, hands-on learning experiences and industry-relevant skill development are being prioritized in China’s higher education expansion, in addition to the traditional emphasis on imparting theoretical knowledge (Indrawati & Kuncoro, 2021). China hopes to produce a workforce that can power long-term economic growth, technological innovation, and sustainable development by encouraging innovation, creativity, and entrepreneurship in its educational institutions (Zhang et al., 2021).

In addition, skill-based competition theory stresses how educational investments can promote a positive feedback loop of productivity, innovation, and economic growth. Research and development, knowledge creation, and technology transfer are all boosted by China’s higher education system, which consistently graduates highly skilled professionals (Chen et al., 2022). This, in turn, benefits the country’s innovation ecosystem. This boosts China’s competitiveness in established and emerging markets by increasing the country’s ability to create and sell innovative technologies. The strategic approach to educational investments in China and its implications for global competitiveness can be better understood with the help of the skill-based competition theory (Ratten & Usmanij, 2021). To drive economic growth, technological innovation, and sustainable development, China is prioritizing STEM education and cultivating a skilled workforce to leverage its human capital advantage (Zhou et al., 2021). In an ever-more-complex and interdependent world, the skill-based competition theory will continue to play a significant role in molding China’s educational policies and strategies, ensuring the country’s success as it navigates the complexities of the global economy (Min & Zhu, 2019).

To better comprehend China’s goal of cultivating a knowledge-based workforce through the expansion of its higher education system, it is helpful to refer to knowledge economy theory. Knowledge, innovation, and intellectual capital are deemed crucial in this theory because of their importance in propelling economic growth and prosperity in the modern global economy (Clarke & Gholamshahi, 2018). As a country that wants to use knowledge and innovation to drive its economic development and global competitiveness, China is investing heavily in higher education, which aligns with this idea. China’s endeavors to foster a highly educated and trained labor force reflect the country’s understanding of the significance of information and creativity as engines of economic progress (Kang & Xiong, 2021). A knowledge-based workforce that can solve complex problems and drive industrial transformation is China’s goal, and the country plans to get there by increasing access to higher education and putting more money into R&D (Jacob et al., 2018). The importance of these investments in cultivating a spirit of innovation, entrepreneurship, and lifelong learning in China’s educational institutions is highlighted by knowledge economy theory (Mei & Symaco, 2022). Knowledge economy theory goes even further by highlighting how all parts of the economy rely on one another to produce, distribute, and consume new knowledge. As part of its push to modernize its higher education system, China embraces interdisciplinary methods that bring together experts from different fields to foster creativity and new perspectives (Holford, 2019). China aims to accelerate technological advancements and knowledge diffusion across various industries by promoting interdisciplinary research and innovation hubs within its universities and research institutions. This will break down silos and foster synergies. According to the knowledge economy theory, human capital is critical in driving innovation and economic competitiveness (Sun & Cao, 2020). China is pouring resources into higher education to prepare its workforce for the dynamic global economy and cultivate individuals who can think critically, creatively, and adapt. China aspires to become a frontrunner in knowledge-driven sectors like clean energy, artificial intelligence, and biotechnology by educating its citizens to take advantage of new technology and overcome complicated obstacles (Nevalainen et al., 2021).

Moreover, another theoretical framework to consider in this study is the Theory of Technological Pedagogical Content Knowledge (TPACK), which emphasizes the intersection of pedagogy, content knowledge, and technology in educational settings (Koehler et al., 2014). Understanding how digitalization affects teaching methods, curriculum design, and student learning outcomes is crucial in the context of China’s higher education expansion. Furthermore, incorporating elements of constructivist learning theory offered valuable insights into the role of higher education in promoting innovation and creativity. According to constructivist learning theory, learners actively construct their understanding of the world through experiences and reflection (Zajda & Zajda, 2021). In the context of China’s higher education expansion, this theory can shed light on how educational institutions are moving beyond traditional lecture-based approaches to embrace more student-centered, experiential learning methodologies. Chinese universities can nurture innovative thinking skills to drive economic growth and technological advancement by encouraging collaboration, problem-solving, and experimentation.

Additionally, drawing on theories of transformative learning could deepen the discussion on the role of higher education in fostering critical thinking and societal change. Transformative learning theory posits that learning experiences have the potential to transform individuals’ perspectives, beliefs, and behaviors (Cranton, 2016). In the context of China’s expansion of higher education, exploring how universities promote critical reflection, challenge existing paradigms, and foster a culture of social responsibility could provide valuable insights into the broader societal impacts of education. By empowering students to question assumptions, engage in meaningful dialogue, and take action on pressing social issues, Chinese universities can contribute to positive societal change and sustainable development.

The Role of China’s Universities in Building Human Capital

When investigating the role that higher education plays in the development of human capital, it is essential to take into account not only the inherent value and impact of education but also the nuanced manifestations of education within a variety of national contexts and cultural frameworks. Scholars have conducted extensive research on this topic, developing various theories and points of view (Aboramadan et al., 2020; Passaro et al., 2018). Early proponents of the human capital theory, such as Baharin Roziana and Attanasio Orazio, emphasized education as an investment that increases individual productivity and income potential. This laid the groundwork for understanding the significance of higher education for both the growth of the economy and the accumulation of human capital (Galiakberova, 2019; Sellar & Zipin, 2019). This idea has been supported by subsequent research conducted by academics such as Wiswall Matthew, which has shown a positive correlation between education and individual income; this correlation was demonstrated through empirical analysis.

Additionally, research conducted by Antoniuk Valentyna has shed light on the challenges that are faced by higher education in countries such as Ukraine, as well as the pivotal role that Ukrainian higher education plays in the process of European integration (Huisman, 2019; Shevchenko, 2019). In addition, studies that have been conducted to investigate the role that work engagement plays as a mediator in higher education have attempted to investigate the impact that human resource management practices have on organizational commitment. Researchers such as Hsu Ching-Chi and Lauder Hugh have conducted extensive research on the rapid expansion in China’s higher education sector, which occurred at the end of the twentieth century (Aboramadan, 2022; Shoaib et al., 2021). They emphasized the extremely significant implications that this expansion would have on the development of China’s economy and the composition of its human capital. Studies conducted by academics such as Wang Huimin, who examined the contribution of higher education to human capital in the context of “rural revitalization,” and Chen Xiaoli, who utilized the MRW (Mankiw-Romer-Weil) model to evaluate the contribution of human capital factors to economic growth, provide evidence that during this time period, there was a significant increase in the number of students enrolling in universities in China, as well as an increase in the quantity and quality of higher education institutions (Ahmed et al., 2020; Wan, 2024; Wang et al., 2023).

Several researchers, including Liu et al. (2023), have investigated human capital levels in China and found significant differences between regions. They examined the impact of regional differences on education quality and human capital levels. In contrast, others, such as Guo et al. (2020), brought attention to the limitations placed on higher education due to the unequal distribution of human capital during urbanization. In this discussion, academics such as Huwei et al. (2023) discussed policy interventions to reduce the disparity in human capital between regions and foster balanced educational growth. According to the most recent research findings, higher education dynamics are significantly impacted by factors such as the levels of economic development in a region, the orientations of policy, and the amount of social capital (Klofsten et al., 2019). As a result of research by Mbithi et al. (2021), which demonstrates how technological advancement stimulates demand for higher education and spurs reforms within the education system, technological progress emerges as a key factor driving human capital growth. This is evidenced by technological progress being a key factor driving human capital growth. Despite the abundance of research conducted on the development of technology and the expansion of capital, there is still a lack of comprehension regarding the connection between higher education and the expansion of human capital in China (Tian & Zhang, 2023). The current studies do not present a comprehensive theoretical and methodological framework that considers the specific contributions of human capital within the context of regional disparities, educational quality, and disciplinary diversity (Faggian et al., 2019). This article aims to fill this void by constructing a theoretical and empirical analysis framework to conduct an in-depth investigation into the influence that the expansion of higher education in China has on human capital growth (Cooke et al., 2021).

Data Sources

The research data utilized in this study has been meticulously gathered from the China Science and Technology Statistical Yearbook, covering the extensive timeframe from 2005 to 2020. This comprehensive dataset encompasses a plethora of vital information crucial for analyzing various facets of China’s educational and economic landscape. The collected data can be systematically categorized into three main sections:

  1. (a)

    Total capital formation and composition: This section presents a detailed overview of the total capital formation and its composition across different regions within China. Total capital formation, measured in 100 million yuan, serves as a primary indicator of economic activity and investment. Furthermore, this section delves into the breakdown of total capital formation, delineating between total fixed capital formation and inventory changes. The composition of total capital formation, expressed as a percentage (total capital formation = 100), provides insights into the relative contribution of fixed capital formation and inventory changes to the overall economic landscape.

  2. (b)

    Information on schools, faculty, and full-time teachers: This segment offers a comprehensive snapshot of the educational infrastructure in China, encompassing all levels and types of educational institutions. Higher education, secondary education, primary education, work-study schools, schools for special education, and preschool education are all included in this dataset. Moreover, the information extends beyond mere institutional enumeration to encompass the number of faculty members and full-time teachers associated with each educational level and type. Such granular insights are invaluable for understanding the educational ecosystem and workforce dynamics across various sectors.

  3. (c)

    Summary of students in various levels and types of education: This section provides a nuanced depiction of student demographics across different educational levels and types. Crucial metrics such as the number of graduates, enrollment figures, and current student populations are meticulously documented for each educational tier. By delineating these statistics, researchers can discern trends in educational attainment, enrollment patterns, and demographic shifts over the specified timeframe.

Overall, the utilization of data sourced from the China Science and Technology Statistical Yearbook facilitates a comprehensive analysis of China’s educational and economic landscape spanning from 2005 to 2020. The systematic organization of information enables researchers to glean valuable insights into capital formation dynamics, educational infrastructure, and student demographics, thereby fostering a deeper understanding of China’s socio-economic development trajectory.

Statistics

Using a wide range of statistical data and methodologies, this research examines the evolution of China’s educational system and the effects of these shifts from 2005 to 2020. A look at regional differences in economic growth, changes in the number of educational institutions, and student enrollment at different levels all shed light on the intricate dynamics of China’s educational system. The report uses panel data analysis and the Malmquist index method to shed light on the intricate connection between variables related to higher education and human capital growth.

The results reveal a number of important takeaways. There were major shifts in China’s educational landscape during the study period. There was considerable variation among universities, a steady uptick in secondary schools, and a marked decline in primary schools. The report also highlights how educational offerings are changing by emphasizing changes in the availability of vocational and special education institutions. A decline in enrollment in elementary schools and an increase in enrollment in secondary and tertiary institutions are among the changes in student demographics highlighted by the study. This demographic change reflects broader social trends and exemplifies China’s concerted efforts to expand access to higher education.

Provinces in the Northeast, in particular, lag behind the rest of the country in terms of GDP growth. In contrast, others, like Shandong and Jiangsu, display consistently high rates. The need for targeted policies to address regional disparities in development and promote inclusive growth is underscored by this disparity. The report elucidates the relationship between higher education and human capital growth using in-depth panel data analysis and the Malmquist index.

Education Pattern and Human Capital Statistics

The optimization of education human capital structure in various regions of China from 2005 to 2020 was statistically analyzed. The number of school institutions at all levels is shown in Fig. 1, and the number of students at all levels is shown in Fig. 2.

Fig. 1
figure 1

Statistics on the number of school institutions at all levels (a 2005–2010; b 2011–2015; c 2016–2020)

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Fig. 2
figure 2

Statistics of the number of students at all levels (unit: 10,000 people) (a population ratio of primary education, secondary education, and higher education; b ratio of college students, undergraduate students, and graduate students)

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Figure 1 shows an apparent increase in universities from 2005 to 2010, followed by a decrease and a stabilization beginning in 2011. The early growth was likely spurred by China’s efforts to expand access to higher education. Possible reasons for the subsequent decline include changes in government policy, resource shortages, or oversaturation in specific industries. There has been a steady increase in secondary schools from 2005 to 2020. The rising population of China and the government’s initiatives to provide secondary education to all residents likely explain this trend. A steep fall in the number of elementary schools is visible on the graph between 2005 and 2015, with a slower decline between 2016 and 2020. Fewer students are enrolled in primary schools in certain parts of China due to urbanization and declining birth rate, which could contribute to this trend. The number of students enrolled in vocational schools decreased marginally from 2005 to 2015 but increased marginally from 2016 to 2020. Also, there are fluctuations in special education institutions over time, with 2016 being a level year. We must explore the causes of these tendencies and the changing policies and priorities within China’s special and vocational education system. In 2011, the number of preschools dropped significantly, but it has leveled out, according to the statistics. Various policy initiatives or economic shifts may have affected the supply and demand for preschool programs during that time, which could explain this trend.

From 2005 to 2020, Fig. 2 displays the percentage of students enrolled in various levels of education in China. Providing data on student demographics and possible changes in educational pathways enhances the understanding of the number of educational institutions provided by Fig. 1. Comparing 2005 and 2020, we see that the percentage of students enrolled in elementary school dropped from 53.8 to 41.1%, and the percentage enrolled in secondary school rose from 34.7 to 39.5%. Given China’s falling birth rate and, consequently, a smaller population of children in elementary school, this trend is probably indicative of the country’s shifting demographics. On the flip side, China’s emphasis on increasing access to higher education is seen in the sharp rise of the proportion enrolled in higher education from 3.4 to 5.8%. Figure 2 b provides additional insight into the demographics of college students. Possible explanations for the slow decline in the percentage of students enrolled in 4-year institutions include shifts in government policy regarding higher education or the rise of non-traditional career tracks such as vocational education. While the percentage of undergraduates has remained relatively constant at 58%, the percentage of graduate students has increased from 4.5 to 6.3%, indicating a shifting focus towards research and graduate school. The changes in GDP of various provinces and cities in China from 2005 to 2020 were further statistically analyzed. The result is shown in Fig. 3.

Fig. 3
figure 3

Changes in gross domestic product of various provinces and cities in China (2005–2020) (a North China; b Northeast; c East China; d Central China; e Southwest; f Northwest)

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Figure 3 is a set of graphs depicting the growth of GDP in various Chinese regions from 2005 to 2020. Province and city formats are used to organize the graphs. This dataset provides valuable insights into the economic dynamics of China and can help us better understand any potential regional differences in development. Significant regional disparities in GDP growth are shown by the graphs throughout the analyzed time period. Compared to provinces like Shandong, Jiangsu, and Beijing, which consistently show high GDP growth rates, provinces in the Northeast region, in particular, show slower rates. This persistent disparity clearly shows how difficult it is to achieve balanced economic development across China’s vast territory. Among the six recognized regions, a few provinces stand out in terms of GDP. This is illustrated, among other things, by the fact that Liaoning is located in northeast China, Beijing and Hebei in north China, and Jiangsu, Shandong, and Zhejiang in east China. Among China’s provinces, Hunan represents the heart of the country, Sichuan represents the west, and Shaanxi is the northwest’s crown jewel. A number of factors, including robust infrastructure, an abundance of resources, and established industrial sectors, likely contribute to these regional leaders’ persistent economic vitality. Data suggests that although there were evident disparities in growth among regions, regional GDP growth was generally stable from 2005 to 2020. Although there has been some regional variation in the results, the overall consistency does point to progress in China’s economic development efforts. The resilience and adaptability of the Chinese economy are on full display in this persistent expansion, which has continued despite varied regional conditions and challenges.

Panel Data Analysis and Malmquist Index

The study uses panel data analysis and the Malmquist index method to explore the impact of Chinese higher education variables on human capital growth. Panel data analysis is used for statistical purposes to process datasets with multiple time point observations. Combining the characteristics of cross-sectional data and time-series data, the same sample is observed and analyzed at multiple time points, and the dynamic relationships between variables are explored. The panel data model is represented as follows:

$${y}_{it}=\alpha +\beta {X}_{it}+{\mu }_{i}+{\varepsilon }_{it}$$
(1)

Here, \({y}_{it}\) is the dependent variable; \({X}_{it}\) is the explanatory variable; \(\alpha\) and \(\beta\) are parameters; \({\mu }_{i}\) is an unobservable individual-specific effect; \({\varepsilon }_{it}\) is the error term; \(i\) represents an individual (province); \(t\) represents time. Based on panel data analysis, individual heterogeneity that does not change over time is controlled to improve estimation accuracy. In this study, considering technological progress and efficiency changes, the Malmquist index method is used to measure changes in total factor productivity. The index is based on the distance function concept and decomposed into technical efficiency and technical change. The mathematical expressions are as follows:

$$MP{I}_{it}=\frac{TF{P}_{it}}{TF{P}_{it-1}}=E{C}_{it}\times T{C}_{it}$$
(2)
$$E{C}_{it}=\frac{{D}_{it}({x}_{it},{y}_{it})}{{D}_{i,t-1}({x}_{it},{y}_{it})}$$
(3)
$$T{C}_{it}=\frac{{D}_{i,t-1}({x}_{it},{y}_{it})}{{D}_{i,t-1}({x}_{i,t-1},{y}_{i,t-1})}$$
(4)

Among them, \({D}_{it}({x}_{it},{y}_{it})\) is the distance function between the input \({x}_{it}\) and output \({y}_{it}\) given at the production possibility boundary at time t. \(EC\) measures the efficiency change relative to the previous period, while \(TC\) measures the technological change, that is, the movement of the production possibility boundary. To further capture the dynamic relationship over time, a dynamic panel data model is used using the following equation:

$${y}_{it}=\alpha {y}_{i,t-1}+\beta {X}_{it}+\gamma {Z}_{i}+{\mu }_{i}+{\varepsilon }_{it}$$
(5)

Here, \({y}_{i,t-1}\) is the lagged dependent variable, representing the impact of the previous period, and \({Z}_{i}\) is other covariates that may affect the dependent variable. Based on the above methods, the study reveals the complex relationship between higher education variables and human capital growth. Combining fixed and random effects models allows individual specificity and unobservable heterogeneity to be more accurately controlled. At the same time, by decomposing technical efficiency and introducing dynamic panel models, the dynamic changes and long-term impacts in time series can be deeply explained.

The choice of variables, including the number of educational institutions at different levels and student enrollment trends, serves to capture the structural shifts within the system. By tracking changes in educational offerings and enrollment demographics, the study sheds light on the dynamic nature of educational access and priorities over the study period. Additionally, the incorporation of regional GDP growth data adds an important economic context, highlighting disparities across regions and underscoring the need for targeted policy interventions to promote inclusive development.

Statistical methods such as panel data analysis and the Malmquist index offer robust frameworks for analyzing the complex dynamics within the educational system. Panel data analysis allows for the examination of both cross-sectional and time-series data, controlling for individual heterogeneity and time-specific effects. Employing a dynamic panel data model, the study captures the relationship between higher education variables and human capital growth, accounting for lagged effects and other covariates. This approach enhances the accuracy of estimations and provides insights into the long-term impacts of educational policies and interventions.

Furthermore, the application of the Malmquist index method enables the measurement of changes in total factor productivity over time, decomposing these changes into components such as technical efficiency and technological change. By quantifying efficiency improvements and technological advancements, this method offers a deeper understanding of the factors driving educational outcomes and human capital growth. The utilization of distance function calculations further elucidates the relationships between inputs and outputs in the educational system, providing valuable insights for policymakers and stakeholders.

Empirical Results

The empirical results section offers a comprehensive analysis of the efficiency and productivity of higher education systems in different regions of China from 2005 to 2020. The study commences by detailing the comprehensive dataset, comprising economic indicators such as regional GDP and educational metrics like the quantity of higher education institutions and student enrollment. The study aims to utilize panel data analysis techniques with cross-sectional and time-series data to reveal intricate relationships between variables, enhancing estimation accuracy while accounting for individual variations. The production possibility boundaries were determined using the data envelopment analysis (DEA) method in conjunction with the Charnes, Cooper, and Rhodes (CCR) model. The study assesses the effectiveness of decision-making units in allocating resources and fostering human capital growth in higher education by delineating the production possibility frontier (PPF) for each province and city. This methodology enhances the traditional CCR model by integrating multiple inputs and outputs, creating a comprehensive evaluation framework—empirical findings on efficiency ratings in various regions of China spanning from 2005 to 2020. The results highlight significant variations in efficiency trends across regions during the specified period, demonstrating the country’s diverse economic conditions and developmental trajectories.

Data Input

The economic and educational data of 31 regions in China from 2005 to 2020 were integrated. The data input includes economic indicators such as regional GDP, added value of tertiary industries, added value by industry, and per capita regional GDP, as well as educational indicators such as the number of higher education institutions, faculty and staff, student enrollment, and number of students enrolled. The relationships between variables were dynamically explored using panel data analysis, combined with cross-sectional and time-series data, to improve estimation accuracy by controlling individual heterogeneity. The Malmquist index method was used to analyze changes in total factor productivity, deconstruct technological efficiency and technological changes, and explain the dynamic changes and long-term impacts of time series.

Constructing Production Possibility Boundaries

The study is based on the DEA method, referring to the evaluation of rural resilience and factor contribution analysis by scholars such as Wang Huimin in western Guangdong. The CCR model is used to construct each province and city’s PPF. The method is extended based on the CCR model, considering multiple inputs and outputs to evaluate the efficiency of decision-making units (provinces and cities) in higher education resource allocation and human capital growth. It is assumed that \({x}_{ij}\) represent the input of the \(j\) th province or city on the \(i\) th input, and \({y}_{rj}\) represent the output on the \(r\) th output. For each decision-making unit (DMU), namely, each province and city, the CCR model is represented as a linear programming problem as follows:

$$\text{maximize }\theta =\sum_{r=1}^{s}{u}_{r}{y}_{r{j}_{0}}$$
(6)
$$\text{subject to }\sum_{i=1}^{m}{v}_{i}{x}_{ij}=1,$$
(7)
$$\sum_{r=1}^{s}{u}_{r}{y}_{rj}-\sum_{i=1}^{m}{v}_{i}{x}_{ij}\le 0,j=1,\dots ,n,$$
(8)
$${u}_{r},{v}_{i}\ge 0,r=1,...,s;i=1,...,m.$$
(9)

Here, \({u}_{r}\) and \({v}_{i}\) are model decision variables representing the weights of the \(r\) th output and the \(i\) th input, respectively. The goal of this model is to maximize the weighted total output of decision unit \({j}_{0}\), while ensuring that the weighted total output of any decision unit does not exceed its weighted total input. In this way, the efficiency value \(\theta\) can be explained as the maximum proportion of output that can increase without changing the amount of input given. Table 1 lists the efficiency scores of six regions in China (North China, Northeast, East China, Central China, Southwest, and Northwest) from 2005 to 2020.

Table 1 Efficiency ratings for various regions in China
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Efficiency ratings for six regions in China from 2005 to 2020 are displayed in Table 1. From 2005 to 2020, North China’s efficiency ratings have been relatively high, ranging from 0.72 to 0.92. On the other hand, efficiency ratings in the Northeast region go through more dramatic swings, ranging from 0.51 in 2015 to 0.99 between 2015 and 2020. Efficiency trends in the Southwest, Central China, and East China also vary over time. For example, East China fell from 2005’s 0.61 to 2018’s 0.55, and then slightly recovered to 2019’s 0.57. Central China’s efficiency ratings fluctuated between 0.50 in 2017 and 0.90 in 2013. Efficiency ratings in the Southwest region also fluctuate, from 0.63 in 2006 to 0.91 in 2018. Compared to other regions, the Northwest region’s efficiency ratings remain reasonably constant throughout the years, consistently hovering around the mid-range. Specifically, efficiency ratings in this area varied from 0.53 in 2007 to 0.97 in 2006. These regional differences in efficiency ratings show that China’s economic landscape and development paths are very diverse. They also highlight the significance of recognizing regional dynamics and enacting focused policies to reduce inequalities and foster national economic development and growth that is equitable.

Calculating the Distance Function

The distance function of each province and city at different time points was calculated to quantify the difference between the actual output of the decision-making unit and the maximum possible output on the production possibility boundary. The distance function in the study is a key indicator for evaluating the efficiency of decision-making units (provinces and cities), reflecting the distance between actual output and optimal output (points on the PPF). For each decision unit j (representing the province and city) and time point t, the distance function \({D}_{jt}({x}_{jt},{y}_{jt})\) is represented as follows:

$${D}_{jt}\left({x}_{jt},{y}_{jt}\right)\text{ = max}\{\theta |(\theta {y}_{jt},{x}_{jt})\in PPF\}$$
(10)

Among them, \({x}_{jt}\) and \({y}_{jt}\) represent the input and output variables of the jth decision unit at time t. The calculation results for each region are shown in Fig. 4.

Fig. 4
figure 4

Calculation of distance function values for different regions from 2005 to 2020

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By comparing the actual output of decision-making units in each province and city with the maximum possible output on the production possibility boundary, Fig. 4 shows the distance function values of six regions in China from 2005 to 2020. As a crucial metric for gauging the efficacy of decision-making units, the distance function illustrates the disparity between various regions’ real and ideal performance in allocating resources for human capital and higher education. In 2007, Central China had a distance function value close to 1, which means that the region’s resource utilization and output efficiency were relatively low. It also means a significant gap with the ideal maximum output. The opposite is true in the northwest region, where relatively low distance function values across a number of years suggest a higher output efficiency and proximity to the production possibility boundary.

Hypothesis Verification

Regarding hypothesis H1, the human capital index (HCI), combined with demographic data, education level, employment situation data, time series analysis, and spatial analysis, were used to evaluate the changes in HCI before and after the expansion of higher education. Regression analysis was used to determine the correlation strength, as shown in Table 2. Based on hypothesis H2, an innovation index includes indicators such as the number of patent applications and the R&D expenditure ratio. PDA was used to associate the innovation index with higher education quality–related indicators (teacher education level, research funding), and the dynamic relationship between the two was tested. The results are shown in Table 3. In response to hypothesis H3, the Labor Market Adaptability Index (LMAI) was developed, taking into account factors such as the unemployment rate, job vacancy rate, and labor mobility rate. The structural equation model (SEM) was used to analyze the relationship between changes in higher education structure and LMAI, and the specific impact of structural optimization on the labor market was explored. The results are shown in Table 4.

Table 2 Impact of higher education on regional human capital index
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Table 3 Relationship between higher education quality and innovation index
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Table 4 The impact of changes in higher education structure on the labor market adaptability index
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The validation process of hypothesis H1 is reflected in Table 2, which provides a detailed presentation of the significant impact that the popularization and expansion of higher education have had on the regional human capital index. It is possible to observe the shifts in HCI that occur in various regions throughout various time periods. When we take the North China region as an example, the HCI was 0.65 prior to the expansion of education from 2005 to 2010, and it increased to 0.68 after the expansion. The change rate was 4.6%, and the regression analysis coefficient was 0.85 following the expansion. The HCI in North China gradually increased over time, from 2011 to 2015 and from 2016 to 2020, reaching 0.69 and 0.70, respectively, with a decreasing rate of change. This occurred between the years 2011 and 2015. The regression analysis coefficients, on the other hand, have been steadily increasing, reaching 0.86 and 0.87, respectively. This indicates that there has been a significant improvement in the levels of human capital in the region as a result of the ongoing expansion of education, which is closely and positively related to the expansion of higher education. Additionally, the trend of changes in the HCI index and regression analysis coefficients in other regions, such as Northeast China and East China, supports hypothesis H1.

In order to validate hypothesis H2, Table 3 conducted an analysis of the relationship between higher education quality and innovation activities. This analysis included indicators that measure innovation level, such as patent applications and R&D expenditure ratios, as well as indicators that relate to higher education quality, such as the level of research funding and the number of teachers who have completed their education. As an illustration, the educational level of teachers in the North China region was 0.8 between 2005 and 2010, the amount of funding for research was 22.133 million yuan, there were 153 patent applications, and the ratio of R&D expenditures was 5%. The comprehensive innovation index and the PDA analysis coefficient were found to be 0.78 and 0.9, respectively. By the time the years 2016–2020 roll around, all of these indicators have been improved, which indicates that the advancement of technology and the activities that promote innovation have also been significantly promoted along with the improvement in the quality of higher education. This pattern is also demonstrated by the data from other regions, which provides additional confirmation for hypothesis H2.

Table 4 presents an analysis of hypothesis H3 and investigates the impact that changes in the structure of higher education have on the adaptability of the labor market. The purpose of this study was to demonstrate, through the utilization of the labor market adaptability index and the coefficients of structural equation modeling (SEM), how the adjustability of the labor market was affected by variations in the composition of higher education in various geographical areas and time periods. For example, the North China region’s unemployment rate was 4% from 2005 to 2010, the job vacancy rate was 5%, the labor mobility rate was 8%, the LMAI was 0.75, and the SEM analysis coefficient was 0.88. The improvement of these indicators continued through the years 2016–2020. As a result of the decrease in the unemployment rate to 3.8%, the vacancy and labor mobility rates increased to 5.2% and 8.2%, respectively. Furthermore, the SEM analysis coefficient was 0.9, and the LMAI increased to a value of 0.77. The findings demonstrate that the adaptability of the labor market has been improved as a result of the optimization of the structure of higher education, which provides evidence in support of hypothesis H3. Additionally, the data from other regions support this conclusion, indicating that optimizing the structure of higher education plays a significant role in enhancing the adaptability and flexibility of the labor market.

Facing hypothesis H4, the Malmquist index method was used to measure the changes in total factor productivity of various provinces and cities in China from 2005 to 2020. The calculation results of total factor productivity by region are shown in Fig. 5. At the same time, it was decomposed into changes in technical level and technical efficiency. The overall results are summarized in Fig. 6.

Fig. 5
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TFP calculation results by region in China (2005–2020) (a North China; b Northeast; c East China; d Central China; e Southwest; f Northwest)

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Fig. 6
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Statistics on changes in China’s total factor productivity (2005–2020) (a from 2005 to 2015; b from 2016 to 2020)

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Figure 5 shows 2005–2020 total factor productivity (TFP) calculations for Chinese regions. TFP measures output and input factors to assess production efficiency. The graphs reveal several key findings. The data shows significant differences in TFP between regions during the analyzed period. East and North China have high TFP periods, indicating efficient production and strong economic growth. Northeast China consistently has lower TFP, suggesting production efficiency and economic output may be limited. Regional differences aside, most regions show an increasing trend in TFP. The rising trend shows that technology, management, and resource utilization have improved production efficiency across China. Certain areas show TFP trends. Beijing’s TFP in North China increased between 2005 and 2009, indicating improved production efficiency and economic performance. Shanghai’s TFP increased significantly during the same period, indicating production sector improvements in East China. However, TFP in Liaoning shows a rise, decrease, and stabilization in Northeast China. This pattern suggests potential barriers to local production and economic development, requiring further investigation into the causes.

Figure 6 illustrates a detailed analysis of TFP divided into technical change (TC) and efficiency change (EC) components from 2005 to 2020 in China. TFP is a key indicator of production efficiency, taking into account both output and input factors. TC represents improvements in technology and knowledge, while EC reflects changes in efficiency in using current technologies. The graph shows a steady rise in TFP and total costs (TC) from 2005 to 2010. This increasing trend indicates significant enhancements in overall production efficiency due to technological advancements and the implementation of new knowledge-intensive processes. After the initial growth phase, the data shows that TFP stabilized at a lower level after 2010, while TC remained relatively constant. There is a notable rise in EC during this period, nearing a value of 1. Efficiency gains from using current technologies more effectively made up for the slowdown in technological progress during this period. From 2016 to 2020, there was a period of stagnation in both TFP and TC, with economic contribution (EC) consistently surpassing a value of 1. This trend indicates a deficiency in significant advancements in technological innovation or efficiency in utilizing current technologies during this period.

Discussion

Through its findings, the study uncovered a number of noteworthy patterns and anticipated outcomes. Variations in the number of educational institutions at different levels of education clearly indicate that the educational environment is undergoing a discernible transformation (Dwivedi et al., 2020). The number of students attending secondary schools is increasing, while the number of students attending primary schools is decreasing. Additionally, there are distinct trends in vocational and special education (Webster & Blatchford, 2019). The observations are in line with the outcomes that were anticipated to occur as a result of China’s expanding higher education system and concurrent changes in demographics. Furthermore, there is a discernible shift in the demographics of students, as evidenced by an increase in the number of students enrolling in secondary and higher education institutions, while the number of students enrolling in elementary education is decreasing (Marcus & Zambre, 2019). This trend is consistent with the anticipated demographic trends and the strategic focus that China has placed on increasing the number of opportunities for higher education. Last but not least, the research sheds light on the widespread issue of unequal regional development by demonstrating the substantial disparities in economic growth rates between different regions (Rodríguez-Pose & Storper, 2020). The differences highlight the necessity of specific policy actions to promote more even development, closely related to the primary research question the study attempts to answer (Schroeder et al., 2019).

Unanticipated findings and novel points of view have emerged as a result of the study, which has shed light on potential directions for future research and improved the methodology. First, the fluctuations in the number of universities and the noticeable changes in curriculum focus, particularly in special and vocational schools, raise some interesting questions about possible policy changes or changing priorities in the education system (Monroe et al., 2019). These questions are raised as a result of the fact that the number of universities has been fluctuating. As a result of these findings, additional research into the factors that are driving this trend is required. Educational institutions are continuously changing. Second, thanks to panel data analysis that utilized both fixed and random effects models, a better understanding of the intricate relationship between increased human capital and higher education degrees has been achieved (Angrist et al., 2021). This methodological approach offers valuable insights that can be used to inform future research endeavors in this domain. It does this by taking into account individual specifics as well as factors that cannot be observed. The fact that it can comprehend the nuances and complexities of the data highlights its value in enhancing our understanding of the connection between education and the development of human capital (Nocker & Sena, 2019).

The study results align with what is already known, especially when seen through the lens of human capital theory, which stresses the importance of education in developing workers’ abilities and stimulating the economy (Francois Koukpaki et al., 2020). Supporting this understanding is that increasing access to higher education has a beneficial effect on human capital growth. Furthermore, skill-based competition theory is consistent with the goals of STEM education and multidisciplinary learning (Delgado, 2021). A workforce with the diverse skill sets needed to thrive in a competitive global environment is a strategic goal of China, and this emphasis reflects that (Zhao et al., 2021). By comparing these findings to existing theoretical frameworks, these findings help us understand the complex relationship between education, skill acquisition, and economic competitiveness, especially in China’s changing educational landscape (Li, 2023).

There are several caveats to keep in mind, even though the study provides helpful information about the state of education in China. Since the education sector is changing at a rapid pace, a timeframe of 2005–2020 may miss some of the most recent advances (Iivari et al., 2020). Another area that could be explored further in future studies is the evaluation of educational quality and the employability outcomes for graduates, as these were not included in this particular investigation. These areas could be explored further in future research by examining the effects of curricular changes on graduates’ employability and skill sets (Behle, 2020; Ornellas et al., 2019). Furthermore, in order to optimize educational outcomes and workforce readiness, it could be beneficial to investigate how higher education policies interact with market demands and individual career goals (Jackson & Tomlinson, 2020). Future policy decisions and educational practices could be informed by longitudinal studies that follow the career paths of graduates (Williamson, 2019).

The findings of the study offer crucial insights into the evolving landscape of China’s educational system and its implications for human capital growth. These insights can inform policymakers, educators, and higher education administrators in formulating effective strategies to address the identified challenges and optimize educational outcomes. Firstly, policymakers should prioritize addressing the disparities in regional development highlighted by the study. Concrete policy actions, such as targeted investments in infrastructure, education, and economic development programs, are necessary to promote more equitable growth across different regions (Turok & Parnell, 2009). By reducing regional disparities, policymakers can ensure that all segments of the population have access to quality education and opportunities for economic advancement. Additionally, efforts to enhance collaboration and knowledge-sharing between regions with varying economic growth rates can foster innovation and drive sustainable development on a national scale (Zhou et al., 2020).

Secondly, there is a need for policy reforms aimed at optimizing technical efficiency and integrating technological advancements into the curriculum. Educational institutions and administrators should adapt their curricula to align with emerging technological trends and industry needs. This may involve enhancing STEM education, promoting multidisciplinary learning, and providing students with practical skills relevant to the modern workforce (Mian et al., 2020). Furthermore, investments in teacher training and professional development programs can ensure that educators are equipped to effectively integrate technology into their teaching methods and prepare students for success in a rapidly evolving job market (Popova et al., 2022).

Conclusions

This study concludes that higher education does contribute to human capital development and economic growth. It also highlights the importance of constantly improving educational quality, achieving regional equity, and integrating technology. Chinese and global policymakers and educators can build an education system that can withstand the test of time by applying the suggested strategies and conducting additional research. This system will enable individuals to take charge of their own learning and will contribute to long-term economic prosperity.

Theoretical Implications

This research significantly contributes to the theoretical landscape of human capital development by unveiling the intricate interplay between higher education expansion, regional economic disparities, and technological advancements. Through its empirical investigation, the study validates existing human capital theories and extends them by emphasizing the dynamic and multifaceted nature of the relationship between education and economic progress. By elucidating how regional variations in educational infrastructure and technological innovation intersect with higher education expansion, the research underscores the need for more nuanced theoretical frameworks to adequately capture these complexities. Such frameworks should go beyond traditional models and incorporate elements that account for the diverse socio-economic contexts within which educational policies operate, providing a more comprehensive understanding of the mechanisms driving human capital development in contemporary societies. Moreover, the study’s findings underscore the importance of considering both technical efficiency and technological change when assessing the impact of higher education on total factor productivity. The research underscores the dynamic nature of the educational-economic nexus by shedding light on how educational investments interact with technological advancements to influence economic output. This highlights the significance of adopting a holistic approach to evaluating the returns on educational investments, which acknowledges the role of innovation and knowledge diffusion in shaping productivity growth. Overall, this research advances theoretical discourse in human capital development and offers valuable insights for policymakers and practitioners seeking to design effective educational strategies that promote sustainable economic growth and regional development.

Managerial Implications

The research results provide valuable practical insights that have important implications for policymakers and educational institutions. Optimizing the education system is crucial and necessitates a multifaceted approach. This involves improving educational standards and promoting technological advancements in teaching methods and curricula to prepare students with the necessary skills for the constantly changing job market. It is crucial to optimize resource distribution among different regions to guarantee fair access to high-quality education. This includes enhancing vocational and continuing education programs to bridge skill deficiencies and encourage continuous learning. By focusing on these measures, policymakers can develop a highly skilled workforce capable of fostering innovation and maintaining economic growth in a competitive global environment. Encouraging collaboration between universities and businesses is a crucial strategy to connect education with industry requirements. Policymakers can enhance graduate employability and facilitate smoother transitions into the workforce by promoting partnerships that align educational offerings with market demands.

Furthermore, promoting equity in educational access and quality is essential to enhance social cohesion and guarantee inclusive economic development. Implementing policies to tackle regional disparities in educational resources and opportunities is crucial for ensuring equal access to education for all individuals, irrespective of their geographic location or socio-economic status. Improving educational standards through global partnerships and adopting best practices can improve the quality and significance of education, leading to a more competitive and adaptable workforce that can support sustainable economic growth.

Ideas for Future Research

Future research in the field of education in China can explore several critical areas to deepen our understanding and enhance the impact of educational reforms. Firstly, longitudinal studies are essential to track the long-term effects of higher education on graduates’ professional trajectories, innovation output, and GDP growth. By following graduates over extended periods, researchers can assess how their skills evolve in response to market demands and technological advancements, providing valuable insights into the effectiveness of educational interventions in workforce preparation. Additionally, qualitative research can delve into the subjective experiences of stakeholders such as students, teachers, and employers, shedding light on learning outcomes, skill development, and career pathways.

Furthermore, comparative studies across different higher education systems can offer evidence-based recommendations for ongoing improvement. By examining the strengths and weaknesses of various systems, policymakers can identify successful strategies and policies to enhance human capital and stimulate economic growth in China. This comparative approach can help pinpoint new methods and initiatives that have proven effective elsewhere, guiding future reforms and investments in higher education.

In addition to these areas of focus, future research could delve into the longitudinal effects of higher education reforms and the role of digital technologies in education. Investigating how educational reforms implemented over time impact graduates’ career trajectories and societal outcomes can provide valuable insights into the effectiveness of policy interventions. Similarly, examining the integration of digital technologies in teaching and learning processes can illuminate their potential to enhance educational outcomes and prepare students for the digital economy.

Moreover, exploring the impact of international collaboration and exchange programs on human capital development could offer interesting insights into the benefits of global cooperation in education. Researchers can identify best practices and strategies for fostering international collaboration to support human capital development and drive economic growth by studying the effects of cross-border partnerships and exchange initiatives.