Spatiotemporal Coupling Relationship between Higher Education and Economic Development in China: Based on Interprovincial Panel Data from 2012 to 2023
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors1. In the introduction, provide more details about the background of the issue being discussed and its significance.
2. Literature review can benefit from the addition of more recent studies, especially from 2024-2023. for example, can consult the following,
(2023). Review of Teaching innovation in university education: Case studies and main practices. edited by Jose Ramon Saura, Hershey. The Social Science Journal. DOI: 10.1080/03623319.2023.2201973.
(2024). Digital Learning Demand and Applicability of Quality 4.0 for Future Education: A Systematic Review. International Journal of Engineering Pedagogy (iJEP), 14(4), pp. 38–53. https://doi.org/10.3991/ijep.v14i4.48847
3. Add limitations of this study.
4. Rewrite the conclusion section and make it more comprehensive and coherent. Avoid 2-3 sentence paragraphs.
Author Response
RE: Following the reviewer's suggestion and the structural requirements of the paper, we have enriched the introduction by providing additional details about the background of the issue and summarizing its significance. For details, see the revised introduction.
The revised introduction now includes:
- Enhanced Background Information: We have expanded the section to offer a comprehensive overview of the historical and contemporary context of higher education modernization and economic development in China. This includes an in-depth discussion of the historical trends, policy shifts, and socio-economic factors influencing both higher education and economic growth. The specific research background content added is as follows:
Higher education, as a crucial nexus of science, talent, and innovation, plays a pivotal role in the historical process of achieving Chinese-style modernization. It serves as a key driver of high-quality economic development and a fundamental supporter of regional economic advancement [1-6]. As the capacity of higher education to enhance China’s economic progress continues to grow, its role in promoting sustainable development becomes increasingly interconnected [7,8]. However, the structural, typological, and resource disparities within higher education are increasingly at odds with the demands of China’s high-quality economic development. These disparities, coupled with significant differences in economic conditions and the status of higher education across China’s provinces, result in varying levels of effectiveness in how higher education supports regional economic development. Consequently, both the nation and society are placing higher expectations on higher education, stressing the need for it to maintain a service-oriented approach, proactively adapt to changes, and support high-quality economic development [9,11]. In response, this paper establishes an evaluation model for the coupling and coordinated development of "higher education–economy" tailored to China’s unique context. This model is urgently needed in the current era to promote the construction of a high-quality education system and the development of a strong educational nation [10,11]. It provides a basis for quantitatively evaluating the coordinated development of higher education and the economy and offers data references for assessing the relationship between higher education and regional economic development.
2.Significance of the Study: We have refined the statement of significance to clearly articulate the importance of this research. This includes explaining how the findings contribute to a better understanding of the relationship between higher education and economic development, and how they can inform policy-making and strategic planning for balanced regional development. The additional research significance is as follows:
This study follows the logic of mutual integration between education and the economy, as well as the synchronized progress of educational development and economic growth. Drawing on literature analysis and the five major functions of universities, it utilizes official data from China’s 31 provinces and cities, sourced from official websites and statistical yearbooks. Focusing on the period from 2012 to 2023, the study establishes a "higher education–economy" composite system coupling coordination evaluation model tailored to China. This model quantifies the "quantity" and "quality" of China’s higher education modernization using the entropy weight method and a comprehensive development level evaluation model.
The study employs the coupling coordination degree model to empirically analyze how well the "supply" of higher education aligns with the "demand" for high-quality regional economic development under the new development paradigm. Additionally, the obstacle degree model is introduced to identify the factors hindering the coupling and coordination between higher education and regional economic development in the 31 provinces, municipalities, and autonomous regions. This provides a foundation for the quantitative evaluation of the coordination between higher education and economic development in China and offers data references for assessing the development of higher education and regional economies.
The findings will support the formulation and implementation of policies aimed at promoting coordinated and sustainable development between higher education and the economy across China’s 31 provinces, municipalities, and autonomous regions. Ultimately, this will contribute to creating a favorable scenario where high-quality economic development and higher education in China are harmoniously aligned and coordinated.
The updated introduction now better contextualizes the study and underscores its relevance in the broader landscape of regional development and educational reform.
Comment 2:2. Literature review can benefit from the addition of more recent studies, especially from 2024-2023. for example, can consult the following,
(2023). Review of Teaching innovation in university education: Case studies and main practices. edited by Jose Ramon Saura, Hershey. The Social Science Journal. DOI: 10.1080/03623319.2023.2201973.
(2024). Digital Learning Demand and Applicability of Quality 4.0 for Future Education: A Systematic Review. International Journal of Engineering Pedagogy (iJEP), 14(4), pp. 38–53. https://doi.org/10.3991/ijep.v14i4.48847
RE: Thank you for your valuable suggestion. In response, we have significantly updated the literature review by incorporating 23 recent studies from 2022 to 2024. This has increased the number of references from the original 22 to a total of 45. The added references include recent and relevant research that enhances the depth and currency of our literature review. The specific additional 23 references are as follows:
These additions not only update the review but also ensure that the analysis reflects the latest developments in the field. We appreciate your guidance in improving the comprehensiveness of our literature review.
(2023). The Chinese Ministry of Education. The Ministry of Education Has Issued the “Guidelines for Evaluating the Quality of Regular High School Education”. Availableonline: http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/s5987/202201/t20220110_593455.html (accessed on 5 June 2023).
(2023). The State Council of China. Outline of the National Medium- and Long-Term Education Reform and Development Plan (2010–2020). Available online: http://www.gov.cn/jrzg/2010-07/29/content_1667143.htm (accessed on 5 June 2023).
(2024). Lu, Z. The role of higher education in promoting regional coordinated development: Mechanisms and empirical evidence. Journal of Higher Education Science.2024,02, 77-87.
(2023). Alam, G.M.; Forhad, M.A.R. The Impact of Accessing Education via Smartphone Technology on Education Disparity-A Sustainable Education Perspective. Sustainability. 2023, 15, 10979.
(2022). Bertoletti, A.; Berbegal-Mirabent, J.; Agasisti, T. Higher Education Systems and Regional Economic Development in Europe: A Combined Approach Using Econometric and Machine Learning Methods. Socio-Economic Planning Sciences. 2022, 82, 101231.
(2024). Gui, P.P.; Alam, G.M. Does Socioeconomic Status Influence Students’ Access to Residential College and Ameliorate Performance Discrepancies among Them in China? Discover Sustainability. 2024, 5, 20.
(2023). Geng, Y.; Chen, L.; Li, J.; Iqbal, K. Higher Education and Digital Economy: Analysis of Their Coupling Coordination with the Yangtze River Economic Belt in China as the Example. Ecological Indicators. 2023, 154, 110510.
(2023). Zhou, G., Zhao, Z., & Geng, M. Spatial layout of higher education resources and its impact on regional technological innovation capability: An empirical study based on the five major city clusters in China. Modern University Education.2023,39(01), 66-75+112.(In Chinese)
(1962). Schultz, T.W. Reflections on Investment in Man. Journal of Political Economy. 1962, 70, 1–8.
(2009). Hanushek, E. A., & Woessmann, L. “Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation,” Working Paper 14633, National Bureau of Economic Research. Cambridge, 2009.
(1986). Romer, P.M. Increasing Returns and Long-Run Growth. Journal of Political Economy. 1986, 94, 1002–1037.
(2019). Denison, M. Reimagining Advocacy: Rhetorical Education in the Legal Clinic. Argumentation and Advocacy. 2019, 55(1), 87-89.
(2019). Anna Valero; John Van Reenen. The Economic Impact of Universities: Evidence from Across the Globe. Economics of Education Review. 2019, 68(1), 53-67.
(2024). Sun, G., & Yang, H. Coupling of urban clusters and higher education clusters and its impact on economic development: An empirical analysis based on statistical data from three major city clusters (2012–2021). Journal of Tianjin Academy of Educational Sciences, 2024,36(01), 33-46.
(2023). Andrews, M. How Do Institutions of Higher Education Affect Local Invention? Evidence from the Establishment of US Colleges. American Economic Journal: Economic Policy. 2023, 15, 1–41.
(2023). Wu, W., & Zhu, J. The structure of educational investment, industrial structure, and the quality of economic growth. Education and Economy.2023,39(05), 19-26.
(2024). Peng, C., & Xu, H. Universities and cities: The impact of higher education on urban innovation, entrepreneurship, and economic growth. Asian Economic Papers. 2024,23(2), 33-56..
(2024). Zhang, M., Li, M., & Fan, X. How education promotes common prosperity: An empirical test based on provincial panel data from 2003 to 2020. Educational Research. 2024,45(05), 132-149.
(2024). Jiang, M., Fan, Q., & Kuang, Y. Coupling and coordination effects of higher vocational education and common prosperity: An empirical analysis based on panel data from 30 provinces. Vocational Education Forum.2024 ,40(04), 93-103.
(2024). Huang, S., Shi, W., & Jiao, Y. Coupling and coordination effects of higher vocational education and rural revitalization: Logical mechanisms and empirical testing—Based on provincial panel data from 2012 to 2021. Vocational and Technical Education.2024,45(10), 65-72.
(2024). Sun, J., & Wan, Y. Higher education, regional innovation capability, and digital economy development. Higher Education Management.2024,18(02),1-12+52.
(2023). Geng, M.; Tian, H. Research on the Coupling and Coordination between Higher Education and Industry and Its Economic Effect — Empirical Analysis Based on Inter-Provincial Panel Data and Spatial Dubin Model. Chongqing Higher Education Research. 2023, 2, 1–14.
(2023). Huang, H.; Wu, S.; Qu, Y. Higher Education and High-Quality Economic Development: Mechanism, Path and Contribution. Journal of East China Normal University (Educational Sciences Edition). 2023, 41, 26–40. (In Chinese)
(2023). Yi, S.; Zou, C. Assessing Transformation Practices in China under Energy and Environmental Policy Goals: A Green Design Perspective. Sustainability. 2023, 15, 2948.
(2024). Guan, C.; Chen, C.; Shen, X. Time Value, Practical Performance and Future Prospect of Sustainable Development Education. Education Economics Review. 2023, 8, 3–22.
(2023). Huang, R., & Gu, Y. Evaluation and characteristics analysis of regional higher education development levels in China. Higher Education Management.2023,17(05),25-41.
Comment 3: Add limitations of this study.
RE: Thank you for your valuable feedback. Based on your suggestion, we have added a new section titled "7. Limitations and Future Recommendations" at the end of the paper. This section addresses the following limitations:
Despite the valuable insights provided by this study, there are several limitations and areas for improvement:
(1) Data Constraints: The study relies on provincial panel data from 2012 to 2023 due to data availability issues. Prior to 2012, detailed provincial breakdowns for educational indicators were not available, limiting the study to this time frame. While the sample size generally meets minimum requirements for data analysis, it remains relatively small, which may affect the robustness of the findings.
(2) Geographical Scope: The research is based on provincial-level data. Future studies could benefit from incorporating data at the municipal or county level, which would allow for more precise and targeted empirical analyses.
(3) Comprehensive Indicator Selection: Future research should consider a broader range of indicators, including ecological factors. High-quality economic development is a multifaceted concept that encompasses not only economic scale, quality, and structure but also social, ecological, and environmental dimensions.
Future work will involve expanding the data set and incorporating variables such as ecology and the environment in the selection of indicators. Additionally, we will use econometric methods, including spatial autocorrelation models and the Dagum Gini coefficient. These efforts aim to enrich existing research findings and provide empirical evidence and policy recommendations for the coordinated development of higher education and the economy in China.
We hope these additions address the concerns raised and contribute to a more robust and comprehensive understanding of the study's limitations and future research directions.
Comment4：Rewrite the conclusion section and make it more comprehensive and coherent. Avoid 2-3 sentence paragraphs.
RE: Thank you for this suggestion. We have revised the conclusion section to enhance its comprehensiveness and coherence, ensuring that it no longer contains brief paragraphs. The updated conclusion now provides a detailed and integrated summary of our findings, discusses the broader implications of our research, and outlines recommendations for future studies. It offers a well-structured synthesis of the study’s contributions, highlighting the key insights and their relevance to the field. As follows:
Based on relevant theories and previous research, this study has developed a systematic, comprehensive, and scientific evaluation index system for assessing the coupling and coordination between higher education and economic development. The study utilized panel data from 31 provinces in China from 2012 to 2023 to explore spatiotemporal differentiation and identify obstacle factors constraining coupling and coordination. Key findings include:
(1) For higher education modernization, an index system was developed comprising 3 primary indicators, 4 secondary indicators, and 16 tertiary indicators. Likewise, a separate index system for high-quality economic development includes 3 primary indicators, 5 secondary indicators, and 13 tertiary indicators.
(2) The level of higher education modernization across China's 31 provinces, municipalities, and autonomous regions exhibited a fluctuating upward trend. The ranking is as follows: East > Central > National > Northeast > West. In terms of growth rates, the figures are as follows: Northeast (2.739%) > National (0.912%) > East (0.542%) > Central (0.357%) > West (0.271%). Nationwide, the comprehensive evaluation index for higher education modernization increased from 0.423 in 2012 to 0.467 in 2023, reflecting an average annual growth rate of 0.912%. The periods with the highest growth rates were 2015-2016 and 2020-2021, with rates of 4.687% and 4.325%, respectively. Conversely, the largest decrease in growth rate occurred during 2018-2019, with a reduction of 2.075%.
(3) The level of high-quality economic development in China's 31 provinces, municipalities, and autonomous regions showed a slow, fluctuating upward trend, with the ranking as follows: East > National > Central > Northeast > West. Nationwide, the average level of high-quality economic development remained stable at 0.370. In the East, Central, and West regions, the average levels of high-quality economic development increased from 0.503, 0.351, and 0.277 in 2012 to 0.520, 0.395, and 0.298 in 2023, respectively. In contrast, the Northeast region's average level decreased from 0.359 in 2012 to 0.268 in 2023.
(4) The temporal variation in the coupling and coordination between higher education and economic development in China displayed a fluctuating trend of "rising - falling - rising." In 2012, the average degree of coupling and coordination was 0.432, which increased to 0.444 by 2017 and slightly decreased to 0.442 by 2023.
(5) The obstacle degree of higher education systems across regions ranges from 24.514% to 70.234%, while for economic development systems, it ranges from 23.311% to 54.142%. It is evident that the obstacle degree for higher education systems is significantly higher than that for economic development systems in each region. The provinces with the highest obstacle degrees in higher education development are Qinghai, Ningxia, Hainan, Tibet, and Inner Mongolia. For economic development, the provinces with the highest obstacle degrees are Jilin, Tibet, Hainan, Qinghai, and Guizhou.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper is interesting, but needs significant additional work.
The title is too long.
It is strange to call articles foreign and domestic in an international journal.
The literature review is very strange. Firstly, it is very short - although (as correctly noted in the introduction) the topic of the connection between economics and education is very widely developed. Secondly, it contains practically no references to literature (there are only 3 references) - which is generally a paradox. And the highlighted subparagraphs themselves do not help to reveal the topic.
I am not a mathematician, and I think many readers of the journal are not either, so a more detailed explanation of what is calculated, how and why would be needed.
At the very beginning of the section on materials and methods, a table with numbers appears - where did these coefficients (Criteria Layer and Weight) come from?
The legend in Fig. 2 and 5 is practically unreadable - the font needs to be increased - at least the year should be written in large letters.
The conclusions lack development and depth. Did the authors get some numerical data and that's it? Or is it possible to reach some level of understanding of the existing connections and regularities (this is where the very weak theoretical base comes into play). General conclusions need to be made and compared with those in the literature.
Author Response
Comment 1: The title is too long.
RE: Thank you for your valuable feedback. In response to your suggestion, we have carefully reviewed and revised the title of our paper. The updated title is: " Spatiotemporal Coupling Between Higher Education and Economic Development in China: Based on Interprovincial Panel Data from 2012 to 2023"
We believe this revised title maintains clarity while being more concise.
Comment 2:It is strange to call articles foreign and domestic in an international journal.
RE: Thank you for this important and insightful suggestion. The use of terms such as "foreign" and "domestic" in the context of an international journal reflects a habit from writing in Chinese, which may not be suitable for an international audience. We have revised the terminology in the Literature Review section to better align with the conventions of international publications. This change not only improves the clarity of this article but will also be beneficial for future papers written in English.
The changes are detailed in 2. Literature review。
Comment 3:The literature review is very strange. Firstly, it is very short - although (as correctly noted in the introduction) the topic of the connection between economics and education is very widely developed. Secondly, it contains practically no references to literature (there are only 3 references) - which is generally a paradox. And the highlighted subparagraphs themselves do not help to reveal the topic.
RE: Thank you for your insightful comments. In response to your feedback, we have significantly revised the literature review to address these concerns. We have expanded the review by incorporating an additional 23 relevant references, ensuring a more comprehensive exploration of the existing research on the connection between economics and education. Additionally, we have restructured and rewritten the literature review to better align with the standards expected in academic papers and to more effectively elucidate the topic. We believe these revisions enhance the depth and clarity of the literature review, providing a more thorough foundation for our study.
Comment 4:I am not a mathematician, and I think many readers of the journal are not either, so a more detailed explanation of what is calculated, how and why would be needed.
RE: Thank you for this practical suggestion. We recognize the importance of making our methodology more accessible to readers who may not have a mathematical background. In response, we have revised the methodology section to provide a clearer and more detailed explanation of our calculations and their purposes. Here is a breakdown of the key methods used in our study:
1.Normalization of Data: This is a fundamental data analysis technique used to eliminate differences in measurement units between indicators, making them comparable. This method is widely used in fields such as scientific research, machine learning, and economic management. In Table 1, the values under "Criteria Layer" and "Weight" represent the weights assigned to different levels of indicators, with the sum of weights for each indicator equaling 1. The weight values indicate the relative importance of each indicator.
2.Entropy Weight Method: The entropy weight method determines the objective weights of indicators based on their variability. This approach is preferred over the Analytic Hierarchy Process (AHP) for its accuracy and efficiency. The weights can be calculated using statistical software like SPSS or Excel.
3.Evaluation Model Construction: After normalization and weight calculation, we use these weights to construct a comprehensive evaluation model for development levels. This model integrates the weighted indicators to assess overall development.
4.Coupling Coordination Degree Model: This model analyzes the interaction, synergy, and coordination between different subsystems within a regional economic system, and evaluates its impact on regional economic development. The Coupling Coordination Degree is computed using ArcGIS software, which simplifies the process with user-friendly tools.
We hope this detailed explanation clarifies the calculations and methodologies used in our study, making them more comprehensible for all readers.
Comment 5: At the very beginning of the section on materials and methods, a table with numbers appears - where did these coefficients (Criteria Layer and Weight) come from?
RE: Thank you for your question. In response to Comment 4, we clarify that the coefficients displayed in Table 1 under "Criteria Layer" and "Weight" represent the weights of different indicators. These weights are derived from the normalization and standardization process, followed by the application of the entropy weight method. This approach calculates the weights of first-level and second-level indicators based on their relative importance. The size of the weight values reflects the significance of each indicator in the evaluation model. We hope this explanation clarifies the origin and meaning of the coefficients presented in the table.
Comment 6:The legend in Fig. 2 and 5 is practically unreadable - the font needs to be increased - at least the year should be written in large letters.
RE: Thank you for pointing this out. In response to your feedback and in accordance with journal guidelines, we have revised all figures, with particular attention to Fig. 2 and Fig. 5. The font size in the legends has been increased to enhance readability, and the year labels are now displayed in larger text. We believe these adjustments will improve the clarity and accessibility of the figures.
Comment 7: The conclusions lack development and depth. Did the authors get some numerical data and that's it? Or is it possible to reach some level of understanding of the existing connections and regularities (this is where the very weak theoretical base comes into play). General conclusions need to be made and compared with those in the literature.
RE: Thank you for your constructive feedback. We have revised the conclusions to address these concerns and enhance their depth and development. The revised conclusions now include a more comprehensive analysis of the findings, providing insights into the underlying connections and patterns identified through our research. We have also strengthened the theoretical framework by linking our findings with relevant literature, offering a broader context and comparison with existing studies. Additionally, we have included general recommendations based on our analysis to provide practical implications. We believe these revisions offer a more thorough and nuanced understanding of the study's results. The specific revised conclusions are as follows:
Based on relevant theories and previous research, this study has developed a systematic, comprehensive, and scientific evaluation index system for assessing the coupling and coordination between higher education and economic development. The study utilized panel data from 31 provinces in China from 2012 to 2023 to explore spatiotemporal differentiation and identify obstacle factors constraining coupling and coordination. Key findings include:
(1) For higher education modernization, an index system was developed comprising 3 primary indicators, 4 secondary indicators, and 16 tertiary indicators. Likewise, a separate index system for high-quality economic development includes 3 primary indicators, 5 secondary indicators, and 13 tertiary indicators.
(2) The level of higher education modernization across China's 31 provinces, municipalities, and autonomous regions exhibited a fluctuating upward trend. The ranking is as follows: East > Central > National > Northeast > West. In terms of growth rates, the figures are as follows: Northeast (2.739%) > National (0.912%) > East (0.542%) > Central (0.357%) > West (0.271%). Nationwide, the comprehensive evaluation index for higher education modernization increased from 0.423 in 2012 to 0.467 in 2023, reflecting an average annual growth rate of 0.912%. The periods with the highest growth rates were 2015-2016 and 2020-2021, with rates of 4.687% and 4.325%, respectively. Conversely, the largest decrease in growth rate occurred during 2018-2019, with a reduction of 2.075%.
(3) The level of high-quality economic development in China's 31 provinces, municipalities, and autonomous regions showed a slow, fluctuating upward trend, with the ranking as follows: East > National > Central > Northeast > West. Nationwide, the average level of high-quality economic development remained stable at 0.370. In the East, Central, and West regions, the average levels of high-quality economic development increased from 0.503, 0.351, and 0.277 in 2012 to 0.520, 0.395, and 0.298 in 2023, respectively. In contrast, the Northeast region's average level decreased from 0.359 in 2012 to 0.268 in 2023.
(4) The temporal variation in the coupling and coordination between higher education and economic development in China displayed a fluctuating trend of "rising - falling - rising." In 2012, the average degree of coupling and coordination was 0.432, which increased to 0.444 by 2017 and slightly decreased to 0.442 by 2023.
(5) The obstacle degree of higher education systems across regions ranges from 24.514% to 70.234%, while for economic development systems, it ranges from 23.311% to 54.142%. It is evident that the obstacle degree for higher education systems is significantly higher than that for economic development systems in each region. The provinces with the highest obstacle degrees in higher education development are Qinghai, Ningxia, Hainan, Tibet, and Inner Mongolia. For economic development, the provinces with the highest obstacle degrees are Jilin, Tibet, Hainan, Qinghai, and Guizhou.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThis manuscript discuss the spatiotemporal coupling and coordination relationship between China's higher education system and economic development levels in China from 2012 to 2023, using provincial panel data.
It establishes a comprehensive evaluation index system, employs the entropy weight method for weighting, and utilizes a coupling coordination degree model to assess the interaction between higher education supply and regional economic demand. The study identifies factors hindering their coordination using an obstacle degree model. While the paper presents a comprehensive analysis, several areas for improvement can be considered. Firstly, the paper lacks a clear explanation of the theoretical framework underpinning the relationship between higher education and economic development. It would be beneficial to explicitly state the theoretical assumptions guiding the research and how they relate to the chosen indicators. Secondly, the paper could benefit from a more detailed discussion of the limitations of the chosen methodologies, particularly the entropy weight method and the coupling coordination degree model. Addressing the potential biases and limitations of these methods would enhance the robustness of the findings. Finally, the paper could benefit from a more nuanced discussion of the policy implications of the findings. It should also discuss (with relevant references) the higher education and its vital role in enhancing the elements of economic development, as education contributes to qualifying human cadres with the skills necessary to support the production and innovation sectors, and also contributes to enhancing scientific research and developing technology to achieve sustainable progress. While the paper identifies obstacles to coupling coordination, it could provide more specific recommendations for policymakers to address these challenges and promote a more coordinated and sustainable development trajectory.
Comments on the Quality of English LanguageThis manuscript should edit for proper English language, grammar, punctuation and overall style by one or more of the highly qualified native.
Author Response
Comment 1:
Firstly, the paper lacks a clear explanation of the theoretical framework underpinning the relationship between higher education and economic development. It would be beneficial to explicitly state the theoretical assumptions guiding the research and how they relate to the chosen indicators.
RE: The author's suggestion is highly valuable and professional. In the original draft of the paper, there was a section titled '2.5 The Interactive Mechanism Between the Modernization of Chinese Higher Education and High-Quality Economic Development' included in the literature review.
Due to space limitations, this section was not included in the final version of the paper. The author intends to write a new paper to further explore and empirically investigate the relationship between the development level of higher education in China and high-quality economic development. This content will be incorporated into the new paper.
2.5 The interaction between Chinese higher education modernization and high-quality economic development is characterized by mutual reinforcement.
Modernized higher education systems act as catalysts for economic transformation, innovation, and sustainable growth. This coupling mechanism manifests in three key aspects:
Direct Impact: Internal investment in higher education directly influences economic development.
Indirect Impact: External investment in higher education indirectly affects the economy.
Spillover Effect: Talent cultivation and scientific research output in higher education positively impact worker productivity and the broader economy.
Drawing on education economics and human capital theory, we elucidate the coupling mechanism between education and high-quality economic development, highlighting the following specific mechanisms (See Figure1 for details) .
Figure1 : The Interactive Mechanism between Higher Education Modernization and High-Quality Economic Development.
The meso-level mechanism of education in high-quality economic development revolves around talent and innovation as intermediary variables. This mechanism encompasses two dimensions: education-talent-high-quality economic development and education-innovation-high-quality economic development. It aligns with and elaborates on the foundational and strategic support for building a socialist modernized country as outlined in the report of the 20th National Congress of the Communist Party of China. This report highlights education, science, technology, and talent as essential and strategic pillars for comprehensive socialist modernization.
At the macro-level mechanism, education contributes to high-quality economic development in two primary ways. Firstly, education cultivates a greater quantity and higher quality of scientific research talents, thereby driving improvements in technological proficiency. Secondly, education facilitates the acquisition of technological advancements through channels such as foreign investment and imports.
A review of relevant domestic and international research reveals that domestic studies primarily focus on two aspects [26,27]. The first aspect is literature review and related policy and theoretical research. By tracing, summarizing, and reviewing relevant findings, we can assess the existing research results and identify gaps in the current literature. The second aspect involves examining theories related to high-quality development in higher education, intensive development, the modernization of higher education with Chinese characteristics, and regional economic coordination.
Second, based on the availability and validity of data, develop a scientific, systematic, and practical multidimensional evaluation system and conduct empirical testing. This includes creating an evaluation indicator system for the modernization of higher education with Chinese characteristics, targeting the year 2035. Investigate the mechanisms of this modernization and its relationship with high-quality economic development through theoretical analysis, statistical analysis, empirical analysis, and differential analysis . Demonstrate the "quantity" and "quality" premium effects of higher education across the eastern, central, and western regions, and analyze the issue of uneven distribution of high-quality higher education resources across provinces and regions in China.
Based on theoretical foundations and empirical data, assess the relationship between regional higher education development levels and economic development levels. Identify the factors affecting the modernization of higher education with Chinese characteristics and propose targeted implementation pathways and policy recommendations. Provide suggestions for the future development of higher education and China's sustainable economic development.
Comment 2:
Secondly, the paper could benefit from a more detailed discussion of the limitations of the chosen methodologies, particularly the entropy weight method and the coupling coordination degree model. Addressing the potential biases and limitations of these methods would enhance the robustness of the findings.
RE: This study thoroughly considers data, indicators, and other relevant factors, employing research paradigms from disciplines such as statistics and econometrics to measure the relationship between higher education levels and economic development. Specifically, the entropy weight method and the coupling coordination degree model are utilized. The advantages and disadvantages of these methods are as follows:
1.Entropy Weight Method: The entropy weight method assigns objective weights to indicators based on their variability. Compared to subjective weighting methods such as the Delphi method and the Analytic Hierarchy Process (AHP), it is more objective and provides a clearer explanation of the results. However, it has two main shortcomings: (1) It is highly dependent on the sample, and improper use can lead to distorted weights; (2) It does not account for the horizontal interactions between indicators.
- Coupling Effect and Coupling Coordination Degree:These have become effective and widely used evaluation tools. Choosing appropriate evaluation indicators is crucial for constructing a robust evaluation system. A well-designed indicator system ensures that the resulting calculations of coupling coordination are more scientific and accurate.
When summarizing the limitations of the paper, the authors identified three key points, incorporating the reviewer's suggestions as follows:
(1) Data Constraints: The study relies on provincial panel data from 2012 to 2023 due to data availability issues. Prior to 2012, detailed provincial breakdowns for educational indicators were not available, limiting the study to this time frame. While the sample size generally meets minimum requirements for data analysis, it remains relatively small, which may affect the robustness of the findings.
(2) Geographical Scope: The research is based on provincial-level data. Future studies could benefit from incorporating data at the municipal or county level, which would allow for more precise and targeted empirical analyses.
(3) Comprehensive Indicator Selection: Future research should consider a broader range of indicators, including ecological factors. High-quality economic development is a multifaceted concept that encompasses not only economic scale, quality, and structure but also social, ecological, and environmental dimensions.
Future work will involve expanding the data set and incorporating variables such as ecology and the environment in the selection of indicators. Additionally, we will use econometric methods, including spatial autocorrelation models and the Dagum Gini coefficient. These efforts aim to enrich existing research findings and provide empirical evidence and policy recommendations for the coordinated development of higher education and the economy in China.
Comment 3:
Finally, the paper could benefit from a more nuanced discussion of the policy implications of the findings. It should also discuss (with relevant references) the higher education and its vital role in enhancing the elements of economic development, as education contributes to qualifying human cadres with the skills necessary to support the production and innovation sectors, and also contributes to enhancing scientific research and developing technology to achieve sustainable progress.
RE: As noted by the reviewer in Comment 1,The interaction between Chinese higher education modernization and high-quality economic development is characterized by mutual reinforcement. Modernized higher education systems act as catalysts for economic transformation, innovation, and sustainable growth. This coupling mechanism manifests in three key aspects:
Direct Impact: Internal investment in higher education directly influences economic development.
Indirect Impact: External investment in higher education indirectly affects the economy.
Spillover Effect: Talent cultivation and scientific research output in higher education positively impact worker productivity and the broader economy.
Given the mechanisms through which higher education and economic development interact, the authors propose in the recommendation that:
1.Efforts should be focused on improving the development of higher education in China, particularly in the central and western provinces, where significant disparities in educational and economic development persist. First, it is crucial to increase investment in higher education resources and direct policies to support the western and northeastern regions. Second, optimize the allocation of higher education resources by facilitating the transfer of resources from the eastern and central regions to the western and northeastern regions. This strategy aims to address the uneven distribution of quality higher education resources and ensure more equitable opportunities across the country.
2.Efforts should be directed towards narrowing the economic development gap between the eastern, central, and western regions. The eastern region, leveraging its leading position, should focus on sharing innovations, promoting resource spillovers, and engaging in joint scientific and technological advancements. This approach will enhance collaborative interactions among the regions, allowing the stronger areas to support and uplift the weaker ones in higher education and economic development.
Comment 4:
While the paper identifies obstacles to coupling coordination, it could provide more specific recommendations for policymakers to address these challenges and promote a more coordinated and sustainable development trajectory.
RE: Thank you for the reviewer's suggestion. As noted, a major highlight of this paper is that the data-driven conclusions offer significant policy recommendations.
Barrier analysis has been employed to identify the factors that constrain the modernization of higher education and high-quality economic development across China’s 31 provinces. The results reveal that the causes of the imbalance between regional economic and higher education development are multifaceted. Each province should perform a comprehensive analysis tailored to its unique development characteristics and specific barriers.
Based on these research findings, the following recommendations are proposed to further enhance the high-quality development of higher education and economic development in China:
- Efforts should be focused on improving the development of higher education in China, particularly in the central and western provinces, where significant disparities in educational and economic development persist. First, it is crucial to increase investment in higher education resources and direct policies to support the western and northeastern regions. Second, optimize the allocation of higher education resources by facilitating the transfer of resources from the eastern and central regions to the western and northeastern regions. This strategy aims to address the uneven distribution of quality higher education resources and ensure more equitable opportunities across the country.
- Efforts should be directed towards narrowing the economic development gap between the eastern, central, and western regions. The eastern region, leveraging its leading position, should focus on sharing innovations, promoting resource spillovers, and engaging in joint scientific and technological advancements. This approach will enhance collaborative interactions among the regions, allowing the stronger areas to support and uplift the weaker ones in higher education and economic development.
- The factors constraining higher education and economic development differ across provinces. Each province should focus on addressing its specific weaknesses in academic disciplines and regional development.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
Comments and Suggestions for AuthorsThe current version of the manuscript is appropriate
