Education and Innovation: The Input-Output Efficiency of Research in Chinese Universities

In terms of scale, China’s higher education system holds a leading position globally. The nation has implemented a strategic framework that integrates higher education development with technological innovation, aiming to establish scientific hubs and innovation-driven centers. Drawing on data from 31 provincial-level regions in China, this study employs the super-efficiency Data Envelopment Analysis (DEA) model and the Malmquist Productivity Index (MPI) to systematically evaluate the research performance of Chinese universities from 2011 to 2022. The findings reveal that throughout the study period, Chinese universities maintained a relatively high level of research efficiency, demonstrating an overall upward trend. Although pure technical efficiency (PTE) remained strong, scale efficiency (SE) still leaves room for improvement. Conversely, the total factor productivity (TFP) of Chinese universities exhibited a downward trajectory, particularly after 2017, when a significant decline in the contribution of technological progress became a primary bottleneck for TFP growth. Therefore, optimizing the research efficiency of Chinese universities requires prioritizing originality-driven investments, optimizing the national layout of research resources, and reforming management mechanisms to stimulate innovative vitality.

References

UNESCO, 2015, UNESCO Science Report: Towards 2030, UNESCO Publishing.

Xia Z, Zheng Z, Garell JM, et al., 2024, Scientific and Technological Innovation and Sustainable Development of Higher Education [Panel Discussion], China Higher Education Research, (12): 6–11.

Ministry of Education of the People’s Republic of China, 2024, 2023 National Education Development Statistics Bulletin.

Wu J, Lu L, 2025, Research on the Historical Position and Construction Path of World-Class Universities with Chinese Characteristics from the Perspective of a Strong Nation Strategy. Jiangsu Higher Education, (2): 1–12.

Wang Z, Li Y, 2024, Anchoring the Gold of Building a Leading Country in Education and Accelerating the Construction of World-Class University Highland. Journal of National Academy of Education Administration, (11): 3–16.

Que M, Shen Y, 2024, The Value Implication, Internal Mechanism and Practical Path of the Integrating Trinity of Education, Science and Technology, Talents. Modern Education Management, (9): 22–33.

Xiao G, Tang K, 2024, The Integrated Construction of Education, Science and Technology, and Human Resources in the Process of Chinese Path to Modernization. Journal of Shandong University (Philosophy and Social Sciences), (1): 1–10.

Feng Y, Zhao D, Gao Y, et al., 2023, Accelerating the Construction of a Powerful Educational Country to Provide Strong Support for the Great Rejuvenation of the Chinese Nation. Modern Education Management, (10): 24–45.

National Bureau of Statistics of China, 2023, 2022 National Statistical Bulletin on Science and Technology Funding Input, https://www.stats.gov.cn/sj/zxfb/202309/t20230918_1942920.html

Nature Index, 2023, Annual Tables 2023, https://www.nature.com/nature-index/annual-tables/2023

Zhuo Z, 2024, High Technology-Oriented Self-Reliance and Self-Improvement Empowered by Research Universities: Dilemmas and Breakthroughs. Educational Research, 45(11): 26–38.

Peng S, Xiao G, 2024, The Logic, Dilemma and Approach of Organized Research in Research Universities under the Background of Scientific and Technological Self-Reliance. Scientific Management Research, 42(3): 26–34.

Liefner I, 2003, Funding, Resource Allocation, and Performance in Higher Education Systems. Higher Education, 46: 469–489.

Brintseva O, 2024, A DEA-Based Malmquist Productivity Index for Analysing University Performance and Competitiveness. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie, 1006(4): 5–22.

Ren X, Kongkaew T, 2024, Evaluation of Educational Resource Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index. Journal of Arts Management, 8(4): 512–531.

Avkiran NK, 2001, Investigating Technical and Scale Efficiencies of Australian Universities through Data Envelopment Analysis. Socio-Economic Planning Sciences, 35(1): 57–80.

Castano MCN, Cabanda E, 2007, Sources of Efficiency and Productivity Growth in the Philippine State Universities and Colleges: A Non-Parametric Approach. International Business Economics Research Journal, 6(6): 93–102.

Kuah CT, Wong KY, 2011, Efficiency Assessment of Universities through Data Envelopment Analysis. Procedia Computer Science, 3: 499–506.

Wolszczak-Derlacz J, 2018, Assessment of TFP in European and American Higher Education Institutions: Application of Malmquist Indices. Technological and Economic Development of Economy, 24(2): 467–488.

Khan MZS, Mazumder S, 2021, Efficiency of Public and Private Universities in Bangladesh: A Comparative Approach Using Data Envelopment Analysis. Journal of Education and Research, 11(2): 28–52.

Lee BL, Johnes J, 2022, Using Network DEA to Inform Policy: The Case of the Teaching Quality of Higher Education in England. Higher Education Quarterly, 76(2): 399–421.

Loganathan M, Subrahmanya MB, 2023, Efficiency of Entrepreneurial Universities in India: A Data Envelopment Analysis. Journal of the Knowledge Economy, 14(2): 1120–1144.

Dipierro AR, De Witte K, 2024, The Underlying Signals of Efficiency in European Universities: A Combined Efficiency and Machine Learning Approach. Studies in Higher Education, 49(3): 1–20.

You L, Kong QP, 2021, Research on Evaluation and Influencing Factors of Higher Education Resource Allocation Efficiency: Based on Super Efficiency DEA-Malmquist Method and Tobit Model. Education Economy, 37(6): 30–37.

Bao SM, Huang YY, Peng WY, 2023, Research on Comprehensive Evaluation of Resource Input-Output Efficiency of Universities Directly under the Ministry of Education in China: Calculation Based on Super-Efficiency DEA Model and Malmquist Productivity Index. University Education Science, (4): 60–71.

Zhao QN, Zhang Y, 2024, Resource Allocation Efficiency of China’s Higher Education System: Based on the Whole-Process Perspective of Achievement and Economization. Education Science, 40(1): 87–96.

Cha DL, Chen S, Yang Q, 2022, Empirical Research on Scientific Research Efficiency and Influencing Factors for Double-First Class Universities: Based on Super-Efficient SBM-Malmquist-Tobit Model. Education Economy, 38(3): 9–16.

Guo J, Liu C, Liu X, et al., 2024, Empirical Analysis of the Efficiency and Influencing Factors of Scientific and Technological Innovation in Chinese Universities: A Two-Stage Perspective of Scientific and Technological Output and Transformation. Science and Technology Management Research, 44(6): 97–106.

Li N, Lü L, Liu P, 2024, Research on the Measurement and Spatial Differences of Scientific and Technological Innovation Efficiency in Local Universities. Statistics Decision, 40(14): 79–83.

Ma Y, Wei S, 2023, Scientific Research Efficiency and Influencing Factors of Universities in Western China under the Perspective of High-Quality Development: Based on Super-Efficiency DEA Window-Malmquist-Tobit Model. Science and Technology Management Research, 43(21): 11–19.

Ren J, Li Y, 2023, Can First-Class Universities be More Efficient? A Perspective of Government Financial Investment. Higher Education Exploration, (5): 25–34.

Han Z, Liu S, Yao H, 2025, Is the “Double World-Class Project” Initiative Effective in Enhancing University Research Innovation Efficiency: A Panel Data Analysis of 72 Universities Directly under the Ministry of Education from 2010 to 2020. Journal of Educational Science of Hunan Normal University, 24(1): 91–102.

Charnes A, Cooper WW, Rhodes E, 1978, Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2(6): 429–444.

Banker RD, Charnes A, Cooper WW, 1984, Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9): 1078–1092.

Charnes A, Rousseau JJ, Semple JH, 1996, Sensitivity and Stability of Efficiency Classifications in Data Envelopment Analysis. Journal of Productivity Analysis, 7: 5–18.

Malmquist S, 1953, Index Numbers and Indifference Surfaces. Trabajos de Estadística, 4(2): 209–242.

Färe R, Grosskopf S, Lindgren B, et al., 1992, Productivity Changes in Swedish Pharmacies 1980–1989: A Non-Parametric Malmquist Approach. Journal of Productivity Analysis, 3(1–2): 85–101.

Färe R, Grosskopf S, Norris M, et al., 1994, Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. American Economic Review, 84(1): 66–83.

Cobb CW, Douglas PH, 1928, A Theory of Production. American Economic Review, 18(1): 139–165.

Chen Z, Yang Z, 2021, Exploring the Effect of Resource Misallocation on the Basic Research Efficiency. Science and Technology Management Research, 41(9): 69–76.

Ren S, 2022, Exploring the Path of Leading Innovation Oriented to the World’s Scientific and Technological Frontiers. China Higher Education, (20): 4–6.

Xie H, Tang Y, 2021, Research on the Comprehensive Improvement of University Scientific and Technological Innovation Capability Based on the “Four Orientations.” China University Science Technology, (6): 54–58.

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