Journal of Data and Information Science ›› 2019, Vol. 4 ›› Issue (1): 32-59.doi: 10.2478/jdis-2019-0003

• Research Paper • Previous Articles     Next Articles

Measuring Scientific Productivity in China Using Malmquist Productivity Index

Yaoyao Song1,2,Torben Schubert3,4,Huihui Liu1,2,Guoliang Yang1,2†()   

  1. 1Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
    3Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany
    4 Center for Innovation, Research and Competence in the Learning Economy (CIRCLE) at Lund University, Lund, Sweden
  • Received:2018-06-29 Revised:2018-11-06 Online:2019-01-31 Published:2019-01-31
  • Contact: Guoliang Yang
  • Supported by:
    Number of graduates, Total amount of external grants and contracts for research;Number of graduates weighted by their degree classification, Research grants;Undergraduates in medicine or dentistry, Undergraduate science students, Undergraduate non-science students, Postgraduate students in all disciplines, Quality-related funding and research grants, Income from other services


Purpose: This paper aims to investigate the scientific productivity of China’s science system.

Design/methodology/approach: This paper employs the Malmquist productivity index (MPI) based on Data Envelopment Analysis (DEA).

Findings: The results reveal that the overall efficiency of Chinese universities increased significantly from 2009 to 2016, which is mainly driven by technological progress. From the perspective of the functions of higher education, research and transfer activities perform better than the teaching activities.Research limitations: As an implication, the indicator selection mechanism, investigation period and the MPI model can be further extended in the future research.

Practical implications: The results indicate that Chinese education administrative departments should take actions to guide and promote the teaching activities and formulate reasonable resource allocation regulations to reach the balanced development in Chinese universities.

Originality/value: This paper selects 58 Chinese universities and conducts a quantified measurement during the period 2009-2016. Three main functional activities of universities (i.e. teaching, researching, and application) are innovatively categorized into different schemes, and we calculate their performance, respectively.

Key words: Data Envelopment Analysis (DEA), Chinese higher education, Scientific productivity, Malmquist Productivity Index (MPI)