efficiency and productivity of 211Us. Methods like DEA and Malmquist index are
employed, and data come from “Higher Education Science and Technology
Statistics”. More specifically, we intend to focus on the following three aspects.
First, based on the theory of economic efficiency and relevant literature, we will
construct indicator systemfit for evaluating Chinese higher education research
efficiency and productivity. The input indicator system is consisted offinancial
input, human capital input, real capital input in research production. The output
indicator system is consisted of science and technology achievements, high-level
academic publications, and commercialization of science and technology achieve-
ments. The construction of the indicators considers not only the quantity and scale
of input and output, but also the quality of input and output.
Second, by dividing all the research activities into two large categories as
Natural sciences, Engineering, Agriculture and Medicine (NEAM) disciplines and
Humanities and Social Sciences (HSS) disciplines, we evaluate research efficiency
and productivity in 211Us separately. We evaluate the research efficiency and
productivity of NEAM disciplines and HSS disciplines in 211Us during 2006– 2010
respectively, in order to unravel the changes of university research efficiency and
productivity, and discover the underlying key factors.
- According to the input–output indicator system, we do descriptive analysis to
every input and output indicator of NEAM and HSS disciplines respectively.
Next step, we do comparative analysis to input and output changes of NEAM
and HSS disciplines from the angles of university locations (namely, eastern,
central and western, and the eastern region is further subdivided into Jing-Jin-Ji
Areas, Hu-Su-Zhe Areas, Other Areas) and university level (985Us or
non-985Us). - Apply DEA model (Constant Returns to Scale (CRS) and Variable Returns to
Scale (VRS)) to evaluate 2006–2010 research efficiency (technical effect and
scale effect) of NEAM and HSS disciplines respectively. Make comparisons of
the efficiency changes of both NEAM and HSS disciplines by university loca-
tion and level. - Apply Malmquist Index approach to illustrate the dynamic productivity changes
of NEAM and HSS disciplines during 2006–2010, and compare the
between-university differences from the angles of university location and level,
and discover the key factors impacting on research productivity of NEAM and
HSS disciplines based on decomposing Malmquist Index into catch-up effect,
scale effect and growth effect. - Based on production function and the construction of impacting indicator sys-
tem in university research efficiency from three layers—macro environment,
higher education sector, and micro mechanism inside university, we use the
efficiency scores of DEA as the dependent variable, and build up Tobit model, to
take an in-depth examination on the key factors impacting on university research
efficiency.
1.3 Overview and Analytical Framework 7