Chinese universities, it’s a reality that female teachers prefer teaching to research.
Second, as a result of different social roles for man and woman, the balance of
family and work is usually a hard choice for female university staff. They are forced
to endure more housework, especially in the aspect of bearing and raising children,
which impedes their progress and growth in research. An investigation on those
who have PhDs done by Mason and Goulden ( 2002 ) shows that, if a female
doctoral graduate gave birth to a baby in less than 5 years after graduation, her
probability of being promoted to tenured track in 12–14 years is 20% lower than
those male peers with similar qualifications. Third, the different proportions of
female in different disciplines is another key factors impacting on their research
efficiency. In 211Us, the percentage of those with a doctor degree in male faculty
members is 34.85%, while that in female faculty members is 20.16%. This lower
percentage of PhD also results into the overall lower research efficiency of females.
To sum up, gender preference in teaching and research, professional development
barrier caused by social roles in both genders, and the endogenous differences on
gender composition and disciplinary research productions (or the features of
research production in different disciplines) all might result into the negative impact
of gender composition on university research efficiency.
Among human capital variables,“DocPerc”has positive impact on research
efficiency in both NEAM and HSS samples. This is consistent with the conclusion
of Tongtong Jiang ( 2011 ). Therefore, for 211Us, it’s necessary to raise the
threshold of recruitment, attracting high-profile talents with doctor degree, and at
the same time, encourage those staff without doctor degree to apply for one in the
future.
6.2.5.2 Factors Functioning in Different Patterns
The inclusion of TYPE variable in the model is to examine the impacts of higher
education policies and institutions formed by history on the university research
efficiency. TYPE variable can distinguish universities in the aspects of research
foundation, research strength and research funds. In all these aspects, 985Us are
much better than non-985Us. According to the results, in the NEAM sample, the
effect of TYPE is negative, while in HSS sample, the effect of TYPE is significantly
positive. This implies that HSS research productions are much more influenced by
higher education policies and institutions.“ARWU”is also an indicator used to
measure the type and attribution of a university, and it is used to examine the impact
of overall academic level of a university. Our research found that in NEAM sample,
those universities on the list of AWRU tend to be less efficient, while in HSS
sample, the result is completely opposite, yet neither is statistically significant.
There might be two reasons for 985U and AWRU variables have opposite effects
on the research efficiency of NEAM and HSS disciplines. First, to the NEAM
samples, their research staff have entered the developmental stage of chasing sci-
ence and technology innovations and improving research quality. Limited with
data, the outputs data is mainly focus on quantity, which results into their low
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