Measuring Scientific Productivity in China Using Malmquist Productivity Index

The Cultural Revolution in China was a period that can be characterized as “order out of chaos”, which had a tremendous negative impact on all aspects of society. Since the end of the Cultural Revolution, China’s political orientation has undergone tremendous changes, and science and education policies also improved in all aspects.

In 1977, the Chinese college entrance examination system restored, which had been interrupted since the impact of the Cultural Revolution for more than ten years. Thus, China resumed its respect for knowledge and talents and ushered in new development opportunities in the field of science and technology. In the same year, the Chinese President Xiaoping Deng declared that China should consider science, technology and education as the primary tool to catch up with the developed countries at the National Work Symposium on Education and Science. In 1983, this new doctrine was reemphasized by Deng, and he announced that education should be oriented toward modernization, the world and the future.

Following this general guideline, the first step towards reorganizing the science system was taken in 1985. The central government issued “The Decision of the Communist Party of China Central Committee on Education Reform” and decided to increase the autonomy of universities. A certain autonomy on the student admission was granted to the universities, which used to be fully determined by national plans. Furthermore, in the same year, the central government also issued the “Notice on the Research and Pilot of Assessment of Higher Engineering Education”, which started the pilot of education assessment in China. This policy shift was furthered by the “Interim Provisions on the Evaluation of Regular Institutions of Higher Education” issued in 1990, which tried to guide the universities to train students, conduct scientific research and promote societal development. It was the first legislation on higher education assessment in China, which clearly regulated the basic elements of higher education assessment, including its purpose, mission, guiding ideology, basic forms, etc. Subsequently, the central government started organizing a higher education assessment in universities, and the assessment was mainly conducted on teaching activities.

Since 1992, governments at all levels made considerable efforts to strengthen the construction of China’s scientific system. On the one hand, they announced that the development of education and science was the key to the overall development of China, and the education and scientific activities were guided accordingly. On the other hand, they improved the science and education system by increasing the autonomy of the scientific system and thereby improve the productivity and creativity of scientific activities. Although there were still many restrictions on the autonomy of China’s scientific system compared with the developed countries at that time, it was still a breakthrough in China’s traditional science and education system under the strict top-down bureaucratic control.

In the subsequent years, the overall scale of the science system increased dramatically, which was especially reflected in the rapidly increased overall number of college students. Since 1998, more and more Chinese universities obtained the right to expand the scale of enrollment, so that an increasing number of high school graduates got access to colleges and universities. In January 1999, the Ministry of Education (MOE) announced the “21st Century Education Revitalization Action Plan”, which defined the goal of expanding students as “China’s higher education enrollment rate should be increased from the current level of 9% to about 15% in 2010”. Table 1 shows the rapid expansion of Chinese higher education from 1998 to 2007. It is obvious that the enrollment number, which denotes the number of newly enrolled students in Chinese colleges and universities, increased by more than 4 times from 1998 to 2007. The university number, which refers to the number of Chinese colleges and universities, nearly doubled to the original number in 1998. The enrollment rate, which refers to the ratio of the number of higher education students and the school-age population, increased from 9.8% to 23.0%. The rapid growth of these data shows China’s emphasis on higher education and the positive impact of implementing various science policies.

This overall increase was accompanied not only by a large number of resources devoted to this field but also by an increasing need for the efficiency and productivity of resource utilization in the science system. To achieve this goal, the country’s administrative controls were further reduced to release the redundant restrictions on universities. In 2013, the central government decided to gradually reduce the administrative control of universities and promote their autonomy. In January 2014, the MOE issued the “Regulations of Academic Committee of Higher Education Institutions”, which prescribed that Chinese universities should establish an academic committee according to the law to be the core of the academic management system and organization structure. At the same time, the role of competition was emphasized on both individual and organizational levels to align the incentives in the science system with societal needs. Thus, like in most other science systems in developed countries, resources are increasingly awarded based on performance.

In recent years, a widespread phenomenon occurred in China’s science and education system. The incentive mechanism is extremely biased towards scientific research rather than teaching. Most of the improvement in China’s scientific productivity comes from scientific research at the expense of teaching activities as we will describe below. Therefore, although China’s policymakers promote equal importance to the development of scientific research and teaching of students, more and more education practitioners consider producing scientific achievements rather than improving the teaching skills as a career goal. To gain a deeper understanding of the problems in China’s scientific system, we will review the resource allocation and the incentive mechanisms that have been developed from the state to universities as well as from universities to their employees (researchers).

Since 1993, the state has formulated a series of educational programs aimed at improving the structure of China’s science system and improving the overall competitiveness of Chinese universities at the same time. According to these programs, the Chinese government allocated a large number of resources to a selected number of universities and guided these universities on their way towards becoming internationally competitive.

Among the numerous projects, Project 211, Project 985, and Plan 2011 are the three most influential programs. The details of these national education programs are described as follows: (1) Project 211. This program, launched in 1993, aims to select and support about 100 universities in China and build frontier universities in the 21st century, with an investment scale of RMB 18 billion Yuan (at current year prices); (2) Project 985. Launched in 1998, it aims to select and support dozens of universities in China to build the excellent and internationally competitive universities with a total investment of more than RMB 30 billion Yuan (at current year prices); (3) Plan 2011. The Higher Education Innovation Capacity Improvement Plan, launched in 2011, is referred to as the Plan 2011, which aims to improve the universities’ innovation capability to face the scientific frontier and continue to build several world-leading universities and disciplines.

The choice of schools that can enter these programs was based on past achievements. Although the general principles of the selection emphasized that research and education are equally important, the most important selection criteria in practice were the number of high-quality international publications. Therefore, many resources for the development of the Chinese science system were concentrated in a handful of universities that had scientific achievements. Until July 2014, there were 2,246 different kinds of higher education institutions in China, among which 782 were public. The institutions involved in Project 211 and Project 985, which represent high research level in China, are only 112, which accounts for less than 15% in public institutions and less than 5% in all higher education institutions. According to the data released by the MOE, from 2009 to 2013, the funding allocated by MOE to all higher education institutions in China reached RMB 264.769 billion Yuan, but Project 211 and Project 985 universities received about 72% of the total funding. Therefore, the remaining more than 2000 higher education institutions received only about RMB 70 billion Yuan with a proportion of about 28% in total

These words refer to http://wap.sciencenet.cn/info.aspx?id=307525, and the statistical data is from the China Education Statistical Yearbooks (2010–2014) issued by MOE.

The Higher Education Evaluation Center (HEEC), established in August 2004, is an administrative organization under the auspices of the MOE. The main responsibilities of HEEC include organizing the evaluation of baccalaureate and associate degree programs offered at different universities and colleges in China, conducting research on regulations and policies on higher education reforms and providing recommendations for the decision-makers. HEEC also shoulders the responsibility to develop international cooperation and exchanges with other higher education evaluation/accreditation agencies from other countries as well as from Hong Kong, Macao and Chinese Taiwan (Luo Wang, 2007). On behalf of MOE, HEEC evaluates its subordinate universities in China every five years. The establishment of HEEC indicates the formal higher education assessment system has been built. Among its evaluation framework, international publication (i.e. SCI/SSCI) is also a very important indicator. The evaluation results will affect the resource allocation of the university and the future of directors of universities. Thus, the directors have a strong motivation to push researchers to publish an increasing number of international papers.

The existence of the university assessment system directly affects the design of the university’s internal management system, especially the evaluation and reward system for researchers. To inspire good scientific achievements and obtain more resources from the government, the universities mainly incentivize researchers through the following mechanisms.

First, the salary of researchers is not a fixed amount but largely depends on performance. Normally, it consists of a basic salary and a performance salary. The basic salary is the same for researchers from the same occupational category. However, the performance salary varies largely according to different performance indicators, e.g. teaching, research funding, publications. The workload of teaching activities is based on the teaching time and its quality, which is difficult to measure. Publications, especially international publications (e.g. SCI/SSCI papers), are one of the main factors for researchers in the universities to compete for research funding. Furthermore, the amount of research funding is highly correlated with the researchers’ salary, reputation and the academic impact in this research field. Having enough research funding is also the prerequisite to be able to recruit master and Ph.D. students, post-docs, and other temporary staff.

Second, publication lists are a key factor for the researchers’ promotion. Being a full professor is obviously a prestigious state in the research community, and a relatively big gap exists in salaries between researchers with and without professorship. Besides, researchers without professorship find it hard to conduct research independently because most of the research projects can only be applied and granted to full professors. Therefore, the demand for promotion also stimulates the desire of researchers to publish papers and engage in academic research.

Third, in most Chinese universities, the primary authors (normally the first author or the corresponding author) of international publications will be awarded extra bonus at the end of the year according to the impact factors or other criteria. For example, universities normally award RMB 100 thousand Yuan to the researcher if he (she) could publish a research paper in top journals (e.g. Science, Nature, and Cell) as the primary author. It is very common in Chinese universities, and almost every university does so, but the amount of bonus depends on the university. It is also recorded and termed as a cash-per-publication reward policy in Quan et al. (2017). To the best of our knowledge, the highest bonus reaches RMB 500 thousand Yuan for one top paper on Science, Nature, or Cell in Wuhan University, which is one of the most famous universities in China

http://news.whu.edu.cn/info/1002/41417.htm

Fourth, the maintenance of the working position is also related to scientific research outcomes. Researchers without enough publications on either number or quality can be changed to a less important position for logistics or service (e.g. secretary), and the salary will be relatively low. Recently, more and more universities, especially high-level research universities, have enabled the tenure track system, which means the researchers without professorship must leave if they fail to publish enough high-quality papers during a certain period, e.g. six or eight years.

Fifth, for students (especially Ph.D. candidates) in the better Chinese universities, one of the most important qualifications for graduation is to publish a certain number of SCI/SSCI papers (Liu & Zhi, 2012). If the Ph.D. candidates failed to publish enough SCI/SSCI papers, they will not be awarded the Ph.D. degree. For post-docs, they cannot stay in the universities and transform to a permanent position or tenure-track as a lecturer, if they cannot publish satisfactory publications.

Finally, research and development (R&D) application can also improve the income of researchers. This kind of technology transfer behavior is common and popular across the academia (Yong, 1996), and it has greatly promoted the university-industry collaboration. If the research results or outcomes of one researcher can be transferred to the company and obtain revenue from business, the researcher will receive a bonus in return. Thus, it may attract some researchers who do not intend to become full professors in the future, because good publications often need to invest a lot of time and vigor.

Due to these reasons, researchers, especially young researchers without professorship, endure relatively high pressure for promotion, income, and even the positions, which mainly depend on the publications, especially high impact SCI/SSCI publications. Therefore, Chinese researchers in high-level universities have very strong motivation to increase their publication list. This force makes them spend most of their time in writing papers instead of using their time to ensure the high quality of teaching because it is hard to be promoted to a full professor through teaching.

The issue of higher education efficiency in different countries has been investigated by many researchers. According to the time span covered by the research methods, this literature can be divided into studies on efficiency at a certain point in time and studies on the productivity during a time period with respect to the higher education.

In the first category, Agasisti (2011) conducted a cross-country comparison on the efficiency of tertiary education and analyzed the role of the public sector. Agasisti and Bianco (2009) computed efficiency rankings and scores of Italian universities using DEA and Stochastic Frontier analysis (SFA). Barra et al. (2018) measured the efficiency of Italian higher education using both DEA and SFA techniques and used the results to provide guidance to university managers and policymakers. De França et al. (2010) evaluated the impact of information asymmetry on Brazil organizational efficiency based on an additive DEA approach. Johnes (2013) evaluated the efficiency of higher education institutions in England using the traditional black-box DEA and network DEA. The results suggested that the employability issue was the main reason constraining the growth of institutional efficiency. Singh and Ranjan (2017) proposed a single stage DEA approach, which can measure the efficiency of DMUs and their parallel sub-units simultaneously. Indian universities, colleges and stand-alone institutions were taken as non-homogeneous sub-units in the assessing of their efficiency. Wolszczak-Derlacz (2017) assessed the technical efficiency of European and American higher education institutes (HEIs) using DEA and determined different frontiers (global, regional, and country-specific). The impact of the external factors on the HEI inefficiency were also examined.

Among the productivity researches, Agasisti and Johnes (2009) investigated the technical efficiency and the Malmquist index of Italian and English higher education institutions and found that English institutions were more efficient than Italian ones. Agasisti and Pohl (2012) examined and compared the efficiency of Italian and German public universities and the Malmquist index from 2001 to 2007, which aims to provide useful managerial and policy-making suggestions for the policy-makers in both countries. Barra and Zotti (2016) investigated the technical efficiency of two main activities, teaching and research, on two large groups, the Science and Technology sector and the Humanity and Social Science sector, in a big public university in Salerno from 2005 to 2009. Barros et al. (2011) employed DEA to investigate the nature of the technical change in the French labor market and the productivity change in a sample of higher education leavers from 1999 to 2004. Edvardsen et al. (2017) investigated the productivity development for Norwegian institutions of higher education from 2004 to 2013 using a bootstrapped Malmquist index based on DEA. Thanassoulis et al. (2011) applied DEA and SFA to assess the efficiency of English higher education institutions and revealed that the increase in students and student mix are factors in the improvement of efficiency. They also adopt the Malmquist index in the assessing of the productivity change in the UK higher education and found a downward movement of the institution productivity on average.

In this paper, we use the MPI to investigate efficiency changes of the sample universities. This index was first introduced by Malmquist (1953) and has been further studied and developed in a non-parametric framework by several authors (e.g. Färe et al. 1994). Odeck (2000) used the MPI in an efficiency and productivity analysis of the Norwegian motor vehicle inspection agencies during 1989–1991. Asmild et al. (2004) conducted the performance over time measurement in Canadian banking industry through the proposed approach combining MPI with DEA window analysis technique, which can treat the panel data with the advantage of dealing with small-size samples. The labor productivity in the European Union area was investigated in Färe et al. (2006). Cooper et al. (2007) showed that the MPI is an index which represents the Total Factor Productivity (TFP) growth of a DMU, and it can reflect (1) the progress or regress in technical efficiency, (2) the changes of the frontier technology between two time periods under the multiple inputs and multiple outputs framework.

Suppose there are m input indicators and s output indicators, and the observation of DMU_j (j = 1,2,...,n) in period t(t = 1,2,...,T) can be denoted as (xjt,yjt)∈Rm+×Rs+.$$\left( x_{j}^{t},y_{j}^{t} \right)\in R_{m}^{+}\times R_{s}^{+}.$$The production possibility set (PPS) can be defined as

where l^t_j denotes the intensity variable. In this paper, we assume the production technology satisfies the variable returns to scale assumption, so ∑j=1nλjt=1$$\sum{_{j=1}^{n}\lambda _{j}^{t}=1}$$is incorporated in Eq. (1) to specify the shape of the PPS. The PPS is characterized by the frontiers, which are formulated by (x^t , y^t ) ∈T^t so that under the current technology, the combination of less input and more output cannot be obtained.

In the framework of Malmquist analysis, the efficiency of DMU₀ is evaluated by frontiers in different periods through various ways. The MPI is defined as the product of the Catch-up term and the Frontier-shift term (Caves et al., 1982; Färe et al., 1989; Tone, 2004). The Catch-up term captures the efficiency change of a DMU. The Frontier-shift term reflects the technology change between two periods. Therein, the Catch-up term is measured as

The efficiency of every DMU can be measured by suitable DEA models. We use a simple case with a single input and a single output to illustrate the Catch-up effect, as shown in Figure 2.

Figure 2

Hence, the input-oriented Catch-up effect can be measured as

The Catch-up value larger than (equals to, smaller than) unity represents an increase (invariability, decrease) in the relative efficiency of DMU₀.

The Frontier-shift term can be embodied in the quotient of the efficiency of (x01,y01)$$\left( x_{0}^{1},\,y_{0}^{1} \right)$$under different frontiers as

where the measurement of the denominator adopts the efficiency of (x01,y01)$$\left( x_{0}^{1},y_{0}^{1} \right)$$relative to the frontier in period 2. Similarly, the Frontier-shift effect of (x02,y02)$$(x_{0}^{2},y_{0}^{2})$$is estimated by

Therefore, the Frontier-shift effect is defined as the geometric mean of Eq. (4) and Eq. (5) as

Using the illustrative case in Figure 2, the input-oriented Frontier-shift effect can be measured as

The Frontier-shift value larger than (equals to, smaller than) unity denotes progress (invariability, regress) in the frontier technology. Consequently, the MPI can be written as the product of the Catch-up term and the Frontier-shift term

Following the technical efficiency derived from the input-based DEA model and the traditional radial directional distance in Shephard (1970), the efficiency score of DMU₀ in period t relative to the frontier in period t + 1 can be notated using the corresponding directional distance function as

which measures the maximum extent that DMU₀ can contract its input and maintain its output. Using this kind of notation, the Catch-up term in Eq. (2) is rewritten as

The Frontier-shift term in Eq. (7) is expressed as

If we multiply these two terms together, the MPI is obtained as

An MPI index exceeding one indicates progress in the TFP of DMU₀ from period t to period t + 1, while an MPI index equalling one or less than one indicates the stability or deterioration in the TFP, respectively.

Interested readers can refer to Cooper et al. (2007) for details. Pastor and Lovell (2005) argued that MPI fails to satisfy circularity and may encounter infeasibility problem. Concretely, the geometric mean MPI is not circular because the choice of time interval will cause the alteration of frontiers and different measures of productivity change. The infeasibility problem sometimes occurs when the data points are outside the frontier and they are impossible to project onto the frontier. However, in this paper, circularity is not our focus, and we observe no infeasibility problem in the case study.

The higher education system absorbs a large amount of governmental financial investment, and it plays an important role in creating, producing, and popularizing knowledge in society. With rapid economic development and social progress, the issue of higher education efficiency has always been a topic of concern, and an increasing number of scholars conducted research on the performance of the higher education system. We summarize some of the existing literature on the higher education issue, and the indicators they used are listed in Table 2.

As stated in Schmoch and Schubert (2009) and Schmoch et al. (2010), teaching, researching and serving the society (mainly reflects in the application of R&D outputs and technology services) are three main functional activities undertaken by universities. The independent functional activities are distinguished into three abstract schemes. Following the indicators used in the previous literature, we select three input indicators corresponding to the three functions, and each one is used in

the input-based DEA model to measure the MPI in a scheme separately. The indicators include the number of teaching staff (TEACHING STAFF), the number of staff who spend most of their time on R&D activities (R&D STAFF), and the number of staff who spend most of their time on the application of R&D outputs and technology services (APPLICATION STAFF). TEACHING STAFF stands for personnel with teacher qualifications and specialized in teaching activities. R&D STAFF refers to personnel belongs to the affiliated research organization of universities. APPLICATION STAFF is represented by personnel engaged in the application and service of scientific research achievements.

Furthermore, we use the total number of staff (TOTAL STAFF) at the university as the single input of Scheme 1, which aims to reflect the overall input-output condition within the university. All these four schemes employ single input indicator because there is heavy overlap between staff and the allocated funding in the universities. To identify the potential driving force of the overall productivity evolution (Scheme 1), we designed three sub-schemes to analyze the productivity evolution in teaching (Scheme 2), researching (Scheme 3), and application (Scheme 4) activities in Chinese higher education. Figure 3 shows how these input and output indicators are organized in each of the four schemes.

Figure 3

The output indicators are the number of students in the university each year (STUDENTS), SCI/SSCI publications (SCI PUB.), and technology transfer income (TT INCOME). In details, STUDENTS denotes the number of undergraduate, master and Ph.D. students taught or trained in the universities each year. It seems that research as an important mission of universities has often dominated the other core mission of higher education, teaching, in recent years. However, it is necessary to recognize that teaching or training students to provide high-quality human resources for the future is of considerable importance for the sustainability of universities in the long run. A variety of measures of teaching or training students have been developed and used in extensive existing literature (e.g. Avkiran, 2001;

Glass et al., 2006; Schubert, 2014; Worthington & Lee, 2008). Merton (1957, 1968) pointed out that publication is an important step to establish the priority of scientific discovery, which is one of the main objectives for scientists. Therefore, most research institutions (including universities and research institutes) prefer to assess the performance of the researchers using publications and citations (Zhang et al. 2011). Thus, publications are one of the important output indicators for the measurement of efficiency or scale characteristics of research institutions in practice (e.g. Rousseau & Rousseau 1997; Schubert, 2009; Schmoch & Schubert, 2009; Schubert, 2014, Yang et al., 2014). Technology transfer is a process through which technical information and products developed by the Federal Government and are provided to potential users, in a manner that encourages and accelerates their evaluation and/or use

http://www.usgs.gov/tech-transfer/

This paper intends to conduct research on the universities directly managed by the MOE of China. These universities have been always leading the direction of China’s higher education. China Education Statistical Yearbook 2015 reveals that more than RMB 67.5 billion Yuan concentrated in the 75 HEIs under the direct management of MOE, which accounts for almost half of the Science and technology expenditure in Chinese regular HEIs. In terms of publications, these universities published 47.4% articles of Chinese regular HEIs on foreign journals and 18.1% on domestic journals in 2014. Besides, 43.6% of the actual revenue of the technology transfer in Chinese regular HEIs came from these universities in 2014. We further remove a few special universities, including art universities, finance universities, and language universities (e.g. Central Conservatory of Music, China Central Academy of Fine Arts) because those universities do not aim to promote scientific and technical progress. Besides, China University of Mining, China University of Petroleum and China University of Geosciences are three universities possess branch schools. The branch schools do not exist separately but independently accounted in Chinese statistical system, so these six universities are also eliminated from our samples. Hence, 58 Chinese universities are considered as our samples. Those sample universities are all Project 211 universities, which are high-level research universities and are representatives of Chinese high-level research universities.

Our data of indicators TEACHING STAFF, R&D STAFF, APPLICATION STAFF and TT INCOME are collected from the Compilation of Science and technology statistics material of colleges and universities (2010–2017) issued by MOE of China. Data of R&D STAFF, APPLICATION STAFF and another indicator, teaching and research staff, are directly provided in the compilations. Teaching and research staff represents the personnel that engaged in teaching and R&D activities in higher education institutions including foreign experts and visiting scholars who have been engaged in scientific research activities for more than one month. R&D STAFF is denoted as teaching and research personnel who spend more than 10% of their working time in scientific research activities. Therefore, we obtain the number of TEACHING STAFF by subtracting the number of R&D STAFF from the number of teaching and research staff. Data of indicator SCI PUB. are obtained from the InCites database

https://clarivate.com/products/incites/

As the highest-level educational institutions and the major research institutions in the country, Chinese universities assume the three basic responsibilities for students training, scientific researching and social services. The input and output efficiency and productivity of universities not only have a direct impact on the quality of talents and the level of scientific research but also embody the potential of science and technology innovation in this country. In this Section, we use the MPI to investigate the efficiency evolution of the sample universities in the period 2009–2016.

The measurement of the MPI index of Scheme 1 reflects the trend of overall productivity of the sample universities during the investigated period. Furthered by decompositions results, a comprehensive understanding of the development of the higher education industry can be obtained from the improvement of the industry’s internal frontier technology and the progress of universities in the pursuit of the best-performed universities. The results are shown in Table 3. The overall TFP consistently exceeded unity, which shows that these sample universities continuously improved their development in all aspects and achieved an average 10.3% increase in the overall efficiency. On average, the technical efficiency was growing at an

average annual rate of 0.8%, and the technological progress increased by 9.5% annually. Therefore, the technological progress (Frontier-shift effect) was the main reason for the growth of overall TFP in Chinese universities. Concretely, the Frontier-shift effect declined from 1.272 to 0.812 from 2009–2010 to 2011–2012 and then performed decently with the around 1.1 scores during the rest investigated periods. The overall performance of the Catch-up effect was unstable because the Catch-up scores fluctuate around unity, and it exceeded unity only in the periods 2011–2012 and 2014–2015. These trends are exhibited in the three polylines in Figure 4. There is an evidently different period 2011–2012, compared with other periods, with an extremely high level of overall TFP but abnormal Catch-up and Frontier-shift values, which may be caused by the promulgation of Outline of Chinese National Medium- and Long-Term Education Reform and Development Plan in 2010. In response to the problems in Chinese higher education, it summarized the experience gained in practice and formulated several strong policies, especially the university de-administrative policy. It aims to weaken the administrative characteristic in colleges and universities and explore the full potential of academic resources. Meanwhile, a batch of management systems in line with the characteristics of colleges and universities have also been introduced.

Figure 4

To examine the changes in the efficiency evolution of teaching activities during the studied period and identify their contributions to the overall TFP, Scheme 2 intends to measure the changes in the efficiency of teaching activities. According to the calculation results shown in Table 4, the teaching TFP was consistently stable at around unity with an average teaching TFP value of 0.992, indicating that higher education practitioners failed to improve the efficiency of teaching activities during this time. The changes in the two decomposition components show an exactly opposite trend. During the periods 2009–2010, 2012–2014 and 2015–2016, the Catch-up effect stays at a high level, but the Frontier-shift effect represents a poor performance. During the remaining periods, the Frontier-shift effect performs better

than the Catch-up effect. We also employ the line chart to display the calculation results, as shown in Figure 5. Comparing with the well-performing overall TFP in Scheme 1, we can determine that the reason for the well-performing overall TFP was not the productivity of teaching activities. Actually, the low average TFP value of the teaching activities has seriously hindered the growth of the overall TFP.

Figure 5

The productivity of scientific research activities during 2009–2016 and their decompositions are also calculated in Scheme 3, and the results can be adopted to confirm the contribution of scientific research activities on the overall TFP. The results are shown in Table 5, illustrating that the research TFP maintained at a high level above 1.1. This indicates that the efficiency of the scientific research activities continues to improve, which is consistent with the scientific researchers’ enthusiasm for researching activities and the government’s incentive schemes promoting research. The two decompositions of the research TFP show different trends during the studied period. From the period 2009–2010 to the period 2012–2013, the Catch-up effect increased continuously from 0.839 to 1.367, while the Frontier-shift

effect decreased from the initial 1.327 to 0.826. Afterward, the Catch-up effect dropped to 0.949 promptly, while the Frontier-shift effect bounced back to 1.184 until the end of the period. Therefore, we can conclude that the relatively high scores of the research TFP in Scheme 3 derive from two balanced and high-valued decompositions. The changes in the values of research TFP and its decompositions of Scheme 3 are shown in Figure 6. What’s more, the excellent performance of the research TFP is highly consistent with the overall TFP in Scheme 1, which shows that the scientific research activities of universities have played a positive role in promoting the efficiency of the entire higher education system.

Figure 6

As the main innovator of science and technology, universities have the responsibility and ability to carry out scientific and technological achievements transformation activities. The efficiency of the technology transformation has a crucial impact on the overall efficiency of the university. The evaluation of the technology transformation efficiency is also an indispensable part of the performance

analysis of the university. It should be noted that the technology transformation function is not involved in the operation of certain universities, so these DMU are removed from the calculation to guarantee the reasonableness of the scores. The results in Table 6 indicate that the transfer TFP fluctuated back and forth around unity during the whole period. According to the volatility of the two decompositions, the whole research period can be divided into two parts. Prior to the period 2012–2013, the transfer TFP shows an obvious consistency with the fluctuation of the Catch-up effect. Thereafter, the Frontier-shift effect reveals dominance over the transfer TFP. The calculation results of Scheme 4 are shown in the form of line chart in Figure 7. In general, the value of transfer TFP is relatively low, and it may make a weak negative impact on the promotion of the overall TFP.

Figure 7

In summary, the TFP values calculated by the four schemes can reflect the main features of Chinese universities’ development during 2009–2016. The average value of the overall TFP (Scheme 1) for this period is 1.103, and the Frontier-shift effect contributed as the main influencing factor. This indicates that Chinese universities

have successfully improved their overall performance by improving the frontier technology during the investigated period. From the perspective of the performance of different functions of universities, the average values of the research TFP (Scheme 3) and the transfer TFP (Scheme 4) are 1.140 and 1.011, respectively, while the corresponding teaching TFP (Scheme 2) is less than 1 (0.992). To accurately determine the relationship between the overall TFP and the TFPs of different schemes, we merged the TFPs in four schemes together and demonstrate them in the radar map in Figure 8. The results show that the polyline which represents the overall TFP lies mostly inside the polyline of the research TFP, and the polyline of the teaching TFP always locates mostly outside the polyline of the overall TFP. However, the polylines of the transfer TFP and the overall TFP are interlaced during the investigated period, and the transfer TFP only exceeds the overall TFP in the periods of 2010–2011 and 2014–2015. This apparent difference fully demonstrates that the current guideline of the Chinese government, which attaches great importance to the scientific research in the evaluation and incentive policies, has made a real impact on the performance of different activities carried out in the universities. If such trends continue, the Chinese higher education quality may run a risk of deterioration, and the training and reservation of talents will be severely affected. Furthermore, this implies a hidden danger for the rapid development of the society.

Figure 8