Efficiency, Technology and Productivity Change of Higher Educational Institutions Directly under the Ministry of Education of China in 2007-2012

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Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science (2018) 000–000 Procedia Computer Science 13900 (2018) 598–604

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The International Academy of Information Technology and Quantitative Management, the Peter The International Academy Kiewit of Information Quantitative Management, the Peter Institute,Technology University and of Nebraska Kiewit Institute, University of Nebraska

Efficiency, Technology and Productivity Change of Higher Efficiency, Technology and Productivity Change of Higher Educational Institutions Directly under the Ministry of Education of Educational Institutions Directly under the Ministry of Education of China in 2007-2012 China in 2007-2012 Rong Yaohuaaa , Li Muyubb , Cheng Weihuaa , Chang Xianyucc Rong Yaohua , Li Muyu , Cheng Weihu , Chang Xianyu a College of Applied Sciences, Beijing University of Technology, Beijing 100124, China

a College of Applied Sciences, Beijing University of Technology, Beijing 100124, China b National Association of Financial Market Institutional Investors, Beijing 100045, China b NationalcAssociation of Financial Market Institutional Investors, Beijing 100045, China

China Huarong Asset Management Co., Ltd., Beijing 100033, China Huarong Asset Management Co., Ltd., Beijing 100033, China

c China

Abstract Abstract Introducing the times of papers being cited to reflect the quality and international influence of scientific research, and the reputation Introducing times of of teaching papers being citedthis to reflect quality and international andtechnology the reputation to reflect thethe quality activity, paper the uses Malmquist Index basedinfluence on DEAoftoscientific measureresearch, efficiency, and to reflect the change quality of of 72 teaching this paper uses Malmquist Index based DEA to efficiency, and productivity Higheractivity, Educational Institutions (HEIs) directly under on Ministry of measure Education of Chinatechnology in 2007-2012. productivity 72 Higher (HEIs) directly under Ministry of Education of China in on 2007-2012. Results revealchange that theofTotal Factor Educational Productivity Institutions (TFP) of HEIs manifests a reasonable growing tendency; the increase technical Results reveal the Total Factor Productivity (TFP)onofTFP, HEIswhile manifests a reasonabledegression growing tendency; the increase on technical efficiency is thethat mainly driving force of the increase the technological has an inhibiting effect; there are efficiency the mainlybetween driving different force of HEIs, the increase TFP, while the end, technological degression an inhibiting effect; there are significantisdifferences regionsonand types. In the some suggestions arehas provided to promote continuous, significant differences between different regions and types. In the end, some suggestions are provided to promote continuous, stable and balance development of HEIsHEIs, in China. stable and balance development of HEIs in China. c 2018  2018 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © c  2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under thethe CCscientific BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) review under responsibility ofof the scientific committee of The International Academy of Information Technology and QuanPeer review under responsibility committee of The International Academy of Information Technology and Peer review under responsibility of the scientific committee The International Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University ofofNebraska. Quantitative Management, the Peter Kiewit Institute, University of Nebraska. titative Management, the Peter Kiewit Institute, University of Nebraska. Keywords: higher educational institutions; running efficiency; total factor productivity; technological progress; technical efficiency Keywords: higher educational institutions; running efficiency; total factor productivity; technological progress; technical efficiency

1. Introduction 1. Introduction China’s medium and long-term education reform and development plan (2010-2020) makes internationally China’s distinctive, medium and long-term education reform Education and development planas(2010-2020) internationally renowned, high-level institution of Higher Institutions the goals. Inmakes order to achieve this renowned, distinctive, high-level institution of Higher Education Institutions as the goals. In order to this goal, the government, education management departments, and educational institutions need to advanceachieve their work goal, the government, education management departments, and educational institutions need to advance their work in terms of resource input and efficiency improvement. In recent years, education expenditures investment from govin terms of resource input and efficiency improvement. In recent years, education expenditures investment from gov∗ ∗

Li Muyu. Tel.: +86-010-66539194 Li Muyu. Tel.: +86-010-66539194 E-mail address: [email protected] E-mail address: [email protected]

c 2018 The Authors. Published by Elsevier B.V. 1877-0509  1877-0509 © 2018 Thearticle Authors. Published by B.V. c 2018 1877-0509  The Authors. Published by Elsevier Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This isis an anopen openaccess accessarticle articleunder under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the CC scientific committee of The International Academy of Information Technology and Quantitative ManagePeer review under responsibility of the scientific committee of The International Academy of Information Technology and Quantitative Peer review under responsibility of the scientific committee of The International Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University of Nebraska. Management, the Peter Kiewit Institute, University of Nebraska. ment, the Peter Kiewit Institute, University of Nebraska. 10.1016/j.procs.2018.10.204

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ernment has increased rapidly, and reached 4% of GDP in 2012. Therefore, efficiency of HEIs, with its trend and decomposition, had become one of the most important research objects of government departments and information management scholars. There are three main categories of research on the efficiency of HEIs: the first is to study the efficiency of teaching. Johnes (2006) [1] studied teaching efficiency of economics department based on the data of 2,547 economics graduates of British universities in 1993. The second category is to study the efficiency of research. Johnes and Yu (2008) [2] studied research efficiency of 109 HEIs in China, found that the efficiency of comprehensive universities was higher than that of colleges. ChuNg and Li (2009) [3] studied the research efficiency of humanities and social sciences in Chinese universities. The regional differences in productivity of colleges and universities between 1998 and 2002 were not obvious. Although technological progress has a positive effect, scale efficiency and technical efficiency have led to deterioration of university productivity. The third category is to study the running efficiency, that is efficiency of both teaching and research. Worthington and Lee (2008) [4] studied the productivity growth of 35 Australian universities in 1998-2003 and pointed out that technological progress was the main driver of productivity growth. Johnes (2008) [5] studied the productivity of 113 universities in the UK from 1996 to 2004, found that the index grew at an average annual rate of 1% and technological progress was the main cause of productivity growth while technical efficiency was inhibiting. In general, the existing research mainly has three shortcomings: First, the research time is earlier, the research results and recommendations are not realistic; the second is the lack of comprehensive evaluation of research and teaching activities. Most of the work was based on scientific research as the main body of evaluation, or the cultivation of talents as the main body of evaluation, which is impossible to comprehensively evaluate a HEI. The third is that the data used was not comprehensive enough, mainly based on the survey data of the Ministry of Education, did not cover social reputation, academic influence and other aspects of the output. This paper intends to use updated data (2007-2012), reputation scores, international influence of academic papers and other indicators to comprehensively analyze the running efficiency of HEIs, as well as regional and type differences, in order to provide materials for the decision-making of relevant government departments and universities. 2. Data and Models 2.1. Data Source ”Compilation of Basic Statistics of Universities under the Ministry of Education”, Essential Science Indicators (ESI) database, the reputation scores of HEIs from the netbig(www.netbig.com), and National Bureau of Statistics of China. ESI, established by American Institute of Scientific Information, is comprehensive and timely and has become an important database for the evaluation of scientific research performance in universities worldwide. 2.2. Indicators By introducing the number of papers being cited to reflect the quality and international influence of scientific research, and the reputation to reflect the quality of teaching activity, this paper constructs an input-output indicator system as table 1. STUD and REPU are two indicators of teaching output. SCIE, SSCI, INCO and ESIC are indicators of research output. Specifically, the vertical research funding INCO obtained through competition reflects the quality of scientific research achievements to some extent [6, 7], which could be considered as one of the output indicators for scientific research. STUD is the weighted summation of undergraduate, graduate, and doctoral students whose weights is 1, 1.5, 2, respectively[8]. SCIE is the weighted summation of domestic papers, foreign papers, monographs, awards, and intellectual property rights while SSCI is the weighted summation of humanities and social sciences domestic papers, foreign papers, and monographs, where the weight is the total number of every research achievement in 2007-2012. ESIC counts the citations of the literature collected in the SCI and SSCI databases of all universities or research institutions around the world for nearly 11 years. This index comprehensively reflects the international level and influence of academics for HEIs and is an indicator commonly used to measure the quality of scientific research.

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Meaning the number of full-time teachers in the sub-high and above titles the national and regional education funding allocation, excluding the special appropriation the number of students in school the scores of reputation the scores of humanities and social science research achievements the scores of scientific research achievements the income of vertical research funds the total times of citations of papers in ESI

In order to eliminate the impact of inflation, this paper uses the GDP deflator to adjust the FUND and INCO. 2.3. DEA-based Malmquist index The classic DEA models (CCR [9] , BCC [10] ) mainly evaluate the relative efficiency of the decision making unit (DMU) for the cross-section data, that is, the evaluated DMU is only compared with other DMUs in the same period. In order to investigate the trend of DMU efficiency, this paper intends to use the DEA-based Malmquist index(MI) to measure the change and decomposition of DMU’s Total Factor Productivity (TFP). Fare et al. (1994) [11] proposed this method. Ray and Desli (1997) [12] modified to decompose the Malmquist Index based on the variable scale return (VRS) assumption. t t t Let the input and output vectors of DMUo in t-period be xot = (x1o , x2o , . . . xmo , )t , and yto = (yt1o , yt2o , . . . ytmo , )t , o = 1, . . . , n, where n is the number of DMUs. Then the input and output matrix of DMUo in t-period are X t and Y t ,   t  t t    x11 . . . x1n  y11 . . . yt1n         X t =  ... . . . ...  Y t =  ... . . . ...  (1)     t t t t  xm1 . . . xmn ym1 . . . ymn Set δt+1 (xot , yto ) is the efficiency value of the frontier surface relative to the t + 1-period. In the BCC model, δt+1 (xot , yto ) = minθ,λ θ X t+1 λ ≤ xot 1 t t+1 λ≤0 θ yo − Y L ≤ eλ ≤ U , λ ≥ 0

s.t. :

(2)

Where L, U are the upper and lower bounds of the vector λ respectively, and e is the unit vector. Under the assumption of Variable Returns to Scale (VRS), the MI measuring the change in TFP from t to t + 1 is:  t t+1 t+1   t+1 t+1 t+1 1/2 δ (xo , yo ) δ (xo , yo ) ) = (3) MI(xot , yto ; xot+1 , yt+1 o δt (xot , yto ) δt+1 (xot , yto ) MI can be further decomposed into the product of the Technology Efficiency Change (EC) and the Technology Change (TC),where EC =

δt+1 (xot+1 , yt+1 o ) t t t δ (xo , yo )

TC =



δt (xot+1 , yt+1 o ) δt+1 (xot+1 , yt+1 o )

(4) 

δt (xot , yto ) δt+1 (xot , yto )

1/2

(5)

The EC measures the degree of change in the relative efficiency of the DMU from t to t+1, called the catch-up effect. The TC measures the movement of the production frontier in two periods, called the frontier surface movement effect.

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A MI greater than (less than) 1 indicates that the TFP level of the t+1 period is higher (lower) than the t period, and an equal to 1 indicates that the TFP level unchanged. An EC greater than 1 indicates an increase in technical efficiency, and an equal to (or less than) 1 indicates that the technical efficiency is constant (or decreased). A TC greater than 1 indicates a technological advance, and an equal to (or less than) 1 indicates unchange in technology (or a technical decline). 3. Analysis of the TFP Based on the output-oriented BCC weight constraint model, this paper uses EMS software to calculate the TFP index (MI), technical efficiency change index (EC) and technological progress change index (TC) of 72 HEIs directly under the MoE from 2007 to 2012. 3.1. Overall analysis Table 2 shows that the change of TFP measured by MI of HEIs has basically maintained a positive growth trend, but did not continue to grow steadily in the study period. Efficiency increased in 2007-2008, 2008-2009, 2011-2012, and decreased in 2009-2010, 2010-2011. Specifically, in the year of 2008 and 2009 EC and TC progress jointly promoted the significant improvement of the TFP (MI greater than 1). In 2010, technological progress index increased significantly, but the productivity decreased slightly due to the sharp decline in the technical efficiency index (EC is 0.97). In 2011, technical efficiency promotion effect was obvious (EC is 1.04), but the technological recession further (TC is 0.95) inhibited the productivity increase. Technology recession still played a depressing role in 2012, but productivity continued to increase due to the strong technical efficiency. Overall, the improvement of the TFP of HEIs under the MoE is mainly caused by the improvement of technical efficiency. This reflects that the efficiency of most HEIs has been continuously improved, and it is closer to the effective frontier, the catch-up effect is very obviously. At the same time, technological recession has played a opposite role in restraining the growth of productivity, which reflects the effective frontier has moved downward as a whole, especially after 2010. Observing the input-output data, it is not difficult to find that the growth rate of education funding in 72 colleges and universities in 2011 and 2012 was as high as 54.34% and 12.42%, while the rapid growth of education funding did not bring about the same growth of teaching and research outputs in the same year, resulting in the significant recession in the effective frontier. Table 2. Malmquist Index from 2007 to 2012 Year 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 MEAN

EC 1.04 1.00 0.97 1.04 1.13 1.04

TC 1.01 1.02 1.03 0.95 0.90 0.98

MI 1.05 1.02 1.00 0.98 1.01 1.01

3.2. HEIs comparative analysis Table 3 shows the average values of MI, EC, TC for 72 HEIs in 2007 to 2012. MI shows that 50 HEIs had a significant growth in productivity from 2007 to 2012, and the rest 22 HEIs suffered a certain degree of decline. The value of MI ranges from 0.95 to 1.11 with a standard deviation of 0.03, reflects a significant differences in the productivity changes. Specifically, three HEIs with the highest annual growth rate of TFP were BEIJING JIAOTONG UNIV, NE NORMAL UNIV, E CHINA UNIV SCI & TECHNOL, with a growth rate of more than 6%. Because these universities have lower starting points for efficiency, they have more room for efficiency improvement. The three HEIs with the lowest productivity are WUHAN UNIV, LANZHOU UNIV, CHONGQING UNIV, with an average annual decline rate of more than 3.72%. Due to the substantial increase in education funding for these larger comprehensive

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universities in 2011 and 2012, the school output has not achieved the same level of growth, resulting in a decline in efficiency, which has led to a decline in productivity. Compared with the technological progress (TC) index, the technical efficiency (EC) shows that 60 universities in 72 universities has improved, which indicates that the improvement of technical efficiency is the main reason for the growth of total factor productivity in HEIs. Specifically, during the inspection period, there are significant differences in the motivations for the growth of HEIs’ productivity: 5 HEIs such as RENMIN UNIV CHINA and SHANGHAI JIAOTONG UNIV are the result of the combined promotion of technical efficiency (EC) and technological progress (TC); ZHEJIANG UNIV and SHANGDONG UNIV are characterized by the promotion of technological progress and the inhibition of technological efficiency; 49 HEIs such as UNIV SCI & TECHNOL BEIJING and BEIJING UNIV CHEM TECHNOL have shown the promotion of technological efficiency and the suppression of technological decline; 9 HEIs such as NANKAI UNIV and JILIN UNIV, suffered both inhibitions of technical efficiency and technological progress. Table 3. Malmquist Index of HEIs HEI PEKING UNIV RENMIN UNIV CHINA TSING HUA UNIV BEIJING JIAOTONG UNIV UNIV SCI & TECHNOL BEIJING BEIJING UNIV CHEM TECHNOL BEIJING UNIV POSTS & TELECOMMUN CHINA AGR UNIV BEIJING FORESTRY UNIV BEIJING UNIV CHINESE MEDs BEIJING NORMAL UNIV BEIJING UNIV FOREIGN BEIJING LANGUAGE UNIV COMMUNICATION UNIV CHINA CENT UNIV FINANCE & ECON INTER ECON & BUSI UNIV CENT MUSIC ACADEMY CENT ARTS ACADEMY CENT DRAMA ACADEMY CHINA UNIV LAW CHINA UNIV PETR NANKAI UNIV TIANJIN UNIV DALIAN UNIV TECHNOL NORTHEASTERN UNIV CHINA JILIN UNIV NE NORMAL UNIV NE FORESTRY UNIV FUDAN UNIV TONGJI UNIV SHANGHAI JIAO TONG UNIV E CHINA UNIV SCI & TECHNOL DONGHUA UNIV E CHINA NORMAL UNIV SHANGHAI UNIV FOREIGN SHANGHAI UNIV FINANCE & ECON

EC 1.00 1.00 1.00 1.09 1.05 1.06 1.05 1.05 1.05 1.04 1.01 1.02 1.02 1.06 1.05 1.04 1.01 1.01 0.98 1.04 1.05 0.99 1.02 1.04 1.04 0.97 1.11 1.07 1.00 0.99 1.01 1.07 1.09 1.00 1.08 1.02

TC 1.00 1.00 1.00 1.02 0.99 0.97 0.96 0.98 0.97 0.99 0.99 1.00 0.99 0.99 0.98 0.98 1.00 1.00 1.00 0.98 0.96 0.98 0.98 0.97 1.00 1.00 0.98 0.97 0.99 1.00 1.03 1.02 0.97 0.98 1.00 0.99

MI 1.00 1.00 1.00 1.11 1.03 1.02 1.00 1.03 1.02 1.03 1.00 1.01 1.01 1.04 1.03 1.02 1.01 1.01 0.98 1.02 1.00 0.97 1.00 1.00 1.03 0.97 1.09 1.03 0.99 0.98 1.04 1.08 1.05 0.97 1.04 1.01

HEI NANJING UNIV SOUTHEAST UNIV CHINA UNIV MIN & TECHNOL HOHAI UNIV JIANGNAN UNIV NANJING AGR UNIV CHINA PHARMACEUT UNIV ZHEJIANG UNIV HEFEI UNIV TECHNOL XIAMEN UNIV SHANDONG UNIV OCEAN UNIV CHINA WUHAN UNIV HUAZHONG UNIV SCI & TECHNOL CHINA UNIV GEOSCI WUHAN UNIV TECHNOL HUAZHONG AGR UNIV CENT CHINA NORMAL UNIV ZHONGNAN UNIV ECON & LAW HUNAN UNIV CENT S UNIV SUN YAT SEN UNIV S CHINA UNIV TECHNOL CHONGQING UNIV SOUTHWEST UNIV SICHUAN UNIV SW JIAOTONG UNIV UNIV ELECT SCI & TECHNOL CHINA SW UNIV FINANCE & ECON XIAN JIAOTONG UNIV XIDIAN UNIV CHANGAN UNIV NW A&F UNIV SHANXI NORMAL UNIV LANZHOU UNIV N CHINA ELECT POWER UNIV

EC 0.97 1.05 1.04 1.09 1.09 1.05 1.07 0.98 1.03 1.02 1.00 1.08 0.98 1.00 1.06 1.04 1.11 1.04 1.07 1.02 1.00 1.00 1.05 1.01 1.03 0.99 1.04 1.07 1.09 0.98 1.05 1.09 1.06 1.06 1.00 1.07

TC 0.98 0.97 0.95 0.96 0.97 0.96 0.98 1.03 1.02 0.98 1.01 0.98 0.99 1.00 0.96 0.95 0.97 0.97 0.96 0.97 0.97 1.02 0.98 0.95 0.97 0.99 0.97 0.99 0.97 0.97 0.96 0.95 0.97 0.97 0.96 0.97

MI 0.95 1.02 0.98 1.03 1.05 1.01 1.03 1.01 1.04 1.00 1.00 1.05 0.96 1.00 1.01 0.98 1.06 1.01 1.02 0.98 0.97 1.02 1.02 0.96 1.00 0.98 1.00 1.05 1.05 0.95 1.00 1.02 1.02 1.02 0.96 1.03

3.3. Regional comparative analysis China has a vast territory, and its social, economic and education development is relatively uneven. Therefore, the analysis of different geographical differences is meaningful and valuable. This paper divides 72 HEIs into three

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categories according to its regions: eastern, central, and western. The average efficiency change of HEIs in different regions is shown in Table 4. Table 4 reveals significant differences in TFP and the technological progress of HEIs in three regions, while the technical efficiency similar relatively. During the inspection period, the productivity of HEIs in the eastern, central and western regions maintained a positive growth with rates of 1.58%, 0.95%, and 0.03%, respectively. This shows that the productivity growth of the eastern HEIs is the fastest, followed by the central and the slowest is the western. Affected by both technological efficiency increase and technology decrease, the productivity change index MI of three regions is between the EC and the TC. For the increase of productivity, the increase of technical efficiency is the main driving force, and the decline of technological progress has different degrees of impact on different regions. Among them, the technological recession has the strongest inhibitory effect on the productivity growth of the western region, followed by the central and eastern regions. This indicates that the effective frontier of western HEIs during the inspection period has the largest decline. We think this is because the investment in the western region has experienced the fastest growth during the inspection period, but the output growth has not kept up. Table 4. Malmquist Index between HEIs in different regions Region EC EASTERN 1.03 CENTRAL 1.04 WESTERN 1.04

TC 0.99 0.98 0.97

MI 1.02 1.01 1.00

3.4. Type comparison analysis The 72 HEIs are divided into three categories: comprehensive, science & engineering, college with 21, 24, and 27 HEIs respectively. Table 5 shows that the productivity growth of the three types HEIs are obviously different. The productivity of college and science & engineering HEIs maintained a good growth trend during the inspection period, with an average annual growth rate of 2.14% and 1.96% respectively, while the productivity of comprehensive universities declined slightly, with an average annual decrease of 0.82%. The productivity growth of colleges is the fastest, the science & engineering category is second, the comprehensive category is not rising. Three types of HEIs’ productivity index are between the technical efficiency index and the technological progress index. The decline in technology has hindered the growth of productivity in the three types of HEIs, and the increase in technological efficiency has promoted productivity. The productivity index, technical efficiency index and technological progress index of science & engineering and college are relatively close, while the technical efficiency index of comprehensive HEIs is low, but the technological progress index falls the slowest. This is because during the inspection period, the efficiency of comprehensive HEIs has a higher starting point, and the space for efficiency improvement is very limited. However, the efficiency of HEIs in college and science & engineering is relatively low, and efficiency has more room for improvement. The technological progress index of science & engineering has the largest decline, with an average annual decline of 2.27%. This indicates that science & engineering have experienced a significant decline in effective frontiers, and it is necessary to vigorously improve management levels to promote technological progress. Table 5. Malmquist Index between HEIs in different types TYPES COMPREHENSIVE SCIENCE& ENGINEERING COLLEGE

EC 1.01 1.05 1.05

TC 0.99 0.98 0.98

MI 0.99 1.02 1.02

4. Conclusions This paper builds an indicator system to comprehensive reflect the quantity and quality of teaching and research results and the influence of internationalization of HEIs, by introducing the ESI, reputation scores, etc. Based on the

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variable returns of scale assumption, using the Malmquist Index based on DEA model, this paper measures the TFP changes and its decompostions of HEIs directly under MoE of China in 2007-2012. During the inspection period, TFP of 72HEIs has a clear upward trend, although there has been a slight decline in two years. Among them, technical efficiency maintained a steady and steady rise, and the significant decline of technology progress after 2010 affected the increase of TFP. Overall, the improvement of technical efficiency is the main reason for the growth of HEIs, and the decline of technology has played a negative role. From a structural point of view, research shows that the development of 72 HEIs is obviously uneven. This imbalance is reflected in two aspects. First, the difference in efficiency growth is huge. Second, the driving force for efficiency growth are different.From a geographical perspective, the productivity growth of HEIs of eastern is the fastest, followed by the central and the slowest of the western. From the perspective of the type of school, the productivity growth of professional classes is the fastest, the science & engineering category is second, and the comprehensive category is not rising. Acknowledgements We would like to thank the Editor and the two referees for their constructive comments and suggestions. Rongs work was partially supported by National Natural Science Foundation of China (No. 11701021), National Statistical Science Research Project (No. 2017LZ35), Fundamental Research Foundation of Beijing University of Technology and Beijing Outstanding Talent Foundation (No. 2014000020124G047) References [1] Johnes J. Data envelopment analysis and its application to the measurement of efficiency in higher education[J]. Economics of Education Review, 2006,25(3):273-288. [2] Johnes J, Li Y U. Measuring the research performance of Chinese higher education institutions using data envelopment analysis[J]. China Economic Review, 2008,19(4):679-696. [3] Ng Y C, Li S. Efficiency and productivity growth in Chinese universities during the post-reform period[J]. China economic review, 2009,20(2):183-192. [4] Worthington A C, Lee B L. Efficiency, technology and productivity change in Australian universities, 1998C2003[J]. Economics of education review, 2008,27(3):285-298. [5] Johnes J. Efficiency and productivity change in the English higher education sector from 1996/97 to 2004/5[J]. The Manchester School, 2008,76(6):653-674. [6] Flegg A T, Allen D O, Field K, et al. Measuring the efficiency of British universities: a multi-period data envelopment analysis[J]. Education Economics, 2004,12(3):231-249. [7] Abbott M, Doucouliagos C. The efficiency of Australian universities: a data envelopment analysis[J]. Economics of Education review, 2003,22(1):89-97. [8] Ministry of Education of China, Basic school conditions indicators for ordinary colleges and universities, 2014. [9] Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units[J]. European journal of operational research, 1978,2(6):429-444. [10] Banker R D, Charnes A, Cooper W W. Some models for estimating technical and scale inefficiencies in data envelopment analysis[J]. Management science, 1984,30(9):1078-1092. [11] Fare R, Grosskopf S, Lindgren B, et al. Productivity developments in Swedish hospitals: a Malmquist output index approach[M]//Data envelopment analysis: theory, methodology, and applications. Springer, 1994:253-272. [12] Ray S C, Desli E. Productivity growth, technical progress, and efficiency change in industrialized countries: comment[J]. The American Economic Review, 1997:1033-1039.