Elsevier

China Economic Review

Volume 19, Issue 4, December 2008, Pages 679-696
China Economic Review

Measuring the research performance of Chinese higher education institutions using data envelopment analysis

https://doi.org/10.1016/j.chieco.2008.08.004Get rights and content

Abstract

This study uses data envelopment analysis (DEA) to examine the relative efficiency in the production of research of 109 Chinese regular universities in 2003 and 2004. Output variables measure the impact and productivity of research; input variables reflect staff, students, capital and resources. Mean efficiency is just over 90% when all input and output variables are included in the model, and this falls to just over 80% when student-related input variables are excluded from the model. The rankings of the universities across models and time periods are highly significantly correlated. Further investigation suggests that mean research efficiency is higher in comprehensive universities compared to specialist universities, and in universities located in the coastal region compared to those in the western region of China. The former result offers support for the recent merger activity which has taken place in Chinese higher education.

Introduction

Data envelopment analysis (DEA) has become a popular tool for measuring the efficiency of non-profit institutions such as hospitals, schools and universities. Its popularity in these contexts derives from the fact that it is based on a distance function approach and hence can handle multiple outputs and multiple inputs; it does not assume any specific behavioural assumptions of the firm (e.g. cost minimization or profit maximization); it makes no assumption regarding the distribution of efficiencies; and it requires no a priori information regarding the prices of either the inputs or the outputs. Despite there being a plethora of studies which examine the efficiency of the higher education sectors of various countries such as the UK, the USA, Canada, Finland, Israel and Australia (Abbott et al., 2003, Ahn et al., 1989, Arcelus and Coleman, 1997, Athanassopoulos and Shale, 1997, Avkiran, 2001, Breu and Raab, 1994, Coelli et al., 1998, El Mahgary and Lahdelma, 1995, Flegg and Allen, 2007, Friedman and Sinuany-Stern, 1997, Haksever and Muragishi, 1998, Johnes, 2006a, Worthington and Lee, 2008), little work has been done on measuring the efficiency in producing any of the outputs of higher education institutions (HEIs) in China. Recent studies by Ng and Li (2000) and Liu (2001) are exceptions but are based on data for the 1990s. A more up-to-date analysis of the Chinese higher education sector is therefore overdue.

It is generally agreed that the main functions of HEIs are teaching and research. Chinese HEIs have been expanding in both these activities in recent years. The 15.5million students in enrolled in regular HEIs in China in 2005 represented an increase of 181% compared to 2000 (China Statistical Yearbook 2006). Funding for education in regular HEIs in China has risen by 133% between 2000 and 2004 (China Statistical Yearbook 2002–2006). In 2005, 978,610 postgraduates were enrolled in Chinese HEIs (of which almost 20% were studying for a doctorate) and this represented an increase of 225% compared to 2000 (China Statistical Yearbook 2006). In addition, patents applied for by HEIs rose from 1942 in 2000 to 14,643 in 2005 (www.sts.org.cn/), an increase of 654%. With such rapid expansion in all aspects of activity undertaken by HEIs, and given the allocation of public resources to higher education, it is essential that the resources are used efficiently and that quality is maintained. Indeed, ‘… quantitative growth can get nowhere in the absence of guaranteed quality’ (Ji, 2006 p278). The purpose of this paper is therefore to examine the technical efficiency in the production of research of 109 top Chinese regular1 universities based on data for 2003 and 2004, i.e. during the period of rapid expansion.

The paper is in 6 parts of which this is the first. Section 2 provides some background on the Chinese higher education system and its development over the last 50 years. The methodology applied to the data is described in Section 3, while the data and the models are presented in Section 4. The results of the analysis are in Section 5, while conclusions which can be drawn from the study are presented in the final section.

Section snippets

Chinese higher education2

Since the People's Republic of China (PRC) was founded in 1949, China's higher education sector has experienced a number of distinct phases. The primary characteristics of these phases, and their implications for the funding, management and admissions criteria of the HEIs are summarised in Table 1.

In 1949, the Chinese higher education sector was very small with just 205 institutions (China Education Yearbook 1949), the majority of which were publicly owned. In addition, the geographical spread

Methodology

Technical efficiency is defined using Farrell's (1957) approach: by comparing a HEI's actual production point with the point which might have been achieved had it operated on the frontier. In Fig. 4, HEIs A, B and C produce at points PA, PB and PC, respectively. FCRS represents the constant returns to scale (CRS) production frontier showing the efficient levels of output, y, which can be produced from a given level of input, x. Technical efficiency is measured as the ratio of 0yA/0yACRS which

Data

The data for this analysis were obtained from the netbig Chinese university rankings (www.netbig.com). The netbig ranking is an unofficial one, and is available for 9 consecutive years. Changes in the universities which comprise the sector (through growth of the sector and merger activity), and variations in the way the data are reported make it difficult to obtain a series of consistent data.7

Technical efficiency

DEA can be sensitive to the specification of the inputs and outputs in the model. In addition, the RES output variable has some very low values (close to zero)13 which can cause some instability in the efficiencies when this variable is included in the DEA. Since the correlation between REPUT and RES is high (r = 0.896) the two variables are likely to reflect

Conclusions

There are few empirical studies of efficiency in Chinese higher education, and none of these is based on recent data covering the period of rapid expansion experienced in the twenty-first century. This study therefore attempts to fill this gap and to highlight areas which should be investigated further in future empirical studies. This study applies four DEA models to a sample of 109 top Chinese HEIs in an attempt to measure the efficiency of Chinese HEIs in producing research. The analysis

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