کارایی و رشد بهره وری در دانشگاه های چین در دوران پس از اصلاحات

The social science research performance of Chinese universities is examined using panel data. The universities are found to be very inefficient in general, with not much difference between regions. By far the largest single cause of universities′ overall technical efficiency is pure technical efficiency, along with a considerable amount of scale inefficiency and a modest amount of congestion. No obvious regional differences in the universities′ productivity growth are apparent between 1998 and 2002. Decomposition of the Malmquist productivity index indicates that although there has been technological progress over the years, poor scale efficiency and technical efficiency have resulted in deterioration in the universities′ average productivity. There are signs of increasing congestion during the period studied.

The remarkable economic growth in China since the 1980s has highlighted the importance of human capital investment. It is well documented that investment in human capital is one of the key factors sustaining the growth of any economy. Changes in the education sector have long-term impacts on an economy, but particularly on an economy like China′s undergoing economic transition. Previous assessments of the education sector at the school district level or at the higher education level in developed countries have presented a mixed picture, but there has been only a smattering of research work in this area from the developing world. Assessing the education sector in China during its education reforms thus provides a real-world case for understanding the education sector′s responses to the changing environment during economic transition. Interpreting the effects of the recent education reforms in China is complicated by the economic reforms (enterprise and industrial reforms) in progress at the same time. It is well documented that the economic reforms of the past two decades have resulted in growth imbalances among China′s regions. The coastal region has consistently been the fastest growing region, with annual growth averaging 10%, compared with the non-coastal (central and west) region with annual growth of 7.4%–8.4% during the course of reform (Bao, Chang, Sachs, and Woo, 2002). This coastal-led development effect has been especially strong since 1990 (Jian, Sachs, and Warner, 1996). Not until the late 1990s did the growth disparities narrow as the government directed more support towards the inland provinces. The regional disparities have had implications for the self-funding of higher education institutions granted by the education reforms. The shrinking of the central education fund over time has meant that Chinese higher education institutions have had to rely on fund-raising for both current expenses and development. Although institutions in the coastal region should be in a better position to raise funds, only if they can manage their resources effectively would they outperform institutions in the other region. Accordingly, an analysis of the productive efficiency of Chinese institutions of higher education by region is warranted. Before the education reforms, the management and the operation of higher education institutions in China was strictly regulated through communist-style central planning. Since the mid-1980s, the reforms have granted management autonomy and freedom in raising funds for the institutions. These changes in practice raise the issue of underused or overused resources. One particular concern is the congestion of some inputs in the sense that an increased use of these inputs is found to cause a fall in one or more outputs. Together with standard efficiency measures, this study examines the extent of congestion in Chinese higher education institutions by region. An assessment at a given point in time only reveals part of the picture for economies in transition. Taking advantage of the availability of five-year panel data, this study seeks to document productivity growth or regression at higher education institutions in China during the post-reform period. Malmquist productivity indices are calculated to provide a more thorough understanding of the performance of the higher education institutions. The sources of productivity change are analyzed by decomposing the Malmquist indices into changes in efficiency and technological change. Such information should be of great value to both policy makers and researchers involved with the education reforms implemented since the mid-1980s. The rest of the paper is organized as follows. Section 2 highlights the situation of the higher education sector in China since the reform, followed by the methodology section. Findings of the analyses are presented in Section 4. Section 5 concludes.

The estimated efficiency measures indicate that universities in China remained very inefficient in their social science research even after a decade of reform. There was no notable regional difference in overall efficiency for the study period 1998–2002. The computed scale efficiency scores indicate that the universities′ performance was influenced by their size (the scale of production). This may be due to the expansion of universities since the late 1990s. The approach adopted in this study suggests that although the problem of congestion was minor in both regions relative to other types of efficiency, it could be an issue as time moves on. The yearly efficiency measures only make reference to the particular year's frontier for a given technology. Over time, universities may improve their efficiency and experience change in technological know-how. The Malmquist index and its decomposition components highlight the average performance of university groups over time. The efficiency frontier shifted upwards between 1998 and 2002, which is probably attributable to the reforms of that period. However, overall technical efficiency remained low. The non-coastal universities had become even less efficient by 2002. It seems that some universities could not catch up with others which were at the frontier. The falling trend of scale efficiency (Table 5) echoed the point that universities were probably expanding too fast. With worsening scale inefficiency over time and potential congestion problems, there was definitely room for improvement. As with all empirical work, a few limitations are worth mentioning. First, the results rely heavily on the available data on inputs and outputs, so the empirical analyses can only serve as an example in understanding the performance of Chinese universities. Second, this evaluation of university performance has been restricted to technical efficiency, scale efficiency, congestion and productivity change. In the computation of the various efficiency measures, Farrell′s input-oriented approach was used. As pointed out by Flegg and Allen, 2007a, Flegg and Allen, 2007b and Flegg and Allen, in press, the amount of congestion uncovered varies depending on the approach, in particular the assumptions about returns to scale. With relatively serious pure technical inefficiency revealed in this estimation, the congestion estimates probably give a reasonable picture of the Chinese universities sampled in this regard. Nevertheless, one has to bear this issue in mind in interpreting the results. Third, only the social science research activities of the universities were considered. Taking no account of the teaching component means that caution is called for when interpreting the results.