Does Expansion Cause Congestion? The Case of the Older British Universities, 1994–2004

Abstract

This paper examines whether the rapid growth in the number of students in British universities in recent years has led to congestion, in the sense that certain universities' output could have been higher if this expansion had been less rapid. The focus of the paper is on 45 older universities that were in existence prior to 1992. The analysis covers the period 1994/95-2003/04. Several alternative methods of measuring congestion are examined and, to check the sensitivity of the results to different specifications, three alternative Data Envelopment Analysis models are formulated. The results indicate that congestion was present throughout the decade under review, and in a wide range of universities, but whether it rose or fell is uncertain as this depends on which congestion model is used. A crucial point here is whether one assumes constant or variable returns to scale. Nonetheless, all models point to a rise in congestion between 2001/02 and 2003/04, and this may well be a result of the rapid growth that occurred in this period. All models also record a sharp drop in mean technical efficiency in 2003/04. A possible explanation of the absence of a clear-cut trend in congestion is that the student:staff ratio in these universities was relatively stable in the decade under review, rising only gently from 2000/01 onwards.

Efficiency and Input- and Output-Substitutability in English Higher Education 1996/97 to 2007/08: A Parametric Distance Function Approach

“…As in previous studies (Abbott and Doucouliagos 2003;Flegg and Allen 2007a;2007b;Worthington and Lee 2008) raw material inputs and teaching outputs are not adjusted for quality (see Johnes 2008 for a discussion of this) mainly because of a lack of suitable published data, particularly in the context of input quality. This inevitably means that results regarding substitutability between inputs should be interpreted cautiously because the model ignores the potential effect on quality of such substitutions.…”

Congestion in the Chinese automobile and textile industries revisited

“…32 33] that, as compared with Cooper"s method, their own procedure would tend to yield a smaller amount of congestion. 26 For some more discussion of the implications of using VRS rather than CRS, see [23] and [25]. 27 Indeed, Cooper et al [11] found that using the second stage made no difference to their results.…”

“…3 What is more, the theoretical discussions have focused, to a large extent, on a relatively narrow issue: whether congestion does or does not exist in a particular case, as opposed to how much congestion there is likely to be. Unfortunately, apart from the earlier study by Cooper et al [11] and the work on British universities by Flegg and Allen [23,24,26], there is very little published evidence available to offer guidance as to whether the competing approaches are apt to yield very different results in reality. Our aim is to augment this limited stock of empirical evidence.…”

“…Whereas strong disposability precludes congestion, weak disposability makes it possible to identify congestion if it exists. 23 In Table 3, the input responsible for congestion is indicated by the L or K appended to the CE score. Here it is interesting to see that the CRS-based results show a continuous period of labour congestion between 1985 and 1992.…”

Distinguishing Technical Inefficiency from Desirable and Undesirable Congestion with an Application to Regional Industries in China

“…Cooper et al [13] explore an integrative model to estimate congestion and identify the sources of congestion. Flegg [14] finds that the generation of congestion may be different for three alternative methods. Simões and Marques [15] determine and compare the influence of congestion on efficiency using different methodologies.…”