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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.

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This paper presents methods to analyze convergence in cross-sectional data collected over time using distribution free statistics that are not sensitive to the magnitudes involved. Measures of concordance and discordance are employed in the empirical analysis of real personal income per capita for 48 U.S. States over the period . Although most States are converging with each other, some are converging faster than others. The methods used have the flexibility to focus on specific characteristics such as convergence in absolute differences or convergence in the ratio of rewards. The methods may also be used to consider convergence without switching and additionally be applied to other features such as the percentiles of the distributions.

The shape of the relationship between the rate of environmental degradation and income per capita has been the subject of much empirical examination. When test results based around this so-called 'environmental Kuznets curve' are compared, the empirical evidence is neither consistently supportive of its traditional inverted-U shape nor uniform across pollutants. A deeper understanding of the characteristics of pollutants and of the derived demand and derived supply of pollutants needs to be achieved if environmental Kuznets curves are to be useful.