Jill Johnes

Research funding and performance in U.K. University Departments of Economics: A frontier analysis

Data envelopment analysis (DEA) is used to investigate the technical efficiency of U.K. university departments of economics as producers of research. Particular attention is paid to the role of external funding of research as an input into the research process. The data set used is an extended version of the one which informed the 1989 Universities Funding Council peer review, and the results obtained here are compared with those obtained by the Council. We conclude that DEA has a positive contribution to make in the development of meaningful indicators of university performance.

Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993

Data envelopment analysis (DEA) is applied to 2568 graduates from UK universities in 1993 in order to assess teaching efficiency. Following a methodology developed by Thanassoulis & Portela (2002), each individual s efficiency is decomposed into two components: one attributable to the university at which the student studied, and the other attributable to the student himself. From the former component, a measure of each institution s teaching efficiency is derived and compared to efficiency scores derived from a conventional DEA applied using each institution as a decision making unit (DMU). The results suggest that efficiencies derived from DEAs performed at an aggregate level include both institution and individual components, and are therefore misleading. Thus the unit of analysis in a DEA is highly important. Moreover, an analysis at the individual level can give institutions insight into whether it is the students own efforts or the institution s efficiency which are a constraint on increased efficiency.

International Handbook on the Economics of Education

Contents: Introduction 1. Human Capital and Rates of Return 2. Signalling and Screening 3. The Economic Assessment of Training Schemes 4. Education and Economic Growth 5. Skill-Biased Technical Change and Educational Outcomes 6. The Social and External Benefits of Education 7. School Finance 8. Funding Higher Education 9. Exploring the Effect of Class Size on Student Achievement: What Have We Learned Over the Past Two Decades? 10. The Economics of Secondary Schooling 11. Determinants of Educational Success in Higher Education 12. Standards and Grade Inflation 13. The School-to-Work Transition 14. The Labour Market for Teachers 15. Multi-product Cost Functions for Universities: Economies of Scale and Scope 16. Efficiency Measurement 17. Education, Child Labour and Development 18. Education and Housing Index Contributors: S.L. Averett, S. Bradley, S. Brown, E. Cohn, S.T. Cooper, P.J. Dolton, D. Greenaway, M. Haynes, W.H. Hoyt, S. Jafarey, G. Johnes, J. Johnes, S. Lahiri, S. Machin, M.C. McLennan, W.W. McMahon, D. Mitch, R.A. Naylor, A.N. Nguyen, H.A. Patrinos, G. Psacharopoulos, P. Santiago, J.G. Sessions, J. Smith, P. Stevens, J. Taylor, M. Weale

Performance assessment in higher education in Britain

All public sector organisations in the UK have witnessed changes in funding arrangements during the 1980s as part of the Governments drive to make them more accountable to the tax-payer. The development of performance indicators is seen as an essential step to ensure that such organisations provide value for money. This paper examines the possibility of constructing measures of the performance of UK universities. A methodology is developed in the framework of production theory and uses multiple regression techniques to estimate the relationship between the outputs and inputs of universities. Around 80% of the inter-university variation in four output measures can be explained by corresponding variations in several input measures. This highlights the need to take into account the inputs available to a university when comparing its output performance with that achieved by other institutions. The problems of interpreting an array of performance indicators are also clearly demonstrated.

Higher education institutions' costs and efficiency: Taking the decomposition a further step

A multiproduct cost function is estimated for English higher education institutions using a panel of data from recent years. The panel approach allows estimation by means of a random parameter stochastic frontier model which provides considerable new insights in that it allows the impact on costs of inter-institutional differences in the cost function itself to be distinguished from inter-institutional differences in efficiency. The approach used here therefore resembles in some respects the non-parametric methods of efficiency evaluation. We report also on measures of average incremental cost of provision and on returns to scale and scope.

Determinants of student wastage in higher education

ABSTRACT This paper investigates the possibility of identifying potential non-graduates, using information obtained before entry to university. Statistical analysis of a sample of the 1979 entry cohort to Lancaster University indicates that the likelihood of non-completion is determined by various characteristics, the main ones being the students academic ability (reflected by A level results), his work experience prior to coming to university, his school background and the location of his home in relation to the university. An examination of wastage separately amongst males and females identifies striking differences between the two groups in the characteristics associated with non-completion. Further analysis of the sample as a whole reveals that a vast improvement in the prediction of non-completion can be achieved by using the results of first-year examinations at university rather than A level results. The main conclusion is that raising the academic requirements for entry into university may not be...

Costs and Efficiency of Higher Education Institutions in England: A DEA Analysis

As student numbers in higher education in the UK have expanded during recent years, it has become increasingly important to understand its cost structure. This study applies Data Envelopment Analysis (DEA) to higher education institutions in England to assess their cost structure, efficiency and productivity. The paper complements an earlier study that used parametric methods to analyse the same panel data. Interestingly, DEA provides estimates of subject-specific unit costs that are in the same ballpark as those provided by the parametric methods. The paper then extends the previous analysis and finds that further student number increases of the order of 20–27% are feasible through exploiting operating and scale efficiency gains and also adjusting student mix. Finally the paper uses a Malmquist index approach to assess productivity change in the UK higher education. The results reveal that for a majority of institutions productivity has actually decreased during the study period.

Efficiency and Productivity Change in the English Higher Education Sector from 1996/97 to 2004/5

In this study we use a distance function approach to derive Malmquist productivity indexes for 112 English higher education institutions (HEIs) over the period 1996/97 to 2004/5. The analysis shows that HEIs have experienced an annual average increase in productivity of 1 per cent. Further investigation reveals that HEIs have enjoyed an annual average increase in technology of 6 per cent combined with a decrease in technical efficiency of 5 per cent. Rapid changes in the higher education sector appear to have had a positive effect on the technology of production but this has been achieved at the cost of lower technical efficiency.

Measuring Efficiency: A Comparison of Multilevel Modelling and Data Envelopment Analysis in the Context of Higher Education

Data envelopment analysis (DEA) and multilevel modelling (MLM) are applied to a data set of 54,564 graduates from UK universities in 1993 to assess whether the choice of technique affects the measurement of universities’ performance. A methodology developed by Thanassoulis and Portela (2002; Education Economics, 10(2), pp. 183–207) allows each individuals DEA efficiency score to be decomposed into two components: one attributable to the university at which the student studied and the other attributable to the individual student. From the former component, a measure of each institutions teaching efficiency is derived and compared to the university effects from various multilevel models. The comparisons are made within four broad subjects: pure science, applied science, social science and arts. The results show that the rankings of universities derived from the DEA efficiencies which measure the universities’ own performance (i.e., having excluded the efforts of the individuals) are not strongly correlated with the university rankings derived from the university effects of the multilevel models. The data were also used to perform a university-level DEA. The university efficiency scores derived from these DEAs are largely unrelated to the scores from the individual-level DEAs, confirming a result from a smaller data set (Johnes, 2006a; European Journal of Operational Research, forthcoming). However, the university-level DEAs provide efficiency scores which are generally strongly related to the university effects of the multilevel models.

Undergraduate non-completion rates: Differences between UK universities

The non-completion rate of university students differs substantially between UK universities. This paper provides estimates of non-completion rates for the 1979 and 1980 entry cohorts into each university and suggests a number of reasons which may have contributed to these inter-university differences. Statistical analysis indicates that a large proportion of the inter-university variation in the non-completion rate can be explained by three main factors: the scholastic ability of each universitys new entrants (as reflected by A-level score), the subject mix of each university, and the proportion of each universitys students accommodated in a hall of residence. The main conclusion is that inter-university comparisons in the non-completion rate are of little value unless account is taken of differences in the scholastic ability of each universitys intake of students.