Educators in OECD countries and beyond are trying to balance the broader societal mission of universities with growing managerial pressure for measurable performance indicators. There is an abundance of methods to evaluate universities’ effectiveness and outcomes. Those methods are often criticized for bias in favor of research. Measuring of teaching-related outcomes is much more challenging. Still, the quality of teaching can be assessed through peer and students evaluation of a particular professor or division. Yet it is very difficult to use the information in making further decisions regarding establishing new departments or new educational programs. Are there any instruments to help us decide, for instance, whether a particular PhD program would be successful? Risk evaluation seems to be at the core of any reasonable resource allocation decision which university management are now facing in European countries and elsewhere. Thus the university management might be extremely interested in having a working methodology of risk assessment. In their groundbreaking work Baumol and Bowen (1966, 1996) paved the way to understanding education as a “stagnant service”, i.e. a service with constant labor productivity and ever-growing costs. If that is always the case, there is no point in spending money on developing new products in education at all which is obviously not helpful. On the other hand, program ‘slicing’ causes time and money losses bringing about an influx of overeducated youngsters to the labor market. We need a more reliable set of instruments in order to estimate risks attached to individual decisions of university management. Bent Flyvbjerg and his coauthors (2002) have suggested a referential classes methodology to estimate possible costs of infrastructural projects. The idea of this method is that costs per each project should not be evaluated apart from the costs estimation per other projects of the same kind (e.g. new railroad or bridge). Therefore we need to have a reference class of actions comparable on the basis of fixed set of variables in order to make reasonable forecast of possible risks. This paper addresses the opportunities suggested by referential classes methodology in the field of higher education. Our core question is helpful for costs estimation while making decisions regarding opening new educational programs. What variables we can use for costs estimation of educational process? Making a step back to Baumol’s and Bowen’s theory we’ll see that for them ‘stagnant services’ are characterized by a ‘handicraft attribute’ (i.e. dependence on unique expertise of particular educator), low standardization and low turnover rate (i.e. educational practices are usually time-consuming). We may transform those negative characteristics of a ‘stagnant services’ into a set of variables. Thus, the rate of ‘handicraftness’, standardization and turnover would serve as indicators of risk attached to a certain educational product.

Baumol, W.J. (1996) ‘Children of ‘Performing Arts, the Economic Dilemma: The Climbing Costs of Health Care and Education’, in Journal of Cultural Economics, 20:4, 183-206. Baumol, W.J., W.G. Bowen (1966) Performing Arts, the Economic Dilemma. New York: Twentieth Century Fund. Dolton, P.J, M. Silles (2008) ‘The Effects of Over-Education on Earnings in the graduate Labour Market’ in Economics of Education Review, 27:2, 125-139. Flyvbjerg, B., M. S. Holm & S. Buhl (2002) ‘Underestimating Costs in Public Works Projects: Eror or Lie?’, in Journal of the American Planning Association, 63:3, 279-295. Kahneman, D., A. Tversky (1979) ‘Prospect Theory: An Analysis of Decisions under Risk’, in Econometrica, 47, 313-327