Student characteristics and academic success: An econometric study of the performance of "equity groups"

Like previous volumes in the Educational Innovation in Economics and Business Series, this book is genuinely international in terms of its coverage. With contributions from nine different countries and three continents, it reflects a global interest in, and commitment to, innovation in business education, with a view to enhancing the learning experience of both undergraduates and postgraduates. It should prove of value to anyone engaged directly in business education, defined broadly to embrace management, finance, marketing, economics, informational studies, and ethics, or who has responsibility for fostering the professional development of business educators. The contributions have been selected with the objective of encouraging and inspiring others as well as illustrating developments in the sphere of business education. This volume brings together a collection of articles describing different aspects of the developments taking place in today’s workplace and how they affect business education. It describes strategies for breaking boundaries for global learning. These target specific techniques regarding teams and collaborative learning, transitions from academic settings to the workplace, the role of IT in the learning process, and program-level innovation strategies. This volume addresses issues faced by professionals in higher and further education and also those involved in corporate training centers and industry.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.