Project Business: a quasi-experimental study of co- operative interaction between schools and industry

This project is a quasi-experimental study involving eight classrooms in two senior elementary schools in St. Catharines, Ontario which received a Project Business Program and were pre- and post-tested to determine the growth of knowledge acquisition in the area of business concepts. Four classrooms received a Project Business treatment while four classrooms acted as a control. The Project Business Program is sponsored by Junior Achievement of Canada; it occurred during a twelveweek period, February to May 1981, and is run by business consultants who, through Action, Dialogue and Career Exploration, teach children about economics and business related topics. The consultants were matched with teacher co-ordinators in whose classrooms they taught and with whom they discussed field trips, students, lesson planning, etc. The statistical analysis of pre- and post-test means revealed a significant statistical growth in the area of knowledge acquisition on the part of those students who received the Project Business Program. This confirms that Project Business makes a difference. A search of the literature appears to advocate economic programs like Project Business, whfch are broadly based, relevant and processoriented. This program recommends itself as a model for other areas of co-operative curricular interactions and as a bridge to future trends and as a result several fruitful areas of research are suggested.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.