Performance analysis of Ontario credit unions

grantor: University of TorontoThe theoretical contributions of this work to DEA science includes the development of an algorithm to find 'all' the facets of a VRS DEA frontier. This enables the projection of inefficient units to the closest point on the efficient frontier, or with the 'minimum' sum of slacks. The proportion of full dimensional facets affects the degree of freedom for movement along the frontier. The dimensionality of any region of the frontier can be determined and is discussed. An efficiency measure is introduced for inefficient units, relative to their closest point on the frontier. This efficiency measure can also be used with any projection to the frontier, including the traditional input or output oriented radial projections. With radial projections, slack is accounted for in this measure, and the measure collapses to the traditional radial measures when there is no slack. The practical application part of this work is based on data from Credit Unions in Ontario. This is an important sector of the Financial Services Industry, with 1996 assets of $13.5 billion and 1.7 million members. The health of the Credit Union industry is monitored by a government agency, the Deposit Insurance Corporation of Ontario (DICO), the collaborating entity that provided data for this research. In this section DEA models are developed to detect weaknesses in Credit Unions so that potential financial failures can be predicted. DEA with a VRS frontier and input orientation is utilised to create various production models that are compared with each other, and with current models used by DICO. In total, fifteen models are constructed and compared with DICO's current z-score model. A simple equity/asset ratio is shown to provide as good a prediction of failure as any of the other models, and is not improved upon the z-score model. The best DEA model provides results comparable to the equity/asset ratio when the new slack adjusted efficiency score is used to measure efficiency, particularly for larger asset sizes. For each Credit Union, comparison is made with a peer group of efficient entities that the inefficient institution's management can emulate, in order to improve their performance.Ph.D