This article examines the attributes of a successful contracting model for the financing and support of nonprofit organizations. It describes how, through government initiative, a program can be built in which transaction costs are minimized through a cooperative approach to contracting based on mutual trust. It shows how investment in a long-term, trust-based, cooperative relationship underlined by professional standards and a continuous focus on a common mission by all levels of actors within and without governmentcan provide the impetus for a system in which high standards of service are maintained, accountability is organic, and organizations feel supported in their mission but not controlled. The example presented is a provincial government program for the prevention of family violence in Manitoba, Canada, but the features that make it successful can be applied widely.

Conservation contracts, Conservation easements, Land preservation, Multi-unit auction, Uniform price auction, Valuation,

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non-linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non-linearity in the unemployment series. Only recently have there been some developments in applying non-linear models to estimate and forecast unemployment rates. A major concern of non-linear modelling is the model specification problem; it is very hard to test all possible non-linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non-linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back-propagation model and a generalized regression neural network model to estimate and forecast post-war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out-of-sample forecast results obtained by the ANN models with those obtained by several linear and non-linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.

We examine whether a sex-based salary gap identified at the University of Manitoba in 1993 and 2003 persists in 2013. We apply decomposition techniques to analyze the factors contributing to the salary gap in each year and to its changes across the two decades. We find that a smaller but substantial 12 percent gap persists in 2013. In contrast to previous years, the 2013 gap is completely explained by sex differences in faculty, experience, and, more important, type of appointment and rank. The distribution of values of these control variables changed considerably between the earlier years and 2013 in ways that influenced the gap.

This paper examines salaries at the University of Manitoba to determine whether a 1994 remedy, paid in response to a 1993 salary study that demonstrated a gap between the salaries of males and females, has eliminated these differences. We use 1993 and 2003 data to approximate the earlier analysis, and apply a Blinder-Oaxaca decomposition to examine the evolution in the wage gap between time periods. Our results indicate that the gap remains largely unchanged in magnitude, but its determinants have shifted somewhat. Women's overrepresentation at lower-paying ranks and underrepresentation at the highest-paying ranks, as well as differences in highest degree and experience explain much of the wage gap.