This paper presents the details of an effort by Indiana State University to apply multiple regression analysis to the problem of eliminating faculty salary inequities. The services of a consultant were used to develop a multiple regression model to predict an individual's salary while excluding any independent variable addressing race or gender. Residuals were then computed by gender, gender by rank, race, and race by rank. The results showed no evidence of a consistent pattern of salary bias due to gender or race. The consultant's analysis appeared somewhat contradictory when considering salary compression in that a variable representing the number of internal promotions (a previously undocumented measure of compression) was statistically significant in the model, even though the analysis stated that no evidence of significant compression existed. After much discussion, the internal promotions variable was dropped. The revised model was run and residuals analysis was used to verify the presence of significant salary compression. The residuals became an important consideration in the adjustment of salaries to address the compression problems. The use of the model demonstrates how quantitative models can aid in addressing salary inequities and improve faculty salary allocation decisiorw by combining a data-driven approach with more traditional peer evaluation methods.

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Using a vertically related multilayer newsvendors framework, this paper analyzes the impacts of market uncertainties and asymmetric information between firms at successive stages of a supply chain on their optimal stocking (and/or pricing) decisions. The asymmetric information along the supply chain effectively transmits the downstream uncertainty backward to the preceding stages of the supply chain. Our results imply that for industries in which there are lead times and asymmetric information in multiple successive stages along the supply chain, it is essentially important for the upstream firms to study the market conditions facing the vertically related downstream firms, and it is important for them to design incentive-compatible mechanisms to facilitate efficient information sharing among vertically related entities. In these industries, the entities might also have stronger incentives to invest in lead time reduction and in information acquisition, if such options exist. Our framework offers a good starting point for a more comprehensive study in the future on the interactions among and the impact of differential lead times across successive stages in supply chains.

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Providing and maintaining roads are major public services. Costs of these services are influenced by many factors. This paper examines the influence of two factors, size of operation and type of administrative organization, on costs of rural roads.An administrative unit providing roads for a small area, county, or township, with a given density of roads might experience higher costs per unit of area than an administrative unit providing road service for a larger area, all other conditions being the same. It likely would experience disadvantages in buying supplies and equipment, making full use of equipment, hiring competent help, and in other ways. However, the unit with a large area could run into diseconomies through high administrative and supervisional costs. Actually, it may not be that simple. This study tests the idea empirically.