Increased competition within the financial services industry has raised concerns about the ability of small banks to adequately fund local rural development. To address these concerns, the Gramm-Leach-Bliley Act of 1999 broadened small-bank access to Federal Home Loan Bank (FHLB) financing. Statistical analyses indicate that the following factors were significantly associated with the decisions of small banks headquartered in nonmetropolitan counties to obtain FHLB membership: bank size, affiliation with a bank holding company, exposure to interest rate risk, loan portfolio quality, liquidity pressure, dividend rates on FHLB stock, and binding membership requirements related to residential real estate-related assets. Many, but not all, of these factors were also significantly associated with the membership decisions of small banks headquartered in metropolitan counties. The decisions of both nonmetropolitan and metropolitan banks to use FHLB funding is significantly related to interest rate risk exposure, liquidity pressure, and net interest margins. Neither population trend nor rural county type variables are consistently significant in explaining either which small banks join FHLBs or which member banks borrow from FHLBs.

Courts have decided dozens of discrimination cases within higher education over the past twenty years. During this time, increasing attention has been given to inferential statistical investigations, such as regression analyses, to determine liability in these lawsuits, particularly with respect to an institution' s pay, hiring, and promotion decisions of its faculty. In this case, regression analysis is used to evaluate the relationship between a set of independent (explanatory) variables with a single, dependent variable. Discrete (dichotomous) decisions within academia, such as hiring and granting tenure, can be analyzed using logistic regression methods; faculty salary, a continuous variable, can be investigated using multiple regression analysis.This chapter reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of male and female faculty in institutional studies or to establish or contest a prima facie case of discrimination in faculty salary equity cases and as a procedure for determining compensation and computing relief during settlement proceedings of faculty pay disparity cases. A brief review of significant case law and court decisions that have defined the role and scope of regression analysis in gender pay disparity cases is also provided. From this review, it will be clear that while statistical analyses are being used by the courts, the various interpretations of these 85 6 NEW DIRECTIONS FOR INSTITUTIONAL RESEARCH, no. 138, Summer 2008