We examine a vertical integration decision within the commercial banking industry. During the last quarter of the 20th century, some community banks reduced their traditional reliance on correspondent banks for upstream products and services by joining bankers' banks, a form of business cooperative. Research on vertical integration focuses primarily on firm-specific investment, market power, and government regulation. However, this case is difficult to explain in terms of these standard vertical integration motives. Our evidence suggests that bankers' banks are a response to technological change and deregulation that results in increased costs faced by community banks in dealing with correspondent banks as both suppliers and potential competitors. For instance, loan participations require sharing proprietary information about major loan customers, something a community bank would not want to provide to a potential competitor.

Agency theory implies that asset ownership and decision authority are complements. Using 1998 data from Texas commercial banks, we test whether the likelihood of local ownership of bank offices increases with the importance of granting local managers greater decision authority (for example, due to location or customer base). Our empirical evidence is consistent with this hypothesis. It suggests that complementarities between strategy and organizational structure can foster differentiation among firms in terms of location, customers, and products. It also supports the growing view that small locally-owned banks have a comparative advantage over large banks within specific environments.

Theory suggests that shareholders who own blocks of stock have a stronger incentive to invest in voting on corporate issues than nonblockholders. Our evidence indicates that institutional investors and other blockholders vote more actively on antitakeover amendments than nonblockholders, and opposition by institutions is greater when the proposal appears to harm shareholders. Our evidence suggests that institutions that are less subject to management influence, such as mutual funds, foundations, and public-employee pension funds, are more likely to oppose management than banks, insurance companies, and trusts, which frequently derive benefits from lines of business under management control.

We develop a positive theory of the hedging behavior of value-maximizing corporations. We treat hedging by corporations simply as one part of the firm's financing decisions. We examine (1) taxes, (2) contracting costs, and (3) the impact of hedging policy on the firm's investment decisions as explanations of the observed wide diversity of hedging practices among large, widely-held corporations. Our theory provides answers to the questions: (1) why some firms hedge and others do not; (2) why firms hedge some risks but not others; and (3) why some firms hedge their accounting risk exposure while others hedge their economic value.

This paper reexamines the anomalous evidence concerning the efficiency of the listed options exchanges. We focus on the structure of trading costs in that market, and note several costs which generally have been ignored, the largest of which is the bid-ask spread. When we adjust the published trading rules for our estimates of these trading costs, the reported abnormal returns are eliminated.

We assume executives managing corporate financial policy consider the firm's current and target leverage, investment plans, anticipated cash flows, and consequences of alternative sequences of financing transactions, operating within efficient markets. Our analysis yields time-series and cross-sectional predictions for management of investment spending and leverage; use of maturity, priority, and convertibility covenants; and management of dividends, share repurchases, cash balances, and credit lines. Our evidence from 8608 SEOs covering 1970–2015 is consistent with implications of our theory, helps to resolve an array of issues in corporate finance, and offers a step toward a more unified analysis of rational corporate financial management.

John Formby, Terry Seaks, and W. Smith (hereafter FSS) argue that the P-Gini coefficient is affected by the arbitrary choice of the age to a degree which brings the validity of [the] age-related measure into question (FSS, 1989, p. 2). More pointedly, a sufficiently narrow age partition, the P-curve can always be driven to the L-curve. Convergence [of the P-Gini] to a nonzero estimate does not occur... (p. 4). These conclusions I believe result from a misapplication of Gastwirth's theorem on disaggregation, and a failure to observe statistical rules relating to sample size and sampling error. I will show that when these rules are observed, the value of the P-Gini does not converge to zero but properly reflects the relative importance of the nonlife-cycle factors affecting the distribution. When calculating the traditional L-Gini, the more disaggregation the better; the number and accuracy of the sample points are the only consideration since all are thrown into one conceptual box and compared in terms of income size. But if we try to identify the factors which account for income inequality in terms of age versus nonage related factors, we are setting up two conceptual boxes (the age-Gini and the P-Gini) and we are no longer simply dealing with a Gastwirth-type problem. Statistical considerations come into play; for example, we must have a sufficient number of sample points in each conceptual box in order to give a reliable estimate of the importance of each factor. The key-allocating device which I employ is the age-Gini, derived from the average age-income profile. The age-Gini shows the amount of inequality that would exist if all nonage-related sources of inequality were eliminated. When calculating this coefficient, the means of the age-groups are used in order to wash out all random and nonagerelated influences, but this separating device works well only if the means are based on large samples. Otherwise, sampling errors create spurious variation and impart an upward bias to the value of the age-Gini. FSS (p. 4) drive the age-Gini value up to the L-Gini by increasing the number of agegroups until they equal the number in the sample. Since the means of the age-groups are now based on samples of one, they become as erratic as the individual incomes, and impart the maximum upward bias to the age-Gini. It is true that the age-income profile (and the age-Gini) are conceptually refined by using smaller age intervals, but unless sample size is large compared to the number of age intervals, the gains from conceptual purification will be more than offset by the greater sampling errors of the age means. This kind of limitation is shared by many other statistical measures which do not thereby lose their validity or usefulness. Under what conditions will the true or limiting value of the P-Gini emerge? FSS in their footnote 3 state that there is no limiting value other than zero. Let us test this claim. Assume we have a scatter diagram of income (Y) and age (X), and wish to show average income in relation to age. We start with a finite number of age-groups and plot their mean incomes on the diagram. By continuously reducing the age interval and increasing sample size, we end up with a curve passing through the true means of infinitely small age intervals: this defines the average age-income profile. Since for each person we have data on income and age, we can with this curve (or an approximation of it) calculate the age-Gini and L-Gini without grouping for age or income. The age-income curve allows us to determine the mean income (u) at any given age and for all persons. *Department of Economics, Portland State University, P.O. Box 751, Portland, OR 97207.