I. Introductory. Concept of the "region, " 533.— II. Labor cost per unit of labor and per unit of product, 536.—III. Regional differences in wages, 537. — Gratuities and payments in kind, 542. — Real wages, 544. — IV. The supply of labor, 545.—Legislative and other restrictions, 548.—V. Differences in productivity, 552. — Spinners and weavers, 558. — VI. Conclusions, 564.

This study examines whether three factors—the transparency of expense disclosures, donor evaluation focus, and organization performance—influence how directors monitor management expense misreporting in nonprofit organizations. An experiment with 189 nonprofit directors finds that the enhanced transparency of expense disclosures increases director monitoring by reducing the tendency to accept management expense misreporting. Further, an organization's nonfinancial performance and the perceived fairness of donor evaluation focus interact to influence director monitoring practices. Specifically, when directors know an organization's nonfinancial performance is poor and understand that this performance will negatively influence the willingness of donors to contribute, directors monitor less if they think that donors are adopting a more balanced approach to organizational evaluation that focuses on both financial and nonfinancial performance; that is, there is a reverse fair process effect as this donor approach is perceived as being fairer than if donors focus solely on financial performance. However, monitoring is equally strong regardless of donor evaluation focus when directors know that an organization's nonfinancial performance is good and a donation is forthcoming.

In this article I analyze the role of cooperation between firm divisions in the budgeting process. I study a setting in which cooperation is a necessary condition for information sharing among division managers, which in turn benefits the principal. The results in this article can help reconcile the differing views between practitioners and academic researchers on the desirability of cooperation in the budgeting process. The results also have implications for some common budgeting processes observed in practice, including bundling budgeting and bottom‐up budgeting.

Gaussian empirical Bayes methods usually maintain a precision independence assumption: The unknown parameters of interest are independent from the known standard errors of the estimates. This assumption is often theoretically questionable and empirically rejected. This paper proposes to model the conditional distribution of the parameter given the standard errors as a flexibly parameterized location‐scale family of distributions, leading to a family of methods that we call close . The close framework unifies and generalizes several proposals under precision dependence. We argue that the most flexible member of the close family is a minimalist and computationally efficient default for accounting for precision dependence. We analyze this method and show that it is competitive in terms of the regret of subsequent decision rules. Empirically, using close leads to sizable gains for selecting high‐mobility Census tracts.

This paper quantifies the importance of the granular channel for the U.S. economy by taking into account that large firms are less volatile than small firms, a feature also known as the size-variance relationship. Intuitively, the largest firms, whose shocks drive granularity, are the least volatile; thus, their influence on aggregates is mitigated. By imposing estimates from the universe of employers for the size-variance relationship in a simple, quantitative framework, I find that the granular hypothesis can rationalize 15% of U.S. aggregate fluctuations, establishing a lower bound for the role of granularity in the U.S. economy.

Valuation risk of a security—uncertainty about its fair value—is a subject of considerable concern in the mutual fund industry. If funds report different values for identical securities, investors cannot easily compare their performance. Yet it is not unusual to see identical illiquid stocks, small-cap stocks, stocks with high analyst dispersion, stocks with less analyst coverage, and newly listed stocks valued differently across mutual funds. An equity fund that has positive price dispersion in its portfolio holdings, that performs poorly, that belongs to a fund family with an inclination for aggressive reporting, that holds more stocks subject to stale prices, that holds more pre-IPO firms, or that experiences net outflows will tend to show positive price dispersion again in the next quarter. This behavior is significant in a volatile market. Aggressive reporting helps funds gain in the mutual fund tournament.

This paper examines the role of focus versus diversification in explaining the economic impact of corporate capital investments. I find that the stock market's responses to announcements of capital investments are more favorable for focused firms than for diversified firms. I also show that focused firms exhibit significantly better post-investment operating performance than diversified firms. The overall findings in this study suggest that the investment opportunities hypothesis dominates the internal capital markets hypothesis in terms of the net economic impact of capital investments on the investing firms.

Security behavior in bull and bear markets has received some attention in recent years. Fabozzi and Francis [5] first documented evidence that security betas are not influenced by the alternating forces of bull and bear markets. Their subsequent study of mutual fund betas also indicated that mutual funds generally respond indifferently to bull and bear market conditions. Using the concept of bull and bear market variations, Kim and Zumwa1t [9] developed and tested the risk premiums associated with the upside and the downside portions of returns variation. They concluded that investors expect to receive a risk premium for downside risk and pay a premium for upside variation of returns. From their results, Kim and Zumwalt [9] suggested that the down-market beta measuring downside risk (downside variation of returns) may be a more appropriate measure of portfolio risk than the single beta in the market model.

Over the past years the beta coefficient has been widely used as a measure of systematic risk in investment and portfolio analysis. The validity of using the beta coefficient as the proper measure of systematic risk is dependent upon the assumption that the beta coefficient is stationary over time. Unfortunately, this assumption has been challenged by a number of empirical studies which have found the beta coefficient to be unstable over time. Examples of such empirical investigations are those documented by Blume [4], Levy [12], Levitz [11], Baesel [2], Altman, Jacquillat, and Levasseur [1], and Roenfelt, Griepentrong, and Pflaum [16]. Most recently, Fabozzi and Francis [9] reported that some security beta coefficients tend to be random over time. Their findings also support the regression tendency of the beta coefficients towards the mean over time, as found by Blume [4]. Thus, because the beta coefficient is changing over time, the use of the ordinary least-squares (OLS) method in investment and portfolio analysis will yield an inefficient estimate of systematic risk. Furthermore, the OLS estimates of security and portfolio residual risks will be influenced by the variability of beta coefficient. Therefore, the purpose of this paper is to investigate the relationship between the variability of the beta coefficient and portfolio residual risk, and hence to provide a real picture of the process of portfolio diversification under the condition of beta nonstationarity. It is shown that the use of the OLS method to estimate security and portfolio residual risks will produce an incorrect conclusion that larger residual risks tend to be associated with higher variability in the beta coefficient.

The problems associated with the investment horizon and systematic risk estimation have been investigated in some detail. Jensen [7] has shown that investment horizon has some impact on the estimated systematic risk; Cheng and Deets [1] have raised some questions about Jensen's instantaneous systematic risk estimation method; Lee [9] has derived the relationship between the estimated instantaneous systematic risk and the estimated finite systematic risk; Levhari and Levy [11] have shown that there exist some relationships between the magnitude of estimated systematic risk and the length of investment horizon; based upon Zellner and Montimarquette's [19] time aggregation technique, Lee and Morimune [10] have shown that the investment horizon problem can be treated either as a time aggregation problem or as a specification problem. However, systematic risk estimates in terms of additive and multiplicative rates of return have not been investigated in detail. The purpose of this paper is to employ the time aggregation technique proposed by Zellner and Montimarquette [19] to investigate the impact of time aggregation on systematic risk associated with the market model. It is shown that autocorrelation and variation in market rates of return are two important factors in determining the magnitude of the estimated systematic risk associated with additive as well as multiplicative models.