ABSTRACT We develop a measure of how information events impact investors' expectations of risk. The measure is broadly applicable and simple to implement. We derive it from an option‐pricing model, where investors anticipate an announcement that simultaneously conveys information on the announcer's expected future cash flows and risk profile. We empirically implement the measure using firms' earnings announcements, showing that it closely aligns with our model's predictions and offers strong forecasting power for firms' risk profiles, costs of capital, and future investments. We further highlight pitfalls of using simple changes in option‐implied volatilities to study information gleaned from earnings announcements. Finally, we apply our measure to study disclosure regulation, the efficacy of text‐based proxies, and market‐wide events, which we use to illustrate our measure's uses, and illuminate its potential limitations.

Research examining the relationship between work stress and well-being has flourished over the past 20 years. At the same time, research on physiological stress processes has also advanced significantly. One of the major advances in this literature has been the emergence of the Allostatic Load model as a central organizing theory for understanding the physiology of stress. In this article, the Allostatic Load model is used as an organizing framework for reviewing the vast literature that has considered health outcomes that are associated with exposure to psychosocial stressors at work. This review spans multiple disciplines and includes a critical discussion of management and applied psychology research, epidemiological studies, and recent developments in biology, neuroendocrinology, and physiology that provide insight into how workplace experiences affect well-being. The authors critically review the literature within an Allostatic Load framework, with a focus on primary (e.g., stress hormones, anxiety and tension) and secondary (e.g., resting blood pressure, cholesterol, body mass index) mediators, as well as tertiary disease end points (e.g., cardiovascular disease, depression, mortality). Recommendations are provided for how future research can offer deeper insight into primary Allostatic Load processes that explain the effects of workplace experiences on mental and physical well-being.

Is there too much or too little research and development (R&D)? In this paper we bridge the gap between the recent growth literature and the empirical productivity literature. We derive in a growth model the relationship between the social rate of return to R&D and the coefficient estimates of the empirical literature and show that these estimates represent a lower bound. Furthermore, our analytic framework provides a direct mapping from the rate of return to the degree of underinvestment in research. Conservative estimates suggest that optimal R&D investment is at least two to four times actual investment.

Increasing manufacturing flexibility is a key strategy for efficiently improving market responsiveness in the face of uncertain future product demand. Process flexibility results from being able to build different types of products in the same plant or production facility at the same time. In Part I of this paper, we develop several principles on the benefits of process flexibility. These principles are that 1) limited flexibility (i.e., each plant builds only a few products), configured in the right way, yields most of the benefits of total flexibility (i.e., each plant builds all products) and 2) limited flexibility has the greatest benefits when configured to chain products and plants together to the greatest extent possible. In Part II, we provide analytic support and justification for these principles. Based on a planning model for assigning production to plants, we demonstrate that, for realistic assumptions on demand uncertainty, limited flexibility configurations (i.e., how products are assigned to plants) have sales benefits that are approximately equivalent to those for total flexibility. Furthermore, from this analysis we develop a simple measure for the flexibility in a given product-plant configuration. Such a measure is desirable because of the complexity of computing expected sales for a given configuration. The measure is ∏(M*), the maximal probability over all groupings or sets of products (M) that there will be unfilled demand for a set of products while simultaneously there is excess capacity at plants building other products. This measure is easily computed and can be used to guide the search for good limited flexibility configurations.

[Macroeconomic models with rational expectations find a new justification if these models appear as limits of some learning procedures. In this paper we consider the case in which, during the learning period, the predictions are obtained by regression. We exhibit the necessary and sufficient condition on the parameter of the model ensuring the convergence of the learning process. The limit is the solution of a rational expectations model in which the information set only includes the exogenous variables used in the auxiliary regression.]