搜索结果：cost

BACKGROUND: Current estimates of the costs of cancer care in the United States are based on data from 2003 and earlier. However, incidence, survival, and practice patterns have been changing for the majority of cancers. METHODS: Cancer prevalence was estimated and projected by phase of care (initial year following diagnosis, continuing, and last year of life) and tumor site for 13 cancers in men and 16 cancers in women through 2020. Cancer prevalence was calculated from cancer incidence and survival models estimated from Surveillance, Epidemiology, and End Results (SEER) Program data. Annualized net costs were estimated from recent SEER-Medicare linkage data, which included claims through 2006 among beneficiaries aged 65 years and older with a cancer diagnosis. Control subjects without cancer were identified from a 5% random sample of all Medicare beneficiaries residing in the SEER areas to adjust for expenditures not related to cancer. All cost estimates were adjusted to 2010 dollars. Different scenarios for assumptions about future trends in incidence, survival, and cost were assessed with sensitivity analysis. RESULTS: Assuming constant incidence, survival, and cost, we projected 13.8 and 18.1 million cancer survivors in 2010 and 2020, respectively, with associated costs of cancer care of 124.57 and 157.77 billion 2010 US dollars. This 27% increase in medical costs reflects US population changes only. The largest increases were in the continuing phase of care for prostate cancer (42%) and female breast cancer (32%). Projections of current trends in incidence (declining) and survival (increasing) had small effects on 2020 estimates. However, if costs of care increase annually by 2% in the initial and last year of life phases of care, the total cost in 2020 is projected to be $173 billion, which represents a 39% increase from 2010. CONCLUSIONS: The national cost of cancer care is substantial and expected to increase because of population changes alone. Our findings have implications for policy makers in planning and allocation of resources.

This paper revisits the problem of optimal learning and decision-making when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrix is reasonable, and we show how to avoid the mistake of defining a cost matrix that is economically incoherent. For the two-class case, we prove a theorem that shows how to change the proportion of negative examples in a training set in order to make optimal cost-sensitive classification decisions using a classifier learned by a standard non-costsensitive learning method. However, we then argue that changing the balance of negative and positive training examples has little effect on the classifiers produced by standard Bayesian and decision tree learning methods. Accordingly, the recommended way of applying one of these methods in a domain with differing misclassification costs is to learn a classifier from the training set as given, and then to compute optimal decisions explicitly using the probability estimates given by the classifier. 1 Making decisions based on a cost matrix Given a specification of costs for correct and incorrect predictions, an example should be predicted to have the class that leads to the lowest expected cost, where the expectation is computed using the conditional probability of each class given the example. Mathematically, let the entry in a cost matrix be the cost of predicting class when the true class is. If then the prediction is correct, while if the prediction is incorrect. The optimal prediction for an example is the class that minimizes

IMPORTANCE: Since publication of the report by the Panel on Cost-Effectiveness in Health and Medicine in 1996, researchers have advanced the methods of cost-effectiveness analysis, and policy makers have experimented with its application. The need to deliver health care efficiently and the importance of using analytic techniques to understand the clinical and economic consequences of strategies to improve health have increased in recent years. OBJECTIVE: To review the state of the field and provide recommendations to improve the quality of cost-effectiveness analyses. The intended audiences include researchers, government policy makers, public health officials, health care administrators, payers, businesses, clinicians, patients, and consumers. DESIGN: In 2012, the Second Panel on Cost-Effectiveness in Health and Medicine was formed and included 2 co-chairs, 13 members, and 3 additional members of a leadership group. These members were selected on the basis of their experience in the field to provide broad expertise in the design, conduct, and use of cost-effectiveness analyses. Over the next 3.5 years, the panel developed recommendations by consensus. These recommendations were then reviewed by invited external reviewers and through a public posting process. FINDINGS: The concept of a "reference case" and a set of standard methodological practices that all cost-effectiveness analyses should follow to improve quality and comparability are recommended. All cost-effectiveness analyses should report 2 reference case analyses: one based on a health care sector perspective and another based on a societal perspective. The use of an "impact inventory," which is a structured table that contains consequences (both inside and outside the formal health care sector), intended to clarify the scope and boundaries of the 2 reference case analyses is also recommended. This special communication reviews these recommendations and others concerning the estimation of the consequences of interventions, the valuation of health outcomes, and the reporting of cost-effectiveness analyses. CONCLUSIONS AND RELEVANCE: The Second Panel reviewed the current status of the field of cost-effectiveness analysis and developed a new set of recommendations. Major changes include the recommendation to perform analyses from 2 reference case perspectives and to provide an impact inventory to clarify included consequences.

Abstract The effect of disclosure level on the cost of equity capital is a matter of considerable interest and importance to the financial reporting community. However, the association between disclosure level and cost of equity capital is not well established and has been difficult to quantify. In this paper I examine the association between disclosure level and the cost of equity capital by regressing firm-specific estimates of cost of equity capital on market beta, firm size and a self-constructed measure of disclosure level. My measure of disclosure level is based on the amount of voluntary disclosure provided in the 1990 annual reports of a sample of 122 manufacturing firms. For firms that attract a low analyst following, the results indicate that greater disclosure is associated with a lower cost of equity capital. The magnitude of the effect is such that a one-unit difference in the disclosure measure is associated with a difference of approximately twenty-eight basis points in the cost of equity capital, after controlling for market beta and firm size. For firms with a high analyst following, however, I find no evidence of an association between my measure of disclosure level and cost of equity capital perhaps because the disclosure measure is limited to the annual report and accordingly may not provide a powerful proxy for overall disclosure level when analysts play a significant role in the communication process.

In this study, we propose an alternative technique for estimating the cost of equity capital. Specifically, we use a discounted residual income model to generate a market implied cost‐of‐capital. We then examine firm characteristics that are systematically related to this estimate of cost‐of‐capital. We show that a firm's implied cost‐of‐capital is a function of its industry membership, B/M ratio, forecasted long‐term growth rate, and the dispersion in analyst earnings forecasts. Together, these variables explain around 60% of the cross‐sectional variation in future (two‐year‐ahead) implied costs‐of‐capital. The stability of these long‐term relations suggests they can be exploited to estimate future costs‐of‐capital. We discuss the implications of these findings for capital budgeting, investment decisions, and valuation research.

This paper takes issue with the Porter-van der Linde claim that traditional benefit-cost analysis is a fundamental misrepresentation of the environmental problem. They contend that stringent environmental measures induce innovative efforts leading to improvements in abatement and production technologies that offset the costs of the regulations. Drawing both on basic economic theory and existing data on control costs, the authors argue that such offsets are special cases. The data indicate offsets are minuscule relative to control costs. There is no free lunch here: environmental programs must justify their costs by the benefits that improved environmental quality provides to society.

ABSTRACT: We examine a potential benefit associated with the initiation of voluntary disclosure of corporate social responsibility (CSR) activities: a reduction in firms’ cost of equity capital. We find that firms with a high cost of equity capital in the previous year tend to initiate disclosure of CSR activities in the current year and that initiating firms with superior social responsibility performance enjoy a subsequent reduction in the cost of equity capital. Further, initiating firms with superior social responsibility performance attract dedicated institutional investors and analyst coverage. Moreover, these analysts achieve lower absolute forecast errors and dispersion. Finally, we find that firms exploit the benefit of a lower cost of equity capital associated with the initiation of CSR disclosure. Initiating firms are more likely than non-initiating firms to raise equity capital following the initiations; among firms raising equity capital, initiating firms raise a significantly larger amount than do non-initiating firms.

The potential advantages of the market-value approach have long been appreciated; yet analytical results have been meager. What appears to be keeping this line of development from achieving its promise is largely the lack of an adequate theory of the effect of financial structure on market valuations, and of how these effects can be inferred from objective market data. It is with the development of such a theory and of its implications for the cost-of-capital problem that we shall be concerned in this paper. Our procedure will be to develop in Section I the basic theory itself and to give some brief account of its empirical relevance. In Section II we show how the theory can be used to answer the cost-of-capital questions and how it permits us to develop a theory of investment of the firm under conditions of uncertainty. Throughout these sections the approach is essentially a partial-equilibrium one focusing on the firm and industry. Accordingly, the of certain income streams will be treated as constant and given from outside the model, just as in the standard Marshallian analysis of the firm and industry the prices of all inputs and of all other products are taken as given. We have chosen to focus at this level rather than on the economy as a whole because it is at firm and the industry that the interests of the various specialists concerned with the cost-of-capital problem come most closely together. Although the emphasis has thus been placed on partial-equilibrium analysis, the results obtained also provide the essential building block for a general equilibrium model which shows how those prices which are here taken as given, are themselves determined. For reasons of space, however, and because the material is of interest in its own right, the presentation of the general equilibrium model which rounds out the analysis must be deferred to a subsequent paper.

Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or "resting" state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06-0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties-supporting efficient parallel information transfer at relatively low cost-which are differently impaired by normal aging and pharmacological blockade of dopamine transmission.

In this article, we consider the problem of detecting multiple changepoints in large datasets. Our focus is on applications where the number of changepoints will increase as we collect more data: for example, in genetics as we analyze larger regions of the genome, or in finance as we observe time series over longer periods. We consider the common approach of detecting changepoints through minimizing a cost function over possible numbers and locations of changepoints. This includes several established procedures for detecting changing points, such as penalized likelihood and minimum description length. We introduce a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost, which, under mild conditions, is linear in the number of observations. This compares favorably with existing methods for the same problem whose computational cost can be quadratic or even cubic. In simulation studies, we show that our new method can be orders of magnitude faster than these alternative exact methods. We also compare with the binary segmentation algorithm for identifying changepoints, showing that the exactness of our approach can lead to substantial improvements in the accuracy of the inferred segmentation of the data. This article has supplementary materials available online.

Limits on health-care resources mandate that resource-allocation decisions be guided by considerations of cost in relation to expected benefits. In cost-effectiveness analysis, the ratio of net health-care costs to net health benefits provides an index by which priorities may be set. Quality-of-life concerns, including both adverse and beneficial effects of therapy, may be incorporated in the calculation of health benefits as adjustments to life expectancy. The timing of future benefits and costs may be accounted for by the appropriate use of discounting. Current decisions must inevitably be based on imperfect information, but sensitivity analysis can increase the level of confidence in some decisions while suggesting areas where further research may be valuable in guiding others. Analyses should be adaptable to the needs of various health-care decision makers, including planners, administrators and providers.

This paper addresses a little examined intersection between the problem loan literature and the bank efficiency literature. We employ Granger-causality techniques to test four hypotheses regarding the relationships among loan quality, cost efficiency, and bank capital. The data suggest that problem loans precede reductions in measured cost efficiency; that measured cost efficiency precedes reductions in problem loans; and the reductions in capital at thinly capitalized banks precede increases in problem loans. Hence, cost efficiency may be an important indicator of future problem loans and problem banks. Our results are ambiguous concerning whether or not researchers should control for problem loans in efficiency estimation.

The data centers used to create cloud services represent a significant investment in capital outlay and ongoing costs. Accordingly, we first examine the costs of cloud service data centers today. The cost breakdown reveals the importance of optimizing work completed per dollar invested. Unfortunately, the resources inside the data centers often operate at low utilization due to resource stranding and fragmentation. To attack this first problem, we propose (1) increasing network agility, and (2) providing appropriate incentives to shape resource consumption. Second, we note that cloud service providers are building out geo-distributed networks of data centers. Geo-diversity lowers latency to users and increases reliability in the presence of an outage taking out an entire site. However, without appropriate design and management, these geo-diverse data center networks can raise the cost of providing service. Moreover, leveraging geo-diversity requires services be designed to benefit from it. To attack this problem, we propose (1) joint optimization of network and data center resources, and (2) new systems and mechanisms for geo-distributing state.

Cost-benefit analysis (CBA) is the systematic and analytical process of comparing benefits and costs in evaluating the desirability of a project or programme – often of a social nature. It attempts to answer such questions as whether a proposed project is worthwhile, the optimal scale of a proposed project and the relevant constraints. CBA is fundamental to government decision making and is established as a formal technique for making informed decisions on the use of society’s scarce resources. This timely sixth edition of the classic Cost-Benefit Analysis text continues to build on the successful approach of previous editions, with lucid explanation of key ideas, simple but effective expository short chapters and an appendix on various useful statistical and mathematical concepts and derivatives. The book examines important developments in the discipline, with relevant examples and illustrations as well as new and expanded chapters which build upon standard materials on CBA. Highlights include: updated historical background of CBA extended non-market goods valuation methods the impact of uncertainty evaluation of programmes and services behavioural economics decision rules and heuristics CBA and regulatory reforms CBA in developed and developing countries value of household production other topics frequently encountered in CBA, such as costs of diseases and air pollution, and value of statistical life. This book is a valuable source and guide to international funding agencies, governments, interested professional economists and senior undergraduate and graduate students. The text is fully supported by a companion website, which includes discussion questions and PowerPoint slides for each chapter.

Abstract This is a unique, in-depth discussion of the uses and conduct of cost-effectiveness analyses (CEA) as decision-making aids in the health and medical fields. The product of over two years of deiberation by a multi-disciplinary Public Health Service appointed panel that included economists, ethicists, psychometricians, and clinicians, it explores cost-effectiveness in the context of societal decision-making for resource allocation purposes. It proposes that analysts include a “reference-case” analysis in all CEA’s designed to inform resource allocation and puts forth the most expicit set of guidelines (together with their rationale) ever outlined of the conduct of CEAs. Important theoretical and practical issues encountered in measuring costs and effectiveness, valuing outcomes, discounting, and dealing with uncertainty are examined in separate chapters. These discussions are complemented by additional chapters on framing and reporting of CEAs that aim to clarify the purpose of the analysis and the effective communication of its findings. Primarily intended for analysts in medicine and public health who wish to improve practice and comparability of CEAs, this book will also be of interest to decision-makers in government, managed care, and industry who wish to consider the roles and limitations of CEA and become familiar with criteria for evaluating these studies.

ABSTRACT We investigate the role of information in affecting a firm's cost of capital. We show that differences in the composition of information between public and private information affect the cost of capital, with investors demanding a higher return to hold stocks with greater private information. This higher return arises because informed investors are better able to shift their portfolio to incorporate new information, and uninformed investors are thus disadvantaged. In equilibrium, the quantity and quality of information affect asset prices. We show firms can influence their cost of capital by choosing features like accounting treatments, analyst coverage, and market microstructure.

A sequel to his frequently cited Cost and Production Functions (1953), this book offers a unified, comprehensive treatment of these functions which underlie the economic theory of production. The approach is axiomatic for a definition of technology, by mappings of input vectors into subsets of output vectors that represent the unconstrained technical possibilities of production. To provide a completely general means of characterizing a technology, an alternative to the production function, called the Distance Function, is introduced. The duality between cost function and production function is developed by introducing a cost correspondence, showing that these two functions are given in terms of each other by dual minimum problems. The special class of production structures called Homothetic is given more general definition and extended to technologies with multiple outputs. Originally published in 1971. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These paperback editions preserve the original texts of these important books while presenting them in durable paperback editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

OBJECTIVE: To develop consensus-based recommendations for the conduct of cost-effectiveness analysis (CEA). This article, the second in a 3-part series, describes the basis for recommendations constituting the reference case analysis, the set of practices developed to guide CEAs that inform societal resource allocation decisions, and the content of these recommendations. PARTICIPANTS: The Panel on Cost-Effectiveness in Health and Medicine, a nonfederal panel with expertise in CEA, clinical medicine, ethics, and health outcomes measurement, was convened by the US Public Health Service (PHS). EVIDENCE: The panel reviewed the theoretical foundations of CEA, current practices, and alternative methods used in analyses. Recommendations were developed on the basis of theory where possible, but tempered by ethical and pragmatic considerations, as well as the needs of users. CONSENSUS PROCESS: The panel developed recommendations through 2 1/2 years of discussions. Comments on preliminary drafts prepared by panel working groups were solicited from federal government methodologists, health agency officials, and academic methodologists. CONCLUSIONS: The panel's methodological recommendations address (1) components belonging in the numerator and denominator of a cost-effectiveness (C/E) ratio; (2) measuring resource use in the numerator of a C/E ratio; (3) valuing health consequences in the denominator of a C/E ratio; (4) estimating effectiveness of interventions; (5) incorporating time preference and discounting; and (6) handling uncertainty. Recommendations are subject to the ¿rule of reason,¿ balancing the burden engendered by a practice with its importance to a study. If researchers follow a standard set of methods in CEA, the quality and comparability of studies, and their ultimate utility, can be much improved.

ABSTRACT In this paper we examine whether and how accounting information about a firm manifests in its cost of capital, despite the forces of diversification. We build a model that is consistent with the Capital Asset Pricing Model and explicitly allows for multiple securities whose cash flows are correlated. We demonstrate that the quality of accounting information can influence the cost of capital, both directly and indirectly. The direct effect occurs because higher quality disclosures affect the firm's assessed covariances with other firms' cash flows, which is nondiversifiable. The indirect effect occurs because higher quality disclosures affect a firm's real decisions, which likely changes the firm's ratio of the expected future cash flows to the covariance of these cash flows with the sum of all the cash flows in the market. We show that this effect can go in either direction, but also derive conditions under which an increase in information quality leads to an unambiguous decline in the cost of capital.