What's an Oscar worth?

This article examines the impact of an Academy Award nomination and award for best picture, best actor/actress, and best supporting actor/actress on a film's (i) market share of theaters, (ii) average revenue per screen, and (iii) its probability of survival. The model is estimated using weekly box-office data for a matched sample of nominated and non-nominated films. The results indicate substantial financial benefits for a nomination and award for best picture and best actor/actress. The structure of rewards is consistent with that found in two-stage, single-elimination tournaments.     

On Mixture Failure Rates Ordering

Mixtures of increasing failure rate distributions (IFR) can decrease at least in some intervals of time. Usually, this property can be observed asymptotically as t → ∞. This is due to the fact that the mixture failure rate is “bent down” compared with the corresponding unconditional expectation of the baseline failure rate, which was proved previously for some specific cases. We generalize this result and discuss the “weakest populations are dying first” property, which leads to the change in the failure rate shape. We also consider the problem of mixture failure rate ordering for the ordered mixing distributions. Two types of stochastic ordering are analyzed: ordering in the likelihood ratio sense and ordering in variances when the means are equal.     

WHAT GOOD IS WEALTH WITHOUT HEALTH? THE EFFECT OF HEALTH ON THE MARGINAL UTILITY OF CONSUMPTION

We estimate how the marginal utility of consumption varies with health. To do so, we develop a simple model in which the impact of health on the marginal utility of consumption can be estimated from data on permanent income, health, and utility proxies. We estimate the model using the Health and Retirement Study's panel data on the elderly and near‐elderly, and proxy for utility with measures of subjective well‐being. Across a wide range of alternative specifications and assumptions, we find that the marginal utility of consumption declines as health deteriorates, and we are able to clearly reject the null of no state dependence. Our point estimates indicate that a one‐standard‐deviation increase in the number of chronic diseases is associated with a 10%–25% decline in the marginal utility of consumption relative to this marginal utility when the individual has no chronic diseases. We present some simple, illustrative calibration results that suggest that state dependence of the magnitude we estimate can have a substantial effect on important economic problems such as the optimal level of health insurance benefits and the optimal level of life‐cycle savings.     

Analysis of familial aggregation in the presence of varying family sizes

Summary.  Family studies are frequently undertaken as the first step in the search for genetic and/or environmental determinants of disease. Significant familial aggregation of disease is suggestive of a genetic aetiology for the disease and may lead to more focused genetic analysis. Of course, it may also be due to shared environmental factors. Many methods have been proposed in the literature for the analysis of family studies. One model that is appealing for the simplicity of its computation and the conditional interpretation of its parameters is the quadratic exponential model. However, a limiting factor in its application is that it is not reproducible , meaning that all families must be of the same size. To increase the applicability of this model, we propose a hybrid approach in which analysis is based on the assumption of the quadratic exponential model for a selected family size and combines a missing data approach for smaller families with a marginalization approach for larger families. We apply our approach to a family study of colorectal cancer that was sponsored by the Cancer Genetics Network of the National Institutes of Health. We investigate the properties of our approach in simulation studies. Our approach applies more generally to clustered binary data.     

The economic toll of intimate partner violence against women in the United States

This study provides estimates of the economic cost of intimate partner violence perpetrated against women in the US, including expenditures for medical care and mental health services, and lost productivity from injury and premature death. The analysis uses national survey data, including the National Violence Against Women Survey and the Medical Expenditure Panel Survey, to estimate costs for 1995. Intimate partner violence against women cost $5.8 billion dollars (95% confidence interval: $3.9 to $7.7 billion) in 1995, including $320 million ($136 to $503 million) for rapes, $4.2 billion ($2.4 to $6.1 billion) for physical assault, $342 million ($235 to $449 million) for stalking, and $893 million ($840 to $946 million) for murders. Updated to 2003 dollars, costs would total over $8.3 billion. Intimate partner violence is costly in the US. The potential savings from efforts to reduce this violence are substantial. More comprehensive data are needed to refine cost estimates and monitor costs over time.     

Good Boundaries Make Good Neighbors: Boundary Management—Tearing Down the Walls and Fostering Organizational Change

This article focuses on the ubiquitous problem of bureaucratic fragmentation experienced by organizations of all kinds. It proposes the strategy and practice of boundary management as a way of addressing the problem while creating organizational change. Based on a classic case of an aging Midwestern steel mill, the purpose of the article is threefold: (i) to define the problem of bureaucratic segmentalism and fragmentation as having to do with the formation of divisive and impenetrable barriers and walls, (ii) to suggest the importance of developing and maintaining permeable boundaries designed to open up and integrate the organization, and (iii) to develop the concept and practice of boundary management and its usefulness in tearing down the walls and creating organizational change.     

Multivariate logistic regression for familial aggregation in age at disease onset

Familial aggregation studies seek to identify diseases that cluster in families. These studies are often carried out as a first step in the search for hereditary factors affecting the risk of disease. It is necessary to account for age at disease onset to avoid potential misclassification of family members who are disease-free at the time of study participation or who die before developing disease. This is especially true for late-onset diseases, such as prostate cancer or Alzheimer's disease. We propose a discrete time model that accounts for the age at disease onset and allows the familial association to vary with age and to be modified by covariates, such as pedigree relationship. The parameters of the model have interpretations as conditional log-odds and log-odds ratios, which can be viewed as discrete time conditional cross hazard ratios. These interpretations are appealing for cancer risk assessment. Properties of this model are explored in simulation studies, and the method is applied to a large family study of cancer conducted by the National Cancer Institute-sponsored Cancer Genetics Network (CGN).     

Are baby boomers who care for their older parents planning for their own future long-term care needs?

A rapidly expanding number of baby boomers provide care to aging parents. This study examines associations between caregiver status and outcomes related to awareness and anticipation of future long-term care (LTC) needs using 2007 Connecticut Long-Term Care Needs Assessment survey data. Baby boomers who were adult child caregivers (n = 353) versus baby boomers who were not (n = 1242) were more likely to anticipate some future LTC needs and to have considered certain financing strategies. Although baby boomer adult child caregivers more readily anticipate some future LTC needs, they are not taking specific actions. It is important to address the need for public education directed toward those who are currently (or have recently completed) caring for aging parents.     

A note on ‘curable’ shock processes

In most conventional shock models, the events caused by an external shock are initiated at the moments of its occurrence. Recently, Cha and Finkelstein (2012) had considered the case when each shock from a nonhomogeneous Poisson processes can trigger a failure of a system not immediately, as in the classical shock models, but with delay of some random time. In this paper, we suggest the new type of shock models, where each delayed failure can be cured (repaired) with certain probabilities. These shock processes have not been considered in the literature before. We derive and analyze the corresponding survival and failure rate functions and consider a meaningful reliability example of the stress–strength model.     

Some results on discrete mixture failure rates

Some properties of the discrete mixture failure rates are studied. Specifically, similar to the continuous case, it is shown that the population mixture failure rate is always smaller than the unconditional expectation in the family of subpopulations failure rates. The analog of the multiplicative and the additive frailty models is introduced via the corresponding survival function. Another approach via the alternative discrete failure rate is also discussed. Stochastic comparisons for two mixed distributions with equal and different mixing distributions are studied.