Employee exposure to coworker substance use and negative consequences: the moderating effects of work group membership.

The current study assesses: (1) whether the relationship between individual exposure to coworker substance use and negative consequences resulting from exposure depends on work group membership, and (2) whether group-level characteristics moderate the relationship between exposure and consequences. At the group-level, we assessed occupations involving safety risk or high mobility and social factors of drinking climate and group cohesiveness. We conducted Hierarchical Linear Modeling (HLM) across two samples of municipal employees (ns = 650, 878; n of groups = 50, 49). Our results revealed that groups with higher proportions of jobs involving risk (e.g., machine work) and, to a lesser extent, groups with a higher level of drinking climate were those most vulnerable to consequences under conditions of exposure. Importantly, our findings controlled for individual risk factors (e.g., personal drinking, job stress). Our discussion examines the implications of this study for theory and policy related to workplace substance abuse.     

THE PHYSICIAN LABOR MARKET IN A MANAGED CARE-DOMINATED ENVIRONMENT

The belief that doctors respond to declining demand by treating patients more aggressively has created skepticism about relying on market forces to restructure physician supply. We argue that even if the physician labor market is dysfunctional under fee-for-service incentives, it can perform better as managed care becomes dominant. Our model implies a nonlinear effect of managed care penetration on incomes. Physicians can offset most or all of initial declines in demand, but cannot insulate themselves indefinitely. This may explain the observation that, until recently, the growth of managed care has not been accompanied by large physician income changes. ( JEL III, J31)     

The coming medical apocalypse.

Is there a medical apocalypse in our future? Will it happen soon? No one can say for sure, but five ominous trends suggest that a medical meltdown could occur at any time. These trends are: (1) The practice of providing medical care becoming too complex from both a business and a legal perspective; (2) Less money being spent on medical care without any corresponding reduction in services provided, creating long-term operating deficits; (3) Investor-owned, for-profit corporations changing the focus of medicine by putting shareholder concerns ahead of patient care; (4) Employment-linked health care insurance creating a growing uninsured population, adding extra financial stress to our hospitals; and, (5) Providers losing faith in their future and becoming increasingly demoralized about practicing the healing arts. These dangerous trends are considered, along with some suggestions that physician executives and organizations might take to protect themselves.     

A Discrete Variable Chain Graph for Applicants for Credit

A bank offering unsecured personal loans may be interested in several related outcome variables, including defaulting on the repayments, early repayment or failing to take up an offered loan. Current predictive models used by banks typically consider such variables individually. However, the fact that they are related to each other, and to many interrelated potential predictor variables, suggests that graphical models may provide an attractive alternative solution. We developed such a model for a data set of 15 variables measured on a set of 14 000 applications for unsecured personal loans. The resulting global model of behaviour enabled us to identify several previously unsuspected relationships of considerable interest to the bank. For example, we discovered important but obscure relationships between taking out insurance, prior delinquency with a credit card and delinquency with the loan.     

Observable trend-projecting state-space models

Much attention has focused in recent years on the use of state-space models for describing and forecasting industrial time series. However, several state-space models that are proposed for such data series are not observable and do not have a unique representation, particularly in situations where the data history suggests marked seasonal trends. This raises major practical difficulties since it becomes necessary to impose one or more constraints and this implies a complicated error structure on the model. The purpose of this paper is to demonstrate that state-space models are useful for describing time series data for forecasting purposes and that there are trend-projecting state-space components that can be combined to provide observable state-space representations for specified data series. This result is particularly useful for seasonal or pseudo-seasonal time series. A well-known data series is examined in some detail and several observable state-space models are suggested and compared favourably with the constrained observable model.     

Testing the Effects of Similarity on Risky Choice: Implications for Violations of Expected Utility

Our aim in this paper was to establish an empirical evaluation for similarity effects modeled by Rubinstein; Azipurua et al.; Leland; and Sileo. These tests are conducted through a sensitivity analysis of two well-known examples of expected utility (EU) independence violations. We found that subjective similarity reported by respondents was explained very well by objective measures suggested in the similarity literature. The empirical results of this analysis also show that: (1) the likelihood of selection for the riskier choice increases as the pair becomes more similar, (2) these choice patterns are consistent with well-known independence violations of expected utility, and (3) a significant proportion of individuals exhibit intransitive choice patterns predicted under similarity effects, but not allowed under generalized expected utility models for risky choice.     

Should mothers work? Age and attitudes in Germany,Italy and Sweden

Welfare states are constructed around values and political and economic preferences, creating social relationships between the genders. As women increase their labour market participation, new patterns of conflict within families and around policies arise. In this article, attitudes towards family and gender relations among men and women in different age groups in Germany, Italy and Sweden are analysed. The findings show that national policies seem to influence the level of attitudes among men and women. Despite the differences between the countries, an overall pattern emerges in which both age and gender influence people's understanding of women's paid work within and between the three countries. Finally, some implications of changing attitudes on welfare state policies for gender equality are discussed.     

Accountability and the Camera Perspective Bias in Videotaped Confessions

Prior research indicates that altering the perspective from which a videotaped confession is recorded influences assessments of the confession's voluntariness. The present study examined whether increasing decision makers' sense of accountability attenuates this biasing effect of camera perspective. Participants in a high-accountability (but not a low-accountability) condition were told that they would have to justify their judgments concerning the voluntary status of a video-taped confession to a trial judge. Although supplementary measures indicated that high-accountability participants processed information contained in the video-taped confession more carefully and thoroughly, the camera perspective bias persisted. This result adds to a growing body of work indicating that the criminal justice system needs to be seriously concerned with how it acquires and utilizes videotaped confession evidence.     

Property Loss Estimation for Wind and Earthquake Perils

This article describes the development of a generic loss assessment methodology, which is applicable to earthquake and windstorm perils worldwide. The latest information regarding hazard estimation is first integrated with the parameters that best describe the intensity of the action of both windstorms and earthquakes on building structures, for events with defined average return periods or recurrence intervals. The subsequent evaluation of building vulnerability (damageability) under the action of both earthquake and windstorm loadings utilizes information on damage and loss from past events, along with an assessment of the key building properties (including age and quality of design and construction), to assess information about the ability of buildings to withstand such loadings and hence to assign a building type to the particular risk or portfolio of risks. This predicted damage information is then translated into risk-specific mathematical vulnerability functions, which enable numerical evaluation of the probability of building damage arising at various defined levels. By assigning cost factors to the defined damage levels, the associated computation of total loss at a given level of hazard may be achieved. This developed methodology is universal in the sense that it may be applied successfully to buildings situated in a variety of earthquake and windstorm environments, ranging from very low to extreme levels of hazard. As a loss prediction tool, it enables accurate estimation of losses from potential scenario events linked to defined return periods and, hence, can greatly assist risk assessment and planning.