Preventing HIV in injection drug users: exploring the tradeoffs between interventions

Injection drug users (IDUs) transmit the human immunodeficiency virus (HIV) via both needle sharing and sex. Available interventions for this population have varying costs and effectiveness and focus on different risk behaviors. In this analysis, we look at two interventions. One is inexpensive, broad-based and provides modest risk reductions (street outreach (SO)); the other is narrowly focused, expensive and relatively effective (methadone maintenance). This analysis explores the effects of population risk behavior, intervention effectiveness, intervention costs, and decision constraints when allocating funds between these two interventions to maximize effectiveness. We develop a model of the spread of HIV, dividing IDUs into susceptibles (uninfected) and infectives, and separately portraying sex and injection risk. We simulate the epidemic in New York City for time periods from the mid-1980s to the early 1990s, and incorporate the behavioral effects of two interventions performed singly or in combination to find the allocation that maximizes the number of infections averted in the IDUs and their noninjecting sex partners, assuming interventions have increasing marginal costs. We find that the optimal allocation nearly always involves spending the maximum allowable amount on SO. This result is largely insensitive to variations in risk parameters, intervention efficacy, or cost. The model's structure, however, makes clear that many factors contribute to this insensitivity, namely the scope of the interventions, the dual drug/sex nature of HIV risk in the population, the asymmetry of sexual risk for men and women, and the potential benefits to nonIDUs.     

Are generalized call-spreads efficient?

In order to explain coexistence of a deductible for low values of the loss and an upper limit for high values of the loss in insurance contracts, we consider the exchange of risk between two rank dependent expected utility maximizers. It is shown that if the insurer (insured) takes more into account the lowest outcomes – hence maximal losses – than the insured (insurer), then the optimal contract has an upper limit (includes a deductible for high values of the loss). If furthermore, the insured (insurer) neglects the highest outcomes while the insurer (insured) does not, the optimal contract includes a deductible (full insurance) for low values of the loss.     

Ein verallgemeinerter Geburten- und Todesprozeß zur Erklärung der Entwicklung des deutschen Volkseinkommens, 1851–1939

Ohne Zusammenfassung     

The occurrence of outliers in the explanatory variable considered in an errors-in-variables framework

Summary The problem of estimating the slope of a linear relationship between two jointly normally distributed random variables is considered when outliers may occur in the explanatory variable. It will be studied as a special case of an errors-in-variables problem where the explanatory variable is measured which a nonnormally distributed error. In this more general model and under certain conditions a consistent estimator can be given with a normal limiting distribution. Applications to cases of outliers in the explanatory variable will be presented.     

Unlocking the potential of advanced manufacturing technologies

This research examines whether investments in advanced manufacturing technologies (AMTs) such as flexible manufacturing systems (FMS), computer aided design (CAD), computer aided manufacturing (CAM), robotics, etc., are more likely to lead to improved performance if they are supported by improvements in the manufacturing infrastructure of the company. This question is evaluated using data gathered from 202 manufacturing plants chosen from industries generally considered to have relatively high investments in technology.Multiple item scales are developed and adapted from sources in the literature to measure investments in technology, infrastructure, and the performance of the plant. Evidence supporting the reliability and validity of these scales is provided. Hierarchical regression is used to analyze the relationship between technology, infrastructure, and performance. The results suggest that there is an important interaction between the adoption of advanced manufacturing technologies and investments in infrastructure. Firms that invest in both AMTs and infrastructure perform better than firms which only invest in one or the other. Separate analyses on sub-samples of firms with the highest and lowest investments in AMTs show that infrastructural investments have a stronger relationship with performance in the high investment group. Thus, the data indicate that infrastructural investments provide a key to unlocking the potential of advanced manufacturing technologies.     

Legalization of drugs: Effects on the workplace

In the last few years bipartisan support for legalizing drugs has grown to a surprising level. Because of the lack of involvement of the business community in this debate, we surveyed the human resource managers of 127 firms about their perceptions of the effects of legalization. Their responses were uniformly negative, with expectations that drug use, absenteeism, worksite crime, and liability costs would increase, while the quality and quantity of work would decline. They also forecast increases in drug testing, performance monitoring, and education and rehabilitation programs as a result.     

The Impact of Non-Normality and Estimation Methods in SEM on Satisfaction Research in Marketing

This paper discusses consequences of violating the normal distribution assumption imbedded in Structural Equation Modeling (SEM). Based on real data from a large sample customer satisfaction survey we follow the procedures as suggested in leading textbooks. We document consequences of this practice and discuss its impact on decision making in marketing.     

Pseudo empirical likelihood method in the presence of missing data

In this paper, we propose an estimator for the population mean when some observations on the study and auxiliary variables are missing from the sample. The proposed estimator is valid for any unequal probability sampling design, and is based upon the pseudo empirical likelihood method. The proposed estimator is compared with other estimators in a simulation study.