Private Ownership versus Institutional Ownership: Exclusion Pricing of Initial Public Offerings

The authors examine the literature with respect to the pricing of initial public offerings and focus upon the relationship of pricing to the structure and conduct of the investment banking industry. Using a data base of all share offerings undertaken in the United States over a two and a half year period, the authors find that there is considerable evidence for the proposition that large, prestigious, and well capitalised investment banks tend to price their share offerings at a higher absolute level than those not meeting such characteristics. Using classical statistical methods, the authors find that the pricing strategy of investment banks is connected to their affiliation with investment funds and unit trusts. The motives for such pricing strategies, the authors argue, lie with the affiliation of investment banks with investment funds, suggesting that the pricing of new share offerings may be a means of excluding retail investors from participating in the strong returns such issues exhibit. The authors raise legal and regulatory implications of their findings in the context of the general consolidation observed within the investment banking industry. This revised version was published online in July 2006 with corrections to the Cover Date.     

Incorporating Time‐Dose‐Response into Legionella Outbreak Models

A novel method was used to incorporate in vivo host–pathogen dynamics into a new robust outbreak model for legionellosis. Dose‐response and time‐dose‐response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best‐fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best‐fit TDR from the murine dosing study was the beta‐Poisson with exponential‐reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non‐time‐dependent distributions to explore the performance of the time‐dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host‐pathogen system.     

The Impact of Job Quality on Wellbeing: Evidence from Kyrgyzstan

Social Indicators Research - Income and hours worked are insufficient to measure job quality yet these domains dominate literature aimed at understanding its relationship with wellbeing. More so,...     

The Women’s Empowerment in Livestock Index

Social Indicators Research - The empowerment of women in the livestock sector is fundamental to achieve gender equality. It also is instrumental for increased household productivity and improved...     

Utilization of Mixed Distributions in the Calculation of Polarization: The Case of Spain

Social Indicators Research - This study analyzes the evolution of salary polarization in the Spanish labor market. The paper proposes the use of mixed distributions to examine the consequences of...     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

Linking Decision Theory and Quantitative Microbial Risk Assessment: Tradeoffs Between Compliance and Efficacy for Waterborne Disease Interventions

Achieving health gains from the U.N. Sustainable Development Goals of universal coverage for water and sanitation will require interventions that can be widely adopted and maintained. Effectiveness—how an intervention performs based on actual use—as opposed to efficacy will therefore be central to evaluations of new and existing interventions. Incomplete compliance—when people do not always use the intervention and are therefore exposed to contamination—is thought to be responsible for the lower‐than‐expected risk reductions observed from water, sanitation, and hygiene interventions based on their efficacy at removing pathogens. We explicitly incorporated decision theory into a quantitative microbial risk assessment model. Specifically, we assume that the usability of household water treatment (HWT) devices (filters and chlorine) decreases as they become more efficacious due to issues such as taste or flow rates. Simulations were run to examine the tradeoff between device efficacy and usability. For most situations, HWT interventions that trade lower efficacy (i.e., remove less pathogens) for higher compliance (i.e., better usability) contribute substantial reductions in diarrheal disease risk compared to devices meeting current World Health Organization efficacy guidelines. Recommendations that take into account both the behavioral and microbiological properties of treatment devices are likely to be more effective at reducing the burden of diarrheal disease than current standards that only consider efficacy.