Is Speeding a Form of Gambling in Adolescents?

Speeding is a major contributor to motor vehicle accidents, which are the leading cause of death in adolescents. This study compares the extent to which adolescents with gambling behavior and substance use reported driving over the posted speed limits (“speeding”). Florida adolescents ages 13–17 (n = 1051) were surveyed, and asked about gambling activities, problems related to gambling, substance use, demographic questions, and speeding. Of the 562 respondents who were drivers, the gender distribution was 52.1% male and 47.9% female. Of those respondents, 76.9% were Caucasian, 6.8% were African American, 10.1% were Hispanic, and 6.1% were Native American/Asian/Other. Simple correlation analysis revealed that self-reported speeding is significantly related to gambling behavior and substance use. When a linear regression model was used, four factors showed the most significant influence on self-reported speeding: past year gambling tendency, age, trouble with the police due to drinking, and tranquilizer usage. Gambling behavior and high-risk speeding (driving ≥ 10 mph over speed limit) also were noted to be positively correlated. Our data indicate a relationship between risky driving, gambling, and other risk-taking behaviors in adolescents, and support the hypothesis that speeding may be a form of gambling behavior in this age group.     

Prevalence and Diagnostic Correlates of DSM-IV Pathological Gambling in Psychiatric Outpatients

Studies of the prevalence of pathological gambling (PG) in psychiatric and substance abusing patients suggest that the disorder is not rare. Most studies have been of substance abusers in treatment, and the rate of PG has been found to be several times higher than the rate found in community based epidemiological surveys. However, only one study has examined the prevalence of PG in a heterogeneous sample of patients, and this was a study of psychiatric inpatients. We are not aware of any prior study of the prevalence of PG in a psychiatric outpatient sample. In the present report from the Rhode Island Methods to Improve Diagnosis and Services (MIDAS) project we examined the current and lifetime prevalence of PG in 1,709 psychiatric outpatients interviewed with a semi-structured diagnostic interview that included a module to diagnose DSM-IV PG. Forty (2.3%) patients had a lifetime history of DSM-IV PG, all of whom had at least one other DSM-IV axis I disorder. Patients with PG had significantly more axis I disorders than patients without PG, and had significantly higher rates of bipolar disorder, social phobia, panic disorder with agoraphobia, alcohol use disorder, and other impulse control disorders. Possible reasons for the low prevalence of PG in our sample are discussed.     

Going the distance: serving the needs of older youth at scale

Few communities have developed successful strategies for attracting large numbers of older youth to their out-of-school-time programs. In addition to meeting the unique developmental and programmatic needs of this population, communities have struggled with the challenge of creating communitywide integrated approaches to service delivery and resource development. Communities wanting to build a system of supports for older youth must do so in the context of a communitywide strategy as opposed to fragmented, individually operating programs and services. A communitywide strategy creates greater opportunity for strategic mobilization of resources, greater funding leverage, evaluation and assessment consistency, and more powerful input into creating a public voice and public will for supporting and serving older youth as they make the transition to adulthood.     

Development of a Dose‐Response Model for SARS Coronavirus

In order to develop a dose‐response model for SARS coronavirus (SARS‐CoV), the pooled data sets for infection of transgenic mice susceptible to SARS‐CoV and infection of mice with murine hepatitis virus strain 1, which may be a clinically relevant model of SARS, were fit to beta‐Poisson and exponential models with the maximum likelihood method. The exponential model (k= 4.1 × l0²) could describe the dose‐response relationship of the pooled data sets. The beta‐Poisson model did not provide a statistically significant improvement in fit. With the exponential model, the infectivity of SARS‐CoV was calculated and compared with those of other coronaviruses. The does of SARS‐CoV corresponding to 10% and 50% responses (illness) were estimated at 43 and 280 PFU, respectively. Its estimated infectivity was comparable to that of HCoV‐229E, known as an agent of human common cold, and also similar to those of some animal coronaviruses belonging to the same genetic group. Moreover, the exponential model was applied to the analysis of the epidemiological data of SARS outbreak that occurred at an apartment complex in Hong Kong in 2003. The estimated dose of SARS‐CoV for apartment residents during the outbreak, which was back‐calculated from the reported number of cases, ranged from 16 to 160 PFU/person, depending on the floor. The exponential model developed here is the sole dose‐response model for SARS‐CoV at the present and would enable us to understand the possibility for reemergence of SARS.     

Identifying Technology Spillovers and Product Market Rivalry

The impact of R&D on growth through spillovers has been a major topic of economic research over the last thirty years. A central problem in the literature is that firm performance is affected by two countervailing “spillovers” : a positive effect from technology (knowledge) spillovers and a negative business stealing effects from product market rivals. We develop a general framework incorporating these two types of spillovers and implement this model using measures of a firm's position in technology space and productmarket space. Using panel data on U.S. firms, we show that technology spillovers quantitatively dominate, so that the gross social returns to R&D are at least twice as high as the private returns. We identify the causal effect of R&D spillovers by using changes in federal and state tax incentives for R&D. We also find that smaller firms generate lower social returns to R&D because they operate more in technological niches. Finally, we detail the desirable properties of an ideal spillover measure and how existing approaches, including our new Mahalanobis measure, compare to these criteria.     

How Plant Managers' Experiences and Attitudes Toward Sustainability Relate to Operational Performance

Managers are increasingly faced with pressure to think not just about profits, but also about their organization's environmental and social performance. This research provides a first examination of operational managers' experiences with and attitudes about employee well‐being and environmental issues, how these factors impact employee well‐being and environmental performance, and how the three performance measures interrelate. We use violations of Occupational Safety and Health Administration regulations and Toxic Release Inventory reports of emissions as proxies for employee well‐being and environmental performance. Our findings suggest that operational managers do not (yet) think in sustainability terms. However, employee well‐being and environmental performance do interact in a significant way with operational performance. Hence, operational managers would benefit from a more complete understanding of the relationships among the elements of the triple bottom line.     

Mixture of Bivariate Exponential Distributions

The exponential distribution is one of the most used type of distribution because of its importance in many lifetime applications and its properties. So is its bivariate form. Simply used, there can be limitations specially for the heterogeneous type population. Its mixture form adds a lot of characters and desirable properties. We propose a mixture of bivariate exponential distribution, study properties of the associated parameters and predict the elements of the mixture. We include the presence of covariate information through a linear relationship, capturing the now famous idea by Marshall and Olkin.     

Tolerance regions for random vectors and best linear predictors

In the case that vectors X and Y have a joint multivariate normal distribution, tolerance regions are found for the best linear predictor of Y using X if samples are used to estimate the regression coeffierante. Tolerance regions are also found for Y. In addition, simultaneous tolerance intervals for all linear functions of Y or of the best linear predictor of Y using X are found.     

How Do Behavioral Assumptions Affect Structural Inference? Evidence From a Laboratory Experiment

We develop a model of labor force status (federal employment, nonfederal employment, unemployment, and out of the labor force) that depends on human capital variables, local labor market conditions, and personal characteristics. According to the estimated model for white non-Hispanic males and females a substantial difference exists between blacks and white non-Hispanics even after correction for the control variables. However, the control variables explain almost all of the difference between Hispanics and white non-Hispanics.