Kevin F. Forbes

Volunteerism: The Influences of Social, Religious, and Human Capital

This article uses data from the 2006 Social Capital Community Survey to examine the impact of social capital, religious capital, human capital, and attitudes on volunteerism. Five alternative struc...This article uses data from the 2006 Social Capital Community Survey to examine the impact of social capital, religious capital, human capital, and attitudes on volunteerism. Five alternative structural models are estimated. Tests reveal unambiguously the inferiority of the Tobit model and point to a double-hurdle model with independent errors as the best alternative. Major findings are that more diversity in friendships and more education increase the likelihood of volunteering, greater intensity of religious belief increases the level of volunteerism, and more informal social networking and formal group involvement along with greater religious participation increase both the likelihood and level of volunteering. Study results suggest strongly that the nonprofit voluntary sector has a vested interest in promoting policies that expand educational opportunities and foster civil engagement, social interaction, and religious participation.

Increased long-term mortality in patients with cardiovascular implantable electronic device infections.

Device infection is associated with increased mortality in patients receiving cardiovascular implantable electronic device (CIED) therapy. However, long‐term mortality associated with CIED infections has not been systematically analyzed in larger studies. This study sought to determine the long‐term mortality associated with CIED infection in a large cohort of Medicare beneficiaries.

Bone fracture nonunion rate decreases with increasing age: A prospective inception cohort study

BACKGROUND Fracture nonunion risk is related to severity of injury and type of treatment, yet fracture healing is not fully explained by these factors alone. We hypothesize that patient demographic factors assessable by the clinician at fracture presentation can predict nonunion. METHODS A prospective cohort study design was used to examine ~2.5 million Medicare patients nationwide. Patients making fracture claims in the 5% Medicare Standard Analytic Files in 2011 were analyzed; continuous enrollment for 12months after fracture was required to capture the ICD-9-CM nonunion diagnosis code (733.82) or any procedure codes for nonunion repair. A stepwise regression analysis was used which dropped variables from analysis if they did not contribute sufficient explanatory power. In-sample predictive accuracy was assessed using a receiver operating characteristic (ROC) curve approach, and an out-of-sample comparison was drawn from the 2012 Medicare 5% SAF files. RESULTS Overall, 47,437 Medicare patients had 56,492 fractures and 2.5% of fractures were nonunion. Patients with healed fracture (age 75.0±12.7SD) were older (p<0.0001) than patients with nonunion (age 69.2±13.4SD). The death rate among all Medicare beneficiaries was 4.8% per year, but fracture patients had an age- and sex-adjusted death rate of 11.0% (p<0.0001). Patients with fracture in 14 of 18 bones were significantly more likely to die within one year of fracture (p<0.0001). Stepwise regression yielded a predictive nonunion model with 26 significant explanatory variables (all, p≤0.003). Strength of this model was assessed using an area under the curve (AUC) calculation, and out-of-sample AUC=0.710. CONCLUSIONS A logistic model predicted nonunion with reasonable accuracy (AUC=0.725). Within the Medicare population, nonunion patients were younger than patients who healed normally. Fracture was associated with increased risk of death within 1year of fracture (p<0.0001) in 14 different bones, confirming that geriatric fracture is a major public health issue. Comorbidities associated with increased risk of nonunion include past or current smoking, alcoholism, obesity or morbid obesity, osteoarthritis, rheumatoid arthritis, type II diabetes, and/or open fracture (all, multivariate p<0.001). Nonunion prediction requires knowledge of 26 patient variables but predictive accuracy is currently comparable to the Framingham cardiovascular risk prediction.

The impacts of religion, political ideology, and social capital on religious and secular giving: evidence from the 2006 Social Capital Community Survey

Using a double hurdle model and data from the 2006 Social Capital Community Survey (SCCS2006) we examine religious and secular giving, focusing on the impacts of religion, political ideology and social capital. Our main results indicate that greater participation in religious activities positively impacts religious and secular giving, the intensity of religious belief increases religious giving at the expense of secular giving, religious giving by very conservative individuals is higher than for other ideological groups, and measures of social capital are very important in raising the level and likelihood of philanthropic giving.

An Assessment of Alternative Structural Models of Philanthropic Behavior

In this article, the question of whether differences in four structural models of charitable behavior make any difference to the findings regarding the major determinants of philanthropic contributions is addressed. Using data from Independent Sector’s Giving and Volunteering Survey, giving and volunteering equations using the standard Tobit model, the “Heckit” model, the Cragg model with uncorrelated errors, and the Cragg model with correlated errors are estimated. Results indicate that the generalized two-stage approaches are far superior to the standard Tobit model for both monetary donations and volunteer time. For monetary giving, parameter estimates of the second-stage contribution equations are similar across the three alternative two-stage methods and there is no evidence of correlation between the first- and second-stage error terms. Second-stage estimates for volunteering from the Heckit and Cragg models with correlated errors are also similar and offer compelling evidence of correlated error terms.

Technology and the exploratory success rate in the U.S. onshore

Abstract According to the Energy Information Administration, the exploratory success rate in the U.S. onshore increased to 34.2% in 1998 from 27.5% in 1978. It is tempting to conclude that this increase can be attributed to the many advances in seismic and drilling technology that have occurred over the same period. However, such a conclusion may be premature. While the average success rate for the industry is substantially higher, the success rate for a group of large technologically progressive producers including Exxon Mobil, Shell, Chevron, and Texaco was only marginally higher in 1998 than in 1978. Building on the work of Forbes and Zampelli (2000) that examined the impact of technology on the offshore success rate, this paper develops an econometric model that attempts to disentangle and quantify the effects of the major factors hypothesized to affect the onshore exploratory success rate. The analysis relies on a panel of company level data from the Energy Information Administration’s Financial Reporting System (FRS).

Pricing of related products by a multiproduct monopolist

This paper examines the pricing behavior of a multiproduct monopolist (MPM). For a firm selling two products, the profit maximizing price of a particular item is found to depend upon its marginal cost, the own and cross-price elasticities and the budget shares of both products. Conditions are identified under which the price charged by a MPM will be greater than, less than, and equal to the price charged by an otherwise identical single product monopolist as well as the circumstances under which it is profitable for a MPM to utilize one product as a loss leader.

Market structure and cooperative advertising

Abstract Cooperative advertising is advertising whose demand-stimulating effects do not accrue exclusively to the firm that makes the advertising expenditure. This paper examines the effect of market structure on the equilibrium level of cooperative advertising.

The Social and Economic Impacts of Moderate and Severe Space Weather

Abstract Although space weather is known to interrupt and damage technologies critical to modern society, there have been a limited number of studies on the social and economic impacts. A better understanding of the impacts is challenging but essential for enhancing our preparedness and strategically reducing our risks. This chapter provides a brief overview of an ongoing effort to advance research on this topic by identifying, describing, and quantifying the social and economic impacts of space weather in the United States. The study examines impacts resulting from both moderate and severe space weather events across four technological sectors: Electric power, commercial aviation, satellites, and Global Navigation Satellite System (GNSS) users. It considers the full range of impacts identified during an extensive literature review and from additional conversations with more than 30 sector stakeholders of diverse expertise from engineering to operations to end users. We organize and discuss our findings in terms of five broad but interrelated impact categories including defensive investments, mitigating actions, asset damages, service interruptions, and health effects. Our team is currently developing simple, tractable models in close collaboration with stakeholders to quantify impacts for each sector that are apt to be largest and also most plausible during moderate and more severe space weather scenarios. The systematic approach we develop can be easily modified as new scientific knowledge is acquired and as our technological infrastructure changes; it can also be readily applied to other impacted sectors (e.g., railway and pipelines). We hope that our systematic exploration of the social and economic impacts stimulates important discussions and provides a foundation for the future work that is necessary and critical for designing technologies and implementing policies that can effectively reduce our known and evolving vulnerabilities to this natural hazard.

The accuracy of wind energy forecasts and prospects for improvement

Wind energy forecast errors, while modest when weighted by wind energy capacity, are quite large relative to the average level of actual wind energy generation. For example, while the capacity weighted root mean squared error (CWRMSE) of day-ahead wind energy forecasts for the 50Hertz control area in Germany over the period 1 January 2015 through 31 December 2015 is just 4.5 percent, the energy-weighted root-mean-squared-error (EWRMSE) is almost five times as large at 21.67 percent. Our analysis also indicates that the errors in 50Hertzs wind energy forecasts are statistically related to forecasted weather conditions. Based on this finding and the time-series attributes of the forecast errors, an ARCH/ARMAX model was formulated to predict wind energy generation. The models forecasting accuracy was evaluated using out-of-sample data over the period 1 January 2015 through 31 December 2015. The out-of-sample period-ahead predictions have a EWRMSE of about 2.93 percent and CWRMSE of about 0.60 percent.