Explaining the Gender Wealth Gap

To assess and explain the United States’ gender wealth gap, we use the Wisconsin Longitudinal Study to examine wealth accumulated by a single cohort over 50 years by gender, by marital status, and limited to the respondents who are their family’s best financial reporters. We find large gender wealth gaps between currently married men and women, and between never-married men and women. The never-married accumulate less wealth than the currently married, and there is a marital disruption cost to wealth accumulation. The status-attainment model shows the most power in explaining gender wealth gaps between these groups explaining about one-third to one-half of the gap, followed by the human-capital explanation. In other words, a lifetime of lower earnings for women translates into greatly reduced wealth accumulation. After controlling for the full model, we find that a gender wealth gap remains between married men and women that we speculate may be related to gender differences in investment strategies and selection effects.     

Treatment Utilization Among Metropolitan and Nonmetropolitan Participants of Corrections-Based Substance Abuse Programs Reentering the Community

ABSTRACT

This study examines differences in drug use and recidivism outcomes at 12 months postrelease from jail or prison as a function of geographic location and treatment utilization. Participants (N= 700) were previously enrolled in drug treatment during the last 6 to 9 months of their incarceration and were released to the community on either parole or serve-out. Findings from this study indicated that offenders returning to metropolitan areas reported more use of community treatment services following release than offenders in nonmetropolitan areas. Although offenders in both metropolitan and nonmetropolitan areas who reported community treatment following release were less likely to be reincarcerated, community treatment utilization was not a significant correlate of relapse. Findings are discussed, and future directions include favorable outcomes when community treatment is utilized.     

Recuerdos de Nuestro Pasado

Following is an interview with Erin Segal, group worker and coauthor of Recuerdos de Nuestro Pasado (Memories of our Past), a multivocal memoir about growing up in El Salvador and growing old in Washington DC, which emerged from a storytelling group at Mary’s Center’s Bernice Fonteneau Senior Wellness Center. The group members and coauthors are Angela Celaya, Sergio Guzmán, Jose Lovos, and Gloria Revelo. Using alternating English-language stanzas, the book expresses the life stories of four senior citizens and includes the images of their photographs and objects as well as illustrations by the book designer Julie Cho. Segal and Cho are cofounders of Thick Press. All profit from this book will go to St. Jude Children’s Hospital.     

Management of Energy Technology for Sustainability: How to Fund Energy Technology Research and Development

Operations management methods have been applied profitably to a wide range of technology portfolio management problems, but have been slow to be adopted by governments and policy makers. We develop a framework that allows us to apply such techniques to a large and important public policy problem: energy technology R&D portfolio management under climate change. We apply a multi‐model approach, implementing probabilistic data derived from expert elicitations into a novel stochastic programming version of a dynamic integrated assessment model. We note that while the unifying framework we present can be applied to a range of models and data sets, the specific results depend on the data and assumptions used and therefore may not be generalizable. Nevertheless, the results are suggestive, and we find that the optimal technology portfolio for the set of projects considered is fairly robust to different specifications of climate uncertainty, to different policy environments, and to assumptions about the opportunity cost of investing. We also conclude that policy makers would do better to over‐invest in R&D rather than under‐invest. Finally, we show that R&D can play different roles in different types of policy environments, sometimes leading primarily to cost reduction, other times leading to better environmental outcomes.     

New roles for families in child welfare: Strategies for expanding family involvement beyond the case level

This paper examines the strategies and approaches child welfare agencies used to integrate meaningful family involvement into their service delivery systems, under the Children's Bureau Improving Child Welfare Outcomes Through Systems of Care demonstration initiative. Through a series of retrospective interviews with child welfare agency staff, systems of care project staff, family members actively involved in implementing the systems of care initiative, and local program evaluators, researchers investigated the family involvement planning and capacity building activities of child welfare agencies during the initiative's implementation. Findings indicate that child welfare agencies' capacity building efforts primarily centered on human resource development, focusing on three areas: program staffing, family engagement, and agency buy-in. These findings illustrate the importance of developing the capacity of child welfare agency staff and family members before fully implementing family involvement programs and activities. Although more research is needed to document the impact of family involvement, the lessons learned from these grant communities' experiences provide critical information and can inform development of policies and practices to help child welfare and other child- and family-serving systems promote and implement meaningful and sustainable family involvement.     

Illegitimate tasks are not created equal: Examining the effects of attributions on unreasonable and unnecessary tasks

ABSTRACT

Illegitimate tasks are tasks that violate norms for what the employee should do as part of the job, and have been found to harm employees’ well-being. The current research uses a mixed methods design to examine the role of attributions on the two types of illegitimate tasks: unreasonable and unnecessary tasks. A sample of 432 engineers described a specific illegitimate task that was assigned to them, the attributions they made and their response. They also completed a quantitative questionnaire. Results from both the qualitative (event level) and quantitative (person level) portions of our study portray differences in the attributions made to unreasonable and unnecessary tasks, as well as differential negative effects on employees’ emotions. In addition, hostile attribution bias was found to moderate the relationship between illegitimate tasks and negative emotions, particularly for unreasonable tasks. This supports the theoretical basis for illegitimate tasks because unreasonable tasks pose a potentially greater risk to the employee’s self-worth than unnecessary tasks that are more often assigned at random.     

On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value

Abstract

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko that recommends the “D-value,” which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states “The D-value has a clear interpretation as the proportion of patients who get worse after the treatment” with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.     

Minority Report: Prevalence of Fear of Violent and Property Crimes Among a Diverse College Sample

Social Indicators Research - Fear is associated with several adverse health behaviors and outcomes. Fear of crime is associated with less exercise and outdoor activity, worse self-rated health...     

Relationships Between the Great Recession and Widening Maternal and Child Health Disparities: Findings from Washington and Florida

The purpose of this study was to explore relationships between the Great Recession in the United States and maternal and child health (MCH) disparities in