Racial and Ethnic Disparities in Health Insurance Coverage: Dynamics of Gaining and Losing Coverage Over the Life-Course

Health insurance coverage varies substantially between racial and ethnic groups in the United States. Compared to non-Hispanic whites, African Americans and people of Hispanic origin had persistently lower insurance coverage rates at all ages. This article describes age- and group-specific dynamics of insurance gain and loss that contribute to inequalities found in traditional cross-sectional studies. It uses the longitudinal 2008 Panel of the Survey of Income and Program Participation (N = 114,345) to describe age-specific patterns of disparity prior to the Affordable Care Act (ACA). A formal decomposition on increment–decrement life tables of insurance gain and loss shows that coverage disparities are predominately driven by minority groups’ greater propensity to lose the insurance that they already have. Uninsured African Americans were faster to gain insurance compared to non-Hispanic whites, but their high rates of insurance loss more than negated this advantage. Disparities from greater rates of loss among minority groups emerge rapidly at the end of childhood and persist throughout adulthood. This is especially true for African Americans and Hispanics, and their relative disadvantages again heighten in their 40s and 50s.     

The Role of Cognitive and Noncognitive Skills in Overeducation

Using the National Education Longitudinal Study, this paper investigates the role of cognitive and noncognitive skills in overeducation. The results indicate that noncognitive skills as well as cognitive skills substitute for education but the substitutability decreases with an increasing level of attained education. Also, both types of skills are positively related to wages, and their returns depend on number of years of required education. In contrast, they play a modest role in job satisfaction and in receiving trainings. Finally, workers who have attained more education than their cognitive and noncognitive skills predict behave just like overeducated workers.     

Bayesian estimation procedure in multiprocess non-linear dynamic generalized model

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers arid is subject to abrupt changes in pattern. In this paper we extend the multiprocess dynamic generalized linear model to allow for a known non-linear parameter evolution and predictor functions. This is done by approximating the non-linear function by a linear function based on a first order Taylor series expansions. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.     

On the estimation of the true probability of a correct selection

For ranking and selection problems, the true probabiIity of a correct selection P(CS) is unknown even if a selection is made under the indifference-zone approach. Thus to estimate the true P(CS) some Bayes estimators and a bootstrap estimator are proposed for two normcal populations with common known variance. Also a bootstrap estimator and a bootstrap confidence interval are proposed for normal populations with common unknown variance. Some comparisons between proposed estimators and some other known estimators are made via Monte Carlo simulations.     

Differences in Perceptions of Child Sexual Abuse Based on Perpetrator Age and Respondent Gender

Child sexual abuse changes the lives of countless children. Child sexual abuse victims experience short and long term negative outcomes that affect their daily functioning. In this study, undergraduate students' perceptions of CSA were obtained using vignettes with an adult or child perpetrator and a general questionnaire. Results indicated participants receiving the child-on-child vignette were less likely to rate the vignette as abuse, saw the abuse as less severe, and assigned less blame to the perpetrator than participants reading the adult-on-child vignette. On a general questionnaire, male participants saw child-on-child abuse as less severe and more encouraged by society than did female participants. The information can be utilized by professionals in treatment planning and preventing revictimization at disclosure.     

Ethnicity or race,area characteristics,and sexual partner choice among American adolescents

The aim of this paper is to examine the association of individual demographic variables and area characteristics with the characteristics of sexual partners of American adolescents. Data for the study were drawn from the National Longitudinal Study of Adolescent Health. The data indicate that the community characteristics of ethnic or racial composition of the population and region were most strongly related to the ethnicity or race of the partner. Regional differences in the ethnicity or race of partners were significant even after controlling for the ethnic or racial composition of the community. Differences in partner's age, grade, neighborhood, and school or residence were not related to school and community characteristics. In addition, the individual characteristics of age, gender, and race or ethnicity were also related to ethnicity or race of partner.     

Factors Contributing to the Size of Nonprofit Sector: Tests of Government Failure,Interdependence, and Social Capital Theory

Based on the county-level longitudinal data in the USA, this study finds that racial diversity is positively associated with the nonprofit sector size. We further find that the share of children below poverty level is negatively associated with the size of nonprofit sectors. Our findings support the government failure theory. Random effects models also show that federal funding and local funding are positively associated with the size of nonprofit sectors, which confirms interdependence theory. Lastly, we do not find statistically significant relationship between social capital—measured by the number of associations—and the nonprofit sector size.     

A monte carlo study of variable selection and inferences in a two stage random coefficient linear regression model with unbalanced data

A growth curve analysis is often applied to estimate patterns of changes in a given characteristic of different individuals. It is also used to find out if the variations in the growth rates among individuals are due to effects of certain covariates. In this paper, a random coefficient linear regression model, as a special case of the growth curve analysis, is generalized to accommodate the situation where the set of influential covariates is not known a priori. Two different approaches for seleaing influential covariates (a weighted stepwise selection procedure and a modified version of Rao and Wu’s selection criterion) for the random slope coefficient of a linear regression model with unbalanced data are proposed. Performances of these methods are evaluated by means of Monte-Carlo simulation. In addition, several methods (Maximum Likelihood, Restricted Maximum Likelihood, Pseudo Maximum Likelihood and Method of Moments) for estimating the parameters of the selected model are compared Proposed variable selection schemes and estimators are appliedtotheactualindustrial problem which motivated this investigation.