An exploratory study of differences in self-esteem, kinship social support, and coping responses among African American ACOAs and Non-ACOAs

The author sought to identify differences in kinship social support, self-esteem, and coping responses between African American college students who identify themselves as adult children of alcoholics (ACOAs) and adult children of nonalcoholics (non-ACOAs) at 2 separate universities. The results indicate that there were no differences in levels of self-esteem, kinship social support, and coping responses among ACOAs and non-ACOAs. The author addresses implications for practice, policy, and research.     

Victoria Climbie Inquiry Data Corpus Project: Using the Delphi Method in Multidisciplinary Child Protection Research

This paper introduces the Delphi research method and analysesits usefulness in multi-agency research. In particular, theauthors examine how it was used in a project aimed at makingthe material generated by the Victoria Climbié Inquiryaccessible to child welfare/protection professionals throughorganizing the oral evidence in such a way that it can becomea web-based data corpus. The thematic analysis of research data-setsusually only reflects the theoretical and analytical ideas ofthe researchers; however, our research demonstrates that usingthe Delphi method incorporates thematic ideas both from researchersand from active professionals, thereby pooling expertise. Thuswe argue that the Delphi method represents a valuable tool inmultidisciplinary child welfare research as it embeds a breadthof expertise throughout the research process.     

The Occupational Cost of Being Illegal in the United States: Legal Status,Job Hazards,and Compensating Differentials

Considerable research and pervasive cultural narratives suggest that undocumented immigrant workers are concentrated in the most dangerous, hazardous, and otherwise unappealing jobs in U.S. labor markets. Yet, owing largely to data limitations, little empirical work has addressed this topic. Using data from the 2004 and 2008 panels of the Survey of Income and Program Participation, we impute legal status for Mexican and Central American immigrants and link their occupations to Bureau of Labor and Statistics (BLS) data on occupational fatalities and occupational hazard data from the U.S. Department of Labor to explore racial and legal status differentials on several specific measures of occupational risk. Results indicate that undocumented workers face heightened exposure to numerous dimensions of occupational hazard – including higher levels of physical strain, exposure to heights, and repetitive motions – but are less exposed than native workers to some of the potentially most dangerous environments. We also show that undocumented workers are rewarded less for employment in hazardous settings, receiving low or no compensating differential for working in jobs with high fatality, toxic materials, or exposure to heights. Overall, this study suggests that legal status plays an important role in determining exposure to job hazard and in structuring the wage returns to risky work.     

Do Interactions between Unemployment Insurance and Sickness Insurance Affect Transitions to Employment?

Previous research suggests that there are substantial interactions between the unemployment and sickness insurance in Sweden. Moral hazard arises in the interplay between these insurance systems, as by reporting sick an unemployed person can postpone unemployment insurance expiration and sometimes receive considerably higher benefits. I examine whether these interactions affect the transition rate to employment. To do this I use a reform that greatly reduced the incentives to transfer to sickness benefits. Although there is evidence that this reform effectively lowered the incidence of sick reports among the unemployed, I do not find that the reduced sick report rate in turn affected the transition rate to employment.     

Inference in Arch and Garch Models with Heavy–Tailed Errors

ARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be particularly heavy tailed. However, little is known about properties of ARCH or GARCH models in the heavy–tailed setting, and no methods are available for approximating the distributions of parameter estimators there. In this paper we show that, for heavy–tailed errors, the asymptotic distributions of quasi–maximum likelihood parameter estimators in ARCH and GARCH models are nonnormal, and are particularly difficult to estimate directly using standard parametric methods. Standard bootstrap methods also fail to produce consistent estimators. To overcome these problems we develop percentile–t, subsample bootstrap approximations to estimator distributions. Studentizing is employed to approximate scale, and the subsample bootstrap is used to estimate shape. The good performance of this approach is demonstrated both theoretically and numerically.