The origins and consequences of public trust in government: a time series analysis

The study of citizens' trust in the national government has been primarily individual-level, cross-sectional analysis. In the current research, we develop a quarterly time series measure of trust in the U.S. national government from 1980 to 1997 and conduct the first multivariate time series examination of public trust in government. We find that negative perceptions of the economy, scandals associated with Congress, and increasing public concern about crime each lead to declining public trust in government. Declining trust in government in turn leads to less positive evaluations of Congress and reduced support for government action to address a range of domestic policy concerns. These results provide new evidence of the influence of public concern about crime and the centrality of Congress in understanding public evaluations of the national government and new evidence of how declining levels of trust in government may influence elections and domestic policy making.     

The identification of the social sex-role stereotypes

The purpose of this study was to identify the social sex-role stereotypes that exist in the nation. The hypothesis for this study was that the stereotypes for femininity and masculinity are dimorphous. To determine the extent of dimorphism, four social sex-role stereotypes were examined: masculine men, feminine women, feminine men, and masculine women. Three hundred female and male respondents from all regions of the nation, who varied in age, ethnicity, education, sexual orientation, and social sex-role, completed a questionnaire by listing the characteristics people expected a masculine man, a feminine woman, a feminine man, and a masculine woman to have. The results supported the hypothesis that the stereotypes for femininity and masculinity are dimorphous.     

“Every Shut Eye, Ain’t Sleep”: The Role of Racism-Related Vigilance in Racial/Ethnic Disparities in Sleep Difficulty

Although racial/ethnic disparities in health have been well characterized in biomedical, public health, and social science research, the determinants of these disparities are still not well understood. Chronic psychosocial stress related specifically to the American experience of institutional and interpersonal racial discrimination may be an important determinant of these disparities, as a growing literature in separate scientific disciplines documents the adverse health effects of stress and the greater levels of stress experienced by non-white compared to white Americans. However, the empirical literature on the importance of stress for health and health disparities specifically due to racial discrimination, using population-representative data, is still small and mixed. In this paper, we explore the association between a novel measure of racially salient chronic stress—“racism-related vigilance”—and sleep difficulty. We found that, compared to the white adults in our sample, black (but not Hispanic) adults reported greater levels of vigilance. This vigilance was positively associated with sleep difficulty to similar degrees for all racial/ethnic groups in our sample (white, black, Hispanic). Black adults reported greater levels of sleep difficulty compared to white adults. This disparity was slightly attenuated after adjustment for education and income. However, this disparity was completely attenuated after adjustment for racism-related vigilance. We found similar patterns of results for Hispanic compared to white adults, however, the disparities in sleep difficulty were smaller and not statistically significant. Because of the importance of sleep quality to health, our results suggest that the anticipation of and perseveration about racial discrimination is an important determinant of racial disparities in health.     

Geometric programming for optimal allocation of integrated samples in quality control

We apply geometric programming, developed by Duffin, Peterson Zener (1967), to the optimal allocation of stratified samples. As an introduction, we show how geometric programming is used to allocate samples according to Neyman (1934), using the data of Cornell (1947) and following the exposition of Cochran (1953).

Then we use geometric programming to allocate an integrated sample introduced by Schwartz (1978) for more efficient sampling of three U. S. Federal welfare quality control systems, Aid to Families with Dependent Children, Food Stamps and Medicaid.

We develop methods for setting up the allocation problem, interpreting it as a geometric programming primal problem, transforming it to the corresponding dual problem, solving that, and finding the sample sizes required in the allocation problem. We show that the integrated sample saves sampling costs.