All Politics is Not Local: National Forces in State Abortion Initiatives

Objective. Although research suggests that national forces can play a role in local and state elections, most of this work has only recently begun to examine the potential role of national forces in state or local ballot initiative or referenda elections. Methods. Our research addresses this gap in the literature by exploring the influence of national forces, such as the timing of elections, Supreme Court rulings, the activities of interest groups, and public opinion, on state direct legislation elections. We incorporate national forces into the morality politics framework and derive specific hypotheses. We then test these hypotheses by conducting a multivariate analysis of county–level voting patterns across 16 abortion–related direct legislation elections. Results. Our results confirm most of the hypotheses derived from the morality politics framework, including those concerning the role of national forces. Conclusions. Voting patterns on abortion tend to be influenced by the presence of presidential elections, Supreme Court rulings, interest–group activity, public opinion, partisanship, college education, and conservative religious forces. We discuss the implications of our findings for research on elections, abortion policy, and morality politics.     

HEALTH NEEDS OF MIGRANT VIETNAMESE WOMEN IN SOUTH‐WEST BRISBANE: AN EXPLORATORY STUDY

This study was conducted in the southwest area of Brisbane, Australia, and is designed to explore and assess the health needs of Vietnamese migrant women. The needs of this group are becoming increasingly urgent due to ageing of the original immigrant refugee community and decreased capacity for support from their children and families. The study used a qualitative research strategy involving focus groups and in‐depth interviews with Vietnamese women between 18–65 years and interviews with Vietnamese health care providers. It shows that the women have had to deal with culture shock, low self‐esteem, lack of friends and relatives, unrecognized professional skills and most importantly in health terms, low socioeconomic status. Significant barriers to access health services and to improved health and well‐being were also identified, such as language difficulties, transportation, time, and knowledge about health education. Recognition of specific requirements of sub‐populations as well as broader socio‐economic and cultural determinants of health should be a guide to more effective planning and implementation of health promotion strategies. The changing needs, over time, of these sub‐populations should also be recognized.     

Sociotropic Concerns and Support for Counterterrorism Policies*

Objective. An individual's personal experiences and perception of the collective experience are often linked to political attitudes, especially those concerning the national economy. In this article, we examine whether personal concern about terrorism and perceptions of public concern about terrorism affect attitudes about counterterrorism policies. In addition, we evaluate which factor is the strongest predictor across several counterterrorism policies. Method. We analyze individual‐level survey data collected during the fall of 2001. Results. Our results indicate that perceptions of public concerns are the strongest and most consistent predictor of policy attitudes about terrorism. Conclusion. The implications for theory about perceptions of public opinion and the competing role of personal interest and sociotropic concern are discussed.     

We are not walking wave functions. A response to “Quantum Mind and Social Science” by Alexander Wendt

Alexander Wendt raises many interesting questions in this book, but to get to the answers he wants, he relies on a misunderstanding of the nature of the quantum states of macroscopic objects.     

A cross-cultural comparison of death anxiety: a brief note

Data are reported from samples of undergraduates around the world who have been administered Templer's Death Anxiety Scale. Data from 24 American samples and from 16 nations were identified. Strong sex differences were found and an association between the scores of men and women.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Cross‐Section Regression with Common Shocks

This paper considers regression models for cross‐section data that exhibit cross‐section dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context. The results of the paper allow for any form of cross‐section dependence and heterogeneity across population units. The probability limits of the LS estimators are determined, and necessary and sufficient conditions are given for consistency. The asymptotic distributions of the estimators are found to be mixed normal after recentering and scaling. The t, Wald, and F statistics are found to have asymptotic standard normal, χ², and scaled χ² distributions, respectively, under the null hypothesis when the conditions required for consistency of the parameter under test hold. However, the absolute values of t, Wald, and F statistics are found to diverge to infinity under the null hypothesis when these conditions fail. Confidence intervals exhibit similarly dichotomous behavior. Hence, common shocks are found to be innocuous in some circumstances, but quite problematic in others. Models with factor structures for errors and regressors are considered. Using the general results, conditions are determined under which consistency of the LS estimators holds and fails in models with factor structures. The results are extended to cover heterogeneous and functional factor structures in which common factors have different impacts on different population units.     

A Bias–Reduced Log–Periodogram Regression Estimator for the Long–Memory Parameter

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^d_r, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^d_r, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^d_r. These results show that the bias of ^d_r goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^d_r attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.     

Dealing with noncompliance and missing outcomes in a randomized trial using Bayesian technology: Prevention of perinatal sepsis clinical trial,Soweto, South Africa

The success of interventions designed to address important issues in social and medical science is best addressed by randomized experiments. With human beings there are often complications, however, such as noncompliance and missing data. Such complications are often addressed by statistically invalid methods of analysis, in particular, intention-to-treat and per-protocol analyses. Here we address these two complications using a statistically valid approach based on principal stratification with a fully Bayesian analysis. This analysis is applied to a randomized trial of a potentially important intervention designed to reduce the transmission of bacterial colonization between mothers and their infants through vaginal delivery in South Africa: the Prevention of Perinatal Sepsis (PoPs).