CAN THE PRESIDENT REALLY AFFECT ECONOMIC GROWTH? PRESIDENTIAL EFFORT AND THE POLITICAL BUSINESS CYCLE

Presidential elections are often seen as referendums on the health of the economy; however, little evidence exists on the president's ability to influence gross domestic product (GDP). This study examines the effect of the incentive to be reelected and the resulting increase in presidential effort on GDP growth. Growth is found to rise in reelection years for first‐term presidents after 1932 and to fall in election years before 1932, when reelection was uncommon, and for second‐term presidents generally. This effect is largest for high‐quality presidents—who probably have the highest return to effort—and is spread across multiple sectors of the economy. (JEL D78, D72, E32, J24)     

Assessing accuracy of a continuous screening test in the presence of verification bias

Summary.  In studies to assess the accuracy of a screening test, often definitive disease assessment is too invasive or expensive to be ascertained on all the study subjects. Although it may be more ethical or cost effective to ascertain the true disease status with a higher rate in study subjects where the screening test or additional information is suggestive of disease, estimates of accuracy can be biased in a study with such a design. This bias is known as verification bias. Verification bias correction methods that accommodate screening tests with binary or ordinal responses have been developed; however, no verification bias correction methods exist for tests with continuous results. We propose and compare imputation and reweighting bias-corrected estimators of true and false positive rates, receiver operating characteristic curves and area under the receiver operating characteristic curve for continuous tests. Distribution theory and simulation studies are used to compare the proposed estimators with respect to bias, relative efficiency and robustness to model misspecification. The bias correction estimators proposed are applied to data from a study of screening tests for neonatal hearing loss.     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

State Immigration Policies: The Role of State Compacts and Interest Groups on Immigration Legislation

US states are active in enacting immigration policies, which vary widely and have substantial impact on the lives of immigrants. Our understanding of what produces these divergent state laws remains limited. Qualitative research demonstrates the importance of a 2010 immigration compact, supported by a powerful religious organization, in shaping immigration policies in Utah, and the Utah Compact was held up as a model for other states. But is the experience of Utah applicable across other states? We test the effects of compacts and interest groups on immigration policy adoption across all 50 states between 2005 and 2013. Our findings suggest that compacts are actually associated with more restrictive immigration policy. Although states with compacts are more likely to pass inclusive immigration laws, these are counterbalanced by higher numbers of exclusive laws. Both religious and non‐religious interests groups are associated with policy, but they do not explain the effects of compacts.     

Time‐Space Distanciation: An Interdisciplinary Account of How Culture Shapes the Implicit and Explicit Psychology of Time and Space

The growing body of research on temporal and spatial experience lacks a comprehensive theoretical approach. Drawing on Giddens’ framework, we present time‐space distanciation (TSD) as a construct for theorizing the relations between culture, time, and space. TSD in a culture may be understood as the extent to which (1) time and space are abstracted as separate dimensions and (2) activities are extended and organized across time and space. After providing a historical account of its development, we outline a multi‐level conceptualization of TSD supported by research on cultural differences in the experience of time and space. We impact this conceptualization by examining two ethnographic case studies. We conclude by highlighting future research directions. TSD is an integrative, interdisciplinary, multi level construct with the potential to guide the burgeoning social science of time and space.     

Strong convergence rates of probabilistic integrators for ordinary differential equations

Probabilistic integration of a continuous dynamical system is a way of systematically introducing discretisation error, at scales no larger than errors introduced by standard numerical discretisation, in order to enable thorough exploration of possible responses of the system to inputs. It is thus a potentially useful approach in a number of applications such as forward uncertainty quantification, inverse problems, and data assimilation. We extend the convergence analysis of probabilistic integrators for deterministic ordinary differential equations, as proposed by Conrad et al. (Stat Comput 27(4):1065–1082, 2017. https://doi.org/10.1007/s11222-016-9671-0), to establish mean-square convergence in the uniform norm on discrete- or continuous-time solutions under relaxed regularity assumptions on the driving vector fields and their induced flows. Specifically, we show that randomised high-order integrators for globally Lipschitz flows and randomised Euler integrators for dissipative vector fields with polynomially bounded local Lipschitz constants all have the same mean-square convergence rate as their deterministic counterparts, provided that the variance of the integration noise is not of higher order than the corresponding deterministic integrator. These and similar results are proven for probabilistic integrators where the random perturbations may be state-dependent, non-Gaussian, or non-centred random variables.

     

Information Seeking about Global Climate Change among Adolescents: The Role of Risk Perceptions, Efficacy Beliefs and Parental Influences

Global climate change is likely to have significant impacts on public health. Effective communication is critical to informing public decision making and behavior to mitigate climate change. An effective method of audience segmentation, the risk perception attitude (RPA) framework has been previously tested with other health behaviors and classifies people into 4 groups on the basis of their perceptions of risk and beliefs about personal efficacy. The 4 groups - indifference (low risk, weak efficacy), proactive (low risk, strong efficacy), avoidance (high risk, weak efficacy), and responsive (high risk, strong efficacy) - are hypothesized to differ in their self-protective behaviors and in their motivations to seek information. In this paper, we extend the RPA framework in two ways. First, we use it at the household level to determine whether parental classifications into the 4 groups are associated with their teenage children's classification into the same 4 groups. Second, we predict adolescent information-seeking behaviors on the basis of their and their parents' membership in the 4 RPA groups. Results (N = 523 parent-adolescent pairs) indicated that parental membership in the 4 RPA groups was significantly associated with children's membership in the same 4 groups. Furthermore, the RPA framework was a significant predictor of adolescent information-seeking: those in the responsive and avoidance groups sought more information on climate change than the indifference group. Family communication on global warming was positively associated with adolescents' information-seeking. Implications for interventions are discussed.     

Classroom sex composition and first-grade school outcomes: The role of classroom behavior

Using nationally representative data from the Early Childhood Longitudinal Study-Kindergarten Cohort (N = 21,409; 10,452 girls and 10,957 boys; mean age = 7.24 years), the association between first-grade classroom sex composition (CSC), measured as the percentage of female students, and end of the year academic (reading, mathematics) and socio-emotional (externalizing problems, internalizing problems, self-control, interpersonal skills) outcomes was examined. Using multilevel modeling techniques and controlling for prior achievement levels, CSC was positively associated with children’s reading achievement at the end of first grade; students performed better in reading in classes with a higher percentage of female students. CSC was also associated with three of the socio-emotional outcomes; controlling for prior levels, students in classrooms with a higher percentage of girls had better self-control and interpersonal skills and fewer internalizing problems. Classroom behavior mediated the effects of CSC on reading achievement and the socio-emotional outcomes. Implications for the composition of first-grade classrooms are discussed.