The Limits of Black Politics in the Post–Civil Rights Era

Pluralist theorists have long contended that to exercise power and influence in the American political system, blacks should come together like any other organized interest group, form coalitions with other like-minded people (white liberals), and advocate for policy reforms. Beginning during the “Black Power” era, the goal of putting black faces in high places to help address black concerns became a driving force in black politics. The deteriorating social and economic status of many in the black community, however, has not been prevented by either the political incorporation of more than 11,000 black elected officials or even the election of the nation’s first black president. This article will show how the inherent limitations of the pluralist interest-group strategy and a growing white backlash fueled by an awareness of changing demographics in the country has spawned an economic and political crisis in black America.     

Changes in anti-fat weight bias in women after exposure to thin and plus-sized models

We examined whether exposing women to female models with different body sizes lead to changes in weight-based attitudes and whether evaluation focus altered the effects. Female college students (N = 214) were exposed to 22 images of either thin or overweight models. Participants rated models on either appearance or non-appearance characteristics and completed measures of anti-fat attitudes, thin ideal internalization, and demographics. Participants rated thin and overweight models equally attractive and sexy. Viewing overweight models showed a significant reduction in anti-fat attitudes. There were no effects for thin models or evaluation ratings. Discussion focuses on the implications of positive images of overweight individuals on social attitudes.     

Elementary School-age Children's Attitudes Toward Children in Same-Sex Parent Families

As numbers of families with same-sex parents increase in the United States, children are more likely to encounter diverse family structures. Given that young children can demonstrate in-group bias, prejudicial attitudes, and social exclusion, it is important to understand how children perceive their peers in diverse families. To our knowledge, no studies have assessed elementary-school-age children's attitudes about same-sex parent families. Here, 131 elementary school students (M_age = 7.79 years; 61 girls) viewed images of same-sex (female and male) and other-sex couples with a child and then were asked about their perceptions of these families, particularly the children. Results indicated participants' preferences toward children with other-sex versus same-sex parents. Developmental and practical implications about children's attitudes toward sexual minority parent families are discussed.     

Inter-group contact and out-group altruism after violence

Altruism among humans is common. It is especially prominent among in-group members. However, we are struck by results from laboratory-in-the-field experiments with out-group members of different ethnic or religious backgrounds. In all instances, the groups were rivals in civil wars. While we find almost no altruistic behavior among groups that were engaged in fighting, out-group altruism emerges with the passage of time. To address the underlying mechanism that might explain this puzzling result, we use a laboratory experiment that sorts between a norms revision explanation and a contact hypothesis explanation. Our findings show that contact with out-group members, in a mutually beneficial task, results in a reversion to prior levels of out-group altruism following conflict. Our findings reinforce the idea that deeply held attitudes toward a stigmatized out-group change following productive interactions with those out-group members. It is clear that hostility between groups need not persist over time.     

Semi-doubly optimal concentric circles fitting with presence of heteroscedasticity

Previous studies focus on homogeneous and isotropic assumptions about the noisy data. Many methods have been developed recently for fitting concentric circles to data. In this paper, these statistical assumptions have been relaxed. To the best of our knowledge, only one iterative method has been recently developed. Due to its complexity, no such algorithm is available to compute the reliable maximum likelihood estimator (MLE). Accordingly, we have developed four new methods that outperform the existing methods including the orthogonal distance regression (ODR). We also discuss which of these methods is superior according to the four principles: statistical efficiency, accuracy, robustness, and computational efficiency. Numerical experiments on synthetic and real images have been conducted to validate our findings.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Compensation or competition: Bias in immigrants' access to active labour market measures

Whether participation in active labour market programmes (ALMPs) pushes individuals back into employment depends on the programme's characteristics. On the basis of encompassing registry data that allow us to control for usually unobserved employability, we find evidence of a systematic access bias whereby jobcentre caseworkers in Switzerland assign unemployed persons to activation measures based on a competition logic that is mainly driven by an economic rationale and the jobcentre's performance evaluation. This practice seems problematic because it results in an overrepresentation of immigrants in measures with little efficacy rather than in measures that could compensate for employability disadvantages. Conversely, Swiss citizens are more likely to enter beneficial human-capital-intensive measures. It is plausible that this discrepancy in programme participation amplifies the general labour market disadvantages of immigrants.     

Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression

We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved efficiency. We propose a semi‐parametric approach using empirical likelihood to combine data from the two sources. The method possesses favourable large and moderate sample properties. We use the method to investigate wage regression using data from the National Longitudinal Survey of Youth Study.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada