The Rise and Prominence of Skip-Generation Households in Lower- and Middle-Income Countries

Investigations into changes in household formations across lower- and middle-income countries (LMICs) rarely consider skip-generation households. Yet, demographic, social, and economic forces increasingly encourage skip-generation household formations. We examine trends and changes in the prevalence of skip-generation households from 1990 to 2016, examining households, adults aged 60+, and children under 15, across 49 countries using household roster data from Demographic and Health Surveys. Analysis takes place in stages, first describing trends in skip-generation households across countries and next providing explanatory analyses using multilevel modeling to assess whether, and the degree to which, country-level characteristics like AIDS mortality and female labor force participation explain trends in the probability that a household is, or that an individual resides in, a skip-generation household. Results indicate extensive increases in skip-generation households in many LMICs, although there is also variation. The increases and variations are not well-explained by the country-level characteristics in our models, suggesting other underlying reasons for the rise and prominence of skip-generation households across LMICs.     

Can Children Recognize Bodily Expressions of Emotion?

Journal of Nonverbal Behavior - Past research has demonstrated that children understand distinct emotion concepts and can accurately recognize facial expressions of distinct emotions by a young...     

The Value of Information in Decision‐Analytic Modeling for Malaria Vector Control in East Africa

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced‐based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5–21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria‐transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs.     

Sex on the brain?: an examination of frequency of sexual cognitions as a function of gender, erotophilia, and social desirability

It is commonly believed that men think about sex much more often than do women, but the empirical evidence in this area is fairly weak. By means of a golf tally counter, 283 college students kept track of their thoughts pertaining to food, sleep, or sex for one week. Males reported significantly more need-based cognitions overall, but there was no significant interaction between sex of participant and type of cognition recorded. Therefore, although these young men did think more about sex than did young women, they also thought more about food and sleep. In contrast, a retrospective estimated frequency of need-based cognitions obtained at the start of the study revealed a sex difference in sexual cognitions, but not thoughts about eating or sleeping. Erotophilia and sexual desirability responding were significant predictors of frequency of sexual cognitions for women, but not for men. Overall, erotophilia was a better predictor of sexual cognition than was sex of participant. Taken as a whole, the results suggest that, although there may be a sex difference in sexual cognitions, it is smaller than is generally thought, and the reporting is likely influenced by sex role expectations.     

Racialized customer service in restaurants: a quantitative assessment of the statistical discrimination explanatory framework

Despite popular claims that racism and discrimination are no longer salient issues in contemporary society, racial minorities continue to experience disparate treatment in everyday public interactions. The context of full-service restaurants is one such public setting wherein racial minority patrons, African Americans in particular, encounter racial prejudices and discriminate treatment. To further understand the causes of such discriminate treatment within the restaurant context, this article analyzes primary survey data derived from a community sample of servers (N = 200) to assess the explanatory power of one posited explanation—statistical discrimination. Taken as a whole, findings suggest that while a statistical discrimination framework toward understanding variability in servers’ discriminatory behaviors should not be disregarded, the framework’s explanatory utility is limited. Servers’ inferences about the potential profitability of waiting on customers across racial groups explain little of the overall variation in subjects’ self-reported discriminatory behaviors, thus suggesting that other factors not explored in this research are clearly operating and should be the focus of future inquires.     

Tolerance limits under Poisson regression based on small-sample asymptotic methodology

This article explores the calculation of tolerance limits for the Poisson regression model based on the profile likelihood methodology and small-sample asymptotic corrections to improve the coverage probability performance. The data consist of n counts, where the mean or expected rate depends upon covariates via the log regression function. This article evaluated upper tolerance limits as a function of covariates. The upper tolerance limits are obtained from upper confidence limits of the mean. To compute upper confidence limits the following methodologies were considered: likelihood based asymptotic methods, small-sample asymptotic methods to improve the likelihood based methodology, and the delta method. Two applications are discussed: one application relating to defects in semiconductor wafers due to plasma etching and the other examining the number of surface faults in upper seams of coal mines. All three methodologies are illustrated for the two applications.