Settlement size and fertility in the Nordic countries

While the variation in childbearing patterns across countries and between socio-economic groups within a country has been studied in detail, less is known about the differences in fertility patterns across settlements within a country. Using aggregate and individual-level register data, we examine fertility variation across settlements in Denmark, Finland, Norway, and Sweden. We observe a significant variation in fertility level by settlement size in all four of these Nordic countries - the larger the settlement, the lower the fertility. Second, the variation in fertility level has decreased over time, but significant differences in fertility between settlements of different size persist. Third, the timing of childbearing also varies across settlements - the larger the settlement, the later the peak of fertility. Fourth, our analysis of parity-specific fertility in Sweden shows that the major socio-economic characteristics of women account for only a small portion of fertility variation across settlements.     

Is There an Ingroup Advantage in Recognizing Spontaneously Expressed Emotions?

Evidence for the ingroup advantage hypothesis in recognizing emotions comes from studies using specific types of posed expressions. A proposed source of this advantage has been culturally-specific ways of expressing emotions, known as cultural dialects (Elfenbein et al. Emotion 7(1):131–146, 2007). But to date, no study has used spontaneously produced expressions as stimuli in testing the hypothesis. We do so here. American and Japanese observers judged expressions produced by American and Japanese athletes immediately at the completion of a medal match from the 2004 Athens Olympic Games. The ingroup hypothesis was not supported, suggesting that it occurs when posers mime an expression, but not when they spontaneously produce it in real life.

A ‘weak and small’ race in China’s southwest: Yi elites and the struggle for recognition in Republican China

Studies on ethnic minorities for the Republican period (1912–1949) highlight the political agency these groups displayed in their negotiations with the modern Chinese state. Most of this work has focused on those non-Han groups officially recognized as part of the Five-Race Republic (wuzu gonghe). Little is known, however, about those excluded from the early Republic’s flag such as the Yi inhabiting southwest China. This article discusses the role played by a group of Yi leaders who engaged with Sun Yat-sen’s ideology of nationalism, racial equality, and anti-imperialism in their attempts to obtain both recognition and aid from the Chinese nation–state. Rather than rejecting the commonly used term to identify China’s non-Han population of ‘weak and small races’ present in Sun Yat-sen’s ideology, Yi elites appropriated this term to their advantage seeking aid from the Guomindang but at the same time placing boundaries to what they perceived to be a Han-centered state.     

Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models

We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the ?_∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on ?₁ and ?₂ estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at a much finer scale than would have been possible using classical oracle inequalities. Thus, our sup-norm bound is tailored to consistent variable selection via thresholding. Our simulations show that thresholding the scaled Lasso yields substantial improvements in terms of variable selection. Finally, we use our estimator to shed further empirical light on the long-running debate on the relationship between the level of debt (public and private) and GDP growth. Supplementary materials for this article are available online.     

Evacuating People with Mobility‐Challenges in a Short‐Notice Disaster

In past disasters, arrangements have been made to evacuate people without their own transportation, requiring them to gather at select locations to be evacuated. Unfortunately, this type of plan does not help those people who are unable to move themselves to the designated meeting locations. In the United States, according to the Post‐Katrina Emergency Management Reform Act of 2006, state or local governments have the responsibility to coordinate evacuation plans for all populations. These include those with disabilities. However, few, if any, have plans in place for those who are mobility‐challenged. The problem of evacuating mobility‐challenged people from their individual locations in a short‐notice disaster is a challenging combinatorial optimization problem. In order to develop the model and select a solution approach, we surveyed related literature. Based on our review, we formulate the problem and develop an Ant Colony Optimization (ACO) algorithm to solve it. We then test two different versions of the ACO algorithm on five stylized datasets with several different parameter settings.     

Autocorrelation in real-time continuous monitoring of microenvironments

Interpretation of continuous measurements in microenvironmental studies and exposure assessments can be complicated by autocorrelation, the implications of which are often not fully addressed. We discuss some statistical issues that arose in the analysis of microenvironmental particulate matter concentration data collected in 1998 by the Harvard School of Public Health. We present a simulation study that suggests that Generalized Estimating Equations, a technique often used to adjust for autocorrelation, may produce inflated Type I errors when applied to microenvironmental studies of small or moderate sample size, and that Linear Mixed Effects models may be more appropriate in small-sample settings. Environmental scientists often appeal to longer averaging times to reduce autocorrelation. We explore the functional relationship between averaging time, autocorrelation, and standard errors of both mean and variance, showing that longer averaging times impair statistical inferences about main effects. We conclude that, given widely available techniques that adjust for autocorrelation, longer averaging times may be inappropriate in microenvironmental studies.     

Family Factors,Theft, Vandalism,and Major Deviance Among a Multiracial/Multiethnic Sample of Adolescent Girls

This article reports on a study of 503 African-American, Hispanic, and White non-Hispanic adolescent girls attending public schools in Miami, Florida. The primary objectives of the study were to determine the prevalence of 13 self-reported delinquent behaviors in the sample, to compare these rates among the three groups of students, and to explore the predictive influences of several family factors that correlate with delinquency. It was found that 37.5% of the sample engaged in one or more acts of serious delinquency, with African-Americans reporting they had engaged in significantly more of these behaviors. The best predictors of theft/vandalism were low family pride and family substance abuse for Hispanics, low family communication for African-Americans, and low family pride for White non-Hispanics. The findings indicate that traditional family factors that have been used repeatedly to understand delinquency by male adolescents were not strong predictors of delinquency among the adolescent girls in the sample.     

On the Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions

We show by example that empirical likelihood and other commonly used tests for moment restrictions are unable to control the (exponential) rate at which the probability of a Type I error tends to zero unless the possible distributions for the observed data are restricted appropriately. From this, it follows that for the optimality claim for empirical likelihood in Kitamura (2001) to hold, additional assumptions and qualifications are required. Under stronger assumptions than those in Kitamura (2001), we establish the following optimality result: (i) empirical likelihood controls the rate at which the probability of a Type I error tends to zero and (ii) among all procedures for which the probability of a Type I error tends to zero at least as fast, empirical likelihood maximizes the rate at which the probability of a Type II error tends to zero for most alternatives. This result further implies that empirical likelihood maximizes the rate at which the probability of a Type II error tends to zero for all alternatives among a class of tests that satisfy a weaker criterion for their Type I error probabilities.