Interplay between Friends' Aggression and Friendship Quality in the Development of Child Aggression during the Early School Years

The aim of this study was to investigate the unique and combined role of friendship quality and friends' aggression in regard to the persistence of young children's physical aggression from kindergarten to grade 2. The sample included 1555 children (808 girls) assessed annually using teacher ratings. Two theoretical perspectives (i.e., the social learning and the social bonding perspectives) served as frameworks to guide the analyses and interpret the results. In line with the social learning perspective, friends' aggression was related to a significant increase in children's physical aggression. However, in line with the social bonding perspective, good friendship quality played both a compensatory and a protective role, by, respectively, reducing children's initial level of physical aggression and by mitigating, albeit marginally, the associations between friends' and children's physical aggression. These results suggest that fostering a positive relationship between friends in the early school years may decrease physical aggression even if the friends are aggressive.     

Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits

We consider assortment problems under a mixture of multinomial logit models. There is a fixed revenue associated with each product. There are multiple customer types. Customers of different types choose according to different multinomial logit models whose parameters depend on the type of the customer. The goal is to find a set of products to offer so as to maximize the expected revenue obtained over all customer types. This assortment problem under the multinomial logit model with multiple customer types is NP‐complete. Although there are heuristics to find good assortments, it is difficult to verify the optimality gap of the heuristics. In this study, motivated by the difficulty of finding optimal solutions and verifying the optimality gap of heuristics, we develop an approach to construct an upper bound on the optimal expected revenue. Our approach can quickly provide upper bounds and these upper bounds can be quite tight. In our computational experiments, over a large set of randomly generated problem instances, the upper bounds provided by our approach deviate from the optimal expected revenues by 0.15% on average and by less than one percent in the worst case. By using our upper bounds, we are able to verify the optimality gaps of a greedy heuristic accurately, even when optimal solutions are not available.     

Further Beyond the Basic Background Check: Predicting Future Unethical Behavior

In this paper, we analyze the ethical issues of using honesty and integrity tests in employment screening. Our focus will be on the United States context: legal requirements related to applicant privacy differ in other countries, but we posit that our proposed balancing test is broadly applicable. We start by discussing why companies have ethical and legal obligations, based on a stakeholder analysis, to assess the integrity of potential employees. We then move to a consideration of how companies currently use background checks as a pre‐employment screening tool, noting their limitations. We then take up honesty and integrity testing, focusing particularly on the problems of false positives and due process. We offer a balancing test for the use of honesty and integrity testing that takes in three factors: (1) the potential harm posed by a dishonest employee in a particular job, (2) the linkage between the test and the assessment process, and (3) the accuracy and validity of the honesty and integrity test. We conclude with implications for practice and future research.     

A Proportional Hazards Regression Model for the Subdistribution with Covariates‐adjusted Censoring Weight for Competing Risks Data

With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution and the covariates are independent. Covariate‐dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate‐dependent censoring. We consider a covariate‐adjusted weight function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate‐adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate‐adjusted weight approach works well for the variance estimator as well. We illustrate our methods with bone marrow transplant data from the Center for International Blood and Marrow Transplant Research. Here, cancer relapse and death in complete remission are two competing risks.     

Barrios and burbs: residential context and health-risk behaviors among Angeleno adolescents

The increasing size of the Latino immigrant population in the United States underscores the need for a more complete understanding of the role that social context plays in influencing the health of immigrants and their children. This analysis explores the possibility that residential location influences the health-risk behaviors of Latino youth in Los Angeles County, California. The data come from the Los Angeles Family and Neighborhood Survey. We apply multivariate, multilevel Rasch models to two scales of adolescent health-risk behaviors (substance use and delinquency). The findings suggest that residence in Census tracts characterized by above-county-average levels of Latinos and above-county-average levels of poverty is associated with increased odds of health-risk behaviors for Latino adolescents, particularly for those born in the United States. The findings lend support to the contention, put forth in the segmented assimilation literature, that disadvantaged urban contexts increase the risk that U.S.-born children of immigrants will experience downward assimilation.     

Foreign-born emigration: A new approach and estimates based on matched CPS files

The utility of postcensal population estimates depends on the adequate measurement of four major components of demographic change: fertility, mortality, immigration, and emigration. Of the four components, emigration, especially of the foreign-born, has proved the most difficult to gauge. Without "direct" methods (i.e., methods identifying who emigrates and when), demographers have relied on indirect approaches, such as residual methods. Residual estimates, however are sensitive to inaccuracies in their constituent parts and are particularly ill-suited for measuring the emigration of recent arrivals. Here we introduce a new method for estimating foreign-born emigration that takes advantage of the sample design of the Current Population Survey (CPS): repeated interviews of persons in the same housing units over a period of 16 months. Individuals appearing in a first March Supplement to the CPS but not the next include those who died in the intervening year, those who moved within the country, and those who emigrated. We use statistical methods to estimate the proportion of emigrants among those not present in the follow-up interview. Our method produces emigration estimates that are comparable to those from residual methods in the case of longer-term residents (immigrants who arrived more than 10 years ago), but yields higher--and what appear to be more accurate--estimates for recent arrivals. Although somewhat constrained by sample size, we also generate estimates by age, sex, region of birth, and duration of residence in the United States.     

Correction to: Climate variability and health in extremely vulnerable communities: investigating variations in surface water conditions and food security in the West African Sahel

Population and Environment - A correction to this paper has been published: https://doi.org/10.1007/s11111-021-00381-x     

A Generalization of Wendell's Tolerance Approach to Sensitivity Analysis in Linear Programming

A criticism of linear programming has been that the data which are available in practice are too inexact and unreliable for linear programming to properly work. Managers are therefore concerned with how much actual values may differ from the estimates that were used in the model before the results become irrelevant. Sensitivity analysis emerged to help deal with the uncertainties inherent in the linear programming model. However, the ranges calculated are generally valid only when a single coefficient is varied. An extension of sensitivity analysis, the 100 Percent Rule, allows the simultaneous variation of more than one element in a vector, but does not permit the independent variation of the elements. A tolerance approach to sensitivity analysis enables the consideration of simultaneous and independent change of more than one coefficient. However, the ranges developed are unnecessarily restricted and may be reduced in width to zero when primal or dual degeneracy exists. This paper presents an extension of the tolerance approach which reduces the limitations of both the traditional and tolerance approaches to sensitivity analysis.     

Multi-Item Grouping Algorithm Yielding Near-Optimal Logistics Cost

This article describes an algorithm used to formulate an inbound consolidation strategy when multi-items are replenished in groups and when total logistics cost is to be minimized. The importance of this algorithm is threefold (1) no optimal procedure exists for grouping multi-items when minimizing total logistics cost, (2) a complete enumeration of all possible groups (from which the optimal grouping set can be identified) is impractical due to the combinatorial nature of the grouping problem, and (3) no other heuristics have been developed that adequately reflect shipping cost in the analysis like this one does. We report the experience of using this algorithm to group and reorder 75 selected ensembles containing a total of 517 inventory items of a retail merchandising firm.     

Linking Migration Intentions with Flows: Evidence and Potential Use

In the absence of reliable, internationally available migration flow data necessary for statistical forecasting, policymakers increasingly turn to survey data on emigration intentions to evaluate future migration trends. The important assumption – i.e. that there is a measurable and systematic relationship between the intention to migrate and actual migration – has not been firmly established at the international level. We examine the association between estimated population averages of emigration intentions and official migration flow data based on data for more than 160 countries. The results show a strong association between emigration intentions and recorded bilateral flows to industrialized countries, as well as between intentions and aggregated out‐migration. The results provide policymakers with a reliability assessment of survey data on emigration intentions and encourage future attempts to incorporate survey data in formal statistical migration forecasting models.