The winner of the United States presidential election is determined by which candidate is the first to reach 270 electoral votes. The winning candidate in most states can be reliably predicted in the months leading up to the election from historical trends and polling data. The remaining states are referred to as “battleground states” or “swing states”; these are the states which typically end up deciding the electoral winner. Campaigns are known to funnel a sizable portion of their finite resources (money, time, rallies, advertising, etc.) to these swing states in the months leading up to the election. Yet, to this point in time, very few mathematical procedures have been developed to quantify the influence of each swing state to a candidate’s election odds. In this paper, we focus on the seven tightest states for the 2024 presidential election and use conditional probabilities to quantify the importance each swing state has to each candidate’s probability of winning the electoral college. This methodology has the potential to serve as a guide for campaigns in the future, helping them determine how to allocate their limited amounts of time and resources to maximize their odds of winning the campaign.
Childhood malnutrition is a serious health issue facing Egypt, owing to its negative impacts on children’s physical growth. Growth charts are important tools for evaluating children’s health, growth, and nutritional status and hence providing public health authorities with accurate and timely information for appropriate intervention. As physical growth can be influenced by genetic, socio-cultural, environmental, and economic factors, using international growth standards can be misleading. This study aims at developing reliable local reference growth curves for Egyptian pre-school children using parametric and nonparametric methods and comparing them against the WHO standards. The results of the different methods were consistent, yet the nonparametric methods provided slightly better estimates when distributional assumptions are not valid for some measurements at some ages. In contrast, the study results highlighted discrepancies between the WHO and newly estimated Egyptian growth curves. This emphasizes the importance of using national growth charts and standards for monitoring children growth for the accurate diagnosis of children growth problems in Egypt. Bivariate national height-weight quantile regression contours are also estimated for the first time in Egypt and compared to the univariate nationally estimated ones. The bivariate growth contours provided a more comprehensive tool for monitoring growth in height and weight simultaneously.
Through censuses and election counts, the modern state regularly requires popular participation in data collection to guide and authorise its policymaking. Both exercises suffer from nonresponse, which generates a missing data problem. This missing data represents both a statistical challenge (because selection biases introduce inferential uncertainty) and a policy problem (because decision-making based on these data can contribute to underrepresentation of non-participating groups). The co-occurrence of local elections and a census in Scotland in 2022 provides a unique opportunity to test hypotheses about this nonresponse, including whether missingness in the two contexts is correlated, and if it is, whether there is sufficient evidence of common causes to posit a theory of generalised nonresponse. Using a geospatial approach, we also investigate the relationship between nonresponse and contextual covariates including age, deprivation, and cohabitation rates. We conclude that understanding these relationships represents an opportunity for improving policy practice to mitigate against decreasing response and turnout rates in future.
In the United States, the use of criminal history information in employment decision-making is ubiquitous. However, employers’ decision-making about job candidates’ criminal records is often nontransparent and inconsistent, with disproportionate negative effects on Black and Hispanic Americans. Here, we consider whether statistical models can produce a more accurate, interpretable, and fair assessment of the recidivism risk of job candidates with criminal records relative to current approaches. We review existing approaches and policy guidance on the use of criminal records in employment decision-making. Then, using data from seven states from the Criminal Justice Administrative Records System (CJARS), spanning 1992-2021, we build a Cox proportional hazards model to predict the risk of recidivism based on a job candidate’s criminal record. We assess the predictive performance, fairness, and generalizability of this model. We find that our candidate model outperforms some existing approaches, although challenges remain in the domains of fairness and usability in practice.
