Understanding ideological positioning is essential for analyzing party systems and electoral competition in Europe. This paper uses the data from the 2024 wave of the Chapel Hill Expert Survey (CHES 2024) to investigate European political parties with two objectives: (i) identifying ideological extremes and (ii) examining cross-country patterns in party positioning. We employ Archetypoid Analysis (ADA), a novel statistical method that detects representative ideological extremes in multivariate data. Focusing on general left-right, economic, and sociocultural orientations, and specific policy areas such as immigration, redistribution, and climate change, we map the European ideological space and identify parties that occupy its edges. We then analyze how party positions relative to ideological archetypoids relate to stances on key political issues and EU policies, and how these relationships vary across European countries. In detail, we assess the influence of party agreement with political issues and EU policies on their proximity to left, center, or right positions and, using linear mixed-effects models, we also account for cross-country variation. This approach allows us to evaluate whether observed patterns are consistent across national contexts or differ significantly between countries. The findings also carry policy relevance by clarifying which ideological configurations are associated with support for or opposition to key EU policies. The observed cross-country heterogeneity suggests that European-level initiatives may encounter differentiated political constraints, with implications for policy feasibility and coalition dynamics across national contexts.

The article is about methods for estimating a table of vote transition counts between two elections, ensuring consistency with the observed marginals, given an estimated table of transition probabilities obtained by some method of ecological inference. We argue that count data are essential for conducting in-depth investigations into voting behavior. Several new methods are compared with Iterative Proportional Fitting (IPF), known for its speed and reliability. To evaluate their performance, we use both simulated data and real electoral results from the 2011 New Zealand general election. An algorithm for simulating artificial electoral data according to the modified Brown and Payne model is presented and models that account for strategic voting are applied to the New Zealand data to reduce ecological bias. Among the methods initially considered, only two valid competitors of IPF emerged, constrained maximum likelihood and minimum Chi-square, with the latter performing significantly better than IPF. We illustrate the potential insights which may be gained from tables of transition counts by an application to transitions from a parliamentary and a regional elections in Umbria, Italy.

This article examines how homestead policies, which opened vast frontier lands for settlement, influenced the development of American frontier states. It uses a treatment propensity-weighted matrix completion model to estimate the counterfactual size of these states without homesteading. In simulation studies, the method shows lower bias and variance than other estimators, particularly in higher complexity scenarios. The empirical analysis reveals that homestead policies significantly and persistently reduced state government expenditure and revenue. These findings align with continuous difference-in-differences estimates using 1.46 million land patent records. This study’s extension of the matrix completion method to include propensity score weighting for causal effect estimation in panel data, especially in staggered treatment contexts, enhances policy evaluation by improving the precision of long-term policy impact assessments.

This note, inspired by the Pearson article, draws on the experience of the development of the UK statistical system over the last 20 years. Incremental steps were taken, which eventually led to statistical legislation now recognised as guaranteeing the professional independence of the UK statistical system. This lengthy journey was probably necessary to develop the case for change and could not have been taken in a single step.

This letter to the Editor comments on the paper “Three-Way ROCs for Forensic Decision Making” by Nicholas Scurich and Richard S. John, published in Statistics and Public Policy (2023, 10, 1–10).

Using the COVID-19 Trends and Impact Survey, we estimate the average effect of COVID-19 vaccinations on self-reported feelings of depression and anxiety, isolation, and worries about health among vaccine-accepting respondents in February 2021, and find 3.7, 3.3, and 4.3 percentage point reductions in the probability of each outcome, respectively, with particularly large reductions among respondents aged 18 and 24 years old. We show that interventions targeting social isolation account for 39.1% of the total effect of COVID-19 vaccinations on depression, while interventions targeting worries about health account for 8.3%. This suggests that social isolation is a stronger mediator of the effect of COVID-19 vaccinations on depression than worries about health. We caution that these causal interpretations rely on strong assumptions. Supplementary materials for this article are available online.

In December 2020, Texas filed a motion to the U.S. Supreme Court claiming that the four battleground states: Pennsylvania, Georgia, Michigan, and Wisconsin did not conduct their 2020 presidential elections in compliance with the Constitution. Texas supported its motion with a statistical analysis purportedly demonstrating that it was highly improbable that Biden had more votes than Trump in the four battleground states. This article points out that Texas’s claim is logically flawed and the analysis submitted violated several fundamental principles of statistics.

Recently, Gastwirth (2016) proposed two transformations and of the Lorenz curve, which calculates the proportion of a population, cumulated from the poorest or middle respectively, needed to have the same amount of income as top . Economists and policy makers are often interested in the comparative status of two groups, e.g. females versus males or minority versus majority. This paper adapts and extends the concept underlying the and curves to provide analogous curves comparing the relative status of two groups. Now one calculates the proportion of the minority group, cumulated from the bottom or middle needed to have the same total income as the top qth fraction of the majority group (after adjusting for sample size). The areas between these curves and the line of equality are analogous to the Gini index. The methodology is used to illustrate the change in the degree of inequality between males and females, as well as between black and white males, in the United States between 2000 and 2017, and can be used to examine disparities between the expenditures on health of minorities and white people.

We estimate the change in the reported number of voter fraud cases when states switch to conducting elections by mail. We consider two types of states in which voting is facilitated by mail: states where a large number of voters receive ballots by mail (receive-by-mail states, RBM) and a subset of these states where registered voters are automatically sent ballots by mail (vote-by-mail states, VBM). We then compare the number of voter fraud cases in RBM (VBM) states to the number of cases in non-RBM (non-VBM) states, using two approaches standard in the social sciences. We find no evidence that voting by mail increases the risk of voter fraud overall. Between 2016 and 2019, RBM (VBM) states reported similar fraud rates to non-RBM (non-VBM) states. Moreover, we estimate Washington would have reported 73 more cases of fraud between 2011 and 2019 had it not introduced its VBM law. While our analysis of the data considers only two of many possible approaches, we argue our findings are unlikely were fraud more common when elections are held by mail.

ABSTRACTThe recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states’ reform efforts has been the inclusion of competitiveness metrics, or scores that evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested.In this article, we examine several classes of competitiveness metrics motivated by recent reform proposals and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states—Utah, Georgia, Wisconsin, Virginia, and Massachusetts—chosen to represent a range of partisan attributes. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics. These results demonstrate the importance of (1) avoiding writing detailed metric constraints into long-lasting constitutional reform and (2) carrying out careful mathematical modeling on real geo-electoral data in each redistricting cycle.