In legislative redistricting, most states draw their House and Senate maps separately. Ohio and Wisconsin require that their Senate districts be made with a 3:1 nesting rule, that is, out of triplets of adjacent House districts. We study the impact of this requirement on redistricting, specifically on the number of seats won by a particular political party. We compare two ensembles generated using Markov chain Monte Carlo methods; one which uses the ReCom chain to generate Senate maps without a nesting requirement, and the other which uses a chain that generates Senate maps with a 3:1 nesting requirement. We find that requiring a 3:1 nesting rule has minimal impact on the distribution of seats won. Moreover, we probe how 3:1 nesting can mitigate partisan gerrymandering, and find that nesting reduces the ability of a party to bias the Senate map.

International human trafficking is a growing problem, driven by conflict, forced migration, and increasing numbers of refugees. Using data from the US Department of State’s Trafficking in Persons reports, we study human trafficking in the context of three possible explanatory variables: a common language, cost of transportation, and difference in national median income. All three factors are predictive of trafficking flows. These findings provide useful information for mitigation policies.

The importance of household budgets is hard to overstate, given their relevance for informing social issues such as minimum wage, targeted basic income, and measures of economic deprivation. The budget components must be relevant at small geographic levels to be effective. Previously, data of sufficient quality and geographic coverage to quantify adequacy standards were unavailable. Using publicly available data, it is now possible to construct budgets for different household combinations within census tracts. We define this Household Living Budget (HLB) as the income necessary to meet a household’s needs and function at a modest yet adequate standard of living. That is the minimum income needed to unlock opportunities and provide choices to participate in society. It is a benchmark against poverty. The budget components include housing, food, transportation, healthcare, childcare, broadband, and other necessities such as clothing, household supplies, personal care, nonprescription medicine, and school supplies, and federal and state income taxes. The HLB assumes the total cost of each need without government subsidies or nonprofit or informal assistance. Finally, using small area synthetic populations for households in Washington, DC, we demonstrate how component adequacy standards change for various household combinations as a percentage of the HLB and compare the living wage calculated using HLB to the Washington, DC minimum wage.

When evaluating a program or policy, a randomized experiment is typically designed to test a single confirmatory hypothesis about the average treatment effect, although subgroup and moderator effects may also be explored. The resulting average treatment effect estimate is then reported in research clearinghouses and used to inform policy and practice decisions for units not in the study. This use suggests that the purpose of these randomized trials is not only the testing of hypotheses, but rather the prediction of treatment effects for a broad set of units in a population. In this article, we consider the optimal design of a randomized experiment focused on the prediction of unit-specific effects. We consider how different sampling processes and models affect the mean squared error of these predictions. The results indicate, for example, that problems of generalizability—differences between study samples and target populations—can greatly increase prediction error. We also identify the conditions under which the best unit-specific treatment effect is the average treatment effect estimate. Throughout, we use simple regression models to connect the predictive and hypothesis testing literatures and to provide implications for the design of randomized experiments.

In this article, we study the main determinants of housing tension, for the case of the Emilia-Romagna Region in Italy, by integrating administrative data sources and official statistics into a new dataset, which combines socio-demographic and wealth attributes with aspects of housing supply and housing market at a municipality level. This dataset is employed in conjunction with cluster-weighted models, able to handle both the intrinsic heterogeneity of local housing tension and the presence of mildly atypical municipalities. The obtained results demonstrate that a source of unobserved heterogeneity over space characterizes the dataset; they also confirm that wealth attributes play a significant role in determining housing tension, while the same result holds true for demographic and housing market aspects for only some of the clusters of municipalities detected by the analysis. This study also provides useful suggestions for the future development of housing policies in the Region in favor of families in need of public economic support in accessing social housing.

The Swedish Research Council is a Swedish government agency and one of the major research financiers in Sweden. As such, its operations are governed by legislation and governmental instructions and policies, which prohibit discrimination and include equal treatment. Consequently, the Swedish Research Council has for a large number of years been working actively against gender bias in their reviews of grant applications. We use data published by the Swedish Research Council to analyze potential gender bias in reviews of grant applications. We can show using several multiple linear regression models, that after controlling for a number of other factors there is still a significant gender bias in the grants awarded, at least for some review panels. We explore the possibility of mitigating this bias by using statistical methodology through the post hoc intervention, GIIU. We argue that GIIU is applicable to the dataset and could be used to mitigate the bias. However, we believe that a post hoc intervention, such as GIIU, would be more effective if it were implemented earlier during the review process on the more detailed data available to the research council.

The current study aims to investigate the impact of Goods and Services Tax (GST) revenue on the economic growth of the Indian economy. The study has used the Auto Regressive Distributed Lag (ARDL) modeling by collecting the data from August, 2017 to March, 2024. The results depict that GST revenue has a positive impact on the economic growth of the Indian economy in both short and long run. Similarly, the results assert that foreign direct investment and government expenditure also exert a positive impact on the economic growth in India. Conversely, the results affirm that gross fiscal deficit and inflation have adverse impact on the Indian economy. The findings assert that the policymakers should devise policies to curb the inflation and fiscal deficit to attain long run economic growth for the Indian economy. Similarly, proper consideration should be given to boost the GST revenue and FDI inflow in the Indian economy. The findings have major implications for the policymakers, GST council and government to boost the economic growth and GST revenue of the nation.

According to the 2020 U.S. Census more than 60% of the U.S. population lives in towns with fewer than 50,000 residents, yet this is not in proportion with the research and public data surrounding policing, which focus on large and dense urban areas. One reason for this disparity is that studying small-town police departments presents unique obstacles. We present some of the challenges that we have encountered in studying small-town police activity such as data availability, quality, and identifiability, and our solutions to these challenges using computational tools. Finally, we give our recommendations in getting involved in this space based on our efforts to-date.

The Bradley-Terry Regression Trunk (BTRT) model combines the log-linear Bradley-Terry model, including subject-specific covariates, with a particular tree-based model, the so-called regression trunk. It aims to consider simultaneously the main effects and the interaction effects of covariates on data expressed as paired comparisons. We apply this model to financial data expressed as rankings and then transformed into paired comparisons. Tax revenues differentiated by category represent the statistical units of the analysis (i.e., taxes on income, social security contributions, taxes on property, and taxes on goods and services). We combine data from OECD, World Bank, and IMF databases for the year 2018 to investigate the effect size of socio-economic covariates and their interaction on the composition of tax revenues for a set of 100 countries worldwide. We also present a comparison with a more established method proposed in tax determinants literature and with two alternative models used for matched pairs. Finally, we discuss the implications of reported results for stakeholders and policymakers.

The Department of Commerce (DOC) uses differential pricing analysis in order to detect whether a foreign exporter dumps goods in the U.S. market at prices lower than the exporter sells the goods for in its domestic market. A dumping duty is then levied on the exporter, the amount of which depends on the dumping margin. Several recent cases at the Federal Circuit Court of Appeals have challenged the DOC’s methodology on statistical grounds. In this article, the DOC’s procedure for calculating the dumping margin is described in detail, including the rules for the controversial zeroing policy. Several statistical issues with the DOC’s approach are identified and some potential improvements are proposed.