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.
Social determinants of health (SDOH) are estimated to drive nearly 80% of health outcomes, making the accurate measurement of neighborhood disadvantage essential for equitable public health policy and resource allocation. The Area Deprivation Index (ADI) serves as a critical multidimensional proxy for SDOH; however, its utility is frequently hindered by a “change of support" problem, where the geographic resolution of available data (census block groups) does not align with the scales required for policy implementation, such as ZIP Code Tabulation Areas (ZCTAs). This mismatch can lead to biased inferences and the misidentification of high-risk communities. This study employs a spatiotemporal change of support (STCOS) model to bridge these scales, specifically addressing the challenge that ADI rankings lack the known variance measures required for robust statistical modeling. We propose and evaluate several variance estimation techniques, including multiple linear regression and finite mixture models, to generate reliable, uncertainty-aware ADI estimates for South Dakota at the ZCTA level. Our findings demonstrate that these refined estimates can successfully identify areas of elevated social risk, even in regions with missing data. By providing a statistically rigorous framework for harmonizing mismatched health data, this research offers a vital tool for decision-makers to support health equity initiatives, such as the Centers for Medicare & Medicaid Services Accountable Care Organization Realizing Equity, Access, and Community Health Model, known as the ACO REACH Model, and to ensure that interventions and resources are precisely directed toward the most underserved populations.
The correlation structure of attitudes is a critical feature for characterizing the ideological beliefs of an electorate. Recent research has shown that, contrary to conventional wisdom, social and economic conservatism are negatively correlated in most countries. In this article, we use a geospatial within-country approach to extend this theoretical model, showcasing the utility of Multidimensional Item Response Theory models and Correlational Class Analysis clustering techniques for processing electorally-salient data. We find that many Westminster constituencies demonstrate a local organization of attitudes that is orthogonal to the correlations of attitudes evident amongst British parties - a phenomenon we term "ideological misalignment". Using data from the British Election Study Internet Panel, we demonstrate the explanatory power of ideological misalignment on voter behavior, highlighting in particular the associations of ideological misalignment with political disengagement; social and geographical disadvantage; and anti-status-quo political behavior.
In this article, we evaluate the lasting impact of the first-wave COVID-19 pandemic policies on unemployment during the post-first-wave period. Specifically, we analyze the causal effects of the Swedish first-wave policies relative to the Norwegian ones on unemployment during the first-wave period (Q2 and Q3 2020, where Q stands for quarter), the post-first-wave period (Q4 2020 and 2021-2024), and the complete period (Q2 2020 - Q4 2024). The Swedish first-wave policies led to an increase of unemployment with an estimate of 506 (95% CI: 406, 607) more unemployment per 100, 000 labor force members during the first-wave period in Sweden. However, the Swedish first-wave policies led to a reduction of unemployment with an estimate of 585 (95% CI: 485, 684) less unemployment during the post-first-wave period in Sweden. Furthermore, the Swedish first-wave policies led to a reduction of unemployment with an estimate of 471 (95% CI: 375, 567) less unemployment during the complete period in Sweden. This provides causal evidence supporting the view that mild first-wave public-health policies helped preserve supply chains and strengthen long-term economic resilience.
This article develops a novel data-driven framework to detect electoral fragmentation regimes using municipal data from Italian national elections (1948-2018), for both the Camera dei Deputati and the Senato della Repubblica, the two Italian chambers with nation-wide legislative authority. We integrate the B-ary entropy with rank-size modelling based on the Universal Law and apply k-means clustering to identify structural shifts and latent electoral regimes over time. Our analysis reveals distinct periods of electoral behavior associated with major institutional reforms, such as the Mattarellum (1993), Porcellum (2005), and Rosatellum (2017). We observe that the evolution of fragmentation differs significantly between the two chambers due to divergent electoral rules and electorate compositions. Particularly notable are the 1992, 1994, and 1996 elections, which reflect the transitional turbulence between the First and Second Republic. This study provides a replicable methodology for electoral regime identification and underscores the value of entropy-based diagnostics combined with machine learning to explore political dynamics.
This study seeks to enhance understanding of the impact of mafia infiltration on urban waste management public services. It examines whether organized crime represents a major obstacle to the transition to a circular economy and the advancement of the waste hierarchy. We investigated the effects of municipal council dissolution-a legal measure by which the central government removes and replaces an elected local government due to documented mafia infiltration-on Italian municipalities on three key variables relevant to urban waste management: the rate of separate waste collection, per-capita total waste generation, and per-capita unsorted waste generation. To achieve the research objective, a panel event study using annual municipal-level data for the period 2010-2021 was conducted. Results show that municipalities, following dissolution and subsequent administrative reorganization under a government-appointed extraordinary commission, increase their separate collection rate and reduce waste generation, aligning with EU waste hierarchy targets and underscoring the connection between mafia infiltration, maladministration, and diminished environmental performance in urban waste management.
Understanding ideological positioning is essential for analyzing party systems and electoral competition in Europe. This article 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 bivariate spatiotemporal study primarily investigates clusters of homelessness and evictions on the county level in the contiguous United States between 2007 and 2018. Further investigation using a negative binomial regression model explores factors commonly associated with homelessness and evictions, as well as the relationship, if any, between the two. Using software for spatial statistics (SaTScanTM), mapping (ArcGIS Pro), and statistical analysis (SAS), the following are identified: geographic variations in clusters, numerous significant covariates, and no strong evidence suggesting an association between clusters of homelessness and evictions in the contiguous United States. This study uses an epidemiological spatiotemporal approach to pinpoint and address regions that exhibit abnormally high counts or rates of evictions and of homelessness. These clusters of counties could be of interest for future investigations into underlying factors contributing to these disproportionate counts or rates.
This study investigates the network of co-sponsorships in COVID-19-related legislation in the 117th Congress, uncovering a clear partisan divide that aligns with existing literature on increasing political polarization. The data shows strong evidence of partisan homophily, where senators are more likely to cosponsor bills with members of their own party. Co-sponsorship clusters predominantly occur within party lines, with minimal crossover between parties. A block model analysis reveals four distinct roles, with the bridging role mainly occupied by Democrats, except for Republican Senator Susan Collins, known for her bipartisan stance. The network structure highlights two peripheral blocks-one Democratic and one Republican-connected through this bridging block, with isolated nodes consisting entirely of Republicans. This analysis reinforces the notion that contemporary politicians are more polarized than ever, especially in legislative co-sponsorships.
Representative democracy in the United States relies on election systems that transmit votes into representatives in three key bodies: the two chambers of the federal legislature (House of Representatives and Senate) and the Electoral College, which selects the president and vice-president. This happens through a process of re-weighting based on geographic units (congressional districts and states) that can introduce substantial distortion. In this article, I propose quantitative measures of this distortion that can be applied to demographic groups, using Census data, to assess and visualize these distorting effects. These include the absolute weight of votes under these systems and the excess population represented in the bodies through the distortions. Visualizing these metrics from 2000-2020 shows persistent malapportionment in key demographic categories. White (non-Hispanic) residents, residents of rural areas, and owner-occupied households are overrepresented in the Senate and Electoral College; Black and Hispanic people, urban dwellers, and renter-occupied households are underrepresented. For urban residents, this underrepresentation is the equivalent of 25 million fewer residents in the Senate and nearly 5 million in the Electoral College. I discuss implications for further research on the effects of these distortions and their interactions with other features of the electoral system.
Access to healthy food is key to maintaining a healthy lifestyle and can be quantified by the distance to the nearest grocery store. However, calculating this distance forces a trade-off between cost and correctness. Accurate route-based distances following passable roads are cost-prohibitive, while simple straight-line distances ignoring infrastructure and natural barriers are accessible yet error-prone. Categorizing low-access neighborhoods based on these straight-line distances induces misclassification and introduces bias into standard regression models estimating the relationship between disease prevalence and access. Yet, fully observing the more accurate, route-based food access measure is often impossible, which induces a missing data problem. We combat bias and address missingness with a new maximum likelihood estimator for Poisson regression with a binary, misclassified exposure (access to healthy food within some threshold), where the misclassification may depend on additional error-free covariates. In simulations, we show the consequence of ignoring the misclassification (bias) and how the proposed estimator corrects for bias while preserving more statistical efficiency than the complete case analysis (i.e., deleting observations with missing data). Finally, we apply our estimator to model the relationship between census tract diabetes prevalence and access to healthy food in northwestern North Carolina.
This research analyzes Indonesia's potential trade deficit in the RCEP (Regional Comprehensive Economic Partnership) cooperation. RCEP has member countries which have varied commodities with different quality and quantity. RCEP cooperation was formed to expand relations between countries in the region with a focus on economic development, such as economy, investment, services, technical cooperation, intellectual property, competition, conflict resolution, e-commerce, MSMEs and so on. In this regard, Indonesia has several consequences when joining this RCEP, first internal policy in determining the need for exports of goods. Second, Indonesia needs a strategy for determining goods and mapping the potential of export destination countries. Third, Indonesia needs a policy of importing goods based on domestic needs. From this point of view, further research is needed to see Indonesia's trade deficit in the RCEP cooperation. This study uses the Balance of Trade (BOT) approach to see the balance of transactions between RCEP member countries. The variables used are export and import for the last ten years. The result of this study is that Indonesia has a trade deficit, which is due to the total imports being bigger than total exports.
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.
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.
