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Understanding the distribution of socioeconomic status (SES) and its temporal dynamics within a population is critical to ensure that policies and interventions adequately and equitably contribute to the well-being and life chances of all individuals. This study assesses the dynamics of SES in a typical rural South African setting over the period 2001–2013 using data on household assets from the Agincourt Health and Demographic Surveillance System. Three SES indices, an absolute index, principal component analysis index and multiple correspondence analysis index, are constructed from the household asset indicators. Relative distribution methods are then applied to the indices to assess changes over time in the distribution of SES with special focus on location and shape shifts. Results show that the proportion of households that own assets associated with greater modern wealth has substantially increased over time. In addition, relative distributions in all three indices show that the median SES index value has shifted up and the distribution has become less polarized and is converging towards the middle. However, the convergence is larger from the upper tail than from the lower tail, which suggests that the improvement in SES has been slower for poorer households. The results also show persistent ethnic differences in SES with households of former Mozambican refugees being at a disadvantage. From a methodological perspective, the study findings demonstrate the comparability of the easy-to-compute absolute index to other SES indices constructed using more advanced statistical techniques in assessing household SES.

Gender equality is a fundamental human right and one of the core values of the European Union (EU). Great efforts have been made to defend this right and to promote gender equality within the member states and around the world. However, there are still significant differences between men and women, especially in terms of income. The main objective of the paper is to compare income distributions for gender groups across four Central European countries, Poland, Slovakia, Czechia and Hungary, i.e., the members of the Visegrád Group (V4). These countries share similar histories and similar economic development, but there are substantial differences between their approaches to economic reforms, including labour market policy. This, in turn, is reflected in different income distributions and income inequality patterns. There is a debated research issue regarding the methodology of measuring the gender gap – the traditional methods based on comparing means and medians seem unsatisfactory as they do not consider the shape of income distributions. The paper’s novelty lies in the application of the relative distribution concept, which goes beyond the typical focus on average income differences toward a full comparison of the entire distribution of women’s earnings relative to men’s. In the paper, we implement a parametric approach for estimating the relative distribution, which allows us to compare and visualise the “gap” between the gender groups at each distribution quantile. The basis for the calculations was the microdata from the Luxembourg Income Study (LIS). The statistical methods applied in the study were appropriate to describe the gender gap over the entire income range. The results of the empirical analysis helped to reveal similarities and substantial differences between the countries.

High-income inequality, accompanied by substantial regional differentiation, is still a great challenge for social policymakers in many European countries. One of the important elements of this phenomenon is the inequality between income distributions of men and women. Using data from the European Union Statistics on Income and Living Conditions, the distributions of income for Italy and Poland were compared, and the gender gap in these countries was assessed. No single metric can capture the full range of experiences, so a set of selected tools were adopted. The Dagum model was fitted to each distribution, summary measures, like the Gini and Zenga inequality indices, were evaluated, and the Zenga curve was employed to detect changes at each income quantile. Afterward, empirical distributions were compared through a relative approach, providing an analytic picture of the gender gap for both countries. The analysis moved beyond the typical focus on average or median earnings differences, towards a focus on how the full distribution of women’s earnings relative to men’s compares. The analysis was performed in the different macroregions of the two countries, with a discussion of the results. The study revealed that income inequality in Poland and Italy varies across gender and regions. In Italy, the highest inequality was observed in the poorest region, i.e. the islands. On the contrary, in Poland, the highest inequality occurred in the richest region, the central one. The relative distribution method was a powerful tool for studying the gender gap.

Through an analysis of the cumulative data of the American National Election Studies between 1984 and 2008, this study presents evidence of growing mass polarization in terms of standard ANES measures of ideological orientation using the public policy issue dimensions. The empirical findings here suggest that the degree of polarization among US citizens increased as the distributional center of measures of political ideology have progressively declined, though the opinion distribution of the later periods do not dramatically exhibit a text-book style polarized distribution (e.g., bimodal distribution). According to the findings, attitudes toward government guarantees have shifted back and forth between more liberal and more conservative positions while public opinion on cultural issues has generally moved more liberal positions over years.

We present an outline of relative distribution methods, with an application to recent changes in the U.S. wage distribution. Relative distribution methods are a nonparametric statistical framework for analyzing data in a fully distributional context. The framework combines the graphical tools of exploratory data analysis with statistical summaries, decomposition, and inference. The relative distribution is similar to a density ratio. It is technically defined as the random variable obtained by transforming a variable from a comparison group by the cumulative distribution function (CDF) of that variable for a reference group. This transformation produces a set of observations, the relative data, that represent the rank of the original comparison value in terms of the reference group's CDF. The density and CDF of the relative data can therefore be used to fully represent and analyze distributional differences. Analysis can move beyond comparisons of means and variances to tap the detailed information inherent in distributions. The analytic framework is general and flexible, as the relative density is decomposable into the effect of location and shape differences, and into effects that represent both compositional changes in covariates, and changes in the covariate-outcome variable relationship.

The chapter looks “behind” standard summary measures of inequality to identify where distributional changes occurred in eight societies over the longest period available in the Luxembourg Income Study Database, focusing on how inequality has grown in these societies (e.g., upgrading, downgrading, polarization). Methods based on the relative distribution are used to decompose overall distributional change into changes in location and shape. This is done for four high-income societies, three transitional societies, and Taiwan. A similar analysis is performed for female-headed households in the United Kingdom and the United States. The goal of this research is to use information on change in the first two moments of the income distribution to explore the degree to which various accounts of rising inequality and middle-class decline are consistent with the actual pattern of distributional change, and to generate new insights into this process.

On 27 August 2016, the Juno spacecraft acquired science observations of Jupiter, passing less than 5000 kilometers above the equatorial cloud tops. Images of Jupiter's poles show a chaotic scene, unlike Saturn's poles. Microwave sounding reveals weather features at pressures deeper than 100 bars, dominated by an ammonia-rich, narrow low-latitude plume resembling a deeper, wider version of Earth's Hadley cell. Near-infrared mapping reveals the relative humidity within prominent downwelling regions. Juno's measured gravity field differs substantially from the last available estimate and is one order of magnitude more precise. This has implications for the distribution of heavy elements in the interior, including the existence and mass of Jupiter's core. The observed magnetic field exhibits smaller spatial variations than expected, indicative of a rich harmonic content.

U.S. data reveal three facts: (1) the share of goods in total expenditure declines at a constant rate over time, (2) the price of goods relative to services declines at a constant rate over time, and (3) poor households spend a larger fraction of their budget on goods than do rich households. I provide a macroeconomic model with non-Gorman preferences that rationalizes these facts, along with the aggregate Kaldor facts. The model is parsimonious and admits an analytical solution. Its functional form allows a decomposition of U.S. structural change into an income and substitution effect. Estimates from micro data show each of these effects to be of roughly equal importance.

Background Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. Results We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl . Conclusions The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity. Key points RankCompV3 is a method for identifying differentially expressed genes (DEGs) in either bulk or single-cell RNA transcriptomics. It is based on the counts of relative expression orderings (REOs) of gene pairs in the two groups. The contingency tables are tested using McCullagh’s method. RankCompV3 has comparable or better performance than that of other conventional methods. It has been shown to be effective in identifying DEGs in both single-cell and pseudo-bulk profiles. Pseudo-bulk method is implemented in RankCompV3, which allows the method to achieve higher computational efficiency and improves the concordance with the bulk ground-truth. RankCompV3 is effective in identifying functionally relevant DEGs in weak-signal datasets. The method is not biased towards highly expressed genes.

Aerosol optical depth (AOD) has become a crucial metric for assessing global climate change. Although global and regional AOD trends have been studied extensively, it remains unclear what factors are driving the inter-decadal variations in regional AOD and how to quantify the relative contribution of each dominant factor. This study used a long-term (1980–2016) aerosol dataset from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis, along with two satellite-based AOD datasets (MODIS/Terra and MISR) from 2001 to 2016, to investigate the long-term trends in global and regional aerosol loading. Statistical models based on emission factors and meteorological parameters were developed to identify the main factors driving the inter-decadal changes of regional AOD and to quantify their contribution. Evaluation of the MERRA-2 AOD with the ground-based measurements of AERONET indicated significant spatial agreement on the global scale (r= 0.85, root-mean-square error = 0.12, mean fractional error = 38.7 %, fractional gross error = 9.86 % and index of agreement = 0.94). However, when AOD observations from the China Aerosol Remote Sensing Network (CARSNET) were employed for independent verification, the results showed that MERRA-2 AODs generally underestimated CARSNET AODs in China (relative mean bias = 0.72 and fractional gross error =−34.3 %). In general, MERRA-2 was able to quantitatively reproduce the annual and seasonal AOD trends on both regional and global scales, as observed by MODIS/Terra, although some differences were found when compared to MISR. Over the 37-year period in this study, significant decreasing trends were observed over Europe and the eastern United States. In contrast, eastern China and southern Asia showed AOD increases, but the increasing trend of the former reversed sharply in the most recent decade. The statistical analyses suggested that the meteorological parameters explained a larger proportion of the AOD variability (20.4 %–72.8 %) over almost all regions of interest (ROIs) during 1980–2014 when compared with emission factors (0 %–56 %). Further analysis also showed that SO2 was the dominant emission factor, explaining 12.7 %–32.6 % of the variation in AOD over anthropogenic-aerosol-dominant regions, while black carbon or organic carbon was the leading factor over the biomass-burning-dominant (BBD) regions, contributing 24.0 %–27.7 % of the variation. Additionally, wind speed was found to be the leading meteorological parameter, explaining 11.8 %–30.3 % of the variance over the mineral-dust-dominant regions, while ambient humidity (including soil moisture and relative humidity) was the top meteorological parameter over the BBD regions, accounting for 11.7 %–35.5 % of the variation. The results of this study indicate that the variation in meteorological parameters is a key factor in determining the inter-decadal change in regional AOD.