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We examine the cardinal gap between wage distributions of the incumbents and newly hired workers based on entropic distances which are well-defined welfare theoretic measures. Decomposition of several effects is achieved by identifying several counterfactual distributions of different groups. These go beyond the usual Oaxaca–Blinder decompositions at the (linear) conditional means. Much like quantiles, these entropic distances are well-defined inferential objects and functions whose statistical properties have recently been developed. Going beyond these strong rankings and distances, we consider weak uniform ranking of these wage outcomes based on statistical tests for stochastic dominance. The empirical analysis is focused on employees with at least 35 hours of work in the 1996–2012 monthly Current Population Survey (CPS). Among others, we find incumbent workers enjoy a better distribution of wages, but the attribution of the gap to wage inequality and human capital characteristics varies between quantiles. For instance, highly paid new workers are mainly due to human capital components, and in some years, even better wage structure.

Although in principle prior information can significantly improve inference, incorporating incorrect prior information will bias the estimates of any inferential analysis. This fact deters many scientists from incorporating prior information into their inferential analyses. In the natural sciences, where experiments are more regularly conducted, and can be combined with other relevant information, prior information is often used in inferential analysis, despite it being sometimes nontrivial to specify what that information is and how to quantify that information. In the social sciences, however, prior information is often hard to come by and very hard to justify or validate. We review a number of ways to construct such information. This information emerges naturally, either from fundamental properties and characteristics of the systems studied or from logical reasoning about the problems being analyzed. Borrowing from concepts and philosophical reasoning used in the natural sciences, and within an info-metrics framework, we discuss three different, yet complimentary, approaches for constructing prior information, with an application to the social sciences.

We replicate and extend the social history treatment of the Berg, Dickhaut, and McCabe (1995) investment game, to further document how the reporting of financial history influences how laboratory societies organize themselves over time. We replicate Berg et al. (1995) by conducting a No History and a Financial History session to determine whether a report summarizing the financial transactions of a previous experimental session will significantly reduce entropy in the amounts sent by Investors and returned by Stewards in the investment game, as Berg et al. (1995) found. We extend Berg et al. (1995) in two ways. First, we conduct a total of five sessions (one No History and four Financial History sessions). Second, we introduce Shannon’s (1948) measure of entropy from information theory to assess whether the introduction of financial transaction history reduces the amount of dispersion in the amounts invested and returned across generations of players. Results across sessions indicate that entropy declined in both the amounts sent by Investors and the percentage returned by Stewards, but these patterns are weaker and mixed compared to those in the Berg et al. (1995) study. Additional research is needed to test how initial conditions, path dependencies, actors’ strategic reasoning about others’ behavior, multiple sessions, and communication may mediate the impact of financial history. The study’s multiple successive Financial History sessions and entropy measure are new to the investment game literature.

We examine the potential effect of naturalization on the U.S. immigrants’ earnings. We find the earning gap between naturalized citizens and noncitizens is positive over many years, with a tent shape across the wage distribution. We focus on a normalized metric entropy measure of the gap between distributions, and compare with conventional measures at the mean, median, and other quantiles. In addition, naturalized citizen earnings (at least) second-order stochastically dominate noncitizen earnings in many of the recent years. We construct two counterfactual distributions to further examine the potential sources of the earning gap, the “wage structure” effect and the “composition” effect. Both of these sources contribute to the gap, but the composition effect, while diminishing somewhat after 2005, accounts for about 3/4 of the gap. The unconditional quantile regression (based on the Recentered Influence Function), and conditional quantile regressions confirm that naturalized citizens have generally higher wages, although the gap varies for different income groups, and has a tent shape in many years.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

Building on recent advances in inverse probability weighted identification and estimation of counterfactual distributions, the authors examine the history of wage earnings for women and their potential wage distributions in the United States. These potentials are two counterfactuals, what if women received men’s market “rewards” for their own “skills,” and what if they received the women’s rewards but for men’s characteristics? Using the Current Population Survey data from 1976 to 2013, the authors analyze the entire counterfactual distributions to separate the “structure” and human capital “composition” effect. In contrast to Maasoumi and Wang (2019), the reference outcome in these decompositions is women’s observed earnings distribution, and inverse probability methods are employed, rather than the conditional quantile approaches. The authors provide decision theoretic measures of the distance between two distributions, to complement assessments based on mean, median, or particular quantiles. We assess uniform rankings of alternate distributions by tests of stochastic dominance in order to identify evaluations robust to subjective measures. Traditional moment-based measures severely underestimate the declining trend of the structure effect. Nevertheless, dominance rankings suggest that the structure (“discrimination”?) effect is bigger than human capital characteristics.

Despite a legacy of research that emphasizes contradictions and their role in explaining change, less is understood about their character or the mechanisms that support them. This gap is especially problematic when making causal claims about the sources of institutional change and our overall conceptions of how institutions matter in social meanings and organizational practices. If we treat contradictions as a persistent societal feature, then a primary analytic task is to distinguish their prevalence from their effects. We address this gap in the context of US electoral discourse and education through an analysis of presidential platforms. We ask how contradictions take hold, persist, and might be observed prior to, or independently of, their strategic use. Through a novel combination of content analysis and computational linguistics, we observe contradictions in qualitative differences in form and quantitative differences in degree. Whereas much work predicts that ideologies produce contradictions between groups, our analysis demonstrates that they actually support convergence in meaning between groups while promoting contradiction within groups.

Our physical universe is 1.5×10¹⁰ years old. It began with the Big Bang. There is some debate about what happened in the first tenth of a second! The first 3×10⁵ years were radiation dominated. Since then it has been matter dominated. (This in accordance with the first law of thermodynamics which states that total mass-energy is conserved.) The universe has continuously expanded in space and in the future either this may continue, or expansion may stabilise at a fixed size or the universe may contract in the Big Crunch (depending on the spatial curvature). At a certain scale the universe is spatially isotropic and homogeneous. Its trajectory exhibits increasing entropy in accordance with the second law of thermodynamics. These statements are in accordance with certain models and empirical data: distant galaxies are receding from us at a velocity proportional to their distance; there is greater spatial uniformity at greater distances from us; there is uniform presence in space of radiation with a temperature of 2.7K; etc.