Measuring Racial Discrimination in Bail Decisions.

We develop new quasi-experimental tools to measure disparate impact, regardless of its source, in the context of bail decisions. We show that omitted variables bias in pretrial release rate comparisons can be purged by using the quasi-random assignment of judges to estimate average pretrial misconduct risk by race. We find that two-thirds of the release rate disparity between white and Black defendants in New York City is due to the disparate impact of release decisions. We then develop a hierarchical marginal treatment effect model to study the drivers of disparate impact, finding evidence of both racial bias and statistical discrimination.

Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination.

As people around the world work to stop the COVID-19 pandemic, mutated COVID-19 (Delta strain) that are more contagious are emerging in many places. How to develop effective and reasonable plans to prevent the spread of mutated COVID-19 is an important issue. In order to simulate the transmission of mutated COVID-19 (Delta strain) in China with a certain proportion of vaccination, we selected the epidemic situation in Jiangsu Province as a case study. To solve this problem, we develop a novel epidemic model with a vaccinated population. The basic properties of the model is analyzed, and the expression of the basic reproduction number R 0 is obtained. We collect data on the Delta strain epidemic in Jiangsu Province, China from July 20, to August 5, 2021. The weighted nonlinear least square estimation method is used to fit the daily asymptomatic infected people, common infected people and severe infected people. The estimated parameter values are obtained, the approximate values of the basic reproduction number are calculated R 0 ≈ 1.378 . Through the global sensitivity analysis, we identify some parameters that have a greater impact on the prevalence of the disease. Finally, according to the evaluation results of parameter influence, we consider three control measures (vaccination, isolation and nucleic acid testing) to control the spread of the disease. The results of the study found that the optimal control measure is to dynamically adjust the three control measures to achieve the lowest number of infections at the lowest cost. The research in this paper can not only enrich theoretical research on the transmission of COVID-19, but also provide reliable control suggestions for countries and regions experiencing mutated COVID-19 epidemics.

Optimal control and cost-effectiveness analysis of a new COVID-19 model for Omicron strain.

Omicron, a mutant strain of COVID-19, has been sweeping the world since November 2021. A major characteristic of Omicron transmission is that it is less harmful to healthy adults, but more dangerous for people with underlying disease, the elderly, or children. To simulate the spread of Omicron in the population, we developed a new 9-dimensional mathematical model with high-risk and low-risk exposures. Then we analyzed its dynamic properties and obtain the basic reproduction number R 0 . With the data of confirmed cases from March 1, 2022 published on the official website of Shanghai, China, we used the weighted nonlinear least square estimation method to estimate the parameters, and get the basic reproduction number R 0 ≈ 1 . 5118 . Finally, we considered three control measures (isolation, detection and treatment), and studied the optimal control strategy and cost-effectiveness analysis of the model. The control strategy G is determined to be the optimal control strategy from the purpose of making fewer people infected. In strategy G, the three human control measures contain six control variables, and the control strength of these variables needs to be varied according to the pattern shown in Figure 11, so that the number of infections can be minimized and the percentage of reduction of infections can reach more than 95%.

Seeing Covid-19 Through a Subprime Crisis lens: How Structural and Institutional Racism Have Shaped 21st-Century Crises in the U.K. and the U.S.

This special issue aims to use historical examples to gain insight into the socio-economic impact of, and possibilities of recovery from, the Covid-19 pandemic for Black communities. We approach this question by comparing the impact of the pandemic on Black Britons in the United Kingdom with that of the 2008 subprime crisis on Black Americans. We find that, in both cases, a pattern of racially asymmetric losses and race-neutral policy responses that have systematically ignored the disparate losses borne by Black and racial/ethnic minority communities. Both patterns are manifestations of these countries' institutional racism. Relying on insights from stratification economics and using the concept of "racial formation" introduced by Harold Baron in 1985, we show how these nations' historical relationships to slavery and imperialism have led to different structures of racial control. Our review of U.K. government policy includes a critique of the March 2021 report of the U.K. Commission on Race and Ethnic Disparities.

Minority student and teaching assistant interactions in STEM.

Graduate student teaching assistants from underrepresented groups may provide salient role models and enhanced instruction to minority students in STEM fields. We explore minority student-TA interactions in an important course in the sciences and STEM - introductory chemistry labs - at a large public university. The uncommon assignment method of students to TA instructors in these chemistry labs overcomes selection problems, and the small and active learning classroom setting with required attendance provides frequent interactions with the TA. We find evidence that underrepresented minority students are less likely to drop courses and are more likely to pass courses when assigned to minority TAs, but we do not find evidence of effects for grades and medium-term outcomes. The effects for the first-order outcomes are large with a decrease in the drop rate by 5.5 percentage points on a base of 6 percent, and an increase in the pass rate of 4.8 percentage points on a base of 93.6 percent. The findings are similar when we focus on Latinx student - Latinx TA interactions. The findings are robust to first-time vs. multiple enrollments in labs, specifications with different levels of fixed effects, limited choice of TA race, limited information of TAs, and low registration priority students. The findings have implications for debates over increasing diversity among PhD students in STEM fields because of spillovers to minority undergraduates.

The determinants of racial disparities in obesity: baseline evidence from a natural experiment.

This paper uses baseline data from an observational study to estimate the determinants of racial and gender disparities in obesity. Samples of low-income workers in Minneapolis and Raleigh reveal that respondents in Minneapolis have lower Body Mass Indices (BMIs) than respondents in Raleigh. There are large, statistically significant race and gender effects in estimates of BMI that explain most of the disparity between the two cities. Accounting for intersectionality - the joint impacts of being Black and a woman - reveals that almost all the BMI gaps between Black women in Minneapolis and Raleigh can be explained by age and education differences.

Employment Discrimination against Indigenous Peoples in the United States: Evidence from a Field Experiment.

We conducted an audit study - a resume correspondence experiment - to measure discrimination in hiring faced by Indigenous Peoples in the United States (Native Americans, Alaska Natives, and Native Hawaiians). We sent employers 13,516 realistic resumes of Indigenous or white applications for common jobs in 11 cities. We signalled Indigenous status in one of four different ways. Interview offer rates do not differ by race, which holds after an extensive battery of robustness checks. We discuss multiple concerns such as the saliency of signals, selection of cities and occupations, and labour market tightness that could affect the results of our audit study and those of others. We also conduct decompositions of wages, unemployment rates, unemployment durations, and employment durations to explore if discrimination might exist in contexts outside our experiment. We conclude by highlighting the essential tests and considerations that are important for future audit studies, regardless of if they find discrimination or not.

A New H 2 Regularity Condition of the Solution to Dirichlet Problem of the Poisson Equation and Its Applications.

We study the regularity of the solution of Dirichlet problem of Poisson equations over a bounded domain. A new sufficient condition, uniformly positive reach is introduced. Under the assumption that the closure of the underlying domain of interest has a uniformly positive reach, the H 2 regularity of the solution of the Poisson equation is established. In particular, this includes all star-shaped domains whose closures are of positive reach, regardless if they are Lipschitz domains or non-Lipschitz domains. Application to the strong solution to the second order elliptic PDE in non-divergence form and the regularity of Helmholtz equations will be presented to demonstrate the usefulness of the new regularity condition.

Deporting "Bad Hombres"? The Profile of Deportees under Widespread versus Prioritized Enforcement.

U.S. President Trump's administration has vowed to boost immigration enforcement to get rid of "bad hombres," or undocumented immigrants with criminal records. Using past data on the alleged detention motives for a representative sample of Mexican deportees, we evaluate how prior widespread and prioritized enforcement has fared in that regard. We find that while the early sweeping approach to enforcement raised deportees' propensity of being detained for minor offenses, the trend reversed with prioritized enforcement. These findings inform policy tactics that, aside from proving effective in prioritizing serious criminal offenses, can also lead to significant savings to taxpayers.

How Does Household Income Affect Child Personality Traits and Behaviors?

We examine the effects of a quasi-experimental unconditional household income transfer on child emotional and behavioral health and personality traits. Using longitudinal data, we find that there are large beneficial effects on children's emotional and behavioral health and personality traits during adolescence. We find evidence that these effects are most pronounced for children who start out with the lowest initial endowments. The income intervention also results in improvements in parental relationships which we interpret as a potential mechanism behind our findings.

Greater accuracy and broadened applicability of phase reduction using isostable coordinates.

The applicability of phase models is generally limited by the constraint that the dynamics of a perturbed oscillator must stay near its underlying periodic orbit. Consequently, external perturbations must be sufficiently weak so that these assumptions remain valid. Using the notion of isostables of periodic orbits to provide a simplified coordinate system from which to understand the dynamics transverse to a periodic orbit, we devise a strategy to correct for changing phase dynamics for locations away from the limit cycle. Consequently, these corrected phase dynamics allow for perturbations of larger magnitude without invalidating the underlying assumptions of the reduction. The proposed reduction strategy yields a closed set of equations and can be applied to periodic orbits embedded in arbitrarily high dimensional spaces. We illustrate the utility of this strategy in two models with biological relevance. In the first application, we find that an optimal control strategy for modifying the period of oscillation can be improved with the corrected phase reduction. In the second, the corrected phase reduced dynamics are used to understand adaptation and memory effects resulting from past perturbations.

Optimal control analysis of a tuberculosis model.

In this paper, we extend the model of Liu and Zhang (Math Comput Model 54:836-845, 2011) by incorporating three control terms and apply optimal control theory to the resulting model. Optimal control strategies are proposed to minimize both the disease burden and the intervention cost. We prove the existence and uniqueness of optimal control paths and obtain these optimal paths analytically using Pontryagin's Maximum Principle. We analyse our results numerically to compare various strategies of proposed controls. It is observed that implementation of three controls is most effective and less expensive among all the strategies. Thus, we conclude that in order to reduce tuberculosis threat all the three controls must be taken into consideration concurrently.

CONFOUNDER ADJUSTMENT IN MULTIPLE HYPOTHESIS TESTING.

We consider large-scale studies in which thousands of significance tests are performed simultaneously. In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e.g., treatment variable, phenotype) and the outcome. Over the past decade, many statistical methods have been proposed to adjust for the confounders in hypothesis testing. We unify these methods in the same framework, generalize them to include multiple primary variables and multiple nuisance variables, and analyze their statistical properties. In particular, we provide theoretical guarantees for RUV-4 [Gagnon-Bartsch, Jacob and Speed (2013)] and LEAPP [Ann. Appl. Stat. 6 (2012) 1664-1688], which correspond to two different identification conditions in the framework: the first requires a set of "negative controls" that are known a priori to follow the null distribution; the second requires the true nonnulls to be sparse. Two different estimators which are based on RUV-4 and LEAPP are then applied to these two scenarios. We show that if the confounding factors are strong, the resulting estimators can be asymptotically as powerful as the oracle estimator which observes the latent confounding factors. For hypothesis testing, we show the asymptotic z-tests based on the estimators can control the type I error. Numerical experiments show that the false discovery rate is also controlled by the Benjamini-Hochberg procedure when the sample size is reasonably large.

Variable-coefficient parabolic theory as a high-dimensional limit of elliptic theory.

This paper continues the study initiated in Davey (Arch Ration Mech Anal 228:159-196, 2018), where a high-dimensional limiting technique was developed and used to prove certain parabolic theorems from their elliptic counterparts. In this article, we extend these ideas to the variable-coefficient setting. This generalized technique is demonstrated through new proofs of three important theorems for variable-coefficient heat operators, one of which establishes a result that is, to the best of our knowledge, also new. Specifically, we give new proofs of L2→L2 Carleman estimates and the monotonicity of Almgren-type frequency functions, and we prove a new monotonicity of Alt-Caffarelli-Friedman-type functions. The proofs in this article rely only on their related elliptic theorems and a limiting argument. That is, each parabolic theorem is proved by taking a high-dimensional limit of a related elliptic result.

Economic and labour market impacts of migration in Austria: an agent-based modelling approach.

This study examines the potential economic and labour market impacts of a hypothetical but plausible migration scenario of 250,000 new migrants inspired by Austria's experience in 2015. Using the agent-based macroeconomic model developed by Poledna et al. (Eur Econ Rev, 151:104306, 2023. 10.1016/j.euroecorev.2022.104306, the study explores the detailed labour market outcomes for different groups in Austria's population and the macroeconomic effects of the migration scenario. The analysis suggests that Austria's economy and labour market have the potential to be resilient to the simulated migration influx. The results indicate a positive impact on GDP due to increased aggregate consumption and investment. The labour market experiences an increase in the unemployment rates of natives and previous migrants. In some industries, the increase in the unemployment rates is more significant, potentially indicating competition among different groups of migrants. This research provides insights for policymakers and stakeholders in Austria and other countries that may face the challenge of managing large-scale migration in the near future. Supplementary Information: The online version contains supplementary material available at 10.1186/s40878-024-00374-3.

Beyond Black and White: Color and Mortality in Post Reconstruction Era North Carolina.

A growing empirical literature in economics and sociology documents the existence of differences in social and economic outcomes between mixed-race blacks and other blacks . However, few researchers have considered whether the advantages associated with mixed-race status may have also translated into differences in mortality outcomes between subgroups of blacks and how both groups compared to whites. We employ previously untapped 1880 North Carolina Mortality census records in conjunction with data from the 1880 North Carolina Population Census to examine whether mulatto, or mixed-race blacks may have experienced mortality advantages over to their colored, or non-mixed race counterparts. For men between the ages of 20-44, estimates demonstrate that all black males are more likely than whites to die. Although our results indicate that there are no statistically significant differences in mortality between mulatto and colored blacks, there are some indications that mulatto males may have enjoyed a slight mortality advantage compared to their colored counterparts. However, we find a substantial mortality advantage associated with mixed-race status among women. These findings indicate that mixed-race women, rather than men, may have accrued any mortality advantages associated with color and white ancestry.

Transcriptome Analysis Reveals That C17 Mycosubtilin Antagonizes Verticillium dahliae by Interfering with Multiple Functional Pathways of Fungi.

Verticillium wilt is a kind of soil-borne plant fungal disease caused by Verticillium dahliae (Vd). Vd 991 is a strong pathogen causing cotton Verticillium wilt. Previously, we isolated a compound from the secondary metabolites of Bacillus subtilis J15 (BS J15), which showed a significant control effect on cotton Verticillium wilt and was identified as C17 mycosubtilin. However, the specific fungistatic mechanism by which C17 mycosubtilin antagonizes Vd 991 is not clear. Here, we first showed that C17 mycosubtilin inhibits the growth of Vd 991 and affects germination of spores at the minimum inhibitory concentration (MIC). Morphological observation showed that C17 mycosubtilin treatment caused shrinking, sinking, and even damage to spores; the hyphae became twisted and rough, the surface was sunken, and the contents were unevenly distributed, resulting in thinning and damage to the cell membrane and cell wall and swelling of mitochondria of fungi. Flow cytometry analysis with ANNEXINV-FITC/PI staining showed that C17 mycosubtilin induces necrosis of Vd 991 cells in a time-dependent manner. Differential transcription analysis showed that C17 mycosubtilin at a semi-inhibitory concentration (IC50) treated Vd 991 for 2 and 6 h and inhibited fungal growth mainly by destroying synthesis of the fungal cell membrane and cell wall, inhibiting its DNA replication and transcriptional translation process, blocking its cell cycle, destroying fungal energy and substance metabolism, and disrupting the redox process of fungi. These results directly showed the mechanism by which C17 mycosubtilin antagonizes Vd 991, providing clues for the mechanism of action of lipopeptides and useful information for development of more effective antimicrobials.

A non-linear mathematical model for analysing the impact of COVID-19 disease on higher education in developing countries.

This study proposes a non-linear mathematical model for analysing the effect of COVID-19 dynamics on the student population in higher education institutions. The theory of positivity and boundedness of solution is used to investigate the well-posedness of the model. The disease-free equilibrium solution is examined analytically. The next-generation operator method calculates the basic reproduction number (R0). Sensitivity analyses are carried out to determine the relative importance of the model parameters in spreading COVID-19. In light of the sensitivity analysis results, the model is further extended to an optimal control problem by introducing four time-dependent control variables: personal protective measures, quarantine (or self-isolation), treatment, and management measures to mitigate the community spread of COVID-19 in the population. Simulations evaluate the effects of different combinations of the control variables in minimizing COVID-19 infection. Moreover, a cost-effectiveness analysis is conducted to ascertain the most effective and least expensive strategy for preventing and controlling the spread of COVID-19 with limited resources in the student population.

Causal Inference of Social Experiments using Orthogonal Designs.

Orthogonal Arrays are a powerful class of experimental designs that has been widely used to determine efficient arrangements of treatment factors in randomized controlled trials. Despite its popularity, the method is seldom used in social sciences. Social experiments must cope with randomization compromises such as noncompliance that often prevents the use of elaborate designs. We present a novel application of orthogonal designs that addresses the particular challenges arising in social experiments. We characterize the identification of counterfactual variables as a finite mixture problem in which choice incentives, rather than treatment factors, are randomly assigned. We show that the causal inference generated by an orthogonal array of incentives greatly outperforms a traditional design.

Metabolites from Bacillus subtilis J-15 Affect Seedling Growth of Arabidopsis thaliana and Cotton Plants.

Bacillus subtilis J-15 is a plant growth-promoting rhizobacteria isolated from the soil rhizosphere of cotton and is resistant to cotton verticillium wilt. This study evaluated the effects of metabolites of J-15 (J-15-Ms), including mycosubtilin, on plant growth using Arabidopsis and cotton plants. The results showed that J-15-Ms promoted Arabidopsis seeding growth at lower concentrations of 0.2 µg/mL but inhibited the growth at higher concentrations, such as 20 µg/mL. Similar results were obtained in cotton. Thus, J-15-Ms-treated plants showed low-concentration-induced growth promotion and high-concentration-induced growth inhibition. The J-15-Ms components were analyzed by liquid chromatography-mass spectrometry. Correlation analysis using the J-15 genomic databases suggested that J-15 may synthesize indoleacetic acid via the indole-3-pymvate pathway and indole-3-acetamide pathway. Treatment with mycosubtilin, a purified peptide from J-15-Ms, showed that the peptide promoted Arabidopsis growth at a low concentration (0.1 µg/mL) and inhibited plant growth at high concentrations (higher than 1 µg/mL), which also significantly increased plant lateral root number. Transcriptomic analysis showed that mycosubtilin might promote lateral root development and inhibit plant primary root growth by regulating the expression of the plant hormone signaling pathway. This study reveals the mechanism of Bacillus subtilis J-15 in affecting plant growth.