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A 70-year-old simmering debate has erupted into vigorous battles over the most effective ways to conduct research. Well-established beliefs are being forcefully challenged by advocates of new research models. While there can be no final resolution to this battle, this paper offers the Twin-Win Model to guide teams of researchers, academic leaders, business managers, and government funding policymakers. The Twin-Win Model favors a problem-oriented approach to research, which encourages formation of teams to pursue the dual goals of breakthrough theories in published papers and validated solutions that are ready for widespread dissemination. The raised expectations of simultaneously pursuing foundational discoveries and powerful innovations are a step beyond traditional approaches that advocate basic research first. Evidence from citation analysis and researcher interviews suggests that simultaneous pursuit of both goals raises the chance of twin-win success. [ABSTRACT FROM AUTHOR]

In this article, we present the results from a large-scale field experiment designed to measure racial discrimination among the American public. We conducted an audit study on the general public--sending correspondence to 250,000 citizens randomly drawn from public voter registration lists. Our within-subjects experimental design tested the public's responsiveness to electronically delivered requests to volunteer their time to help with completing a simple task--taking a survey. We randomized whether the request came from either an ostensibly Black or an ostensibly White sender. We provide evidence that in electronic interactions, on average, the public is less likely to respond to emails from people they believe to be Black (rather than White). Our results give us a snapshot of a subtle form of racial bias that is systemic in the United States. What we term everyday or "paper cut" discrimination is exhibited by all racial/ethnic subgroups--outside of Black people themselves--and is present in all geographic regions in the United States. We benchmark paper cut discrimination among the public to estimates of discrimination among various groups of social elites. We show that discrimination among the public occurs more frequently than discrimination observed among elected officials and discrimination in higher education and themedical sector but simultaneously, less frequently than discrimination in housing and employment contexts. Our results provide a window into the discrimination that Black people in the United States face in day-to-day interactions with their fellow citizens. [ABSTRACT FROM AUTHOR]

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers. [ABSTRACT FROM AUTHOR]

The US global leadership in science and technology has greatly benefitted from immigrants from other countries, most notably from China in the recent decades. However, feeling the pressure of potential federal investigations since the 2018 launch of the China Initiative, scientists of Chinese descent in the United States now face higher incentives to leave the United States and lower incentives to apply for federal grants. Analyzing data pertaining to institutional affiliations of more than 200 million scientific papers, we find a steady increase in the return migration of scientists of Chinese descent from the United States to China. We also conducted a survey of scientists of Chinese descent employed by US universities in tenured or tenure- track positions (n = 1,304), with results revealing general feelings of fear and anxiety that lead them to consider leaving the United States and/or stop applying for federal grants. If the situation is not corrected, American science will likely suffer the loss of scientific talent to China and other countries. [ABSTRACT FROM AUTHOR]

This study investigates Black and White consumers' preferences for Black versus White people in United States advertising contexts over 66 y, from 1956 until 2022, a time in which the United States has experienced significant ethno-racial diversification. Examining Black and White consumers' reactions to visual advertising over more than half a century offers a unique and dynamic view of interracial preferences. Mass advertising reaches an audience of billions and can shape people's attitudes and behavior, emphasizing the relevance of clarifying the influence of race in advertising, how it has evolved over time, and how it may contribute to mitigating discrimination based on racial perceptions. A meta-analysis of extant experiments into the relationship between the depicted endorser's race (i.e., the model in a visual ad) and the reaction of Black and White viewers pertains to 332 effect sizes from 62 studies reported in 52 scientific papers, comprising 10,186 Black and White participants. Our results are anchored in a conceptual framework, including a comprehensive set of perceiver (viewer), target (endorser), social/societal context, and publication characteristics. Without accounting for temporal dynamics, the results indicate ingroup favoritism, such that White viewers prefer White models and Black viewers prefer Black models. But by controlling for the publication year, it is possible to observe a time-dependent trend: Historically, White consumers preferred endorsers of the same race, but this preference has significantly shifted toward Black endorsers in recent years. In contrast, the level of Black consumers' reactions to endorsers of the same race remains largely unchanged over time. [ABSTRACT FROM AUTHOR]

This paper examines whether school COVID-19 policies influenced enrollment differently by student age and race/ethnicity. Unlike much prior research, we i) analyze enrollments for virtually the entire U.S. public school population for both the 2020- 2021 and 2021-2022 school years, ii) compare enrollment trends within districts in order to isolate subgroup heterogeneity from district characteristics, and iii) account for district selection into preferred learning modes. Analyzing data on over 9,000 districts that serve more than 90% of public school students in the United States, we find enrollment responses to COVID policies differed notably. We find that White enrollments declined more than Black, Hispanic, and Asian enrollments in districts that started the 2020-2021 school year virtually, but in districts that started in-person the reverse was true: Non-White enrollments declined more than White enrollments. Moreover, Black, Hispanic, and Asian families responded more than White families to higher COVID-19 death rates in the months preceding the start of the 2021 school year. In 2021-2022, enrollment differences by the previous year's learning mode persisted. Racial/ethnic differences did not vary by whether the district required masking in classrooms. These findings are consistent with the greater risk faced by communities of color during the pandemic and demonstrate an additional source of disparate impact from COVID policies. [ABSTRACT FROM AUTHOR]

Concern over democratic erosion has led to a proliferation of proposed interventions to strengthen democratic attitudes in the United States. Resource constraints, however, prevent implementing all proposed interventions. One approach to identify promising interventions entails leveraging domain experts, who have knowledge regarding a given field, to forecast the effectiveness of candidate interventions. We recruit experts who develop general knowledge about a social problem (academics), experts who directly intervene on the problem (practitioners), and nonexperts from the public to forecast the effectiveness of interventions to reduce partisan animosity, support for undemocratic practices, and support for partisan violence. Comparing 14,076 forecasts submitted by 1,181 forecasters against the results of a megaexperiment (n = 32,059) that tested 75 hypothesized effects of interventions, we find that both types of experts outperformed members of the public, though experts differed in how they were accurate. While academics' predictions were more specific (i.e., they identified a larger proportion of ineffective interventions and had fewer false-positive forecasts), practitioners' predictions were more sensitive (i.e., they identified a larger proportion of effective interventions and had fewer false-negative forecasts). Consistent with this, practitioners were better at predicting best-performing interventions, while academics were superior in predicting which interventions performed worst. Our paper highlights the importance of differentiating types of experts and types of accuracy. We conclude by discussing factors that affect whether sensitive or specific forecasters are preferable, such as the relative cost of false positives and negatives and the expected rate of intervention success. [ABSTRACT FROM AUTHOR]

Many studies anticipate that carbon capture and sequestration (CCS) will be essential to decarbonizing the U.S. economy. However, prior work has not estimated the time required to develop, approve, and implement a geologic sequestration site in the United States. We generate such an estimate by identifying six clearance points that must be passed before a sequestration site can become operational. For each clearance point (CP), we elicit expert judgments of the time required in the form of probability distributions and then use stochastic simulation to combine and sum the results. We find that, on average, there is a 90% chance that the time required lies between 5.5 and 9.6 y, with an upper bound of 12 y. Even using the most optimistic expert judgements, the lower bound on time is 2.7 y, and the upper bound is 8.3 y. Using the most pessimistic judgements, the lower bound is 3.5 y and the upper bound is 19.2 y. These estimates suggest that strategies must be found to safely accelerate the process. We conclude the paper by discussing seven potential strategies. [ABSTRACT FROM AUTHOR]

Renewable Portfolio Standards (RPSs) are one of the most prevalent and impactful clean energy policies implemented by states in the United States. This paper investigates the regional spillover effect of RPS policies using a directed dyad panel dataset of renewable electricity generation in US states from 1991 to 2021. Regional spillover effect is measured in two ways: by considering the influence of an RPS enacted in neighboring states and in states in the same regional transmission organization or independent system operator region. We use dyadic fixed effects estimation and conclude that the neighboring state's RPS stringency score is a strong determinant of a state's total renewable electricity generation. For states without an RPS, the positive influence of an RPS in a neighboring state is larger when the non-RPS state has more abundant renewable energy resources than the neighboring RPS state. Our findings suggest that past RPS policy evaluation research using a confined within-state focus may have underestimated the holistic impact of an RPS, as the impacts of an RPS policy can extend beyond the enacting state's borders. Overall, this study contributes to an improved understanding of the holistic impact of state RPS policies. [ABSTRACT FROM AUTHOR]

A longstanding line of research in urban studies explores how cities can be understood through their appearance. However, what remains unclear is to what extent urban dwellers' everyday life can be explained by the visual clues of the urban environment. In this paper, we address this question by applying a computer vision model to 27 million street view images across 80 counties in the United States. Then, we use the spatial distribution of notable urban features identified through the street view images, such as street furniture, sidewalks, building façades, and vegetation, to predict the socioeconomic profiles of their immediate neighborhood. Our results show that these urban features alone can account for up to 83% of the variance in people's travel behavior, 62% in poverty status, 64% in crime, and 68% in health behaviors. The results outperform models based on points of interest (POI), population, and other demographic data alone. Moreover, incorporating urban features captured from street view images can improve the explanatory power of these other methods by 5% to 25%. We propose "urban visual intelligence" as a process to uncover hidden city profiles, infer, and synthesize urban information with computer vision and street view images. This study serves as a foundation for future urban research interested in this process and understanding the role of visual aspects of the city. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making. [ABSTRACT FROM AUTHOR]

Major changes to the operation of local newsrooms--ownership restructuring, layoffs, and a reorientation away from print advertising--have become commonplace in the last few decades. However, there have been few systematic attempts to characterize the impact of these changes on the types of reporting that local newsrooms produce. In this paper, we propose a method to measure the investigative content of news articles based on article text and influence on subsequent articles. We use our method to examine over-time and cross-sectional patterns in news production by local newspapers in the United States over the past decade. We find surprising stability in the quantity of investigative articles produced over most of the time period examined, but a notable decline in the last 2 y of the decade, corresponding to a recent wave of newsroom layoffs. [ABSTRACT FROM AUTHOR]

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non- White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities. [ABSTRACT FROM AUTHOR]

This paper estimates the financial benefits accruing to fossil fuel producers (i.e., the producer incidence) that arise because of implicit fossil fuel subsidies in the United States. The analysis takes account of coal, natural gas, gasoline, and diesel, along with the implicit subsidies due to externalized environmental damages, public health effects, and transportation-related costs. The direct benefit to fossil fuel producers across all four fuels is estimated at $62 billion per year, a sum calculated due to the higher price that suppliers receive because of inefficient pricing compared to the counterfactual scenario where environmental and public health externalities are internalized. A significant portion of these benefits accrue to relatively few companies, and specific estimates are provided for companies with the largest production. The financial benefit because of unpriced costs borne by society is comparable to 18% of net income from continuing domestic operations for the median natural gas and oil producer in 2017-2018, and it exceeds net income for the majority of coal producers. The results clarify what the domestic fossil fuel industry has at stake financially when it comes to policies that seek to address climate change, adverse health effects from local pollution, and inefficient transportation. [ABSTRACT FROM AUTHOR]

This paper presents estimates of the prevalence of dementia in the United States from 2000 to 2016 by age, sex, race and ethnicity, education, and a measure of lifetime earnings, using data on 21,442 individuals aged 65 y and older and 97,629 person-year observations from a nationally representative survey, the Health and Retirement Study (HRS). The survey includes a range of cognitive tests, and a subsample underwent clinical assessment for dementia. We developed a longitudinal, latent-variable model of cognitive status, which we estimated using the Markov Chain Monte Carlo method. This model provides more accurate estimates of dementia prevalence in population subgroups than do previously used methods on the HRS. The age-adjusted prevalence of dementia decreased from 12.2% in 2000 (95% CI, 11.7 to 12.7%) to 8.5% in 2016 (7.9 to 9.1%) in the 65+ population, a statistically significant decline of 3.7 percentage points or 30.1%. Females are more likely to live with dementia, but the sex difference has narrowed. In the male subsample, we found a reduction in inequalities across education, earnings, and racial and ethnic groups; among females, those inequalities also declined, but less strongly. We observed a substantial increase in the level of education between 2000 and 2016 in the sample. This compositional change can explain, in a statistical sense, about 40% of the reduction in dementia prevalence among men and 20% among women, whereas compositional changes in the older population by age, race and ethnicity, and cardiovascular risk factors mattered less. [ABSTRACT FROM AUTHOR]

Studies in the United States have shown that minority students might face a trade-off between better academic performance and peer acceptance, which has been termed "acting White." This paper investigates racial differences in the relationship between grades and popularity in five Brazilian schools. Popularity is measured using friendship ties among students, assigning a higher value to students more central in the network. The racial composition of friendship ties is generally diverse, although they tend to favor racial peers, especially among Black students. We find a positive correlation between grades and popularity of non-White students that is driven by their friendships with their White classmates. This contrasts with patterns associated with acting White, where a negative correlation between minorities' grades and their popularity among racial peers is not compensated by their status among White students. We also investigate how academic performance is associated with racial identity choice conditional on skin color, finding a weak negative relationship between higher grades and the odds of classification as mixed race. [ABSTRACT FROM AUTHOR]

In response to coronavirus disease 2019 (COVID-19), many scholars and policy makers are urging the United States to expand voting-by-mail programs to safeguard the electoral process. What are the effects of vote-by-mail? In this paper, we provide a comprehensive design-based analysis of the effect of universal vote-by-mail--a policy under which every voter is mailed a ballot in advance of the election--on electoral outcomes. We collect data from 1996 to 2018 on all three US states that implemented universal vote-by-mail in a staggered fashion across counties, allowing us to use a difference-in-differences design at the county level to estimate causal effects. We find that 1) universal vote-by-mail does not appear to affect either party's share of turnout, 2) universal vote-by-mail does not appear to increase either party's vote share, and 3) universal vote-by-mail modestly increases overall average turnout rates, in line with previous estimates. All three conclusions support the conventional wisdom of election administration experts and contradict many popular claims in the media. [ABSTRACT FROM AUTHOR]

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs (P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water suppliesand runoff-related risks. [ABSTRACT FROM AUTHOR]

Vascular disease is a leading cause of morbidity and mortality in the United States and globally. Pathological vascular remodeling, such as atherosclerosis and stenosis, largely develop at arterial sites of curvature, branching, and bifurcation, where disturbed blood flow activates vascular endothelium. Current pharmacological treatments of vascular complications principally target systemic risk factors. Improvements are needed. We previously devised a targeted polyelectrolyte complex micelle to deliver therapeutic nucleotides to inflamed endothelium in vitro by displaying the peptide VHPKQHR targeting vascular cell adhesion molecule 1 (VCAM1) on the periphery of the micelle. This paper explores whether this targeted nanomedicine strategy effectively treats vascular complications in vivo. Disturbed flow-induced microRNA-92a (miR-92a) has been linked to endothelial dysfunction. We have engineered a transgenic line (miR-92aEC-TG/Apoe-/-) establishing that selective miR-92a overexpression in adult vascular endothelium causally promotes atherosclerosis in Apoe-/- mice. We tested the therapeutic effectiveness of the VCAM-1–targeting polyelectrolyte complex micelles to deliver miR-92a inhibitors and treat pathological vascular remodeling in vivo. VCAM-1–targeting micelles preferentially delivered miRNA inhibitors to inflamed endothelial cells in vitro and in vivo. The therapeutic effectiveness of anti–miR-92a therapy in treating atherosclerosis and stenosis in Apoe-/- mice is markedly enhanced by the VCAM-1–targeting polyelectrolyte complex micelles. These results demonstrate a proof of concept to devise polyelectrolyte complex micelle-based targeted nanomedicine approaches treating vascular complications in vivo [ABSTRACT FROM AUTHOR]

The form of political polarization where citizens develop strongly negative attitudes toward out-party members and policies has become increasingly prominent across many democracies. Economic hardship and social inequality, as well as intergroup and racial conflict, have been identified as important contributing factors to this phenomenon known as “affective polarization.” Research shows that partisan animosities are exacerbated when these interests and identities become aligned with existing party cleavages. In this paper, we use a model of cultural evolution to study how these forces combine to generate and maintain affective political polarization. We show that economic events can drive both affective polarization and the sorting of group identities along party lines, which, in turn, can magnify the effects of underlying inequality between those groups. But, on a more optimistic note, we show that sufficiently high levels of wealth redistribution through the provision of public goods can counteract this feedback and limit the rise of polarization. We test some of our key theoretical predictions using survey data on intergroup polarization, sorting of racial groups, and affective polarization in the United States over the past 50 y. [ABSTRACT FROM AUTHOR]

This paper empirically examines how the opening of K--12 schools is associated with the spread of COVID-19 using county-level panel data in the United States. As preliminary evidence, our event-study analysis indicates that cases and deaths in counties with in-person or hybrid opening relative to those with remote opening substantially increased after the school opening date, especially for counties without any mask mandate for staff. Our main analysis uses a dynamic panel data model for case and death growth rates, where we control for dynamically evolving mitigation policies, past infection levels, and additive countylevel and state-week "fixed" effects. This analysis shows that an increase in visits to both K--12 schools and colleges is associated with a subsequent increase in case and death growth rates. The estimates indicate that fully opening K--12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the association of K--12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These findings support policies that promote masking and other precautionary measures at schools and giving vaccine priority to education workers. [ABSTRACT FROM AUTHOR]

Ethnoracial residential segregation is a complex, multiscalar phenomenon with immense moral and economic costs. Modeling the structure and dynamics of segregation is a pressing problem for sociology and urban planning, but existing methods have limitations. In this paper, we develop a suite of methods, grounded in information theory, for studying the spatial structure of segregation. We first advance existing profile and decomposition methods by posing two related regionalization methods, which allow for profile curves with nonconstant spatial scale and decomposition analysis with nonarbitrary areal units. We then formulate a measure of local spatial scale, which may be used for both detailed, within-city analysis and intercity comparisons. These methods highlight detailed insights in the structure and dynamics of urban segregation that would be otherwise easy to miss or difficult to quantify. They are computationally efficient, applicable to a broad range of study questions, and freely available in open source software. [ABSTRACT FROM AUTHOR]

Land conservation efforts throughout the United States sustain ecological benefits while generating wealth in the housing market through capitalization of amenities. This paper estimates the benefits of conservation that are capitalized into proximate home values and quantifies how those benefits are distributed across demographic groups. Using detailed property and household-level data from Massachusetts, we estimate that new land conservation led to $62 million in new housing wealth equity. However, houses owned by low-income or Black or Hispanic households are less likely to be located near protected areas, and hence, these populations are less likely to benefit financially. Direct study of the distribution of this new wealth from capitalized conservation is highly unequal, with the richest quartile of households receiving 43%, White households receiving 91%, and the richest White households receiving 40%, which is nearly 140% more than would be expected under equal distribution. We extend our analysis using census data for the entire United States and observe parallel patterns. We estimate that recent land conservation generated $9.8 billion in wealth through the housing market and that wealthier and White households benefited disproportionately. These findings suggest regressive and racially disparate incidence of the wealth benefits of land conservation policy. [ABSTRACT FROM AUTHOR]

The abundance of media options is a central feature of today's information environment. Many accounts, often based on analysis of desktop-only news use, suggest that this increased choice leads to audience fragmentation, ideological segregation, and echo chambers with no cross-cutting exposure. Contrary to many of those claims, this paper uses observational multiplatform data capturing both desktop and mobile use to demonstrate that coexposure to diverse news is on the rise, and that ideological self-selection does not explain most of that coexposure. We show that mainstream media outlets offer the common ground where ideologically diverse audiences converge online, though our analysis also reveals that more than half of the US online population consumes no online news, underlining the risk of increased information inequality driven by self-selection along lines of interest. For this study, we use an unprecedented combination of observed data from the United States comprising a 5-y time window and involving tens of thousands of panelists. Our dataset traces news consumption across different devices and unveils important differences in news diets when multiplatform or desktop-only access is used. We discuss the implications of our findings for how we think about the current communication environment, exposure to news, and ongoing attempts to limit the effects of misinformation. [ABSTRACT FROM AUTHOR]

The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the statelevel-- a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States. [ABSTRACT FROM AUTHOR]

The “income inequality hypothesis” holds that rising income inequality affects the distribution of a wide range of social and economic outcomes. Although it is often alleged that rising income inequality will increase the advantages of the well-off in the competition for college, some researchers have provided descriptive evidence at odds with the income inequality hypothesis. In this paper, we track long-term trends in family income inequalities in college enrollment and completion (“collegiate inequalities”) using all available nationally representative datasets for cohorts born between 1908 and 1995. We show that the trends in collegiate inequalities moved in lockstep with the trend in income inequality over the past century. There is one exception to this general finding: For cohorts at risk for serving in the Vietnam War, collegiate inequalities were high, while income inequality was low. During this period, inequality in college enrollment and completion was significantly higher for men than for women, suggesting a bona fide “Vietnam War” effect. Aside from this singular confounding event, a century of evidence establishes a strong association between income and collegiate inequality, providing support for the view that rising income inequality is fundamentally changing the distribution of life chances. [ABSTRACT FROM AUTHOR]

One of the most basic predictions of almost any model of crime is that individual time preferences matter. However, empirical evidence on this fundamental property is essentially nonexistent. To our knowledge, this paper provides the first pieces of evidence on the link between time discounting and crime. We use a unique dataset that combines a survey-based measure of time discount rates (at age 13) with detailed longitudinal register data on criminal behavior spanning over 18 y. Our results show that individuals with short time horizons have a significantly higher risk of criminal involvement later in life. The magnitude of the relationship is substantial and corresponds to roughly one-third of the association between intelligence and crime. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) air pollution has been recognized as a major source of mortality in the United States for at least 25 years, yet much remains unknown about which sources are the most harmful, let alone how best to target policies to mitigate them. Such efforts can be improved by employing high-resolution geographically explicit methods for quantifying human health impacts of emissions of PM2.5 and its precursors. Here, we provide a detailed examination of the health and economic impacts of PM2.5 pollution in the United States by linking emission sources with resulting pollution concentrations. We estimate that anthropogenic PM2.5 was responsible for 107,000 premature deaths in 2011, at a cost to society of $886 billion. Of these deaths, 57% were associated with pollution caused by energy consumption [e.g., transportation (28%) and electricity generation (14%)]; another 15% with pollution caused by agricultural activities. A small fraction of emissions, concentrated in or near densely populated areas, plays an outsized role in damaging human health with the most damaging 10% of total emissions accounting for 40% of total damages. We find that 33% of damages occur within 8 km of emission sources, but 25% occur more than 256 km away, emphasizing the importance of tracking both local and long-range impacts. Our paper highlights the importance of a fine-scale approach as marginal damages can vary by over an order of magnitude within a single county. Information presented here can assist mitigation efforts by identifying those sources with the greatest health effects. [ABSTRACT FROM AUTHOR]

In light of calls for improving people's skill in collaboration, this paper examines strengths in processes of collaboration of Mexican immigrant children. Sibling pairs (6-10 years old) in California were asked to collaborate in planning the shortest route through a model grocery store. On average, 14 sibling pairs with Mexican Indigenous-heritage backgrounds engaged together collaboratively as an ensemble, making decisions in common and fluidly building on each other's ideas, more often than 16 middle-class European American sibling pairs, who on average more often divided decision making into a solo activity (often ignoring the other or simply bossing the other). Siblings who spent more time collaborating fluidly as an ensemble in the shared planning task were also more likely to collaborate with initiative at home, according to their mothers, which suggests that family socialization practices may contribute to cultural differences in collaboration. [ABSTRACT FROM AUTHOR]

Many scholars suggest that visa restrictions push individuals who would have otherwise migrated legally toward illegal channels. This expectation is difficult to test empirically for three reasons. First, unauthorized migration is clandestine and often unobservable. Second, interpersonal ties between migrants and would-be migrants form a self-perpetuating system, which adapts in ways that are difficult to observe or predict. Third, empirical evaluations of immigration policy are vulnerable to endogeneity and other issues of causal inference. In this paper, we pair tailor-made empirical designs with an agent-based computational model (ABM) to capture the dynamics of a migration system that often elude empirical analysis, while grounding agent rules and characteristics with primary data collected in Jamaica, an origin country. We find that some government-imposed restrictions on migrants can deter total migration, but others are ineffective. Relative to a system of free movement, the minimal eligibility conditions required to classify migrants into visa categories alone make migration inaccessible for many. Restrictive policies imposed on student and high-skilled visa categories have little added effect because eligible individuals are likely able to migrate through alternative legal categories. Meanwhile, restrictions on family-based visas result in significant reductions in total migration. However, they also produce the largest reorientation toward unauthorized channels--an unintended consequence that even the highest rates of apprehension do not effectively eliminate. [ABSTRACT FROM AUTHOR]

Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts--e.g., the women's movement in the 1960s and Asian immigration into the United States--and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science. [ABSTRACT FROM AUTHOR]

Ensuring safe water supply for communities across the United States is a growing challenge in the face of aging infrastructure, impaired source water, and strained community finances. In the aftermath of the Flint lead crisis, there is an urgent need to assess the current state of US drinking water. However, no nationwide assessment has yet been conducted on trends in drinking water quality violations across several decades. Efforts to reduce violations are of national concern given that, in 2015, nearly 21 million people relied on community water systems that violated healthbased quality standards. In this paper, we evaluate spatial and temporal patterns in health-related violations of the Safe Drinking Water Act using a panel dataset of 17,900 community water systems over the period 1982-2015. We also identify vulnerability factors of communities and water systems through probit regression. Increasing time trends and violation hot spots are detected in several states, particularly in the Southwest region. Repeat violations are prevalent in locations of violation hot spots, indicating that water systems in these regions struggle with recurring issues. In terms of vulnerability factors, we find that violation incidence in rural areas is substantially higher than in urbanized areas. Meanwhile, private ownership and purchased water source are associated with compliance. These findings indicate the types of underperforming systems that might benefit from assistance in achieving consistent compliance. We discuss why certain violations might be clustered in some regions and strategies for improving national drinking water quality. [ABSTRACT FROM AUTHOR]

Corn production, and its associated inputs, is a relatively large source of greenhouse gas emissions and uses significant amounts of water and land, thus contributing to climate change, fossil fuel depletion, local air pollutants, and local water scarcity. As large consumers of this corn, corporations in the ethanol and animal protein industries are increasingly assessing and reporting sustainability impacts across their supply chains to identify, prioritize, and communicate sustainability risks and opportunities material to their operations. In doing so, many have discovered that the direct impacts of their owned operations are dwarfed by those upstream in the supply chain, requiring transparency and knowledge about environmental impacts along the supply chains. Life cycle assessments (LCAs) have been used to identify hotspots of environmental impacts at national levels, yet these provide little subnational information necessary for guiding firms' specific supply networks. In this paper, our Food System Supply-Chain Sustainability (FoodS³) model connects spatial, firm-specific demand of corn purchasers with upstream corn production in the United States through a cost minimization transport model. This provides a means to link county-level corn production in the United States to firm-specific demand locations associated with downstream processing facilities. Our model substantially improves current LCA assessment efforts that are confined to broad national or state level impacts. In drilling down to subnational levels of environmental impacts that occur over heterogeneous areas and aggregating these landscape impacts by specific supply networks, targeted opportunities for improvements to the sustainability performance of supply chains are identified. [ABSTRACT FROM AUTHOR]

Electric school buses have been proposed as an alternative to reduce the health and climate impacts of the current U.S. school bus fleet, of which a substantial share are highly polluting old diesel vehicles. However, the climate and health benefits of electric school buses are not well known. As they are substantially more costly than diesel buses, assessing their benefits is needed to inform policy decisions. We assess the health benefits of electric school buses in the United States from reduced adult mortality and childhood asthma onset risks due to exposure to ambient fine particulate matter (PM2.5). We also evaluate climate benefits from reduced greenhouse-gas emissions. We find that replacing the average diesel bus in the U.S. fleet in 2017 with an electric bus yields $84,200 in total benefits. Climate benefits amount to $40,400/bus, whereas health benefits amount to $43,800/bus due to 4.42*10-3 fewer PM2.5-attributable deaths ($40,000 of total) and 7.42*10-3 fewer PM2.5-attributable new childhood asthma cases ($3,700 of total). However, health benefits of electric buses vary substantially by driving location and model year (MY) of the diesel buses they replace. Replacing old, MY 2005 diesel buses in large cities yields $207,200/bus in health benefits and is likely cost-beneficial, although other policies that accelerate fleet turnover in these areas deserve consideration. Electric school buses driven in rural areas achieve small health benefits from reduced exposure to ambient PM2.5. Further research assessing benefits of reduced exposure to in-cabin air pollution among children riding buses would be valuable to inform policy decisions. [ABSTRACT FROM AUTHOR]

Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice. [ABSTRACT FROM AUTHOR]

Despite experimental and observational studies demonstrating that biodiversity enhances primary productivity, the best metric for predicting productivity at broad geographic extents-functional trait diversity, phylogenetic diversity, or species richness-remains unknown. Using >1.8 million tree measurements from across eastern US forests, we quantified relationships among functional trait diversity, phylogenetic diversity, species richness, and productivity. Surprisingly, functional trait and phylogenetic diversity explained little variation in productivity that could not be explained by tree species richness. This result was consistent across the entire eastern United States, within ecoprovinces, and within data subsets that controlled for biomass or stand age. Metrics of functional trait and phylogenetic diversity that were independent of species richness were negatively correlated with productivity. This last result suggests that processes that determine species sorting and packing are likely important for the relationships between productivity and biodiversity. This result also demonstrates the potential confusion that can arise when interdependencies among different diversity metrics are ignored. Our findings show the value of species richness as a predictive tool and highlight gaps in knowledge about linkages between functional diversity and ecosystem functioning. [ABSTRACT FROM AUTHOR]

This article highlights the significance of including noncontiguous regions of the United States in climate research. It argues that excluding these areas leads to inaccurate national climate risk assessments and hampers policy decisions. The article acknowledges the challenges of data collection in these regions but suggests that advancements in modeling and regional climate models can help address this issue. Collaboration between the research community, funders, project managers, and local experts is emphasized as crucial. The article also discusses the need to improve climate data coverage in areas outside of the continental United States to ensure equitable access to climate information and address historical exclusion and social injustices. Incorporating local and traditional climate knowledge, improving monitoring and observations, and building partnerships with underrepresented communities are suggested as ways to supplement limited datasets. The article underscores the importance of including all states and territories in research efforts to better serve communities and make informed choices. [Extracted from the article]

The warnings of potential climate migration first appeared in the scientific literature in the late 1970s when increased recognition that disintegrating ice sheets could drive people to migrate from coastal cities. Since that time, scientists have modeled potential climate migration without integrating other population processes, potentially obscuring the demographic amplification of this migration. Climate migration could amplify demographic change--enhancing migration to destinations and suppressing migration to origins. Additionally, older populations are the least likely to migrate, and climate migration could accelerate population aging in origin areas. Here, we investigate climate migration under sea-level rise (SLR), a single climatic hazard, and examine both the potential demographic amplification effect and population aging by combining matrix population models, flood hazard models, and a migration model built on 40 y of environmental migration in the United States to project the US population distribution of US counties. We find that the demographic amplification of SLR for all feasible Representative Concentration Pathway-Shared Socioeconomic Pathway (RCP-SSP) scenarios in 2100 ranges between 8.6-28 M [5.7-53 M]--5.3 and 18 times the number of migrants (0.4-10 M). We also project significant aging of coastal areas as youthful populations migrate but older populations remain, accelerating population aging in origin areas. As the percentage of the population lost due to climate migration increases, the median age also increases--up to 10+ y older in some highly impacted coastal counties. Additionally, our population projection approach can be easily adapted to investigate additional or multiple climate hazards. [ABSTRACT FROM AUTHOR]

Biomass-derived sustainable aviation fuel holds significant potential for decarbonizing the aviation sector. Its long-term viability depends on crop choice, longevity of soil organic carbon (SOC) sequestration, and the biomass-to-biojet fuel conversion efficiency. We explored the impact of fuel price and SOC value on viable biojet fuel production scale by integrating an agroecosystem model with a field-to-biojet fuel production process model for 1,4-dimethylcyclooctane (DMCO), a representative high-performance biojet fuel molecule, from Miscanthus, sorghum, and switchgrass. Assigning monetary value to SOC sequestration results in substantially different outcomes than an increased fuel selling price. If SOC accumulation is valued at $185/ton CO2, planting Miscanthus for conversion to DMCO would be economically cost-competitive across 66% of croplands across the continental United States (US) by 2050 if conventional jet fuel remains at $0.74/L (in 2020 US dollars). Cutting the SOC sequestration value in half reduces the viable area to 54% of cropland, and eliminating any payment for SOC shrinks the viable area to 16%. If future biojet fuel prices increase to $1.24/L-Jet A-equivalent, 48 to 58% of the total cultivated land in the United States could support a more diverse set of feedstocks including Miscanthus, sorghum, or switchgrass. Among these options, only 8–14% of the area would be suitable exclusively for Miscanthus cultivation. These findings highlight the intersection of natural solutions for carbon removal and the use of deep-rooted feedstocks for biofuels and biomanufacturing. The results underscore the need to establish clear and consistent values for SOC sequestration to enable the future bioeconomy. [ABSTRACT FROM AUTHOR]

apidly changing wildfire regimes across the Western United States have driven more frequent and severe wildfires, resulting in wide-ranging societal threats from wildfires and wildfire-generated smoke. However, common measures of fire severity focus on what is burned, disregarding the societal impacts of smoke generated from each fire. We combine satellite-derived fire scars, air parcel trajectories from individual fires, and predicted smoke PM2.5 to link source fires to resulting smoke PM2.5 and health impacts experienced by populations in the contiguous United States from April 2006 to 2020. We quantify fire-specific accumulated smoke exposure based on the cumulative population exposed to smoke PM2.5 over the duration of a fire and estimate excess asthma-related emergency department (ED) visits as a result of this exposure. We find that excess asthma visits attributable to each fire are only moderately correlated with common measures of wildfire severity, including burned area, structures destroyed, and suppression cost. Additionally, while recent California fires contributed nearly half of the country’s smoke-related excess asthma ED visits during our study period, the most severe individual fire was the 2007 Bugaboo fire in the Southeast. We estimate that a majority of smoke PM2.5 comes from sources outside the local jurisdictions where the smoke is experienced, with 87% coming from fires in other counties and 60% from fires in other states. Our approach could enable broad-scale assessment of whether specific fire characteristics affect smoke toxicity or impact, inform cost-effectiveness assessments for allocation of suppression resources, and help clarify the growing transboundary nature of local air quality. [ABSTRACT FROM AUTHOR]

This paper documents a marked increase in the all-cause mortality of middle-aged white non-Hispanic men and women in the United States between 1999 and 2013. This change reversed decades of progress in mortality and was unique to the United States; no other rich country saw a similar turnaround. The midlife mortality reversal was confined to white non-Hispanics; black non-Hispanics and Hispanics at midlife, and those aged 65 and above in every racial and ethnic group, continued to see mortality rates fall. This increase for whites was largely accounted for by increasing death rates from drug and alcohol poisonings, suicide, and chronic liver diseases and cirrhosis. Although all education groups saw increases in mortality from suicide and poisonings, and an overall increase in external cause mortality, thosewith less education saw the most marked increases. Rising midlife mortality rates of white non-Hispanics were paralleled by increases in midlife morbidity. Self-reported declines in health, mental health, and ability to conduct activities of daily living, and increases in chronic pain and inability to work, as well as clinically measured deteriorations in liver function, all point to growing distress in this population. We comment on potential economic causes and consequences of this deterioration. [ABSTRACT FROM AUTHOR]

Coastal wetlands dampen the impact of storm surge and strong winds. Studies on the economic valuation of this protective service provided by wetland ecosystems are, however, rare. Here, we analyze property damage caused by 88 tropical storms and hurricanes hitting the United States between 1996 and 2016 and show that counties with more wetland coverage experienced significantly less property damage. The expected economic value of the protective effects of wetlands varies widely across coastal US countieswith an average value of about $1.8 million/km2 per year and a median value of $91,000/km2. Wetlands confer relatively more protection against weaker storms and in states with weaker building codes. Recent wetland losses are estimated to have increased property damage from Hurricane Irma by $430 million. Our results suggest the importance of considering both natural and human factors in coastal zone defense policy. [ABSTRACT FROM AUTHOR]

Emissions of most pollutants that result in fine particulate matter (PM2.5) formation have been decreasing in the United States. However, this trend has not been uniform across all sectors or regions of the economy. We use integrated assessment models (IAMs) to compute marginal damages for PM2.5-related emissions for each county in the contiguous United States and match location-specific emissions with these marginal damages to compute economy-wide gross external damage (GED) due to premature mortality. We note 4 key findings: First, economy-wide, GED has decreased by more than 20% from 2008 to 2014. Second, while much of the air pollution policies have focused to date on the electricity sector, damages from farms are now larger than those from utilities. Indeed, farms have become the largest contributor to air pollution damages from PM2.5-related emissions. Third, 4 sectors, comprising less than 20% of the national gross domestic product (GDP), are responsible for ~75% of GED attributable to economic activities. Fourth, uncertainty in GED estimates tends to be high for sectors with predominantly ground-level emissions because these emissions are usually estimated and not measured. These findings suggest that policymakers should target further emissions reductions from such sectors, particularly in transportation and agriculture. [ABSTRACT FROM AUTHOR]

A longitudinal analysis of small-scale solar energy generation in the United States is used to demonstrate how transition studies can explain nonlinearity in multidecade changes of consumption-production systems. Nonlinearity involves uneven development of sustainability innovations with episodes of rapid growth but also periods of slow growth, stalling, or even collapse. Factors that affect the increasing feasibility and attractiveness of small-scale solar include technological improvements, declining costs, and changes in global energy markets. However, a more complete explanation of nonlinearity highlights the importance of a type of systems analysis that also includes strategic action and broader societal and policy changes. Specifically, efforts by the utilities constrained the growth of small-scale solar by weakening policy support because of the perceived threat, but the solar industry and advocates responded with countervailing action in a changing context. As the transition developed, strategic action (including goals, targets, tactics, and coalition partners) changed and became more conflictual. However, by the beginning of the 2020 decade, the development of microgrids, digital technologies, storage, and virtual power plants in combination with net-zero energy policies provided indications of potential for a reconfiguration of the relationship that could be less polarized and conflicted. [ABSTRACT FROM AUTHOR]

In this speculative article, I argue that the expansion of economic activity in space may offer a uniquely promising way to escape indefinitely from what economists call “secular stagnation,” a state of self-fulfilling, persistently sluggish economic growth that has increasingly threatened high-income countries. Economists have pointed to both supply-side and demand-side drivers of secular stagnation, and space as a focal point for investment can—at least in principle—address both. On the supply side, space is an unlimited frontier that, as have frontiers in the past, may inspire the individualism, innovation, and world-building needed to sustainably increase productivity and population growth. On the demand side, public investment toward increased economic activity in space could meaningfully add to aggregate demand if it reached historical peak benchmarks in the United States. [ABSTRACT FROM AUTHOR]

Cyanobacteria are infamous producers of toxins. While the toxic potential of planktonic cyanobacterial blooms is well documented, the ecosystem level effects of toxigenic benthic and epiphytic cyanobacteria are an understudied threat. The freshwater epiphytic cyanobacterium Aetokthonos hydrillicola has recently been shown to produce the "eagle killer" neurotoxin aetokthonotoxin (AETX) causing the fatal neurological disease vacuolar myelinopathy. The disease affects a wide array of wildlife in the southeastern United States, most notably waterfowl and birds of prey, including the bald eagle. In an assay for cytotoxicity, we found the crude extract of the cyanobacterium to be much more potent than pure AETX, prompting further investigation. Here, we describe the isolation and structure elucidation of the aetokthonostatins (AESTs), linear peptides belonging to the dolastatin compound family, featuring a unique modification of the C-terminal phenylalanine-derived moiety. Using immunofluorescence microscopy and molecular modeling, we confirmed that AEST potently impacts microtubule dynamics and can bind to tubulin in a similar matter as dolastatin 10. We also show that AEST inhibits reproduction of the nematode Caenorhabditis elegans. Bioinformatic analysis revealed the AEST biosynthetic gene cluster encoding a nonribosomal peptide synthetase/polyketide synthase accompanied by a unique tailoring machinery. The biosynthetic activity of a specific N-terminal methyltransferase was confirmed by in vitro biochemical studies, establishing a mechanistic link between the gene cluster and its product. [ABSTRACT FROM AUTHOR]

Air pollution poses well-established risks to physical health, but little is known about its effects on mental health. We study the relationship between wildfire smoke exposure and suicide risk in the United States in 2007 to 2019 using data on all deaths by suicide and satellite-based measures of wildfire smoke and ambient fine particulate matter (PM2.5) concentrations. We identify the causal effects of wildfire smoke pollution on suicide by relating year-over-year fluctuations in county-level monthly smoke exposure to fluctuations in suicide rates and compare the effects across local areas and demographic groups that differ considerably in their baseline suicide risk. In rural counties, an additional day of smoke increases monthly mean PM2.5 by 0.41 μg/m³ and suicide deaths by 0.11 per million residents, such that a 1-μg/m³ (13%) increase in monthly wildfire-derived fine particulate matter leads to 0.27 additional suicide deaths per million residents (a 2.0% increase). These effects are concentrated among demographic groups with both high baseline suicide risk and high exposure to outdoor air: men, working-age adults, non-Hispanic Whites, and adults with no college education. By contrast, we find no evidence that smoke pollution increases suicide risk among any urban demographic group. This study provides large-scale evidence that air pollution elevates the risk of suicide, disproportionately so among rural populations. [ABSTRACT FROM AUTHOR]

We use three indexes to identify how age-specific mortality rates in the United States compare to those in a composite of five large European countries since 2000. First, we examine the ratio of agespecific death rates in the United States to those in Europe. These show a sharp deterioration in the US position since 2000. Applying European age-specific death rates in 2017 to the US population, we then show that adverse mortality conditions in the United States resulted in 400,700 excess deaths that year. Finally, we show that these excess deaths entailed a loss of 13.0 My of life. In 2017, excess deaths and years of life lost in the United States represent a larger annual loss of life than that associated with the COVID-19 epidemic in 2020. [ABSTRACT FROM AUTHOR]

The future of the American academic research enterprise is considered. Data are presented that characterize the resources available for the 160 best-resourced research universities, a small subset of the 2,285 4-year, nonprofit, higher education institutions. A computational model of research universities was extended and used to simulate three strategic scenarios: status quo, steady decline in foreign graduate student enrollments, and downward tuition pressures from high-quality, online professional master's programs. Four specific universities are modeled: large public and private, and small public and private. The former are at the top of the 160 in terms of resources, while the latter are at the bottom of the 160. The model's projections suggest how universities might address these competitive forces. In some situations, it would be in the economic interests of these universities to restrict research activities to avoid the inherent subsidies these activities require. The computational projections portend the need for fundamental change of approaches to business for universities without large institutional resources. [ABSTRACT FROM AUTHOR]

Prior studies linking grit-defined as perseverance and passion for long-term goals-to performance are beset by contradictory evidence. As a result, commentators have increasingly declared that grit has limited effects. We propose that this inconsistent evidence has occurred because prior research has emphasized perseverance and ignored, both theoretically and empirically, the critical role of passion, which we define as a strong feeling toward a personally important value/preference that motivates intentions and behaviors to express that value/preference. We suggest that combining the grit scale- which only captures perseverance-with a measure that assesses whether individuals attain desired levels of passion will predict performance. We first metaanalyzed 127 studies (n = 45,485) that used the grit scale and assessed performance, and found that effect sizes are larger in studies where participants were more passionate for the performance domain. Second, in a survey of employees matched to supervisor-rated job performance (n = 422), we found that the combination of perseverance, measured through the grit scale, and passion attainment, measured through a new scale, predicted higher performance. A final study measured perseverance and passion attainment in a sample of students (n = 248) and linked these to their grade-point average (GPA), finding that the combination of perseverance and passion attainment predicted higher GPAs in part through increased immersion. The present results help resolve the mixed evidence of grit's relationship with performance by highlighting the important role that passion plays in predicting performance. By adequately measuring both perseverance and passion, the present research uncovers grit's true predictive power. [ABSTRACT FROM AUTHOR]