A key part of an engineer’s purpose is to create products and services that benefit society, or, in other words, to create products with a positive social impact. While engineers have many predictive models to aid in making design decisions about the functional performance or safety of a product, very few models exist for estimating or planning for the wide range of social impacts an engineered product can have. To model social impact, a model must contain representations of both the product and society. Agent-based modeling is a tool that can model society and incorporate social impact factors. In this paper, we investigate factors that have historically limited the usefulness of product adoption agent-based models and predictive social impact models through a systematic literature review. Common themes of limiting factors are identified, steps are presented to improve the usefulness of agent-based product adoption models and predictive social impact models, and a general process for the creation of agent-based social impact models is presented. Improving the usefulness of these predictive models can aid engineers in making better design decisions. Predictive social impact models can help identify areas in the design space to improve the social impact of products. When coupled with existing design methods, agent-based predictive social impact models can help increase the probability that a product achieves positive social impact.

The floodplains of the Southeast’s stream network once hosted immense brakes of rivercane (Arundinaria gigantea), a disturbance-dependent native bamboo with both cultural and ecological significance. Ecologically, rivercane alters its local environments, boosts biodiversity and biomass, and protects streambanks from erosion and storm damage. Indigenous peoples of the Southeast have used rivercane for millennia, for material and cultural purposes, and formerly maintained its health and extent through harvesting and fire. Settler-colonial incursions largely destroyed rivercane, through the more intense disturbance of different land management practices, and brakes now exist mainly in relatively small areas. While numerous rivercane restoration projects are in process throughout its natural range, no comprehensive inventory of living rivercane exists. In this paper, we present both human context and the results of LiDAR analysis that identifies canebrakes based on the physical characteristics of the plant and brake. In our study area on the Little Tennessee River in western North Carolina, we found rivercane on about 9 percent of the floodplain area, based on QL1 LiDAR data available from the state of North Carolina. The technique can be applied in any part of rivercane’s range, and the resulting inventory used in support of both cultural and ecological goals.

Researchers have identified inequities in health insurance and usual source of care in relation to either race/ethnicity or sexual orientation among U.S. women and men. However, intersectionality suggests that racism and heterosexism may have a compounding negative impact on health care outcomes in relation to both race/ethnicity and sexual orientation. Using 2013–2018 National Health Interview Survey data, we used multivariable logistic regression to examine differences in health insurance and usual source of care among U.S. women (N = 70,855) and men (N = 61,032) aged 18–64 years in relation to both race/ethnicity and sexual orientation. Compared to white heterosexual women, Black ([odds ratio =]0.89; [95% confidence interval:] 0.80–0.98) and Latina (OR = 0.42; 0.38–0.46) heterosexual women and white (0.78; 0.64–0.95), Black (0.51; 0.35–0.73), and Latina (0.53; 0.36–0.78) sexual minority women (SMW) had significantly lower adjusted odds of having health insurance. Latina heterosexual women (0.80; 0.73–0.88) and white (0.79; 0.65- 0.95), Black (0.56; 0.37–0.83), and Latina (0.60; 0.41–0.88) SMW also had significantly lower adjusted odds of having a usual source of care. Compared to white heterosexual men, Black (0.74; 0.67–0.81) and Latino (0.42; 0.39–0.46) heterosexual men had significantly lower adjusted odds of health insurance coverage, and Latino heterosexual men (0.73; 0.67–0.80) had significantly lower adjusted odds of having a usual source of care. Some observed disparities were attenuated upon adjustment for socioeconomic factors. Additional research is needed to identify and address the other structural and social factors that contribute to health insurance disparities among marginalized populations at diverse intersections of race/ethnicity, sexual orientation, and gender identity.

We investigate the origins and implications of zero-sum thinking: the belief that gains for one individual or group tend to come at the cost of others. Using a new survey of 20,400 U.S. residents, we measure zero-sum thinking, political preferences, policy views, and a rich array of ancestral information spanning four generations. We find that a more zero-sum mindset is strongly associated with more support for government redistribution, race- and gender-based affirmative action, and more restrictive immigration policies. Zero-sum thinking can be traced back to the experiences of both the individual and their ancestors, encompassing factors such as the degree of intergenerational upward mobility they experienced, whether they immigrated to the United States or lived in a location with more immigrants, and whether they were enslaved or lived in a location with more enslavement.

Objective: Researchers have developed numerous indices to identify vulnerable sub-populations. The Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) is the most common and highly serviceable, but it has some temporal limitations considering that some variables used in calculating the CDC-SVI were not available before 1980. Changes in societal composition over time can impact social vulnerability. This study defines an alternate, but similar, index that could serve as a surrogate for the CDC-SVI without the temporal limitations. Methods: An inventory analysis of the historical census data (1960-2018) was used to develop a Modified SVI that allows for historic analyses. To consider the chronic effect of social vulnerabilities, a longitudinal SVI was introduced to elucidate how a community’s multidimensional experiences exacerbate vulnerability to disaster events, such as the COVID-19 pandemic. We use Harris County, Texas, in this case study to examine how the Modified SVI performs against the original CDC-SVI. Results: This Modified SVI was used to generate historical maps, find temporal patterns, and inform a longitudinal SVI measure. The results showed a good agreement among the developed indices and the CDC-SVI. We also observed satisfactory performance in identifying the areas that are most vulnerable to the COVID-19 pandemic. Conclusions: The Modified SVI overcomes temporal limitations associated with the CDC-SVI, and the longitudinal SVI captures a community’s multidimensional experiences that exacerbate a community’s vulnerability to disaster events over time.

We provide new evidence on the role of domestic geographic integration in a country's integration to the international market. The geographic concentration of exporters may reduce trade frictions for other potential exporters, for example, through learning by doing or establishing reliable distribution channels. We examine the existence of such spillover effects in the export market by leveraging a quasi-natural experiment: The expansion of China's high-speed rail (HSR) provides plausibly exogenous variation in the extent of (effective) geographic integration among Chinese exporters. From a simple model of export behavior, we derive the empirical specification to examine how export performance responds to the improved geographic integration. The analysis reveals strong evidence that the improved within-sector integration of exporters positively impacts a firm's export performance, such as sales, number of export markets , and product quality. We also provide evidence consistent with that an HSR connection strengthens knowledge spillovers among exporters to overcome informal trade barriers when accessing foreign markets.

Soil moisture reflects the amount of water available to crops in the top layer of soil. As such, considering soil moisture provides important insight into water availability and ultimately crop yields in agricultural settings. In studies of climate change, food security, and health, however, soil moisture is rarely empirically considered despite its connection to crop health and yields. In this project, we aim to advance understanding of climate impacts on food security by incorporating soil moisture into quantitative models of child health. Combining spatially referenced health survey data from the Demographic and Health Surveys for 2005 and 2010 in Senegal and 2007, 2011, and 2014 in Bangladesh, with soil moisture data from the Famine Early Warning System Network Land Data Assimilation System, we explore the linkages between sub-annual and sub-seasonal climate conditions and child malnutrition in two rainfed agriculture dependent countries—Bangladesh and Senegal. Results suggest that soil moisture, measured on very short time scales, may be associated with reductions in anthropometric weight-for-height z-scores, but the relationship is highly dependent upon geographic context.

The precepts of the culture of honor in the southern region of the United States promoted violence for retribution, ostensibly for affronts, and the redemption of honor. Typically, the narratives of lynch lawlessness examine the lynching of African American men by Caucasian mobs. More recently, researchers have begun examining intra-racial mob violence, a new field in the study of lynching. This discourse examines the intersection of violence, the culture of honor, and African American intra-racial mobocracy in the Cotton Belt region of Georgia in 1892.

Scientists use models to further their understanding of phenomena and inform decision-making. A confluence of factors has contributed to an exponential increase in spatial data volumes. In this study, we describe our methodology to identify spatial variation in the performance of classification models. Our methodology allows tracking a host of performance measures across different thresholds for the larger, encapsulating spatial area under consideration. Our methodology ensures frugal utilization of resources via a novel validation budgeting scheme that preferentially allocates observations for validations. We complement these efforts with a browser-based, GPU-accelerated visualization scheme that also incorporates support for streaming to assimilate validation results as they become available.

In the United States, age at first marriage was lowest and the education gap between husbands and wives was highest during the 1950s. The conventional explanation for such a negative correlation is that early marriage leads to earlier and higher fertility, which in turn prevents women from acquiring education. Here, we propose that early marriages enabled couples to overcome credit constraints in education. A model that includes this motive and mechanism can replicate not only the marriage and education patterns observed in the middle of the century in the United States but also the overall trends over the twentieth century.

The Universal Child Care Act recently proposed in the New York State Senate would begin the creation of a universal child care system in New York State. This would involve a large scale-up of child care service supply, precipitating a large increase in employment in the child care sector, while increasing wages for jobs in that sector. Who will benefit from these new jobs and wages? We use the Levy Institute Micro Model (LIMM) to simulate the distribution of these new jobs and wages to the population of New York State. Two econometric contributions are made to the LIMM which improve dispersion and result in allocations which are more representative of predicted likelihood distributions. The distribution of jobs and wages is found to be highly income-progressive, making it an effective pro-equity and anti-poverty measure. The distribution of jobs and wages favors women, especially women of color, across the state.

Standard estimates based on the main household survey used to shed light on labor markets—the Current Population Survey (CPS)—suggest that after a significant drop during the pandemic, recent rapid growth has brought the foreign-born population back to, or above, levels predicted by the pre-pandemic trend. However, we document that the weighting factors used to make the CPS nationally representative have recently displayed some unusual movements and conclude that standard estimates of the foreign-born population may currently be too high. We also show that recent labor market indicators are inconsistent with increased foreign-born induced slack.

The spatial distribution of people is incredibly concentrated: 8 percent of the US population lives in the ten largest US cities, but those cities take up less than 0.1 percent of total US land area. Why this concentration? More generally, what determines the distribution of people and economic activity across space? And how can economic policies affect the spatial distribution of economic activity? This essay will show that these questions can be answered through the familiar lens of supply and demand curves. We begin by applying this intuition to the well-known Rosen-Roback framework (Rosen 1979; Roback 1982). But as we will discuss, the distribution of economic activity in this early spatial model depends only on local geography, not on what happens to other regions. For example, a change in one location—say, a large infrastructure investment that improves its productivity—is predicted to have an identical impact on all other locations, regardless of where they are. Thus, intuitive spatial features like where a location is located on a map and who its neighbors are entirely absent: it is a spatial model where space does not matter. In reality, spatial linkages create rich interactions between locations. One implication of these interactions is that a large infrastructure investment that improves the productivity in one location will have greater impacts on close-by locations than locations further away. To account for such spatial linkages, we extend the intuition of the Rosen-Roback model to modern economic geography frameworks where locations are connected through the flow of goods, based on our earlier work in Allen and Arkolakis (2014). In this framework, the economic fate of a location depends not only on its own “local” geography but also on the local geography of its neighbors, the effect of which is mediated by the strength of the economic ties, creating a “global geography.” Despite this added complexity, we show the same tools based on supply and demand used to understand predictions of the earlier Rosen-Roback framework extend readily to a globally integrated world. This globally integrated framework can be applied to understand both the direct and indirect impacts of real world economic policies that change either the local or global geography. We discuss how the framework can be applied to spatial data, while also highlighting the most common pitfalls and offering strategies for traversing them. Finally, we provide a brief overview of the many ways in which this framework has been applied thus to understanding the spatial distribution of economic activity, as well as pointing out several interesting and still unexplored questions for future researchers. To keep the discussion as straightforward and accessible as possible, we relegate all mathematical details and derivations to the Appendix, where we also provide a companion Matlab toolkit to help researchers apply these techniques on their own.

Essay 1: Consumers have faced rapidly changing food prices in recent years—meat prices have been particularly volatile—leading individuals to be frequently surprised by the prices they encounter. In contrast to neo-classical assumptions, applications of prospect theory to consumer choice have hypothesized that consumers evaluate prices relative to a reference price, are loss averse, and experience diminishing sensitivity. Accordingly, I tested for reference price effects in consumer choice for protein and demonstrated the implications in post-estimation analysis. I leveraged choice experiment data in a random utility framework while progressively incorporating various reference price features and found including reference price effects improves model performance, both within and outside of the estimation sample. The magnitude of reference price effects varies by product and across marketing channels, with implications for elasticity estimates, market share predictions, and welfare analysis. My results are consistent with previous research but adds an application to a previously unstudied product group across market channels, while also demonstrating the implications of various modelling approaches. This additional information provides insights into protein markets and important guidance to researchers and policy analysts. Essay 2: Benefits of government subsidized farm programs may pass through the production sector to agricultural input prices. Likewise, publicly supported insurance programs can increase expected future revenue and reduce risk, thus altering production incentives and potentially impacting input prices and quantities. Accordingly, I examined the impact of Pasture, Rangeland, and Forage (PRF) Index Insurance on agricultural land values (price) and pastureland area (quantity). I leveraged the staggered rollout of PRF at the county level in a non-traditional Difference-in-Differences framework and found a positive effect on both farmland value and acres of pastureland. However, higher percentages of public land in a county are associated with smaller effects on land value and larger effects on pasture area. My results are in line with previous research and provide additional detail on the geographical impact. This additional nuance gives policy makers localized insights into the distribution of program effects.

In the United States, the most common form of health insurance coverage is through employer-sponsored insurance (ESI) plans. ESI plans cover the workers and their dependents—typically the spouse and children. As a means of increasing young-adult insurance coverage, states and the federal government have enacted “dependent” mandates that expand the definition of a dependent for ESI plans. At the federal level, the Affordable Care Act (ACA) dependent mandate increased the maximum age of an adult child dependent from 18 to 25. In addition to the federal expansion, the majority of states enacted dependent mandates which extended dependent status to children over the age of 18. State mandates commonly required that to be eligible the adult child must be unmarried or a student.

We investigate the effect of education on public job choice using quarter of birth as an instrumental variable. We find that an additional year of education increases the probability of public sector employment by 1.87 percentage points. However, this positive effect is driven by females, whites, and those with high school degrees or less. For those with college or higher degrees, we observe a decrease in public employment with more education. Our results imply that highly-educated individuals go into high-risk, high-return occupations.

The rich continue to get richer, and everyone else either only kind of earns more or stays where they’re at. In the income chart below, the top line for Americans in the 99th percentile, or the top 1%, separates from the bottom more over the years.

Since the beginning of the new millennium, social scientists have found that, much like the general population, Latin American and Caribbean migrants have been settling in greater numbers in Southern and Mid-Western cities and towns. This “new settlement” literature has focused on how race and racism, along with ethnic identities and tensions, have generated new opportunities and challenges for migrants in new settlement areas or emerging gateways. However, a handful of studies have accounted for how racial inequality outside and within the Latin American and Caribbean favors white or lightskinned Latinx. Likewise, few studies have looked at how differences in nationality and ethnicity shape migrant experiences in new destinations with a long-standing history of Hispanic migration. To address the shortcomings of these fields of study, I address three main empirical questions: 1) What is the residential distribution of Venezuelan, Cuban, and Puerto Rican migrants, and what can that this tells us about their residential mobility and neighborhood preferences?, 2) What are the effects, if any, these new migrant groups have on neighborhood characteristics?, and 3) How do these migrants understand and describe the factors that affect their reception and experiences in a new migrant destination versus an established migrant destination? I answer these research questions using statistical, visual, and ethnographic analysis of the neighborhoods, communities and homes of foreign-born Puerto Rican, Venezuelan and Cuban migrants in the suburbs of Houston, TX. Houston is not a traditional migrant destination for any of these three groups: U.S.-bound Puerto Rican migration has traditionally been associated with the Northeast and Chicago, while Cuban and Venezuelan migration has generally gravitated towards South Florida. I found that Puerto Rican and Venezuelan migrants were fairing better than Mexicans and Cubans. In turn, the influx of Cubans had started to change the neighborhood composition in downtown Houston. These changes could be explained by racial and class inequality within the migrant waves, and the position migrants maintained in the social hierarchies of Houston.

In the age of mass migration, the US became economically crucial with the development of Atlantic trade and attracted labour flows from the rest of the world. Meanwhile, the Ottoman Empire was suffering severe economic and political problems. The Ottoman citizens of various ethnic origins, such as Turks, Jews, Armenians and Arabs, moved to different countries, including the US, for economic and political reasons. This paper examines the effect of institutional reforms in the Ottoman Empire during the Second Constitutional Era on immigration decisions of different ethnic groups to the US. Data come from the US census data of IPUMS (the Integrated Public Use Microdata Series) for 1910. The empirical results show that the institutional reforms could reduce emigration from the Ottoman Empire through granting additional civil liberties. However, this effect works heterogeneously for different ethnic groups. This result points out the importance of institutional improvements in home countries on reducing migration outflows.

The purpose of this study is to develop and test a local measure of walkability in San Francisco County and evaluate walkability by race, ethnicity, and educational attainment at the census block group level. We used data collected as part of the 2019-2020 Bay Area Young Adult Health Study, wherein we conducted audits of 1525 randomly selected census blocks to assess domains, such as aesthetics, access to amenities, street safety, and topography, which we then imputed to the remaining blocks in the city for overall coverage. We linked the walkability index to 2015-2019 American Community Survey Data aggregating walkability to the block group level. We performed a geographically weighted regression to measure associations between walkability, race/ethnicity, and educational attainment and found signicant negative associations between walkability and the percentage of non-Hispanic Black residents in specic neighborhoods and non-Hispanic Asian residents in all neighborhoods. We also found signicant positive associations between the percentage of residents with a bachelor's or higher degree and walkability. Inequities in walkability across neighborhoods in San Francisco County largely mirrored sociodemographic inequities. Our approach to constructing a walkability index points to a relatively ecient means of estimating ne-scale neighborhood characteristics in urban areas, and our ndings suggest that investments in neighborhood infrastructure may positively impact walkability and address broader inequity concerns.