Given increasingly accessible spatially-referenced environmental data, environmental demographers have the unique opportunity to use spatial data and spatial analytical techniques to unpack the complex relationships between demographic processes and the biophysical environment. A distinguishing feature of spatial studies, demographic or otherwise, is the explicit consideration of one location relative to others. This chapter provides an overview of how spatial data and analyses are used in research at the population-environment nexus and identifies how spatial data and analytic strategies might advance our theoretical knowledge of today’s most significant environmental issues. In Part 1, we provide general definitions, and foundational theoretical and methodological concepts. In Part 2, we highlight compelling spatial work occurring in the population and environment literature organized by demography’s traditional themes: migration, fertility, and mortality. We conclude with suggestions for future directions researchers might take to incorporate spatial thinking from both methodological and theoretical standpoints. By underscoring spatial differentiation within population-environment research via reflexive theory and appropriate modeling strategies, environmental demographers are uniquely positioned to draw connections between demographic processes, social inequality, and environmental hazards.

More than 3.1 million immigrants reside in New York City, comprising more than a third of the city’s total population. The boroughs of Brooklyn and Queens are home to nearly 940,000 and more than 1 million immigrants, respectively. According to the New York City Department of Health and Mental Hygiene’s (DOHMH) Community Health Survey (CHS), foreign-born New Yorkers have poorer health and less access to healthcare than their US-born counterparts. For this study, the Center for Migration Studies of New York (CMS) focused on six neighborhoods in these two boroughs whose immigrant residents were identified by a previous CMS study, Virgin and Warren (2021), as most at risk of poor health outcomes: Brooklyn • Bay Ridge/Dyker Heights • Bushwick • Sunset Park/Windsor Terrace Queens • Elmhurst/South Corona • Flushing/Whitestone/Murray Hill • Jackson Heights/North Corona Immigrants comprise between 31 to 63 percent and undocumented immigrants comprise between 5 to 18 percent of the total populations in these neighborhoods. Though many of New York City’s health and social service programs are open to all residents, immigrants – especially the undocumented – remain at greater risk of poor health outcomes than US-born residents due to other barriers to access to healthcare. The CMS research team conducted a survey of 492 immigrants across these six neighborhoods and convened one focus group to collect data on immigrants’ health and well-being. CMS also surveyed 24 service providers including community health clinics, health focused community based organizations (CBOs), and hospitals that work with immigrants in the studied neighborhoods. Analysis of these data, together with the US Census Bureau’s American Community Survey (ACS) and the DOHMH's CHS, provides insight into the factors that affect immigrants’ health and wellbeing across these neighborhoods. This study highlights several social determinants of health that likely contribute to the native-immigrant health gap. Finally, the study describes steps that can be taken to close this gap.

This study examines the employment status of older Americans in the months immediately before and after the peak COVID-19 lockdown in April 2020. We construct longitudinal employment data from 2019-2020 Current Population Surveys. To account for seasonal fluctuations in employment and retirement patterns that are not unique to the COVID-19 recession, we implement a difference-in-differences analysis using multinomial logistic regressions. We find that the onset of the pandemic immediately and adversely affected all workers, but the extent of the employment disruptions varied by age group, sex, and whether the worker has a college degree. Reemployment patterns after the peak lockdown month also varied but did not simply reverse the earlier patterns. Our findings imply that the employment effects of the COVID-19 recession are substantially different from those of previous recessions.

This paper uses birth records from California and mothers who move to quantify the absolute and relative importance of birth location in early-life health. Using a model that includes mother and location fixed effects, we find that moving from a below- to an above-median birth weight location leads to a 19-gram increase in average birth weight. These causal place effects explain 16 percent of geographic variation in birth weight, with family-specific factors accounting for the remaining 84 percent. Place effects are more influential for children of non-college-educated mothers, and are most strongly correlated with local levels of pollution. The improvement in birth weight from moving to a higher-quality area compares favorably to policies that target maternal health, and could have a small, lasting effect on long-run outcomes.

This report compares the educational composition and average earnings of immigrants from Italy to the educational composition and average earnings of people born in the US over time using data for the years: 1960, 1970, 1980, 1990, 2000, and 2010. The following analysis is based on data from the American Community Survey (ACS), as obtained from IPUMS-USA (Ruggles et al, 2022). The variables used in this report are: “bpl”, “educ” “incwage”. The variable “bpl” documents a person’s place of birth. Values of this variable that record a person’s place of birth not being from Italy or the United States are excluded from the analysis. The variable “educ” notes the educational attainment of the individual. Individuals are grouped into 12, ranging from no schooling to a Doctorate. No group was removed, for instance, the “no schooling” was maintained, considering the selected years of the analysis. The 12 groups were then divided initially into four categories by education level as follows: grade eleven or below, a high school diploma, some college, and a bachelor’s degree or above. In the first analysis, the groups are condensed into two educational attainments: “lower education” and “higher education”. People having a lower education are defined as having some college or below and people with higher education as having a bachelor’s degree or above. Lastly, the variable “incwage” reports yearly wages and salaries of individuals, not adjusted for inflation. The missing values and not applicable observations are replaced. The educational attainment of immigrants from Italy and people born in the US are compared, with a detailed focus on the years 1960 and 2010; further, the relationship between educational attainment and yearly income is presented. The analysis will include six graphs. The first graph will compare the educational attainment between Italian immigrants and people born in the US, outlining the greater picture. The next two graphs will compare in detail the educational attainment between Italian immigrants and people born in the US for two specific years: 1960 and 2010. The last three graphs show first a greater picture of the earnings across years, and lastly a comparison of the educational attainment and the yearly earnings between Italian immigrants and people born in the US, for the years 1960 and 2010 The sample contains 36,450,459 observations, of these 36,269,933 are people born in the US, and 180,256 are Italian immigrants. For the year 1960, the sample contains 5,610,619 observations of which 61,004 are immigrants from Italy. For the year 2010, the sample contains 2,109,609 observations of which 4,530 are immigrants from Italy

This paper shows that the growing regional disparities in the U.S. since 1980 can be explained by firms endogenously responding to a skill-biased technology shock. With the introduction ofa new skill-biased technology that is high fixed cost but low marginal cost, firms endogenously adopt more in big cities, cities that offer abundant amenities for high-skilled workers, and citiesthat are more productive in using high-skilled labor. In cities with more adoption, small and unproductive firms are more likely to exit the market, increasing the equilibrium rate of turnoveror business dynamism—a selection effect similar to Melitz (2003). Differences in technology adoption and selection account for three key components of the growing regional disparities known as the Great Divergence: (1) big cities saw a larger increase in the relative wages and supply of skilled workers, (2) big cities saw a smaller decline in business dynamism, and (3) firms in big cities invest more intensively in Information and Communication Technology (ICT).

Purpose: Seasonal influenza vaccination is an important public health strategy to reduce preventable illness, hospitalization, and death. Because of overlapping risk factors for severe illness from seasonal influenza and COVID-19, uptake of the seasonal influenza vaccination has heightened importance during the COVID-19 pandemic. We analyzed receipt of seasonal influenza vaccination among COVID-19 priority groups and further examined socio-demographic and behavioral factors associated with receiving the seasonal influenza vaccine among US adults. Methods: Using the 2018 National Health Interview Survey, we classified 24,772 adults into four COVID-19 priority groups: healthcare workers, medically vulnerable, non-healthcare essential workers, and the general population. We performed multiple logistic regression to compare the relative odds of receiving the influenza vaccine by COVID-19 priority group, socio-demographics, and health-related factors. Results: Healthcare workers, medically vulnerable adults, essential workers, and the general population comprised 8.9%, 58.4%, 6.6%, and 26.1 % of the US population, respectively. Compared with healthcare workers, the adjusted odds ratio (aOR) of receiving influenza vaccine were significantly lower in medically vulnerable adults (aOR=0.43, 95% CI=0.37, 0.48), essential workers (aOR=0.28, 95% CI=0.23, 0.34), and the general population (aOR=0.32, 95% CI=0.28, 0.37). Being young, male, Black, and having no health insurance were associated with lower relative odds of receiving the flu vaccine. Conclusions: Patterns of influenza vaccine cause concern for under-coverage of populations at high risk for both seasonal influenza and COVID-19. Achieving optimal protection against vaccine-preventable respiratory illness in US adults will require emphasis on those employed outside of the healthcare sector, younger age groups, and adults with lower socioeconomic resources.

This dissertation uses evidence from Marketplace Lending (MPL) platforms to examine how newly emerged Fintech lending performs during risky times. In the first chapter, I use the COVID-19 pandemic as a setting to examine the role of marketplace lending (MPL) as an alternative credit provider during a long-term and market-wide economic crisis. Using data from LendingClub, I find that marketplace lenders extend more credit to counties with larger COVID19 exposure, as measured by its local economic impact and an area’s infection rate. This relation is more prominent in areas with fewer banks, suggesting that marketplace lenders fill borrowing needs in underbanked areas and substitute the traditional lending sector. However, in contrast to prior work showing that marketplace lenders increase credit availability to riskier borrowers, I find that the increase in loans goes primarily to higher credit-quality borrowers during the pandemic. This result is consistent with a “flight-to-quality” reaction. Yet, there is some evidence of altruistic lending behaviors, as medical-related loans increase in areas more affected by the pandemic. In the second chapter, I use mass shootings as a setting to examine how short-term salient events affect FinTech lending in consumer credit markets. Unlike other risky events that only increase fundamental risks, mass shootings also change how MPL investors perceive risks in affected areas and generate altruism towards lower credit borrowers who may be disproportionally affected by the events. I find that loans originated by higher-credit borrowers in areas that have experienced at least one mass shooting in the past three months receive lower inflows of credit and are charged higher interest rates, which is consistent with investors viewing this group as riskier. In contrast, loans originated by lower credit borrowers in affected areas receive higher inflows of credit and are charged lower interest rates, which is consistent with investors demonstrating altruistic behaviors. These effects are short-lived and not driven by new information, suggesting that temporary behavioral biases induced by salient events have real economic implications.

The 2019 public charge rule published by the Trump administration has left substantial impacts on public safety net participation for immigrant families. Utilizing data from the Current Population Survey (CPS), I examine the effects of the the updated public charge rule (UPCR) on public safety net participation on citizen children with non-citizen mothers (treatment group), using citizen children with citizen mothers as a control group. I employ a difference-in-differences model to explore the before and after effects of the rule to look at participation rates in Medicaid, SNAP, and lunch subsidies. I find statistically significant results showing that the odds of SNAP participation is approximately 27% lower for the treatment group in comparison with the control group during the years that the UPCR is in effect. Interestingly, I did not find significant results on the effect of the UPCR on Medicaid and lunch subsidy participation. The UPCR exacerbated food insecurity among children living in immigrant households in a time when immigrants were already facing economic hardship as a result of the ongoing COVID-19 pandemic.

Envisioning a future 100% electrified transportation sector, this paper uses socio-economic, demographic, and geographic data to assess electric energy demand from commuter traffic. We explore the individual mode choices, which allows to create mode-mix scenarios for the entire population, and quantify the electric energy demand for each scenario using technical specifications of battery and electric drives technology in combination with different charging scenarios. Using data sets for New York City, our results highlight the need for infrastructure investments, the usefulness of flexible charging policies, and the positive impact of incentivizing micromobility and mass-transit options. Our model and results are publicly available as interactive dashboard.

The life-cycle consumption and permanent income hypotheses predict that if workers face greater likelihood of unemployment in the future that lowers expected future income, they will save more today. In this paper, we test this hypothesis by looking at the expenditure response of workers to the change in unemployment risk measured at the occupational level. We find that occupational unemployment risk does not have a large impact on consumption expenditure. However, despite investigating multiple forms of occupational unemployment risk for multiple expenditure categories in two expenditure surveys (PSID, CEX), we do not obtain narrow confidence intervals for our estimates, so there remains a possibility of a limited impact.

The empirical impact of housing costs on commuting is still relatively poorly understood. This impact is especially salient in California given the state's notoriously high housing costs, which have forced many lower- and middle-class households to move inland in search of affordable housing at the cost of longer commutes. To investigate this linkage, we relied on Generalized Structural Equation Modeling and analyzed 2012 CHTS data for Los Angeles County – the most populous county in the U.S. Our model, which jointly explains commuting distance and time, accounts for residential self-selection and car use endogeneity, while controlling for household characteristics and land use around residences and workplaces. We find that households who can afford more expensive neighborhoods have shorter commute distances (−2.3% and − 3.1% per additional $100 k to median home values around workplaces and residences respectively). Job density, distance to the CBD, and land-use diversity around workplaces have a relatively greater impact on commuting than the corresponding variables around commuters' residences. Compared to non-Hispanics, Hispanic workers commute longer distances (+3.5%), and so do African American (+5.1%) and Asian (+2.0%) workers compared to Caucasians, while college educated workers have shorter (−2.6% to −3.6%) commutes. Furthermore, commuters in the top income brackets tend to have faster commutes than lower income workers. Finally, women's commutes are ~41% shorter than men's, possibly because they are balancing work with domestic responsibilities. Better understanding the determinants of commuting is critical to inform housing and transportation policy, improve the health of commuters, reduce air pollution, and achieve climate goals.

Hispanics are important contributors to the self-employment sector. Their entrepreneurial activity varies by immigration status and ethnonational subgroup. We comparatively examine the self-employment of Hispanics who immigrated as adults, those who immigrated as children, and non-immigrants of four groups in the United States: Mexicans, Puerto Ricans, Cubans, and Southern South Americans. We investigate intergenerational assimilation through self-employment into the three trajectories posited by segmented assimilation theory. We estimate regression models using a sample from the American Community Survey of Hispanics (n = 585,279) and US-born non-Hispanic Whites (n = 2,848,456). In a subsequent exploratory analysis, we estimate models for Hispanic origin and immigrant status groups to compare key predictors. We find that self-employment probabilities indicate distinct assimilation patterns for our origin groups. The exploratory analysis reveals different effects of important characteristics across groups. This work highlights the need for policies tailored toward the heterogeneity in Hispanics’ assimilation processes.

Unemployment Insurance (UI) benefits were a central part of the social safety net during the Covid-19 recession. UI benefits, however, are severely understated in surveys. Using administrative tax data, we find that over half of UI benefits were missed in major survey data, with a greater understatement among low-income workers. As a result, 2020 official poverty rates were overstated by about 2 percentage points, and corrected poverty reached a six-decade low. We provide data to correct underreporting in surveys and show that, compared to UI benefits, the UI exclusion tax expenditure was less targeted at low incomes.

Background: this study analyzes the association between limited language proficiency and screening for colorectal cancer. Methods:This is a retrospective cohort study from the 2015 sample of the National Health Interview Survey database utilizing univariate and multivariate regression analysis. The study population includes subjects between 50 and 75 years of age. The main outcome analyzed was rates of screening colonoscopies between limited English-language proficiency (LEP) subjects and those fluent in English. Secondary outcomes included analysis of baseline, socioeconomic, access to health care variables, and other modalities for colorectal cancer screening between the groups. Results Incidence of limited language proficiency was 4.8% (n = 1978, count = 4 453 599). They reported lower rates of screening colonoscopies (61% vs 34%, P < .001), less physician recommendation for a colonoscopy (87 vs 60%, P < .001), fewer polyps removed in the previous 3 years (24% vs 9.1%; P < .001), and fewer fecal occult blood samples overall (P < .001). Additionally, Hispanic non-LEP subjects have higher rates of colonoscopies compared to those with language barriers (50% vs 33%, P < .001). On multivariate analysis, LEP was associated with a lower likelihood to have a screening colonoscopy (OR .67 95% CI .49-.91). A second regression model with “Spanish language” and “other language” variables included, associated Spanish speakers with a lower likelihood for a screening colonoscopy (OR .71 95% CI .52-.97) when controlling for baseline, socioeconomic, and access to health care covariates. Discussion:Patients with limited English-language proficiency are associated with lower rates of screening for colorectal cancer, in particular the Spanish speaking subgroup.

After several decades of Whites fleeing large metropolitan areas, they are now increasingly gentrifying urban neighborhoods and communities. This analysis uses data from the 2000 decennial census and the 2012 and 2017 American Community Survey to assess the growing presence of Whites in U.S. cities. The analysis examines the extent to which Whites have experienced an increase in their percentage share of the populations of 212 majority non-White communities with 50,000 or more inhabitants over two time periods (2000 to 2008–2012 and 2008–2012 to 2013–2017). The results show that 39 communities have experienced an expanding relative presence of Whites in one or both periods. Whites generally are growing at a faster pace than Blacks and Latinos in these communities and there are large socioeconomic gaps favoring Whites. The article concludes with a discussion of the policy implications of the findings.

There has been rapid growth in professional certifications in the health care sector, but little is known about the rewards to workers for attaining professional certifications, especially in low social closure occupations where the barriers to entry (e.g., higher education, degrees, licensure) are relatively limited. In this study, we focus on the attainment and rewards for professional certifications in four health care occupations – personal care aides, medical transcriptionists, medical assistants, and community health workers – where certification is generally not required by state or federal regulation but may be attractive to employers. Using the Current Population Survey (IPUMS CPS) from 2015 to 2020, we find that workers of color have significantly lower odds of attaining a certification, while women are 1.2 times more likely than men to an earn a certification. On average, workers who have earned a professional certification have weekly earnings that are 4.8% higher than workers who do not have a certification. Men experience the largest increase in weekly earnings (11.3%) when they have a professional certification as compared to those without, while women experience lower gains from professional certification (3.8%). Black and Hispanic workers experience modest rewards for certification (weekly earnings that are 1.2% and 5% higher, respectively) that are lower than the rewards gained by white workers (6% higher weekly earnings). Our findings suggest that professional certifications may have modest benefits for workers, but professional certifications often come with significant costs for individuals. Strategies for reducing inequality in the return to credentials and for improving job quality in the care sector are discussed.

Physicians’ engagement with the political process, particularly through voting, plays an important role in shaping public policy, affecting patient health and clinical practice.1 Historically, physicians in the US voted at lower rates than the general population in elections.2 However, physician voter engagement may have evolved, particularly given the changing political landscape and importance of US health care reform, as highlighted by the COVID-19 pandemic and differing policy proposals between the 2 major political parties. We assessed the trends in physician-voter turnout and reported reasons for not voting from 2000 through 2020.

An increasing share of urinary tract infections (UTIs) are caused by extraintestinal pathogenic Escherichia coli (ExPEC) lineages that have also been identified in poultry and hogs with high genetic similarity to human clinical isolates. We investigated industrial food animal production as a source of uropathogen transmission by examining relationships of hog and poultry density with emergency department (ED) visits for UTIs in North Carolina (NC). ED visits for UTI in 2016–2019 were identified by ICD-10 code from NC's ZIP code-level syndromic surveillance system and livestock counts were obtained from permit data and aerial imagery. We calculated separate hog and poultry spatial densities (animals/km2) by Census block with a 5 km buffer on the block perimeter and weighted by block population to estimate mean ZIP code densities. Associations between livestock density and UTI incidence were estimated using a reparameterized Besag-York-Mollié (BYM2) model with ZIP code population offsets to account for spatial autocorrelation. We excluded metropolitan and offshore ZIP codes and assessed effect measure modification by calendar year, ZIP code rurality, and patient sex, age, race/ethnicity, and health insurance status. In single-animal models, hog exposure was associated with increased UTI incidence (rate ratio [RR]: 1.21, 95 % CI: 1.07–1.37 in the highest hog-density tertile), but poultry exposure was associated with reduced UTI rates (RR: 0.86, 95 % CI: 0.81–0.91). However, the reference group for single-animal poultry models included ZIP codes with only hogs, which had some of the highest UTI rates; when compared with ZIP codes without any hogs or poultry, there was no association between poultry exposure and UTI incidence. Hog exposure was associated with increased UTI incidence in areas that also had medium to high poultry density, but not in areas with low poultry density, suggesting that intense hog production may contribute to increased UTI incidence in neighboring communities.

This article reviews evidence on the labor market performance of Hispanics in the United States, with a particular focus on the US-born segment of this population. After discussing critical issues that arise in the US data sources commonly used to study Hispanics, we document how Hispanics currently compare with other Americans in terms of education, earnings, and labor supply, and then we discuss long-term trends in these outcomes. Relative to non-Hispanic Whites, US-born Hispanics from most national origin groups possess sizeable deficits in earnings, which in large part reflect corresponding educational deficits. Over time, rates of high school completion by US-born Hispanics have almost converged to those of non-Hispanic Whites, but the large Hispanic deficits in college completion have instead widened. Finally, from the perspective of immigrant generations, Hispanics experience substantial improvements in education and earnings between first-generation immigrants and the second-generation consisting of the US-born children of immigrants. Continued progress beyond the second generation is obscured by measurement issues arising from high rates of Hispanic intermarriage and the fact that later-generation descendants of Hispanic immigrants often do not self-identify as Hispanic when they come from families with mixed ethnic origins.