IPUMS Demographic and Health Surveys (IPUMS DHS), through its intuitive website (http://dhs.ipums.org/), eliminate barriers to overtime and cross-national analyses with the DHS. IPUMS DHS recently released simplified reproductive calendar data. These calendar data are harmonized across samples, distinguish “not in universe” cases from “no” responses, and do not require destringing. Variable names are hot links to important documentation, such as survey-question text and comparability concerns. Analysts can also select consistently coded variables relating to the woman, her household, and her social and environmental context without merging files.

In this dissertation, I explore the socioeconomic contributions of landscape ecosystem C production in context of conventional biophysical regulations, resulting in spatiotemporal changes of global warming potential (GWP). To accurately model landscape-scale C production and estimate GWP at ecosystem and landscape levels, both socioeconomic and biophysical processes must be considered. Using an interdisciplinary approach, this work identifies factors that drive landscape C production and evaluates the sensitivity of terrestrial C sinks and sources. Chapter 2 uses fine spatiotemporal resolution remote sensing imagery to estimate terrestrial carbon production in heterogeneous agroecosystems. In Chapter 3, a structural equation model (SEM) assesses the relationships between historical agricultural intensification, land tenure, abiotic climate factors, and land cover land use change (LCLUC) on landscape terrestrial carbon production. Chapter 4 discusses the potential use of these estimates in a spatiotemporal life cycle assessment (LCA) for calculating carbon offset for carbon neutral goals. This work provides an innovative protocol that integrates geospatial and engineering methods to measure, validate, and summarize terrestrial and anthropogenic C emissions in life cycle assessment for calculating CO2-equivalent (CO2-eq)—GWP. Key contributions and lessons from this research will advance policy- and decision-making at landscape scales, where social and ecological processes must be considered for sustainable climate mitigation.

We analyze time use studies to describe how people allocate their time as they age, especially among paid work, unpaid work, leisure, and personal care. We emphasize differences in time allocation between older (i.e., those aged 65+) and younger people; between developed and developing countries; and by other demographic characteristics such as gender, marital status, health status, and educational attainment. We summarize related economic literature, assess the adequacy of global data resources, and propose a framework for thinking about key conceptual issues involving time allocation over the life cycle. We conclude by discussing some promising opportunities to fill salient gaps in the literature.

Do patterns of unequal policing persist or transform within gentrifying neighborhoods? Using an original survey of Chicago residents, we assess whether gentrifiers and longtime residents experience policing differently. Building on macro-level studies which rely on aggregate population data and micro-level studies which rely on ethnographies and interviews, we conduct a meso-level study that compares the experiences and views of differently positioned residents. We find that the phenomenon of being “over-policed and under-protected” that characterizes race-class subjugated neighborhoods is replicated within gentrifying neighborhoods for longtime residents. Meanwhile, gentrifiers express less concern about crime and report fewer interactions with police. While the average gentrifier has low levels of police contact, we find some evidence that a subset of gentrifiers are more likely to call the police about quality-of-life issues compared to neighbors. Our methodological approach provides a blueprint for how survey research can provide insights on individual-level experiences and attitudes in gentrifying neighborhoods.

In December 2021, with COVID-19 cases about to reach their peak, US employment still remained below its pre-pandemic level, with data indicating a strong labour demand and a weak labour supply. Many US individuals did not work, out of a concern about Covid-19. At the same time, the pandemic affected businesses of different sizes differently. At first, the pandemic hit especially hard small businesses. But later in 2020 the recovery was stronger at smaller businesses than at larger ones. These developments call for a renewed attention to how the labour supply varies across employers of different sizes. I focus on two important determinants of labour supply during a pandemic: the vulnerability to the severe effects of the virus because of pre-existing health conditions and the ability to work from home. I study how these two factors vary across establishments of different sizes. I find that the ability to work from home is higher at larger establishments, for both workers with and without COVID-19 health risk. However, the difference in ability to work from home across these two types of workers does not decrease at larger employers.

In the last three decades, numerous studies in different countries have corroborated the main postulates of the Fundamental Cause Theory (FCT), providing evidence showing how health inequalities are reproduced as society increases its capacity to control disease and/or avoid its consequences through preventive innovations. However, documenting the reproductive logic proposed by the theory requires the development of a dynamic analytical approach to consider socioeconomic disparities in the incorporation of multiple preventive innovations over time, which could act as mediating mechanisms of the durable relationship between socioeconomic status and health/mortality. This study draws on data from different waves of the National Health Interview Survey and the National Health and Nutrition Examination Survey to analyze the diffusion processes of various innovations in the U.S. The results of the study show that educational inequalities emerge, are amplified, and are reduced by the continuous diffusion of preventive innovations, supporting the meta-hypothesis of substitution of mediating mechanisms according to the interconnections of FCT and Diffusion of Innovation Theory.

Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.

The New Deal era’s Home Owners Loan Corporation (HOLC) programme has garnered notoriety for denying Black communities financial investment based on their race through the practice known as redlining. It is possible that redlining influenced future investment in these neighbourhoods by making them more appealing to gentrifiers through creating a rent gap or increasing the percentage of non-White populations. To explore the link between HOLC redlining and gentrification, we drew upon a sample of 58 cities across the United States from the Mapping Decline project. We also leveraged historical Census data collected by IPUMS and the Longitudinal Tract Database, as well as data on urban renewal from Renewing Inequality to control for intervening factors. Findings indicate that HOLC redlining can either directly or indirectly relate to gentrification depending on when gentrification begins. These findings encourage more consideration of the role of racist government policies in determining when gentrification will occur.

Background Ensuring universal access to safe sanitation by 2030 is a development priority for India. Doing so can help ensure improved physical and mental health outcomes. While the proportion of people in India with safe sanitation has risen dramatically over the past thirty years, much less is known about who has been most at risk for not having access to safe sanitation across India’s states and Union Territories (UT) over this time period. We introduce the concept of zero-sanitation to fill this gap. Methods Data from five National Family Health Surveys (NFHS) conducted in 1993, 1999, 2006, 2016, and 2021 from 36 states and UT were used for this study. The study population consisted for all household individuals regardless of age in each survey round. Zero-sanitation was defined as those who have no access to a household toilet, and thus defecate in the open. We analyzed the percent prevalence of zero-sanitation in every state /UT at each time period in both urban and rural communities, as well as the population headcount burden in 2021. We calculated the absolute change on an annual basis to assess the change in percentage points of zero-sanitation across time periods at the all-India and state /UT levels. Results The all-India prevalence of zero-sanitation declined from 70.3% (95% confidence interval (CI)=70.2%-70.5%) in 1993 to 17.8% (95% CI=17.7%-17.9%) in 2021. The median percent prevalence of zero-sanitation across states and UTs was 65.9% in 1993. By 2021, the median percent prevalence of zero-sanitation across states and UTs was 5.7%. This reduction corresponded with a reduction in the between state /UT inequality in zero-sanitation. Nevertheless, as of 2021, the prevalence of zero-sanitation was still above 20% in Bihar, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, and Uttar Pradesh. Additionally, as of 2021, almost 92% of individuals who were defecating in the open were experiencing zero-sanitation. Zero-sanitation remains most common in states such as Bihar, Punjab, Uttar Pradesh, and Assam. Nevertheless, at this current rate of improvement, every state and UT except for Sikkim and Chandigarh are on track to end open defecation by 2030. Conclusions The concept of zero-sanitation is a useful tool in helping policy makers assess the extent to which sanitation coverage remains incomplete. When viewed through this lens, we see that open defecation remains most common among those who do not have a toilet. Addressing the myriad social determinants of sanitation access can help fill these gaps and ensure equitable sanitation coverage throughout India

The common method of calculating economic magnitudes is to multiply the regression coefficient of the variable of interest by its sample standard deviation. This method is often problematic in finance settings when researchers use granular fixed effects. For many common finance variables, the sample standard deviation is much larger than the within-group variation that identifies the regression coefficient, and within-group changes of this magnitude are rare. Without additional assumptions, the common approach significantly inflates the economic magnitude of the identified effect and can bias comparisons between different variables of interest. We provide approaches to measure within-group variation for economic magnitude calculations. Abstract The common method of calculating economic magnitudes is to multiply the regression coefficient of the variable of interest by its sample standard deviation. This method is often problematic in finance settings when researchers use granular fixed effects. For many common finance variables, the sample standard deviation is much larger than the within-group variation that identifies the regression coefficient, and within-group changes of this magnitude are rare. Without additional assumptions, the common approach significantly inflates the economic magnitude of the identified effect and can bias comparisons between different variables of interest. We provide approaches to measure within-group variation for economic magnitude calculations.

Air quality in the United States has improved significantly over the past few decades. According to the Environmental Protection Agency (EPA), total emissions of the six principal air pollutants (carbon monoxide, lead, nitrogen oxides, ground -level ozone, particulate matter, and sulfur oxides) fell by 78% between 1970 and 2020 (US EPA, 2016). However, compared to countries like New Zealand, Sweden, Australia, and Canada, the United States still has much to do with regard to pollution management or reduction (McCarthy, 2020). As pollution control has increasing marginal costs, the cost of reducing pollution in a country like the United States, where pollution levels have been brought down significantly, becomes more and more expensive with each additional effort aimed at reducing pollution. Despite the level of spending on air pollution, exposure to pollution in the US is still quite high. The American Lung Association reported in a 2021 report that four in ten Americans live where pollution levels frequently make the air too dangerous to breathe (American Lung Association, 2021). The amount of spending on air pollution mitigation pales is negligible compared to the cost of the effects of air pollution. The effects of air pollution are very costly, as they directly affect human wellbeing and environmental conditions (Santana et al., 2020; Chen & Chen, 2021; Delucchi et al., 2002; Gouveia & Fletcher, 2000). Between 2010 and 2020, mortality from exposure to ozone, and from exposure to PM.2.5 among adults were expected to increase by about 65% and 44%, respectively (US EPA, 2011). Adverse health conditions are not the only negative effect of air pollution. Other effects of exposure to air pollution can be seen in reduced visibility, restricted activity, and increased social misbehavior (Lu, 2020; Hyslop, 2009; Yan et al., 2019). 2 It is important to know the full costs of air pollution so that governments (federal and state) can make policies whose benefits will exceed those costs. It is hard to measure the full cost of air pollution as some of the effects of air pollution are not as easily observable and quantifiable as health effects. One such effect of air pollution is the change in behavior to mitigate exposure to air pollution. These changes in behavior are important of themselves as they represent a loss in utility because people give up enjoyable activities in order to avoid being exposed to pollution. Behavioral changes are also important because they indirectly affect the cost of health effects of pollution by reducing the probability of getting sick from exposure to pollutants. This type of behavior is usually referred to as avoidance behavior or averting behavior. Avoidance behavior with regards to air pollution constitutes any behavior that enables a person to stay clear of or protect themselves in environments that could potentially expose them to air pollutants. It may include staying indoors on days with high levels of air pollution, avoiding highly polluted areas, using protective coverings such as face masks, and in the longer term, migrating from highly polluted areas to less polluted areas. Estimates of the cost of the health effects (and the total costs) of air pollution that do not take into account the cost of these behavioral adjustments are biased downwards because these actions affect the frequency and severity with which people’s health are affected by pollution. The estimates from this study measure the causal effects of air quality and mobility. Measures of air quality have been found to be endogenous in the existing body of research about the relationship between air quality and human mobility. To control for this endogeneity, I employ a control function model which corrects the endogeneity problem by modelling the endogeneity in the error term. Though similar in many ways to a two-stage least squares estimation, the control function method is more advantageous as it is compatible with more model specifications (including non-linear models) and allow for heterogeneous effects. Thus, the control function method allows me to test the robustness of my results to different model specifications without changing the underlying statistical method. To the best of my knowledge, this is the first research work that employs the use of the control function method in correcting for endogeneity in the relationship between air quality and human mobility. This study finds evidence of avoidance behavior in the United States as shown by a 1.4 percentage point increase in time spent at home, a 4.2 percentage point decrease in time spent away from home, and a 4.5 percent reduction in distance traveled from home by a device on average on days of unhealthy air quality. These translate to an 8-minute increase in time spent at home, a 10- minute reduction in time spent away from home and a 385-meter reduction in distance traveled from home on days that the EPA reports unhealthy air quality compared to days on which good air quality is reported. The study uses observed data from across the entire country and thus, findings are more reliable and generalizable than others in the literature. Results from heterogeneity analyses done as part of the study show variations in response to air pollution among different demographic groups. Minority communities, for example, are shown to be more responsive to changes in air quality than non-minority communities. Knowledge of the extent to which avoidance measures are effective in reducing exposure to air pollution is important to policy makers, as it could serve as the basis for the development of policies geared towards making individuals play more active roles in reducing the adverse effects of pollution. If avoidance behavior reduces exposure to pollution, then it might be prudent to encourage people to practice these behaviors while the government finds more efficient ways to reduce pollution levels. It must be noted, however, that avoidance behavior has many costs aside from the cost of implementing information systems to make people aware of the levels of pollution. As there has been a general failure to limit pollution (in the form of greenhouse gases, for instance) globally, finding a way to measure these costs is important for governments to make decisions on whether to make environmental policy more focused on mitigation and adaptation practices. Avoidance behavior, which is the focus of this paper, is a type of mitigation practice. As such, the findings from this paper contribute valuable knowledge to the decision-making process in matters concerning environmental policy. Also, the finding that different groups respond differently to air pollution should emphasize to policymakers the importance of considering vulnerable populations when making policies related to encouraging avoidance behavior.

French-Canadian child mortality in an early twentieth-century North American industrial city. The analysis is based on the exploitation of an original dataset constructed by linking the 1910 census data (IPUMS-Full Count) for Manchester, New Hampshire to Quebec Catholic marriage records (BALSAC) and geocoding census data at the household level (Sanborn Fire Insurance Maps). Our results suggest that the presence of maternal and paternal grandmothers in the city living in different households were associated with reduced child mortality and that French-Canadian women who arrived in the United States as children or young adults experienced higher child mortality compared to second-generation French Canadians and those who migrated at a later age.

The climbing costs of housing, is one of the main concerns across the United States. Housing is considered affordable when it consumes less than 30 percent of total household income, and although the average household spends a quarter of its income in housing, low-income household devote between 50 and 70 percent of their income to housing. The issues with housing affordability have become evident since the 1970s, around the same time that incomes at the top of the distribution started to experience sharp growth. This thesis examines the effects of income inequality on housing affordability for the years 2011-2017 post-Great Recession, at the different levels of the income distribution and by demographic characteristics in the State of California. This study uses data from the Integrated Public Use Microdata Series (IPUMS) from the American Community Surveys (ACS). Data includes seven sample years from 2011 to 2017, with a total of 649,766 observations of households from the State of California with individual characteristics filtered by head of household. Based on existing literature and the results of the empirical model presented in this paper, income inequality has a significant effect on housing affordability as it increases housing costs as a share of income (house price income ratio and rent price income ratio). This effect is most noteworthy for the lowest income households, and it fades as it gets closer to the top of the income distribution.

Following the June 24, 2022 Dobbs v. Jackson Supreme Court ruling, which overturned the federal right to abortion established in Roe v. Wade, hundreds of employers publicly announced policies covering out-of-state employee travel for abortions and related care. Leveraging data from Indeed and Glassdoor, we first document that companies with more female and more Democratic-leaning employees and executives were more likely to announce these policies. We then examine the causal impact such announcements had on recruitment, job satisfaction, and pay by introducing a new methodology to recover similar employers who did not make announcements using workers’ revealed preferences in job search. Difference-in-differences estimates reveal that for announcing companies: (i) vacancies received more job seeker interest, particularly in Democratic-leaning states and female-dominated jobs in states with “trigger” laws that outlawed abortion, (ii) satisfaction with management fell amongst existing employees, particularly in male-dominated jobs, and (iii) posted wages increased, especially for companies where employee sentiment declined. These results highlight the complicated trade-off employers face from engaging in sociopolitical dialogue, in particular how signals of company culture can help recruit new workers but alienate current ones.

Evidence has emerged demonstrating that whites no longer reject negative, explicit racial appeals as they had in the past. This seeming reversal of the traditional logic of the powerlessness of explicit appeals raises the question: Why are explicit racial appeals accepted sometimes but rejected at other times? Here, I test whether the relative acceptance of negative, explicit racial appeals depends on whites’ feelings of threat using a two-wave survey experiment that manipulates participants’ feelings of threat, and then examines their responses to an overtly racist political appeal. I find that when whites feel threatened, they are more willing to approve of and agree with a negative, explicit racial appeal disparaging African Americans—and express willingness to vote for the candidate who made the explicit racial appeal.

We present a new series on the trend of occupational task content between 1900 and 2020. We find that Black workers were concentrated in tasks that declined in demand over the last 120 years, while white workers were concentrated in tasks that increased in demand. We then use longitudinal data to show that transitions across occupational task content were racially biased, where Black workers ended up in occupations with lower-rewarded task content than their white counterparts. This bias also existed across generations in pre-World War II data, but not for post-World War II years. These results suggest that a given task-displacing technological shock will impact Black-white inequality within one generation, but not across generations.

This paper studies the spatial distribution of household income inequality across 722 U.S. local labor markets—or Commuting Zones—from 2005–2019. The Theil index, its within and between components, and the decomposition of the within component into a shared and differentiated structure are estimated using the American Community Survey. Inequality increased in 72% of CZs. The dynamics within markets drive the rising income inequality pattern due to high accumulation of income in specific CZs. One-fourth of inequality comes from the shared distribution and two-fifths from the differentiated. New York and DC are the labor markets with the highest positive contribution to inequality. San Francisco’s, San Jose’s, and Seattle also contributed to overall inequality in the last few years. CZs with a lower share of income than households tend to be touristic, formerly industrial, or border labor markets. Texas and Oregon displayed an attractive reduction of income inequality in many of their CZs.

While cultural competence has been proposed as an important framework for enhancing health care equity, how members of different racial/ethnic groups consider the importance of cultural competence and their access to culturally competent health care are insufficiently understood. Despite continuously increasing immigrants into the US, it is unclear how immigration status intersects with race/ethnicity to shape individuals’ perception of and access to culturally competent care in the US health care system. To fill this research gap, this study examined how the intersection of race/ethnicity and immigration status is associated with people’s perception of and access to culturally competent health care and among immigrants, whether their length of stay matters, using data from the 2017 National Health Interview Survey. The results show that while racial and ethnic minority members reported greater importance of culturally competent care than non-Hispanic whites, Asian, black and other-race immigrants reported even greater importance than their US-born counterparts. Additionally, although racial/ethnic minorities reported greater limited access to culturally competent care than their white peers, this gap in access was observed primarily among US-born racial/ethnic minorities. Shorter length of residence (fewer than 15 years) was associated with greater perceived importance than residence of at least 15 years among immigrants, but access to culturally competent care did not differ by length of residence. The findings speak to racial/ethnic minorities’ greater desire for culturally competent care and their unmet needs.

Objectives: The purpose of this study is to use the National Health Interview Service (NHIS) database to assess the impact of socioeconomic factors on fall risk in older adults in the US. Methods: The NHIS database was retrospectively analyzed to assess several variables related to falls and resulting injuries in older adults. The database covered the period from 2000 to 2018. Results: Our study identified several correlations that have not been previously reported in the literature. Welfare recipiency was associated with more injuries from falls (aOR = 18.8, 95% CI: 1.9-185.6, P =.01), while US citizenship was associated with a lower risk of falls in older adults in the US (aOR =.4, 95% CI:.2-.9, P =.02). Discussion: These findings highlight the importance of evaluating patients for social determinants of health (SDOH) in both the inpatient and outpatient setting. Based on this data, we believe SDOH should be incorporated into fall risk assessments.

Objective: The objective of this study was to examine health care unaffordability for rural and urban residents and by postpartum status. Methods: We used cross-sectional survey data on female-identifying respondents ages 18-44 (n=17,800) from the 2019 to 2021 National Health Interview Study. Outcomes of interest were 3 measures of health care unaffordability. We conducted bivariate and multivariable regression models to assess the association between health care unaffordability, rurality, and postpartum status. Results: Bivariate analyses showed postpartum people reported statistically significantly higher rates of being unable to pay medical bills and having problems medical paying bills, as compared with nonpostpartum people. Rural residents also reported statistically significantly higher rates of being unable to pay their medical bills and having problems paying medical bills as compared with urban residents. In adjusted models, the predicted probability of being unable to pay medical bills among postpartum respondents was 12.8% (CI, 10.1-15.5), which was statistically significantly higher than among nonpostpartum respondents. Similarly, postpartum respondents had statistically significantly higher predicted probabilities of reporting problems paying medical bills (18.4%, CI, 15.4-21.4) as compared with nonpostpartum respondents. The rural residency was not significantly associated with the health care unaffordability outcome measures in adjusted models. Conclusions: Both postpartum and rural respondents reported higher rates of being unable to pay medical bills and having problems paying medical bills; however, after adjusting for covariates, only postpartum respondents reported statistically significantly higher rates of these outcomes. These results suggest that postpartum status may present challenges to health care affordability that span the urban/rural context.