Flooding, including hurricanes and tornadoes, accounts for approximately 40 percent of natural disasters worldwide and kills 100 people on average in the United States each year, which is more than any other single weather hazard. Since flooding is a common hazard in the U.S. and flood-related casualties have been increasing in recent years, it is important to understand the spatial patterns of different vulnerable population groups in the flooding regions. To achieve this objective, spatial scan statistics were used to identify the spatial clusters of different demographic groups (children and elderly, poor, White, African American, and Hispanic) in the 100-year floodplain areas of Birmingham. Using the decennial census data from 1990 to 2015, this research examined whether these vulnerable population groups had aggregated more in the flooding areas or moved away from the flooding areas in the past thirty years. The findings of this research indicate that most of the minorities are increasingly aggregating in the floodplain areas of Village Creek in Birmingham. The findings also suggest that the non-minorities are moving away from the flooding regions in Birmingham, AL. As part of the minorities and non-minorities group, approximately 50 percent of African Americans and 4 percent of White populations aggregated in the Village Creek flooding areas in 2015. Although the percentage of White populations is very low, the findings suggest that they are still exposed to floods. The multi-decadal analysis of flood risk will help the local governments to understand which population groups could be more affected by floods historically and need more attention in future flood hazards. This understanding will help them prepare for future flood hazards by allocating resources efficiently among the different racial and ethnic groups.

This article explores the relationships between supervisor–supervisee gender difference and the scientific impact of doctoral dissertations. We use the China Doctoral Dissertations Full-text Database and pay special attention to the fields of Humanities and Social Sciences in China in our empirical study. By establishing regression models, we find that the ranks of the scientific impact regarding doctoral dissertations are female–female (first), female–male (second), male–male (third) and male–female (fourth) pairs (sequence: student gender and then supervisor gender). The finding has many interesting implications for science policy and gender inequality.

In this study, I develop a novel general equilibrium life cycle model composed of finitely-lived households that differ according to age, skill level, and access to employer-provided health insurance. After introducing a “Medicare for all” health insurance system to the model, I examine how the welfare response to this policy change will differ according to household characteristics. Then, I compare this system to a completely privatized health insurance system that achieves universal health insurance coverage through the creation of utilization-based premium subsidies. In general, both systems tend to improve the welfare of young households at the expense of old households. However, when using average value-of-life as the primary measure of welfare, Medicare for all either benefits unskilled households at the expense of skilled households, or makes both worse off. In contrast, the privatized system improves the average value-of-life of all household groups, regardless of skill level or prior access to employer-provided health insurance.

Background: Medicaid plays a critical role during the perinatal period, but pregnancy-related Medicaid eligibility only extends for 60 days post partum. In 2014, the Affordable Care Act's (ACA's) Medicaid expansions increased adult Medicaid eligibility to 138% of the federal poverty level in participating states, allowing eligible new mothers to remain covered after pregnancy-related coverage expires. We investigate the impact of ACA Medicaid expansions on insurance coverage among new mothers living in poverty. Methods:We define new mothers living in poverty as women ages 19 to 44 with incomes below the federal poverty level who report giving birth in the past 12 months. We use 2010-2017 American Community Survey data and a difference-in-differences approach using parental Medicaid-eligibility thresholds to estimate the effect of ACA Medicaid expansions on insurance coverage among poor new mothers. Results: A 100-percentage-point increase in parental Medicaid-eligibility is associated with an 8.8-percentage-point decrease (P < .001) in uninsurance, a 13.2-percentage-point increase (P <.001) in Medicaid coverage, and a 4.4-percentage-point decrease in private or other coverage (P = .001) among poor new mothers. The average increase in Medicaid eligibility is associated with a 28% decrease in uninsurance, a 13% increase in Medicaid coverage, and an 18% decline in private or other insurance among poor new mothers in expansion states. However, in 2017, there were ∼142 000 remaining uninsured, poor new mothers. Conclusions: ACA Medicaid expansions are associated with increased Medicaid coverage and reduced uninsurance among poor new mothers. Opportunities remain for expansion and nonexpansion states to increase insurance coverage among new mothers living in poverty.

Bikeshare programs in their current form have been in place for several years in many cities across the United States. Encouraging people to use bikeshare for their daily routine travel has numerous social, economic, environmental, and health benefits. Therefore, it is important to understand factors influencing bikeshare use in different urban areas to improve the system and encourage more use. This paper investigates how the built environment at both local and regional scales influences bikeshare use in seven large metropolitan areas in the U.S. The study areas include Boston, Chicago, Philadelphia, Minneapolis, San Francisco, San Jose, and Washington, D.C., and the data consists of about 12 million bike trips from approximately 2,000 stations over a one-year period. In addition to linear regression models built for each individual city for comparison purposes, a multi-level mixed effect regression model is built to predict the number of trips originated from each station with respect to the local and regional built environment pattern. The results are consistent with previous research on the effect of land use at the local level on bikeshare demand and show that residential density, regional diversity, pedestrian-oriented road network density, and job accessibility via transit all have a significant positive effect on bikeshare demand. At the regional level, results suggest that the overall level of mixed-use development and overall bike-friendliness in the region (i.e., exclusive bike routes, right-of-way, and bike facilities) and higher congestion level in the region are significant factors influencing bikeshare activities and demand. Models developed in this study could be applied to other communities that are seeking to improve and/or expand their bikeshare systems, as well as cities planning to launch new bikeshare programs.

The Current Population Survey (CPS) has been the nation’s primary source of information about employment and unemployment for decades. The data are widely used by social scientists and policy makers to study labor force participation, poverty, and other high-priority topics. An underutilized feature of the CPS is its short-run panel component. This paper discusses the unique challenges encountered when linking basic monthly data in the 1976-1988 period as well as linking the March basic monthly data to the Annual Social and Economic Supplement (ASEC) in the 1976-1988 period. We describe strategies to address linking obstacles and document linkage rates.

The standard assumption in growth accounting is that an hour worked by a worker of given type delivers a constant quantity of labor services over time. This assumption may be violated due to vintage effects, which were shown to be important in the United States since the early 1980s, leading to an underestimation of the growth of labor input (Bowlus anA1d Robinson, 2012). We apply their method for identifying vintage effects to a comparison between the United States and six European countries. We find that vintage effects led to increases of labor services per hour worked by high‐skilled workers in the United States and United Kingdom and decreases in Continental European countries between 1995 and 2005. Rather than a productivity growth advantage of the US and UK, the primary difference with Continental European countries was human capital vintage effects instead.

Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns related to the training data and unfair behaviors of some decisions with regard to certain attributes (e.g., sex, race) are becoming more critical. Thus, constructing a fair machine learning model while simultaneously providing privacy protection becomes a challenging problem. In this paper, we focus on the design of classification model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. In order to enforce $\epsilon$-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint. We further propose an utility-enhancement scheme, called relaxed functional mechanism by adding Gaussian noise instead of Laplace noise, hence achieving $(\epsilon,\delta)$-differential privacy. Based on the relaxed functional mechanism, we can design $(\epsilon,\delta)$-differentially private and fair classification model. Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms.

Objective: We describe disparities in civic engagement along two axes: the modal, describing the extent to which civic engagement is structured, and its content, describing the extent to which civic engagement has partisan objectives. Accordingly, this structure creates four domains of civic engagement: associational–partisan (e.g., voting), social–partisan (e.g., boycotting), associational–civic (e.g., participating in parent–teacher organizations), and social–civic (e.g., talking with neighbors). Method: Using data from the Current Population Survey and item response theory methods, we generate civic engagement scores in each of these domains for as many as 35,618 U.S. respondents. Results and Conclusions: Similar to prior studies, income and educational attainment are associated with large disparities in civic engagement across all domains. However, in contrast to prior studies, young Americans are not outpacing older Americans in social–partisan engagement; rather, older Americans are more engaged in every sector of engagement we measure.

Objectives. To examine the relationship between minoritized identity and barriers to health care in the United States.Methods. Nationally representative data collected from the 2013 to 2017 waves of the National Health Interview Survey were used to conduct descriptive and logistic regression analyses. Men and women were placed in 1 of 4 categories: no minoritized identities, minoritized identities of race/ethnicity (MIoRE), minoritized identities of sexuality (MIoS), or minoritized identities of both race/ethnicity and sexuality (MIoRES). Five barriers to health care were considered.Results. Relative to heterosexual White adults and after controlling for socioeconomic status, adults with MIoRE were less likely to report barriers, adults with MIoS were more likely to report barriers, and adults with MIoRES were more likely to report barriers across 2 of the study measures.Conclusions. Barriers to care varied according to gender, minoritized identity, and the measure of access to health care itself.Public Health Implications. Approaching health disparities research using an intersectional lens moves the discussion from examining individual differences to examining the role of social structures such as the health care system in maintaining and reproducing inequality.

Importance: Psychological distress affects health and health care utilization. Hearing loss (HL) is highly prevalent and undertreated, and it may be a potentially modifiable risk factor for psychological distress. Objective: To investigate the association between HL, psychological distress, and mental health care utilization among adults in the United States. Design, Setting, and Participants: This cross-sectional study included 25 665 individuals aged 18 years and older who participated in the 2017 National Health Interview Survey, a home-based nationally representative cross-sectional survey of noninstitutionalized US adults conducted by trained interviewers. Exposures: Self-reported hearing and hearing aid use. Main Outcomes and Measures: Kessler psychological distress scale (scores ≥5 indicating moderate distress), self-reported use of medication to treat depression or anxiety symptoms, and use of mental health services in the past year. Results: A total of 25 665 adults (mean [SD] age 47.0 [18.1] years, 51.7% [95% CI, 51.0%-52.5%] women, weighted to be representative of the US adult population) were included in the analysis. Compared with 11 558 participants (49.3%; 95% CI, 48.2%-50.5%) with no HL, the 9390 (35.3%; 95% CI, 34.4%-36.2%) with mild HL and 4717 (15.4%; 95% CI, 14.8%-16.0%) with moderate or worse HL had increased odds of moderate psychological distress (mild HL: odds ratio [OR], 1.49; 95% CI, 1.35-1.62; moderate HL: OR, 2.12; 95% CI, 1.87-2.41) and were more likely to report antidepressant medication use (mild HL: OR, 1.39; 95% CI, 1.17-1.67; moderate HL: OR, 2.07; 95% CI, 1.70-2.57), and antianxiety medication use (mild HL: OR, 1.39; 95% CI, 1.16-1.67; moderate HL: OR, 1.94; 95% CI, 1.57-2.39). Moderate HL was associated with increased odds of use of mental health services (moderate HL: OR, 1.53; 95% CI, 1.30-1.79). Among individuals with moderate HL, those with hearing aids (1066 [22.6%]) were less likely to experience psychological distress than those without hearing aids (3651 [77.4%]; OR, 0.66; 95% CI, 0.53-0.83). Conclusions and Relevance: In a large nationally representative sample of US adults, self-reported HL was associated with both greater psychological distress and increased rates of antidepressant and antianxiety medication use and utilization of mental health services. Further research is needed to investigate whether HL may be a modifiable risk factor for these outcomes.

Naturalization bestows economic benefits to immigrants, their families, and communities through greater access to employment opportunities, higher earnings, and homeownership. It is the cornerstone of immigrant assimilation in the United States. Yet, less than 720,000 of the estimated 8.5 million legal permanent residents (LPRs) eligible to naturalize do so on a yearly basis. Using data from the 2008–2018 American Community Survey, we analyze how the expansion of interior immigration enforcement impacts the decision to naturalize. We find that the adoption over an entire year of one enforcement initiative, or of two enforcement initiatives for half a year, raises the naturalization hazard by 2 percent. The effect is more pronounced among LPRs who are Mexican, women, or reside in non-mixed status households. Finally, the impact is driven by police-based, as opposed to employment-based, interior immigration enforcement. In sum, immigrants who naturalize in response to intensified enforcement may be doing so out of fear or uncertainty about their ability to secure their citizenship status and rights in a rapidly changing immigration policy environment increasingly hostile towards immigrants.

School psychologists often consider index- and subtest-level discrepancy scores from intelligence tests when making decisions regarding students’ special education eligibility. Best practices for clinical decision-making indicate that scores may only be considered if they meet an established standard of reliability. Therefore, it is essential to assess whether an interpretation of discrepancy scores can be considered reliable. This research used data provided in the supplemental manual of the Kaufman Assessment Battery for Children, Second Edition Normative Update (KABC-II NU) to calculate internal reliability coefficients (ICR) for discrepancy scores for each of the sample age group batteries, ages 3-6 and ages 7-18. Subtest-level discrepancy score ICR for ages 3-6 ranged from .61 to .94 and index-level ICR ranged from .00 to .93. Subtest-level discrepancy score ICR for ages 7-18 ranged from .56 to .94 and index-level ICR ranged from .61 to .94. These scores are compared to established reliability standards and a discussion of implications for practitioners is provided.

Telephone operation, one of the most common jobs for young American women in the early 1900s, provided hundreds of thousands of female workers a pathway into the labor force. Between 1920 and 1940, AT&T adopted mechanical switching technology in more than half of the U.S. telephone network, replacing manual operation. We show that although automation eliminated most of these jobs, it did not affect future cohorts' overall employment: the decline in demand for operators was counteracted by growth in both middle-skill jobs like secretarial work and lower-skill service jobs, which absorbed future generations. Using a new genealogy-based census linking method, we show that incumbent telephone operators were most impacted by automation, and a decade later were more likely to be in lower-paying occupations or have left the labor force entirely.

Rapid technological development makes skills depreciate faster than in the past while new technologies generate gaps in workers’ skills and call for the acquisition of proper skills and lifelong learning. Proper skill mixes for future jobs include strong cognitive skills, basic information and communication technology, and analytical skills, as well as a range of noncognitive skills such as creativity, problem-solving, critical thinking, and communication. Retraining and reskilling workers is also crucial. All these changes lead to a major rethinking of education and skill training throughout a person’s life. This paper reviews the recent studies on human capital and skill formation in the era of rapid technological progress. Findings from these studies particularly in labor economics can shed light on new directions for lifelong education policies.

Over time, automobile ownership in the US has increased. Yet low-income households remain less likely to have access to automobiles than higher-income households. Today, given the continued dispersion of US metropolitan areas and the growing number of jobs, as well as low-income families living in the suburbs, the evidence suggests that low-income women who do not have access to automobiles are increasingly disadvantaged. To engender greater economic and social sustainability, the evidence suggests that low-income women would benefit from policies to increase their access to automobiles.

Hong Kong is highly developed yet heavily influenced by traditional social norms, such as the preference for sons over daughters. This paper studies son preference in Hong Kong by exploring the effect of child gender on parents’ fertility and sex selection decisions in the 1991 to 2016 Hong Kong population census/by census. In a society heavily influenced by son preference, both fertility and child gender might be correlated with unobservable family characteristics. To overcome the potential bias from omitted variables, I follow Dahl and Moretti (2008) to explore the randomness of the gender of the first child. Thus, this paper focuses on the effect of firstborn girls versus firstborn boys on fertility and sex selection. I find that son preference is persistently strong in Hong Kong. Firstborn girls increased fertility as well as the probability of boys in the future. Compared with firstborn boys, firstborn girls caused mothers to have on average 6.74% more children in the 1991 to 2016 censuses, and the magnitude of the effect decreased with time. Concurrent with the diminishing effect of firstborn girls on fertility, son-biased sex selection might became more extensive. In the most recent data from the 2016 census, parents without sons were 20% more likely to have sons at the third parity than parents who already had at least one son. The results suggest that son preference is persistently strong in Hong Kong, and parents might be increasingly likely to substitute the son-biased stopping rule with sex selection to have sons such that they could reach the desired number of sons within the small family.

Housing affordability is a major problem for many Americans. The increase in residential rents in the past few decades, alongside stagnant and even lower incomes, is forcing households to spend a larger share of their income on housing. The high costs of housing relative to income mean that some households cannot afford non-housing goods and services like food, healthcare, and education. Within the affordability debate, lowering transportation costs by using transit is often viewed as a potential solution to affordability problems. While housing might be expensive, if transportation costs are low, the overall costs of living in a specific neighborhood might still be affordable. Hence, housing and transportation advocates call for improving public transport options that allow households to access destinations without needing a private vehicle. In this dissertation, I examine housing and transportation costs and affordability in twentyseven U.S. metropolitan areas with intra-urban rail systems. The objective of the study is to understand whether transit-rich neighborhoods, especially those served by rail, are affordable, with an emphasis on lower-income households. To this end, the dissertation adopts a multilevel approach to examining housing and transportation costs and affordability cross-sectionally and over time. Adopting a multilevel approach allows examining how neighborhood- and metropolitan-level factors interact with one another and affect housing and transportation costs and affordability. Neighborhoods (i.e., block groups and census tracts) are classified based on their proximity to rail and their built environments to examine how costs vary between different types of neighborhoods. Finally, affordability is calculated based on metropolitan-wide income levels to assess whether housing and transportation costs are affordable to households at different income levels. The results indicate that the majority of neighborhoods in the sampled metropolitan areas are affordable to median and moderate-income households. Moreover, transit-rich neighborhoods are found to be more affordable than auto-oriented neighborhoods, mainly thanks to lower transportation costs. Still, only small share of neighborhoods is affordable to households earning 50% or less of area median income. Even in transit-rich neighborhoods, the lower transportation costs typically do not translate into more affordable locations for very low-income households. This is because many households still rely on the private vehicle even in the most transit-rich neighborhoods. Housing in transit-oriented development is expensive, in part, due to the high levels of transit job accessibility these neighborhoods offer. However, housing costs in these neighborhoods are also high because of low long-run elasticities of housing supply. Despite an increase in the demand for compact walkable neighborhoods in recent decades, land-use regulations and local opposition direct denser development to rail-station areas. As a result, a higher supply of housing in transit-oriented development is associated with higher housing costs regionwide due to induced demand for these neighborhoods. At the same time, increasing the supply of housing in alternative pedestrian-friendly and transit-rich neighborhoods has a moderating effect on housing costs in transit-oriented development as it allows separating the demand for walkable urban form from the demand for transit accessibility. Hence, rather than focusing on developing more housing only in transit-oriented development, efforts should focus on expanding the housing options in a diversity of neighborhood types both near and away from rail stations.

This thesis studies the relationship between socio-cultural contexts and entrepreneurship. It departs from the question “Given that entrepreneurs are rare and economically valuable, are there certain socio-cultural contexts which are more efficient at ‘producing’ and ‘enabling’ them?” As such, it is rooted in the comparative stream of international entrepreneurship research (Coviello, McDougall, & Oviatt, 2011; Jones, Coviello, & Tang, 2011; Terjesen, Hessels, & Li, 2016; Verbeke & Ciravegna, 2018). The role of socio-cultural as well as economic and formal institutional contexts in understanding variation in entrepreneurship manifests itself at three levels of analysis: at the national level, within countries across regions, and within countries across immigrant groups from different countries of origins. Since variations in entrepreneurial activity across contexts are at the core of this thesis, I begin by describing differences in entrepreneurship as captured by self-employment rates across nations, regions, and immigrant groups to motivate the subsequent analyses. First, cross-national differences in current self-employment rates around the world are substantial (see Figure 1.1). The rate of self-employment is below 10% in, amongst others, Denmark, Estonia, Libya, Norway, Russia, Saudi Arabia, and Sweden, while it is above 70% in, for example, Angola, India, Madagascar, and Tanzania (WDI 2016-2018, World Bank, 2019). Moreover, these international differences in self-employment rates are also of considerable persistence. This is illustrated in Figure 1.2 which plots current levels of self-employment against past self-employment rates 20 years ago. As the figure highlights, self-employment rates 20 years ago can explain 96% of the variation in current self-employment rates.

How does ethnic diversity within a jurisdiction impact residents’ demand for local public goods? This question is central to the literature on diversity and local spending but remains relatively untested. To fill this gap, we study outcomes from California school district ballot measures to increase school funding. We use an instrumental variable approach to account for endogeneity in diversity. We find no evidence that diversity impacts willingness to be taxed to fund local schools. Our findings suggest that the negative effects of diversity on spending may be occurring through other channels, such as the decision-making of local officials.