Car use is associated with negative social, economic, and environmental externalities. Encouraging people to adopt a car-free lifestyle is one way to address these harms, since car ownership is a major predictor of driving. Nevertheless, few people in the US voluntarily live without a car. In this dissertation, I seek 1) to understand why people decide to live car-free, and 2) to investigate the potential that more people could adopt car-free lifestyles. I use threads scraped from Reddit, an online social media platform, to address these issues, employing content analysis to examine data from seven US cities in which Reddit commenters discuss car ownership decisions. I find that the car-free are multimodal, employ contingency plans, rely on ridehailing and carsharing services to fill mobility gaps, and readily get used to car-free living. Among car owners, I find that while some need cars, others keep them for convenience. Costs are a primary motivator in ownership decisions, especially parking costs. Awareness of these costs motivates shedding of cars, while lowered costs, e.g., via free parking, encourage ownership. Accessible neighborhoods facilitate car-free life, with commenters describing walking and using transit to access common destinations. Changes in car ownership are catalyzed in two ways: 1) Key life events (e.g., moving) cause changes in travel needs or available choices, or 2) People reevaluate their available options and make new choices, unspurred by a key event. I conclude that there are groups, like those who keep cars for convenience, or those whose needs could be met by alternative modes, that display high potential to become car-free. Changes in cost structure and policy may be leveraged to bring these changes about.

Background: Financial toxicity (FT) is a well-established side-effect of the high costs associated with cancer care. In recent years, studies have suggested that a significant proportion of those with atherosclerotic cardiovascular disease (ASCVD) experience FT and its consequences. Objectives: This study aimed to compare FT for individuals with neither ASCVD nor cancer, ASCVD only, cancer only, and both ASCVD and cancer. Methods: From the National Health Interview Survey, we identified adults with self-reported ASCVD and/or cancer between 2013 and 2018, stratifying results by nonelderly (age <65 years) and elderly (age ≥65 years). We defined FT if any of the following were present: any difficulty paying medical bills, high financial distress, cost-related medication nonadherence, food insecurity, and/or foregone/delayed care due to cost. Results: The prevalence of FT was higher among those with ASCVD when compared with cancer (54% vs. 41%; p < 0.001). When studying the individual components of FT, in adjusted analyses, those with ASCVD had higher odds of any difficulty paying medical bills (odds ratio [OR]: 1.22; 95% confidence interval [CI]: 1.09 to 1.36), inability to pay bills (OR: 1.25; 95% CI: 1.04 to 1.50), cost-related medication nonadherence (OR: 1.28; 95% CI: 1.08 to 1.51), food insecurity (OR: 1.39; 95% CI: 1.17 to 1.64), and foregone/delayed care due to cost (OR: 1.17; 95% CI: 1.01 to 1.36). The presence of ≥3 of these factors was significantly higher among those with ASCVD and those with both ASCVD and cancer when compared with those with cancer (23% vs. 30% vs. 13%, respectively; p < 0.001). These results remained similar in the elderly population. Conclusions: Our study highlights that FT is greater among patients with ASCVD compared with those with cancer, with the highest burden among those with both conditions.

Overall project objective: Provide policy recommendations that can effectively decrease time poverty among women in sub-Saharan Africa; Establish the relationship between women’s decision making and household production time allocation; Present the theoretical literature on women’s empowerment and bargaining power; Review the empirical literature on Women’s empowerment in Sub-saharan Africa.

This article advances the field by integrating insights from intersectionality perspectives with the emerging literatures on structural racism and structural sexism—which point to promising new ways to measure systems of inequality at a macro level—to introduce a structural intersectionality approach to population health. We demonstrate an application of structural intersectionality using administrative data representing macrolevel structural racism, structural sexism, and income inequality in U.S. states linked to individual data from the Behavioral Risk Factor Surveillance System to estimate multilevel models (N = 420,644 individuals nested in 76 state-years) investigating how intersecting dimensions of structural oppression shape health. Analyses show that these structural inequalities: (1) vary considerably across U.S. states, (2) intersect in numerous ways but do not strongly or positively covary, (3) individually and jointly shape health, and (4) are most consistently associated with poor health for black women. We conclude by outlining an agenda for future research on structural intersectionality and health.

In a recession, increased competition forces inexperienced job market entrants to accept lower wages than those who start their careers during an economic boom. Yet despite years of improvement in labor market conditions following a recession, a wage disparity, known as scarring, persists between these cohorts. I use Salary History Ban laws (SHBs) to test whether job mobility for scarred workers is constrained because employers screen on prior compensation. For scarred workers who began their careers during a moderate-to-severe recession, or a 5 percentage point higher state unemployment rate, I find SHBs increase job mobility by 0.6%, hourly wages by 3.4%, and weekly earnings by 5.45% relative to workers who graduated in baseline labor market conditions. These estimates represent a substantial reduction in the original scarring effect and provide evidence this effect partially persists due to salary disclosure.

The starting point of this paper is to document considerable cross-country variation in the “labor wedge”, a common measure of labor market frictions. Its variation is theoretically isomorphic to differences in a preference-for-leisure parameter. The paper proceeds to investigate what might explain the variation. It presents three separate empirical exercises supportive of the view that, to a substantial extent, cross-sectional labor-wedge differences are capturing systematic differences in leisure preferences. Firstly, in cross-country regressions, a cultural measure of preferences for leisure, elicited from the World Values Survey, contains economically larger and statistically more robust explanatory power than do traditional measures of labor market frictions. Secondly, following the epidemiological approach, individual-level data on labor-supply choices of descendants of immigrants in the United States and Sweden line up with what an “inherited” preference for leisure would predict. Thirdly, in the spirit of an out-of-sample test, the paper looks at the implication of differences in preferences for cross-country differences in optimal labor taxation. Economic theory suggests a negative association between preferences for leisure and labor taxes; empirical data verify the theoretical prediction.

We examine the long-run impacts of exposure to a Black teacher for both Black and white students. Leveraging data from the Tennessee STAR class-size experiment, we show that Black students randomly assigned to at least one Black teacher in grades K-3 are 9 percentage points (13%) more likely to graduate from high school and 6 percentage points (19%) more likely to enroll in college than their same-school, same-race peers. No effect is found for white students. We replicate these findings using quasi-experimental methods to analyze a richer administrative data set from North Carolina. The increase in postsecondary enrollments is concentrated in two-year degree programs, which is somewhat concerning because two-year colleges have both lower returns and lower completion rates than four-year colleges and universities. These long-run effects are also concentrated among Black males from disadvantaged backgrounds, which is not evident in short run analyses of same-race teachers' impacts on test scores. These nuanced patterns are of policy relevance themselves and also underscore the importance of directly examining long-run treatment effects as opposed to extrapolating from estimated short-run effects.

One of the objectives of the AI WATCH is to calculate the EU robotics market shares over the past ten years. To this end, this report, first, provides a review of the robotics industry, and looks at the official definitions of both industrial and services robots. Second, the report offers a review of the scientific and institutional literature looking at the economic impact of robotics. Third, it describes the different statistical data sources, identified through a comprehensive search, offering relevant information about the robotics industry. Fourth, it provides an initial overview of the European robotics market shares from the different data sources identified. The identification of existing robotics data sources will contribute to the construction of a methodology to assess the EU shares concerning adoption and production of robots. The main objective is to establish the basis for a suitable database that will allow tracing the evolution of EU shares in the global robotics market over the past ten years, ideally disentangling between industrial and service robots. This report sketches such methodology, while it also identifies the main data gaps and challenges to integrating the heterogeneous information from different data sources into a coherent database, in order to derive consistent estimates of the EU market share in robotics. Such methodology will have to account for data challenges (e.g., missing data, development of sound merging techniques) so that the EU trends of robotics can be assessed along the most important dimensions (i.e. demand vs supply, industrial vs service robots), and aiming to provide relevant information to the policies of the European Commission for Robotics and Artificial Intelligence.

Children comprise a significant share of immigrants around the world, yet scholarship has largely treated children as adult-like or adult-following actors in migration. We explore how the early life course and parents' migration structured children's migration from Mexico to the USA from 2002 to 2005, using the Mexican Family Life Survey, national survey data from Mexico that tracked 854 migrants, including 375 children, to the USA. We find that while parents' migration decisions matter at all ages, young children who migrate are nearly always accompanied by their parents, whereas the minority of adolescents are. Primary school-aged children and accompanied adolescents migrate in response to community violence and barriers to education, suggesting that their migration reflects concerns about where it is best to raise children. Adolescents who migrate without their parents do so in response to economic factors, much like adults; however, adolescents also respond to youth community migration prevalence, suggesting that youth-specific norms of migration frame their decision-making. The results show how the early life course structures three distinct profiles of child migration: complete dependents, children whose location choices reflect concerns about schools and safety, and near independents. More generally, the determinants and process of migration shift as parental oversight declines and social structures beyond the family-community violence, access to education, youth norms, gender, and labor markets-emerge as important.

Diabetes and hypertension are socially patterned by individual race/ethnicity and by neighborhood economic context, but distributions among Asian subgroups are undercharacterized. We examined variation in prevalence for both conditions, comparing between US Asian subgroups, including within South Asian nationalities, and comparing within subgroups by neighborhood economic context. We obtained data on a non-probability sample of 633,664 patients ages 18–64 in New York City, NY, USA (2014–2017); 30,138 belonged to one of seven Asian subgroups (Asian Indian, Bangladeshi, Pakistani, Chinese, Korean, Japanese, and Filipino). We used electronic health records to classify disease status. We characterized census tract economic context using the Index of Concentration at the Extremes and estimated prevalence differences using multilevel models. Among Asian men, hypertension prevalence was highest for Filipinos. Among Asian women, hypertension prevalence was highest for Filipinas and Bangladeshis. Diabetes prevalence was highest among Pakistanis and Bangladeshis of both genders, exceeding all other Asian and non-Asian groups. There was consistent evidence of an economic gradient for both conditions, whereby persons residing in the most privileged neighborhood tertile had the lowest disease prevalence. The economic gradient was particularly strong for diabetes among Pakistanis, whose prevalence in the most deprived tertile exceeded that of the most privileged by 9 percentage points (95% CI 3, 14). Only Koreans departed from the trend, experiencing the highest diabetes prevalence in the most privileged tertile. US Asian subgroups largely demonstrate similar neighborhood economic gradients as other groups. Disaggregating Asian subgroups, including within South Asian nationalities, reveals important heterogeneity in prevalence.

Chapter 1 examines how land use regulation affects residential segregation by income. Residential segregation by income severely limits access to opportunity for low-income households, restricting prospects of upward mobility. Land use regulations are one potential determinant of such segregation. However, establishing a causal link between such regulation and segregation faces two econometric challenges. First, regulation is potentially endogenous. Second, proper measurement of segregation is difficult. Concerning the first challenge, the prior literature relies on instrumental variables that may not be valid. Concerning the second challenge, the prior literature relies on measures that ignore the spatial dimension of segregation. This paper uses a new instrumental variable strategy and measures of segregation that account for the spatial distribution of neighborhoods within US metropolitan areas. The key findings are that stricter overall land use regulation decreases segregation within metropolitan areas and that accounting for the spatial aspect of segregation matters. However, the negative effect appears to be driven by state involvement and approval delay; other types of regulation, such as residential density restrictions and local zoning approval complexity, may increase segregation. Chapter 2 examines how property tax assessment caps affect new home building permits and housing stock growth using county-level panel data from the US Census Bureau and longitudinal state-level tax policy data from the Lincoln Institute of Land Policy. Property tax assessment growth limits ensure smaller, more predictable changes in taxable value of property, reducing the share of property taxes on rapidly appreciating property. This helps cash-poor homeowners keep appreciating homes if tax rates don’t rise. However, these limits distort decisions on whether to move, whether to invest in property, and where to locate by conditioning reassessment on changes in property ownership, use, size, or zoning. This paper constructs a county-level panel dataset for a fixed effects regression analysis to estimate how homestead property tax assessment caps affect new homebuilding. The findings are inconclusive. Results using a levels regression are consistent with homestead assessment caps increasing homebuilding by reducing the expected future tax costs of owner-occupied housing assets. When the dependent variable is the inverse hyperbolic sine of the number of new housing units issued building permits, results are consistent with assessment caps decreasing homebuilding by increasing the tax cost of property changes.

Among the economic demands of the 1963 March on Washington for Jobs and Freedom was a demand for a federal jobs program that would eliminate unemployment for African Americans. From the 1960s to today, Black Americans have been about twice as likely as White Americans to be unemployed. Consequently, Black people never achieve low unemployment. They can be said to be living in a permanent economic recession. This Article presents a suite of policies to end high unemployment in African American communities. The policies include those that work indirectly by increasing the demand for goods and services, and those that directly create jobs. Since anti-Black racial discrimination in the labor market is at the root of the persistently high rate of Black joblessness, a strong affirmative action program to counteract discrimination will also be needed. Some might think that a universal basic income is an acceptable alternative to a jobs program, but a job has economic, psychological, and sociological benefits beyond an income. A society that denies many African Americans the opportunity to work denies them not just an income, but also opportunities for identity, self-esteem, service, and social relationships. Ending the permanent recession in Black America is an important step toward providing equal opportunity in America,

Background Studies suggest that agricultural workers and rural residents may have an elevated suicide risk. However, suicide is relatively rare, and rural and farming populations have significantly declined, limiting their representation in national surveys. Many studies have inadequate samples for meaningful analysis. Methods We pooled 29 years of data from the Mortality-Linked National Health Interview Survey, 1986–2014, then measured suicide mortality in groups including agriculture workers, and variation in suicide across rural and urban areas. Exposure variables indicated whether participants worked in a farm-related occupation or industry, or lived in a rural area. We used survey-weighted Poisson regression to estimate suicide mortality rates and rate ratios. Results Age-adjusted suicide mortality rate per 100,000 was: 22.3 for farmers and farm managers; 21.6 for farmworkers; 28.7 in farming, forestry, and fishing; 15.3 across all other occupations; 16.1 among rural residents. Among farmworkers, age-adjusted rates were 28.3 in rural areas, 17.1 in urban areas (not significantly different). The age-adjusted suicide mortality rate ratio (RR) comparing workers in the agriculture, forestry, and fishery industries to those in all other industries was 1.34 (95% confidence interval, [CI]: 1.05–1.72) (not statistically significant after further adjustment for demographic characteristics). Age-adjusted results were consistent with a higher suicide risk for workers in forestry and fishing than in all other occupations (RR: 1.88, 95% CI: 0.79–4.46). Conclusion Workers in agriculture, forestry, and fishing may have an elevated suicide risk. National surveys should consider oversampling of rural residents, who have increased morbidity and mortality risks.

Introduction: Residential racial desegregation has demonstrated improved economic and education outcomes. The degree of racial community segregation relative to surgical outcomes has not been examined. Patients and Methods: Patients undergoing pancreatic resection between 2013 and 2017 were identified from Medicare Standard Analytic Files. A diversity index for each county was calculated from the American Community Survey. Multivariable mixed-effects logistic regression with a random effect for hospital was used to measure the association of the diversity index level with textbook outcome (TO). Results: Among the 24,298 Medicare beneficiaries who underwent a pancreatic resection, most patients were male (n = 12,784, 52.6%), White (n = 21,616, 89%), and had a median age of 72 (68–77) years. The overall incidence of TO following pancreatic surgery was 43.3%. On multivariable analysis, patients who resided in low-diversity areas had 16% lower odds of experiencing a TO following pancreatic resection compared with patients from high-diversity communities (OR 0.84, 95% CI 0.72–0.98). Compared with patients who resided in the high-diversity areas, individuals who lived in low-diversity areas had higher odds of 90-day readmission (OR 1.16, 95% CI 1.03–1.31) and had higher odds of dying within 90 days (OR 1.85, 95% CI 1.45–2.38) (both p < 0.05). Nonminority patients who resided in low-diversity areas also had a 14% decreased likelihood to achieve a TO after pancreatic resection compared with nonminority patients in high-diversity areas (OR 0.86, 95% CI 0.73–1.00). Conclusion: Patients residing in the lowest racial/ethnic integrated counties were considerably less likely to have an optimal TO following pancreatic resection compared with patients who resided in the highest racially integrated counties.

How does body weight affect Intimate Partner Violence (IPV) reporting throughout the regions of South Africa?

A common narrative is that COVID-19 cost Trump re-election. But, we do not find supporting evidence. Rather, our results highlight the political salience of the trade war and health insurance coverage in the 2020 US Presidential election. US trade war tariffs boosted Trump’s support and foreign retaliation hurt Trump. In particular, the pro-Trump effects of US trade war tariffs were crucial for Trump getting inside the recount thresholds in Georgia and Wisconsin. Even more important politically, voters abandoned Trump in counties with large increases in health insurance coverage since the Affordable Care Act, presumably fearing the roll-back of such expansion. Absent this anti-Trump effect, Trump would have been on the precipice of re-election by winning Georgia, Arizona, Nevada, and only losing Wisconsin by a few thousand votes. These effects cross political and racial lines. Thus, our results suggest a mechanism based around the local economic impact of Trump administration policies rather than a mechanism of political polarization.

This study uses American Community Survey data to examine the impact of the Great Recession on college graduates majoring in the arts. Arts graduates play important roles in an economy, through both artistic creation and in careers outside of the arts. While the Great Recession took a significant toll on the US economy generally, arts majors faced additional vulnerabilities as industries that rely on discretionary spending, like the arts and entertainment, are especially hard hit in times of economic downturn. This paper assesses the impact of graduating during or shortly after the recession relative to graduating shortly before this period on educational choices, including choice of major, double majoring, and completing an advanced degree, and career outcomes, including employment status, type of employment, hours worked, and earnings, for college graduates majoring in the arts. Graduating before or after the recession is found to have a negative impact on the share of graduates majoring in traditional arts fields, but a positive impact on the share majoring in related creative fields. Using a difference-in-difference estimation strategy, relative to non-art college graduates, traditional arts majors graduating during or after the Great Recession are more likely to complete a double major, be self-employed, be unemployed, work longer hours, and earn less income than those graduating prior to the recession. These impacts are likely to have a negative effect on the pipeline of college-educated artists working in the arts into the future.

Economic phenomena—such as the rise of automation or the recent COVID-19 pandemic—can result in lost jobs for long-tenured workers in New England and the United States. While reemployment in a different industry typically leads to lower earnings compared with same-sector reemployment for such displaced workers, sectoral mobility is almost certainly an attractive alternative to nonemployment and may offer additional benefits to some workers. The research in this report shows that successful sectoral mobility is further facilitated by additional general and industry-specific skills, and it is most relevant when business cycles and other factors increase the chance of nonemployment following a job loss. Policymakers interested in displaced-worker reemployment may wish to ensure that related legislation can facilitate industry changes, with consideration of skills training and differential vulnerability to nonemployment. This report finds that broad patterns of job displacement and sectoral mobility for long-tenured workers from 1996 through 2019 are similar in New England and the United States. The study shows that being displaced increases the probability of changing industries by 59.8 percent within 15 months. This industry-switching effect grows as the time following a job separation increases. Additionally, pre-displacement education and industry structure facilitate post-displacement sectoral mobility. This finding suggests the importance of both general and industry-specific skills. The report also examines which factors cause displaced workers to be reemployed in the same sector after a job loss rather than nonemployed. Examining those alternatives to sectoral mobility, the analysis identifies several key individual and market influences, including business cycles, a worker’s age, and the presence of any children in a worker’s household. Given the existing policy landscape, the findings of this report have multiple implications for related future workforce and economic development policy in New England. For instance, policymakers should keep in mind that sectoral mobility after job loss need not be viewed negatively. Rather, such industry switching might mitigate unwanted nonemployment and may be a necessary mechanism to reemploy displaced workers following harmful economic shocks. Additionally, to facilitate sectoral mobility, policies should encourage the accumulation of general skills—especially a high school diploma or equivalent—and industry-specific skills. Targeted acquisition of the latter skills may be aided by assessing which industryto-industry transitions are most frequent and/or which industries have the greatest overlap of occupations or tasks.

With the use of the Yemen National Health and Demographic Survey 2013, I’ve attempted to understand the effects of wealth and access to healthcare on Yemen maternal complications. Responses from around 10,000 women, aged 15-49, who had a child between 2009-2013 were utilized for this project. The data collected focused on complications from the most recent pregnancy and delivery process. Outcome variables were separated into two different categories: Pregnancy Complications and Delivery Complications.

Federated learning (FL), as a type of distributed machine learning frameworks, is vulnerable to external attacks on FL models during parameters transmissions. An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP). In this paper, we aim to propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms (e.g., Krum and Trimmed mean) implemented at the server without being noticed, i.e., covert MP (CMP). Specifically, we first formulate the MP as an optimization problem by minimizing the Euclidean distance between the manipulated model and designated one, constrained by a defensive aggregation rule. Then, we develop CMP algorithms against different defensive mechanisms based on the solutions of their corresponding optimization problems. Furthermore, to reduce the optimization complexity, we propose low complexity CMP algorithms with a slight performance degradation. In the case that the attacker does not know the defensive aggregation mechanism, we design a blind CMP algorithm, in which the manipulated model will be adjusted properly according to the aggregated model generated by the unknown defensive aggregation. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.