Background: Hearing loss is associated with a greater risk of death in older adults. This relationship has been attributed to an increased risk of injury, particularly due to falling, in individuals with hearing loss. However, the link between hearing loss and mortality across the life span is less clear. Methods: We used structural equation modeling and mediation analysis to investigate the relationship between hearing loss, falling, injury, and mortality across the adult life span in public-use data from the National Health Interview Survey and the National Death Index. We examined (a) the association between self-reported hearing problems and later mortality, (b) the associations between self-reported hearing problems and the risk of injury and degree and type of injury, (c) the mediating role of falling and injury in the association between self-reported hearing problems and mortality, and (d) whether these relationships differ in young (18–39), middle-aged (40–59), and older (60+) age groups. Results: In all 3 age ranges, those reporting hearing problems were more likely to fall, were more likely to sustain an injury, and were more likely to sustain a serious injury, than those not reporting hearing problems. While there was no significant association between hearing loss and mortality in the youngest category, there was for middle-aged and older participants, and for both, the fall-related injury was a significant mediator in this relationship. Conclusions: Fall-related injury mediates the relationship between hearing loss and mortality for middle-aged as well as older adults, suggestinga need for further research into mechanisms and remediation.

In today’s aging societies, a good deal of the older population is faced with the decision of when to exit the labor force. The decision is often made jointly with a locational choice and a choice about housing consumption (Banks et al. 2012). Prior to retirement, the workplace location strongly influences where people live. Upon retirement, however, other factors such as closeness to children and grandchildren, climate, and amenities take on a pivotal role (Whisler et al. 2008; Wiseman and Roseman 1979; Serow 2001). Moreover—upon retirement—households typically receive less income and change their consumption patterns, spending relatively more on housing, food, and healthcare than on clothing, transportation, and household furnishing (Lee et al. 2014). As a result, many households downsize their home (Bian 2016) and move to places that are more affordable and easily accessible in anticipation that health and strength may become compromised (Abramsson and Andersson 2016).

In this article, we develop and make available measures of public ideology in 2010 for the 50 American states, 435 congressional districts, and state legislative districts. We do this using the geospatial statistical technique of Bayesian universal kriging, which uses the locations of survey respondents, as well as population covariate values, to predict ideology for simulated citizens in districts across the country. In doing this, we improve on past research that uses the kriging technique for forecasting public opinion by incorporating Alaska and Hawaii, making the important distinction between ZIP codes and ZIP Code Tabulation Areas, and introducing more precise data from the 2010 Census. We show that our estimates of ideology at the state, congressional district, and state legislative district levels appropriately predict the ideology of legislators elected from these districts, serving as an external validity check.

State-level labor market disparities have been linked to health outcomes. The current study examines how labor market disparities may shape different patterns of sex/gender inequalities in cognition across race/ethnicity, place, and time. We leverage cognitive outcome data from multiple cohort and nationally representative longitudinal studies, as well as historical data on labor force participation and occupational status from IPUMS CPS. Multilevel modeling analyses was used to examine heterogeneity in sex/gender inequalities in cognitive trajectories within and between race/ethnicity and U.S. state of birth and determine whether such variability is explained by a state-level labor market opportunity composite. We expect women to demonstrate an advantage over men on cognitive measures. Women’s advantage will be more pronounced in states with a small sex/gender gap in labor market opportunities and less pronounced in states with a large gap. The magnitude of this advantage will be greater for White women compared with Black women.

Over the last decade, fast-food restaurants have proliferated in the United States, with the largest increase in Los Angeles County. Fast food is an integral part of the food sector in Los Angeles, comprising nearly 150,000 restaurant workers. This report investigates working conditions in fast food prior to the pandemic, profiles the industry’s demographics and cost to the public, and examines the impact of COVID-19 on the sector. Even before COVID-19, the fast-food sector was characterized by difficult working conditions and high public costs. 1. Fast-food workers faced labor issues related to safety and injury, workplace violence, harassment, retaliation, and wage theft. 2. The franchise model, which predominates in fast food, incentivized labor violations. 3. Fast food’s low wages have made it difficult for workers to meet their basic needs. More than two-thirds of the families of fast-food workers in Los Angeles County were enrolled in a safety net program at a public cost of $1.2 billion a year. Because workplaces are a common vector of COVID-19 transmission, fast-food worksites are particularly vulnerable. 1. One-third of fast-food worksites had 20 or more employees, suggesting shared equipment, work spaces, bathrooms, and break areas. Other research found that food workers work in moderately close to close proximity; cooks in particular have had the highest increase in mortality of any occupation during the pandemic. 2. Worker testimony and complaints show COVID-19 outbreaks and employer failures to communicate these outbreaks to workers. Fast-food workers and their communities face a disproportionate risk of COVID-19 transmission and its negative impacts. 1. Black, Latinx, and Asian populations had disproportionately higher rates of infection, hospitalizations, and deaths. Nine in ten fast-food workers in Los Angeles were workers of color, and nearly three-quarters were Latinx. 2. Women in fast food were already vulnerable to sexual harassment, and that has been exacerbated by COVID-19. Nearly seven in ten fast-food workers were women. 3. Though fast-food workers skewed young, over two-thirds lived in households with four or more people, and a third included household members over age 55. 4. The majority of fast-food workers earn low wages, often at or near the minimum wage, but research indicates those wages constituted 40% of their family’s total income. 5. Fast-food workers were twice as likely as other workers to fall below the federal poverty line, and over half of those who rent their housing were rent-burdened, spending over 30% of their household income on rent and utilities. 6. Fast-food workers were one and half times more likely to be uninsured and two and a half times more likely to be enrolled in Medi-Cal than Los Angeles workers as a whole. Only a third of fast food workers received some type of employer-sponsored insurance.

The impact of Proximityh at work on the probability of unemployment during Covid-19 pandemic in the Finance industry.

Many regions around the world suffer from a lack of authoritatively-collected data on factors critical to understanding human well-being. This challenges our ability to understand the progress society is making towards reducing poverty, improving lifespans, or otherwise improving livelihoods. A growing body of research is exploring how deep learning algorithms can be used to produce novel estimates of sparse development data, and how access to such data can impact development efforts. This dissertation contributes to this literature in three parts. First, using Landsat 8 satellite imagery and data from the Armed Conflict Location & Event Data Project, convolutional neural networks are trained to predict locations where conflict is likely to result in fatalities for one year. Second, building on the findings in chapter 1, this dissertation explores the potential to extend predictions to a time series using both yearly and six month intervals. Finally, chapter 3 introduces GeoQuery, a dynamic web application which utilizes a High Performance Computing cluster and novel parallel geospatial data processing methods to overcome challenges associated with integrating, and distributing geospatial data within research communities.

There are two broad views as to why people stay poor. One emphasizes differences in fundamentals, such as ability, talent or motivation. The other, poverty traps view, differences in opportunities which stem from differences in wealth. We exploit a large-scale, randomized asset transfer and panel data on 6000 households over an 11 year period to test between these two views. The data supports the poverty traps view-we identify a threshold level of initial assets above which households accumulate assets, take on better occupations and grow out of poverty. The reverse happens for those below the threshold. Structural estimation of an occupational choice model reveals that almost all beneficiaries are misallocated in the work they do at baseline and that the gains arising from eliminating misallocation would far exceed the program costs. Our findings imply that big push policies which transform job opportunities represent a powerful means of addressing the global mass poverty problem.

The American Voices Project (AVP) relies on immersive interviews to deliver a comprehensive portrait of life across the country. The interview protocol blends qualitative, survey, administrative, and experimental approaches to collecting data on such topics as family, living situations, community, health, emotional well-being, living costs, and income. The AVP is based on a nationally representative sample of hundreds of communities in the United States. Within each of these sites, a representative sample of addresses is selected. In March 2020, recruitment and interviewing began to be carried out remotely (instead of face-to-face), and questions were added on the pandemic, health and health care, race and systemic racism, employment and earnings, schooling and childcare, and new types of safety net usage (including new stimulus programs). The "Monitoring the Crisis" series-which is co-sponsored by the Stanford Center on Poverty and Inequality, the Federal Reserve Bank of Atlanta, and the Federal Reserve Bank of Boston-uses AVP interviews conducted during recent months to provide timely reports on what's happening throughout the country as the pandemic and recession play out. To protect respondents' anonymity, all quotations presented in this series are altered slightly by changing inconsequential details.

The study of the dynamics, causes, and consequences of changes in labor supply is central to understanding modern economies and identifying candidate policies to improve welfare. Each chapter of my dissertation contributes to one or more components of this broad theme by combining applied econometrics techniques with insights from quantitative theoretical models. Using these tools, I aim to address three questions: How do aggregate hours worked change over the business cycle? Can the rise in female labor force participation account for household migration trends? What role do individual labor supply choices play in driving aggregate growth as populations age? In Chapter 1 of my dissertation, I study the finite sample properties of a novel approach to identifying macroeconomic shocks with long-run restrictions. In contrast to past studies, this approach constructs and constrains the long-run impact of shocks directly using local projections rather than inferring them from vector auto regressions. Through a series of Monte Carlo simulations, I show that the local projections approach can yield substantial reductions in both bias and mean squared error, while also boasting decreased sensitivity to the choice of included lag length and assumed order of integration of the endogenous variables. I then use data from the Bureau of Labor Statistics to revisit a long standing debate on the response of aggregate hours worked to positive productivity shocks. I find that labor hours rise in response to positive productivity shocks and follow a hump-shaped profile thereafter. This result is robust to a number of specification choices and provides new evidence in support of the standard real business cycle model. My joint work with Christine Braun and Peter Rupert constitutes Chapter 2 of my dissertation, and studies the relationship between the historical rise in female labor force participation and contemporaneous decline in household migration rates. Between 1964 and 2000, the intercounty migration rate of married couples declined by 15%. Concurrently, female labor force participation among married women and the relative wages of women increased by 39 and 14 percentage points, respectively. Using a two location household level search model of the labor market, we show that both the increase in dual earner households and the rise in women’s wages contributed significantly to the decline in the migration rate of married households, with each explaining 53% and 20% of the decline, respectively. We further show that this co-location problem has important implications for structural models designed to estimate lifetime earnings inequality. Finally, I conclude my dissertation in Chapter 3 with joint work with Thomas Cooley and Espen Henriksen wherein we study the growth effects of aging populations in Europe’s four largest economies – France, Germany, Italy, and the United Kingdom. Since the early 1990’s, GDP per-capita growth in these economies has slowed while at the same time a combination of longer individual life expectancy and declining fertility have led to gradually ageing populations. Using a general equilibrium overlapping generations model, we show that demographic change such as this affects economic growth directly through aggregate savings and labor supply decisions. These decisions are further affected indirectly through additional distortions caused by rising tax rates needed to fund pension systems. We find that the net effect of these forces can account for a significant fraction of the historical growth slowdown and that evolving demographics will continue to drag down growth over the next 20 years. We highlight that the degree to which gains to life expectancy change labor supply decisions is the most important margin through which demographic change affects growth by studying several reforms aimed at increasing late-life labor supply.

Background: High-intensity antitobacco media campaigns are a proven strategy to reduce the harms of cigarette smoking. While buy-in from multiple stakeholders is needed to launch meaningful health policy, the budgetary impact of sustained media campaigns from multiple payer perspectives is unknown. Methods: We estimated the budgetary impact and time to breakeven from societal, all-payer, Medicare, Medicaid and private insurer perspectives of national antitobacco media campaigns in the USA. Campaigns of 1, 5 and 10 years of durations were assessed in a microsimulation model to estimate the 10 and 20-year health and budgetary impact. Simulation model inputs were obtained from literature and both pubic use and proprietary data sets. Results: The microsimulation predicts that a 10-year national smoking cessation campaign would produce net savings of $10.4, $5.1.4.6 and .2 billion from the societal, all-payer, Medicare, Medicaid and private insurer perspectives, respectively. National antitobacco media campaigns of 1, 5 and 10-year durations could produce net savings for Medicaid and Medicare within 2 years, and for private insurers within 6-9 years. A 10-year campaign would reduce adult cigarette smoking prevalence by 1.2 percentage points, prevent 23 500 smoking-attributable deaths over the first 10 years. In sensitivity analysis, media campaign costs would be offset by reductions in medical care spending of smoking among all payers combined within 6 years in all tested scenarios. Conclusions: 1, 5 and 10-year antitobacco media campaigns all yield net savings within 10 years from all perspectives. Multiyear campaigns yield substantially higher savings than a 1-year campaign.

Janitors are California’s unseen essential workers. While grocery store workers, health care workers and delivery drivers receive much deserved attention as essential workers crucial for our wellbeing during the pandemic, janitors’ contributions to Californians’ health and safety remains invisible. The cleaning industry, by its very nature, keeps janitors in the shadows. Most janitors work at night cleaning office buildings, grocery stores, and restaurants. In the morning, consumers expect these spaces to be clean, sanitary, and ready for the public’s use without fully appreciating the plight of the janitors who work nightly to maintain these spaces. Other janitors work during the day or in 24-hour facilities where there is contact with other groups of workers and the public. This is especially true in workplaces serving vulnerable populations, such as hospitals and senior facilities. Despite working in plain sight, these workers still are often neglected because they are employed by subcontractors under substandard conditions in comparison to their co-workers in the same workspace. To understand the experience of these unseen workers during the pandemic, the Maintenance Cooperation Trust Fund conducted a survey of non-union janitors between July and August 2020. The janitors surveyed were from various work locations in California that included office buildings, grocery stores, restaurants, health care facilities and government buildings. In April 2021, MCTF returned to a group of those surveyed to conduct more in-depth interviews to uncover how workers fared through the 2020/2021 winter surge of COVID-19 cases in California. This is the first survey-based study focusing on the experience of this vulnerable group of workers during the pandemic.

During the early twentieth century, industrial-era European immigrants entered the United States with lower levels of education than the U.S. average. However, empirical research has yielded unclear and inconsistent evidence about the extent and pace of their integration, leaving openings for arguments that contest the narrative that these groups experienced rapid integration and instead assert that educational deficits among lower-status groups persisted across multiple generations. Here, we advance another argument, that European immigrants may have "leapfrogged" or exceeded U.S.-born non-Hispanic white attainment by the third generation. To assess these ideas, we reconstituted three-generation families by linking individuals across the 1940 census; years 1973, 1979, and 1981 to 1990 of the Current Population Survey; the 2000 census; and years 2001 to 2017 of the American Community Survey. Results show that most European immigrant groups not only caught up with U.S.-born whites by the second generation but surpassed them, and this advantage further increased in the third generation. This research provides a new understanding of the time to integration for twentieth-century European immigrant groups by showing that they integrated at a faster pace than previously thought, indicative of a process of accelerated upward mobility.

More precisely, this ambitious, thought-provoking paper asks, “How much larger would the US economic pie be if opportunities and outcomes were more equally distributed by race and ethnicity?” The authors evaluate the impact on the labor contribution to GDP over thirty years in light of the question, What if Black and Hispanic workers had outcomes similar to those of non-Hispanic white workers? They focus on five outcomes of interest: employment, hours of work, educational attainment, educational utilization, and earnings gaps not explained by these and other productivity-related indicators. There are three notable features of this paper by comparison with the previous literature on the benefits of closing labor market gaps.

This paper examines the impact of the replacement ratio on re-employment wages during the great recession. This is done using a data set from IPUMS CPS displaced workers supplement between 2005 and 2012. Using OLS analysis, I estimated the impacts of the replacement ratio on the ratio of re-employment wages to pre-unemployment wages. I found that a replacement ratio of one would lead to a 77.6% increase in the ratio of re-employment wages to pre-unemployment wages, without the consideration of any other variables. The findings of the replacement ratio support economic theory and contradict the findings of some major papers on the subject, including the paper I based my model off. The analysis in this paper could be strengthened with weekly wage data as well as with the addition of more variables controlling for the impacts of a recession.

In this article, we describe kmr, a command to estimate a microcompliance function using groups’ nonresponse rates (Korinek, Mistiaen, and Ravallion, 2007, Journal of Econometrics 136: 213–235), which can be used to correct survey weights for unit nonresponse. We illustrate the use of kmr with an empirical example using the current population survey and state-level nonresponse rates.

In recent decades, the racial wealth gap has widened with extant literature reporting that Black and Latinx families hold fewer assets than white families. One such asset that receives substantial attention because of its wealth-generating principles is homeownership. Whereas intergroup homeownership inequalities are found throughout the literature, less is known about racialized inequality within groups. Latinxs provide a novel case for exploring how racialized homeownership inequality is structured within an ethnic group. Using data from the American Community Survey, we examine the odds of homeownership and predicted logged home values among Latinxs. We find that the association between race and housing outcomes varies substantially across Latinx groups. Drawing from theories of Latinx racial identity and the future of racial structures, we discuss the implications of our findings for understanding racial inequality among Latinx groups.

This paper tests for the so-called bilingual advantage, the notion that knowing more than one language improves individuals’ other cognitive skills. We use data on children of Latino immigrants in the United States who have been randomly assigned calculation tests in English or Spanish. After controlling for characteristics of children and their parents, we find that bilingual children perform 0.57 standard deviations better than monolingual children, almost equal to learning gains of two additional school years. Applying the Oster test, we find that selection on unobservables would need to be 10 times stronger than selection on observables to explain away our results. Our heterogeneity analysis reveals that the bilingual advantage is particularly strong among boys.

There is a complex set of public policies and associated programs that constitute the social safety net in the United States. In Life-Course Implications of U.S. Public Policies, the authors encourage others to systematically consider the influence of policies and programs on lives, aging, and the life course, and how the consequences might vary by gender, race/ethnicity, sexual orientation, ability, and social class. The volume aims to foster an appreciation of how policy influences connect and condition the life course. Chapters examine issues relating to health, housing, food security, crime, employment, and care work, amongst other issues, and demonstrate how the principles of the life-course perspective and cumulative inequality theory can be used to inform contemporary public policy debates. Life-Course Implications of U.S. Public Policies will be a great resource for students of gerontology, sociology, demography, social work, public health and public policy, as well as policy makers, researchers in think tanks, and advocates, who are concerned with age-based policy.

Drawing from the lived experiences of Haitian women in Boston and Montreal, this study illustrates how ethnography can augment understandings of race and place in demography by complementing quantitative analyses, showing how race is constructed across place through daily micro-interactions. Building on the work of demographers who examine how race shifts over time and place, this article challenges the practice of engaging with race as a fixed or static category to consider how race is constructed across place, highlighting the nuances of race that are sometimes lost in quantitative studies. The multi-sited ethnographic methodology employed in this study is uniquely suited to uncovering the specificities of race and place. The findings reveal that Haitian women experience race differently in Montreal and in Boston, based largely on the historical context of each place. Haitians in Boston experienced intraracial tensions with African Americans, particularly during the 1960s and 1970s, that shaped their experiences of race and place, while Haitians in Montreal at the same time experienced Blackness that was closely tied with xenophobia in the French Canadian context. The Haitian women in this study experienced race, place, gender, ethnicity, and class simultaneously, necessitating an intersectional approach to understanding the effects of race in and on their daily lives.