The US and other developed nations have experienced two concurrent phenomena over the previous two decades: Slow productivity growth and rapid workforce aging. In this paper I argue that both phenomena are linked through a demand channel. Following an instrumental variable strategy I provide evidence for a causal link between workforce aging and lower innovation.

This study explores the effect of school reopenings during the COVID-19 pandemic on married women's labor supply. We proxy for in-person attendance at US K-12 schools using smartphone data from Safegraph and measure female employment, hours, and remote work using the Current Population Survey. Difference-indifferences estimates show that K-12 reopenings are associated with significant increases in employment and hours among married women with school-aged children, with no measurable effects on labor supply in comparison groups. Employment effects of school reopenings are concentrated among mothers of older school-aged children, while remote work may mitigate effects for mothers of younger children.

This dissertation contains three essays studying topics in applied microeconomics. The first chapter is a co-authored paper in which we use daytime satellite imagery and convolutional neural networks to model economic growth at the neighborhood level. In the second chapter, I use this model to examine the spatial distribution of residential impacts from fracking. The third chapter investigates methods of measuring skill distance between occupations and proposes a new method which matches patterns of observed occupational transition. Each chapter uses unconventional data sources and machine learning techniques to contribute to central questions in labor economics research and policy. In the first chapter we apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. Our model predictions achieve R 2 values of and 0.32 to 0.46 in decadal changes, which have no counterpart in the literature and are 3-4 times larger than for commonly used nighttime lights. Our network has wide application for analyzing localized economic shocks. One such application is my second chapter, which studies changes in total neighborhood income and population in areas near fracking extraction and shale reserves. My microspatial approach identifies that fracking exposure as far as 20 miles away leads to a 2 percent decline in neighborhood income. The spatial gradient and associated mechanisms of this effect indicate that it is driven by local industrialization rather than direct environmental externalities. Examination reveals margins of policy and labor conditions which attenuate the observed impacts. In the third chapter I show that a regression framework generates a novel, empirical occupational skill distance norm which is disciplined by observed occupation switching patterns. This approach relieves key limitations of existing measures such as linearity and symmetry. It also allows for an analysis of which skill dimensions relate to the portability of human capital, and which do not. Implications for existing results on skill portability are discussed, along with immediate policy applications on employee adjustment costs.

The share of young employees who have been with their employer for 3 years or more has remained relatively steady over time. About 44% of 18- to 34-year-old workers in the United States have been with their current employer for 3 years or more, according to Pew Research Center data. Compared with the 1980s, more of today’s young workforce are in management and professional occupations – and seniority tends to correlate with longer spells at the same company. This also matters economically, because worker earnings tend to increase with more time on the job.

Most research on cancer patient survival uses registry-based (e.g., SEER) incidence and survival data that have limited socioeconomic status and health-risk information. In this study, we used the 1997-2015 National Health Interview Survey-National Death Index prospectively-linked pooled cohort database (n = 40,291 cancer patients) to examine disparities in patient survival by a broad range of social determinants, including race/ethnicity, nativity, educational attainment, income/poverty level, occupation, housing tenure, physical and mental health status, smoking, physical activity, body mass index, and alcohol consumption. We used Cox proportional hazards models to estimate mortality hazard ratios and cause-specific 1-year, 5-year, and 10-year survival rates for all-cancer and lung cancer. During 1997-2015, the 10-year age-adjusted (all-cause) survival rate for cancer patients with professional and managerial occupations was 89.66%, significantly higher than the survival rate of 83.17% for laborers or 83.66% for the unemployed. Cancer patients with renting house had significantly lower age-adjusted survival rates than those owning house (82.65% vs. 85.80%). The 10-year age-adjusted survival rates were significantly greater among cancer patients with regular physical activity than those without regular physical activity (90.18% vs. 83.24%). Age-adjusted survival rates were significantly reduced for cancer patients with lower income and education, poor health, and serious psychological distress, and among current and former smokers. The gap in survival narrowed with additional sociodemographic, health, or behavioral adjustment. Similarly large differentials were found in lung cancer survival. Marked disparities in all-cancer and lung cancer survival were found by a wide range of sociodemographic and health characteristics.

The belief that women should specialize in domestic tasks was not challenged until the mid-20th century. At this point, more women sought employment outside of their homes, but they were still expected to complete all their homes’ domestic tasks. The provision of lunch for students by schools, whether at cost or free of cost, was a form of lessening the load for working mothers. Having access to food at school allows students to be fed and parents to have more liberty with their time. Although the intention behind school lunch is to benefit students and parents, the benefits to parents are seldom outlined. One potential effect is additional free time (Datar & Nicosia, 2012). This effect is especially relevant when it comes to categorizing parents’ work in and outside of the home. The question this paper will answer is how the implementation of school lunch programs affects parents’ likelihood to work outside of the home, and the number of hours they work. Previous works can be categorized into three groups: scrutinization of parents for school lunch preparation, the impact of socioeconomic status on the choice to opt in school lunch programs, and the relationship between policies and additional free time. These works provide insight into the factors that could influence school lunch uptake. Parents, but especially mothers, face scrutiny when preparing lunches for their children (Pike & Leahy, 2012). This is heightened when mothers work outside of the home. There is research on socioeconomic status and its impact on school lunch consumption (Cullen, et al., 2009) and on policies that have freed up time for parents (Datar & Nicosia, 2012). These works have found that parents are saving time when they opt for their children to eat school lunch, but they do not address what parents are doing with that additional time. Paniagua 3 In this paper, I study the effects of the Healthy, Hunger-Free Kids Act (2010) on school lunch consumption and parental working trends. Using variables from a national survey on poverty rates and measures of income, I created a linear regression equation. This equation enabled me to analyze the effects of the policy on school lunch uptake. I found that after the policy was implemented, school lunch uptake appeared to increase for children of all income brackets. After observing this effect, I analyzed the effect of the policy on parental working trends by varying subpopulations. Most individuals in these subpopulations appeared to decrease both their likelihood of working and the hours they worked after the policy passed. The following sections will provide context and discourse about my findings.

Hate crime is a rising, and long-standing, concern in New York City. The nation's largest city and one of its most diverse, New York is home to hundreds of bias offenses every year. The number of these offenses has declined far less precipitously over the past 30 years than other types of crime. This report details the scale and contours of offenses motivated by racial, ethnic, and religious bias in NYC, including data on trends over time, who is victimized and where and how, and on the types of offenders. It also discusses the city's infrastructure for dealing with these offenses and the justifications for combating hate crime as hate crime (as opposed to crime per se). Last, this paper proposes a “focused deterrence” approach for dramatically reducing hate crimes in the city. Such an approach would entail: • A zero-tolerance strategy for hate crimes, facilitated by a surge in NYPD resources • An all-of-government messaging campaign in tandem with the surge • A specific focus on identifying and treating serious mental illness • More data transparency from across the criminal justice system

What is the long-run impact of growing up in a segregated enclave? This paper examines the relationship between residential segregation in childhood and wealth outcomes later in life. I use a new sample of Irish, German, and English children linked from their childhood in 1850 to their adult outcomes in 1870. Conditional on childhood characteristics, such as the wealth of the father, I find a small negative association between childhood residential segregation from the US-born in 1850 and an individual’s percentile rank of wealth in 1870, suggesting there is little detriment to growing up in an enclave. This association is also weak by sending country and urban status. These results are robust to measuring wealth outcomes as the change in wealth relative to an individual’s father in 1850. Overall, the results suggest that fears of nativists during the mid-19th century about immigrant enclaves were unfounded.

A substantial fraction of schools and childcare facilities in the United States closed their in-person operations during the COVID-19 pandemic. These closures may carry substantial costs to the families of affected children. In this paper, we examine the impact of school and childcare closures on parental labor market outcomes during the COVID-19 pandemic. In particular, we test whether COVID-19 school closures have a disproportionate impact on parents of school-age children (age 5-17 years old) and whether childcare closures affect parents of young children (age <5 years old) relative to others. Our results suggest that while closures have had little impact on whether parents work at all, they have had significant effects on whether parents work full time (at least 35 hours) and the number of hours worked per week. These effects are concentrated among low-educated parents, suggesting that such individuals had a more difficult time adjusting their work life to closures.

Using multiple decrement life tables based on U.S. marriage and mortality rates from 2019, this paper projects a lifetime chance of marrying for non-Hispanic, single-race White and Black women. Projections show 86.2% for White women, and 61.7% for Black women, eventually marrying for a cohort born and living through conditions prevailing in 2019 (before the COVID-19 pandemic). These projected rates represented continued steep declines in marriage, along the lines observed in recent decades. The method described here is applicable to other family events, and may be readily extended to marriage for other populations as well.

Segregation by race and class has been a foundational feature of U.S. society, and public education is not an exception. However, scholarly discussions have often missed geographic layers in the phenomenon of school segregation and its relevance to the decentralized U.S. education system, in which state and district governments operate as additional political divisions to the federal government. This dissertation consists of three papers investigating the patterns and consequences of, as well as remedies for, segregation across schools and neighborhoods at various geographic levels. The first paper documents trends in racial economic segregation among U.S. public schools during the last three decades and decomposes these trends into different geographic scales (e.g., between-state, between-district, and within-district segregation). It focuses on racial differences in exposure to school poverty, a dimension of school segregation that has faced less academic scrutiny despite its substantive importance as the strongest predictor of racial achievement gaps. The results of this study show that racial economic segregation has decreased since the late 1990s, yet Black students are still considerably isolated in schools with higher poverty rates. Between-district segregation has been the largest component of racial economic segregation, whereas within-district segregation has steadily grown during the last three decades. These findings suggest the imperativeness of inter-district policy remedies while also highlighting intra-district policy needs for promoting student diversity across schools in the district. The second paper examines state-level socioeconomic achievement gaps and their association with between-district income segregation. Socioeconomic achievement gaps have long been a central focus of educational research. However, not much is known about how (and why) between-district gaps vary among states, even though states are a primary organizational level in the decentralized education system in the United States. Using data from the Stanford Education Data Archive (SEDA), this paper describes statelevel socioeconomic achievement gradients and their growth from grades 3 to 8. It also examines state-level correlates of the gradients and their growth, including school system funding equity, preschool enrollment patterns, the distribution of teachers, income inequality, and economic segregation. Findings show that socioeconomic gradients and their growth rates vary considerably among states and that between-district income segregation is positively associated with the socioeconomic achievement gradient. The third paper simulates intra-district desegregation policy options that focus on different dimensions of student disadvantage: race, income, and the joint condition of race and income. Student assignment policy (SAP) has emphasized within-district local control and the use of students’ socioeconomic status rather than race since the 1990s. Although prior research on SAP has examined how a new generation of SAP affects school segregation, none of these studies investigate how it impacts racial economic segregation, the strongest predictor of racial achievement gaps. Moreover, none of the prior studies scrutinize how the race- and income-based SAP contribute to desegregation differently, even though the distinction between these two strategies has been an ongoing legal question since the Seattle School District No.1 case in 2007. This study simulates three different within-district student assignment strategies, each of which focuses on student race, income, and the joint condition of race and income. Findings from this study show that the race- and income-based strategies reduce racial economic segregation across schools at a similar pace, although the income-based strategy is slightly less efficient. The inefficiency of the income-based strategy compared to the race-based one becomes more pronounced if aiming for a larger reduction in school segregation. As expected, the race-and-income-based strategy most efficiently decreases racial economic segregation. Moreover, the relative efficiency of the race-based strategy is more salient among districts with higher school poverty rates and racial economic segregation.

Across cultures, the majority of individuals will have a sibling relationship in their lifetime. Of particular importance to parents and children is the transition to siblinghood, which refers to the process wherein a child becomes a sibling. Although many parents are concerned about how their children will adjust during the transition to siblinghood, scant empirical work on this subject exists. Available empirical work on the transition to siblinghood has stemmed from developmental psychology; however, social work researchers have yet to deeply explore this body of work. The transition to siblinghood is a topic that is rife with opportunity for social work researchers, who have the ability to study this transition from culturally sensitive, person-in-environment, and social justice perspectives. This article provides an overview of the elements and importance of the transition to siblinghood, introduces a theoretical framework relevant to social workers, reviews existent empirical work on the transition to siblinghood, and elucidates limitations in the literature that social work researchers are adept to address. The article concludes with four recommendations to researchers who are interested in studying the transition to siblinghood through a social work lens.

The Paycheck Protection Program (PPP) provided small businesses with roughly $800 billion dollars in uncollateralized, low-interest loans during the pandemic, almost all of which will be forgiven. With 93 percent of small businesses ultimately receiving one or more loans, the PPP nearly saturated its market in just two months. We estimate that the program cumulatively preserved between 2 and 3 million job-years of employment over 14 months at a cost of $170K to $257K per job-year retained. These estimates imply that only 23 to 34 percent of PPP dollars went directly to workers who would otherwise have lost jobs; the balance flowed to business owners and shareholders, including creditors and suppliers of PPP-receiving firms. Program incidence was highly regressive, with about three-quarters of PPP funds accruing to the top quintile of households. This compares unfavorably to the other two major pandemic aid programs, enhanced UI benefits and Economic Impact Payments (i.e. stimulus checks). PPP’s breakneck scale-up, its high cost per job saved, and its regressive incidence have a common origin: PPP was essentially untargeted because the United States lacked the administrative infrastructure to do otherwise. The more targeted pandemic business aid programs deployed by other high-income countries exemplify what is feasible with better administrative systems. Building similar capacity in the U.S. would enable greatly improved targeting of either employment subsidies or business liquidity when the next pandemic or other large-scale economic emergency occurs, as it surely will.

Using CPS data, we examine whether occupation mobility patterns during the first year of the Covid-19 pandemic differ from occupation mobility patterns of a pre-pandemic benchmark period from 2016 to 2019. In aggregate, we find that occupation mobility rates neither increased nor decreased during the first year of the pandemic. However, we do find that individuals who went through a period of unemployment in 2020 were less likely to have changed occupations than individuals who were unemployed during the benchmark era. This is consistent with the high rates of recall unemployment that have been documented in the literature. We also document that the quality of occupation switches changed during the pandemic compared to the benchmark period. Individuals who went through a period of unemployment during the pandemic are more likely to switch into lower income occupations and less likely to switch into higher income occupation, relative to individuals who went through a period of unemployment during the benchmark era. This scarring effect of the Covid-19 pandemic is not present for individuals who did not experience unemployment in 2020, who are slightly more likely to switch into higher income occupations compared to benchmark transitions. We find no evidence that this scarring effect is more prevalent among workers who worked in occupation that were heavily impacted by the pandemic.

Objective: Many patients with osteoarthritis experience pain which can lead to higher healthcare expenditures. It is important to understand the factors that drive the excess expenditures associated with pain in osteoarthritis. Design: Cross-sectional. Study sample: Our study sample consisted of adults (age ⩾ 18 years) from the Medical Expenditure Panel Survey (MEPS, 2018). Methods: Adults who were alive during the calendar year and had pain status were included in this study (N =2804 weighted N =32.03 million). Osteoarthritis was identified from the medical conditions file and household file. We used multivariable ordinary least squares regression to identify the statistically significant association of pain with direct healthcare expenditures. The Blinder-Oaxaca post-linear decomposition on log-transformed total direct healthcare expenditures was used to estimate the extent to which differences in characteristics contribute to the excess expenditures associated with pain. Results: Adults with osteoarthritis and pain had higher average expenditures ($21 814 vs $10 827, P < .001; 9.318 vs 8.538 in logtransformed expenditures) compared to those without pain. Pooled regression weights explained 62.9% of excess expenditures differences in characteristics between the 2 groups. The 2 main drivers of excess healthcare expenditures among adults with osteoarthritis and pain were (i) comorbidities (diabetes, asthma, chronic obstructive pulmonary disease, depression, heart diseases, cancer, and non-cancer pain conditions and (ii) prescription medications (NSAIDs, opioids, and polypharmacy). Conclusion: Need factors such as comorbid conditions, and prescription treatment explained the excess healthcare expenditures among adults with osteoarthritis and pain. The study findings suggest that reducing polypharmacy and appropriate management of comorbid conditions may be a pathway to reduce excess expenditures among adults with osteoarthritis and pain.

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and the National School Lunch Program (NSLP) are designed to increase food security and reduce hunger for children from low-income households. Since the cutoff age for WIC is five, and school enrollment is required for receiving free or reduced-price NSLP, some children from low-income households cannot receive both WIC and free or reduced-price NSLP. Using data from the Current Population Survey, the partial identification method developed in this paper addresses the problems of self-selection into WIC and systematic underreporting of program participation. Due to this loophole in food assistance programs for children, aging out of WIC is found to increase child food insecurity by at least 1.1 percentage points. This result indicates that the prevalence of child food insecurity would decline by 15% if WIC extended its cutoff age until children enroll in kindergarten.

I examine why Confederate monuments were built in public spaces in counties throughout the US South with particular attention to connections to race. I use event history analysis to examine the overall likelihood of erecting the first monument in a county while accounting for dimensions of time. The data include a comprehensive accounting of public Confederate monuments, covariates spanning 150 years, and several unique historical variables. This is the first study to assess competing explanations for the construction of Confederate monuments in a generalizable analysis. Results provide support for understanding Confederate monuments as part of a larger "memory movement."Moreover, this movement was strongly related to the relationship between race and power within a county. However, my analyses offer limited support for viewing Confederate monuments as part of a countermovement in response to local racial tensions. Incorporating the nuances of how race relates to Confederate monuments will be valuable to future research as scholars develop an understanding of their links to contemporary society.

When employers conduct more internal hiring, does this facilitate upward mobility for low-paid workers or does it protect the already advantaged? To assess the effect of within-employer job mobility on occupational stratification, we develop a framework that accounts for inequality in both rates and payoffs of job changing. Internal hiring facilitates advancement for workers without strong credentials, but it excludes workers at employers with few good jobs to advance into. Analyzing Current Population Survey data, we find that when internal hiring increases in a local labor market, it facilitates upward mobility less than when external hiring increases. When workers in low-paid occupations switch jobs, they benefit more from switching employers than from moving jobs within the same employer. One-third of this difference is due to low-paid workers isolated in industries with few high-paying jobs to transfer into. An occupationally segregated labor market therefore limits the benefits that internal hiring can bring to the workers who most need upward mobility.

Background The purpose of the current study was to use a social determinants of health (SDOH) framework and latent class analysis (LCA) to identify risk classes among mothers with young children. The risk classes were then used to predict food insecurity severity and stability/change of food insecurity over time. Method The secondary data from the Fragile Families and Child Wellbeing Study (n = 2,368; oversampled for non-marital births) was used in this study. Household food insecurity was assessed using the 18-items USDA Food Security Survey. A seventeen-item inventory of educational, economic stability, incarceration (i.e. social context), neighborhood safety (i.e. neighborhood and built environment), health and health care, and substance use behaviors at baseline/Year-1 were included to identify SDOH risk indicators in the LCA. Covariate-adjusted multinomial logistic regression models were used to examine the relation between risk classes at Year-1 and the severity of food insecurity at Year-3 and stability/change of food insecurity between Year-3 and Year -5. Results LCA identified five risk classes: High utility and medical hardship (Class 1), high housing and employment hardship, high substance use, and incarceration (Class 2), high housing and medical hardship, poor health, and health care (Class 3), high employment hardship and low-income (Class 4) and low-risk (Class 5). The Class 1, Class 2 and Class 3 had greater odds of low food security and very low food security at Year-3 compared to Class 4. In addition, compared to Class 4, Class 1, Class 2 and Class 3 had greater odds unstable food insecurity and persistent food insecurity over time. Conclusions LCA could be used to identify distinctive family system risk profiles predictive of food insecurity. The generated risk profiles could be used by health care providers as an additional tool to identify families in need for resources to ensure household food security.

We propose a complete framework for model-free difference-in-differences analysis with covariates, where model-free means data-driven, in particular nonparametric estimation and testing, variable and scale choice. We start with searching for the preferred data setup by simultaneously choosing confounders and a scale of the outcome variable along identification conditions. The treatment effects themselves are estimated in two steps: first, the heterogeneous effects stratified along the covariates, then the average treatment effect(s) for the population(s) of interest. We provide the asymptotic statistics as well as the finite sample behavior of our methods, and suggest bootstrap procedures to calculate standard errors and p-values of significance tests. The pertinence of our methods is shown with a study of the impact of the Deferred Action for Childhood Arrivals program on human capital responses of non-citizen immigrants. We show that past results underestimated the positive impact on school attendance for individuals aged 14-18, and the positive impact on high school completion. Moreover, we find that the parametric methods fail to identify the negative impact on school attendance of college aged individuals. Practical issues including bandwidth selection, sample weights, and implementation are given in the supplement.