While the mental health impacts of the COVID-19 pandemic on the general U.S. public health workforce have been well described, the effects of the COVID-19 response on Black, Indigenous, and People of Color (BIPOC) working in public health have not been adequately characterized. BIPOC public health professionals may have suffered, potentially, greater stress and more negative health impacts during the pandemic due to being part of communities experiencing severe COVID-19 health inequities and the potential for racism-related stress in the workplace. This study utilized a cross-sectional design to investigate the associations between risk factors/predictors and higher levels of burnout among BIPOC public health professionals working during the COVID-19 pandemic. Survey data was collected using the Qualtrics survey platform and SPSS was used for data analysis. Survey items measured multiple domains including professional experience (i.e., years of experience, job functions, hours worked, volunteer work), mental and physical health status (i.e., co-morbidities, BMI, COVID-19 diagnosis, insomnia, anxiety, depression, trauma, burnout), professional and personal stress (before and during the pandemic), and racism-related stress (i.e., discrimination, harassment, heightened vigilance, cultural taxation). Of the total respondents (n = 486), 80% experienced insomnia, 68.5% experienced depression, 81.7% experienced anxiety and 61.3% experienced trauma. BIPOC public health professionals suffered a moderately high overall level of burnout (mean = 2.578, SD = 0.486, min = 1, max = 3.9) and a high level of exhaustion (mean = 2.744, SD = 0.532, min = 1, max = 4). Paired t-tests found respondents’ physical and mental health status were each significantly worse during the pandemic (p < .000). Respondents also had significantly worse professional and personal stress during the pandemic (p < .000). Backward stepwise regression found higher burnout significantly predicted by: not having sought counseling; lower rating of mental health during COVID-19; higher past year mental distress (i.e., depression, anxiety, insomnia and trauma); higher past month perceived stress; and higher vigilance. These findings emerge as important in informing the public health field regarding the current and future needs of BIPOC public health professionals during the pandemic and beyond.

Over the course of the Coronavirus pandemic, works of art explored social isolation, abolition, and climate crisis. The pandemic had ruptured normative curricular practices in schools and learning discourses focused on minimizing those interruptions. Meanwhile, works such as Ellen Reid’s SOUNDWALK, Kamau Ware’s Fighting Dark, and Maya Lin’s Ghost Forest crafted relationships to knowledge through site-specific sounds, familiar materials, and sensory experiences of their environments. A group of curriculum designers, researchers, and educators, including the author of this study, affiliated with a university-based Curriculum Lab engaged with these artworks, while processing the pandemic’s effects on their own curricular practices. Situated within the Lab, this project used ethnographic and speculative methods to research how the artworks’ aesthetic and sensory strategies activated curricular contact zones and contributed to artful practices for curriculum theory and design. This study built on the work of critical curriculum scholarship which has demonstrated that significant forms of knowledge and belonging are produced through informal and null curriculum, and outside of schools entirely. Drawing on aesthetics, affect, and vital materialisms, this study theorized ambient curriculum: a surround through which any variety of onto-epistemological practices might cohere into relationships of knowing and becoming. At the same time, this study recognized that formal curriculum exerts a large influence on the daily lives of teachers and students, and that there are educators searching for forms of curriculum more aligned to their commitments to social and ecological justice; beliefs about the complexity of knowledge and learning; and approach to design as a creative process. This project considers the implications of such creative processes for curriculum design as a nomadic practice and curriculum designers as nomadic becomings, making and made by their creations.

Algorithmic management systems organize many different kinds of work across domains, and have increasingly come under academic scrutiny. Under labels including gig work, piecemeal work, and platform labor, these systems have been richly theorized under disciplines including human-computer interaction, sociology, communications, economics, and labor law. When it comes to the relationships between such systems and their workers, current theory frames these interactions on a continuum between organizational control and worker autonomy. This has laid the groundwork for other ways of examining micro-level practices of workers under algorithmic management. As an alternative to the binary of control and autonomy, this dissertation takes its cue from feminist scholars in Science, Technology, and Society (STS) studies. Drawing on frameworks from articulation, repair, and mutual shaping, I examine workers’ interpretations and interactions, to ask how new subjectivities around identity and community emerge from these entanglements. To shed empirical light on these processes, this dissertation employs a mixed-methods research design examining the introduction of facial recognition into the sociotechnical systems of algorithmic management. Data include 22 in-person interviews with workers in New York City and Toronto, a survey of 100 workers in the United States who have been subjected to facial recognition, and analysis of over 2800 comments gathered from an online workers’ forum posted over the course of four years.Facial recognition, like algorithmic management, suffers from a lack of empirical, on-the-ground insights into how workers communicate, negotiate, and strategize around and through them. Interviews with workers reveals that facial recognition evokes polysemia, i.e. a number of distinct, yet interrelated interpretations. I find that for some workers, facial recognition means safety and security. To others it means violation of privacy and accusations of fraud. Some are impressed by the “science-fiction”-like capabilities of the system: “it’s like living in the future.” Others are wary, and science fiction becomes a vehicle to encapsulate their fears: “I’m in the [movie] The Minority Report.” For some the technology is hyper-powerful: “It feels like I’m always being watched,” yet others decry, “it’s an obvious façade.” Following interviews, I build a body of research using empirical methods combined with frameworks drawn from STS and organizational theory to illuminate workers’ perceptions and strategies negotiating their algorithmic managers. I operationalize Julian Orr’s studies of storytelling among Xerox technicians to analyze workers’ information-sharing practices in online forums, to better understand how gig workers, devices, forums, and algorithmic management systems engage in mutual shaping processes. Analysis reveals that opposing interpretations of facial recognition, rather than dissolving into consensus of “shared understanding,” continue to persist. Rather than pursuing and relying on shared understanding of their work to maintain relationships, workers under algorithmic management, communicating in online forums about facial recognition, elide consensus. After forum analysis, I then conduct a survey, to assess workers’ fairness perceptions of facial recognition targeting and verification. The goal of this research is to establish an empirical foundation to determine whether algorithmic fairness perceptions are subject to theories of bounded rationality and decision-making. Finally, for the last two articles, I turn back to the forums, to analyze workers’ experiences negotiating two other processes with threats or ramifications for safety, privacy, and risk. In one article, I focus on their negotiation of threats from scam attackers, and the use the forum itself as a “shared repertoire” of knowledge. In the other I use the forums as evidence to illuminate workers’ experiences and meaning-making around algorithmic risk management under COVID-19. In the conclusion, I engage in theory-building to examine how algorithmic management and its attendant processes demand that information-sharing mechanisms serve novel ends buttressing legitimacy and authenticity, in what I call “para-organizational” work, a world of work where membership and legitimacy are liminal and uncertain. Ultimately, this body of research illuminates mutual shaping processes in which workers’ practices, identity, and community are entangled with technological artifacts and organizational structures. Algorithmic systems of work and participants’ interpretations of, and interactions with, related structures and devices, may be creating a world where sharing information is a process wielded not as a mechanism of learning, but as one of belonging.

Over the past decade, and accelerated due to the COVID-19 pandemic, there has been increasing adoption of decentralized diagnostic testing, where the testing is brought closer to the patient. This trend has largely been fueled by the development of more accurate diagnostic tools and faster and more reliable data connectivity. Decentralized testing has been shown to greatly reduce turnaround times while increasing accessibility to users in remote regions. However, there are challenges that limit its widespread adoption. In this dissertation, we detail the development of tools and technologies to overcome these barriers and expedite the shift towards decentralized diagnostic testing. First, we demonstrate the ability to develop point-of-care (POC) diagnostic tests with performance that rivals that of traditional lab-based methods. We developed a rapid, multiplexed, microfluidic serological test for Lyme disease, a tick-borne disease caused by the Borrelia burgdorferi bacterium. The recommended testing, the standard 2-tiered (STT) approach, is not sensitive for early-stage infections, is labor-intensive, has long turnaround times, and requires the use of two immunoassays (enzyme-linked immunosorbent assay (ELISA) and the Western Blot). We developed a standalone multiplexed sandwich ELISA assay and adapted it to the mChip microfluidic platform. We validated the assay on a rigorously characterized panel of human serum samples and demonstrated that our approach outperforms the STT algorithm on sensitivity while matching its specificity. The form factor of this technology is amenable to use in physician’s offices and urgent care clinics. We also showed exploratory work towards adapting the mChip platform for diagnosis of Zika disease, a mosquito-borne disease caused by the Zika virus, and acute kidney injury, a syndrome characterized by loss of kidney excretory function. Next, we worked on increasing the adoption of rapid diagnostic tests for self- and partner-testing designed to be used in at-home settings. We developed a smartphone application to be used alongside the INSTI Multiplex test for detecting HIV and syphilis infections. The application was designed to provide users with i) instructions on running the test, ii) an automated deep-learning-based image interpretation algorithm to interpret the rapid test results from a smartphone image, iii) a way to save test results and display/share them, and iv) resources for follow-up care. We adopted a user-centered, iterative design process where we worked with a cohort of study participants composed of men who have sex with men and transgender women at high risk for contracting sexually transmitted infections. We then field tested the application with 48 participants over a duration of three months and found high acceptability for the application, both in terms of functionality and helpfulness. Finally, we sought to address a key limitation with deep-learning-based image classification techniques, specifically, the requirement for large numbers of annotated images for training. We developed a deep-learning image interpretation algorithm that could be quickly adapted to new rapid test kits using only a fraction of the images that would otherwise be needed for training the model. The interpretation algorithm followed a three-step, modular process. First, the rapid test kit and the membrane were extracted from the smartphone image. Second, the constituent zones were cropped from the extracted membrane. Finally, a classifier detected the presence or absence of a line in the individual zones. Fast adaptation was demonstrated by adapting a base model, trained using images of a single COVID-19 rapid test kit, to four different rapid test kits, each with different form factors, using few-shot domain adaptation. After training with 20 or fewer images, the classification accuracies of all the adapted models were > 95%. This approach can provide a digital health platform for improved pandemic preparedness and enable quality assurance and linkage to care for consumers operating new LFAs in widespread decentralized settings. Together, these methods provide a suite of tools that could expedite the shift towards decentralized, POC testing.

This dissertation includes three essays that examine the potential of policies, interventions, and curricula in improving transitions to four-year colleges and universities in the U.S. The first essay provides a novel investigation of New York City’s adoption of SAT School Day (SSD), which provides universal access to college entrance exams for high school juniors. I examine the causal impact of the policy on SAT taking and four-year college enrollment patterns for the first three post-policy cohorts. Using a two-way fixed effects differences-in-differences (DID) model, I find that the SSD policy increases SAT-taking rates by 20% but has little impact on four-year enrollment. These findings suggest that increased college entrance test participation alone is not sufficient to increase enrollment to four-year colleges. The second essay provides a systematic review and meta-analysis of 19 studies on the causal impact of late pre-college advising interventions, which I refer to as LPCAIs. LPCAIs aim to connect juniors and seniors with pre-college advisors to guide them through the complex college application process. The studies use experimental and quasi-experimental designs to identify the causal effect of LPCAIs on postsecondary enrollment outcomes for historically underrepresented students. I find that the offer of an LPCAI, on average, increases postsecondary enrollment and improves institutional choices. However, the in-person advising format accounts for nearly all of the positive impact derived from LPCAIs. Several implications of these findings are discussed, particularly in the context of COVID-19. The third chapter presents a correlational study that uses transcript data from a statewide community college system to estimate how well GPA, credit accumulation, and course taking at community college predict upward transfer and baccalaureate completion. While GPAs and credit accumulation rates in all academic branches are strong, positive predictors of transfer, GPAs in humanities are significantly more predictive of transfer compared to GPAs in non-humanities branches. GPAs and successful credit accumulation rates in humanities courses are also associated with greater likelihoods of baccalaureate completion, conditional on transfer. However, humanities courses are equally valid predictors of baccalaureate completion as courses in other academic branches. In contrast to strong academic performance, taking more courses in humanities, although associated with greater likelihoods of vertical transfer, is associated with lower likelihoods of baccalaureate completion. Taken together, these essays contribute to our understanding of approaches to improve transitions to four-year colleges and universities. They also highlight the importance of academic preparation and advising within transitional pathways.

This dissertation includes three papers that examine the role of child care policy in promoting early childhood education and care and parental labor supply. Paper one investigates the effects of universal pre-kindergarten on center-based early education and care enrollment and child care expenditures by household income with a focus on middle-income children. Paper two considers how the generosity of the Child and Dependent Care Tax Credit (CDCTC) benefits is associated with child care utilization and maternal labor supply. Paper three assesses the role of coresident grandparents in parental labor supply during the COVID-19 pandemic.

Introduction: There is growing evidence for increased prevalence of poor adolescent mental health, including depression, in the United States. Increases in adolescent depression beginning around 2008-2010 coincided with the timing of the Great Recession and there are plausible mechanisms through which economic recessions may influence adolescent depression (e.g., caregiver job loss, household economic hardship). More research is needed to understand the potential relationship between the 2007-2009 Great Recession and long-term impacts on mental health by household poverty, as many mechanisms (e.g., cumulative familial stress) can impact adolescent mental health after the peak of a recession passes. The objective of this dissertation is to examine the associations between economic recessions and adolescent depression. This dissertation includes five chapters: first, an introduction; second, a literature review to examine evidence of time trends and birth cohort effects in depressive disorders and symptoms among adolescents in recent years; third, an empirical study to assess changes in adolescent depression and depression treatment, including differences by household poverty occurring at the beginning of the Great Recession; fourth, an empirical study to estimate potential longer-term impacts of the Great Recession by examining whether young adults from birth cohorts who were adolescents at the time of the Great Recession had higher risk of MDE and mental health treatment use as young adults compared with birth cohorts who were adolescents and surveyed prior to the Great Recession with potential differences by household poverty; and fifth, a conclusion to summarize results and discuss implications for future research. Methods: The integrative systematic literature review included 10 studies related to the United States, adolescent populations, birth year and time trends, and depressive symptoms or disorders. The two empirical aims utilized data from the National Survey on Drug Use and Health (NSDUH), a national survey assessing behavioral health among participants aged 12 and older. For the first empirical aim, I analyzed data for adolescents ages 12-17 participating in the 2004-2019 NSDUH (N = 256,572). For the second empirical aim, I included young adults ages 18-29 from the 2005-2019 NSDUH (N = 135,158). For this aim, the main exposure measure was belonging to birth cohorts (1990-1994) who were adolescents during the Great Recession and surveyed in 2008-2019 versus those from birth cohorts (1976-1989) that did not experience the Great Recession and were surveyed prior to the Great Recession in 2005-2007. For both empirical aims, I measured past year DSM-IV and DSM-5 major depressive episodes (MDE) from self-reported symptoms. MDE treatment was assessed among those with past year MDE, excluding those who were already successfully treated for MDE. For the first empirical aim, I tested how MDE and MDE treatment conditioned on MDE changed from pre-Great Recession (2004 to Fall 2007) to post-Great Recession (Winter 2007 to 2019) using interrupted time-series (ITS) segmented regression models accounting for seasonality (January-March, April-June, July-September, October-December) and autocorrelation. For the second empirical aim, regression models assessed the relationships between the birth cohort exposure measure and MDE and mental health treatment utilization adjusting for age, gender, race/ethnicity, educational attainment, and insurance status. Both empirical aims tested effect modification by household poverty. Results: The review of 10 studies found increases in depressive symptoms and disorders in adolescents across recent survey years with increases observed between 1991 and 2020. Of the 3 articles that assessed birth cohort trends, birth cohort trends were less prominent than time period trends. Proposed explanations for increases included social media, economic-related reasons, changes in mental health screening and diagnosis, changes to mental health stigma and treatment and, in more recent years, the COVID-19 pandemic. In the first empirical study, I illustrated that the Great Recession was not associated with an immediate change in MDE prevalence (β: -0.77, 95% CI: -2.23, 0.69). However, following the Great Recession, the increase in MDE prevalence accelerated (β: 0.29, 95% CI: 0.13, 0.44). The Great Recession was not associated with acute changes in adolescent MDE treatment (β: -2.87, 95% CI: -7.79, 2.04) nor longer-term slope effects (β: 0.03, 95% CI: -0.46, 0.51). Evidence of interaction by household poverty was not observed for either the MDE or MDE treatment outcome. In the second empirical aim, interaction between the birth cohort exposure and household poverty was observed for MDE (F=10.17, df=2, p=<0.0001), but not for mental health treatment use. Great Recession exposure effects were stronger among those at higher levels of household income. For example, among young adults who were living in households at two times the poverty threshold, those from birth cohorts who were exposed during adolescence to the Great Recession had higher odds of MDE compared with young adults from birth cohorts who were unexposed during adolescence to the Great Recession (adjusted odds ratio= 1.16, 95% CI= 1.04, 1.29). Conclusions: Multiple cross-sectional surveys and cohort studies documented rising prevalence of depressive symptoms and disorder among adolescents from 1991-2020. The Great Recession coincided with accelerated trends of increasing MDE, but not MDE treatment of these adolescents. Contrary to my hypothesis, the strength of changes in the rate of increase in MDE did not differ by household poverty and adolescents from households living in poverty, who likely experienced a greater financial burden during the recession, did not experience an increase in the rate of MDE. Birth cohort effects by household poverty were observed and exposure to the Great Recession during adolescence was associated with long-term effects on MDE, but not mental health treatment utilization, during young adulthood compared with those not exposed to the Great Recession. Young adults from higher income households who were exposed to the Great Recession had heightened likelihood of MDE. Future research should explore alternative drivers of MDE during the 2010s, as poverty-specific cohort analyses did not show that those living in poverty who likely experienced the greatest burden of a recession financially had increased risk of MDE.

This dissertation consists of three essays in the field of health economics. The first essay provides the first causal evidence that bargaining power in a relationship shapes pregnancy outcomes and health disparities in the US. A key driver of bargaining power is the availability of potential non incarcerated male partners in the local dating market, which I define at the race by cohort by county level. Because these sex ratios are endogenous, I use a novel instrument that leverages the randomness in sex at birth and the persistence of local demographics to isolate exogenous variation in the relative availability of men. Greater female bargaining power causes better outcomes: fewer out-of-wedlock births, less chlamydia and hypertension among mothers, and fewer infants with APGAR score below the normal level. The marriage market makes a significant contribution to racial disparities in pregnancy health. Specifically, Black women face relatively poor prospects when looking for a partner compared to White women: while there are 102 White men per 100 White women, only 89 Black men are available per 100 Black women. According to my estimates, Black women’s disadvantage accounts for 5-10% of the large racial gap in maternal and neonatal health. The racial difference in male availability is mostly policy-driven, as incarceration accounts for 45% of the gap. A counterfactual policy equalizing county-level incarceration rates for non-violent offenses between Black and White people would prevent 200-700 adverse pregnancy outcomes per year among Black mothers through the bargaining power channel alone. The second essay investigates how reopening hotels and ski facilities in Poland impacted tourism spending, mobility, and COVID-19 outcomes. We used administrative data from a government program that subsidizes travel to show that the policy increased the consumption of tourism services in ski resorts. By leveraging geolocation data from Facebook, we showed that ski resorts experienced a significant influx of tourists, increasing the number of local users by up to 50%. Furthermore, we confirmed an increase in the probability of meetings between pairs of users from distanced locations and users from tourist and non-tourist areas. As the policy impacted travel and gatherings, we then analyzed its effect on the diffusion of COVID-19. We found that counties with ski facilities experienced more infections after the reopening. Moreover, counties strongly connected to the ski resorts during the reopening had more subsequent cases than weakly connected counties. The third essay studies the diffusion of influenza-like illnesses (ILI) through social and economic networks. Using almost two decades of weekly, county-level infection and mortality data from Poland, it studies within and across-counties ILI transmission. Firstly, it evaluates the causal effect of school closures on viral transmission. The results show that closing schools for two weeks decreases the number of within county cases by 30-40%. The decline in infections extends to elderly and pre-school children. In addition, flu-related hospitalizations drop by 7.5%, and mortality related to respiratory diseases among the elderly drops by 3%. Secondly, the paper demonstrates the significant contribution of economic links to diffusion across counties. The disease follows the paths of workers commuting between home and workplace. Together with the structure of the labor mobility networks, these results highlight the central role of regional capitals in sustaining and spreading the virus.

This dissertation consists of three essays in public economics. The three chapters focus on interactions between public and private economic decisions. The first two chapters focus on unemployment insurance (UI) policy in the United States. The third discusses public-private interactions in the education market. The first chapter, a joint work with Arindrajit Dube, Calvin Jahnke, Raymond Kluender, Suresh Naidu, and Michael Stepner estimates the labor supply and spending responses to a large change in UI benefits during the pandemic. We examine the effects of the sudden withdrawal of expanded pandemic unemployment benefits in June 2021 using anonymized bank transaction data for 16,548 individuals receiving UI in April 2021. Comparing the difference in differences between states withdrawing and retaining expanded UI, we find that UI receipt falls 35 p.p. while employment rises by only 4.4 p.p. by early August. Average cumulative UI benefits fall by $1,385 while average cumulative earnings increase by only $93. Heterogeneity by unemployment duration implies that these effects are primarily driven by extensive margin expiration of benefits, rather than intensive margin reductions in the benefit level. The second chapter examines the role of gifts and loans from friends and family during unemployment. These transfers play a largely unstudied informal insurance role in high-income countries, making it difficult to assess their implications for social insurance policy. I present new results on informal insurance paid via person-to-person (P2P) payment platforms using a survey-linked administrative bank transaction dataset covering 130,502 low-income users from the US who were unemployed at least once between July 2019 and September 2020. Event study estimates show average monthly inflows from all P2P platforms increase by $30, or 2% of lost earnings, one month after job loss before returning to baseline over 10 months. Single mothers and the long-term unemployed receive the largest increases, as do those living in high-income areas. I exploit three plausibly exogenous changes to federal pandemic unemployment insurance (UI) policy to estimate that UI benefits crowd out at most $0.04 of informal P2P transfers. Using the social insurance framework introduced in Chetty & Saz (2010), my crowd-out estimates indicate negligible welfare consequences for an additional dollar of benefits. Altogether these results imply that public UI benefits can raise welfare by pooling risk across networks without reducing within-network targeting of informal insurance. The third chapter asks whether public school services fill in gaps left by private school failures. Specifically, it explores what type of schools enter the market and experience an increase in enrollment after reports of abuse by Catholic priests lead to Catholic Schools closures. I use a two-way fixed effects event study method to estimate a change in enrollments and number of different types of schools after a report of priest abuse within the same zip code, school district, or county. I find there are 0.2 fewer Catholic schools and Catholic school enrollment falls by 75 students after six years, which are offset by a 0.2 and 50-student increase in charter school counts and enrollments on average. These increases are unique to charter schools and is not observed in other public or non-Catholic private schools. Altogether, these results suggest that former Catholic schooled families show a preference for charter schools over other public schools, which may be due to the low-cost and similar emphasis on discipline and academic achievement.

In light of health disparities involving African Americans having disproportionately high rates of obesity, type II diabetes, cancer risk, heart disease and hypertension, the study was conducted with an all-Black adult sample (n= 470). Some 53.0% were female and the sample had a mean age of 32 years. The study evaluated the Knowledge of Physical Activity and Dietary Intake Guidelines Survey (KOPA-DIGS-40) as a new True-False knowledge test—with all True items. The study found moderately high levels of knowledge using the KOPA-DIGS-40. Findings using paired t-tests found evidence that ratings for (1) knowledge of guidelines and (2) self-efficacy for adherence to guidelines were significantly higher after taking the test. The study found noteworthy predictors of a higher KOPA-DIGS-40 score to be older age, higher education, and higher Body Mass Index. The study focused on three time periods (1-before the COVID-19 pandemic, 2-during the pandemic, 3-currently) and ratings on four health dimensions (1-physical health, 2-mental/emotional health, 3-physical activity level, 4-eating a healthy diet). Findings showed declines on the four dimensions during the pandemic, yet by Spring 2023 there were significant improvements—with a return by Spring 2023 to pre-pandemic levels. Findings suggested evidence of resilience after experiencing the stress of a global pandemic. Thus, a resilience theory and framework may be vital in guiding a future era of health disparities research with African Americans. Further, the study found an increase in social support during the pandemic, which continued into their current lives. Future research should continue to utilize the innovation of the genre of research reflected in this study’s use of the new Knowledge of Physical Activity and Dietary Intake Guidelines Survey (KOPA-DIGS-40)—as a True-False test with all True items; this follows from evidence the KOPA-DIGS-40 served as a brief online e-health intervention associated with increases in (1) knowledge of the guidelines and (2) self-efficacy for adherence to physical activity and dietary intake guidelines. Given the high rates of obesity, type II diabetes, cancer risk, heart disease and hypertension for African Americans, the findings make an important contribution to the literature—while suggesting future directions in research and interventions.