Using data from the Current Population Survey, we investigate the distributional consequences of the Covid-19 pandemic and the associated public policy response on labor earnings and unemployment benefits in the United States up until February 2021. We find that year-on-year changes in labor earnings for employed individuals were not atypical during the pandemic months, regardless of their initial position in the earnings distribution. The incidence of job loss, however, was substantially higher among low earners, leading to a dramatic increase in labor income inequality among the set of individuals who were employed prior to the onset of the pandemic. By providing very high replacement rates for individuals displaced from low-paying jobs, the initial public policy response was successful in reversing the regressive nature of the pandemic’s impacts. We estimate, however, that recipiency rates for displaced low earners were lower than for higher earners. Moreover, from September 2020 onwards, when policy changes led to a decline in benefit levels, earnings changes became less progressive.

Coastal populations are facing increasing environmental stress from coastal hazards including sea level rise, increasing tidal ranges, and storm surges from hurricanes. The East and Gulf Coasts of the United States (US) are projected to face high rates of sea level rise and include many of the US’ largest urban populations. This study proposes modelling land-use change and coastal change between 1996 and 2019 to project the impacts of intensifying coastal hazards on the US Gulf and East Coast populations and to estimate how coastal populations are growing or retreating from high-risk areas. The primary objective is to develop a multifaceted spatial-temporal (MuST) framework to model coastal change through land-use projections and thorough analysis of the indicators of coastal urban growth or retreat. While urban growth models exist, one that presents an interdisciplinary evaluation of potential growth and retreat due to geographic factors and coastal hazards has not been released. This study proposes modelling urban growth using geospatial metrics including topographic slope, topographic elevation, distance to existing urban areas, distance to existing roads, and distance to the coast. The model will also use historic hurricane data, including storm track and footprint for named storms between 1996 and 2019 and the associated flood claims data from Federal Emergency Management Agency (FEMA), to account for existing impacts from coastal storms. Additionally, climate change data including sea level rise projections and future tidal ranges will be incorporated to project the impacts of future coastal hazards on urban expansion over the next 30 years (2020–2050). The basis of the urban growth model compares land-use change between 1996 and 2019 to complete a geospatial analysis of both the areas shifting from rural (agricultural, forest, wetlands) to urban, indicating change in population data from 2000 to 2020, to evaluate coastal retreat or abandonment over the next 30 years. It is expected that slow or no growth may indicate retreat from coastal areas, while urbanization and increasing population will indicate a shift towards coastal areas and growth.

Objective: This study examines the re-employment prospects and short-term career consequences for mothers and fathers who lost their jobs during the COVID-19 pandemic. Background: The pandemic recession has been dubbed a “shecession,” but few studies have explored whether mothers paid a higher or lower price upon labor market re-entry than fathers. Method: This study draws on March 2020–December 2022 Current Population Survey data and focuses on partnered parents with children under age 13 in the household. Exploiting four-month panels, we use multi-level discretetime event history models to predict re-employment and linear regression models to predict job-level wage upon reemployment, while controlling for a wide array of factors. Results: Partnered fathers were more likely than partnered mothers to find re-employment during the pandemic. The gender gap in re-employment was concentrated only among parents without a bachelor’s degree and persisted when all controls were held constant. Moreover, upon reemployment, fathers had higher job-level wages than mothers, which was consistent across educational levels. Even with the same job-level wage before labor market exit, mothers were penalized on re-entry relative to fathers and this penalty was rooted in gendered job segregation. Conclusion: This study extends previous research by analyzing re-employment and a critical material outcome for parents (i.e., job-level wage upon re-employment) during the entire pandemic, including the “new normal” (late 2022). The results reveal the intersectional inequalities in family and work: Compared to fathers, mothers,particularly less-educated mothers, paid a higher price for their time out of work during the pandemic.

We estimate the effects of permanent legal status on the health of children born to immigrants in the United States using variation from the Immigration Reform and Control Act of 1986 (IRCA). Our empirical approach compares trends in birth outcomes for foreign-born Mexican mothers across counties with different application rates under IRCA’s large-scale legalization programs. Maternal legalization raised birthweight. Effects arose immediately after the application process began – five years before affected women became Medicaid-eligible – suggesting causal mechanisms besides improved access to early prenatal care. Changes in the composition of births, stemming from changes in fertility and family reunification, contribute to but far from fully explain the birthweight impacts. The more likely mechanisms were instead the increases in family income and reductions in stress that came from gaining legal status.

We study the joint process of urbanization and industrialization in the US economy between 1880 and 1940. We show that only a small share of aggregate industrialization is accounted for by the relocation of workers from remote rural areas to industrial hubs like Chicago or New York City. Instead, most sectoral shifts occurred within rural counties, dramatically transforming their sectoral structure. Most industrialization within counties occurred through the emergence of new "factory" cities with notably higher manufacturing shares rather than the expansion of incumbent cities. In contrast, today's shift towards services seems to benefit large incumbent cities the most.

Public transit has received scrutiny as a vector for spreading COVID-19 with much of the literature finding correlations between transit ridership and COVID-19 rates by assessing the role that transportation plays as a vector for human mobility in COVID-19 spread. However, most studies do not directly measure the risk of contracting COVID-19 inside the public transit vehicle. We fill a gap in the literature by comparing the risk and social costs across several modes of transportation. We develop a framework to estimate the spread of COVID-19 on transit using the bus system in Pittsburgh. We find that some trips have demand that exceed their COVID-19 passenger limit, where the driver must decide between: (1) leaving a passenger without a ride or (2) allowing them on the bus and increasing COVID-19 risk. We consider five alternatives for alleviating overcapacity: allow crowding, additional buses, longer buses as substitutes, Transportation Network Company (TNC) rides, or Autonomous Vehicles (AVs) for passed-by passengers. We use transit ridership and COVID-19 data from the spring of 2020 by combining transportation data and an epidemiological model of COVID-19 stochastically in a Monte Carlo Analysis. Our results show that 4% of county cases were contracted on the bus or from a bus rider, and a disproportionate amount (52%) were from overcapacity trips. The risk of contracting COVID-19 on the bus was low but worth mitigating. A cost–benefit analysis reveals that dispatching AVs or longer buses yield the lowest societal costs of $45 and $46 million, respectively compared to allowing crowding ($59 million).

Are reforms towards individual taxation politically feasible? Are they desirable from a welfare perspective? We develop a method to answer such questions and apply it to the US federal income tax since the 1960s. Main findings are: As of today , Pareto-improvements require a move away from joint taxation. Revenue-neutral reforms towards individual taxation are not Pareto-improving, but attract majority-support. Such reforms are rejected by Rawlsian welfare measures and supported by ones with weights that are increasing in the secondary earner's income share. Thus, there is a tension between the welfare of "the poor" and the welfare of "working women."

Synthetic data generation methods, and in particular, private synthetic data generation methods, are gaining popularity as a means to make copies of sensitive databases that can be shared widely for research and data analysis. Some of the fundamental operations in data analysis include analyzing aggregated statistics, e.g., count, sum, or median, on a subset of data satisfying some conditions. When synthetic data is generated, users may be interested in knowing if their aggregated queries generating such statistics can be reliably answered on the synthetic data, for instance, to decide if the synthetic data is suitable for specific tasks. However, the standard data generation systems do not provide "per-query" quality guarantees on the synthetic data, and the users have no way of knowing how much the aggregated statistics on the synthetic data can be trusted. To address this problem, we present a novel framework named DP-PQD (differentially-private per-query decider) to detect if the query answers on the private and synthetic datasets are within a user-specified threshold of each other while guaranteeing differential privacy. We give a suite of private algorithms for per-query deciders for count, sum, and median queries, analyze their properties, and evaluate them experimentally.

Deafblindness refers to a functional restriction of both hearing and vision, and presents at all ages. Determining prevalence of deafblindness, particularly in children, is challenging. The aim of this review was to explore and assess methodologies previously used to determine the prevalence of childhood deafblindness in both peer-reviewed and grey literature. Five databases were included in the search – Medline (OVID), PubMed, Scopus, CINAHL, and PsycINFO. Thirteen peer-reviewed articles and 11 documents from the grey literature met inclusion criteria for the review. In exploring the literature on deafblindness in children, it is evident that the characteristics and needs of this population are not well described. Approaches adopted by researchers show inconsistencies in how deafblindness is defined, assessed and diagnosed, making comparison challenging. To understand the needs of this group, it is critical that the childhood deafblind population is accurately described. Recommendations are made for the assessment of the population of children with deafblindness.

Objectives This paper provides new estimates of disability prevalence for the archipelago and mainland-residing Puerto Rican populations ages 40 and above and compares disability by place of birth and place of residence to investigate drivers of middle and older age health. Methods Large nationally representative samples from 2013 to 2017 American Community Survey and Puerto Rico Community Survey data are used to estimate age-specific disability prevalence for archipelago-born/archipelago-residing, archipelago-born/mainland-residing, mainland-born/mainland-residing Puerto Ricans. Results Mainland-born/mainland-residing Puerto Ricans have the lowest age-adjusted disability rates and archipelago-born/archipelago-residing Puerto Ricans have the highest rates. Differences in education explain part of this disparity. Discussion Similarities in disability prevalence are strongest based on where one was born as opposed to current residence, pointing to early life as a critical period in the disablement process for later-life health. Early life socio-economic disadvantage on the archipelago may have an enduring impact on later-life disability prevalence for archipelago-born Puerto Ricans.

This paper investigates the relationship between high-skill intensive industries and poverty rates among low-skilled residents in a sample of cities in the United States. We estimate a model of poverty rates among low-skill residents to examine whether cities with higher economic concentrations in high-skill intensive industries tend to have higher poverty rates among their low-skill residents. Our results suggest that there is a statistically significant and positive relationship between the two variables. We interpret our results in the context of previous findings and discuss the policy implications.

This article uses state-specific timing of legal changes and data from the Current Population Survey and National Longitudinal Survey of Young Women to estimate effects of confidential access to the pill in late adolescence on earnings, labor force participation, and human capital accumulation over the life cycle, finding no evidence of effects. These results are contrary to past research, which imposed a restriction on the regression model that this article shows explains the past results.

This study examines the substitutability between prime-age (ages 25-54) and marginal-retirement-age (ages 55-64) workers by investigating the impact of internal migration of prime-age workers across 320 U.S. Metropolitan Statistical Areas from 2002 to 2019. A 1% increase in prime-age worker inflows boosts the relative employment of prime-to marginal-retirement-age workers by 1.39%. The inflow shock reduces relative earnings by 0.18%, indicating a substitution elasticity of 7.7. Interpreted via an overlapping generations model, the inflow of prime-age workers, coupled with low substitutability, enhances the welfare of workers nearing retirement. Therefore, I emphasize that policymakers should consider not just relocating people to high-inflow areas but also reducing worker substitutability to improve the welfare of the marginal-retirement-age population.

E-commerce can potentially make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities, particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include the use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection-points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two- echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

This paper studies how belief disagreement across households affects aggregate demand. I develop a model in which households are heterogeneously exposed to business cycles and show that the impact of disagreement can be summarized by a simple statistic–correlated disagreement–which captures the correlation between beliefs and individual business-cycle exposure. I endogenize disagreement via heterogeneous attention, which implies that attention increases with exposure. So, correlated disagreement is positive. Then, I show that disagreement amplifies general-equilibrium effects and acts as a propagation mechanism amplifying business cycles. I also provide evidence of this positive correlation using survey data on expectations. To quantify the implications of disagreement, I extend the analysis to a Heterogeneous-Agent New Keynesian model featuring multiple sources of heterogeneity. I show that belief disagreement can substantially amplify business-cycle fluctuations. Finally, I show that targeting spending to the most cyclical workers can significantly increase the spending multiplier.

Maternal health disparities are a growing health concern that disproportionately subject Black women to a three times greater risk of morbidity than White women. When factoring in an environmental hazard, the risk of morbidity for Black women nearly doubles. Maternal health disparities are defined by the difference in the rate of severe maternal morbidity (SMM) between Black and White women. The overall aim of this dissertation is to examine the impact across multiple geographic scales of an environmental hazard on maternal health disparities. A secondary aim is to identify key areas of interventions to reduce adverse outcomes. This dissertation has three independent but linked research objectives. The first objective is to examine the baseline trend of maternal health disparities in Michigan two years prior to the Flint Water Crisis (FWC). The second objective is to determine the impact of the FWC on maternal health of women in Flint, Michigan. The third objective is to examine the different perspectives on the quality of maternal health care services from women who gave birth in Flint post-FWC. The study timeframe overall is 2012 through 2017. Anonymized data was collected from the Michigan Inpatient Database System (MIDB) and used to address Study 1 and Study 2’s objectives individually. For Study 3, I collaborated with an ongoing, community-based randomized control trial (RCT) to collect additional maternal health disparity data from their baseline survey. Each study was self-contained and deployed a range of statistical analyses including descriptive statistics, odds ratios, differencein-difference (DID) models, and logistic regression analyses. At the broadest geographic scale, I found that the maternal health disparity trend in Michigan has increased over time. At the urban geographic scale, I found that despite an environmental hazard posed by the FWC and living within similar socioeconomic and demographic communities as White women, the burden of maternal health disparities still falls on Black women. At the individual scale, I found that Hispanic women had a greater experience of severe maternal morbidity than non-Hispanic women. I also found that Black women experienced discrimination within the maternal health care system despite delivering in a race concordant city more than White women. In conclusion, there are two driving factors across geographic scales that significantly impact a women’s maternal outcome despite an environmental hazard: race and age. Thus, depending on the age range and if a woman identifies as Black, the odds of experiencing a SMM is significantly higher than a White woman in Flint even when accounting for multi-scale factors such as socioeconomic status, place of residence, community factors, delivering in a race concordant city, and the Flint Effect in the presence of an environmental hazard.

In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent adversarial parties from colluding to steal others' private information. However, guaranteeing the utility of computation outcomes while protecting all parties' data privacy can be challenging, particularly when the parties' privacy requirements are highly heterogeneous. In this paper, we propose a novel privacy framework for multi-party computation called Threshold Personalized Multi-party Differential Privacy (TPMDP), which addresses a limited number of semi-honest colluding adversaries. Our framework enables each party to have a personalized privacy budget. We design a multi-party Gaussian mechanism that is easy to implement and satisfies TPMDP, wherein each party perturbs the computation outcome in a secure multi-party computation protocol using Gaussian noise. To optimize the utility of the mechanism, we cast the utility loss minimization problem into a linear programming (LP) problem. We exploit the specific structure of this LP problem to compute the optimal solution after O(n) computations, where n is the number of parties, while a generic solver may require exponentially many computations. Extensive experiments demonstrate the benefits of our approach in terms of low utility loss and high efficiency compared to existing private mechanisms that do not consider personalized privacy requirements or collusion thresholds.

Analyses of survey data highlight observable person characteristics such as education and occupation as critical factors for rising earnings inequality, while industry has an offsetting effect. In contrast, analysis of administrative data highlights that rising between-firm earnings dispersion and, in turn, between-industry earnings dispersion dominates the rise in earnings inequality. We construct a novel integrated dataset based upon CPS microdata linked with LEHD administrative records. We find that most of the rise in earnings inequality is accounted for by rising between-industry inequality. This finding reflects a substantial contribution of increased sorting and segregation of observable person characteristics between industries.

The composition of the US healthcare workforce relative to citizenship status remains ill-defined. We sought to characterize practice patterns among US doctors relative to citizenship status. Data were extracted from the 2008–2019 American Community Surveys, and citizenship was stratified as: citizens by birth, naturalized citizens for ≥ 10 years or < 10 years, and non-citizens. Multinomial logistic regression models and inverse probability weighting were employed. The data were reported as differences in proportions/means with 95% confidence intervals. Among 97,775,606 respondents, 113,638 were identified as doctors (0.12%). Among the surveyed doctors, 72.4% (95% CI 72.1–72.8%) were citizens by birth, followed by naturalized citizens ≥ 10 years [14.4% (95% CI 14.1–14.6%)], non-citizens [7.2% (95% CI 7.0–7.4%)], and naturalized citizens < 10 years [6.0% (95% CI 5.8–6.1%)]. Naturalized citizens ≥ 10 and < 10 years worked 40.4 (95% CI 12.6–68.2) and 38.2 (95% CI 4.8–71.6) more hours annually compared with citizens by birth, respectively (both p < 0.05). While 22.7% of doctors who were citizens by birth worked in high socially vulnerable counties, immigrant doctors were more likely to work in these areas (difference (95% CI); naturalized citizens ≥ 10 years, 7.7% (6.1–9.4) vs. naturalized citizens < 10 years, 8.0% (5.9–10.1) vs. non-citizens, 4.1% (2.0–6.1)). Furthermore, naturalized citizens ≥ 10 years and < 10 years worked in lower physician density counties that had 29.6 (14.4–44.8) and 59.9 (42.3–77.5) more people, respectively, relative to doctors (all p < 0.001). Immigrant doctors play a vital role in addressing US healthcare needs. Policies that encourage the increased integration and utilization of immigrant doctors and physicians-in-training may help to foster a sustainable healthcare workforce over the coming decades.

The percentage of Americans over 65 who are still working — because they either want to or have to — has nearly doubled since the late 1980s