Health disparities research often focuses on the social patterning of health outcomes. Increasingly, there has been an emphasis on understanding the mechanisms perpetuating disparities, even after issues of patient access to health services are addressed. The following study utilizes a novel dataset of electronic medical records (EMR), radiology records, and U.S. Census data to investigate the racial/ethnic patterning of provider-patient communication among patients diagnosed with incidental medical findings requiring follow-up. My results indicate that racial/ethnic disparities in follow-up adherence stem from initial disparities in provider-patient communication. These communication disparities persist even after accounting for multiple socioeconomic, health, and provider characteristics, indicating a bias in medicine, whereby providers are less likely to communicate information about incidental medical findings to patients of color relative to White patients. This paper has important clinical implications, as it sheds new light on why we might see low adherence to medical advice among patients of color. Findings also have social, political, and policy relevance, as they suggest an important mechanism through which health inequalities persist. To finally eliminate racial/ethnic health inequalities in the United States, racial bias and discrimination within medical and public health infrastructures must be eliminated.

Communities share challenges with the neighborhoods to which former residents move and the neighborhoods from which new residents arrive. A lack of migration data for small geographic areas, however, makes it difficult to identify places that share populations over time. This article uses longitudinal consumer credit data to evaluate the spatial connectivity of neighborhoods. The paper develops a methodology for the construction of Small-Area Moving Ratios (SMvRs), motivating the approach with two applications: (1) the visualization of residential mobility ties across Massachusetts neighborhoods and (2) the application of a community detection algorithm to identify communities of strongly interconnected places. The research produces novel evidence showing that the connectivity between neighborhoods differs for socioeconomically advantaged versus for disadvantaged movers. This work shows how longitudinal, geolocated business administrative datasets can be repurposed to produce planning-relevant insights.

Background and objectives: U.S. Latinos are a heterogeneous population with unique characteristics related to individual-level socioeconomic and contextual factors based on nativity status and country of origin. Population aging and greater public awareness of dementia may contribute to an increasing prevalence of self-reported cognitive impairment. However, population-level trends in self-reported cognitive impairment among Latinos are unclear and it is unknown whether there are differences among Latino subgroups. Thus, this study aims to examine heterogeneity in self-reported cognitive impairment among older U.S. Latino subgroups. Research design and methods: We used data from the 1997-2018 National Health Interview Survey to document age-specific patterns in self-reported cognitive impairment among U.S.-born Mexican, foreign-born Mexican, island-born Puerto Rican, foreign-born Cuban, and U.S.-born non-Latino Whites aged 60 and older. We estimated hierarchical age-period-cohort cross-classified random effects models (HAPC-CCREM) to isolate age patterns in self-reported cognitive impairment across disaggregated Latino subgroups and U.S.-born non-Latino Whites. Results: The overall prevalence of self-reported cognitive impairment increased from 6.0% in 1997 to 7.1% in 2018. This increase was evident among U.S.-born non-Latino Whites and U.S.-born and foreign-born Mexicans but not other Latino subgroups. Fully adjusted HAPC-CCREM estimates indicated that Latinos were more likely to self-report cognitive impairment than U.S-born non-Latino Whites (b = 0.371, p < .001). When disaggregated by Latino subgroup, the difference in the likelihood for self-reported cognitive impairment compared to U.S.-born non-Latino Whites was greatest for island-born Puerto Ricans (b = 0.598, p < .001) and smallest for foreign-born Cubans (b = 0.131, p > .05). Discussion and implications: We found evidence of considerable heterogeneity in the age patterns of self-reported cognitive impairment among U.S. Latino subgroups. We also detected large differences in the likelihood for self-reported cognitive impairment between U.S. Latino subgroups compared to U.S.-born non-Latino Whites. These results underscore the importance of differentiating between unique Latino subpopulations when studying population-level trends in cognitive function.

Abstract Objectives This study investigates associations between hearing impairment, household composition, marital status, and all-cause mortality for a representative sample of United States adults aged 40 and older (N = 198,902). Methods We use data from 11 waves of the National Health Interview Survey (2004–2014) linked to prospective mortality status through 2015. The risk of mortality over the follow-up period is estimated using Cox proportional hazard models. Results Compared to those with good to excellent hearing, adults with moderate to severe hearing impairments and deaf adults had 11% and 21% higher risk of death from any cause over the follow-up period, respectively. Household composition and marital status, as indicators of household social support systems, associated independently with the risk of mortality but did not substantively change the association between hearing impairment and mortality. Discussion Hearing impairment represents an important contributor to the length of life for adults age 40 and older, independent of other important and established determinants of mortality.

The COVID-19 pandemic is taking a toll. The pressures that mothers of young children (defined throughout as having a child under the age of 13 in the household) have faced over the course of the COVID-19 pandemic are legion. Pressure to stay in the labor market as schools closed and child-care networks collapsed (Madowitz and Boesch 2020). Pressure to facilitate their children’s formal education and provide child care as the economy shut down, the labor market contracted, and families struggled to make ends meet. Pressure to work, care, cook, clean, keep safe, keep afloat. From the outset of the pandemic, mothers faced pressure to “do it all” but with fewer resources and support than before. While inequities persist in many aspects of women’s lives, some of the stickier problems for women stem from the difficult choices they face in reconciling competing demands on their time. Even before the pandemic, caregiving and family responsibilities disproportionately fell on women and on mothers; in 2018, a third of women who reported wanting a job but who were not actively looking for work cited family responsibilities as the reason why (Nunn, Parsons, and Shambaugh 2019). For each American to reach their full potential and for the American economy to grow, it is essential to remove barriers to women’s full and equitable participation in the labor market. But to remove those barriers and support mothers’ economic recovery from the COVID-19 pandemic requires an understanding of the problem. As we celebrate Women’s History Month and as we observe the anniversary of the onset of the COVID-19 recession, we review trends in women’s labor force participation and document how mothers of children under age 13 have changed how they spend their time. In this set of economic facts, we detail some of the ways in which work, time, and caregiving have changed for mothers with young children from before the onset of the COVID-19 pandemic through 2020 until early 2021.

It is well understood that adverse economic shocks affect workers nonuniformly. We explore a new channel through which unequal employment outcomes may emerge during a downturn: displacement through the extensive margin of establishment deaths. Intuitively, workers who are concentrated in less resilient establishments prior to an economic decline will be disproportionately affected by its onset. Using rich administrative employment and establishment data for the United States, we show that black workers bore the brunt of the Great Recession in terms of within-industry employment changes arising from establishment deaths. This finding has important implications for the evolution of worker disparities during future downturns.

We investigate effects of plausibly exogenous variation in the value of oil and natural gas production in local economies on self-employment in the United States. We find that self-employment is procyclical, i.e. self-employment increases during a business cycle expansion and is reduced during a contraction. This effect comes entirely from unincorporated self-employed workers (in lieu of incorporated self-employment). We also find that self-employment explains an economically meaningful share of the employment adjustment; point estimates suggest that approximately 8 to 9 percent of the employment adjustment comes from unincorporated self-employed individuals-a group that makes up about 6 percent of total employment. JEL Classification: J24, L26, M13, Q33, Q35 for their helpful comments and suggestions and Mark Agerton for sharing computer code to compile oil & gas data with the research community. We also thank participants of the 5th Annual CEBRA Workshop for Commodities and Macroeconomics hosted by the Federal Reserve Board, especially Tony Cookson for his constructive comments and suggestions.

Time-use data can often be perceived as inaccessible by non-specialists due to their unique format. This article introduces the ATUS-X diary visualization tool that aims to address the accessibility issue and expand the user base of time-use data by providing users with opportunity to quickly visualize their own subsamples of the American Time Use Survey Data Extractor (ATUS-X). Complementing the ATUS-X, the online tool provides an easy point-and-click interface, making data exploration readily accessible in a visual form. The tool can benefit a wider academic audience, policy-makers, non-academic researchers, and journalists by removing accessibility barriers to time use diaries.

As long as humanity has existed, we have altered our environment to provide goods, services, and (more recently) wealth to people. Over the last several centuries, the scope and pace of this transformation has accelerated with the onset of technological innovation, social and economic reorganization, and an ensuing population boom. Today, humanity’s demands on nature have become the dominant force shaping the critical earth systems upon which all life depends. From local landuse change to the global climate many of these anthropogenic pressures pose an existential threat to nature and the dependent social systems that rely on them. Yet, extreme economic and social inequality within and across human societies leads to significant inequality in who reaps the benefits of these transformations, who reaps the harms, and who makes the decisions on that benefit-harm distribution. Here I quantify, at high granularity and over a 20-year period (1996-2015), the GHG emissions footprints of United States households, based on the flow of income, goods and services these emissions enable. I compare the scale and distributions of xi household-level GHG emissions across different social groups and responsibility frameworks and provide policy insights related to those findings.

Three Covid‑19 vaccines have been approved in the US as of March 1, 2021, and the CDC has established broad guidelines for prioritizing their rollout.9 Front-line health care workers and residents of long-term care facilities were the first priority, but even before this phase was complete, many states and localities started vaccinating other essential workers, the elderly, and others at high risk of severe disease from Covid‑19.10 At the end of 2020, the US had fallen well short of a stated goal to have 20 million people vaccinated, but health departments and providers have started to move past early hurdles and the pace of vaccination exceeded 1.5 million doses administered per day as of late February 2021.

We investigate the relationship between ethnic diversity and the rise of individualism in the United States during the 20th-21st centuries. Tests of the historical rates of ethnic diversity alongside individualistic relational structures (e.g., adults living alone, single/multi-child families) from the years 1950-2018 reveal that societal and regional rates of ethnic diversity accompanied individualistic relational structures. These effects hold above and beyond time series trends in each variable. Further evidence from experimental studies (N = 707) suggest that the presence of, and contact with, ethnically diverse others contributes to greater individualistic values (e.g., the importance of uniqueness and personal achievement). Converging evidence across societal, regional, and individual-level analyses suggests a systematic link between ethnic diversity and individualism. We discuss the implications of these findings for socio-cultural livelihood in light of the rising rates of ethnic diversity across the globe.

We study the U.S. rollout of eligibility expansions in the Supplemental Nutrition Assistance Program and show that expanding eligibility raises enrollment among the inframarginal (always-eligible) population. This evidence motivates a general model of the optimal eligibility threshold for welfare programs with incomplete take-up, where inframarginal take-up responds to that threshold. The optimal threshold depends on the size of the inframarginal response and the extent to which information frictions and stigma influence that response. An online experiment provides evidence that the eligibility threshold affects stigma. Given our empirical results and certain modeling assumptions, the SNAP eligibility threshold is lower than optimal.

As the COVID-19 pandemic pushed firms to comply with social distancing guidelines, the relative demand for work that could be performed from home was expected to increase. However, while employment in “remotable" occupations was relatively resilient during the pandemic, online job postings -which measure demand for new hires- for these occupations dropped disproportionately. This apparent contradiction is not explained by prior job “churning" in “non-remote” jobs, nor by the recomposition of the labor market across economic sectors. The underperformance of postings in “remotable” jobs during the pandemic concentrates in essential occupations and occupations with high returns to experience.

Driving rates decline with age as vision, health, and cognitive ability cause some older adults to give up driving. Many older adults first gradually limit their driving as they age and later cease driving. Using data from the Health and Retirement Study (HRS), which surveys 22,000 older Americans every two years, we modeled the extent to which older drivers limit and stop driving. The data are longitudinal, allowing analysis of changes in driving and residential location as well as cohort effects that could not be studied using standard, cross-sectional survey data that only allow comparisons of different people at one point in time. The analysis shows that decisions to limit and eventually stop driving vary in statistically significant ways with sex, age, and health conditions. These relationships also differ by birth cohort. More recent cohorts are less likely to stop and limit driving than older ones. To analyze the relationship between residential location and driving behavior, we linked the HRS data to census-tract level data from the US Census and a categorization of community types. We found that residential density and other urban built environment features are associated with changes in driving and vehicle ownership. HRS survey participants showed a greater propensity to reduce or give up driving if they resided in denser, more diverse, transit-oriented neighborhoods. People who prefer non-automotive modes of transportation may have been more likely than others to self-select into walkable and transit-rich areas. The findings should inform California’s strategic planning for aging and its community development policies. In addition to informing planning for the next generation of older Californians, this study demonstrated the utility of longitudinal information and models for the understanding of older populations and their travel.

Fall 2021 marks a potential inflection point in the US approach to youth policy. Congress is considering the Build Back Better Act, which proposes major changes across the spectrum of programs for youth from early childhood through early career. The enactment of the bill would represent a paradigm shift in the country’s approach to youth and young adults. This fundamental change would align with the policy direction that our research suggests is needed to address the challenges facing young people today. To fully support youth on the journey from birth to young adulthood, we need to provide inclusive, culturally responsive, and data-informed support and guidance; smooth out transitions on the pathway traversing education and work; and expand opportunity at every juncture on the way from youth to adulthood. Incorporating programs and services in areas as diverse as early childhood education, child nutrition, teacher preparation, college affordability, and workforce training, the bill is the closest the United States has come to acknowledging that supporting the transition from youth to adulthood requires an all-one-system approach. In an all-one-system approach, preschools, elementary and secondary schools, community colleges, four-year universities, employers, and governments would all follow an integrated playbook, helping to smooth out young people’s progress from pre-K–12 to college and work. An all-one-system approach is acutely needed and will require comprehensive change to establish a continuum of support along the entire journey to economic independence.

We estimate long-run trends in intergenerational relative mobility for representative samples of the U.S.-born population. Harmonizing all surveys that include father's occupation and own family income, we develop a mobility measure that allows for the inclusion of non-whites and women for the 1910s–1970s birth cohorts. We show that mobility increases between the 1910s and 1940s cohorts and that the decline of Black-white income gaps explains about half of this rise. We also find that excluding Black Americans, particularly women, considerably overstates the level of mobility for twentieth-century birth cohorts while simultaneously understating its increase between the 1910s and 1940s.

The large regional disparities in joblessness across local labor markets in the U.S has been extensively documented in the literature. However, contrary to what conventional economic theories predict, over the past few decades such disparities appeared to be persistent, if not growing. Therefore, local labor market policies may play a larger role in state and local governments’ policy making. In this thesis, I examine the effectiveness of two policy tools commonly adopted by local governments to promote job creation.

We document two robust features of the cross-sectional distribution of usual weekly hours and hourly wages. First, usual weekly hours are heavily concentrated around 40 hours, while at the same time a substantial share of total hours come from individuals who work more than 50 hours. Second, mean hourly wages are non-monotonic across the usual hours distribution, with a peak at 50 hours. We develop and estimate a model of labor supply to account for these features. The novel feature of our model is that earnings are non-linear in hours, with the extent of nonlinearity varying over the hours distribution. Our estimates imply significant wage penalties for individuals that deviate from 40 hours in either direction, leading to a large mass of individuals that work 40 hours and are not very responsive to shocks. This has important implications for the role of labor supply as a mechanism for self-insurance in a standard heterogeneous agent-incomplete markets model and for empirical strategies designed to estimate labor supply parameters.

Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.

Data on COVID-19 cases by town show that the post-holidays surge is here. Daily new cases for all age groups have also spiked following Christmas and New Year’s Eve. Hospitalizations have remained relatively flat since Thanksgiving, but there is no reason to believe that infection rates will return to summertime levels. In fact, Connecticut appears to be caught in a cycle of community transmission that may not slow down for months.