Measuring change over time in areas such as family structure, employment, income, and poverty is of great interest to social scientists. The panel component of the Current Population Survey (CPS) affords the opportunity to observe short-term change in these areas. The Annual Social and Economic supplement (ASEC), with its wealth of information on income, health insurance coverage, benefits receipt, and many other topics, is a particularly popular resource for this purpose. However, commonly used methods for linking CPS ASEC files do not address how to link the ASEC oversample records across years, leading to smaller linked sample sizes. We demonstrate how to recover the linkable oversample cases in the 2005-2020 ASEC, resulting in about 150,000 more linked records (between 13,000 and 19,000 yearly) which represents a 30% increase in the overall linked sample size.

Emerging evidence suggests that the COVID-19 pandemic has extracted a substantial toll on immigrant communities in the United States, due in part to increased potential risk of exposure for immigrants to COVID-19 in the workplace. In this article, we use federal guidance on which industries in the United States were designated essential during the COVID-19 pandemic, information about the ability to work remotely, and data from the 2019 American Community Survey to estimate the distribution of essential frontline workers by nativity and immigrant legal status. Central to our analysis is a proxy measure of working in the primary or secondary sector of the segmented labor market. Our results indicate that a larger proportion of foreign-born workers are essential frontline workers compared to native-born workers and that 70 percent of unauthorized immigrant workers are essential frontline workers. Disparities in essential frontline worker status are most pronounced for unauthorized immigrant workers and native-born workers in the secondary sector of the labor market. These results suggest that larger proportions of foreign-born workers, and especially unauthorized immigrant workers, face greater risk of potential exposure to COVID-19 in the workplace than native-born workers. Social determinants of health such as lack of access to health insurance and living in overcrowded housing indicate that unauthorized immigrant essential frontline workers may be more vulnerable to poor health outcomes related to COVID-19 than other groups of essential frontline workers. These findings help to provide a plausible explanation for why COVID-19 mortality rates for immigrants are higher than mortality rates for native-born residents.

This paper offers estimates of US foreign-born populations that are eligible for special legal status programs and those that would be eligible for permanent residence (legalization) under pending bills. It seeks to provide policymakers, government agencies, community-based organizations (CBOs), researchers, and others with a unique tool to assess the potential impact, implement, and analyze the success of these programs. It views timely, comprehensive data on targeted immigrant populations as an essential pillar of legalization preparedness, implementation, and evaluation. The paper and the exhaustive estimates that underlie it, represent the first attempt to provide a detailed statistical profile of beneficiaries of proposed major US legalization programs and special, large-scale legal status programs. The paper offers the following top-line findings: Fifty-eight percent of the 10.35 million US undocumented residents had lived in the United States for 10 years or more as of 2019; 37 percent lived in homes with mortgages; 33 percent arrived at age 17 or younger; 32 percent lived in households with US citizens (the overwhelming majority of them children); and 96 percent in the labor force were employed. The Citizenship for Essential Workers Act would establish the largest population-specific legalization program discussed in the paper. 7.2 million (70 percent) of the total undocumented population would be eligible for legalization under the Act. Approximately two-thirds of undocumented essential workers reside in 20 metropolitan areas. The populations eligible for the original Deferred Action for Childhood Arrivals (DACA) program and for permanent residence on a conditional basis and removal of the conditions on permanent residence under the Dream Act of 2021 are not only ready to integrate successfully, but in most cases have already done so. A high percentage are long-term residents, virtually all have completed high school (or attend school), a third to one-half have attended college, and the overwhelming majority live in households with incomes above the poverty level. The median household income of California, Illinois, New York, and New Jersey residents that are eligible for the original DACA program is higher than the US median household income. New York and New Jersey residents that are eligible for removal of conditions on permanent residence under the Dream Act of 2021 also have median incomes above the US median household income. The total eligible for removal of conditions on permanent residence under the Dream Act of 2021 have median household incomes that are 99 percent of the US median income. Unlike populations eligible for most special legal status and population-specific legalization programs, childhood arrivals can be found in significant numbers and concentrations in communities throughout the United States, particularly in metropolitan areas. More than 1.8 million persons from El Salvador, Guatemala, and Honduras would be eligible for TPS if the Secretary of the Department of Homeland Security (DHS) designated Guatemala for TPS and re-designated El Salvador and Honduras. Local communities can best prepare for legalization by collaborating on: (1) the hard work of assisting individual immigrants to meet their immigration needs; (2) dividing labor, integrating services, screening the undocumented for status, and building legal capacity; and (3) implementation of special legal status programs. This collective work should be viewed as a legalization program in its own right. The populations eligible for legalization and legal status under the programs analyzed in the paper have overlapping needs and large numbers of immigrants would be eligible for more than one program. However, substantial differences between these populations in size, geography, length of residency, education, socio-economic attainment, and English language proficiency argue for distinct preparedness and implementation strategies for each population. The paper also makes several broad policy recommendations regarding legalization bills, special legal status programs, and community-based preparedness and implementation efforts. In particular, it recommends that: Congress should pass broad immigration reform legislation that includes a general legalization program or, in the alternative, a series of population-specific programs for essential workers, childhood arrivals, agricultural workers, persons eligible for Temporary Protected Status (TPS) and Deferred Enforced Departure (DED), and long-term residents. In the interim, the Biden administration should also designate and re-designate additional countries for TPS. Immigration reform legislation should allow the great majority of US undocumented residents to legalize, should reform the underlying legal immigration system, and should provide for the legalization of future long-term undocumented residents through a rolling registry program. Congress, the relevant federal agencies, and advocates should ensure that any legalization program be properly structured and sufficiently funded, particularly the work of CBOs, states, and localities. Local communities should continue to build the necessary partnerships, capacities, skills, and resources to implement a legalization program. They should do so, in part, by collaborating on special legal status programs such as DACA, TPS, and naturalization campaigns, as well as through the steady-state work of assisting immigrants in their individual immigration cases and funding their representation as necessary in removal proceedings. Section I of the paper describes the populations that would be eligible for legalization under pending bills and that are potentially eligible for special legal status programs. Section II presents top-line findings based on the Center for Migration Studies’ (CMS’s) estimates and profiles of these populations. The report offers estimates of each population by characteristics — such as length of time in the country, English language proficiency, education, household income, health insurance, and homeownership — that are relevant to preparedness and implementation activities. Section III makes the case for immigration reform and a broad legalization program. Section IV offers detailed recommendations on the substance, structure, and implementation of these programs.

Poverty scholarship in the United States is increasingly reliant upon the Supplemental Poverty Measure (SPM) as opposed to the Official Poverty Measure of the United States for research and policy analysis. However, the SPM still faces several critiques from scholars focused on poverty in nonmetropolitan areas. Key among these critiques is the geographic adjustment for cost of living employed in the SPM, which is based solely upon median rental costs and pools together all nonmetropolitan counties within each state. Here, we evaluate the current geographic adjustment of the SPM using both microdata and aggregate data from the American Community Survey for 2014-2018. By comparing housing costs, tenure, and commuting, we determine that median rent is likely an appropriate basis for geographic adjustment. However, by demonstrating the wide variability between median rents of nonmetropolitan counties within the same state, we show that the current operationalization of this geographic adjustment could be improved through the use of more-specific categories such as metropolitan adjacency or Rural Urban Continuum Codes.

The Current Population Survey (CPS) has been the nation's primary source of information about employment and unemployment for decades. The data are widely used by social scientists and policy makers to study labor force participation, poverty, and other high-priority topics. An underutilized feature of the CPS is its short-run panel component. This paper discusses the unique challenges encountered when linking basic monthly data as well as when linking the March basic monthly data to the Annual Social and Economic (ASEC) Supplement in the 1976-1988 period. We describe strategies to address linking obstacles and document linkage rates.

Microdata from U.S. decennial censuses and the American Community Survey are a key resource for social science and policy analysis, enabling researchers to investigate relationships among all reported characteristics for individual respondents and their households. To protect privacy, the Census Bureau restricts the detail of geographic information in public use microdata, and this complicates how researchers can investigate and account for variations across levels of urbanization when analyzing microdata. One option is to focus on metropolitan status, which can be determined exactly for most microdata records and approximated for others, but a binary metro/nonmetro classification is still coarse and limited on its own, emphasizing one aspect of rural-urban variation and discounting others. To address these issues, we compute two continuous indices for public use microdata-average tract density and average metro/micro-area population-using population-weighted geometric means. We show how these indices correspond to two key dimensions of urbanization-concentration and size-and we demonstrate their utility through an examination of disparities in poverty throughout the rural-urban universe. Poverty rates vary across settlement types in nonlinear ways: rates are lowest in moderately dense parts of major metro areas, and rates are higher in both low- and high-density areas, as well as in smaller commuting systems. Using the two indices also reveals that correlations between poverty and demographic characteristics vary considerably across settlement types. Both indices are now available for recent census microdata via IPUMS USA (https://usa.ipums.org).

Poverty is a key indicator of economic hardship. By providing a geographic adjustment for cost of living, the recently developed Supplemental Poverty Measure has upended long-held views that poverty is higher in rural compared to urban America. In this study, we unpack the geographic adjustment underlying the Supplemental Poverty Measure and find that most of the difference is explained by the median rent index component rather than the housing tenure component. Further, we find that six states (Alabama, Georgia, Kentucky, Mississippi, North Carolina, and Ohio) account for over a third of the nonmetro poverty drop due to the median rent adjustment. We demonstrate that the demographic composition of the rural poor remains relatively unaffected despite this large drop, but find that the prevalence of poverty within demographic groups varies considerably. As a result, while the SPM is largely considered a more complete measure of poverty, the use of the median rent adjustment has important implications for the demographic understanding of rural poverty.

The Annual Social and Economic Supplement (ASEC) is the most widely used type of Current Population Survey (CPS) data, but it is cumbersome to use the ASEC as part of a longitudinal CPS panel, especially linking to non-March months. In this paper, we detail the challenges associated with linking the ASEC to monthly CPS data, outline the creation of an identifier that links the ASEC and the March Basic Monthly data from 1989 through 2017, and provide substantive examples that illustrate the value of combining the ASEC with monthly data. The variable, MARBASECID, which we created to link ASEC and March monthly CPS data, represents a significant contribution to social and economic data infrastructure, saving individual researchers from having to duplicate the effort required to create linkages between ASEC and monthly CPS data.

This paper develops the first evidence on how individuals' union membership status affects their net fiscal impact, the difference between taxes they pay and cost of public benefits they receive, enriching our understanding of how labor relations interacts with public economics. Current Population Survey data between 1994 and 2015 in pooled cross-sections and individual first-difference models yield evidence that union membership has a positive net fiscal impact through the worker-level channels studied.

Gary McDowell, Misty Heggeness and colleagues present census data showing how the biomedical workforce is fundamentally different to those of past generations – academia should study the trends, and adapt.

The U.S. biomedical scientific enterprise has a long, deep history of innovation, global leadership, and scientific advancements that have improved the health and wellbeing of humankind. Biomedical scientific careers ebb and flow with funding availability, and funding drives this workforce in terms of size and structure. The stakes are high for becoming a scientist because large amounts of time and capital investments are required to be competitive in this profession and because the labor market is heavily dependent on the availability of external funding. Ideally, students and postdocs will make career decisions based on market data regarding the potential for future advancement and career expectations. Young scientists are struggling today more than they ever have in finding productive tenure-track academic employment. The landscape has drastically changed as numerous young scientists take alternative jobs in industry and government, some of them even choosing these jobs over academia. Here we use historical census data to analyze the size and shape of the U.S. biomedical workforce. We plan to use this data to empower early career scientists and the senior investigators who mentor them with information about today’s labor market that they can use to help young scientists make informed decisions about their career paths.