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Experimental Capture/recapture Estimation Using Census and Administrative Data
June 2026
Working Paper Number:
CES-26-38
This report expands upon the innovation of utilizing administrative records and third-party data implemented in the 2020 Census. The 2020 Census used administrative records and third-party data in address canvassing and nonresponse followup operations. The Census Bureau also has a long history of using administrative records of births, deaths, and other information to produce Demographic Analysis coverage estimates. Since 1980, the Census Bureau has produced capture-recapture coverage estimates by conducting an independent post-enumeration survey and utilizing dual system estimation approaches. This report presents the research results of attempting to see if administrative records and third-party data could be utilized to produce capture-recapture coverage estimates. This work uses an Expectation Maximization Log Linear Modeling approach previously researched by Statistics Netherlands and Statistics New Zealand. This report documents some of the experimental results from an evaluation that was part of the 2020 Census Program for Evaluation, Experiments, and Assessments.
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Flood Risk, Insurance, and Housing in the United States
June 2026
Working Paper Number:
CES-26-37
Flooding is among the most salient natural hazards facing households in the United States. A large body of evidence has documented a pattern of disproportionate social vulnerability in floodplains. However, little evidence exists on how household-level exposure to flood risk is distributed. We fill this gap by combining parcel-level flood risk with confidential linked survey and administrative data held at the US Census Bureau. Although net migration to Census blocks in floodplains has increased in recent years, there has been essentially no net migration to parcels with flood risk or change in the overall share of households living in floodplains. Income gradients in flood risk are highly non-linear at the household level, with slightly negative income gradients for the bottom 90 percentiles of the income distribution that are dwarfed by disproportionate exposure in the top decile, especially when considering multiple property ownership. This nonlinearity is largely driven by differences in building type and homeownership within narrow income groups. In contrast to the conclusions in the literature using aggregate data, our household-level analysis suggests that households in floodplains are less disadvantaged and increasingly protected from the impacts of flooding, even as a vulnerable subpopulation of low-income, uninsured homeowners remains.
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New U.S. Business Establishments: Surging or Stalling?
June 2026
Working Paper Number:
CES-26-36
Since the 1990s, the Bureau of Labor Statistics (BLS) has reported much more rapid growth in U.S. private sector employer establishments than has the Census Bureau' the gap reached roughly 1.6 million by 2023. Using linked BLS-Census microdata, we document two main drivers. First, a large and growing number of employers providing services to the elderly and persons with disabilities are in scope for the BLS frame but not the Census Bureau's. Second, many firms appear with substantially more establishments in the BLS frame. These discrepancies substantially affect the measured establishment size distribution and quantitative policy analysis.
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Employment and Earnings Trajectories of HUD Program Participants
May 2026
Working Paper Number:
CES-26-31
Federal housing assistance programs, such as those run by the U.S. Department of Housing and Urban Development (HUD), have been shown to reduce rent burden and improve housing stability for program participants, which may in turn have downstream impacts on their labor market attachment and career trajectories. However, existing studies from individual cities or states provide mixed evidence on the association of housing assistance with labor market outcomes. By linking HUD administrative records to matched employee-employer earnings records from the Longitudinal Employer-Household Dynamics (LEHD) program, we document how the labor market trajectories of program participants change as they enter and exit federal housing assistance programs, examining outcomes over a 14-year window surrounding entry or exit. In our analysis of entry, we find that the employment rates and earnings of first-time HUD program participants begin to increase upon entering a HUD program, which represents a reversal of prior declining trends in these outcomes. Suggestive of a positive association, these increases in employment and earnings trends exceed those of low-income non-participants from the American Community Survey (ACS). In our analysis of exits, we find that program participants who eventually leave a HUD program have increasing pre-exit trends in employment and earnings that then flatten upon exiting. Comparing these negative changes in trend to the relatively stable trajectories of those who remain in HUD programs throughout the analysis suggests that exits are associated with diminished employment and earnings trajectories.
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The Role of Homophily in Response to Labor Market Opportunities: Differences Across Race and Ethnicity
March 2026
Working Paper Number:
CES-26-22
This paper investigates the role that homophily might play in explaining racial/ethnic disparities in the labor market. We find that Black and Hispanic workers are less responsive than White workers to changes in job opportunities, but responsiveness increases when those opportunities present themselves in locations with a higher share own-race population. The analysis makes use of restricted American Community Survey data, accessible through the Federal Statistical Research Data Centers, allowing us to include commuting zones that may otherwise not be identified because of suppressed location information in the public data
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Integrating Multiple U.S. Census Bureau Data Assets to Create Standardized Profiles of Program Participants
January 2026
Working Paper Number:
CES-26-01
The Foundations for Evidence-Based Policymaking Act of 2018 (Evidence Act) directed federal agencies to systematically use data when making policy decisions. In response, the U.S. Census Bureau established the Evidence Group within its Center for Economic Studies (CES). With an interdisciplinary team of economists, sociologists, and statisticians, the Evidence Group can support the broader federal government in their efforts to use existing data to improve program operations without increasing respondent burden. For federal agencies administering social safety net and business assistance programs in particular, the team provides a no-cost evidence-building service that links program records to Census Bureau data assets and creates a series of standardized tables describing participants, their economic outcomes prior to program entry, and the communities where they live. These tables provide partner agencies with the detailed information they need to better understand their participants and potentially make their programs more accountable and effective in reaching their target populations. In this working paper, we describe the standardized tables themselves as well as the data assets available at the Census Bureau to create these tables, the data files produced by the table production process, and the methodology used to merge and harmonize data on participants and subsequently calculate unbiased and accurate estimates. We conclude with a brief discussion of steps taken to ensure confidentiality and data security. This documentation is intended to facilitate proper use and understanding of the standardized tables by partner agencies as well as researchers who are interested in leveraging these tools to explore characteristics of their samples of interest.
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School-Based Disability Identification Varies by Student Family Income
December 2025
Authors:
Quentin Brummet,
Andrew Penner,
Emily Penner,
Leah R. Clark,
Michelle Spiegel,
Paul Y. Yoo,
Paul Hanselman,
Nicholas J. Ainsworth,
Christopher Cleveland,
Jacob Hibel,
Andrew Saultz,
Juan Camilo Cristancho
Working Paper Number:
CES-25-74
Currently, 18 percent of K-12 students in the United States receive additional supports through the identification of a disability. Socioeconomic status is viewed as central to understanding who gets identified as having a disability, yet limited large-scale evidence examines how disability identification varies for students from different income backgrounds. Using unique data linking information on Oregon students and their family income, we document pronounced income-based differences in how students are categorized for two school-based disability supports: special education services and Section 504 plans. We find that a quarter of students in the lowest income percentile receive supports through special education, compared with less than seven percent of students in the top income percentile. This pattern may partially reflect differences in underlying disability-related needs caused by poverty. However, we find the opposite pattern for 504 plans, where students in the top income percentiles are two times more likely to receive 504 plan supports. We further document substantial variation in these income-based differences by disability category, by race/ethnicity, and by grade level. Together, these patterns suggest that disability-related needs alone cannot account for the income-based differences that we observe and highlight the complex ways that income shapes the school and family processes that lead to variability in disability classification and services.
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Gifted Identification Across the Distribution of Family Income
December 2025
Authors:
Quentin Brummet,
Andrew Penner,
Emily Penner,
Leah R. Clark,
Michelle Spiegel,
Paul Hanselman,
Nicholas J. Ainsworth,
Aaron J. Ainsworth,
Christopher Cleveland,
Jacob Hibel,
Andrew Saultz
Working Paper Number:
CES-25-73
Currently, 6.1 percent of K-12 students in the United States receive gifted education. Using education and IRS data that provide information on students and their family income, we show pronounced differences in who schools identify as gifted across the distribution of family income. Under 4 percent of students in the lowest income percentile are identified as gifted, compared with 20 percent of those in the top income percentile. Income-based differences persist after accounting for student test scores and exist across students of different sexes and racial/ethnic groups, underscoring the importance of family resources for gifted identification in schools.
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The Hidden Costs of Decline: Health Disparities in America's Diminishing Micropolitan Areas
September 2025
Working Paper Number:
CES-25-70
This study examines the relationship between long-term population change and health outcomes in U.S. micropolitan areas, with a focus on life expectancy and mortality disparities. Using a county typology based on the historical population trajectories of micropolitan cores from 1940 to 2020, this analysis reveals that health outcomes are substantially worse in places that experienced sustained decline. These disparities persist even after controlling for demographic and socioeconomic characteristics, suggesting that population loss itself is a key driver of poor public health. Declining micropolitan areas are older, less educated, and report high rates of behavioral risk factors, including smoking, excessive drinking, and physical inactivity. By linking historical demographic trends to tract-level data, this analysis highlights the distinct challenges facing the urban cores of shrinking micropolitan areas. Population decline emerges not only as a demographic trend, but as a marker of structural disadvantage with measurable consequences for community health.
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Optimal Stratified Sampling for Probability-Based Online Panels
September 2025
Working Paper Number:
CES-25-69
Online probability-based panels have emerged as a cost-efficient means of conducting surveys in the 21st century. While there have been various recent advancements in sampling techniques for online panels, several critical aspects of sampling theory for online panels are lacking. Much of current sampling theory from the middle of the 20th century, when response rates were high, and online panels did not exist. This paper presents a mathematical model of stratified sampling for online panels that takes into account historical response rates and survey costs. Through some simplifying assumptions, the model shows that the optimal sample allocation for online panels can largely resemble the solution for a cross-sectional survey. To apply the model, I use the Census Household Panel to show how this method could improve the average precision of key estimates. Holding fielding costs constant, the new sample rates improve the average precision of estimates between 1.47 and 17.25 percent, depending on the importance weight given to an overall population mean compared to mean estimates for racial and ethnic subgroups.
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