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Understanding Criminal Record Penalties in the Labor Market
June 2025
Working Paper Number:
CES-25-39
This paper studies the earnings and employment penalties associated with a criminal record. Using a large-scale dataset linking criminal justice and employer-employee wage records, we estimate two-way fixed effects models that decompose earnings into worker's portable earnings potential and firm pay premia, both of which are allowed to shift after a worker acquires a record. We find that firm pay premia explain a small share of earnings gaps between workers with and without a record. There is little evidence of variable within-firm premia gaps either. Instead, components of workers' earnings potential that persist across firms explain the bulk of gaps. Conditional on earnings potential, workers with a record are also substantially less likely to be employed. Difference-in-differences estimates comparing workers' first conviction to workers charged but not convicted or charged later support these findings. The results suggest that criminal record penalties operate primarily by changing whether workers are employed and their earnings potential at every firm rather than increasing sorting into lower-paying jobs, although the bulk of gaps can be attributed to differences that existed prior to acquiring a record.
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Consequences of Eviction for Parenting and Non-parenting College Students
June 2025
Working Paper Number:
CES-25-35
Amidst rising and increasingly unaffordable rents, 7.6 million people are threatened with eviction each year across the United States'and eviction rates are twice as high for renters with children. One important and neglected population who may experience unique levels of housing insecurity is college students, especially given that one in five college students are parents. In this study, we link 11.9 million student records to eviction filings from housing courts, demographic characteristics reported in decennial census and survey data, incomes reported on tax returns by students and their parents, and dates of birth and death from the Social Security Administration. Parenting students are more likely than non-parenting students to identify as female (62.81% vs. 55.94%) and Black (19.66% vs. 14.30%), be over 30 years old (42.73% vs. 20.25%), and have parents with lower household incomes ($100,000 vs. $140,000). Parenting students threatened with eviction (i.e., had an eviction filed against them) are much more likely than non-threatened parenting students to identify as female (81.18% vs. 62.81%) and Black (56.84% vs. 19.66%). In models adjusted for individual and institutional characteristics, we find that being threatened with an eviction was significantly associated with reduced likelihood of degree completion, reduced post-enrollment income, reduced likelihood of being married post-enrollment, and increased post-enrollment mortality. Among parenting students, 38.38% (95% confidence interval (CI): 32.50-44.26%) of non-threatened students completed a bachelor's degree compared to just 15.36% (CI: 11.61-19.11%) of students threatened with eviction. Our findings highlight the long-term economic and health impacts of housing insecurity during college, especially for parenting students. Housing stability for parenting students may have substantial multigenerational benefits for economic mobility and population health.
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The Composition of Firm Workforces from 2006'2022: Findings from the Business Dynamics Statistics of Human Capital Experimental Product
April 2025
Working Paper Number:
CES-25-20
We introduce the Business Dynamics Statistics of Human Capital (BDS-HC) tables, a new Census Bureau experimental product that provides public-use statistics on the workforce composition of firms and its relationship to business dynamics. We use administrative W-2 filings to combine population-level worker demographic data with longitudinal business data to estimate the demographic and educational composition of nearly all non-farm employer businesses in the United States between 2006 and 2022. We use this newly constructed data to document the evolution of employment, entry, and exit of employers based on their workforce compositions. We also provide new statistics on the interaction between firm and worker characteristics, including the composition of workers at startup firms. We find substantial changes between 2006 and 2022 in the distribution of employers along several dimensions, primarily driven by changing workforce compositions within continuing firms rather than the reallocation of employment between firms. We also highlight systematic differences in the business dynamics of firms by their workforce compositions, suggesting that different groups of workers face different economic environments due to their employers.
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Peer Income Exposure Across the Income Distribution
February 2025
Working Paper Number:
CES-25-16
Children from families across the income distribution attend public schools, making schools and classrooms potential sites for interaction between more- and less-affluent children. However, limited information exists regarding the extent of economic integration in these contexts. We merge educational administrative data from Oregon with measures of family income derived from IRS records to document student exposure to economically diverse school and classroom peers. Our findings indicate that affluent children in public schools are relatively isolated from their less affluent peers, while low- and middle-income students experience relatively even peer income distributions. Students from families in the top percentile of the income distribution attend schools where 20 percent of their peers, on average, come from the top five income percentiles. A large majority of the differences in peer exposure that we observe arise from the sorting of students across schools; sorting across classrooms within schools plays a substantially smaller role.
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The Design of Sampling Strata for the National Household Food Acquisition and Purchase Survey
February 2025
Working Paper Number:
CES-25-13
The National Household Food Acquisition and Purchase Survey (FoodAPS), sponsored by the United States Department of Agriculture's (USDA) Economic Research Service (ERS) and Food and Nutrition Service (FNS), examines the food purchasing behavior of various subgroups of the U.S. population. These subgroups include participants in the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), as well as households who are eligible for but don't participate in these programs. Participants in these social protection programs constitute small proportions of the U.S. population; obtaining an adequate number of such participants in a survey would be challenging absent stratified sampling to target SNAP and WIC participating households. This document describes how the U.S. Census Bureau (which is planning to conduct future versions of the FoodAPS survey on behalf of USDA) created sampling strata to flag the FoodAPS targeted subpopulations using machine learning applications in linked survey and administrative data. We describe the data, modeling techniques, and how well the sampling flags target low-income households and households receiving WIC and SNAP benefits. We additionally situate these efforts in the nascent literature on the use of big data and machine learning for the improvement of survey efficiency.
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Measuring the Business Dynamics of Firms that Received Pandemic Relief Funding: Findings from a New Experimental BDS Data Product
January 2025
Working Paper Number:
CES-25-05
This paper describes a new experimental data product from the U.S. Census Bureau's Center for Economic Studies: the Business Dynamics Statistics (BDS) of firms that received Small Business Administration (SBA) pandemic funding. This new product, BDS-SBA COVID, expands the set of currently published BDS tables by linking loan-level program participation data from SBA to internal business microdata at the U.S. Census Bureau. The linked programs include the Paycheck Protection Program (PPP), COVID Economic Injury Disaster Loans (COVID-EIDL), the Restaurant Revitalization Fund (RRF), and Shuttered Venue Operators Grants (SVOG). Using these linked data, we tabulate annual firm and establishment counts, measures of job creation and destruction, and establishment entry and exit for recipients and non-recipients of program funds in 2020-2021. We further stratify the tables by timing of loan receipt and loan size, and business characteristics including geography, industry sector, firm size, and firm age. We find that for the youngest firms that received PPP, the timing of receipt mattered. Receiving an early loan correlated with a lower job destruction rate compared to non-recipients and businesses that received a later loan. For the smallest firms, simply participating in PPP was associated with lower employment loss. The timing of PPP receipt was also related to establishment exit rates. For businesses of nearly all ages, those that received an early loan exited at a lower rate in 2022 than later loan recipients.
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Potential Bias When Using Administrative Data to Measure the Family Income of School-Aged Children
January 2025
Working Paper Number:
CES-25-03
Researchers and practitioners increasingly rely on administrative data sources to measure family income. However, administrative data sources are often incomplete in their coverage of the population, giving rise to potential bias in family income measures, particularly if coverage deficiencies are not well understood. We focus on the school-aged child population, due to its particular import to research and policy, and because of the unique challenges of linking children to family income information. We find that two of the most significant administrative sources of family income information that permit linking of children and parents'IRS Form 1040 and SNAP participation records'usefully complement each other, potentially reducing coverage bias when used together. In a case study considering how best to measure economic disadvantage rates in the public school student population, we demonstrate the sensitivity of family income statistics to assumptions about individuals who do not appear in administrative data sources.
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Places versus People: The Ins and Outs of Labor Market Adjustment to Globalization
December 2024
Working Paper Number:
CES-24-78
We analyze the distinct adjustment paths of U.S. labor markets (places) and U.S. workers (people) to increased Chinese import competition during the 2000s. Using comprehensive register data for 2000'2019, we document that employment levels more than fully rebound in trade-exposed places after 2010, while employment-to-population ratios remain depressed and manufacturing employment further atrophies. The adjustment of places to trade shocks is generational: affected areas recover primarily by adding workers to non-manufacturing who were below working age when the shock occurred. Entrants are disproportionately native-born Hispanics, foreign-born immigrants, women, and the college-educated, who find employment in relatively low-wage service sectors like medical services, education, retail, and hospitality. Using the panel structure of the employer-employee data, we decompose changes in the employment composition of places into trade-induced shifts in the gross flows of people across sectors, locations, and non-employment status. Contrary to standard models, trade shocks reduce geographic mobility, with both in- and out-migration remaining depressed through 2019. The employment recovery instead stems almost entirely from young adults and foreign-born immigrants taking their first U.S. jobs in affected areas, with minimal contributions from cross-sector transitions of former manufacturing workers. Although worker inflows into non-manufacturing more than fully offset manufacturing employment losses in trade-exposed locations after 2010, incumbent workers neither fully recover earnings losses nor predominately exit the labor market, but rather age in place as communities undergo rapid demographic and industrial transitions.
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CTC and ACTC Participation Results and IRS-Census Match Methodology, Tax Year 2020
December 2024
Working Paper Number:
CES-24-76
The Child Tax Credit (CTC) and Additional Child Tax Credit (ACTC) offer assistance to help ease the financial burden of families with children. This paper provides taxpayer and dollar participation estimates for the CTC and ACTC covering tax year 2020. The estimates derive from an approach that relies on linking the 2021 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) to IRS administrative data. This approach, called the Exact Match, uses survey data to identify CTC/ACTC eligible taxpayers and IRS administrative data to indicate which eligible taxpayers claimed and received the credit. Overall in tax year 2020, eligible taxpayers participated in the CTC and ACTC program at a rate of 93 percent while dollar participation was 91 percent.
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EITC Participation Results and IRS-Census Match Methodology, Tax Year 2021
December 2024
Working Paper Number:
CES-24-75
The Earned Income Tax Credit (EITC), enacted in 1975, offers a refundable tax credit to low income working families. This paper provides taxpayer and dollar participation estimates for the EITC covering tax year 2021. The estimates derive from an approach that relies on linking the 2022 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) to IRS administrative data. This approach, called the Exact Match, uses survey data to identify EITC eligible taxpayers and IRS administrative data to indicate which eligible taxpayers claimed and received the credit. Overall in tax year 2021 eligible taxpayers participated in the EITC program at a rate of 78 percent while dollar participation was 81 percent.
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