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Housing Capital and Intergenerational Mobility in the United States
August 2025
Working Paper Number:
CES-25-55
Housing represents the most important capital asset for most U.S. families. Despite substantial analysis of the intergenerational mobility of income, large gaps in our knowledge of the distribution of housing assets and their transmission over time remain, as housing is generally not reflected by income flows. Using novel linked data that combines survey responses with administrative tax data and information on ownership and valuation from property tax records for over 3.4 million families, we provide new evidence on the intergenerational transmission of housing capital. We find that housing capital is more persistent across generations than labor income. We document important disparities between average housing outcomes for White and Black children. These difference persist even conditional on parent rank in the distribution of housing assets, with the gap growing throughout the parental housing capital distribution. A decomposition shows that average differences in children's labor market outcomes associated with parental assets explain about half of the observed intergenerational persistence (a 'labor income channel'), and that there is also a substantial 'direct channel' ' conditional on children having the same earnings, children of parents with more housing assets have more assets themselves on average. The direct channel is also important for explaining the intergenerational gap in outcomes of Black and White children. Finally, we present quasi-experimental evidence that local housing supply constraints help explain spatial differences in intergenerational persistence across US counties. Our results establish the importance of housing markets, both independently from and jointly with labor markets, in shaping the intergenerational persistence of economic resources.
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Differences in Disability Insurance Allowance Rates
August 2025
Working Paper Number:
CES-25-54
Allowance rates for disability insurance applications vary by race and ethnicity, but it is unclear to what extent these differences are artifacts of other differing socio-economic and health characteristics, or selection issues in SSA's race and ethnicity data. This paper uses the 2015 American Community Survey linked to 2015-2019 SSA administrative data to investigate DI application allowance rates among non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic American Indian/Alaska Native, and Hispanic applicants aged 25-65. The analysis uses regression, propensity score matching, and inverse probability weighting to estimate differences in allowance rates among applicants who are similar on observable characteristics. Relative to raw comparisons, differences by race and ethnicity in multivariate analyses are substantially smaller in magnitude and are generally not statistically significant.
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Earnings Measurement Error, Nonresponse and Administrative Mismatch in the CPS
July 2025
Working Paper Number:
CES-25-48
Using the Current Population Survey Annual Social and Economic Supplement matched to Social Security Administration Detailed Earnings Records, we link observations across consecutive years to investigate a relationship between item nonresponse and measurement error in the earnings questions. Linking individuals across consecutive years allows us to observe switching from response to nonresponse and vice versa. We estimate OLS, IV, and finite mixture models that allow for various assumptions separately for men and women. We find that those who respond in both years of the survey exhibit less measurement error than those who respond in one year. Our findings suggest a trade-off between survey response and data quality that should be considered by survey designers, data collectors, and data users.
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Credit Access in the United States
July 2025
Working Paper Number:
CES-25-45
We construct new population-level linked administrative data to study households' access to credit in the United States. These data reveal large differences in credit access by race, class, and hometown. By age 25, Black individuals, those who grew up in low-income families, and those who grew up in certain areas (including the Southeast and Appalachia) have significantly lower credit scores than other groups. Consistent with lower scores generating credit constraints, these individuals have smaller balances, more credit inquiries, higher credit card utilization rates, and greater use of alternative higher-cost forms of credit. Tests for alternative definitions of algorithmic bias in credit scores yield results in opposite directions. From a calibration perspective, group-level differences in credit scores understate differences in delinquency: conditional on a given credit score, Black individuals and those from low-income families fall delinquent at relatively higher rates. From a balance perspective, these groups receive lower credit scores even when comparing those with the same future repayment behavior. Addressing both of these biases and expanding credit access to groups with lower credit scores requires addressing group-level differences in delinquency rates. These delinquencies emerge soon after individuals access credit in their early twenties, often due to missed payments on credit cards, student loans, and other bills. Comprehensive measures of individuals' income profiles, income volatility, and observed wealth explain only a small portion of these repayment gaps. In contrast, we find that the large variation in repayment across hometowns mostly reflects the causal effect of childhood exposure to these places. Places that promote upward income mobility also promote repayment and expand credit access even conditional on income, suggesting that common place-level factors may drive behaviors in both credit and labor markets. We discuss suggestive evidence for several mechanisms that drive our results, including the role of social and cultural capital. We conclude that gaps in credit access by race, class, and hometown have roots in childhood environments.
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Dynamics of High-Growth Young Firms and the Role of Venture Capitalists
June 2025
Working Paper Number:
CES-25-38
Motivated by the substantial growth and upfront investments of venture capital (VC) backed firms observed in administrative US Census data, this paper develops a firm dynamics model over the life cycle. In the model, startups choose the source of financing from VC, Angel investors, or banks, depending on their growth potential, and invest in innovation. The calibrated model explains the life-cycle dynamics of firms with different sources of financing and implies that venture capitalists' advice accounts for around 22% of the growth of VC-backed firms. A counterfactual economy without VC financing would lose aggregate consumption by around 0.4%.
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Private Equity and Workers: Modeling and Measuring Monopsony, Implicit Contracts, and Efficient Reallocation
June 2025
Working Paper Number:
CES-25-37
We measure the real effects of private equity buyouts on worker outcomes by building a new database that links transactions to matched employer-employee data in the United States. To guide our empirical analysis, we derive testable implications from three theories in which private equity managers alter worker outcomes: (1) exertion of monopsony power in concentrated markets, (2) breach of implicit contracts with targeted groups of workers, including managers and top earners, and (3) efficient reallocation of workers across plants. We do not find any evidence that private equity-backed firms vary wages and employment based on local labor market power proxies. Wage losses are also very similar for managers and top earners. Instead, we find strong evidence that private equity managers downsize less productive plants relative to productive plants while simultaneously reallocating high-wage workers to more productive plants. We conclude that post-buyout employment and wage dynamics are consistent with professional investors providing incentives to increase productivity and monitor the companies in which they invest.
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Geographic Immobility in the United States: Assessing the Prevalence and Characteristics of Those Who Never Migrate Across State Lines Using Linked Federal Tax Microdata
March 2025
Working Paper Number:
CES-25-19
This paper explores the prevalence and characteristics of those who never migrate at the state scale in the U.S. Studying people who never migrate requires regular and frequent observation of their residential location for a lifetime, or at least for many years. A novel U.S. population-sized longitudinal dataset that links individual level Internal Revenue Service (IRS) and Social Security Administration (SSA) administrative records supplies this information annually, along with information on income and socio-demographic characteristics. We use these administrative microdata to follow a cohort aged between 15 and 50 in 2001 from 2001 to 2016, differentiating those who lived in the same state every year during this period (i.e., never made an interstate move) from those who lived in more than one state (i.e., made at least one interstate move). We find those who never made an interstate move comprised 75 percent of the total population of this age cohort. This percentage varies by year of age but never falls below 62 percent even for those who were teenagers or young adults in 2001. There are also variations in these percentages by sex, race, nativity, and income, with the latter having the largest effects. We also find substantial variation in these percentages across states. Our findings suggest a need for more research on geographically immobile populations in U.S.
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Work Organization and Cumulative Advantage
March 2025
Working Paper Number:
CES-25-18
Over decades of wage stagnation, researchers have argued that reorganizing work can boost pay for disadvantaged workers. But upgrading jobs could inadvertently shift hiring away from those workers, exacerbating their disadvantage. We theorize how work organization affects cumulative advantage in the labor market, or the extent to which high-paying positions are increasingly allocated to already-advantaged workers. Specifically, raising technical skill demands exacerbates cumulative advantage by shifting hiring towards higher-skilled applicants. In contrast, when employers increase autonomy or skills learned on-the-job, they raise wages to buy worker consent or commitment, rather than pre-existing skill. To test this idea, we match administrative earnings to task descriptions from job posts. We compare earnings for workers hired into the same occupation and firm, but under different task allocations. When employers raise complexity and autonomy, new hires' starting earnings increase and grow faster. However, while the earnings boost from complex, technical tasks shifts employment toward workers with higher prior earnings, worker selection changes less for tasks learned on-the-job and very little for high autonomy tasks. These results demonstrate how reorganizing work can interrupt cumulative advantage.
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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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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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