This paper examines the empirical relationship between the probability a plant closes and the compensation paid to the employees in the plant. The paper uses data on over 6500 manufacturing plants from the LRD to estimate the market hedonic wage locus and the probability of plant failure. The empirical results reported in this paper indicate that the probability of plant failure is systematically related to the plant's market share, age, recent growth, and variable cost to revenue ratio. The market hedonic wage regression indicates that workers employed by multi-plant firms earn a positive compensating wage differential for the risk of plant closing but workers employed in single-plant firms do not. Additionally, the paper provides evidence on the general pattern of wage variation across heterogeneous employers. Establishment wage rates are significantly affected by plant size, age, geographic location, industry, capital intensity, and value added per worker.
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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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Longitudinal Environmental Inequality and Environmental Gentrification: Who Gains From Cleaner Air?
May 2017
Working Paper Number:
carra-2017-04
A vast empirical literature has convincingly shown that there is pervasive cross-sectional inequality in exposure to environmental hazards. However, less is known about how these inequalities have been evolving over time. I fill this gap by creating a new dataset, which combines satellite data on ground-level concentrations of fine particulate matter with linked administrative and survey data. This linked dataset allows me to measure individual pollution exposure for over 100 million individuals in each year between 2000 and 2014, a period of time has seen substantial improvements in average air quality. This rich dataset can then be used to analyze longitudinal dimensions of environmental inequality by examining the distribution of changes in individual pollution exposure that underlie these aggregate improvements. I confirm previous findings that cross-sectional environmental inequality has been on the decline, but I argue that this may miss longitudinal patterns in exposure that are consistent with environmental gentrification. I find that advantaged individuals at the beginning of the sample experience larger pollution exposure reductions than do initially disadvantaged individuals.
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Fatal Errors: The Mortality Value of Accurate Weather Forecasts
June 2023
Working Paper Number:
CES-23-30
We provide the first revealed preference estimates of the benefits of routine weather forecasts. The benefits come from how people use advance information to reduce mor tality from heat and cold. Theoretically, more accurate forecasts reduce mortality if and only if mortality risk is convex in forecast errors. We test for such convexity using data on the universe of mortality events and weather forecasts for a twelve-year period in the U.S. Results show that erroneously mild forecasts increase mortality whereas erro neously extreme forecasts do not reduce mortality. Making forecasts 50% more accurate would save 2,200 lives per year. The public would be willing to pay $112 billion to make forecasts 50% more accurate over the remainder of the century, of which $22 billion reflects how forecasts facilitate adaptation to climate change.
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Non-Random Assignment of Individual Identifiers and Selection into Linked Data: Implications for Research
January 2026
Working Paper Number:
CES-26-06
The U.S. Census Bureau's Person Identification Validation System facilitates anonymous linkages between survey and administrative records by assigning Protected Identification Keys (PIKs) to person records. While PIK assignment is generally accurate, some person records are not successfully assigned a PIK, which can lead to sample selection bias in analyses of linked data. Using the American Community Survey (ACS) and the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) between 2005 and 2022, we corroborate and extend existing findings on the drivers of PIK assignment, showing that the rate of PIK assignment varies widely across socio-demographic subgroups. Using earnings as a test case, we then show that limiting a survey sample of wage earners to person records with PIKs or successful linkages to W-2 wage records tends to overestimate self-reported wage earnings, on average, indicative of linkage-induced selection bias. In a validation exercise, we demonstrate that reweighting methods, such as inverse probability weighting or entropy balancing, can mitigate this bias.
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The Privacy-Protected Gridded Environmental Impacts Frame
December 2024
Working Paper Number:
CES-24-74
This paper introduces the Gridded Environmental Impacts Frame (Gridded EIF), a novel privacy-protected dataset derived from the U.S. Census Bureau's confidential Environmental Impacts Frame (EIF) microdata infrastructure. The EIF combines comprehensive administrative records and survey data on the U.S. population with high-resolution geospatial information on environmental hazards. While access to the EIF is restricted due to the confidential nature of the underlying data, the Gridded EIF offers a broader research community the opportunity to glean insights from the data while preserving confidentiality. We describe the data and privacy protection process, and offer guidance on appropriate usage, presenting practical applications.
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The Census Historical Environmental Impacts Frame
October 2024
Working Paper Number:
CES-24-66
The Census Bureau's Environmental Impacts Frame (EIF) is a microdata infrastructure that combines individual-level information on residence, demographics, and economic characteristics with environmental amenities and hazards from 1999 through the present day. To better understand the long-run consequences and intergenerational effects of exposure to a changing environment, we expand the EIF by extending it backward to 1940. The Historical Environmental Impacts Frame (HEIF) combines the Census Bureau's historical administrative data, publicly available 1940 address information from the 1940 Decennial Census, and historical environmental data. This paper discusses the creation of the HEIF as well as the unique challenges that arise with using the Census Bureau's historical administrative data.
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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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Income, Wealth, and Environmental Inequality in the United States
October 2024
Working Paper Number:
CES-24-57
This paper explores the relationships between air pollution, income, wealth, and race by combining administrative data from U.S. tax returns between 1979'2016, various measures of air pollution, and sociodemographic information from linked survey and administrative data. In the first year of our data, the relationship between income and ambient pollution levels nationally is approximately zero for both non-Hispanic White and Black individuals. However, at every single percentile of the national income distribution, Black individuals are exposed to, on average, higher levels of pollution than White individuals. By 2016, the relationship between income and air pollution had steepened, primarily for Black individuals, driven by changes in where rich and poor Black individuals live. We utilize quasi-random shocks to income to examine the causal effect of changes in income and wealth on pollution exposure over a five year horizon, finding that these income'pollution elasticities map closely to the values implied by our descriptive patterns. We calculate that Black-White differences in income can explain ~10 percent of the observed gap in air pollution levels in 2016.
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The Geography of Inventors and Local Knowledge Spillovers in R&D
October 2024
Working Paper Number:
CES-24-59
I causally estimate local knowledge spillovers in R&D and quantify their importance when implementing R&D policies. Using a new administrative panel on German inventors, I estimate these spillovers by isolating quasi-exogenous variation from the arrival of East German inventors across West Germany after the Reunification of Germany in 1990. Increasing the number of inventors by 1% increases inventor productivity by 0.4%. I build a spatial model of innovation, and show that these spillovers are crucial when reducing migration costs for inventors or implementing R&D subsidies to promote economic activity.
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Job Referral Networks and the Determination of Earnings in Local Labor Markets
December 2010
Working Paper Number:
CES-10-40
Referral networks may affect the efficiency and equity of labor market outcomes, but few studies have been able to identify earnings effects empirically. To make progress, I set up a model of on-the-job search in which referral networks channel information about high-paying jobs. I evaluate the model using employer-employee matched data for the U.S. linked to the Census block of residence for each worker. The referral effect is identified by variations in the quality of local referral networks within narrowly defined neighborhoods. I find, consistent with the model, a positive and significant role for local referral networks on the full distribution of earnings outcomes from job search.
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