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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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Building the Prototype Census Environmental Impacts Frame
April 2023
Working Paper Number:
CES-23-20
The natural environment is central to all aspects of life, but efforts to quantify its influence have been hindered by data availability and measurement constraints. To mitigate some of these challenges, we introduce a new prototype of a microdata infras tructure: the Census Environmental Impacts Frame (EIF). The EIF provides detailed individual-level information on demographics, economic characteristics, and address level histories ' linked to spatially and temporally resolved estimates of environmental conditions for each individual ' for almost every resident in the United States over the past two decades. This linked microdata infrastructure provides a unique platform for advancing our understanding about the distribution of environmental amenities and hazards, when, how, and why exposures have evolved over time, and the consequences of environmental inequality and changing environmental conditions. We describe the construction of the EIF, explore issues of coverage and data quality, document patterns and trends in individual exposure to two correlated but distinct air pollutants as an application of the EIF, and discuss implications and opportunities for future research.
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The Industrial Revolution in Services
October 2021
Working Paper Number:
CES-21-34
The U.S. has experienced an industrial revolution in services. Firms in service industries, those where output has to be supplied locally, increasingly operate in more markets. Employment, sales, and spending on fixed costs such as R&D and managerial employment have increased rapidly in these industries. These changes have favored top firms the most and have led to increasing national concentration in service industries. Top firms in service industries have grown entirely by expanding into new local markets that are predominantly small and mid-sized U.S. cities. Market concentration at the local level has decreased in all U.S. cities but by significantly more in cities thatwere initially small. These facts are consistent with the availability of a new menu of fixed-cost-intensive technologies in service sectors that enable adopters to produce at lower marginal costs in any markets. The entry of top service firms into new local markets has led to substantial unmeasured productivity growth, particularly in small markets.
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Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes
August 2016
Working Paper Number:
carra-2016-06
While commercial data sources offer promise to statistical agencies for use in production of official statistics, challenges can arise as the data are not collected for statistical purposes. This paper evaluates the use of 2008-2010 property tax data from CoreLogic, Inc. (CoreLogic), aggregated from county and township governments from around the country, to improve 2010 American Community Survey (ACS) estimates of property tax amounts for single-family homes. Particularly, the research evaluates the potential to use CoreLogic to reduce respondent burden, to study survey response error and to improve adjustments for survey nonresponse. The research found that the coverage of the CoreLogic data varies between counties as does the correspondence between ACS and CoreLogic property taxes. This geographic variation implies that different approaches toward using CoreLogic are needed in different areas of the country. Further, large differences between CoreLogic and ACS property taxes in certain counties seem to be due to conceptual differences between what is collected in the two data sources. The research examines three counties, Clark County, NV, Philadelphia County, PA and St. Louis County, MO, and compares how estimates would change with different approaches using the CoreLogic data. Mean county property tax estimates are highly sensitive to whether ACS or CoreLogic data are used to construct estimates. Using CoreLogic data in imputation modeling for nonresponse adjustment of ACS estimates modestly improves the predictive power of imputation models, although estimates of county property taxes and property taxes by mortgage status are not very sensitive to the imputation method.
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State and Local Determinants of Employment Outcomes among Individuals with Disabilities
March 2016
Working Paper Number:
CES-16-21
In the United States, employment rates among individuals with disabilities are persistently low but vary substantially. In this study, we examine the relationship between employment outcomes and features of the state and county physical, economic, and policy environment among a national sample of individuals with disabilities. To do so, we merge a set of state- and county-level environmental variables with data from the 2009'2011 American Community Survey accessed in a U.S. Census Research Data Center. We estimate regression models of employment, work hours, and earnings as a function of health conditions, personal characteristics, and these environmental features. We find that certain environmental variables are significantly associated with employment outcomes. Although the estimated importance of environmental variables is small relative to individual health and personal characteristics, our results suggest that these variables may present barriers or facilitators to employment that can explain some geographic variation in employment outcomes across the United States.
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Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill
January 2016
Working Paper Number:
CES-16-36R
In 1980, Census data indicate, housing prices in large US cities rose with distance from the city center. By 2010, the relationship had reversed. We propose that this development can be traced to high-income households working longer hours. With little non-market time, proximity to work takes on added salience, leading high-income households to forgo suburban amenities and extending the gentrification trend beyond its 1970s niche status. In a tract-level data set covering the 27 largest US cities, years 1980-2010, we find support for our hypothesis. Using a Bartik-type demand shifter for skilled labor we find that full-time skilled workers favor centrality and the rising share in the population can account for the observed price changes in favor of the city center.
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DO HOUSING PRICES REFLECT ENVIRONMENTAL HEALTH RISKS? EVIDENCE FROM MORE THAN 1600 TOXIC PLANT OPENINGS AND CLOSINGS
April 2013
Working Paper Number:
CES-13-14
A ubiquitous and largely unquestioned assumption in studies of housing markets is that there is perfect information about local amenities. This paper measures the housing market and health impacts of 1,600 openings and closings of industrial plants that emit toxic pollutants. We find that housing values within one mile decrease by 1.5 percent when plants open, and increase by 1.5 percent when plants close. This implies an aggregate loss in housing values per plant of about $1.5 million. While the housing value impacts are concentrated within ' mile, we find statistically significant infant health impacts up to one mile away.
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The Dynamics of House Price Capitalization and Locational Sorting: Evidence from Air Quality Changes
September 2012
Working Paper Number:
CES-12-22
Despite extensive use of housing data to reveal valuation of non-market goods, the process of house price capitalization remains vague. Using the restricted access American Housing Survey, a high-frequency panel of prices, turnover, and occupant characteristics, this paper examines the time path of capitalization and preference-based sorting in response to air quality changes caused by differential regulatory pressure from the 1990 Clean Air Act Amendments. The results demonstrate that owner-occupied units capitalize changes immediately, whereas rent capitalization lags. The delayed but sharp rent capitalization temporally coincides with evidence of sorting, suggesting a strong link between location choices and price dynamics.
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The Effect of Power Plants on Local Housing Values and Rents: Evidence from Restricted Census Microdata
July 2008
Working Paper Number:
CES-08-19
Current trends in electricity consumption imply that hundreds of new fossil-fuel power plants will be built in the United States over the next several decades. Power plant siting has become increasingly contentious, in part because power plants are a source of numerous negative local externalities including elevated levels of air pollution, haze, noise and traffic. Policymakers attempt to take these local disamenities into account when siting facilities, but little reliable evidence is available about their quantitative importance. This paper examines neighborhoods in the United States where power plants were opened during the 1990s using household-level data from a restricted version of the U.S. decennial census. Compared to neighborhoods farther away, housing values and rents decreased by 3-5% between 1990 and 2000 in neighborhoods near sites. Estimates of household marginal willingness-to-pay to avoid power plants are reported separately for natural gas and other types of plants, large plants and small plants, base load plants and peaker plants, and upwind and downwind households.
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A Unified Framework for Measuring Preferences for Schools and Neighborhoods
October 2007
Working Paper Number:
CES-07-27
This paper develops a comprehensive framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous model of residential choice to address the endogeneity of school and neighborhood attributes. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number of new results. First, households are willing to pay less than one percent more in house prices ' substantially lower than previous estimates ' when the average performance of the local school increases by five percent. Second, much of the apparent willingness to pay for more educated and wealthier neighbors is explained by the correlation of these sociodemographic measures with unobserved neighborhood quality. Third, neighborhood race is not capitalized directly into housing prices; instead, the negative correlation of neighborhood race and housing prices is due entirely to the fact that blacks live in unobservably lower quality neighborhoods. Finally, there is considerable heterogeneity in preferences for schools and neighbors: in particular, we find that households prefer to selfsegregate on the basis of both race and education.
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