Papers Containing Keywords(s): 'resident'
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American Community Survey - 45
Decennial Census - 27
Census Bureau Disclosure Review Board - 26
Current Population Survey - 21
2010 Census - 21
Viewing papers 21 through 30 of 86
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Working PaperRe-examining Regional Income Convergence: A Distributional Approach
February 2023
Working Paper Number:
CES-23-05
We re-examine recent trends in regional income convergence, considering the full distribution of income rather than focusing on the mean. Measuring similarity by comparing each percentile of state distributions to the corresponding percentile of the national distribution, we find that state incomes have become less similar (i.e. they have diverged) within the top 20 percent of the income distribution since 1969. The top percentile alone accounts for more than half of aggregate divergence across states over this period by our measure, and the top five percentiles combine to account for 93 percent. Divergence in top incomes across states appears to be driven largely by changes in top incomes among White people, while top incomes among Black people have experienced relatively little divergence.View Full Paper PDF
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Working PaperThe Impact of Household Surveys on 2020 Census Self-Response
July 2022
Working Paper Number:
CES-22-24
Households who were sampled in 2019 for the American Community Survey (ACS) had lower self-response rates to the 2020 Census. The magnitude varied from -1.5 percentage point for household sampled in January 2019 to -15.1 percent point for households sampled in December 2019. Similar effects are found for the Current Population Survey (CPS) as well.View Full Paper PDF
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Working PaperImproving Estimates of Neighborhood Change with Constant Tract Boundaries
May 2022
Working Paper Number:
CES-22-16
Social scientists routinely rely on methods of interpolation to adjust available data to their research needs. This study calls attention to the potential for substantial error in efforts to harmonize data to constant boundaries using standard approaches to areal and population interpolation. We compare estimates from a standard source (the Longitudinal Tract Data Base) to true values calculated by re-aggregating original 2000 census microdata to 2010 tract areas. We then demonstrate an alternative approach that allows the re-aggregated values to be publicly disclosed, using 'differential privacy' (DP) methods to inject random noise to protect confidentiality of the raw data. The DP estimates are considerably more accurate than the interpolated estimates. We also examine conditions under which interpolation is more susceptible to error. This study reveals cause for greater caution in the use of interpolated estimates from any source. Until and unless DP estimates can be publicly disclosed for a wide range of variables and years, research on neighborhood change should routinely examine data for signs of estimation error that may be substantial in a large share of tracts that experienced complex boundary changes.View Full Paper PDF
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Working PaperMetropolitan Segregation: No Breakthrough in Sight
May 2022
Working Paper Number:
CES-22-14
The 2020 Census offers new information on changes in residential segregation in metropolitan regions across the country as they continue to become more diverse. We take a long view, assessing trends since 1980 and extrapolating to the future. These new data mostly reinforce patterns that were observed a decade ago: high but slowly declining black-white segregation, and less intense but hardly changing segregation of Hispanics and Asians from whites. Enough time has passed since the civil rights era of the 1960s and 1970s to draw this conclusion: segregation will continue to divide Americans well into the 21st Century.View Full Paper PDF
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Working PaperNeighborhood Income and Material Hardship in the United States
January 2022
Working Paper Number:
CES-22-01
U.S. households face a number of economic challenges that affect their well-being. In this analysis we focus on the extent to which neighborhood economic conditions contribute to hardship. Specifically, using data from the 2008 and 2014 Survey of Income and Program Participation panel surveys and logistic regression, we analyze the extent to which neighborhoods income levels affect the likelihood of experiencing seven types of hardships, including trouble paying bills, medical need, food insecurity, housing hardship, ownership of basic consumer durables, neighborhood problems, and fear of crime. We find strong bivariate relationships between neighborhood income and all hardships, but for most hardships these are explained by other household characteristics, such as household income and education. However, neighborhood income retains a strong association with two hardships in particular even when controlling for a variety of other household characteristics: neighborhood conditions (such as the presence of trash and litter) and fear of crime. Our study highlights the importance of examining multiple measures when assessing well-being, and our findings are consistent with the notion that collective socialization and community-level structural features affect the likelihood that households experience deleterious neighborhood conditions and a fear of crime.View Full Paper PDF
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Working PaperImmigration and the Demand for Urban Housing
August 2021
Working Paper Number:
CES-21-23
The immigrant population has grown dramatically in the US in the last fifty years. This study estimates housing demand among immigrants and discusses how immigration may be altering the structure of US urban areas. Immigrants are found to consume less housing per capita than native born US residents.View Full Paper PDF
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Working PaperCombining Rules and Discretion in Economic Development Policy: Evidence on the Impacts of the California Competes Tax Credit
June 2021
Working Paper Number:
CES-21-13
We evaluate the effects of one of a new generation of economic development programs, the California Competes Tax Credit (CCTC), on local job creation. Incorporating perceived best practices from previous initiatives, the CCTC combines explicit eligibility thresholds with some discretion on the part of program officials to select tax credit recipients. The structure and implementation of the program facilitates rigorous evaluation. We exploit detailed data on accepted and rejected applicants to the CCTC, including information on scoring of applicants with regard to program goals and funding decisions, together with restricted access American Community Survey (ACS) data on local economic conditions. Using a difference-in-differences approach, we find that each CCTC-incentivized job in a census tract increases the number of individuals working in that tract by over two ' a significant local multiplier. We also explore the program's distributional implications and impacts by industry. We find that CCTC awards increase employment among workers residing in both high income and low income communities, and that the local multipliers are larger for non-manufacturing awards than for manufacturing awards.View Full Paper PDF
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Working PaperThe Impacts of Opportunity Zones on Zone Residents
June 2021
Working Paper Number:
CES-21-12
Created by the Tax Cuts and Jobs Act in 2017, the Opportunity Zone program was designed to encourage investment in distressed communities across the U.S. We examine the early impacts of the Opportunity Zone program on residents of targeted areas. We leverage restricted-access microdata from the American Community Survey and employ difference-in-differences and matching approaches to estimate causal reduced-form effects of the program. Our results point to modest, if any, positive effects of the Opportunity Zone program on the employment, earnings, or poverty of zone residents.View Full Paper PDF
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Working PaperChanges in Metropolitan Area Definition, 1910-2010
February 2021
Working Paper Number:
CES-21-04
The Census Bureau was established as a permanent agency in 1902, as industrialization and urbanization were bringing about rapid changes in American society. The years following the establishment of a permanent Census Bureau saw the first attempts at devising statistical geography for tabulating statistics for large cities and their environs. These efforts faced several challenges owing to the variation in settlement patterns, political organization, and rates of growth across the United States. The 1910 census proved to be a watershed, as the Census Bureau offered a definition of urban places, established the first census tract boundaries for tabulating data within cities, and introduced the first standardized metropolitan area definition. It was not until the middle of the twentieth century, however, the Census Bureau in association with other statistical agencies had established a flexible standard metropolitan definition and a more consistent means of tabulating urban data. Since 1950, the rules for determining the cores and extent of metropolitan areas have been largely regarded as comparable. In the decades that followed, however, a number of rule changes were put into place that accounted for metropolitan complexity in differing ways, and these have been the cause of some confusion. Changes put into effect with the 2000 census represent a consensus of sorts for how to handle these issues.View Full Paper PDF
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Working PaperDetermination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report
October 2020
Working Paper Number:
CES-20-33
This report documents the efforts of the Census Bureau's Citizen Voting-Age Population (CVAP) Internal Expert Panel (IEP) and Technical Working Group (TWG) toward the use of multiple data sources to produce block-level statistics on the citizen voting-age population for use in enforcing the Voting Rights Act. It describes the administrative, survey, and census data sources used, and the four approaches developed for combining these data to produce CVAP estimates. It also discusses other aspects of the estimation process, including how records were linked across the multiple data sources, and the measures taken to protect the confidentiality of the data.View Full Paper PDF
