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Whose Neighborhood Now? Gentrification and Community Life in Low-Income Urban Neighborhoods
June 2024
Working Paper Number:
CES-24-29
Gentrification is a process of urban change that has wide-ranging social and political impacts, but previous studies provide divergent findings. Does gentrification leave residents feeling alienated, or does it bolster neighborhood social satisfaction? Politically, does urban change mobilize residents, or leave them disengaged? I assess a national, cross-sectional sample of about 17,500 respondents in lower-income urban neighborhoods, and use a structural equation modeling approach to model six latent variables pertaining to local social environment and political participation. Amongst the full sample, gentrification has a positive association with all six factors. However, this relationship depends upon respondents' level of income, length of residency, and racial identity. White residents and those with shorter length of residency report higher levels of social cohesion as gentrification increases, but there is no such association amongst racial minority groups and longer-term residents. This finding aligns with a perspective on gentrification as a racialized process, and demonstrates that gentrification-related amenities primarily serve the interests of white residents and newcomers. All groups, however, are more likely to participate in neighborhood politics as gentrification increases, drawing attention to the agency of local residents as they attempt to influence processes of urban change.
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Mobility, Opportunity, and Volatility Statistics (MOVS):
Infrastructure Files and Public Use Data
April 2024
Working Paper Number:
CES-24-23
Federal statistical agencies and policymakers have identified a need for integrated systems of household and personal income statistics. This interest marks a recognition that aggregated measures of income, such as GDP or average income growth, tell an incomplete story that may conceal large gaps in well-being between different types of individuals and families. Until recently, longitudinal income data that are rich enough to calculate detailed income statistics and include demographic characteristics, such as race and ethnicity, have not been available. The Mobility, Opportunity, and Volatility Statistics project (MOVS) fills this gap in comprehensive income statistics. Using linked demographic and tax records on the population of U.S. working-age adults, the MOVS project defines households and calculates household income, applying an equivalence scale to create a personal income concept, and then traces the progress of individuals' incomes over time. We then output a set of intermediate statistics by race-ethnicity group, sex, year, base-year state of residence, and base-year income decile. We select the intermediate statistics most useful in developing more complex intragenerational income mobility measures, such as transition matrices, income growth curves, and variance-based volatility statistics. We provide these intermediate statistics as part of a publicly released data tool with downloadable flat files and accompanying documentation. This paper describes the data build process and the output files, including a brief analysis highlighting the structure and content of our main statistics.
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Producing U.S. Population Statistics Using Multiple Administrative Sources
November 2023
Working Paper Number:
CES-23-58
We identify several challenges encountered when constructing U.S. administrative record-based (AR-based) population estimates for 2020. Though the AR estimates are higher than the 2020 Census at the national level, they are over 15 percent lower in 5 percent of counties, suggesting that locational accuracy can be improved. Other challenges include how to achieve comprehensive coverage, maintain consistent coverage across time, filter out nonresidents and people not alive on the reference date, uncover missing links across person and address records, and predict demographic characteristics when multiple ones are reported or when they are missing. We discuss several ways of addressing these issues, e.g., building in redundancy with more sources, linking children to their parents' addresses, and conducting additional record linkage for people without Social Security Numbers and for addresses not initially linked to the Census Bureau's Master Address File. We discuss modeling to predict lower levels of geography for people lacking those geocodes, the probability that a person is a U.S. resident on the reference date, the probability that an address is the person's residence on the reference date, and the probability a person is in each demographic characteristic category. Regression results illustrate how many of these challenges and solutions affect the AR county population estimates.
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Noncitizen Coverage and Its Effects on U.S. Population Statistics
August 2023
Working Paper Number:
CES-23-42
We produce population estimates with the same reference date, April 1, 2020, as the 2020 Census of Population and Housing by combining 31 types of administrative record (AR) and third-party sources, including several new to the Census Bureau with a focus on noncitizens. Our AR census national population estimate is higher than other Census Bureau official estimates: 1.8% greater than the 2020 Demographic Analysis high estimate, 3.0% more than the 2020 Census count, and 3.6% higher than the vintage-2020 Population Estimates Program estimate. Our analysis suggests that inclusion of more noncitizens, especially those with unknown legal status, explains the higher AR census estimate. About 19.8% of AR census noncitizens have addresses that cannot be linked to an address in the 2020 Census collection universe, compared to 5.7% of citizens, raising the possibility that the 2020 Census did not collect data for a significant fraction of noncitizens residing in the United States under the residency criteria used for the census. We show differences in estimates by age, sex, Hispanic origin, geography, and socioeconomic characteristics symptomatic of the differences in noncitizen coverage.
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Estimating the U.S. Citizen Voting-Age Population (CVAP) Using Blended Survey Data, Administrative Record Data, and Modeling: Technical Report
April 2023
Authors:
J. David Brown,
Danielle H. Sandler,
Lawrence Warren,
Moises Yi,
Misty L. Heggeness,
Joseph L. Schafer,
Matthew Spence,
Marta Murray-Close,
Carl Lieberman,
Genevieve Denoeux,
Lauren Medina
Working Paper Number:
CES-23-21
This report develops a method using administrative records (AR) to fill in responses for nonresponding American Community Survey (ACS) housing units rather than adjusting survey weights to account for selection of a subset of nonresponding housing units for follow-up interviews and for nonresponse bias. The method also inserts AR and modeling in place of edits and imputations for ACS survey citizenship item nonresponses. We produce Citizen Voting-Age Population (CVAP) tabulations using this enhanced CVAP method and compare them to published estimates. The enhanced CVAP method produces a 0.74 percentage point lower citizen share, and it is 3.05 percentage points lower for voting-age Hispanics. The latter result can be partly explained by omissions of voting-age Hispanic noncitizens with unknown legal status from ACS household responses. Weight adjustments may be less effective at addressing nonresponse bias under those conditions.
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Immigration 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.
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Individual Social Capital and Migration
March 2018
Working Paper Number:
CES-18-14
This paper determines how individual, relative to community social capital affects individual migration decisions. We make use of non-public data from the Social Capital Community Benchmark Survey to predict multi-dimensional social capital for observations in the Current Population Survey. We find evidence that individuals are much less likely to have moved to a community with average social capital levels lower than their own and that higher levels of community social capital act as positive pull-factor amenities. The importance of that amenity differs across urban/rural locations. We also confirm that higher individual social capital is a negative predictor of migration.
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Developing a Residence Candidate File for Use With Employer-Employee Matched Data
January 2017
Working Paper Number:
CES-17-40
This paper describes the Longitudinal Employer-Household Dynamics (LEHD) program's ongoing efforts to use administrative records in a predictive model that describes residence locations for workers. This project was motivated by the discontinuation of a residence file produced elsewhere at the U.S. Census Bureau. The goal of the Residence Candidate File (RCF) process is to provide the LEHD Infrastructure Files with residence information that maintains currency with the changing state of administrative sources and represents uncertainty in location as a probability distribution. The discontinued file provided only a single residence per person/year, even when contributing administrative data may have contained multiple residences. This paper describes the motivation for the project, our methodology, the administrative data sources, the model estimation and validation results, and the file specifications. We find that the best prediction of the person-place model provides similar, but superior, accuracy compared with previous methods and performs well for workers in the LEHD jobs frame. We outline possibilities for further improvement in sources and modeling as well as recommendations on how to use the preference weights in downstream processing.
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Has Falling Crime Invited Gentrification?
January 2017
Working Paper Number:
CES-17-27
Over the past two decades, crime has fallen dramatically in cities in the United States. We explore whether, in the face of falling central city crime rates, households with more resources and options were more likely to move into central cities overall and more particularly into low income and/or majority minority central city neighborhoods. We use confidential, geocoded versions of the 1990 and 2000 Decennial Census and the 2010, 2011, and 2012 American Community Survey to track moves to different neighborhoods in 244 Core Based Statistical Areas (CBSAs) and their largest central cities. Our dataset includes over four million household moves across the three time periods. We focus on three household types typically considered gentrifiers: high-income, college-educated, and white households. We find that declines in city crime are associated with increases in the probability that highincome and college-educated households choose to move into central city neighborhoods, including low-income and majority minority central city neighborhoods. Moreover, we find little evidence that households with lower incomes and without college degrees are more likely to move to cities when violent crime falls. These results hold during the 1990s as well as the 2000s and for the 100 largest metropolitan areas, where crime declines were greatest. There is weaker evidence that white households are disproportionately drawn to cities as crime falls in the 100 largest metropolitan areas from 2000 to 2010.
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The Effect of Low-Income Housing on Neighborhood Mobility:
Evidence from Linked Micro-Data
May 2016
Working Paper Number:
carra-2016-02
While subsidized low-income housing construction provides affordable living conditions for poor households, many observers worry that building low-income housing in poor communities induces individuals to move to poor neighborhoods. We examine this issue using detailed, nationally representative microdata constructed from linked decennial censuses. Our analysis exploits exogenous variation in low-income housing supply induced by program eligibility rules for Low-Income Housing Tax Credits to estimate the effect of subsidized housing on neighborhood mobility patterns. The results indicate little evidence to suggest a causal effect of additional low-income housing construction on the characteristics of neighborhoods to which households move. This result is true for households across the income distribution, and supports the hypothesis that subsidized housing provides affordable living conditions without encouraging households to move to less-affluent neighborhoods than they would have otherwise.
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