Papers Containing Keywords(s): 'community'
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Viewing papers 1 through 10 of 13
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Working PaperWhose 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.View Full Paper PDF
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Working PaperConnected and Uncooperative: The Effects of Homogenous and Exclusive Social Networks on Survey Response Rates and Nonresponse Bias
January 2024
Working Paper Number:
CES-24-01
Social capital, the strength of people's friendship networks and community ties, has been hypothesized as an important determinant of survey participation. Investigating this hypothesis has been difficult given data constraints. In this paper, we provide insights by investigating how response rates and nonresponse bias in the American Community Survey are correlated with county-level social network data from Facebook. We find that areas of the United States where people have more exclusive and homogenous social networks have higher nonresponse bias and lower response rates. These results provide further evidence that the effects of social capital may not be simply a matter of whether people are socially isolated or not, but also what types of social connections people have and the sociodemographic heterogeneity of their social networks.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 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 PaperValidating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance
February 2020
Working Paper Number:
CES-20-05
This paper furthers a research agenda for modeling populations along spatial networks and expands upon an empirical analysis to a full U.S. county (Gaboardi, 2019, Ch. 1,2). Specific foci are the necessity of, and methods for, validating and benchmarking spatial data when conducting social science research with aggregated and ambiguous population representations. In order to promote the validation of publicly-available data, access to highly-restricted census microdata was requested, and granted, in order to determine the levels of accuracy and error associated with a network-based population modeling framework. Primary findings reinforce the utility of a novel network allocation method'populated polygons to networks (pp2n) in terms of accuracy, computational complexity, and real runtime (Gaboardi, 2019, Ch. 2). Also, a pseudo-benchmark dataset's performance against the true census microdata shows promise in modeling populations along networks.View Full Paper PDF
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Working PaperIn-migration and Dilution of Community Social Capital
June 2018
Working Paper Number:
CES-18-32
Consistent with predictions from the literature, we find that higher levels of in-migration dilute multiple dimensions of a community's level of social capital. The analysis employs a 2SLS methodology to account for potential endogeneity of migration.View Full Paper PDF
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Working PaperIndividual 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.View Full Paper PDF
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Working PaperExamining Multi-Level Correlates of Suicide by Merging NVDRS and ACS Data
January 2017
Working Paper Number:
CES-17-25
This paper describes a novel database and an associated suicide event prediction model that surmount longstanding barriers in suicide risk factor research. The database comingles person-level records from the National Violent Death Reporting System (NVDRS) and the American Community Survey (ACS) to establish a case-control study sample that includes all identified suicide cases, while faithfully reflecting general population sociodemographics, in sixteen USA states during the years 2005 2011. It supports a statistical model of individual suicide risk that accommodates person-level factors and the moderation of these factors by their community rates. Named the United States Multi-Level Suicide Data Set (US-MSDS), the database was developed outside the RDC laboratory using publicly available ACS microdata, and reconstructed inside the laboratory using restricted access ACS microdata. Analyses of the latter version yielded findings that largely amplified but also extended those obtained from analyses of the former. This experience shows that the analytic precision achievable using restricted access ACS data can play an important role in conducting social research, although it also indicates that publicly available ACS data have considerable value in conducting preliminary analyses and preparing to use an RDC laboratory. The database development strategy may interest scientists investigating sociodemographic risk factors for other types of low-frequency mortality.View Full Paper PDF
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Working PaperThe 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.View Full Paper PDF
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Working PaperDisconnected Geography: A Spatial Analysis of Disconnected Youth in the United States
January 2016
Working Paper Number:
CES-16-37
Since the Great Recession, US policy and advocacy groups have sought to better understand its effect on a group of especially vulnerable young adults who are not enrolled in school or training programs and not participating in the labor market, so called 'disconnected youth.' This article distinguishes between disconnected youth and unemployed youth and examines the spatial clustering of these two groups across counties in the US. The focus is to ascertain whether there are differences in underlying contextual factors among groups of counties that are mutually exclusive and spatially disparate (non-adjacent), comprising two types of spatial clusters ' high rates of disconnected youth and high rates of unemployed youth. Using restricted, household-level census data inside the Census Research Data Center (RDC) under special permission by the US Census Bureau, we were able to define these two groups using detailed household questionnaires that are not available to researchers outside the RDC. The geospatial patterns in the two types of clusters suggest that places with high concentrations of disconnected youth are distinctly different in terms of underlying characteristics from places with high concentrations of unemployed youth. These differences include, among other things, arrests for synthetic drug production, enclaves of poor in rural areas, persistent poverty in areas, educational attainment in the populace, children in poverty, persons without health insurance, the social capital index, and elders who receive disability benefits. This article provides some preliminary evidence regarding the social forces underlying the two types of observed geospatial clusters and discusses how they differ.View Full Paper PDF
