Recent work has suggested that higher income inequality may be a desirable attribute of a neighborhood in that it represents diversity, even though high (and rising) inequality appears to be detrimental to the nation as a whole. The research reported here has determined the key characteristics of a census tract that are associated with the level of inequality in 2000 or 2010, and those associated with changes in income inequality between 2000 and 2010. For the change, the strongest influence is a negative effect for the level of income inequality in 2000; that is, higher income inequality in 2000 leads to a decline over the decade, ceteris paribus. Neighborhoods with higher proportions or levels of the following population and housing characteristics tend to have both higher income inequality and a larger increase in income inequality between 2000 and 2010: individuals in poverty, those with a bachelor's degree, older individuals, householders living alone, and median rent, and lower median housing value and household income. Among these, perhaps the most important determinant is the percent in poverty in 2000. Furthermore, as the baseline level of demographic and economic diversity increases, the better the baseline and change characteristics explain the change in the Gini index from 2000 to 2010.
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Who Moves to Mixed-Income Neighborhoods?
August 2010
Working Paper Number:
CES-10-18
This paper uses confidential Census data, specifically the 1990 and 2000 Census Long Form data, to study the income dispersion of recent cohorts of migrants to mixed-income neighborhoods. If recent in-migrants to mixed-income neighborhoods exhibit high levels of income heterogeneity, this is consistent with stable mixed-income neighborhoods. If, however, mixed-income neighborhoods are comprised of older homogeneous lower-income (higher income) cohorts combined with newer homogeneous higher-income (lower-income) cohorts, this is consistent with neighborhood transition. Our results indicate that neighborhoods with high levels of income dispersion do in fact attract a much more heterogeneous set of in-migrants, particularly from the tails of the income distribution, but that income heterogeneity does tend to erode over time. Our results also suggest that the residents of mixed-income neighborhoods may be less heterogeneous with respect to lifetime income.
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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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Neighborhood 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.
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Improving 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.
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School Accountability and Residential Location Patterns: Evaluating the Unintended Consequences of No Child Left Behind
January 2017
Working Paper Number:
CES-17-28
The 2002 to 2015 No Child Left Behind (NCLB) Act is often considered the most significant federal intervention into education in the United States since 1965 with the passage of the Elementary and Secondary Education Act. There is growing evidence that holding schools accountable is leading to some improved educational outcomes for students. There is in contrast very little work examining whether these sweeping reforms have unintended consequences for the communities which these schools are serving. As school attendance, particularly at the elementary school level, is closely tied to one's residence, placing sanctions on a school could have negative repercussions for neighborhoods if it provides new information on school failure. In contrast, if these sanctions also bring new resources, including financial resources or school choice, they could spark additional demand within a neighborhood. Through the use of restricted access census data, which includes local housing values, rents and individual residential choices in combination with the use of a boundary discontinuity identification strategy, this paper seeks to examine how failure to meet Adequate Yearly Progress (AYP), the key enforcement mechanism of NCLB, is shaping local housing markets and residential choices in five diverse urban school districts: New York, Los Angeles, Philadelphia, Detroit and Tucson.
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Who Gentrifies Low Income Neighborhoods?
January 2008
Working Paper Number:
CES-08-02
This paper uses confidential Census data, specifically the 1990 and 2000 Census Long- Form data, to study the demographic processes underlying the gentrification of low income urban neighborhoods during the 1990's. In contrast to previous studies, the analysis is conducted at the more refined census-tract level with a narrower definition of gentrification and more narrowly defined comparison neighborhoods. The analysis is also richly disaggregated by demographic characteristic, uncovering differential patterns by race, education, age and family structure that would not have emerged in the more aggregate analysis in previous studies. The results provide little evidence of displacement of low-income non-white households in gentrifying neighborhoods. The bulk of the income gains in gentrifying neighborhoods are attributed to white college graduates and black high school graduates. It is the disproportionate in-migration of the former and the disproportionate retention and income gains of the latter that appear to be the main engines of gentrification.
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Metropolitan 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.
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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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Poverty Estimates for Places in the United States
September 2005
Working Paper Number:
CES-05-12
This paper first describes some historical poverty trends, overall and for demographic groups and broad locations within the U.S. from an ongoing household survey, and then presents some specific information on poverty for localities by size, from the most recent decennial census (2000). Rural poverty exceeded urban poverty in 1969 and 1979, but urban poverty in 1999 was higher than rural poverty. Non-metropolitan area poverty exceeded metropolitan area poverty in each of the four censuses, but within each of those areas, rural poverty is now less than urban poverty. Within metropolitan areas, poverty is highest for those in central cities. For urbanized areas (50,000 or more population), the poverty rate is lower as the area gets larger, with the exception of the very largest-sized areas. This higher poverty for the largest places is accounted for entirely by the higher poverty rate for the central city or cities in those urban agglomerations, as the poverty rates for the parts of the urbanized areas not in the central place continue to fall as the area itself gets larger. Some of the critical relationships affecting the poverty rate of places appear to be the location of certain types of people - female householders, non-citizens, people of color, and college graduates.
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Neighborhood Effects on High-School Drop-Out Rates and Teenage Childbearing: Tests for Non-Linearities, Race-Specific Effects, Interactions with Family Characteristics, and Endogenous Causation using Geocoded California Census Microdata
May 2008
Working Paper Number:
CES-08-12
This paper examines the relationship between neighborhood characteristics and the likelihood that a youth will drop out of high school or have a child during the teenage years. Using a dataset that is uniquely wellsuited to the study of neighborhood effects, the impact of the neighborhood poverty rate and the percentage of professionals in the local labor force on youth outcomes in California is examined. The first section of the paper tests for non-linearities in the relationship between indicators of neighborhood distress and youth outcomes. Some evidence is found for a break-point at low levels of poverty. Suggestive but inconclusive evidence is also found for a second breakpoint, at very high levels of poverty, for African-American youth only. The second part of the paper examines interactions between family background characteristics and neighborhood effects, and finds that White youth are most sensitive to neighborhood effects, while the effect of parental education depends on the neighborhood measure in question. Among White youth, those from single-parent households are more vulnerable to neighborhood conditions. The third section of the paper finds that for White youth and Hispanic youth, the relevant neighborhood variables appear to be the own-race poverty rates and the percentage of professionals of youths' own race. The final section of the paper estimates a tract-fixed effects model, using the results from the third section to define multiple relevant poverty rates within each tract. The fixed-effects specification suggests that for White and Hispanic youth in California, neighborhood effects remain significant, even with the inclusion of controls for any unobserved family and neighborhood characteristics that are constant within tracts.
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