Segregation by Racial and Demographic Group: Evidence from the San Francisco Bay Area

Miller, Vincent; Quigley, John M.

doi:10.1080/00420989020080011

Cited by 55 publications

(34 citation statements)

References 18 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, the magnitude of this coefficient increases. This is consistent with literature that finds that demographic difference or economic segregation (and to a lesser extent racial preferences for same-race neighborhoods) account for very little of the observed racial segregation patterns (Ihlanfeldt and Scafidi, 2002;Miller and Quigley, 1990). However, the inclusion of these demographic measures does halve the coefficients estimate on job sprawl and knocks out its statistical significance.…”

Section: Model 1 Insupporting

confidence: 79%

Explaining Racial/Ethnic Gaps in Spatial Mismatch in the US: The Primacy of Racial Segregation

Stoll¹,

Covington²

2011

Urban Studies

View full text Add to dashboard Cite

Despite declines in racial segregation across most U.S. metropolitan areas in recent years, racial and ethnic minorities still display uneven geographic access to jobs. In this article, the authors provide a detailed analysis of the factors driving racial and ethnic gaps in spatial mismatch conditions across U.S. metropolitan areas. Using data primarily from the 1990 and 2000 U.S. Censuses, and the 1994 and 1999Economic Censuses, and the Zip Code Business Pattern files, we generate descriptive, multivariate, and decompositional evidence to address why blacks and to a lesser extent Latinos display greater degrees of spatial mismatch than whites. The results indicate that racial segregation in housing markets, among many other factors including job sprawl, is the most important factor. The authors' models indicate that racial differences in spatial mismatch conditions, particularly between blacks and whites, should be eliminated in 45 to 50 years should racial segregation levels continue to decline in the future at similar rates observed over the 1990s.

show abstract

Section: Model 1 Insupporting

confidence: 79%

Explaining Racial/Ethnic Gaps in Spatial Mismatch in the US: The Primacy of Racial Segregation

Stoll¹,

Covington²

2011

Urban Studies

View full text Add to dashboard Cite

show abstract

“…Consider, for example, an index of the residential segregation of households. The exposure index, one of several measures of segregation commonly used by demographers and sociologists (see, for example, White, 1986;and Miller and Quigley, 1990), is a direct measure of the residential contact between and among groups. Specifically, the exposure to a given demographic group is the probability that a randomly selected person residing in the same neighbourhood (say, census tract) is a member of the given group.…”

Section: Metropolitan Measures Of Access and Employment Outcomesmentioning

confidence: 99%

Where Youth Live: Economic Effects of Urban Space on Employment Prospects

O’Regan

Quigley

1998

Urban Studies

Self Cite

View full text Add to dashboard Cite

This paper synthesises a series of empirical analyses investigating the role of urban space in affecting minority employment outcomes. It broadens the focus beyond transport and the 'friction of space' and expands the data available for spatial research. The empirical analyses share a common framework linking 'access' to youth labour market performance. The first set of results is based on aggregate data relating access to employment outcomes for black youth at the metropolitan level. Access is broadly defined to include traditional measures of geographical distance, as well as measures of social isolation or social access. Metropolitan areas in which the black poor are more spatially isolated are also found to have higher black youth unemployment rates. The second body of evidence relies on the same type of metropolitan measures, combined with individual data on youth living with at least one parent. When individual and family characteristics are controlled for, and white and Hispanic youth are also considered, metropolitan measures of social access exert distinguishable effects upon youth employment -youth living in urban areas in which they have less residential contact with whites or the non-poor are less likely to be employed. The final piece of analysis links the individual records of such youth to tract-level measures of access, both social (neighbourhood composition variables) and geographical (job-access measures). This is accomplished through the creation of a unique data set at the Bureau of the Census. Again, after controlling for individual and family characteristics, the residential conditions of youth affect their employment. Ceteris paribus, youth living in census tracts with fewer employed adults, with fewer whites, and which are further from jobs are less likely to be employed. Results suggest that the overall effects of space on employment outcomes are substantial, explaining 10-40 per cent of the observed racial differences in employment in four urban areas examined. Of this 'spatial' effect, the bulk arises from social/informational measures; job access appears to play a much smaller role. However, when measured more precisely, at the census-tract level, job access does have a significant effect on youth employment. This effect is less important than other spatial influences. Spatial influences are less important in explaining outcomes than are differences in human capital.

show abstract

“…In order to use data characterizing the racial composition of smaller geographic areas, such as Census tracts or zip codes, researchers have generally made use of data that are not explicitly linked to individual households. Miller and Quigley (1990) and Harsman and Quigley (1995), for example, compare the degree of racial segregation in a metropolitan area to the degree of stratification on the basis of income and other household characteristics, concluding that 4 sorting on the basis of these other characteristics can explain only a small amount of observed racial segregation. …”

mentioning

confidence: 99%

What drives racial segregation? New evidence using Census microdata

Bayer

McMillan

Rueben

2004

Journal of Urban Economics

186

109

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Notes: Center Discussion Papers are preliminary materials circulated to stimulate discussions and critical comments. Terms of use: Documents in EconStor mayThe authors would like to thank Fernando Ferreira (University of California, Berkeley) for outstanding research assistance, as well as Pedro Cerdan and Jackie Chou for help assembling the data. We would also like to thank Debbie Reed, Steve Ross, Jon Sonstelie, Chris Udry, Junfu Zhang and seminar participants at Johns Hopkins, UCLA, and Yale for helpful comments. We are grateful to the California Census Research Data Center for providing access to the data and to Ritch Milby in particular. All output included in this paper has been subject to thorough disclosure analysis by Census Bureau officials and meets stringent standards to safeguard confidentiality. Financial support from the Public Policy Institute of California is gratefully acknowledged. Please send correspondence to any of the authors: patrick.bayer@yale.edu, mcmillan@chass.utoronto.ca, or rueben@ppic.org This paper can be downloaded without charge from the Social Science Research Network electronic library at: http://ssrn.com/abstract=428742An index to papers in the Economic Growth Center Discussion Paper Series is located at: http://www.econ.yale.edu/~egcenter/research.htm What Drives Racial Segregation? New Evidence Using Census MicrodataPatrick Bayer, Robert McMillan, and Kim Rueben AbstractThis paper sheds new light on the forces that drive residential segregation on the basis of race, assessing the extent to which across-race differences in other household characteristics can explain a significant portion of observed racial segregation. The central contribution of the analysis is to provide a transparent new measurement framework for understanding segregation patterns. This framework allows researchers to characterize patterns of segregation, to decompose them in meaningful ways, and to carry out partial equilibrium counterfactuals that illuminate the contributions of a variety of non-race characteristics in driving segregation. We illustrate our approach using restricted micro-Census data from the San Francisco BayArea that provide a rich joint distribution of household and neighborhood characteristics not previously available to the research community. In contrast to findings in the prior literature, our analysis indicates that individual household characteristics can explain a considerable fraction of segregation by race, explaining...

show abstract

Segregation by Racial and Demographic Group: Evidence from the San Francisco Bay Area

Cited by 55 publications

References 18 publications

Explaining Racial/Ethnic Gaps in Spatial Mismatch in the US: The Primacy of Racial Segregation

Explaining Racial/Ethnic Gaps in Spatial Mismatch in the US: The Primacy of Racial Segregation

Where Youth Live: Economic Effects of Urban Space on Employment Prospects

What drives racial segregation? New evidence using Census microdata

Contact Info

Product

Resources

About