2000115-Strengthening-Communities-with-Neighborhood-Data

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Advances in Analytic Methods for Neighborhood Data 359 lation of these interconnections is not controversial, its implication for empirical studies perhaps is more so. We show that statistical studies in both the fields of neighborhood effects and residential mobility potentially suffer from biasing forces related to geographic selection on un observed individual characteristics and endogeneity. Though the former field has devoted a good deal of effort to develop methods to overcome selection bias, the latter field has almost universally overlooked it. Neither field has made much effort to address the challenge of endogeneity. We believe that prototype efforts to model neighborhood effects and mobility holistically through the use of IVs represent the cutting edge of the next generation of studies in both fields. We have offered our own work as an example of the important empirical difference that such an approach produces, demonstrating that our concerns over bias are not just theoretical or hypothetical. We recognize that the IV approach is rife with challenges of its own. Paramount is the difficulty in identifying valid and powerful variables to serve as instruments. Nevertheless, we believe that the potential payoffs from further explorations in this realm should prove worthwhile. Notes 1. The need for a more holistic and dynamic approach to neighborhoods has previously been emphasized by scholars interested in neighborhood effects (Galster 1987, 2003; Tienda 1991; Galster, Marcotte, et al. 2007). Ioannides and Zabel (2008), van Ham and Manley (2010), Hedman and van Ham (2011), and Hedman, van Ham, and Manley (2012) have argued that one must uncover neighborhood selection processes to accurately assess neighborhood effects. They do not, however, holistically extend the argument conceptually or empirically to consider how neighborhood effects may alter neighborhood selection. 2. We recognize that perceptions of alternative neighborhoods are based on imperfect information and that there are spatial biases to this information. A fuller explanation of the residential search process is beyond the scope of this essay. 3. Despite its vaunted reputation, the Moving to Opportunity demonstration research has major flaws that render any conclusions regarding neighborhood effects based on its data highly suspect. For a thorough review see Galster (2011). 4. We here refer both to neighborhoods with high shares of ethnic minorities and neighborhoods inhabited by an ethnically homogenous majority (native) population. 5. See Bråmå (2006) for an attempt to apply the white flight and white avoidance theories to a Swedish context. 6. The framework is more problematic for long-distance migration (or moves within very large metropolitan areas) as other factors come into play; see Roseman (1983). 7. This is referred to as chain migration or migration network theory.
360 Strengthening Communities with Neighborhood Data 8. The direction of these potential biases is, however, unclear; Jencks and Mayer (1990) and Tienda (1991) argue that effects are overestimated, but Brooks-Gunn, Duncan, and Aber (1997) suggest that the opposite could also occur. 9. A “Heckman correction” variable; see various papers on selection bias by James Heckman from 1979 onward. 10. There is another sort of endogeneity that we do not discuss here. The choice of neighborhood is undertaken as a joint decision including decisions about housing, tenure, and length of residence (Galster 2003, 2008). References Aaronson, D. 1998. “Using Sibling Data to Estimate the Impact of Neighborhoods on Children’s Educational Outcomes.” Journal of Human Resources 33: 915–46. Alba, R. D., and J. R. Logan. 1993. “Minority Proximity to Whites in Suburbs: An Individual Level Analysis of Segregation.” American Journal of Sociology 98: 1388–1427. Andersson, R. 2008. “Neighbourhood Effects and the Welfare State: Towards a European Research Agenda?” Schmollers Jahrbuch 128: 1–14. Andersson, R., and Å. Bråmå. 2004. “Selective Migration in Swedish Distressed Neighbourhoods: Can Area-Based Urban Policies Counteract Segregation Processes?” Housing Studies 19 (4): 517–39. Bolt, G., and R. van Kempen. 2003. “Escaping Poverty Neighbourhoods in the Netherlands.” Housing, Theory and Society 20: 209–22. Bråmå, Å. 2006. “‘White Flight’? The Production and Reproduction of Immigrant Concentration Areas in Swedish Cities 1990–2000.” Urban Studies 43 (7): 1127–46. Bråmå, Å., and R. Andersson. 2005. “Who Leaves Sweden’s Large Housing Estates?” In Restructuring Large Housing Estates in Europe, edited by R. van Kempen, K. Dekker, S. Hall, and I. Tosics. Bristol, United Kingdom: Policy Press. Brooks-Gunn, J., G. Duncan, and J. Aber. 1997. Neighborhood Poverty: Volume 1, Context and Consequences for Children. New York: Russell Sage Foundation. Brooks-Gunn, J., G. Duncan, P. Kato Klebanov, and N. Sealand. 1993. “Do Neighborhoods Influence Child and Adolescent Development?” American Journal of Sociology 99 (2): 353–95. Brown, L. A., and E. G. Moore. 1970. “The Intra-Urban Migration Process: A Perspective.” Geografiska Annaler B 52 (1): 1–13. Burrows, R., and D. Rhodes. 2000. “The Geography of Misery: Area Disadvantage and Patterns of Neighbourhood Dissatisfaction in England.” In Researching Poverty, edited by J. Bradshaw and R. Sainsbury. Aldershot, United Kingdom: Ashgate. Cadwallader, M. 1992. Migration and Residential Mobility: Macro and Micro Approaches. Madison, WI: University of Wisconsin Press. Card, D., A. Mas, and J. Rothstein. 2008. “Tipping and the Dynamics of Segregation.” Quarterly Journal of Economics 123 (1): 177–218. Clampet-Lundquist, S. 2007. “No More ‘Bois Ball: The Impact of Relocation from Public Housing on Adolescents.” Journal of Adolescent Research 22 (3): 298–323.
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ABOUT THE URBAN INSTITUTE The nonpr
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Institutional Context 59 profession
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ESSAY Neighborhood Data and Locally
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412 About the Authors served as dir
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About the Essayists Riccardo Bodini
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About the Essayists 417 community a
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422 Index alternative scenarios, fo
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