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Understanding Neighbourhood Effects: Selection Bias and Residential Mobility

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Neighbourhood Effects Research: New Perspectives

Abstract

This chapter argues that the most severe problem in the identification of causal neighbourhood effects is selection bias as a result of selective sorting into neighbourhoods. People sort themselves into and out of neighbourhoods and selection bias occurs when the selection mechanism into neighbourhoods is not independent from the outcome studied. However, many studies do not include controls in their models of neighbourhood effects for selection bias. As a result it is impossible to say whether correlations between neighbourhood characteristics and individual outcomes are causal effects, or the result of neighbourhood selection. The chapter argues that more knowledge is needed about residential mobility and the selective sorting into and out of neighbourhoods to better understand mechanisms behind neighbourhood effects. An illustration is provided using a case study for Stockholm, Sweden and a conceptual model is presented which links neighbourhood choices made by individuals and households with individual level outcomes. Both real causal effects and selection effects are featured in the model. The conclusion is that in order to further our understanding of neighbourhood effects we should incorporate neighbourhood sorting into our models of neighbourhood effects.

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Notes

  1. 1.

    The survey is part of the project “Den etniska segmenteringens mekanismer - exemplet bostadsmarknaden” [Ethnic housing segmentation and discrimination – a study of institutional practices and preferences], sponsored by the Swedish Council for Working Life and Social Research, and conducted by Roger Andersson, Irene Molina, and Lina Hedman at the Institute for Housing and Urban Reseach, Uppsala University, and by Åsa Bråmå at the Centre for Municipality Studies, Linköping University.

  2. 2.

    All respondents had stayed 2 years (2005–2007) in one of the four selected neighbourhoods (Luthagen, Svartbäcken/Tunabackar, Stenhagen, Gottsunda). About 50% (where possible, otherwise the entire moving population) had moved to a different neighbourhood in 2008 while the other 50% remained in the same neighbourhood. The total number of respondents was 1,257, the response rate was 48.2%.

  3. 3.

    The U.S. literature consistently refers to “race” while the European literature refers to “ethnicity” and “immigrant status”.

  4. 4.

    For formal descriptions of the conditional logit model and how it differs from the multinomial logistic model, see McFadden (1974), Hoffman and Duncan (1988).

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Author information

Authors and Affiliations

  1. Institute for Housing and Urban Research, Uppsala University, Gävle, Sweden

    Lina Hedman

  2. OTB Research Institute for the Built Environment, Delft University of Technology, Delft, The Netherlands

    Maarten van Ham

Authors
  1. Lina Hedman
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  2. Maarten van Ham
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Correspondence to Lina Hedman .

Editor information

Editors and Affiliations

  1. , OTB Research Institute for the Built Environment, Delft University of Technology, Delft, Netherlands

    Maarten van Ham

  2. , Centre for Housing Research, University of St. Andrews, St. Andrews, KY16 9AL, Fife, United Kingdom

    David Manley

  3. , Urban Studies, University of Glasgow, Glasgow, G12 8RS, United Kingdom

    Nick Bailey

  4. , The Cathie Marsh Centre for Census and S, University of Manchester, Manchester, M13 9PL, United Kingdom

    Ludi Simpson

  5. , Centre for Housing Research, University of St. Andrews, St. Andrews, KY16 9AL, Fife, United Kingdom

    Duncan Maclennan

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Hedman, L., van Ham, M. (2012). Understanding Neighbourhood Effects: Selection Bias and Residential Mobility. In: van Ham, M., Manley, D., Bailey, N., Simpson, L., Maclennan, D. (eds) Neighbourhood Effects Research: New Perspectives. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2309-2_4

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