Scottish Longitudinal Study
Development & Support Unit
Impact of Residential Sorting on the Valuation of Environmental Amenties/Disamenities and Estimation of Neighbourhood Effects
Guanpeng Dong (University of Sheffield)
Jon Minton (University of Glasgow)
Nick Bailey (University of Glasgow)
Gwilym Pryce (University of Sheffield)
Stephan Heblich (University of Bristol)
Chris Timmins (Duke University)
23 Sept 2015
Sorting processes have been recognised as one of the main factors that undermine the reliability of existing UK estimates of neighbourhood effects, and of the economic value of various social/environmental (dis)amenities. Lack of data has meant that it has not been possible to resolve the selection bias arising from omitted sorting effects in UK studies. However, the impact could be profound. For example, someone who loses their job through long term ill health will likely need to move to cheaper accommodation. They are then likely to be sorted by the housing market into poorer neighbourhoods with lower rents/house prices. So the observed cross-sectional correlation between neighbourhood deprivation and poor health may not be causal but, to some extent, the result of residential sorting. Similarly, sorting processes can be an important determinant of apparent associations between poverty and proximity to locally undesirable land uses (LULUs) because the siting of a LULU can put downward pressure on prices which makes the area affordable for low income households, which in turn changes the social composition of the area. So there are important social justice implications of residential sorting (Depro, Timmins and O’Neil, 2013).
A reduction in crime or the improvement in school performance may boost house prices. However, such improvements are likely to change the social mix of the affected neighbourhoods. Failure to take into account selection effects leads to bias when using house price change to gauge the economic value of a variety of social and physical environmental effects.
The AQMEN Urban Segregation and Inequality research strand aims to combine innovative measures of social segregation and inequality (Massey & Denton 1988; Galster & Cutsinger 2007, Lee, Minton and Pryce, 2015) with cutting-edge longitudinal and sorting-model techniques (Kuminoff, Smith & Timmins 2013) to explore the drivers of, and constraints on, household location choice (the causes of neighbourhood segmentation, sorting and inequality – Bayer et al. 2004), the effect on life chances and wellbeing (the consequences – Galster 2007) and the implications for how we design interventions (development of policy simulation toolkits).
With the aid of international expert Timmins we aim to address the endogeneity associated with household location decisions, which profoundly affect our ability to understand segmentation (Schelling 1971) and ascribe value to various forms of risk reduction (Bayer et al. 2004). We propose not only the first application of sorting model methodology to UK data but also to advance these techniques because we shall have access to better data (for example, US Census data, which forms the basis for much of the current research on residential sorting, does not permit longitudinal analysis of individuals over time).
Overall, our central hypothesis is that residential sorting processes can have profound effects on both the valuation of (dis)amenities and on the estimation of neighbourhood effects in a Scottish context.
- What is the impact of residential sorting on the economic valuation of (a) crime, (b) access to good schools, (c) environmental (dis)amenities and (d) proximity to social mix?
- What is the impact of residential sorting on the estimation of neighbourhood effects, particularly outcomes arising from prolonged exposure to: (a) concentrated poverty; (b) ethnic/religious segregation; (c) crime; (d) environmental (dis)amenities; (e) proximity to social boundaries; and (f) poor educational opportunities?
- What is the role of residential sorting in mediating the impacts on a variety of outcomes including: (a) educational performance; (b) employment trajectories/life chances; (c) health and wellbeing.
We are in the process of preparing a much larger request to access SLS data in order to construct and test sorting models. In the meantime, this application is to enable us to conduct some preliminary work. We are aiming to get a better understanding of residential mobility rates for different groups and different locations in order to be able to calibrate some very limited sorting models based on non-SLS data. More specifically, we are seeking to extract ‘stayer propensities’ (the proportion of people not moving home) at Scottish and Datazone level. We aim to compare the SLS estimates with those derived from other sources, notably from NHS-CHI data, to validate the use of the latter.
Bayer, P., R. McMillan, and K. Rueben (2004): “An Equilibrium Sorting Model of Sorting in an Urban Housing Market”, NBER Working Paper No. 10865.
Depro, B., O'Neil, M. and Timmins, C. (2014) "White Flight and Coming to the Nuisance: Can Residential Mobility Explain Environmental Injustice?" Working Paper
Galster, G. (2007) "Neighbourhood Social Mix as a Goal of Housing Policy: A Theoretical Analysis," European Journal of Housing Policy, 7(1), 19-43.
Kuminoff, N., Smith, K. and Timmins, C. (2013) "The New Economics of Equilibrium Sorting and Policy Evaluation Using Housing Markets," Nicolai Kuminoff and Kerry Smith. Journal of Economic Literature. 51(4), 1007-1062.
Schelling, T.C. (1971) Dynamic Models of Segregation, Journal of Mathematical Sociology, 1, 143-186.
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