Is Poverty Decentralising? Quantifying Uncertainty in the Decentralisation of Urban Poverty

In this paper we argue that the recent focus on the suburbanisation of poverty is problematic because of the ambiguities and inconsistencies in defining suburbia. To improve transparency, replicability and comparability, we suggest that research on the geographical changes to the distribution of poverty should focus on three questions: (1) How centralised is urban poverty? (2) To what extent is it decentralising? (3) Is it becoming spatially dispersed? With respect to all three questions, the issue of quantifying uncertainty has been under-researched. The main contribution of the paper is to provide a practical and robust solution to the problem of inference based on a Bayesian multivariate conditional autoregressive (CAR) model, made accessible via the R-software package CARBayes. Our approach can be applied to spatio-temporally autocorrelated data, and can estimate both levels of and change in global RCIs (relative centralisation index), local RCIs and dissimilarity indices. We illustrate our method with an application to Scotland's four largest cities. Our results show that poverty was centralised in 2011 in Glasgow, Dundee and Aberdeen. Poverty in Edinburgh, however, was decentralised: non-poor households tend to live closer to the centre than poor ones, and increasingly so. We also find evidence of statistically significant reductions in centralisation of poverty in all four cities. To test whether this change is associated with poverty becoming more dispersed, we estimate changes to evenness and local decentralisation of poverty, revealing complex patterns of change.