There has been a growing emphasis on the spatial targeting of policy options in the areas of poverty and social exclusion. In this chapter, the focus will be on using spatial microsimulation models to look at the local impact of policies related to inequality and poverty. Spatial data typically exist in national census datasets, but very frequently these data do not contain information on incomes. The challenge, therefore, is to generate datasets that are spatially consistent, in order to facilitate the linkage of spatially defined data, such as local-area census data, with nationally representative surveys that contain labour, demographic, and income information. Spatial microsimulation modelling helps with this. The purpose of this chapter is to provide an insight into the rationale, development, and application of the spatial microsimulation method for analysing the spatial distribution of inequality. The policy context for spatial-inequality analysis is discussed initially, before considering the statistical method for synthetically generating spatially consistent, household-income-distribution data. Approaches to validating these methods are then discussed, before applying quantitative methods to measuring spatial inequality in a national setting.
Validation of Spatial Microsimulation Models: a Proposal to Adopt the Bland-Altman Method
