Understanding the Socio-Economic Distribution and Consequences of Patterns of Multiple Deprivation: An Application of Self-Organising Maps
In this paper we apply self organising maps (SOM) to a detailed set of material deprivation indicators from the Irish component of European Union Community Statistics on Income and Living Conditions (EU-SILC). The first stage of our analysis involves the identification and description of sixteen clusters of multiple deprivation that allow us to provide a detailed account of such deprivation in contemporary Ireland. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of the manner in which individuals experience their economic circumstances. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. It suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. This finding, combined with those relating to the role of socio-economic factors in accounting for cluster membership, confirms that a focus on a set of eight SOM macro clusters seems most appropriate if our interest lies in broad patterns stratification. For other purposes differentiation within clusters, which clearly takes a systematic form, may prove to be crucial.
Year of publication: |
2009-06
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Authors: | Whelan, Christopher T. ; Lucchini, Mario ; Pisati, Maurizio ; Maitre, Bertrand |
Institutions: | Economic and Social Research Institute (ESRI) |
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