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SIMD and the flaws of area-based socio-economic profiles

SIMD and the flaws of area-based socio-economic profiles. Paul Lambert, University of Stirling Presentation to the Scottish Civil Society Data Partnership Project (S-CSDP), Webinar 5 on ‘Dealing with data: Taking advantage of data resources about regions and area’

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SIMD and the flaws of area-based socio-economic profiles

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  1. SIMD and the flaws of area-based socio-economic profiles Paul Lambert, University of Stirling Presentation to the Scottish Civil Society Data Partnership Project (S-CSDP), Webinar 5 on ‘Dealing with data: Taking advantage of data resources about regions and area’ www.thinkdata.org.uk, 31 Mar 2016

  2. “The Scottish Index of Multiple Deprivation identifies small area concentrations of multiple deprivation across all of Scotland in a consistent way. It allows effective targeting of policies and funding where the aim is to wholly or partly tackle or take account of area concentrations of multiple deprivation.” What is SIMD? (from http://www.gov.scot/Topics/Statistics/SIMD ) • SIMD versions in 2012, 2009, 2006, 2004 • Ranks of relative deprivation at the data zone level • SIMD 2012 ranks from 1 (most deprived) to 6505 (least deprived) • Commonly converted to quintiles, deciles, or binary summary tools • e.g. in most deprived 15% • Deprivation indicators take account of: income (28%), employment (28%), health (14%), education (14%), geographic access (9%), crime (5%), housing (2%) • http://www.gov.scot/Topics/Statistics/SIMD/BackgroundMethodology S-CSDP, 31 Mar 2016

  3. Usefully seen as one of a number of different ways that social inequalities can be represented What else is SIMD? • Several other area-based measures and a general drift in social science towards area-based socio-economic profiles • [e.g. Dorling 2013; www.viewsoftheworld.net ] • Many other measures at the individual or household level… • See ‘webinar 3’ on standard measures and variables & their scientific attractions [Bulmer et al. 2010]; [Shaw et al. 2007] • Other sensible ways of measuring position in the structure of social inequality include using occupation, education, tenure, wealth, assets, consumption patterns, and plenty more… S-CSDP, 31 Mar 2016

  4. Strengths and weaknesses of SIMD? • High quality, well-documented, preparatory work • Downloadable data resource • Good predictor of individual behaviours • Plausible policy-oriented evidence tool • Some survey datasets already linked to SIMD • Positive features • A few specific and operational flaws • Wider scientific challenges • A rank, not a score, without a simple method of aggregation over areas • Not suited for comparisons with rest of UK • Data linkage may not be easy (or available at all) S-CSDP, 31 Mar 2016

  5. …Why SIMD raises wider scientific challenges… • Not everyone in an area is the same… • Variations esp. by working activities, family status, age and income • Area based policies may misdirect resources • Areal boundaries are different in different situations (e.g. health services, education, transport, crime, travel-to-work areas) • Cultural stereotyping of area-based profiles? • Areas aren’t good units for analytical research S-CSDP, 31 Mar 2016

  6. …Why SIMD raises wider scientific challenges, ctd… • Not everyone in an area is the same… • Areas aren’t good units for analytical research • Cartographies encourage ‘bivariate’ thinking, when most social mechanisms are ‘multivariate’ • SIMD is a combined index, but different aspects of people’s lives are usefully separated • Can introduce disclosure risks in geographical data • Areas can mask individuals’ temporal changes • Aggregating over areas is technically difficult; with SIMD, users adopt simplifying strategies (e.g. % live in a 15% most deprived ward) S-CSDP, 31 Mar 2016

  7. Example: Errors when area-based profiles get things wrong… • Predicting volunteering with SHS 2012… • Worse errors from this model are those with (a) high prob. but not volunteering, and (b) those with low prob. who do volunteer • includes: 73% women, 44% most advantaged jobs, twice as likely to cite ‘lack of time’ as reason than other non-vol.s • Includes: 61% men, 54% not in work, average age, 4 times less likely to be married than other vols S-CSDP, 31 Mar 2016

  8. Summary: Area level socio-economic classifications like SIMD are handy tools with some appealing properties, but it is also worth being a little wary… • Encourage a bivariate, descriptive approach • Neglect (or drift away from) other compelling ways of understanding social processes • Tend to neglect/downplay some important individual level influences upon social behaviours and experience. When studying third sector activity or engagement, especially with regard to working time, type of occupation, and family status References cited • Bulmer, M., Gibbs, J., & Hyman, L. (Eds.). (2010). Social Measurement through Social Surveys: An Applied Approach. Aldershot: Ashgate. • Dorling, D. (2013). The Population of the UK, 2nd Edition. London: Sage. • Shaw, M., Galobardes, B., Lawlor, D. A., Lynch, J., Wheeler, B., & Davey Smith, G. (2007). The Handbook of Inequality and Socioeconomic Position: Concepts and Measures. Bristol: Policy Press. S-CSDP, 31 Mar 2016

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