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The Census Area Statistics Myles Gould Understanding area-level inequality & change.

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Presentation on theme: "The Census Area Statistics Myles Gould Understanding area-level inequality & change."— Presentation transcript:

1 The Census Area Statistics Myles Gould Understanding area-level inequality & change

2 Presentation Content Nature of Census & CAS data Data Tools Research Uses Analysis Issues Examples: Health Variations – CAS, SARS & combining with other data

3 Nature

4 What is the Census? Cross-sectional snapshot of population on single date Source of secondary data Can be examined at many geographical levels Total (nearly!) enumeration (count) & coverage of national population Coverage is consistent (all households asked same questions) Source: Press Association

5 What is the CHCC? Collection of Historical & Contemporary Censuses Census Area Statistics Sample of Anonymised Records (SARs) aggregated to zones 1971, 1981, 1991, 2001 representative sample of individuals 1991 and 2001 Historical Censuses Collection 1851 & 1881 All datasets are available in UK HE & FE sectors

6 Census Statistics A range of products are available Source: Rees et al (2002 )

7 CAS Complex data structure Data aggregated for different geographical units 1991 LBS contain 99 tables for GB, & approx. 20,000 statistical count A relatively simple table…

8 Census Geography Rich source of local statistics for a range of hierarchical geographical units Source: adapted from Martin (1991) 1991 Eng & Wales 2001

9 2001Outputs Headcounts: counts males, females, households for unit postcodes upwards (eg LS2 9JT) Profiles: counts & %s for OAs upwards, but confidentiality protected Key statistics: 50 variables, mainly %s for OAs upwards, LA data already available CAS: 7000 counts in cross-tabulations for OAs upwards: Standard tables: 25,000 counts in more detailed tables for Wards/LAs

10 Tools

11 Casweb Available at

12 CommonGIS

13 Uses & Examples

14 Research Uses Describing demographic & socio-economic profiles of areas –EDA & mapping Selecting & identifying areas for further study Exploring patterns &/or relationships for variables/processes –typically generalized linear modelling Looking at change over time Using as a denominator for calculating other statistics Combining with other secondary data sources in multivariate analysis Identifying & classifying areas with similar characteristics –factor/cluster analysis, composite deprivation indices

15 Census change over time atlas CommonGIS also used to visualise change over time

16 Analysis Issues Ecological fallacy MAUP scale aggregation problem & different results Confounders & ecological analyses Decennial snapshot & out of date quickly Need for standardisation & understanding underlying composition Dealing with unstable population denominators (shrunken estimates) Cross-tabulations of a small number of variables in any one CAS table

17 Some Research Questions Are there variations between different types of people? Are there variations between places? Are there absolute or relative differences? How does place matter? Is it composition (whos in a place) or context that matters? Are variations explained solely by poverty? Do variations vary over time? Is there a widening gap? Are variations becoming more polarized Are there groups & places we should target with policy responses? What aspects of place matter? Health Variations NB not all these questions can be answered with census data

18 Health Variations


20 Local Health Variations

21 Gould & Jones (1996) - Self-reported Limiting Long-Term Illness Analysis National & sub-regional comparisons Consider compositional vs context debate 2% Individual SARs Use multilevel analysis –Individual & area variations at same time 419,550 individuals, 42,073 reported illness 278 SAR Areas - combinations of Local Authority districts (protecting confidentiality of individuals) Health Variations


23 Results Marked relationship illness & age but differences between the sexes are not particularly marked until the older age group there is a 'multiplicative' relationship so that the worst health of all is experienced by partly skilled/unskilled, local-authority rent, with no car geographical variation remain after allowing for individual characteristics – area composition (who lives in a place) Place does make some difference Health Variations

24 Health & Deprivation: Exploratory Survival Analysis Jones, Gould & Duncan (2000) Combine HALS & Census CAS

25 Health Variations Results Deprivation little effects on mortality when in wards where deprivation <0 (mean) –little difference between social classes in areas of relative affluence Marked differences between classes in areas of increasing deprivation

26 Further work SARs, LTLI & Cambridge scores New unpublished work with Kelvyn Jones Looking at absolute & relative variations in morbidity and social advantage Model interaction quartile Cambridge scores (individuals), with area means, & with area Gini coefficients Health Variations

27 Further work SARs, LTLI & Cambridge scores Min 2.13 driver mate; Ql= 19 security officer Md=36 stores controller Qu=46 Farmers; Max=94 General Medical Practitioner In areas with more equality, individual class effects are small Q1 Q2 Q1 Q2 Q3 Q4 Low status High status Q1 Q2 Q1 Q2 Q3 Q4 Health Variations

28 Self-critique Ethnicity in effect treated chaotic conceptions (Sayer 1992) – lumping everything together MAUP, SAR areas big and crude, what do they mean? Some purchase on modelling complex relationships, but still only suggesting reasons for variations ML Point us in right direction for other survey work or some qualitative Caveats

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