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SARs User Group Meeting, Royal Statistical Society, London, 16 November 2006 Research Potential of the Small Area Microdata Tony Champion

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Presentation on theme: "SARs User Group Meeting, Royal Statistical Society, London, 16 November 2006 Research Potential of the Small Area Microdata Tony Champion"— Presentation transcript:

1 SARs User Group Meeting, Royal Statistical Society, London, 16 November 2006 Research Potential of the Small Area Microdata Tony Champion

2 Research Potential of the Small Area Microdata Introduction to SAM Benefits (& downside) of SAM Demonstrating SAMs research potential: approach A SAM-based example in detail: multiply- deprived people Other examples in outline Concluding comments & extra thoughts

3 Introduction to SAM 5% sample of individual persons from 2001 Census of UK 2,964,871 records: 2,478,448 in England, 146,409 in Wales, 254,634 in Scotland, 85,380 Northern Ireland Most analyses would exclude students living away in termtime (28,486), leaving 2,883,566 in households, 52,819 in CE Downloadable from CCSR (59MB zipped, without imputation flags)

4 Benefits (& downside) of SAM Primary benefit is geographical detail (SA): Coding to 405 LAs of GB and 18 PCs of NI (only GB LAs not separate: City of London/ Westminster; Scilly/Penwith; Orkney/Shetland) Geography same as CAMS; infinitely better than ILSARs 13 Regions; even better than 1991 ISARs 278 SAR Districts of GB Secondary benefit: 5% sample size, cf. 3% ILSAR, 2% 1991 ISAR Downside: somewhat fewer variables (72, cf 87 of ILSAR) and generally less breakdown of categories for each variable

5 Demonstrating SAMs research potential Approach: Identified published studies that used 1991 ISARs SAR Districts (from SARs Pubtrawl) Assessed how far a sample of these could be replicated with SAM Found that many can, so worked up basics of five examples using 1991 ISAR study as model Also developed a new topic of personal interest (though taking this further with CAMS) These available on SARs website. Here, present one in detail; overview the other five.

6 A SAM-based example in detail: Multiply-deprived people Chosen partly because the original study was in a rather obscure publication: Coombes M & Atkins D (1996) in Atkins et al.s Urban Trends in England (HMSO), pp Also because it demonstrates the value of microdata over small-area census tables: measuring MD for individual people, not areas, so avoiding ecological fallacy. Central question: To what extent do under-16s live in households experiencing high levels of problems?

7 5 steps in this analysis STEP 1: identify indicators associated with deprivation for this group (SAM variable = ) Living in HH without earner (hnearnra=1) Living in overcrowded HH (densitya=3) Living in HH without car (carsh=0) Living in HH without at least one housing amenity (bathwc=2 and/or cenheat0=2) STEP 2: Extract records of all children in HH by whether these conditions are met, plus code of usual residence (lacode) STEP 3: Crosstabs for % children by all possible combinations of the four problems, as shown:

8 Prevalence of 4 problems for under 16s (% total) All children100.0% NONE of the problems 1 problem only:No earners Overcrowded Without car Lacking amenity 2 problems:No earners & Overcrowded No earners & Without car No earners & Lacking amenity Overcrowded & Without car Overcrowded & Lacking amenity Without car & Lacking amenity 3 problems:ALL except No earners ALL except Overcrowded ALL except Without car ALL except Lacking amenity ALL 4 problems

9 5 steps in this analysis (cont.) STEP 4: Calculate for each LA the % children with severe problems = in HH with no earners (seen as biggest single problem) as well as experiencing at least 2 of the other 3 problems – in 1991 Tower Hamlets top with 64% STEP 5: identify broader geographical dimension by aggregating raw area-level data to types of areas, e.g. ONS District Classification – in 1991 Inner London highest at 44.5%, Principal Metro Cities next 39.9% (using OPCS classn)

10 Taking this type of application further: Follow up other aspects of the original study, eg. Incorporate risk factors alongside problems: - in lone parent family - in HH headed by low-skill person - non-white Apply same steps to other population groups, e.g. over 70s Extend from just England to whole of UK (relevant variables are UK-wide in SAM) Update the deprivation indicator variables etc Use an alternative classification of areas

11 Compare SAM-based results with 1991? Possibly, but not straightforward because of the many changes, e.g. - in coverage/imputation - in population definition (students at termtime address in 2001, can this be handled?) - in variable specs (e.g. amenities) - SAR Districts for 1991, cf individual LAs.

12 Other examples: exploring geographical variation in female labour force particn Replicating Ward C & Dale A (1992) in Regional Studies 26, That study used 1981 data from ONS LS, but possible using 1991 ISAR (see also Fieldhouse & Gould 1998 on unemployment using ISAR) Females in FT & PT work and not working, after controlling for ethnicity, life-course stage and area type (classification of TTWAs) Would need to use best-fit of LAs to TTWAs, or choose any other geography based on LAs But cant replicate some other studies in this field because of no Industry variable in SAM

13 Other examples: analysing perceived limiting long term illness (LLTI) Replicating Gould MI & Jones K (1996) in Social Science & Medicine 42, Morbidity variations linked to sex, age, tenure, ethnicity, car ownership & social class, but using multi-level modelling to control for these, still big variations between SAR Districts All the variables are available in SAM, though a different socio-economic and ethnicity classns and slight change in tenure; LAs now possible Also, Boyle et al (2001) in SS&M is largely replicable, with SAM containing all but one of the 12 variables used – exception = industry

14 Other examples: adding policy value to sub-national household projections Replicating King D & Bolsdon D (1998) in Environment & Planning A 30, This used 1991 HH SAR to add value through data linkage including data for Regions 2001 Licensed HH SAR has no Regions variable, but versions of original analyses can be done at LA level in SAM (incl. urban/rural) SAM contains both household reference person and (ODPMs) household headship Can analyse characteristics of concealed households; match household size with dwelling size; see % with children

15 Other examples: in-migration Replicating Ford T and Champion T (2000) Who moves into, out of and within London? Area 32, Use microdata to classify persons into types that are more rounded than from area tables (using sex, age, econ position, occupation, relation to HRP, family type, HH size, tenure) Calculate composition by person type of in- migrants and compare with non-migrants etc Calculate % contribution of in-migrants to total residents of each person type (i.e. rate) All this at LA level; for Regions, can also cover out-migration (using migorgn as in ILSAR)

16 Other examples: Commuting behaviour of recent in-migrants of rural settlements No equivalent study using 1991 SAR (as far as I can tell) – part of my current research, trialled on SAM but full study uses CAMS Questions: Do recent in-migrants commute further than longer-term residents? Does DTW depend on distance of move or type of origin? Identify rural settlements using classification of LAs on lacode (in CAMS also urbrurew) Classify rural residents by migrant status using miginda and distmova Tabulate rural residents by migrant status and distance to work (dstwrka); model DTW to control for socio-demographic/area differences

17 Concluding comments & extra thoughts SAMs great benefits of LA geog & 5% sample If happy with Regions, then can combine with ILSAR to give 8% sample – useful for smaller population groups in multi-way crosstabs etc Less detail on some variables, but still quite disaggregated for some; e.g. 13 ethnic groups in SAM for England & Wales (cf 16 in ILSAR) Can replicate many 1991-ISAR-based studies, with no or little change in their variable detail, and trial new LA-based jobs pre-CAMS Comparison of 2001-based results with 1991 not straightforward, but can use lacode to aggregate to 1991 Standard Region (cf GOR)

18 SARs User Group Meeting, Royal Statistical Society, London, 16 November 2006 Research Potential of the Small Area Microdata Tony Champion

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