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									CASE: Going local
Applying & developing sub-national analysis

 Increasing ‘localism’
  –More power at a local and sub-local level
  –Reducing role of central government:
  –Not imposing from above

 Limited resources locally to exploit social science
Local needs

 Data: Base-lining, benchmarking, transparency,
 Analysis: Business cases, policy options
 Evidence: relevance, effectiveness and
  persuasiveness (partnerships)
 Others?
What CASE has - Data

 Baselining, benchmarking, accountability
  –‘Regional Insights’
  –Toolkit for asset mapping

 Data (for making evidence)
  –Understanding Society (longitudinal data)
What CASE has - Analysis

 Drivers, Impact and Value project
  –Understanding local population (Drivers)
  –Policy simulation tool (Drivers)
  –Valuing policy impacts (Value)
 Issues:
  –Drivers work based on Taking Part
  –Local conditions not fully accounted for
What CASE has - Evidence

 Drivers, Impact and Value project
  –Sound evidence for learning effects in young
  –Research database: practical benefit
 Issue
  –Not geographic specific
  –Need to make the ‘generalisation’ leap
Where next?

 No guarantee of a future for CASE
 Could focus on a number of areas:
  –More data at local level
  –More analysis at local level
  –Better access to data for local areas
Marshal more data...

 CIPFA data
 Audience data
 Wider economic and social data
 Drawing together local datasets on assets etc

 What else?
More analysis at a local level...

 Eg. GIS analysis
 Nearest neighbour etc
 Danger of top-down analysis
 Resource-heavy for centre
 Needs demand from local areas to justify
Density of all arts, museums, libraries and sports and heritage assets
per head of population as at 2008/9

This map shows the steep differences
in both privately- and publicly-funded
culture and sport opportunities by local

Fewer than 8% of authorities have over
25% of the assets

       1-3 assets per 1000
       3-4 assets per 1000
       4-7 assets per 1000
       7-34 assets per 1000
Count of all arts, museums, libraries and sports and heritage assets
per local authority as at 2008/9

 Removing the population figures from
 the analysis has little effect overall, with
 rural areas remaining higher up the
 scale. However, small town type areas
 now tend the lower part of the scale

 Fewer than 8% of authorities have over
 25% of the assets

        9-250 assets
        251-385 assets
        386-558 assets
        559-4056 assets
             for each local authority
             libraries, sport and heritage
             Density of arts, museums, galleries,

             assets per hectare per 10,000 people

                                                                 00BK: Westminster
                                                                     21UD: Hastings
                                                                   12UB: Cambridge
                                                                  23UB: Cheltenham
                                                                       42UD: Ipswich
                                                                     00AM: Hackney
                                                             00MA: Bracknell Forest
                                                                        00HH: Torbay
                                                                  43UE: Mole Valley
                                                                   00BC: Redbridge
                                                                        00AD: Bexley
                                                                    26UG: St Albans
                                                              40UF: West Somerset
                                                                     43UD: Guildford
                                                                    00BS: Stockport
                                                                        00AK: Enfield
                                                                    00BX: Knowsley
                                                                         00BL: Bolton
                                                                                                                                                                                                  but is not consistent.

                                                                          45UC: Arun
                                                                     26UC: Dacorum
                                                        17UJ: North East Derbyshire
                                                           26UF: North Hertfordshire
317th & 303rd out of 324.
                                                                    42UH: Waveney
                                                                        00CA: Sefton
counties have much lower

                                                                   29UH: Maidstone
                                                                                                                                                                                                  Accessibility is very unevenly

                                                          31UE: Hinckley & Bosworth
                                                                   00CY: Calderdale
                                                            38UD: South Oxfordshire
                                                              42UG: Suffolk Coastal
                                                                                                                                                               Plot of C&S density scores by LA

                                                                       29UB: Ashford
                                                                      18UK: Torridge
                                                             30UP: West Lancashire
such as Leeds and Birmingham are
                                                                                                                                                                                                  distributed. A rural-urban divide exists,

                                                                      00KB: Bedford
accessibility. Key metropolitan areas

                                                                   18UD: Mid Devon
areas of the south coast. Larger, rural
Highest accessibility is in London and

                                                            00HD: S Gloucestershire
                                                    00EW: Cheshire West and Chester
Better access to data...

 Already got Taking Part Netquest
 Could go further with ‘regional insights’ data?
 E.g. Dynamic website, bespoke analysis
 Skills available locally?
 Demand for this?
 Requires resources to develop...
Your views

 We are exploring different channels

 How do you think CASE can be more relevant for
  local areas?

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