CASE: Going local Applying & developing sub-national analysis Issue Increasing ‘localism’ –More power at a local and sub-local level –Reducing role of central government: –Not imposing from above BUT... Limited resources locally to exploit social science Local needs Data: Base-lining, benchmarking, transparency, accountability 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 people –Research database: practical benefit Issue –Not geographic specific –Need to make the ‘generalisation’ leap Where next? No guarantee of a future for CASE BUT... 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 BUT... 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 authority. 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 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 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 BUT... Skills available locally? Demand for this? Requires resources to develop... Your views We are exploring different channels QUESTION IS... How do you think CASE can be more relevant for local areas?
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