Transcript Innovative use of centrally collected data to inform policy at city and

Innovative use of centrally collected
data by regions and cities
Dev Virdee
Office for National Statistics
United Kingdom
Introduction
• NSI’s are custodians of huge amounts of data
- A valuable resource, a national asset, a public good
• Cities and Regions need the evidence base to
develop their strategies
• Regional policy shift
- from “Equity or Efficiency” to “Equity and efficiency”
- all regions should realise their full potential
• Many collect their own data and develop local
information systems, unable to access centrally
held data or not knowing of its existence
Presentation will cover:
• Examples of NSI’s providing regional & local
data
• Regionally and locally developed examples
• The recent UK organisational innovation:
- ONS Regional Statisticians
• Some suggestions
NSI Example 1: Statistics Netherlands
Municipalities & neighbourhoods
443 municipalities
On average 76 km2 &
37 thousand inhabitants
11,500 neighbourhoods
On average 3 km2 &
1430 inhabitants
Example: Utrecht
Database StatLine (http://statline.cbs.nl/)
New tools for disseminating
neighbourhood statistics
• Google Earth:
• Statistics Netherlands’ map layer in
Google Earth
• Google Maps:
• www.CBSinuwbuurt.nl
• (Statistics Netherlands in your
neighbourhood)
Map layer in Google Earth
1. Install Google Earth (from version 4.1)
2. Go to the Statistics Netherlands’ website
3. Click
the ‘Start
Google
Earth’
Button
(Type ‘Google Earth’ in the search engine)
Publication Gemeente
Newsop
and
Maat
articles
Link to database
StatLine
Users
can give comments
Future developments
• Updates
• More subjects
• More possibilities for comparisons between
neighbourhoods
• Finding a neighbourhood using postcodes
NSI Example 2: Neighbourhood Statistics
Background: The Website
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Live since 2001
Part of National Statistics On-line
Freely available
Over 100,000 visits per month
Data retrieval and visualisation
Complements other websites
Background
Data Availability
Data covering these topics..
Supplied by..
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Access to Services
Crime and Safety
Economic Deprivation
Education, Skills & Training
Health
Housing
Income and Lifestyles
Physical Environment
Population and Migration
Work Deprivation
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ONS (inc 2001 Census)
Dept of Health
Department of Work and Pensions
Department for Communities and
Local Government
Dept for Education and Skills
Home Office
Valuation Office
Met Police
Dept for Transport
Registry Trust
Department for Environment Food
and Rural Affairs
and others..
all data provided for small areas..
Who is the service aimed at?
• Designed for people involved in tackling
deprivation at the local level
BUT
• Used by much wider audience
Indicators and analysis
• Indicator development
e.g. benefit claimants
The Indicator Catalogue allows quick
access to data.
LEA
Health
Parish
WPC
OA
LSOA
MSOA
Ward
LAD
County
GOR
Country
Available Geographies
Domain
Dataset Name
Indicator Name / Data
Type
Health and Care
Teenage Conceptions,
2002
Teenage Conceptions (Rate per
1000)
aaaa
2002
Health and Care
Teenage Conceptions,
2003
Teenage Conceptions (Rate per
1000)
aaaa
2003
Health and Care
Years of Potential Life
Lost Indicator, 20002003
Years of Potential Life Lost
Indicator (Ratio)
Housing
Housing
Changes of Ownership by Dwelling
Price, Type of Sale: Cash as
Changes of Ownership Percentage of All Sales
by Dwelling Price, 2001 (Percentage)
Changes of Ownership by Dwelling
Price, Type of Sale: Cash as
Changes of Ownership Percentage of All Sales
by Dwelling Price, 2002 (Percentage)
a
Dataset
Date
2000 2003
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2001
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2002
Indicators and analysis
• Indicator development
e.g. benefit claimants
• Analysis in place of data
e,.g teenage conceptions
Under 18 conceptions data for wards in London
Kensington and
Kensington
and Chelsea
Chelsea
Quintile Groups
Low Pop
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Indicators and analysis
• Indicator development
e.g. benefit claimants
• Analysis in place of data
e,.g teenage conceptions
• Articles about the data
e.g. population turnover
• Different ways of visualising the data
“Regional” example: Scottish Government
The Scottish Government
Government’s Purpose
• To focus Government and public services on creating
a more successful country, with opportunities for all
Scotland to flourish, through increasing sustainable
economic growth
Five Strategic Objectives - to create a:
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Wealthier and Fairer Scotland
Safer and Stronger Scotland
Smarter Scotland
Greener Scotland
Healthier Scotland
“Scotland Performs” website
• Purpose
• Five Strategic Objectives
• 15 National Outcomes
Describe what Government wants to achieve
• 45 National Indicators
Enabling progress to be tracked
“Scotland Performs” website
15 National Outcomes
45 National Indicators
45 National Indicators
Local example: Tower Hamlets, London
Local example: Fife Council (Scotland)
Local example: Norfolk
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90 miles of coast, 250 miles of waterways, 6,329 miles of roads
Over 200 conservation areas, more than 10,000 listed buildings
and more than 350 scheduled ancient monuments
A population of over 832,000
7 districts and 541 parishes
The County’s population
• 38 % in built up areas of Norwich, Great Yarmouth and King's
Lynn
• 18% in Market towns
• 40% in parishes of over 300 pop
• 4% in parishes with less than 300
An increasingly elderly population
A high proportion of second homes
www.norfolkdata.net
Masked pockets of rural deprivation in North Norfolk
20% most deprived SOAs
20% most deprived OAs
www.norfolkdata.net
Hidden deprivation in Holt
20% most deprived SOAs
20% most deprived OAs
But is this enough?
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Good practice exists but only in some local
authorities
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Much more needs to be done to “join up” those with
similar interests
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NSI’s can play a key role by proactive engagement
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The need recognised in UK through report on
improving the information needed for regional policy
making: the “Allsopp Report”
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Included recommendation that ONS should have a
“Regional Presence”
An innovation in organisation
ONS Regional Statisticians
- but what do they do?
Why do we need
Regional Statisticians?
Why do they have to be in the Regions?
Regional statistics – Policy Drivers in UK
• Government’s regional growth target
• Devolution to Scotland, Wales & Northern Ireland
• England - Regional Development Agencies (RDA’s)
• European Structural Funds implemented in regions
• Urban policies, City Regions, London devolution
• Rural policies, Rural Proofing
• Local Area and Multi Area Agreements
• Neighbourhood Renewal
• Sub National Review – joining up the various areabased policies
ONS Regional Statisticians
• Regional Development Agencies contribute
£1 million towards the costs
• Government’s Comprehensive Spending Review
confirmed that they should continue to do so
• Two ONS Statisticians sitting in each RDA or
Regional Observatory since April 2007
Where they are:
•Some based in RDA
offices
•Others in Regional
Observatories
•Line management from
ONS London
•Topic leads
• Regular “get-togethers”
and visits by line
managers
Core functions – 3 types of roles
• Facing the Region – statistical expert/advisor
to the Region, establishing links with regional
stakeholders
• Facing the Centre – feeding regional
knowledge back to HQ to improve ONS
outputs
• Work in collaboration with regional and subregional institutions to improve evidence base
for their region
Closer working with ONS/GSS
• Acting as a first point of ONS contact for key
regional bodies
• Establish two way dialogue
• To act as focus to feed views through from regional bodies
• Establish priorities for improving regional statistics
• Facilitate more efficient consultation and collaboration
• Assisting ONS in its decision-making by
providing ONS a consolidated picture of sub
national data use/dissemination, issues and
developments etc
Support / improve ONS outputs
• To gather intelligence to help improve the quality
of ONS data and processes, keeping ONS up to
date with changes in the regions
• To quality assure final estimates of regional GVA
– Input to quality assurance of surveys
– To help improve the quality of the Inter-Departmental
Business Register (IDBR) and business survey data;
– Identify local business intelligence that can best feed
into the detail of survey processes
– Improve commentary / analysis / metadata
• Feed back to ONS/GSS
Supporting the statistical needs of region
• Providing support and advice on use of statistics
– Providing access to data
– Providing independent advice and briefing on ONS/GSS data
– Facilitating engagement with ONS on technical issues
• Supporting the region
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Aid understanding of their area
Facilitate provision of training to regional partners
Assist in assessment of locally commissioned data
Raising statistical awareness and supporting statistical practitioners
Bringing region together
Improve quality of local information
• Collaborative projects
Examples of collaborative projects
• Priority Sectors for Regional Economic Strategies
Example of intelligent use of Business Register
NE Chemical industry - background
• NEPIC = North East Process Industry Cluster
covering chemicals, pharmaceuticals,
biotechnology and their supporting industries
• pressure group to promote these industries,
consisting of around 500 businesses.
• powerful media presence in region
• Estimated £9bn Gross Value Added (GVA), about
25% of the GVA of whole North East!
• despite lack of credibility, figure widely used in the
press and by senior management of the RDA
Procedure
• Regional Statistician worked with NEPIC – positive,
wanted official stamp of approval for their cluster
• ONS agreed match their list of businesses to the
Business Register, produce a more robust estimate
of employment and GVA
• results in an estimated total GVA for NEPIC of
around £1.86bn, with 3.3% of workforce producing
about 5% of regional GVA.
Examples of collaborative projects
• Priority Sectors for Regional Economic Strategies
• Women in Enterprise in South East
Background to project
• In the South East Regional Economic Strategy,
target of 10,000 extra female owned businesses
in period 2005-10
• Issue of measurement and setting of baseline
• SE England Development Agency (SEEDA)
Chief Executive is head of national task force for
women - important for the South East
• SEEDA very keen to lobby for more and better
data disaggregated by sex
• Regional Statisticians used LFS/Annual
Population Survey data for analysis
Issues to be looked at
• What’s happening with Self-employment in the
South East (rates, levels, emerging issues)
• Why do women become self-employed?
• What sort of work are they doing?
• Can we look beyond self-employment and
create a better measure of business ownership?
Some results ….
Index of full and part time self-employment for women
160
140
120
80
60
40
LFS Quarter
od07
js07
aj07
jm07
od06
js06
AJ06
od05
js05
aj05
jm05
od04
aj04
od03
aj03
od02
aj02
od01
0
JM06
20
aj01
Index
100
Full time
Part time
Male
Female
Saw the
demand/
market
Opportunity
arose –
capital, space,
equipment
available.
Wanted more
money
Other
Nature of
occupation.
Family
commitments/
wanted to work
at home.
To be
independent/
for a change
Percentage of respondents
Why become self employed?
Reasons for becoming self-employed
35%
30%
25%
20%
15%
10%
5%
0%
What do female self-employed do:
• Different occupations from men
- Males dominate in construction
- Females in personal services – childcare, therapists,
cleaning, hairdressing
- Some similarities – shopkeepers, doctors, artists, authors
• Different industries – reflecting above
• Different places of work
- More at home
• Different hours of work
- Fulltime female self-employed work longer than men, part
timers work shorter hours
Implications
• RDA has to consider whether meeting target is
good thing or not
• Are many women working as self-employed
because they have to, or by choice?
• Are they realising their full potential and using
skills or doing what they have to?
• More analysis needed to understand fuller
situation
Other examples of collaborative projects
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Priority Sectors for Regional Economic Strategies
Women in Enterprise
Access to Services
Counterfactuals projects
Analysis of foreign ownership of companies
Rural productivity strategy
Cultural and Creative industries
Innovation
Perceptions indicator development
Olympic Games Global Impact Study
….and
What next?
Evaluation of Regional Statisticians
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Internal and external evaluations conducted
Both strongly endorsed benefits
Significant contribution to regions’ evidence bases
Helped to increase statistical capacity of the regions
and local authorities through statistical training
Helping to improve ONS data by feeding knowledge
of regions back to sources of data
Regions feel that doors have been opened to them
Lessons learnt, need to adapt at centre to be able to
take on feedback more effectively
……and Regions want larger teams…..
Picture courtesy of
David Marlow, EEDA