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United Nations Economic Commission for EuropeStatistical DivisionUnited Nations Economic Commission for EuropeStatistical Division
Regional CollaborationDeveloping statistical processes
and frameworks
Paris 21 Forum: Reinforcing Statistical Co-operation at the Regional Level to Support Sustainable Development, Paris, 5-6 October 2015
Taeke GjaltemaUNECE
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Guide Questions
Forms of cooperation in statistics (intra- and inter-regional)
How balance interest rich/advanced vs countries in transition
Good practices to share Which areas more appropriate at regional
level
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UNECE Established in 1947 by ECOSOC to promote pan-
European economic integration
One of five Regional Commissions
56 Member states (Conference European Statisticians 66) and 9 territories
UNECE Region: Europe (incl. Turkey & Israel), Caucasus, Central Asia, North America (Canada & USA) Territories (e.g. Bermuda, Greenland, Kosovo)
25 DAC members (18 ODA recipients) & 21 countries in development (2 low, 6 low-middle, 13 upper-middle)
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UNECE: Standards and Conventions
Environment: Water conventions, Aarhus convention, industrial accidents etc.
Transport: Vehicle safety, transport of dangerous goods, harmonized labelling chemicals, TiR (custom) convention, Road safety and signs
Trade: trade facilitation and e-business (UN/CEFACT), agricultural quality standards
Sustainable Energy: Gas centre, UNFC (classification mineral resources/fossil fuels)
Statistics: fundamental principles of Official Statistics, and:
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UNECE Statistical Division
Meetings, Workshops, Seminars Technical assistance and training Guidelines and Recommendations
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Conference of European Statisticians (CES) One of the oldest statistical bodies working on
international statistics (founded 1953, origin stems from 1928)
Governing body on statistics of UNECE Steered by CES Bureau (Chief Statisticians from 8
countries and 6 IOs) Membership open to all UN members
(currently 65+ members)
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How we work Collaboration and Coordination Key Through:
• Task Teams/Forces• Expert/Working Groups• Modernisation Committees• Meetings/Workshops/Seminars
By:• National experts (NSOs and other agencies)• Regional actors: Eurostat/OECD/CIS-STAT/reg. UN• International experts: UNSD, World Bank, ILO,
academia etc. Using: Face-to-face meetings, Webex
conferences, Wikis, (virtual) Sprints etc.
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Type of Outputs Recommendations and Guidelines Standards for statistical production (Modernisation) Knowledge bases (over 60 UNECE wikis for
international statistical development work) CES Seminars In-depth reviews (studies on selected strategic issues
to identify gaps and new issues) Global Assessments of national statistical systems in
EECCA countries Other:
• Library of Training Materials• Statistical Database• Database of the International Statistical Activities in
UNECE region
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Recent output
Measuring sustainable development Climate change-related statistics Population and housing censuses Measuring global production Gender equality indicators Time-use surveys Measuring health state Developing statistical business registers Making data meaningful Human resources management and training Standards for statistical production (GSBPM,
GSIM, GAMSO)
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Strengths/why successful: Provides Intra-regional Collaboration platform Sharing & Synergy: countries see benefits Commitment and involvement at highest level Contributions and involvement from substantive
specialist Similar levels of statistical maturity Open to inter-regional participation Coordination and Collaboration with other
regional organizations (Eurostat, OECD, CISSTAT, regional UN offices)
Collaboration with other International Organizations
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Challenges
Diversity/Heterogeneity:• Level of development/statistical maturity differs• Language and working culture
Coordination with other players:• Other Regional bodies, Global organizations• Bi-lateral and private sector
Sharing our efforts: • Access to our products• Awareness of our activities
Our Success: more projects, more outputs, more countries more complex
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Inter-regional collaboration
Limited but:• Joint projects (mainly UN Development Account
funded)• Caucasus and Central Asia shared with ESCAP
And participation of countries from other regions• e.g. China, Japan, Mongolia, South-Korea,
Australia, New Zealand, Brazil, Chile, Colombia, Mexico, South-Africa, United Arab Emirates
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Introducing the HLG
CES High-Level Group for the Modernisation of Official StatisticsCreated by the bureau of the CES in 2010Oversees and coordinate activities that support modernisation of statistical organisations“Within the official statistics community ... take a leadership and coordination role”
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The Challenges
SDGs
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These challenges are too big for statistical organisations to
tackle on their own
We need to work together
SDGs makes this even more evident
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HLG Mission and Roadmap
Stimulate development of global standards and oversee international collaboration activities• Take a leadership and coordination role• Collaboration of the willing• Focused on delivery tangible/practical outputs
Implementing common standards and models for the official statistics “industry”
Promoting collaboration and sharing• From the design stage, not just the outputs
Modular systems giving increased flexibility for new sources / processes / outputs
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HLG Achievements: Generic Statistical Business Process
Model Common Statistical Production
Architecture Generic Statistical Information Model Generic Activity Model for Statistical
Organizations Big Data: Sandbox, quality,
partnerships
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Historically, statistical organizations have produced
specialized business processes and IT systems
The problem CSPA solves
Many statistical organizations are modernising and
transforming using Enterprise Architecture
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….Sharing becomes difficult!
Disseminate
When countries work on their own…
CSPA enables sharing
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CSPA Services built in 2014
1. Seasonal adjustment – France, Australia, New Zealand
2. Confidentiality on the fly – Canada, Australia
3. SVG generator – OECD
4. SDMX transform – OECD
5. Sample selection business registers – Netherlands
6. Linear error localisation – Netherlands
7. Linear rule checking – Netherlands
8. Error correction – Italy
Freely available to any statistical organisation
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Big Data Sandbox The Irish Centre for High-end Computing hosts the
UNECE Big Data ‘sandbox’ containing data and tools for international experiments
“Play is the highest form of
research” – Einstein
UNECE Big Data Wiki: http://www1.unece.org/stat/platform/display/bigdata
Experimenting with: Twitter, traffic loops, scanner data, mobile phone, smart meters, web scrapping data, Wikistat, Comtrade
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7 Sandbox Experiment Teams
75 Individuals from 25 countries / organisations
3 Task Teams
Executive Board, Modernisation Committees
1 Project Manager
2 Coordinators
Partners
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Guide Questions
Forms of cooperation in statistics (intra- and inter-regional)
How balance interest rich/advanced vs countries in transition
Good practices to share Which areas more appropriate at regional
level
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Global vs Regional level
RCs: Follow-up on (sub) regional demands Regional specific knowledge UNECE: advanced and more affluent NSOs
• Forefront of developments (but not always!) When mature to Global level Connecting National to Global level
And reverse: Adjust Global to regional needs• MDGs and SDGs• SEEA, SNA etc.
Be complementary and create synergy
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Balance needs of member states Development of standards and
recommendations and capacity building Sub-regional and Russian translation/interp. Be at the forefront but don’t forget the basics Follow-up and assistance in implementation Structural approach long term vision:
• Modernisation of statistical production• Global assessment to identify gaps
Collaborate at similar level, learn from leaders New Tool: Modernisation Maturity Model?
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Modernisation Maturity Model Modernisation can mean different things depending on
the starting point The level of maturity will vary across organisations (but
also within organisations across domains) In some cases:
• the fundamentals principles of OS not guaranteed• the right of access to administrative data is not guaranteed• the data infrastructure required to exploit secondary data
sources is not in place• the skills required to engage with new data sources are not
in place To Identify Challenges and Starting point to develop a
road map towards continual modernisation
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What next in HLG?
Continue:• Sandbox• Increasing CSPA compliant services• Defining GAMSO
Develop knowledge hub
Data integration is the key: stable outputs with unstable ever changing inputs from multiple sources
Modernisation Maturity Model
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Get involved!Anyone is welcome to contribute!
Contact: [email protected]
More Information HLG Wiki: www1.unece.org/stat/platform/display/hlgbas
LinkedIn group:“Modernising official statistics”
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Additional Info Slides
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Information:
HLG: http://www1.unece.org/stat/platform/x/xIR8Aw UNECE: http://www.unece.org/info/ece-homepage.html UNECE publications: http://www.unece.org/statistics/publications.html
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HLG Structure
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Vision of the High Level Group
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Quality Management / Metadata Management
SpecifyNeeds
Design Build Collect Process Analyse Disseminate
1.1Identify needs
1.2Consult &confirm needs
1.3Establish
outputobjectives
1.5Check dataavailability
1.6Prepare
business case
2.1Design outputs
2.4Design frame
& sample
2.3Design datacollection
2.5Design
processing & analysis
2.6Design
production systems & workflow
3.1Build datacollectioninstrument
3.2Build or enhance
process components
3.4Configure workflows
3.5Test production
system
3.7Finalize
production system
4.1Create
frame & select
sample
4.2Set up
collection
4.3Run
collection
4.4Finalize
collection
5.1Integrate data
5.2Classify & code
5.3Review & validate
5.5Derive new
variables & units
5.7Calculate
aggregates
6.1Prepare draft
outputs
6.2Validate outputs
6.3Interpret &
explain outputs
6.4Apply
disclosure control
6.5Finalizeoutputs
7.1Update output
systems
7.2Produce
dissemination products
7.3Manage
release of dissemination
products
7.5Manage user
support
7.4Promote
dissemination products
5.6Calculate weights
1.4Identify
concepts
Evaluate
8.1Gather
evaluation inputs
8.2Conduct
evaluation
8.3Agree an
action plan
5.4Edit & impute
3.6Test statistical
business process
5.8Finalize data files
2.2Design variable
descriptions
3.3Build or enhance
dissemination components
20142015
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GAMSO
Released in March 2015
Production
Manage buildings
& physical
space
Manage quality
Manage information
& knowledge
Manage consumers
Capability management
Plan capability improvements
Develop capability
improvements
Montitor capabilities
Support capability
implementation
Manage finances
Manage human
resources
Manage IT
Manage business &
performance
Manage data
suppliers
Generic Statistical Business Process Model
Corporate support
Strategy & leadership
Manage strategic collaboration & cooperation
Govern & leadDefine vision
Manage statistical
methodolgy
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Activity / Process / Capability
Activity is what we do Process is how we do it Capability is what allows us to do it