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Taeke Gjaltema UNECE taeke.gjaltema@unece

Regional Collaboration Developing 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 Gjaltema UNECE taeke.gjaltema@unece.org. Guide Questions.

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Taeke Gjaltema UNECE taeke.gjaltema@unece

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  1. Regional CollaborationDeveloping statistical processes and frameworksParis 21 Forum: Reinforcing Statistical Co-operation at the Regional Level to Support Sustainable Development, Paris, 5-6 October 2015 Taeke Gjaltema UNECE taeke.gjaltema@unece.org

  2. 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

  3. 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)

  4. 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:

  5. UNECE Statistical Division • Meetings, Workshops, Seminars • Technical assistance and training • Guidelines and Recommendations

  6. 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)

  7. 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.

  8. 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

  9. 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)

  10. 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

  11. 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

  12. 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

  13. Introducing the HLG • CES High-Level Group for the Modernisation of Official Statistics • Created by the bureau of the CES in 2010 • Oversees and coordinate activities that support modernisation of statistical organisations • “Within the official statistics community ... take a leadership and coordination role”

  14. The Challenges SDGs

  15. These challenges are too big for statistical organisations to tackle on their ownWe need to work togetherSDGs makes this even more evident

  16. 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

  17. 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

  18. The problem CSPA solves Historically, statistical organizations have produced specialized business processes and IT systems Many statistical organizations are modernising and transforming using Enterprise Architecture

  19. ….Sharing becomes difficult! When countries work on their own… Disseminate CSPA enables sharing

  20. CSPA Services built in 2014 • Seasonal adjustment – France, Australia, New Zealand • Confidentiality on the fly – Canada, Australia • SVG generator – OECD • SDMX transform – OECD • Sample selection business registers – Netherlands • Linear error localisation – Netherlands • Linear rule checking – Netherlands • Error correction – Italy Freely available to any statistical organisation

  21. 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 Experimenting with: Twitter, traffic loops, scanner data, mobile phone, smart meters, web scrapping data, Wikistat, Comtrade UNECE Big Data Wiki: http://www1.unece.org/stat/platform/display/bigdata

  22. Partners Executive Board, Modernisation Committees 1 Project Manager 2 Coordinators 3 Task Teams 7 Sandbox Experiment Teams 75 Individuals from 25 countries / organisations

  23. 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

  24. 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

  25. 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?

  26. 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

  27. 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

  28. Get involved! Anyone is welcome to contribute! Contact: support.stat@unece.org More Information • HLG Wiki: www1.unece.org/stat/platform/display/hlgbas • LinkedIn group: “Modernising official statistics”

  29. Additional Info Slides

  30. 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

  31. HLG Structure

  32. Vision of the High Level Group

  33. Quality Management / Metadata Management SpecifyNeeds Design Build Collect Process Analyse Disseminate Evaluate 1.1 Identify needs 2.1 Design outputs 3.1 Build datacollectioninstrument 4.1 Create frame & select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Update output systems 8.1 Gather evaluation inputs 2.2 Design variable descriptions 5.2 Classify & code 1.2 Consult &confirm needs 3.2 Build or enhance process components 6.2 Validate outputs 7.2 Produce dissemination products 8.2 Conduct evaluation 4.2 Set up collection 5.3 Review & validate 2.3 Design datacollection 1.3 Establishoutputobjectives 6.3 Interpret & explain outputs 8.3 Agree an action plan 4.3 Run collection 3.3 Build or enhance dissemination components 5.4 Edit & impute 7.3 Manage release of dissemination products 2.4 Design frame& sample 4.4 Finalize collection 5.5 Derive new variables & units 1.4 Identify concepts 6.4 Apply disclosure control 3.4 Configure workflows 7.4 Promote dissemination products 2.5 Design processing & analysis 1.5 Check dataavailability 5.6 Calculate weights 6.5 Finalizeoutputs 3.5 Test production system 5.7 Calculate aggregates 7.5 Manage user support 1.6 Prepare business case 2.6 Design production systems & workflow 3.6 Test statistical business process 5.8 Finalize data files 2015 2014 3.7 Finalize production system

  34. GAMSO Released in March 2015

  35. Activity / Process / Capability • Activity is what we do • Process is how we do it • Capability is what allows us to do it

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