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

taeke.gjaltema@unece.org

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

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)

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:

UNECE Statistical Division

Meetings, Workshops, Seminars Technical assistance and training Guidelines and Recommendations

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)

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.

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

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)

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

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

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

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”

The Challenges

SDGs

These challenges are too big for statistical organisations to

tackle on their own

We need to work together

SDGs makes this even more evident

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

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

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

….Sharing becomes difficult!

Disseminate

When countries work on their own…

CSPA enables sharing

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

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

7 Sandbox Experiment Teams

75 Individuals from 25 countries / organisations

3 Task Teams

Executive Board, Modernisation Committees

1 Project Manager

2 Coordinators

Partners

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

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

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?

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

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

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”

Additional Info Slides

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

HLG Structure

Vision of the High Level Group

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

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

Activity / Process / Capability

Activity is what we do Process is how we do it Capability is what allows us to do it