The Role of Crowdsourcing for Better Governance in Fragile State Contexts
Fragile Contexts: How Can Data Help?
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Transcript of Fragile Contexts: How Can Data Help?
PARIS21Partnership in Statistics for
Development in the 21st Century
Fragile Contexts: How Can Data Help?
MENA-OECD Competitiveness ProgrammeMay 2016, Beirut
El Iza MohamedouDeputy Manager, PARIS21 Secretariat
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Global Partnership
Promoting data and statistics for development for more than 15 years
Founded by:
Governed by: PARIS21 Board – 50 members
Monitored by: PARIS21 Executive Committee – 10 members
Busan Action Plan for Statistics (BAPS) Secretariat
Secretariat staff: 19 people
Annual Budget: EUR 5 MILLION
Promote, influence and facilitate statistical capacity development and better use of statistics – particularly in developing countries
Partnership in Statistics for Development in the 21st Century
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• Fragile states lagged in MDG reporting on all 8 objectives• They face specific challenges related to insufficient:• general data production• specific data relevant to their own challenges
• They suffer from brain drain of officials, lack of training, inadequate facilities and equipment and difficult safe access to some geographic areas
• Long term investment in any statistical capacity building activity is needed in fragile states – LT is elusive in these contexts
What Are the Main Statistical Development Issues in Fragile States?
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How useful is data in Fragile States?
• By providing data on issues that create fragility – e.g. employment
• By building a stable state through the establishment of strong institutions – e.g. accountable
• By fostering whole-of-government linkages through the coordinating role of the NSO which works across all public institutions – e.g. NSS
• By strengthening governance through the introduction of evidence to policy making
• By helping address inequality and fostering inclusive growth by providing data on the “invisible” and most vulnerable, which are often sources of conflict in fragile states
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NSS Assessment in MENA
Egypt – 2015, in collaboration with UNESWA, AfDB & UNECA
Libya – 2016, in collaboration with UNFPA & Palestine Bureau of Statistics
Sudan – Planned (2016)Jordan – Planned (2016)
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What are some key findings from these assessment?
Egypt• Achivements
Produces quarterly & annual GDP estimates Produces tourism satellite accounts Conducted economic census (2014)– updated business register External trade statistics produced on monthly and annual basis
• But not all administrative data are utilized in GDP estimates –
underestimates the sectors (growth) that are more attractive/unattractive for investment.
Final expenditure of Non-Profit Institutions Serving Households (NPISH) not included in the final consumption of households’ estimates – the estimate is that considerable funds/services are received from these institutions (religious, political, etc)
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What are some key findings from these assessment?
Libya• Produced annual business registers 1992-2013
• However currently not updated• External trade statistics compiled from administrative records
from Customs Authorities 1966-2014• No sharing of data between NSO and Customs Authority
• National accounts produced by Ministry of Planning• No data on informal sector are included
• No definition has been agreed at the national level on informal sector• Undercount or absence of informal sector affects estimates of
national accounts
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How can data help private sector engagement?
• Focus on productive sectors• GDP and Macroeconomic indicators
• Production and Trade snapshot• Business information register
• Access to skills, expertise & core competences• Labour force surveys & employment statistics
• Market opportunities• External and domestic trade statistics, price statistics
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Data Revolution and New data sources as complementary
• Use new sources of data
• Produce right time information• Helping Investors Bring Electricity to the First Mile in Sub-Saharan Africa
(PREMISE)• Follow population displacement (Nepal)• Estimate poverty and key social indicators (Nigeria)• Predict spread of infectious diseases (Ebola)• Estimate harvest size (early warning systems for crop failure)• Use cellphone metadata (who calls whom, when and for how long)
to measure wealth
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Data 4 Development in Africa• Partners: Orange/Sonatel, NSO Senegal• Hypothesis: Mobile phone user behaviour
reveals socio-economic characteristics• Approach:
• Re-build survey data with model using “call logs”• Estimate literacy level on monthly basis • Check consistency with survey results
• CDRs: Location (antenna +/- 2km), time, emitter and receiver (identifiers)
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Opportunities• Cost-effectiveness• Timeliness• Granularity• Data in new areas
Challenges• Competitive risks• Privacy and ethics• Legal constraints• Turning PPPs for statistics into a
viable business model• Technical and statistical challenges
Source: Public-Private Partnerships for Statistics: Lessons Learned, Future Steps, PARIS21 Working Paper
PPPs in data and emerging new data sources
Non-rivaly of data; Diffusion of fixed costs
Spatial granularity; Temporal granularity; Thematic granularity; Unit granularity
Reputational and ethical issues; Decreased data availability
Uncertainty about the demand for unofficial data; Demonstrating the benefits of PPPs
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Why are PPPs particularly relevant in fragile contexts?
• Costs: reduces cost of undertaking frequent surveys• Security: minimises risks of data collectors travelling
to insecure places• Lack of other data: provides data that may not be
collected due to fragility• Timeliness: data is available all the time & on time• Shared resources & risks: including financial,
political, security, infrastructure and human resources
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• Combining data• Complementing official statistics with new
sources of data
So that we move from
Prevention Predictability Reaction Real-time monitoring
What’s Next?
twitter.com/ContactPARIS21
facebook.com/ContactPARIS21
youtube.com/PARIS21OECD
PARIS21 SecretariatOECD/DCD4 Quai du Point du Jour92100 Boulogne-Billancourt, [email protected]