Fragile Contexts: How Can Data Help?
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The document details the activities and challenges faced by the MENA-OECD Competitiveness Programme in promoting statistical capacity development, especially in fragile states. It highlights the need for long-term investments in data production and governance to support development goals, with specific case studies from Egypt, Libya, Sudan, and Jordan. Additionally, it discusses the importance of innovative data sources, public-private partnerships, and addressing challenges related to data privacy and technical constraints.
Fragile Contexts: How Can Data Help?
- 1. MENA-OECD Competitiveness Programme May 2016, Beirut El Iza Mohamedou Deputy Manager, PARIS21 Secretariat
- 2. 2 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
- 3. 3 • 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
- 4. 4
- 5. 5 • 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
- 6. 6 Egypt – 2015, in collaboration with UNESWA, AfDB & UNECA Libya – 2016, in collaboration with UNFPA & Palestine Bureau of Statistics Sudan – Planned (2016) Jordan – Planned (2016)
- 7. 7 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)
- 8. 8 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
- 9. 9 • 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
- 10. 10 • 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
- 11. 11 • 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)
- 12. 12 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 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
- 13. 13 • 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
- 14. 14 • Combining data • Complementing official statistics with new sources of data So that we move from Prevention Predictability Reaction Real-time monitoring
- 15. twitter.com/ContactPARIS21 facebook.com/ContactPARIS21 youtube.com/PARIS21OECD PARIS21 Secretariat OECD/DCD 4 Quai du Point du Jour 92100 Boulogne-Billancourt, France contact@paris21.org www.paris21.org
Editor's Notes
- #12: I discuss cases of public-private partnerships with mobile operators to obtain socio-economic indicators and migration statistics that are more granular, timely and precise than extant estimates. Advantage: cheap, granular, timely
- #14: I discuss cases of public-private partnerships with mobile operators to obtain socio-economic indicators and migration statistics that are more granular, timely and precise than extant estimates. Advantage: cheap, granular, timely