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MENA-OECD Competitiveness Programme
May 2016, Beirut
El Iza Mohamedou
Deputy Manager, PARIS21 Secretariat
2
Global Partnership
Promoting data and statistics for development for more than 15 years
Founded by:
Governed by: PARIS21...
3
• Fragile states lagged in MDG reporting on all 8 objectives
• They face specific challenges related to insufficient:
• ...
4
5
• By providing data on issues that create fragility – e.g.
employment
• By building a stable state through the establish...
6
Egypt – 2015, in collaboration with UNESWA,
AfDB & UNECA
Libya – 2016, in collaboration with UNFPA &
Palestine Bureau ...
7
Egypt
• Achivements
 Produces quarterly & annual GDP estimates
 Produces tourism satellite accounts
 Conducted econom...
8
Libya
• Produced annual business registers 1992-2013
• However currently not updated
• External trade statistics compile...
9
• Focus on productive sectors
• GDP and Macroeconomic indicators
• Production and Trade snapshot
• Business information ...
10
• Use new sources of data
• Produce right time information
• Helping Investors Bring Electricity to the First Mile in S...
11
• Partners: Orange/Sonatel, NSO Senegal
• Hypothesis: Mobile phone user behaviour
reveals socio-economic characteristic...
12
Opportunities
• Cost-effectiveness
• Timeliness
• Granularity
• Data in new areas
Challenges
• Competitive risks
• Priv...
13
• Costs: reduces cost of undertaking frequent surveys
• Security: minimises risks of data collectors travelling
to inse...
14
• Combining data
• Complementing official statistics with new
sources of data
So that we move from
Prevention Predictab...
twitter.com/ContactPARIS21
facebook.com/ContactPARIS21
youtube.com/PARIS21OECD
PARIS21 Secretariat
OECD/DCD
4 Quai du Poin...
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Fragile Contexts: How Can Data Help?

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El Iza Mohamedou, Deputy Manager, PARIS21 Secretariat, 11 May 2016, Regional conference: Investment and inclusive growth in the midst of crisis, Beirut

Published in: Government & Nonprofit
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Fragile Contexts: How Can Data Help?

  1. 1. MENA-OECD Competitiveness Programme May 2016, Beirut El Iza Mohamedou Deputy Manager, PARIS21 Secretariat
  2. 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. 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. 4
  5. 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. 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. 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. 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. 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. 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. 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. 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. 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. 14 • Combining data • Complementing official statistics with new sources of data So that we move from Prevention Predictability Reaction Real-time monitoring
  15. 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

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