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

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

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