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?

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PARIS21 SecretariatOECD/DCD4 Quai du Point du Jour92100 Boulogne-Billancourt, Francecontact@paris21.orgwww.paris21.org