HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress

1. Data Issues for Beyond GDP Africa
Chukwudozie Ezigbalike
Chief, Data Technology Section
UN Economic Commission for Africa
Measurement of Well Being and Development in Africa
Durban International Convention Centre, South Africa
12 – 14 November 2015
@Ezigbalike

2. Indicator: A summary
measure that aims to
describe, in a few
numbers as much detail
as possible about a
system, to help
understand, compare,
predict, improve, and
innovate
– The Good Indicators Guide
Understanding how to use and choose indicators Consensus: GDP is now
being used for what it was
not specifically designed
for

3. The data that
informs the
narrative
dictates the
story
– Carlos Lopes
… an indicator without trustworthy data to feed it, is often
worthless and sometimes dangerous

4. D-I-Y
• Indicators are part of a narrative
 They DESCRIBES a system
• Africa wants to tell its story
 Therefore, countries should be able to calculate own
indicators – not calculated for them by others

5. • Data must be available,
convenient and possible to collect
by indicator maintainers in the
country
• Unambiguous definition that
everybody understands
• Consensus among stakeholders
on what it means
• Try defining “noise pollution” in
a multi-faith community

6. Affordability
• Survey data not available
mainly because of cost
• Example from UNSD’s 2005
publication: Household
sample surveys in
developing and sample
surveys
• In 2005, survey of 3000
households was estimated at
$521,600
• Therefore reliance on donors

7. Pay the piper …
• … and dictate the tune
• Even with NSDS, basket funding, and similar
measures, countries won’t have full control over
timing and content of surveys
• Africa Data Consensus: Countries must own the
prioritisation, financing and leadership of [this
revolution]
• Important consideration for designing indicators

8. Administrative Data
•Bureaucracies maintain records
•But there must be …
 Consistency – same result unless data values change with
changing conditions
 Reproducibility – different people should obtain the same
results
• Review and design protocols to ensure data curation
practices meet fundamental principles

9. Opportunities
Presented by the
Data Revolution,
the Africa Data
Consensus and
Data Communities
Image from Cartagena Data Festival

10. “We [also] call for a data revolution for sustainable
development, with a new international initiative to
improve the quality of statistics and information
available to people and governments.”
[SG’s High-Level Panel on Post-2015 Process]

11. Yet …
The revolution in information technology over the last decade
provides an opportunity to strengthen data and statistics for
accountability and decision-making purposes. There have
been innovative initiatives to use mobile technology and other
advances to enable real-time monitoring of development
results.
[SG’s High-Level Panel on Post-2015 Process]

12. “A true data revolution would draw on existing
and new sources of data to fully integrate
statistics into decision making, promote open
access to, and use of, data and ensure
increased support for statistical systems.”
Try something new
Try data communities

13. What is a Data Community?
• A data community refers to a group of people who
share a social, economic or professional interest
across the entire data value chain – spanning
production, management, dissemination, archiving
and use

14. Global Goals No. 2
Example to explain the data community concept
•End hunger, achieve food security and improved
nutrition and promote sustainable agriculture

15. “By 2030, double the agricultural productivity and incomes of
small-scale food producers, in particular women, indigenous
peoples, family farmers, pastoralists and fishers, including
through secure and equal access to land, other productive
resources and inputs, knowledge, financial services, markets and
opportunities for value addition and non-farm employment”
Global Goals Target 2.3

16. Emphasis on “Implementation”
•Common misconception that “development goals” are
about only “monitoring” and reporting
•Therefore, a tendency to collect data on agreed
indicators to report on situation
 After the fact. Land has degraded, and we fail on the
indicator. But people may have died from floods, etc.
•Focus on Data for Implementation
 Properly defined, should include documenting baseline
situation, planning interventions, delivering services and
monitoring progress to refine plans and actions
 The data will then be available to generate the indicators for
reporting

17. Proposed indicator for 2.3
•Volume of production per labour unit (measured in
constant USD), by classes of farming/pastoral/
forestry enterprise size
Did we double productivity? 
… of small-scale producers? 
… particularly women? 
… secure and equal access to land? 
… financial services? 
… because emphasis was on reporting on the
indicator

18. Data Needs for Secure and Equal Access
Focus on implementation data
• Identification of land parcels or units of holding and/or
use
• Interests and rights recognized in land in the jurisdiction
 Ownership interests, grazing rights, access rights, group rights,
management rights, etc.
• Current holders of the interests
 Including stakeholders
• Land capability and potential
 Suitability analysis
• Current uses
• Taxes and charges assessed; amount paid
• … etc

19. Not Really New
•Land records and cadastres have been around
•Initially with emphasis on conveyancing and taxation
•Became multi-purpose in the 1980s, applying modern
information technologies
•Countries have varying degrees of restrictions to
access
 From complete public access – conditions apply of course
 To near-complete secrecy – except for owner
•Modern economic models recommend openness in
the management of land information
 Country specific decisions on degree of openness

20. Managing Land Records
The Land Data Community – as an example
• There are experts, practitioners, officials, who:
 Understand the concepts associated with this type of data, including
best way to curate them and disseminate to wider society
 Are in a position to collect the data in the course of normal work
 Need them more than others in the course of their work, therefore have
the strongest incentives to keep them updated
• Constitute them into a data community and give it
mandate/credential to collect, curate and
disseminate these datasets for everybody
• Define protocols for data flow to NSS
• Define other data communities for specific data
types

21. Citizen as Data Collector
• Continuous data flows for selected metrics
• Energy costs and types
• Store/grocery prices
 ECA pilot projects in six countries
• Challenge of incentive structure

22. The Africa Data Consensus
• Result of the High Level Conference on Data
Revolution
 18 data communities
• Forward looking towards call by IEAG for a global
consensus on data
• Core principles on which the data revolution will
become a reality in Africa
www.uneca.org/datarevolution

23. ADC Vision
•Vision: A partnership of all data communities that
upholds the principles of official statistics as well as
openness across the data value chain, which creates
a vibrant data ecosystem providing timely, user-driven
and disaggregated data for public good and inclusive
development.

24. Some ADC Principles
•Political will is pivotal to the implementation of the
African data revolution. Countries must own the
prioritisation, financing and leadership of this
revolution.
•Official data belong to the people and should be open
to all. They should be open by default.
•African governments should acknowledge open data
provided by credentialed data communities as
acceptable sources of country statistical information.
•Technology, new forms of data and other innovations
should be actively embraced.

25. Expanding the Data Ecosystem
• NOT all new data sources CAN fit into
traditional/official statistical systems
 And we don’t have to force them to fit, because not all
decisions require data to be stamped as “official” before
using them
• New sources constantly being incorporated into new
indicators, including well-being
 Increasing need for data from scientific sensors; e.g.,
Wetlands, soil cover, water pollution, etc.
• Expand the data ecosystem beyond NSS
 But define technical protocols, policy environments and
legal frameworks

26. Data Ecosystem
•Multiple data communities, all types of data (old and
new), institutions, laws and policy frameworks,
innovative technologies and tools, interacting to
achieve the data revolution
•However, there is need to define clear rules of
engagement, including interfaces with the national
statistical systems and other data communities

27. In Summary
• Indicators are indispensable part of the narrative
• They are as good as the data that informs them
• Africa wants to tell its stories
• Consider availability and affordability of data
systems in designing indicators of well-being
• Articulate needs to be included in data systems that
are now being put in place for the global goals
• Take advantage of the data revolution
• In Africa, adopt principles of the Africa Data
Consensus