The Global Goals:
Challenges for the
statistics community
Edinburgh RSS Group
World Statistics Day
20 October 2015
Neil Jackson, Chief Statistician DFID
Goal 1: Eradicate extreme poverty and
hunger
• Extreme poverty has declined
significantly over the last two
decades. In 1990, nearly half of the
population in the developing world
lived on less than $1.25 a day; that
proportion dropped to 14 per cent in
2015
• Globally, the number of people living
in extreme poverty has declined by
more than half, falling from 1.9
billion in 1990 to 836 million in 2015.
Most progress has occurred since
2000
Goal 2: Achieve universal primary
education
• The number of out-of-school
children of primary school age
worldwide has fallen by almost
half, to an estimated 57 million in
2015, down from 100 million in
2000
• Sub-Saharan Africa has achieved
a 20 percentage point increase in
the net enrolment rate from 2000
to 2015, compared to a gain of 8
percentage points between 1990
and 2000
Challenge to build a coherent set of
goals, targets and indicators
Term
Goal
Targets
Indicators
How it is used in this report
Expresses an ambitious, but
specific, commitment. Always
starts with a verb/action.
Example from MDGs
Reduce child mortality
Quantified sub-components that
Reduce by two-thirds, between
will contribute in a major way to
1990 and 2015, the under-five
achievement of goal. Should be
mortality rate
an outcome variable.
Precise metric from identified
databases to assess if target is
being met (often multiple
indicators are used).
Proportion of 1-year olds
immunized against measles
Challenge to learn the lessons from MDG
Monitoring
• Targets and indicators perceived as “top down”
• The baseline year was set too far away from the
adoption of the framework
• Confusion over whether the targets were global or
national
• Some goals, targets and indicators are not well-aligned
• Some targets lack clear numerical yardsticks, and are
ambiguous and vague
Key SDG themes for the UK Government
• Eradicating extreme poverty (Goal 1)
• Finishing the job on the MDGs
• Climate change (Goal 13)
• Gender equality (Goal 5)
• strong and inclusive institutions (Goal 16)
The UK puts the principle of leave no one behind at the heart
and centre of the post-2015 agenda and sees this as the
defining feature of the SDGs. No goal, target or indicators
should be considered met unless met for every person around
the globe.
Increase over time in number of MDG indicator
series for which trend analysis was possible for
developing countries
There has been some progress in
producing statistics …
 The World Bank’s statistical capacity indicator has improved
from its benchmark level of 54 in 1999 to 68 in 2014.
 After the 2010 census round concluded in 2014, 93 percent
of the world’s population has been counted.
• The percentage of developing countries with at least two
data points for 16-22 MDG data series has increased from
2% in 2003 to 79% in 2013
… but much more is still needed
 Adequate poverty data does not exist for 77 out of 155
countries measured
 One in three children aged 5 years or younger have not had
their births registered and so do not exist officially.
 The deaths and causes of deaths for 60 percent of people
worldwide go uncounted
 Less than 0.3 % of Overseas Development Assistance
(ODA) is given to statistics
DFID support for statistical capacity
FAO/
Agriculture
Title
Improving Statistics for Food Security,
Sustainable Agriculture and Rural
Development
Enhanced Data Dissemination Initiative for
IMF/ Economic
Africa
statistics
OECD
Paris 21 Core Support (2009-2013)
UN Statistics
Division
Improving Collation, Availability &
Dissemination of MDG indicators
World Bank
Support to the Trust Fund for Statistical
Capacity Building
World Bank
World Bank
Making better use of Survey Data
Statistics for Results Facility
Time-scale
Total budget
2012-2016
£16m
2009-2015
£5m
2009-2015
£5m
2009-2014
£4m
2008-2017
£27m
2012-2016
£14m
2009-2017
£50m
Transforming our world: the 2030 agenda for
sustainable development
• Quality, accessible, timely and reliable disaggregated
data will be needed to help with the measurement of
progress and to ensure that no one is left behind.
• Such data is key to decision-making.
• … We agree to intensify our efforts to strengthen statistical
capacities in developing countries.
• We are committed to developing broader measures of
progress to complement gross domestic product (GDP).
What lessons did we learn from the MDGs
on building statistical capacity?
• Strong political commitment and significantly increased
resources will be needed to meet the demand of post-2015
• Strengthening statistical capacity is key
• New technology is changing the way data are collected and
disseminated
• Promoting open, easily accessible data and data literacy is key
for effective use of data for development decision-making
• Global standards and an integrated statistics system are key
elements for effective monitoring
Invisibility
• There are too many gaps in what is counted,
making some people and some issues almost
invisible:
–Too many countries still have poor data for
MDGs
–Data arrives too late
–Too many issues are still barely covered by
existing data
–Entire groups of people, regions and key issues
remain invisible
The call for a Data Revolution
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 citizens.
The Panel recommends establishing a Global Partnership on
Development Data that brings together diverse but interested
stakeholders – government statistical offices, international
organisations, CSOs, foundations and the private sector.
High Level Panel
The data revolution is already happening
• New technologies are creating new data and tools on an
unparalleled scale;
• This is driven by the wide distribution of computers and
mobile devices, the spread of the internet, the falling cost of
data storage, and the increase in remote sensing
technologies
• This is generating much greater demand for data from all
sides
• are There is a ferment of experimentation, innovation and
adaptation amongst governments, companies, researchers
and citizen groups
Need for a broader coalition of partners
 The private sector to share their expertise, and to open up useful
data for development purposes
 Civil society to generate citizen data and to help people use data
to hold their governments to account
 Researchers to develop new and improved data collection and
analytical methods, linking new and traditional data sources, to
enable the disaggregation of data
 Governments, donors and international organisations to commit
to transparency and Open Data, to invest in information systems,
and to modernise national statistical systems
Global Partnership for Sustainable
Development Data (GPSDD)
We support data-driven decision-making by catalyzing more
open, new, and usable data to help end extreme poverty,
combat climate change and ensure a healthy life for all.
Who We Are: The Global Partnership is led by a steering
group of governments, companies, civil society groups,
international organizations, and statistics and data
communities from all regions of the world.
It brings together data producers and users who share a vision
for using data to achieve sustainable development.
Global Partnership for Data:
the opportunities
• Build political support for improved data systems.
• Foster the development and application of innovative
approaches to filling critical data gaps
• Convene events and forums, including a world data forum, to
raise the profile of data challenges and to bring together
diverse communities to find new solutions;
• Engage a wider set of players on data for the global goals,
from the private sector and civil society. Sharing skills,
knowledge, expertise and data
What steam was to the 19th century, and oil has
been to the 20th, data is to the 21st. It’s the driver
of prosperity, the revolutionary resource that is
transforming the nature of social and economic
activity, the capability that differentiates
successful from unsuccessful societies
Data Manifesto, Royal Statistical Society