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 How it is used in this report Example from MDGs

GoalExpresses an ambitious, but specific, commitment. Always starts with a verb/action.

Reduce child mortality

Targets

Quantified sub-components that will contribute in a major way to achievement of goal. Should be an outcome variable.

Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate

Indicators

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

  Title Time-scale Total budget

FAO/ Agriculture

Improving Statistics for Food Security, Sustainable Agriculture and Rural Development 

2012-2016 £16m

IMF/ Economic statistics

Enhanced Data Dissemination Initiative for Africa 

2009-2015 £5m

OECD Paris 21 Core Support (2009-2013)  2009-2015 £5m

UN Statistics Division

Improving Collation, Availability & Dissemination of MDG indicators  2009-2014 £4m

World BankSupport to the Trust Fund for Statistical Capacity Building 

2008-2017 £27m

World Bank Making better use of Survey Data  2012-2016 £14m

World Bank Statistics for Results Facility  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