Counting the invisible: Three priorities for strengthening statistical capacities in the SDG era

By Johannes Jütting, Executive Head PARIS21, Rolando Avendano, Economist, Asian Development Bank and Manuel Kuhm, Research Support Officer (PARIS21)


Better policies need better data. High-quality data and official statistics are vital for governments, civil society, the private sector and the public to make informed decisions, create effective polices, and establish good governance. Under the 2030 Agenda for Sustainable Development, data-driven policy making takes on even greater significance. For if we are to “leave no one behind”, we must first ensure that everyone is counted.

Yet today, more than 110 low and middle-income countries lack functional civil registration and vital statistics systems and under-record or omit vital events of specific populations. Those living in poverty are most likely to be excluded—the poorest 20% of the global population account for 55% of unregistered births. Only 37 countries have statistical legislation that complies with the United Nations (UN) Fundamental Principles of Official Statistics.

If we don’t even know who the poorest are, how can we ensure that they aren’t left behind?

At the same time, while a global Sustainable Development Goal (SDG) indicator framework is an essential part of Agenda 2030, it is putting pressure on national statistical systems. In addition to the demand of compiling 232 national-level indicators, the Agenda requires that data are disaggregated by income, sex and gender, geography, age and disability, far beyond current capacity in many developing countries.

A recent assessment revealed that almost 60% of SDG indicators lack most of the required data, are unavailable or methodologically undefined by the international statistical community. Yet, improvements in Tier I indicators (defined as conceptually clear, methodologically established and regularly produced by at least 50% of countries) are being achieved. In 2019, 101 SDG indicators were classified as Tier I, up from 82 indicators in 2017.

The main challenges of implementing the SDG indicator framework fall into three areas: the overburdening of national statistical systems, internal and external co-ordination challenges, and a fragile funding framework for statistical modernisation.

How can we overcome these challenges?

Avoid overburdening national statistical systems. The 2030 Agenda acknowledges the potential for reporting overburden and recommends that national statistical systems build on existing reporting mechanisms. The compilation of a 100-indicator basket is considered an upper limit by some countries’ national statistical offices, yet risks stimulating uneven development and excluding certain populations.

More importantly, regional discrepancies in SDG monitoring and disaggregation needs are visible. For example, countries in Latin America and the Caribbean tend to prioritise indicators related to social protection and food security, whereas African countries prioritise indicators related to poverty, food security, and water resources management. Disaggregation by income and geographical location is more important in Africa, whereas disaggregation by migrant and disability status is pre-eminent in Asia and the Pacific, and in Latin America and the Caribbean.

Naturally, the most tracked SDG indicators are linked to the policy priorities of governments.

Country-driven, localised SDG monitoring and disaggregation strategies are required to maximise the use of resources and national capacities while responding to national needs. Data planning tools such as PARIS21’s Advanced Data Planning Tool (ADAPT), offer one solution for adapting data production to priority data needs from users.

Supporting the monitoring of SDGs in developing countries: Priorities for data disaggregation

Please indicate what types of data disaggregation require the most immediate support.


Source: HLG-PCCB/PARIS21 (2018). Survey results: new approaches to capacity development and future priorities. Paris: PARIS21. Retrieved from

Reduce co-ordination challenges between stakeholders.
Linked to the challenge of using national data for international monitoring is the need to better align national and international efforts to strengthen statistical capacity. The new data ecosystem requires innovative co-ordination mechanisms and strategies to foster efficient data sharing, interoperability and quality assurance. A coherent, inclusive and politically-backed national statistical plan can guide this process. In 2018, 129 countries were implementing a national statistical plan, an increase of 26% from 102 countries in 2017.

Improving co-ordination also involves strengthening soft skills such as leadership, negotiation and communication, key capabilities for multi-stakeholder data partnerships today. New forms of development co-operation, including triangular and South-South co-operation, can be an additional instruments to improve capacity. Ultimately, successful co-ordination and collaboration in the new data ecosystem depends on political will and trust between all involved stakeholders.

Tackle the lack of financing for more and better SDG data. Investing in national statistical systems needs to become a strategic priority for low- and middle-income countries, and providers of development co-operation alike. Assuming an ambitious scenario of domestic resource mobilisation in low- and middle-income countries, closing the funding gaps for SDG data demands and Cape Town Global Action Plan (CTGAP) objectives, requires a doubling of the current external support for statistics from 0.33% (around USD 600 million) to 0.7% per year (around USD 1.3 billion). Global alliances, including the recently established Bern Network for financing development data represent a positive step in that direction.

However, simply increasing the amount of investment for statistical capacity is unlikely to bring results as long as delivery mechanisms are not improved. A more comprehensive response is required on different levels:

  • Engage national governments to set goals for domestic resource mobilisation for statistical systems. This is important to ensuring long-term sustainability of statistical capacity at the national level. Integrating national statistical plans into the budgeting process will ensure more sustainable funding for the sector.
  • Engage bilateral and multilateral donors to create more sustainable interventions and programmes in statistical capacity, for instance linking statistical support to their own monitoring and evaluation (M&E) agenda.
  • Create a global alliance for financing development data to raise political demand for data, improve alignment with national priorities, promote development partner co-ordination, and speed up access to finance at scale.
  • Encourage innovative ways to make sectoral investments in data better fit into the broader support to national data systems.

Finally, in our efforts to mobilise resources for a more accurate and quantifiable tracking of the implementation of Agenda 2030, we need more compelling narratives and impact stories that demonstrate the power of data to save and improve lives. Better policies demand better data, and better data demand better stories.