The sixth discussion will focus on the effectiveness of regional cooperation and capacity development on national statistical systems’ strengthening with an emphasis on experiences from the Asia Pacific region.
Improving less developed statistical systems – do we apply the correct foundations and are we at risk of leaving some countries behind?
The UN Secretary General’s Independent Expert Advisory Group (IEAG) speaks of the data revolution as an “explosion” in the volume and production of data matched by a “growing demand for data from all parts of society”. This expansion of the data ecosystem is disruptive to national statistical systems as it requires a radical shift in attention and new capacities to deal with the plethora of new, unstructured data sources and demands from new actors and data users (NGOs, civil society and citizens).
The Partnership in Statistics for Development in the 21st Century (PARIS21) promotes the better use and production of statistics throughout the developing world. PARIS21 is helping national data ecosystems respond to and manage this process through a new approach to capacity known as Capacity Development 4.0 or CD4.01. The objectives of the CD4.0 approach go beyond the traditional production-side interventions to also include the strengthening of data use, literacy and results. The CD4.0 approach is guided by a framework of three levels and five target areas for capacity development. The three levels are individuals, organisations and systems. The five target areas are resources, skills and knowledge, management, politics and power, and incentives.
The CD4.0 is a useful framework for asking ourselves whether international capacity development is focused on lifting the capacity of national statistical systems or whether it is focused on its own needs. Is it supply or demand driven?
On the foundations: Are capacity development efforts more focused on transforming capacity development than transforming national statistical systems?
Discussion starter #1: Statistics capacity development suffers from a foundational problem: Most initiatives aim to produce statistics rather than developing organizational and system capacity.
Many of the capacity development initiatives undertaken are focused on statistical production. Be it filling data gaps for the 2030 Agenda for Sustainable Development, supporting revisions to international statistical standards such as the System of National Accounts, or the 2020 round of Censuses of Population and Housing. Training programs too are focused on statistical production. In contrast, organisational and system capacity development initiatives are less frequent and more difficult to secure investment for or to produce tangible results within funding windows. Changing legislation, for instance, requires time. Is there enough focus on organisational and system issues? How best can we develop organisational and system capacity? Which levels of capacity development (individual, organisational, system) deliver an enduring capacity lift? What is the National Statistical Office’s priority for investment by the capacity development system – individual, organisational or system?
Discussion starter #2: Is capacity development focussing on the new or retaining the old?
The capacity development landscape is a mixture of staff with long career’s in the same institution; consultants contributing during their retirement; and rules and procedures which can favour the tried-and-tested rather than the new. The data revolution and the ever-changing data ecosystem call for a revolution to how we think about and deliver our statistical business. Are retired statisticians the best consultants for supporting countries to adapt to the data revolution and other developmental work? Do capacity development staff from the 1960’s inspire youth with innovation and inspiration or with caution and conservatism? Is there a way to strike a balance between gaining from experts’ knowledge of methodological framework while keeping the approach current and up to date?
On leaving countries behind: does our approach risk leaving countries and regions behind?
Discussion starter #3: Is the global statistical system truly inclusive? Or does it resemble a discussion club for statisticians from developed statistical systems, inviting others to observe from the gallery? Are we leaving some countries behind?
The COVID-19 pandemic has seen a whirlwind of activity: new data sources being used; experimental statistics being produced; and near real-time statistics being released. Developed countries have new drivers for collaboration and cooperation and are embracing the data revolution as the Secretary-General encouraged. Do these new collaboratives, often initiated under the umbrella of the UN Statistical Commission, respond to needs and priorities of the few or the many? Has development assistance to developing countries taken a backseat? Are developing countries able to keep up with the pace of change? Are they able to benefit from global collaboration among statisticians? How do we ensure equal and equitable participation of countries based on level of statistical development or capacity (which very much differ from actual income or economic level/status of countries)?
Discussion starter #4: Is capacity development truly inclusive?
PARIS21’s 2019 Partner Report on Support to Statistics (PRESS) reported between 2016 and 2017, official development assistance to data and statistics rose 11 per cent from USD 623 million to USD 689 million, largely driven by the adoption of the Sustainable Development Goal monitoring framework (Figure 1). Africa received the largest share of statistical support with 50 per cent in 2015-2017 whilst the share of commitments received by the Asia and Pacific region was 18 per cent, a much lower share than the 32 per cent received in 2011-2013. A further 13 per cent was committed to global projects and programmes that were not region-specific. What is driving this disparity in funding for this type of statistical activity? Is this investment mix because of donor priorities to invest in Africa? Are we assuming other regions like Asia and the Pacific now require lesser assistance than what is perceived? What incentives are in place to ensure financial assistance to all developing countries? When do we say assistance is enough? What parameters should we be monitoring apart from the SDG indicators (which again means being more focused on data and statistics production.)