- National statistics offices lack independence
- Data is inaccurate, due in part to misaligned incentives between the funders and producers of statistics
- Donor priorities dominate national priorities
- Data is kept behind closed doors (you can read more about each of these challenges in chapter two of the final report.)
The Group also called on governments and donors to focus on the 'building blocks of national statistics systems - or data intrinsically important to the calculation of almost any major economic or social welfare indicator. These include births and deaths; growth and poverty; tax and trade; sickness, schooling and safety; and land and environment. Improving the accuracy, timeliness and availability of these statistics will be critical to the success of the post-2015 development agenda, across every sector.
Making progress in these areas will require willingness by all involved to experiment with new approaches - not revert to business as usual - to change the way data is collected, used, and made public. To this end, the Working Group’s recently released final report identifies three strategies for governments and donors to deliver on the data revolution in the region:
1. Fund more and fund differently: Current funding for national statistical systems is both insufficient and structured in ways that prevent the production and disclosure of accurate and timely data. For instance, in many countries, nearly all core data collection is funded by external sources which intrinsically elevates donor priorities over national priorities and may restrict where investments can be made.
Countries that have greatly improved their national statistical capacity, like South Africa and Rwanda, have had strong national leadership characterised by political ownership and domestic funding. As economies grow across the region, other governments must start allocating more domestic funding to improving national statistics (thus reducing donor dependency). Ideally, these funds would be allocated from revenues as appropriate. Where more creative mechanisms are needed, a government might consider tying a share of sectoral spending to activities aimed at implementing their national strategies for the development of statistics - 1% for data, for example, or a 'data surcharge' added to any donor project to fund data building blocks (a public good). Governments may also experiment with pay-for-performance agreements to enhance mutual accountability for progress on improving the core statistical products.
2. Build institutions that can produce accurate, unbiased data: Many of the political economy problems surrounding data hinge on vulnerability to political and interest group influence, as well as rigidities in civil service and government administration that limit governments’ ability to attract and retain qualified staff. Many countries are moving towards greater legal autonomy, in which national statistics offices function independently of government ministries, and these efforts should be increasingly supported through existing programs and initiatives.
An independent governing board might be one way forward to ensure checks and balances in the system. This has worked in Mexico, where the director of the Instituto Nacional de Estadística Geografía is nominated by a board rather than by the country’s executive. Countries may also experiment with new institutional models like public-private partnerships to improve data collection and dissemination. Such models could support increased functional and financial autonomy while retaining, if not increasing, accountability to stakeholders. They could also free national statistics offices to focus on more oversight functions, including setting norms and standards and providing quality controls for national statistics.
3. Prioritise the accuracy, timeliness, and availability of the data building blocks: A lot of country and donor funding has gone to censuses and surveys over the past decade. This investment has paid off: more than 80 percent of African countries conducted a census in the past decade, and an average of 22 household and firm surveys were conducted each year over the same period. But efforts to ensure the integrity of this data lag.
Governments and donors must build greater quality control mechanisms into data collection and analysis. The Working Group suggests a number of ways this could be done including through increased independent verification of core data, by embedding NSO support in line ministries (as is done in Côte d’Ivoire), or by requiring all data collection activities across line ministries be checked by NSO staff periodically throughout the process (as is done in Rwanda). National governments should also release all non-confidential, publishable data, including metadata, free of charge and online in a format that is analysable and machine readable.
For a truly sustainable data revolution in sub-Saharan Africa, these changes must be initiated and led inside governments in coordination with donors, civil society and private sector. Thus any action around the data revolution must fundamentally modify the relationship between donors, governments, and producers of statistics to work in harmony with national statistical priorities.
So where do we start? Try a data compact: A presidentially-led data compact could help mobilise and focus domestic and donor funding for progress on national statistical priorities. Data compacts would allow governments and donors to express intent to fund, partner and progress on the critical 'building blocks' of a national statistics system over multiple years, with clear and verifiable measures of progress. It would also provide a country-specific framework to innovate on funding mechanisms, engage civil society and mobilise new technologies for data collection and dissemination.
Read more about the Working Group’s findings and recommendations in the final report a and brief. CGD and APHRC will continue to inform and track actions as the data revolution takes shape and welcome your feedback.
The views expressed in the Opinion section of StatsLife are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of The Royal Statistical Society.