African policy makers are increasingly called on to use evidence-based research to inform development decisions. But this requires the rigorous collection of data as well as a coordinated system to disseminate it. This is why Kenya-based African Population Health Research Center is advocating for national policies to enable strong data systems.
Data, and especially data of good quality, are essential for national governments and institutions to accurately plan, fund and evaluate development activities.
Basic development indicators are essential for an accurate picture of a country’s development status. This includes a country’s progress towards specific development goals and improving its citizens’ socio-economic conditions. In fact, solutions to social and economic problems are often inseparable from the statistics.
You cannot build schools without knowing how many children need to be enrolled. Private investors need to know what resources are available in a given country before putting in their money. A country needs to know what it grows and where to prevent famine. Donors can only know whether their aid is changing lives if they have data.
In general, development programs entail measurable results. Development decisions should be informed by data. But more importantly this data must be turned into information that is easy to understand and useful to end users. You sometimes hear people say, “The data speak for themselves.” But they don’t.
Data is the first, crucial step. Then you need smart, objective analysis to make sense of the data and shape the narrative. Once the data supply side is up to par, the hope is that decision makers at all levels will increasingly demand relevant information to lay the foundation for policy making and budgeting.
Like everyone else, African governments and their development partners need good data on basic development metrics. To be of value, such data must be accurate, timely, disaggregated and widely available. This is not the case in many African countries.
Given the circumstances, you can imagine how difficult it is for African governments to make data driven decisions. This situation is often compounded by the lack of an entrenched culture of data use. More often than not it is difficult to ascertain existing programmes’ effectiveness or whether available resources are being allocated to address the most urgent and serious development issues.
You probably heard how Nigeria became the biggest economy in Africa overnight in 2014. This happened simply on account of changing the method of calculating Gross Domestic Product (GDP) – the so-called rebasing. The review ought to have been carried out every three or five years. But in this case it wasn’t done for decades.
This suggests that for years, decisions in one of Africa’s largest economies were based on data that were not credible or accurate or timely. This is the story of many countries in Africa. In 2015, 65% of the Millennium Development Goals’ indicators for countries in Central Africa were either estimated, derived from statistical models, or were last measured prior to 2010.
The truth is that data in Africa are not produced on time, are not frequently produced, are of poor quality and are not accurate. This makes it difficult to make data driven decisions.
A joint working group, the Data for African Development Working Group, of which I was part, posed that very question a few years ago. Actually, the paucity of accurate, reliable and timely data has been a recurring issue. It continues to be a major constraint to the effective monitoring and evaluation of interventions and development programmes across countries in Africa.
Estimates on health and other socio-economic outcomes are often uncertain and are not systematically produced. This makes it difficult to generate evidence about the effectiveness of existing policy.
There have been gains in the frequency and quality of censuses and household surveys over the past 30 years or so. But the building blocks of national statistical systems on the continent remain weak.
The building blocks fundamental to the calculation of almost any major economic or social welfare indicator include data on:
- births and deaths
- growth and poverty
- taxes and trade
- land and the environment, and;
- sickness, schooling, and safety.
As of 2013, none of the 60 countries with complete vital registration is in Africa. This situation is brought about by four main issues that we can call political economy challenges:
- lack of autonomy and stable funding for national statistical systems
- misaligned incentives contributing to inaccurate data
- dominance of donor priorities over national priorities; and
- limited access to and usability of data.
These are the issues that African countries should address to drastically improve data systems and quality of data needed for development.
Mind the gaps
The focus should be on the underlying challenges facing national statistical systems. The following actions would help improve the production, quality and use of data in Africa:
- Fund more and fund differently. For example, increase domestic funding and allocate predictable budget, and experiment pay-for-performance agreements with donor funding
- Build institutions that can produce accurate, unbiased data. For example, enhance national statistical offices autonomy or try out public-private partnerships
- Prioritize the core attributes of data building blocks: accuracy, timeliness, relevance and availability. To achieve this, build quality control mechanism, open data, accountability for improving data quality.
These changes must be initiated and led by governments. But donors and local civil society groups also have a major role to play. This process can help modify the relationship between donors, governments and the producers of statistics to work in harmony with national statistical priorities.
We cannot afford to continue with business as usual. Fortunately, a number of national statistical offices across Africa are taking steps to improve the serious challenges facing their systems. More work and more investment needs to happen. And at an accelerated pace.