Improving public policy in resource-rich African countries by enhancing statistical capacity

By Dr Vessah Mbouombouo Salim Ahmed

Natural resource-rich African countries, such as Nigeria, Angola, and the Democratic Republic of Congo, are endowed with significant mineral, oil, and gas deposits. However, these resources have not consistently led to inclusive and sustainable development. The ongoing failure to convert natural wealth into economic and social progress is largely attributed to the inefficiency of the public policies implemented. Often poorly targeted, insufficiently coordinated, or based on inadequate data, these policies struggle to address the real needs of populations and to anticipate the structural challenges of development. A persistent structural weakness in these public policies is the low statistical capacity, which limits the quality, availability, and use of data necessary for designing and evaluating effective public policies.

According to the World Bank, the Statistical Capacity Index of Sub-Saharan Africa is 55.5%, compared to an average of 81.2% in Europe and Central Asia. More than 20 African countries have not conducted a population census in over ten years, and a third of them lack recent data on poverty. Furthermore, less than 0.1% of the national budget is allocated to statistics in most African countries, while the recommended standard ranges between 0.3% and 0.5%. This deficiency in updated and reliable data on extractive production, taxation, inequality, and employment hinders evidence-based decision-making and the development of sound economic, social, and environmental governance strategies. Additionally, it undermines efforts to combat corruption, monitor progress on the Sustainable Development Goals (SDGs), and implement structural reforms.

In this context, this policy brief aims to highlight the strategic importance of statistical capacity in resource-rich African countries. It seeks to demonstrate how reliable and up-to-date data can facilitate effective and transparent governance oriented toward sustainable development. The brief also proposes concrete measures to strengthen national statistical systems, particularly in terms of funding, capacity-building, and data governance. Finally, it advocates for the systematic integration of data into all stages of public policymaking, from design to evaluation, to enhance decision quality and the impact of public interventions.

What drives successful statistical capacity

Referring to the overall capacity of a statistical system to collect, analyse, and disseminate high-quality data on a country’s population, economy, and society to support evidence-based policymaking, statistical capacity remains a persistent challenge for African countries. This growing importance has led to a significant body of literature on its various challenges, which can be grouped into economic, social, and institutional dimensions.

Economically, the barriers to statistical capacity are numerous and closely linked to dependence on extractive rents. This reliance on natural resources reduces governments’ incentives to develop strong statistical systems, as revenues from extractive activities allow them to finance public policies without relying on data-driven evidence. Consequently, investments in statistical infrastructure such as digital tools, continuous training, and field data collection are often marginalized and underfunded. Additionally, many countries depend on external funding or data provided by international extractive companies, which undermines national statistical systems and aligns priorities with donor or corporate interests rather than national needs.

From a social perspective, weak demand for statistical data within government institutions poses a major obstacle. Civil societies, the media, and even universities often lack the resources and data literacy required to use statistics as tools for advocacy or accountability. Furthermore, in several extractive regions, geographical, security, or logistical challenges hinder the collection of reliable data, particularly regarding local populations, the environment, or informal employment. This situation is exacerbated by widespread distrust of official data, often perceived as incomplete or manipulated, leading to further unreliability. As a result, the lack of trust creates a vicious cycle that weakens demand for improvements in the statistical system.

Institutional barriers reflect the structural weaknesses of national statistical systems. In many resource-rich countries, national statistics offices lack autonomy, a robust legal framework, and effective coordination with technical ministries. This fragmentation results in data production that is difficult to use for policy design. Additionally, a shortage of qualified human resources especially in advanced statistics, big data processing, and monitoring and evaluation limits the ability to turn data into actionable insights for decision-making. Finally, in contexts marked by poor governance, there is often a lack of political will to strengthen transparency through data. Sensitive information, such as mining revenues or pollution levels, may be deliberately ignored or falsified, hindering accountability and inclusive planning.

From statistical capacity to building targeted public policy: how does it work?

As an essential ingredient for developing effective public policies based on evidence, statistical capacity depends on the availability of reliable, disaggregated, and regularly updated data. When properly analysed, this data not only helps diagnose socioeconomic challenges but also guides the prioritization of public actions. Once problems are identified through statistical analysis whether descriptive or econometric decision-makers can design policies aligned with SMART objectives, (Specific, Measurable, Achievable, Realistic, and Time-bound) while also establishing monitoring indicators. In other words, the effectiveness of statistical systems enables policymakers to develop better-informed, more targeted, and evidence-based public policies. For African countries rich in natural resources, this includes assessing extractive revenues, measuring territorial and social inequalities, anticipating investment or redistribution needs, and tracking the outcomes of reforms. Reliable data also fosters greater transparency in public management, thereby enhancing government legitimacy and institutional accountability to citizens. By integrating statistical indicators into decision-making processes, states can design more inclusive development strategies, reduce waste, and quickly adjust their policies in response to economic or social shocks.

For instance, the creation of the National Institute of Statistics of Rwanda (NISR) has supported the National Transformation Plan by providing reliable data from regular surveys. This has significantly contributed to targeted poverty reduction and the development of social policies adapted to local realities. Similarly, Statistics South Africa publishes accurate and up-to-date data on the labour market and inequality in South Africa, thereby supporting fiscal reforms and evidence-based social policies. Following its collaboration with the Extractive Industries Transparency Initiative (EITI), Ghana publishes daily reports on extractive revenues, enhancing budget transparency and enabling more equitable redistribution mechanisms among producing regions. Conversely, in the Republic of Congo, the opacity of figures concerning oil production has led to poor revenue management and exacerbated public debt.

Policy recommendations

To enhance the effectiveness and influence of statistical systems in African countries, it is essential to:

• Increase national budget allocations for statistics to at least 0.3% of national budgets: Many Central African countires allocate less than 0.1% of their budget to the production of statistical data. This results in weak national statistical offices, outdated data of poor quality and excessive dependence on donor funding. The 0.3% mark is a step toward financial autonomy and sustainability. In doing so, governments must integrate statistical needs into annual finance laws and establish sustainable funding mechanisms for National Statistical Institutes (NSIs). This will ensure their technical independence and reduce reliance on external funding. At the supranational level, regional institutions such as the African Union Commission, the African Development Bank (AfDB), and the United Nations Economic Commission for Africa (ECA) can establish indicative thresholds and provide monitoring frameworks to encourage member states to meet this commitment.
• Integrate data into public policy cycles: Sectoral ministries must utilize evidence to plan, implement, and evaluate public policies. Including statistical indicators in planning documents should become a mandatory requirement. Donors and regional institutions should condition their support on the integration of quantitative evaluation mechanisms and promote funding for rigorous impact assessments of public programs.
• Strengthen statistical capacity in Africa through staff training and the adoption of digital technologies: Governments must invest in the continuous training of NSI personnel in statistics, econometrics, big data analysis, and data visualization. South-South partnerships and regional centers of excellence can support this effort. Additionally, the use of digital tools (such as dynamic dashboards and interactive platforms) is essential for improving data quality and dissemination. At the continental level, creating a pan-African ecosystem of open and interoperable data would enhance transparency, innovation, and cooperation on cross-border issues.

Ultimately, strengthening the statistical capacity of African countries rich in natural resources is essential for improving the quality of public policies. Reliable and effectively utilized data facilitate better planning, more transparent governance, and greater accountability. Investing in statistical systems, both in terms of human resources and technology, is a strategic imperative. It is the responsibility of states, technical partners, and regional actors to prioritize statistics to transform natural wealth into shared progress.