Statistical capacity: no reliable data, no reliable policy

Public policy failures are readily blamed on poor political choices. The reality is often more prosaic: many programmes fail because they were designed, targeted and monitored on the basis of figures that were approximate, outdated or simply absent. Statistical capacity, meaning a state s ability to produce, process and disseminate credible data, is not a matter for experts alone: it is the invisible infrastructure of public decision-making. In West Africa, this capacity remains highly uneven, and the gaps widen precisely where the needs are most acute. Yet, as Senegal s recent trajectory shows, nothing about this lag is irreversible.

An invisible but decisive infrastructure

The World Bank s Statistical Performance Indicators programme (SPI) scores each country from 0 to 100 across five dimensions: data use, services, products, sources and infrastructure. In West Africa, the 2023 scores range from 63.4 (Mali) to 76.9 (Senegal), a seemingly narrow band that conceals opposite dynamics. Benin, at 68.0, now sits above the regional median, having started from 48.5 in 2016, one of the strongest gains in the area. These figures confirm a simple intuition: the quality of a statistical system is no gift of nature, it is the product of sustained investment or, conversely, of underinvestment that is paid for in full through the quality of policies.

The Senegalese precedent: proof that lagging behind is no fate

The most instructive comparison is not spatial but temporal. Between 2016 and 2023, Senegal s SPI score rose from 55.1 to 76.9, a gain of nearly 22 points in seven years. In 2024, the country even reached 81.4 out of 100 and was ranked the leading statistical system in Africa by the World Bank, ahead of South Africa, Mauritius and Egypt. This rise was no accident: it stems from two decades of institutional investment around the National Agency for Statistics and Demography (ANSD), successive national strategies for the development of statistics and a general census conducted in 2023 whose preliminary results were released in record time.

Niger, over the same period, progressed more modestly, from 54.0 to 66.9. Yet both countries started from almost the same point in 2016: fewer than two points separated them. By 2023 the gap had reached ten points, and it widens further with Senegal s 2024 score. This divergence is all the more striking because Senegal and Niger are not in the same income bracket: with a GDP per capita more than twice as high (1,773 versus 735 dollars), Senegal certainly had more budgetary room, but it is institutional continuity, not wealth alone, that explains most of the gap. Burkina Faso, despite a lower GDP per capita than Senegal, posts the region s second-best SPI score (74.0), proof that sustained commitment can offset a resource constraint. The lesson is clear: at comparable economic levels, the statistical performance gap is built through institutional and budgetary choices, not through geographic fate.

The three levers that make, or break, a statistical system

Why does a country produce reliable data, or fail to? Statistical performance does not fall from the sky: it breaks down into measurable factors, three of which are decisive in West Africa. The first is digital infrastructure, which conditions collection and dissemination. The second is human capital, without which raw data remain unintelligible. The third is budgetary resource, which funds censuses, surveys and information systems. These three levers do not add up, they multiply: a deficit on any one of them is enough to throttle the entire chain.

Lever 1: digital infrastructure, the first bottleneck

Collecting, transmitting and publishing data requires a minimum level of connectivity. Yet the divide is wide. Ghana (72.2 percent of the population online in 2024) and Senegal (60.1 percent) have a solid foundation for deploying modern statistical systems, from mobile data collection to online dashboards. At the other extreme, Niger tops out at 23.2 percent (2023, latest published year) and Burkina Faso at 28.3 percent, which directly constrains survey coverage and the dissemination of results. Benin, at 34 percent, sits in the regional middle ground: a point of vigilance for anyone seeking to industrialise the data chain from the field to the decision-maker. Connectivity is no luxury: it determines whether a survey can be geolocated, monitored in real time and delivered in weeks rather than years.

Lever 2: human capital, the condition for intelligible data

Producing statistics requires staff able to design questionnaires, code, analyse and interpret. The adult literacy rate offers a first measure of this pool of skills. Ghana (76.5 percent), Togo (72.6 percent) and Nigeria (70.4 percent) provide fertile ground for training statisticians and analysts. Conversely, Mali and Niger (35.6 percent each) along with Burkina Faso (41.6 percent) sharply restrict the available talent pool. Benin, at 51.4 percent, has an intermediate base that should be consolidated through continuing education, notably in monitoring-evaluation and data science. These World Bank figures are not annual, however: they refer to each country s most recent available survey (from 2021 to 2024 depending on the case), so the comparison should be read as an order of magnitude rather than a perfectly synchronous snapshot.

No development policy is sounder than the data on which it rests.

Lever 3: budgetary resources, the lifeblood of public statistics

A census, a national survey or a health information system is costly, and budgetary room for manoeuvre depends largely on income per capita. Côte d Ivoire (2,728 dollars of GDP per capita in 2024) and Ghana (2,391 dollars) have a clearly greater investment capacity than Niger (735 dollars) or Burkina Faso (982 dollars). Nigeria stands at 1,084 dollars, but this figure calls for a cautious reading: it nearly halved between 2023 and 2024 as a result of the sharp devaluation of the naira, not of any real impoverishment. Nigeria remains by far the region s largest economy in aggregate size; what stays narrow is its budgetary room for manoeuvre relative to a population of more than 220 million. Benin, at 1,485 dollars, occupies a middle position that calls for efficiency choices: pooling tools, prioritising digital data collection and concentrating effort on the surveys with the highest decision-making payoff.

Three divides that reinforce one another

The most worrying finding is the convergence of these three deficits in the same territories. Niger combines the lowest connectivity (23.2 percent), one of the lowest literacy rates (35.6 percent) and the most modest GDP per capita (735 dollars). This concentration creates a vicious circle: without reliable data, poverty-reduction, health and education policies are designed and monitored blindly, which degrades their results and, ultimately, the resources available to strengthen statistics. The three levers reinforce one another in cascade:

• Weak connectivity: surveys remain paper-based, hence slow, costly and hard to monitor, which reduces their frequency and coverage.
• Limited human capital: collected data are under-used for lack of analysts, and the rare skills available leak away to the private sector or abroad.
• Constrained budgets: funding remains dependent on one-off donor projects, with no permanent line, ruling out any multi-year planning of censuses and major surveys.

Breaking this circle requires coordinated, sequenced investment rather than isolated operations. This is precisely the path Senegal followed, making the ANSD the pivot of a system-wide reform rather than stacking up projects without coherence.

The cost of inaction: blind spots measured in lives and billions

The absence of reliable data is no abstract technical gap: it carries a measurable human and financial cost. The clearest example is civil registration. In West and Central Africa, only 45 percent of children under five were registered at birth, the lowest rate in the world, around 47 million children without legal existence. Across the continent, of the 164 million children under five who are unregistered worldwide, nearly 91 million live in Africa. An unregistered child is a child who does not exist in planning databases: not counted in school needs, nor in vaccination campaigns, nor protected against early marriage or trafficking. Every missing percentage point of coverage thus translates into poorly sized public services and rights left unguaranteed.

The good news is that this deficit recedes when tackled head-on: over three years, birth-registration coverage in West and Central Africa rose from 45 percent to 53 percent. Here too, progress is anything but automatic; it follows from targeted investment in registration systems and vital statistics. The cost of inaction is its mirror image: each year of delay leaves an entire cohort of children outside the radar of public decision-making.

What national averages hide, and why fine measurement changes the decision

All the figures presented so far are national averages. Yet an average is, by construction, a compromise that dissolves disparities. A national literacy rate of 51 percent may conceal 70 percent in urban areas and 30 percent in rural zones; internet penetration of 34 percent may mean 60 percent in the capital and less than 10 percent in remote regions. Steering a public policy on the average alone means accepting to serve the median reasonably well while leaving the most vulnerable populations out of reach. Statistical capacity is therefore judged not only by the ability to produce national aggregates, but by the ability to produce disaggregated data: by sex, by place of residence, by wealth quintile, by fine administrative unit.

This is precisely CRAD s line of work. Geolocated, disaggregated data transform the decision: they make it possible to target a vaccination campaign on the communes that are genuinely under-covered rather than spreading a national budget thinly, to direct school construction towards the zones where access is weakest, or to measure whether a programme benefits women as much as men. The gender dimension is cardinal here: an aggregate indicator can show average progress while masking a regression for women or girls, as the gaps in literacy and digital access between men and women in several countries of the region demonstrate. Without disaggregation by sex, these blind spots remain invisible, and policies meant to correct inequalities end up reproducing them.

The full chain, from the digital field to the decision dashboard, has value only if it preserves this granularity all the way to the point of decision. This is also what distinguishes statistics for steering from statistics for show: the former descends to the level of the commune, the neighbourhood, the household; the latter settles for a reassuring but inoperative national aggregate. Measuring finely is no academic refinement: it is the condition for efficient and equitable public spending, and it is often what separates a programme that reaches its beneficiaries from one that misses them.

Financing statistics: moving beyond project dependence

Financing remains the weakest link. According to PARIS21, international support to data and statistics reached around 799 million dollars in 2021, up 14 percent, with Africa the only region where this funding increased consistently. But this dependence on external aid, now channelled mostly through multilateral institutions, exposes statistical systems to the volatility of donor priorities. A census funded by project is a census that can be postponed, truncated or abandoned at the mercy of an external budget arbitration. Sustainability requires a permanent base of domestic financing, with aid complementing rather than replacing national commitment. The issue is not only the level of resources, but predictability: a statistical apparatus is planned over ten years, not over the lifetime of a project.

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