|By Babatunji Wusu
The National Bureau of Statistics publishes GDP, inflation, and employment data that the IMF, the World Bank, and Nigeria’s own central bank routinely question. Policy built on uncertain data produces uncertain outcomes. The cost is not hypothetical.
IN November 2023, the National Bureau of Statistics published Nigeria’s third-quarter GDP growth figure: 2.54 percent. Within ten days, two international financial institutions had published commentary questioning the methodology. Within three weeks, the IMF’s Article IV consultation note had included a carefully worded passage describing Nigeria’s statistical capacity as a “constraint on macroeconomic assessment.” It was not the first such note. It is not likely to be the last.
Nigeria’s statistical infrastructure — the system of surveys, administrative records, and measurement methodologies that produces the official data on which policy is built — is, by the assessment of most independent analysts, inadequate for an economy of its size, complexity, and policy ambition. The National Bureau of Statistics operates with an annual budget that the African Development Bank has estimated at less than one-fifth the per-capita statistical expenditure of comparable middle-income economies. Its major surveys, the General Household Survey, the Labour Force Survey, the National Agricultural Sample Survey, are conducted at intervals and with sample sizes that produce estimates with margins of error wide enough to accommodate multiple incompatible policy conclusions.
“The problem with governing on bad data is not that you get the wrong answer,” said Agbetola Ayokanmi Victor, a data analyst who has studied the relationship between data infrastructure quality and economic policy effectiveness. “It is that you don’t know you have the wrong answer. When your statistical margin of error is large enough that both ‘this policy is working’ and ‘this policy is failing’ are consistent with the data, you are not making evidence-based decisions. You are making faith-based decisions with a statistical veneer.”
The economic cost of inadequate statistical infrastructure is difficult to quantify precisely, partly because the measurement problem is itself a symptom of the inadequacy being measured. But its consequences are visible in the divergence between official data and the experience of economic actors.
Nigeria’s official inflation figures, published monthly by the NBS, use a basket of goods and a geographic sampling methodology that the IMF’s 2023 Article IV consultation noted “may not fully capture urban consumption patterns.” The practical consequence was visible in 2023, when the official year-end inflation figure of 28.92 percent coexisted with food price surveys by the Enhancing Financial Innovation and Access initiative showing that specific staple food prices in Lagos markets had increased by 45 to 70 percent over the same period. The official figure was not wrong in any demonstrable sense. It was measuring something different from what the people experiencing the inflation were experiencing.
For monetary policy, this divergence is costly. The CBN’s rate-setting decisions are, in theory, data-driven. In practice, they are driven by data that may be measuring a different economy from the one the rate decisions will affect. “Every monetary policy decision that is calibrated to an inflation estimate that is systematically off by fifteen percentage points is a decision that will produce systematic surprises,” said Dr. Bayo Adeleke, an economist at the Centre for the Study of African Economies at Oxford. “Nigeria’s monetary policy surprises are not random. They are patterned. That pattern is the statistical gap expressing itself in policy outcomes.”
“When your statistical margin of error is large enough that both ‘this policy is working’ and ‘this policy is failing’ are consistent with the data, you are making faith-based decisions with a statistical veneer.”
— Agbetola Ayokanmi Victor, data analyst
One of the most tractable dimensions of Nigeria’s statistical infrastructure problem is the gap between administrative data and analytical use. Nigeria generates substantial administrative data through its government operations: tax records from the Federal Inland Revenue Service, import and export records from the Nigeria Customs Service, company registration data from the Corporate Affairs Commission, vehicle registration data from the Joint Tax Board. This data, properly integrated and analysed, would provide near-real-time economic intelligence at a fraction of the cost of the large-scale surveys that currently anchor official statistics.
The integration and analysis of this data does not currently happen at any meaningful scale. The various agencies that hold administrative data do not share it with the NBS on a systematic basis. The NBS does not have the analytical infrastructure to process and integrate large administrative datasets if they were shared. The result is that Nigeria has, sitting in government databases, a substantial fraction of the data it needs to build significantly better economic statistics, and lacks only the institutional will and technical capacity to use it.
Ayokanmi identifies the technical component as tractable: “The data engineering problem here is not unsolvable. The methodologies for integrating administrative data into national statistics, tax record-based GDP estimation, customs data-based trade statistics, firm registry-based business demography, are well-established. Countries with far smaller technical capacity than Nigeria have implemented them. The barrier is not that the methods don’t exist. It is that the institutional coordination required to implement them has not been achieved.”
In the absence of adequate public statistical infrastructure, Nigeria’s larger private sector organisations have built their own. Stears Business itself maintains some of the most granular economic datasets available for the Nigerian market. The Enhancing Financial Innovation and Access initiative produces financial inclusion surveys that are more methodologically rigorous than most NBS equivalents. Several Nigerian banks publish their own inflation trackers and economic activity indices that their private clients treat as more reliable than official figures.
This private statistical infrastructure is, in aggregate, considerably better than the public infrastructure it supplements. It is also privately held, commercially motivated, and accessible only to those who can pay for it. The economic intelligence that drives the most sophisticated investment decisions in Nigeria is available to institutional investors, consulting firms, and large corporations. It is not available to the SMEs, smallholder farmers, and informal sector workers whose economic decisions it is most relevant to.
“Good data should be a public good,” said Ayokanmi. “When it becomes a private commodity, the information advantage accumulates at the top of the income distribution. The people who most need accurate economic intelligence to make rational decisions, about where to locate a business, which market to target, whether to hold naira or dollars, are precisely the people who can’t afford to pay for the data that would tell them. That is not a neutral outcome. It is a compounding inequality.”
Nigeria’s statistical infrastructure problem is, ultimately, a governance problem wearing a technical mask. The data to govern better exists or can be collected. The institutional capacity to analyse it can be built. The political will to prioritise it over more visible public expenditure has not yet been sustained long enough to produce structural change. Until it is, the country will continue to govern its most complex policy challenges with the bluntest available instruments.
