China’s customs data sheds some light on Africa’s growth, showing that Africa-China trade ballooned to $210 billion last year from $5 billion to $7 billion at the end of the 1990s.
Lending to the private sector in Africa also has surged, with private-sector credit growth more than doubling in real terms between 2000 and 2010.
But little is known about the true magnitude of Africa’s growth surge. Data quality in most Sub-Saharan African economies is weak. In many instances, the official data is too out-of-date to tell us much that is useful.
The lack of data complicates decision-making for both the private sector and governments. It reduces certainty, adds to the cost of doing business and can delay the formulation of much-needed policy.
While Africa has seen surging foreign direct investment and private portfolio inflows in recent years, investors – especially those new to the region – are often shooting in the dark when it comes to data.
Improved data quality can alter our perceptions of the region dramatically. When Ghana released its rebased GDP figures in 2010 (the first rebasing since 1993) the economy turned out to be 63 per cent larger than previously thought.
Nigeria’s rebasing earlier this year was even more dramatic, with the estimated size of the economy increasing by 89 per cent. With its GDP rebasing, Nigeria ‘became’ the largest economy in Africa and the 26th largest in the world.
Some claim even after a decade-and-a-half of growth out performance, some African countries are still as poor as they were at independence, if not poorer. While this sounds counter-intuitive, it is difficult to disprove without better information. In the absence of data, there is much conjecture and little analysis.
Take the question of how African economies might withstand weaker commodity prices. The myth of Africa’s dependence on commodities persists, despite evidence that other sectors contribute more to employment and GDP.
Why is this? Because resource extraction is large-scale and identifiable, lending itself more readily to measurement, we tend to overplay its importance.
Weak government capacity, funding difficulties, eroding capabilities at national statistics bureaus, the prohibitive costs of gathering data beyond urban centres, and poor survey design have all contributed to the current situation.
The information gaps are thought to be so substantial that any such ranking would tell us little that is meaningful.
Better data exists for the private sector, though it is more ‘micro’ in scale and less accessible.
Within banks, we have a good idea of the direction of growth. We can observe loan growth trends to identify the sectors that are gaining ground and those that are fading in relevance.
Corporate profitability and earnings surprises can be monitored. Loan delinquency data may provide an early gauge of sectoral problems, while market liquidity – and its influence on daily interbank rates – may be one of the most valuable sources of information.
If an economy experiencing an unusual surge in liquidity, consistent with a strong rise in pre-election spending, Interbank data would likely indicate this first.
Private sector can play a more meaningful role in improving data collation and accessibility. To test this, Standard Chartered has teamed up with well-known data providers to design a new set of Africa-focused price and business sentiment surveys.
Our price survey – a consumer basket tracker by Premise, a company based in San Francisco – uses a simple smartphone app to track thousands of price observations in real time.
Information gatherers on the ground – starting in Ghana and Nigeria – upload photos of price tags on consumer goods in local stores and markets. This data is then collated by Premise to track price trends.
This technology has already been deployed by Premise in other emerging markets. In India, it highlighted a sustained rise in the price of onions, which soon spilled over into general inflation and triggered a pre-emptive rate increase by the Reserve Bank of India.
The benefits of this technology relative to a monthly CPI survey are obvious. Geographic differences in real-time price trends can be mapped more easily, as can data on the availability of goods.