- •Contact us
- •About us
- •Advertise with the FT
- •Terms & conditions
© The Financial Times Ltd 2013 FT and 'Financial Times' are trademarks of The Financial Times Ltd.
February 24, 2013 3:22 pm
Poor Numbers: How We Are Misled By African Development Statistics and What To Do About It, by Morten Jerven, (Cornell University Press, RRP£40.50, $65)
There are lies, damned lies and then there are African statistics. If economic figures everywhere are a work in progress – regularly rebased and updated to take into account fresh data – those from Africa are the most open to question and the most unreliable in their revision.
This will come as no surprise to academics, investors or development specialists. Shantayanan Devarajan, chief economist of the World Bank for Africa, for example, has talked recently of “Africa’s statistical tragedy”. Yet the extent and implications of the variations are little studied, and the data are widely used.
The point is not simply theoretical. Some innovative recalculations from social surveys last year by London School of Economics professor Alwyn Young suggest living standards in sub-Saharan Africa have risen at 3-4 per cent a year in the past two decades, or three to four times faster than existing data sets claim.
More extreme, in 2010, Ghana recalculated its gross domestic product, adding 60 per cent ($13bn). Overnight, the “low income” country was redefined as low-middle income: good for investment and fulfilling politicians’ pledges; bad for recipients of aid from donors focused on the poorest nations.
Poor Numbers is an important contribution to the subject. Morten Jerven, an assistant professor at the school for international studies at Simon Fraser University, Vancouver, builds the case for renewed scrutiny. Pointing to “huge discrepancies and alarming gaps” in African figures, he writes: “Datasets are like guns. Someone will use them if they are left lying around.”
The book, while technical, is notable for its application of economic analysis without indigestible equations; for drawing out broader policy implications; and the use of colourful insights from the field. Conducting fieldwork in Zambia’s Central Statistical Office in 2007, he found a crop survey delayed by the need for car repairs, computers gone missing, scant records from the previous three decades and just one of the three staff from the national accounts division regularly in the office.
He examines data to show that in 2011, only 17 of a selection of 47 African countries had prepared their own new GDP estimates. Just 10 had a “base year” – the reference point from which subsequent adjustments are made – that was within the past decade. Madagascar’s dated back to 1984.
Yet fundamental changes – in agriculture, industrialisation, the large, unrecorded informal sector – have taken place since. “More than half of the rankings of African economies up to 2009 may be pure guesswork,” Jerven concludes. From his own informal polling of statistics offices in 23 countries, he found just three believed their own GDP calculations covered the whole economy and 18 thought they were underestimated.
So how did such a lamentable situation come about? The author describes how statistical offices are a relic of the colonial period, which had its own extractive priorities. African production and trade not destined for export were considered irrelevant. With decolonisation, funding for data gathering waned further. In mid-1970s Zambia, the university library lacked the money to transport reports from the statistical office to its archives; they are now missing. Ever poorer national statistics meant lower demand and still fewer resources for improvement.
The structural adjustment programmes – reducing fiscal imbalances by cutting public spending – advocated by the International Monetary Fund in the 1980s and 1990s ought to have given new impetus to economic measurement. Yet, Jerven argues, budget cuts and the break-up of large state-controlled companies that undertook centralised data collection worsened the problems.
Today, external donors’ priorities are still influential. Jerven cites a three-year aid programme in Malawi that used Norwegian software that was too complex to manage locally and models inappropriate for the data available.
A recent push towards accountability in meeting the social objectives of the UN millennium development goals has had the perverse effect of creating demand for 48 new indicators – many difficult to quantify – and further stretched limited capacity to measure broader economic growth.
Jerven’s recommendations for reform are a bit thinner, although he is right to call for a focus on strengthened national statistical capacity, the use of “economic anthropologists” and at a minimum greater transparency on the underlying assumptions and weaknesses of existing data.
As he rightly concludes: “Numbers are too important to be ignored, and the problems surrounding the production and dissemination of numbers are too serious to be dismissed.”
The writer is an FT correspondent
Copyright The Financial Times Limited 2013. You may share using our article tools.
Please don't cut articles from FT.com and redistribute by email or post to the web.