African development statistics misleading
The development indicator with the greatest influence on policy and public debates is gross domestic product. It is used to rank countries' progress and wealth - but its interpretation and measurement are tricky.
For Africa, in particular, countries' economic development statistics are misleading as a result.
The difficulties inaccurately measuring GDP are not restricted to the developing world,
but, all other things being equal, poorer economies are likely to have lower-quality statistics.
Lack of data
A poorer economy will have relatively fewer available resources to fund an official statistics
The quality and availability of data, and therefore the cost of collecting robust statistics,
depend on individuals and companies keeping formal records of economic activity or filing
taxes - which are less likely in poorer countries. This information is only occasionally collected
"Our knowledge about growth in African economies is limited."
While defining what constitutes economic activity is a massive problem for measuring
GDP in the developing world, it is dwarfed by the difficulty of actually recording economic
In Sub-Saharan Africa, this basic problem is aggravated by some historical factors. For
example, most countries on the continent have not been collecting taxes on property and
incomes, but on goods crossing borders - therefore states have had weak incentives to monitor
production and domestic economic transactions.
And although African states' statistical capacity was greatly expanded in the late colonial
and early post-colonial period, it suffered greatly during the 1970s economic crisis. Statistical
offices were neglected in the decades of liberal policy reform that followed. This period of
"structural adjustment" in the 1980s and 1990s meant governments had to account for more
with less: informal and unrecorded markets in food and services grew, while public spending
As a result, our knowledge about growth in African economies is limited. Anyone can
download GDP data for 2012 from the World Bank covering 47 Sub-Saharan African countries
and then rank and compare them.
But due to the uneven application of methods and poor availability of data, any comparisons
will be highly misleading. This was made clear in 2010 when Ghana updated data and
methods that had been unchanged since 1993 to publish a new estimate: switching to using
2006 rather than 1993 as the benchmark year for calculating growth almost doubled its
As for any index, when the base year is out of date, GDP estimates become unreliable as
they may no longer reflect a sizeable part of the economy. It is widely expected that when
Nigeria revises its GDP data, probably next year, it may surpass South Africa as Sub-Saharan
Africa's largest economy. The problem is that not all countries in the region use a
recent base year.
The International Monetary Fund recommends updating it every five years, but countries
struggle to do so. Indeed, by that rule, Ghana's GDP estimate is already out of date. Expanding
country-level expertise: What to do about unreliable GDP estimates? The message for
data users is to question your evidence. And data disseminators, such as the World Bank with
its World Development Indicators, need to label their data correctly and clearly acknowledge
knowledge gaps. A great deal of information sold as data is only weak guesses and projections.
But the biggest challenge is to invest in countries' capacity to produce better data.
In the macro analysis of growth and poverty, the distance between the analyst and the economy
being studied has increased since the 1990s, when downloadable data sets became popular.
To some extent, this practice has replaced country experts, who tended to be better informed
about gaps in country-level statistics.
Improving this state of affairs will require a change in scholarly methods, expanding country-
level expertise - such as strengthening domestic universities and think-tanks - as well as
training journalists to report on economic development.
While funds have been available for statistical offices, partly due to the Millennium Development
Goal agenda, they have tended to divert resources from economic statistics towards
social statistics more relevant to MDG monitoring. Moreover, these are generally ad-hoc
funds that support data collection for a donor-funded project. In practice, many statistical
offices operate as a data-collection agency for hire, not an office that provides objective information
needed for day-to-day politics or policy planning.
This system means that donors distort data production rather than expand statistical capacity.
And it stretches resources for manpower and infrastructure. The problem here is lack
of co-ordination: many countries have national strategies for statistical development, but,
more often than not, donors break with these plans' priorities and demand the data they
need, thus adding to the fragility of statistical offices under increasing pressure.
Put monitoring first. Policy debates can drive capacity building too. On a global level,
perhaps the most visible commitment to results-based and evidence-driven policy was the
adoption of the MDGs. But progress in achieving them has been difficult to measure.
In retrospect, it was na´ve to require this extent of measurability without a systematic
understanding of how data can and should be generated by weak statistical systems. There is
a lesson here: statistical capacity must become central to discussions about post-2015 development
targets. In the MDG discussions, for example, targets were identified first, but less
thought was given to where the data needed to monitor them should come from.
With future goals, it may be useful to turn the initial question around: rather than asking
what kind of development we should target, the question should be what kind of development
can we monitor? A new agenda for development data in Sub-Saharan Africa is required
- one that puts local demand, incentives and applicability at the centre. - Scidev.net