Bench marking

The journalist Martin Plaut wrote an excellent article about the Trouble with African Statistics.   He identifies several cases of statistics reported as facts that are based on little more than thin air.  Imagine how difficult it is to track conditions in countries like Angola, which has not conducted a census in over 40 years and has not conducted a nutrition survey since 2008.  He makes it very difficult to miss the point by stating that “often African statistics are just politically driven guesses”.  The article was based on analysis by Good Governance Africa to be released on the 28th October.

The issues of poor quality or missing data also affect economic statistics. The recalculation of the GDP of Nigeria caused a great stir as overnight, the beehive of Africa became the largest economy. The new calculations of economic activity correctly included the Nollywood film industry, banking and the telecoms. Unfortunately they left out the value of breastmilk production which is most likely also huge.  These issues have been researched by economic historian Morten Jerven in his book Poor Numbers and reported as an editorial in the Lancet. The book explores the weaknesses in the National Statistical Offices and the effects on planning when administrative data for the day-to-day operation of governments do not exist.

Now that Ghana and Nigeria have recalculated their GDPs, most other countries in the region are considering to follow suit.  Ben Leo of the Center for Global Development reports that we need to “revisit what we think we really know” about these economies. An enormous number of econometric studies were done on data that has become redundant overnight.  The public spending in Nigeria on nutrition, health, education, etc…  was low to begin with.  Now with the readjustment of the economic condition, we see that social spending is closer to 1 percent of GDP compared to 2 percent formerly reported.

As Morton Jerven stated, “It quickly becomes evident that any statement about the size and direction of global poverty is relying on a lot of assumptions and extrapolations”.

And our data on the chronic and acute emergencies regularly battering the sub-Saharan populations are also problematic. The Active Learning Network for Accountability and Performance in Humanitarian Action (ALNAP) report on use of evidence in humanitarian action finds that are early warning data is often incomplete and monitoring is regularly very poor, inaccurate and non-representative.

We have tremendous opportunities with mobile technologies to collect more information, more quickly and with more granularity than ever before.  To address these weaknesses in data, there are many initiatives, but it clear that greater coordination, harmonization of critical indicators and more rigorous and regular data collection is needed

Apologies for the draft version sent out earlier. It is failure friday, and I am still learning how to work these high tech tools (blogs) for nutrition.  I tried to retract the first posting and ended up here (