On the July 27 2012, exactly a year ago to the day, I wrote about the LIBOR scandal that was rocking the global banking establishment. We, mere mortals, had just discovered that the esteemed British banks that were previously the paragons of trust and rectitude, had been manipulating the data used to compile the London Interbank Offer Rate or LIBOR, for short. By falsifying their daily data submissions, the banks were fraudulently causing LIBOR, which is the benchmark against which the interest rates for financial contracts worth tens of trillions of US Dollars all over the world are set, to move up or down to suit their own commercial interests.
In that column a year ago, I wondered whether it was remotely possible that, just as with LIBOR, the social and economic figures that are bandied about in Uganda could be the product of a manipulated process.
I am not an economist or a statistician but based on hunches, born out of common sense, I asked three questions. I asked how one could explain the fact that Uganda officially can have a very low HIV prevalence rate yet at the same time one of the world’s fastest growing population, meaning that Ugandans are neither abstaining nor using prophylactics but are somehow managing to avoid HIV.
I asked how Uganda had managed to stage one of the most sustained periods of year-onyear economic growth over the last 26 years whilst the Uganda Shilling has been on a steady downward slide against all major foreign currencies throughout that time.
Lastly, I wondered how Uganda could afford to borrow at the rate of 15.1 per cent per annum whereas it was widely accepted that Spain, whose now embattled Prime Minister Mariano Rajoy is infamous here for having said derisively said “Spain is not Uganda!”, could not afford to borrow at the rate of 7.5 per cent per annum.
I concluded that column with these words, “Whilst we wait to hear from whoever has the answers, methinks there is something LIBOResque about many of these figures.” I got no responses, which made me think that my amateur hunches may be right. However, thanks to a “villagemate” who is a keen reader I now know that my hunches have been the subject of an extensive professional study by Dr. Morten Jerven, the assistant professor of International Studies at Simon Fraser University in Vancouver, Canada.
Dr Jerven condensed the findings of his research on the reliability of the African economic statistics into a book entitled “Poor Numbers: How We Are Misled By African Development Statistics and What To Do About It.” Dr. Jerven must have spent some of his spare time assessing African reading habits because his title is so selfexplanatory that many who will never read his book will still be able to get its simple message.
With evidence drawn from research visits to Botswana, Ghana, Kenya, Malawi, Nigeria, Tanzania, Uganda and Zambia, Dr. Jerven proves that indeed many of the numbers that we are fed are of “dubious quality”.
The problems begin with our lack of institutional capacity to collect, store and process data. Statistics Offices are generally understaffed and underfunded. Then the baselines used as benchmarks for many of the statistics that are bandied about today were also flawed.
There are also many social and political pressures to have numbers that read a certain way, meaning that even where numbers could have been corrected, errors or gaps are simply compounded in order to perpetuate the “growth” stories. Apparently gaps in the numbers are routinely filled not with empirical data but with guesstimates and extrapolation.
To illustrate Dr Jerven’s point using examples close to home, consider the following basic facts. Uganda does not have a universal register of births and deaths. Uganda does not have a national identity card. Uganda has not had a national population census for over ten years and whilst there is now a promise to hold one in 2014, there is no guarantee that this will happen. The majority of Ugandans do not pay direct taxes and are engaged in the so-called “informal sector”, which is hard for economists to accurately measure because there are very few statistics collected about it. One hears many stories of young people employed to gather research data, who pocket the per diem and falsify the field entries without ever having stepped outside of Kampala. Then for the statistics collected, lack of institutional capacity to verify means that a lot of this kind of fraud goes unchecked.
So the numbers are LIBOResque. I am glad that a year later, my hunch has been confirmed by an academic study. We need to take this seriously because behind these poor numbers lie real people whose basic needs are being overlooked because of “lies, damned lies and statistics”.