Oliver Kim’s How Much Should We Trust Developing Country GDP?, a review of Morten Jerven’s 2013 book Poor Numbers: How We Are Misled by African Development Statistics and What to Do About It, makes the same point about GDP as well. Improving data collection in underresourced areas in general seems like a cross-cutting ‘cause X’.
Some quotes:
Hollowed out by years of state neglect, African statistical agencies are now often unable to conduct basic survey and sampling work. Jerven writes:
In 2010, I returned to Zambia and found that the national accounts now were prepared by one man alone… Until very recently he had had one colleague, but that man was removed from the National Accounts Division to work on the 2010 population census. To make matters worse, lack of personnel in the section for industrial statistics and public finances meant that the only statistician left in the National Accounts Division was responsible for these data as well. (pg. x)
Without the staff to collect and analyze survey data, statistical agencies are usually forced to improvise, guessing the size of the economy from population figures, which are themselves extrapolated from censuses that are decades-old.
… I’m haunted by the words of the lone Zambian statistician, sitting in his empty office, who asks Jerven plaintively: “What happens if I disappear?”
Many African states are failing at the basic task of knowing how many people live in their borders—let alone accurately measuring their economic activity. The vast, unobserved informal sector (which includes subsistence farming, and something like 60% of working people) is usually estimated just as a direct function of population.5 Lacking direct harvest yields, estimates of agricultural output are often produced using FAO models based on planting-season rainfall data.6 Even the minimal task of measuring the goods traveling across borders—in theory, the easiest thing for a sovereign state to accomplish—is occasionally beyond the reach of statistical agencies. Until 2008, landlocked Uganda only collected trade data on goods that eventually passed through the Kenyan port of Mombasa, ignoring the four other countries on its borders.7
In the absence of good underlying data, the prevailing approach for GDP in developing Africa can be summarized as:
Income estimates… derived by multiplying up per capita averages of doubtful accuracy by population estimates equally subject to error. (p. 39)
Poor Numbers came out in 2013, attracting a wave of scholarly and policy attention (including by Bill Gates). Once you’ve heard its arguments, it’s virtually impossible to look at a GDP statistic the same way again.
But what actual progress has been made in the statistical capacity of nations?
Seemingly, not much. In late 2014, perhaps in response to Jerven’s book, the World Bank relaunched its website for its Statistical Capacity Indicator—a metric on a 0-100 scale which scores countries based on the strength of their “Methodology”, “Source Data”, and “Periodicity and Timeliness”. But even by this clunky internal metric, progress has been glacially slow: in 2004, the average score for African countries was 58.2; in 2019, it was just 61.4.
Moreover, over six years of an economics PhD, I have never heard of any economist using this statistic. Poor Numbers is well-cited and well-read (at least by Africa specialists), but its lessons about the fundamental unreliability of statistics have largely not been absorbed in how we actually do economics.
Oliver Kim’s How Much Should We Trust Developing Country GDP?, a review of Morten Jerven’s 2013 book Poor Numbers: How We Are Misled by African Development Statistics and What to Do About It, makes the same point about GDP as well. Improving data collection in underresourced areas in general seems like a cross-cutting ‘cause X’.
Some quotes: