The ability to make good decisions is contingent
on the availability and utilization of sound evidence. The overarching
assumption is that the evidence is derived from reliable data. Moreover, it is
expected that the data be collected by rigorously established procedures.
Morten Jerven’s book Poor Numbers: How we are misled by African development statistics and what
to do about it provides an insightful analysis of the production and use of
data for development in Africa. The book concludes that the capacities national
statistical agencies have fallen apart.
A World Bank report, Poverty in a Rising Africa, published in 2016 argued that lack of
reliable and comparable data mask complex realities and makes it difficult to
assess Africa’s progress. Hence, sustained and joined efforts are urgently
needed to improve the quality of and timeliness of statistics in the continent.
The concerns Morten Jerven and the World Bank
raise are spot on, and are strong indictment of the incapacity of the
statistical offices in African countries. But there is some data. The real
concern in my view is how little use we make of existing data. Whatever little
data we have and however old or outdated it is, provides invaluable insights
about the long-term impacts of national policy priorities.
At the East African Institute, we looked at
about 48 variables – from publicly available data – across all of Kenya’s 47
counties. Using both basic and fairly sophisticated data analysis and modeling techniques
we uncovered insightful patterns about the differences and similarities among
the 47 counties. What is exciting about the insights is that they are most
indelible fingerprint of the policies were have implemented for over half a
century.
From our analysis
Kenya’s 47 counties divide into four neat groupings. One group comprises
Turkana, Marsabit, Samburu, Garissa, Tana River, Wajir, Mandera and West Pokot.
These defining characteristics of these
counties are: high fertility rates; high maternal mortality rates; low levels
of mothers’ education, poor access to health facilities and stunting.
Another group of
counties comprises Kiambu, Nyeri, Muranga, Kirinyaga, Machakos, Uasin Gishu,
Meru, Nakuru and Nyandarua. A high density of health facilities, high levels of
literacy, and high per capita access to grid power characterize these counties,
unlike the first set of counties. A child born in Meru is three times more
likely to celebrate her fifth birthday compared to a child born in Mandera. Moreover,
a pregnant woman in Turkana is six times more likely to die of pregnancy
related complications than a pregnant woman in Kiambu.
Paucity of data can
no longer be used as an excuse for making bad public policy of investment
decisions. And yes there is so much we can learn from half a century of policy experiments,
which have led to divergent and unequal human wellbeing outcomes.
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