Platforms of Prosperity: The Africa Edition

Abstract

The six-factor model was developed by the Centre for African Management and Markets (CAMM) at the Gordon Institute of Business Science (GIBS), in a country prosperity project that has run since 2009. The research draws on data from 160 countries going back six decades, with the final scorecard removing microstates from the analysis to reduce the survey set to 125 countries spanning 60 years, making for 7 500 country years
of data, which is a treasure trove of economic and industrial intelligence. The model is derived from a multiple linear regression model with a learning component to determine the factors most closely associated with prosperity across the entire sample of countries over time.

Full text

Gordon Institute of Business Science A
CENTRE FOR AFRICAN MANAGEMENT
AND MARKETS (CAMM)
PLATFORMS OF
PROSPERITY:
THE AFRICA EDITION
ADRIAN SAVILLE, IAN MACLEOD AND THERESA ONAJIBENSON
AUGUST 2021

B Gordon Institute of Business Science

Gordon Institute of Business Science 1
www.gibs.co.za
2 The great setback
3 Prosperity by download
4 Methodology: The six-factor model
4 Half a dozen of the other
5 Applying the six-factor model in Africa in 2021
6 Somalia
7 South Africa
7 Botswana
9 Harmonising African economies and trade
23 Conclusion
25 References
Contents
The authors gratefully acknowledge the funding support of
Barloworld Ltd in producing this paper.
AUTHORS
ADRIAN SAVILLE
Full time Faculty and Director
of the Centre for African
Management and Markets
IAN MACLEOD
Research Associate and Manager
special projects with the Centre for
African Management and Markets
THERESA ONAJIBENSON
Full time Faculty and Senior Manager of the
Centre for African Management and Markets

2 Gordon Institute of Business Science
We were on the most sustained and widespread outbreak of
prosperity the world has ever seen (Pinker, 2018). The seven
decades since the end of the Second World War brought greater
improvements in living standards to more people than could
previously have been imagined. In the two decades to 2020
alone, global poverty rates were cut by more than half (United
Nations, 2021). This may have been cold comfort to those left
behind, but the world was undisputedly on an unprecedented
arc of improvement. Nevertheless, COVID-19 rapidly turned
that rising path of improvement sharply downwards in the
closing weeks of 2019.
Prior to the pandemic, economic predictions expected that
poverty rates would decline in 2020 (World Bank, 2021), “Had
the pandemic not convulsed the globe, the poverty rate was
expected to drop to 7.9% in 2020.” The actual estimate was
between 9.1% and 9.4% of the world population living on less
than $1.90 per day in 2020. Thus, in staying with the established
trend of material gain in global welfare, 2020 was expected to
take the world to a better place by the end of the year compared
to the start. Of course, with the benefit of the brilliance of
hindsight, we know that the exact opposite came to pass. The
unprecedented COVID-19 crisis has put individuals, firms, and
The great setback
They want us to join
their fighting
But our answer today
Is to let all our worries
Like the breeze through
our fingers slip away…
When you’re moving in
the positive
Your destination is the
brightest star
~ Stevie Wonder, “Master Blaster (Jammin’)”
countries into a new level of uncertainty, fuelled by the inability
to make coherent strategic decisions for now and the immediate
future. It has marked a significant turning point especially for
countries and businesses, as these units have had to rethink
“normal” and adapt to the newness of the situation.
Economies and societies have been hard hit by the extent of the
pandemic, with significant societal turning points exacerbated by
the George Floyd crisis in America, education crisis in the United
Kingdom (UK), riots in Europe, increased incidences of gender-
based violence in South Africa, and police brutality in Nigeria.
Economically, nations are shaky due to government expenditures
to support employment more than doubling, increased
healthcare costs, and lost income from the freeze on cross-
border travel. COVID-19 and the policy response have resulted
in an unprecedented and global freeze in economic activity.
The grimness of the situation also extends to Africa, though the
crisis seems to be more of an economic crisis than a health crisis,
with the continent doing better than was initially predicted at
the onset of the global pandemic. However, the gains made in
the controlled spread of the virus present heightened economic
costs as a result of harsh lockdowns in a predominantly informal
economy. The high levels of inequality are predicted to rise with

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the increased suffering of the middle class due to high costs of
credit, job losses, and weak policy support from the governments
to ameliorate the economic hardships. Moreover, there is the
concern around the potential debt crises facing at least 19 of the
54 African nations.
While the coronavirus and the scale of the resultant devastation
are new, one admirable facet of human nature has a long
history as a bright shadow to dark times. People are resilient. If
businesses, economies, and nation states can ride out tumult, the
experience can fuel hope of that most endearing narrative: the
comeback story. Consider several cases of people pivoting and
finding prosperity despite the tumult of the COVID-19 recession.
In a recent report, Qureishi (2021) notes a number of success
stories that have been born out of this crisis of 2020, including
manufacturers in Kenya converting factories to produce personal
protective equipment; Rwanda utilising locally assembled drones
and robots to track COVID-19 patients; Ghana producing a low-
cost COVID-19 antibody test; and engineering students in Senegal
developing a multifunctional medical robot to ease the burden on
healthcare workers.
Resilience and ingenuity have their limits, though. If the world
is to look back on a “Great COVID Comeback”, large tools with
deep foundations are needed. We need inclusive solutions,
transportable and customisable to different political and
philosophical systems, across borders, and through boundaries.
We need platforms, if you like, or to use the language of our age,
economic “apps”.
With countries gradually coming out of the crisis, there is a
need for strategic direction at a macro and micro level to ensure
that there is a trajectory of economic and social improvements.
Although this seems to be a task for the strong-minded, history
has shown that individuals, businesses, and nations have the
capacity to reinvent and emerge from a crisis with direction and
purpose. The onus is on economies to think innovatively on how
to overcome the challenges and maximise the opportunities
presented. In this paper, we interrogate macroeconomic growth
models that countries have focused on to recover and respond to
crises. We employ data from our six-pack framework that looks at
savings and investment, demography, education, health, openness,
and policy and institutions as the key focus for prosperity.
“Had the pandemic
not convulsed the
globe, the poverty
rate was expected to
drop to 7.9% in 2020.”
The World Bank (2021)

4 Gordon Institute of Business Science
Prosperity
by download
I should have based my
judgement upon deeds and
not words.
~ Antoine de Saint-Exupéry, The Little Prince
Figure 1: The “Great Divergence” depicted by gross domestic product (GDP) per person in 1990 constant United States (US) dollars (Source: Maddison Project, 2021)
Britain Netherlands Japan India China
1300 1400 1500 1600 1700 1800 1900 2000
5,000
0
20,000
10,000
15,000
25,000
THE GREAT DIVERGENCE
GDP per person, 1990 constant $
Ferguson (2012) advances what he argues is an exhaustive – or at
least comprehensive – list of the six components or institutions
that enabled “the West” to accelerate past “the rest” in quality of
living since approximately 1500AD. In his now iconic TED Talk,
Ferguson (2011) says:
“Let’s call [these six institutions] ‘killer apps’. They’re kind of like
the apps on your phone, in the sense that they look quite simple.
They’re just icons; you click on them. But behind the icon, there’s
complex code. It’s the same with institutions.”

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achieved after 1500AD – only faster.” In his headline note, he
quips: “This is the great re-convergence and it’s the biggest story
of your lifetime.”
Furthermore, Ferguson (2012) concedes that the “apps”
description is an adaption of a complex idea for a TED Talk
audience with 20 minutes to focus. Nevertheless, the principle
that the enablers of long-term prosperity are open-source and
free to use by any government is eminently feasible.
Figure 2: The “Great Re-convergence” depicted by ratios of GDP per capita ratios between countries (Source: Maddison Project Database, 2021)
USA/China UK/India
5
1854
1914
0
1864
1924
1934
1944
1954
1964
19 74
1984
1994
2004
20
1824
1884
10
1844
1904
15
1834
1894
25
1500
1874
Ferguson (2012), a historian by training, identifies six
institutions: competition; scientific revolution; property
rights; modern medicine; consumer society; and work ethic.
He builds his case from centuries of data. However, the
“app” comparison takes on particular importance today. Like
smartphone apps, he says that these are “downloadable” and
can be installed even more efficiently today than in years gone
by. Ferguson (2012) continues, “Any society can adopt these
institutions, and when they do, they will achieve what the West

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Methodology:
The six-factor model
I could see in the distance all the dreams that were clear to me
Every choice that I had to make left you on your own
Somehow the road we started down had split asunder
Too late to realise how far apart we’d grown.
How I wish I, wish I’d done a little bit more
Now “shoulda woulda coulda”, means I’m out of time
‘Cause “shoulda woulda coulda”, can’t change your mind
And I wonder, wonder, wonder what I’m gonna do
“Shoulda woulda coulda” are the last words of a fool
~ Beverley Knight, “Shoulda Woulda Coulda”
The six-factor model was developed by the Centre for African
Management and Markets (CAMM) at the Gordon Institute of
Business Science (GIBS), in a country prosperity project that has
run since 2009. The research draws on data from 160 countries
going back six decades, with the final scorecard removing
microstates from the analysis to reduce the survey set to 125
Table 1:The six-factor model with descriptors for each of the six factors
Factor Proxy descriptors
Savings and investment
Elevated (>25% of GDP), productive (non-rent-seeking assets), and funded domestically, with no more
than modest rates of foreign direct investment (conventionally <3% of GDP). Gross domestic fixed
investment (GDFI) is an excellent proxy.
Demography
More people need to be joining the workforce than going into retirement. A misconception is that
longevity and higher retirement ages lead to “job displacement”. The opposite tends to hold. Lagged
population growth is an excellent proxy.
Policy and institutions Stable policies beat “good” or “bad” policies. Policy has to be backed by capacity and capability (rang-
ing from institutional strength to physical infrastructure).
Education The first 1 000 days are key. Spending on education is not always a good proxy for the eectiveness
of education.
Healthcare Workforces must be physically and mentally healthy. Robust proxies for these are infant mortality
rates and life expectancy.
Openness
Connections must be functional, fed by comparative advantage. Connections to neighbours tend to
have more pronounced and enduring impacts than connections per se. Proxies are flows of trade and
capital, and the movement of people and ideas (TCIP).
countries spanning 60 years, making for 7500 country years
of data, which is a treasure trove of economic and industrial
intelligence. The model is derived from a multiple linear
regression model with a learning component to determine the
factors most closely associated with prosperity across the entire
sample of countries over time.

Gordon Institute of Business Science 7
In addition to asking the learning model to “go and find what works”
by interrogating more than 1200 variables that include demographic,
social, political, economic, geographic, and institutional factors, the
model also identifies factor weights and factor sequencing or – in the
words of Ferguson (2012) – the “download order.”
From Table 2, it can be seen that not all factors are of equal
importance. That is, not all are equally strongly correlated with
prosperity. With a coefficient of 27.5, the savings and investment
factor is the most important, containing more than a quarter of
the model’s explanatory power. The openness factor is nearly as
potent, correlated with just under a quarter of explanatory power.
Demography, the least strong factor, with a coefficient of just 5.1,
marks the lower cut-off point for inclusion in the model.
Table 2: The six-factor model with proxies and weights for each of the factors
Factor Description Multi-factor
constituents
Savings and investment
i. Structural investment rate (10-year average % GDP)
ii. Stability of investment (σ)
iii. Structural rate of saving (10-year average % GDP)
iv. Stability of saving (σ)
v. Savings-investment gap (% GDP)
27.5
Demography i. Population growth (15-year lag) 5.1
Policy and institutions
i. Macroeconomic management rating index
ii. Transparency, accountability, and corruption in the public sector index
iii. Public sector management and institutions cluster strength index
iv. Ease of doing business index, time to open a business (days); cost of business
start-up procedures (% of income per person); logistics performance index
15.0
Education
i. Pre-primary enrolment rate (gross %)
ii. Primary school enrolment rate (net %)
iii. Secondary school enrolment rate (net %)
iv. Tertiary education enrolment rate (gross %)
v. Adolescents out of school (% of lower secondary school age)
12.4
Healthcare i. Infant mortality rate (per 1 000 live births)
ii. Life expectancy at birth (years) 15.7
Openness
i. Imports and exports relative to GDP (%)
ii. Export complexity index
iii. Foreign capital flows relative to GDP (%)
iv. TCIP index
24.3
This distinction between more and less powerful enablers
of long-term prosperity is a feature of the model. Applying
the data to an individual country or region indicates not
only the degree to which each factor is embedded but, read
alongside the coefficient for each factor, the model provides
policymakers with the information to determine areas of
improvement that offer the greatest efficiency. In other
words, it suggests areas where an amount of energy and
social, political, and policy investment ought to provide
the highest relative return in the form of prosperity. “Bang
for policy buck” if you like. A country with low scores on a
factor with a high correlation with long-term prosperity has
such an indicator.

8 Gordon Institute of Business Science
Half a dozen
of the other
The six-factor model of prosperity shares important foundational
arguments with the six killer apps of Ferguson (2012). Chiefly,
both models are agnostic to specific policies. Ferguson (2011)
makes this point using the killer app of property rights: “It’s
not the democracy, it’s having the rule of law based on private
property rights”. The very same can be demonstrated using the
factor of policy and institutions. It says nothing about the content
of policy; it is about stable and predictable policy and capable
institutions. That is not to suggest that any policy is as effective
as any other as an agent of prosperity, but simply that stable
policy is a keystone factor of long-term prosperity. If there is a
key message to policymakers in these findings, it is that policy
stability matters more than policy and that policy only matters if
it is accompanied by capable institutions.
Additionally, the factors and killer apps speak to similar
timelines. In a time of stock tickers, tweets, and 24-hour news,
the availability bias can overemphasise the immediate and short
term. The killer apps are built on data going back centuries. Even
the recent changes in fortunes that are described as the “Great
Re-convergence” are measured in decades. Likewise, the factors
say nothing about currency sell-offs overnight in Asia or even
the impact of last year’s interest rate cuts, quantitative easing,
and negative interest rate policy. We are in the realm of decades,
lifetimes, and generations. We are in the world of prosperity
and well-being, rather than dividend declarations, policy
pronouncements, and business cycles.
There is striking congruence between several of the killer apps
and the six factors, many of which are plain to see – for example,
the app of modern medicine perfectly matches the healthcare
factor, and science and education share a large overlap. Even
elements that appear contradictory find congruence on closer
inspection – for instance, Ferguson’s app of consumer society
might sound incongruous with saving and investment, yet both
models apply to the long run. While we cannot consume what we
save in the short run, saving today enables investment tomorrow,
and therefore consumption in the long run.
Perhaps the most salient difference between the two models
that, by chance, lands on six critical ingredients is the method
of derivation. Ferguson, a British-educated historian now based
at the Hoover Institution at Stanford University in the US,
derives his model qualitatively and historical documents are his
data source. By contrast, the factors in our work are built using
economic data and applying quantitative methods that also
incorporate “machine learning”, where each year brings new data
and a fresh chance to retest the model in what econometricians
call “out of sample”.

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Applying the six-factor
model in Africa in 2021
Two conditions of self-
sustaining growth are that a
country has acquired a cadre
of domestic entrepreneurs and
administrators and, secondly,
that it has attained to adequate
savings and taxable capacity.
~ Sir Arthur Lewis, Nobel Prize Lecture:
The Slowing Down of the Engine of Growth
We have run the six-factor model with the latest available data
for 2021 and applied this to the 125 countries that make up our
global set. Notably, although we have access to data for more than
200 countries, and reliable data for 160 of these 200 countries,
our final data set comprises 125 countries to remove microstates.
The country score is derived by considering the structural
progress and end-weight in each of the six factors across a
20-year measurement period. The two-decade measurement
period ensures factor weights are structural and not cyclical,
fickle or fleeting. From this, we model the country’s “six-factor
growth structure” with a 10-year horizon. This also translates
into a consideration of the country’s potential, which gives a
robust reading for the country’s sustained growth performance
as distinct from economic forecasts that tend to place emphasis
on the “next year or two”. Importantly, in deriving the structural
growth potential, the model is pointing to exactly that, potential;
the model is not forecasting that this will come to pass.
Figure 3: The range of structural growth rates by country, as calculated from the six-factor model
6,7
-1,3
GDP Growth

10 Gordon Institute of Business Science
That caveat aside, the six-factor growth structure gives us the
ability to rank countries based on total six-factor score and
10-year prospects, which, in turn, is a sound basis for policy
formulation in the public sector and strategic intent in the private
sector. The growth structure figure suggests a best possible
economic growth rate given the associated country’s actual six-
factor score. As per Figure 4, global potential growth rates for the
2021–2030 period range from -1.3% (Guinea-Bissau, Somali, and
South Sudan) to 6.7% (Algeria, Mongolia, Mozambique, Qatar,
the Seychelles, and Zambia).
Figure 4: The range of structural growth rates by country, as calculated from the six-factor model
Notably, the growth structure of the world economy post-
COVID-19 points to 3.1% per annum over the decade.
Importantly, this is not to suggest that economic growth is the
be-all and end-all of prosperity – we acknowledge the many
shortcomings of GDP as a measure of progress. However, the
correspondence between GDP and other broader, core measures
of well-being, such as the Human Development Index and
Gross National Happiness is high. Furthermore, we take some
confidence from the fact that the supposed growth structure
of the next 10 years of 3.1% per annum closely resembles the
growth structure of the world economy over the past 100
years. From this, we can focus on more specific regional or
country questions. To this end, Figure 5 shows structural
rates for African countries for the 2021–2030 period.
For the sake of illustration, three African countries have
been selected for further analysis. One country was chosen
from the lowest six-factor scores (Somalia); one from the
middle range (South Africa); and one from the top scorers
(Botswana).
-2,0
-1,0
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
8,0
0 20 40 60 80 100 120
Six Factor Score (0 to 125)
Structural Growth Prospect (% p.a. 2021-2030)
Qatar
Seychelles
Algeria
Mongolia
Tanaznia
Mozambique
Mauritania Nepal
Kyrgyz Republic Ethiopia
Belarus
Sri Lanka
Rwanda
Uganda
Lesotho
Kenya
Myanmar
Niger
Haiti Bangladesh China
Turkey Saudi Arabia
Vietnam
Nicaragua
Guinea
Ghana
Tog o
Tajikistan
Angola
Senegal
Equatorial Guinea
Congo, Dem. Rep.
Namibia
Cameroon
Burkina Faso
Tunisia
Sierra Leone
Nigeria
Mali
Benin
Mauritius
Bolivia
South Africa
Madagascar
Ukraine Brazil
Uruguay
Sudan
Liberia Iraq
Cote d'Ivoire
Gambia
Pakistan
Argentina
Central African Rep.
Mexico
Columbia
Hungary
Azerbaijan
Philippines
Chile
Peru
Slovak
Republic
Russian
Federation
Finland
France
New Zealand
Denmark
Netherlands
Slovenia
LithuaniaSpain
Israel
Luxembourg
Iceland
Italy
Cyprus
Portugal
Greece
Malawi
Eswatini
Zimbabwe
Burundi
Guinea-Bissau Somalia
South Sudan
United Kingdom
Germany
MaltaPoland
Croatia
Cambodia
U/States
Kuwait
Japan
Latvia
Switzerland
United Arab Emirates
Honduras
Dominican Republic Moldova
WORLD
Oman
Botswana
Kazakhstan
Czech Republic
Australia
Ireland Estonia
NorwaySingapore
Thailand
Malaysia
Canada
Austria Sweden Belgium
Chad Ecuador
Gabon
Uzbekistan
Marocco
Indonesia
Zambia
India

Gordon Institute of Business Science 11
Figure 5: Structural growth rates for African countries, as calculated from the six-factor model
5,8
6,4
5,9
3,7
2,7
4,9
4,1 2,0
5,6-1,3
-1,3
-0,5
3,72,9
1,8
1,6
2,9
4,0 3,1
5,2
4,9
6,1
3,8
6,7 6,6
1,7
2,2
1,4 4,2
2,5

12 Gordon Institute of Business Science
Somalia
Whereas in the industrialized
West, poetry – and especially
what is regarded as serious
poetry –seems to be increasingly
relegated to a marginal place
in society, Somali oral verse is
central to Somali life.
~ Somali scholar Said Sheikh Samatar,
explaining the country’s moniker, the “Nation
of Poets”
Our starting point ought to be Somalia’s overall six-factor ranking
of 116 out of a total of 125 countries. This is a country struggling
on the model and in terms of economics and prosperity. A
number of individual factor scores serve as testament to
this. Somalia scores worst out of 125 nations for savings and
investment. Just four countries score worse for healthcare. Both
policy and institutions and education are outside the top 110.
Six factor Rank 116 /125 Somalia
Country
Median
Best
Worst
125
Savings &
Investment
Savings % GDP (10-year average) -5,0 2 1,9 65 ,0 -5 2,9
Investment % GDP (10-year average) 9,0 23,2 44,9 8,7
10
Demography
Population Growth % (t-18) 3,5 1,4 5,2 -3,8
114
Policy &
Institutions
Budget Balance % GDP(10-year average) -0,5 -3,1 19,7 -8,0
Money Supply Growth % (5-year average) 8,0 8,1 -8,6 80,0
Debt % GDP 83,0 50,6 8,4 238,0
Central Bank Policy (0-4) 1 3 4 0
111
Education
Schooling (Years) 4,0 8,7 1,4 14,2
121
Healthcare
Life Expectancy (Years) 57,1 73 ,9 84,2 52,8
Change in Life Expectancy % (20 years) 7,3 5,8 25,0 1 ,1
Infant Mortality Rate (per 1,000 live births) 74,0 14,3 1,6 8 1,0
Change in Infant Mortality % (10 years) -24,6 -25,0 -55,7 41,0
48
Openness
Openness 50 74 1169
Change in Openness % (5 years) 5,0 0,0 -44,4 126,7
Figure 6: Summary of Somalia’s six-factor rank

Gordon Institute of Business Science 13
5,8
6,4
5,9
3,7
2,7
4,9
4,1 2,0
5,6-1,3
-1,3
-0,5
3,72,9
1,8
1,6
2,9
4,0 3,1
5,2
4,9
6,1
3,8
6,7 6,6
1,7
2,2
1,4 4,2
2,5
-1,3% p.a
Six Factor Growth Structure
1999-2018
Growth Sequence,
Contributors & Structure
Angola
Botswana
DRC
Ethiopia
Kenya
Malawi
Northern
Mozambique
Southern
Mozzambique
Namibia
Rwanda
Somalia
South Africa
South Sudan
Tanzania
Zambia
Zimbabwe
Growth
Stability
GDP Growth (% p.a.) 4,5 4,7 4,8 8,3 4,4 4,5 6,7 6,7 3,7 7, 8 2,9 2,8 5,7 6,8 6,4 3,9
Volatility (% s.d.) 8,0 7, 0 4,3 4,6 2,2 7, 6 3,6 3,6 3,4 2,3 0,4 2 ,1 7,0 1,7 4,0 19, 5
Contributors (+/-)
Consumption 2,8 2,6 2,8 5,4 3,6 3,5 3, 2 3,2 3,5 5,3 2,2 1 ,9 0,7 3,5 2,3 2,7
Investment -0,2 1,0 1,9 3,7 1 ,1 1,5 1,4 1,4 0,6 2,9 0,5 0,7 -0,8 3,2 3,2 0,3
Government 0,4 0,8 0,3 0,7 0, 5 0,4 2 ,7 2,7 0,9 1, 2 0,2 0,6 4 ,6 0,6 0,9 1,6
Export 1,5 1,8 2 ,1 0,7 0,6 1,3 3 ,9 3,9 1 ,4 1,6 0,0 0,7 1,7 0,9 3 ,0 0,5
Imports 0,0 -1,5 -2,4 -2,2 -1,3 -2, 2 -4,6 -4,6 -2 ,8 -3, 2 0,0 -1 ,1 -0,5 -1,4 -3,0 -1,2
Contributors (%)
Consumption 62,6 55,8 59,0 65 ,1 80,0 7 7, 5 48,3 48,3 94 ,7 67, 8 7 7,0 67,1 9 4,1 51,8 35,3 68,6
Investment -5,0 21,8 39, 8 44,2 24,7 33,6 21 ,0 21,0 16, 2 37, 2 16,5 23,5 9,5 4 7, 3 49,9 8,3
Government 9,4 16,3 6,2 8,9 1 1,9 8,2 40,3 4 0,3 24,3 15,4 7, 5 23,2 2 7, 5 8,4 14,5 41,3
Export 33,0 3 8,1 44,4 8 ,1 12,6 28,3 58,2 58,2 38,7 20,8 0,3 26,2 12 ,5 13,2 46,8 13,2
Imports 0,0 -32,0 - 49,4 -2 6,3 -2 9,2 -4 7, 5 - 67, 8 - 6 7,8 -73 ,9 -4 1,1 -1,3 -4 0,1 -43, 5 -20,7 -46,6 -31,4
Drivers
(%)
C + G 72,0 7 2 ,1 65,2 74,0 92,0 85,7 88,6 88,6 119,1 83,2 84,5 90,3 92,3 6 0,2 49,9 1 10,0
I + NX 28,0 2 7,9 34,8 26,0 8,0 14,3 11,4 11,4 -1 9,1 16,8 15,5 9, 7 7,7 39,8 5 0,1 -1 0,0
Components
Investment as % GDP (10 years) 34,8 2 9,0 2 2,9 33,3 20,3 2 2 ,9 41,0 41,0 2 3,6 25,4 2 0,1 19,9 9,5 33,5 36,4 10,8
Government as % GDP (10 years) 17,8 18,5 1 2 ,1 8,9 13,6 9,5 24,6 24 ,6 24,8 14,6 8,7 20,7 2 7, 5 9,6 1 2,9 1 9,1
NX as % GDP (10 years) -5,4 1,6 -5,2 -13,8 -1 1,2 - 17,0 -32, 8 -32,8 -1 6,1 - 1 7,2 -1,4 -0,3 -3 1,0 -6,6 - 0,9 -1 3,0
Figure 7: Somalia’s six factor rank in comparison
Descriptor: High growth, low volatility but truncated for 2009 - 2018; modest invetment drive; high government component and strong NX lead.
2021-2030
Country
Median
Best
Worst
Growth Structure (% p.a.) -1,3 2 ,9 6,7 -1,3
Population Growth (% p.a.) 2,9 1,4 3,8 -1,8
Per Capita Income (% p.a.) -4 ,1 1,7 5,8 -4 ,1
GDP/Capita (% p.a. 2010 - 2019) 1,0 1,8 7, 2 - 7,3
As asserted earlier, the six parts of the model provide tools
upon which governments can base policy. One standout factor
for Somalia is the demography ranking. Placing 10th out of 125
suggests this is a foothold upon which to generate prosperity. The
demography factor in the model favours a growing proportion
of young people entering the workforce, and therefore a
demographic dividend. The population pyramids for Somalia and
Japan are depicted in Figures 8 and 9. The Somalian pyramid is a
picture of potential, while the Japanese pyramid suggests a high
proportion of people retiring, with not enough young people to
enter the workforce and grow in seniority. Japan has long suffered
from economic stagnation (albeit at a high standard of living)
and is now responding with policies enticing young immigrants
(Roberts, 2018).
Like any opportunity,
Somalia will need
to act to ensure this
factor does not become
a disaster. Low scores on
education and healthcare
bode poorly for a young
population. However, the
six-factor model suggests
this may be where their
“bang for buck” lies. Even modest improvements in
education and healthcare will be strongly enabling of a
large, young population.

14 Gordon Institute of Business Science
Figure 8: Population pyramid for Somalia, demonstrating high potential for a demographic dividend (Source: PopulationPyramid.net, 2019a)
Figure 9: Population pyramid for Japan, demonstrating low potential for a demographic dividend (Source: PopulationPyramid.net, 2019b)
Somalia 2019
Japan 2019
Population: 15,442,905
Population: 126,860,299
0%
0%
2%
2%
2%
2%
10%
10%
10%
10%
4%
4%
4%
4%
6%
6%
6%
6%
8%
8%
8%
8%
0-49
0-49
5-9
5-9
10-14
10-14
15-19
15-19
20-24
20-24
25-2 9
25-2 9
30-34
30-34
35-39
35-39
40-44
40-44
45-49
45-49
50-54
50-54
55-59
55-59
60-64
60-64
65-69
65-69
70 -74
70 -74
75-79
75-79
80-84
80-84
85-89
85-89
90- 94
90- 94
95-99
95-99
100+
100+
8,8%
1,9%
7, 5 %
2,1 %
2,1 %
2,2%
6,7%
5,7%
4,8%
2,3%
2,4%
3,6%
2,7%
2,7%
2,4%
2,1 %
3,0%
1,8%
3,4%
1,8%
1,6%
3,9 %
3,8%
1,3%
3,3%
1,0%
3,1 %
0,8%
3,0%
0,6%
3,5%
0,4%
3,0%
0,3%
0,1%
0,8%
3,0%
0,6%
3,3%
3,3%
0,4%
0,4%
0,2%
0,1%
0,1%
0,0%
0,0%
0,0%
0,0%
0,0%
Male
Male
Female
Female
0,0%
0,0%
1,0%
0,0%
0,3%
0,0%
0,1%
9,0%
2,0%
7,6 %
2,2%
6,7%
2,3%
2,3%
5,7%
4,8%
2,4%
3,6%
2,5%
2,7%
2,8%
2,1 %
3,1 %
1,7%
3,5%
1,7%
2,4%
1,5%
4,0%
1,2%
3,4%
1,0%
3,1 %
1,0%
Like any opportunity, Somalia will need to act to ensure
this factor does not become a disaster. Low scores
on education and healthcare bode poorly for a young
population. However, the six-factor model suggests this
may be where their “bang for buck” lies. Even modest
improvements in education and healthcare will be
strongly enabling of a large, young population.

Gordon Institute of Business Science 15
South Africa
South Africa has advanced
politically by disasters and
economically by windfalls.
~ C.W. de Kiewiet, Historian
Long the economic powerhouse of Africa, South Africa falls in the
lower-middle region of six-factor scores in 2021, with a structural
growth rate of 1.8% per annum. Notably, this is only modestly
ahead of the population growth estimated at 1.3% per annum,
which translates into per person income growth trapped at 0.5%
per annum. Although South Africa is the most industrialised,
technologically advanced and diversified economy on the African
continent, factors point to the country being “trapped”. Lacking
any extreme individual scores, the lowest-ranking factor of
healthcare at 107 of 125, indicates the key importance of the
ongoing debate around universal access to effective healthcare.
Against the backdrop of a high-quality private healthcare system,
to which fewer than 17% of South Africans have access (Stats SA,
General Household Survey, 2018), the National Health Insurance
Bill (Parliament of the Republic of South Africa, 2019) proposes a
nationally provided system for healthcare.
If anything, the distance between policy (43 out of 125) and
effective impact is evidenced by the distance to healthcare (107)
and education (56), where South Africa boasts the highest level
of public-sector spend as a percentage of GDP amongst its income
category globally. The low saving-and-investment score (97) also
identifies stubbornly low investment confidence in the critical
factor of GDFI, including public- and private-sector spend on
infrastructure. Squaring up to these two elements of healthcare
and investment would see South Africa leapfrog the table.
Figure 10:Summary of South Africa’s six-factor rank
Six factor
Rank 89 /125 South Africa
Country
Median
Best
Worst
97
Savings &
Investment
Savings % GDP (10-year average) 19, 5 21 ,9 65,0 - 52 ,9
Investment % GDP (10-year average) 19, 5 23,2 44,9 8,7
67
Demography
Population Growth % (t-18) 1,3 1,4 5,2 -3,8
43
Policy &
Institutions
Budget Balance % GDP(10-year average) -4,5 -3,1 19,7 -8,0
Money Supply Growth % (5-year average) 7,1 8 ,1 -8,6 80,0
Debt % GDP 62,2 50,6 8,4 238,0
Central Bank Policy (0-4) 4 3 4 0
56
Education
Schooling (Years) 9,3 8,7 1,4 14,2
107
Healthcare
Life Expectancy (Years) 63 ,9 7 3,9 84,2 52,8
Change in Life Expectancy % (20 years) 5,5 5,8 25,0 1,1
Infant Mortality Rate (per 1,000 live births) 27, 5 14,3 1,6 81,0
Change in Infant Mortality % (10 years) -24,7 -25,0 -55,7 41,0
59
Openness
Openness 56 74 1169
Change in Openness % (5 years) 12,0 0,0 44,4 126,7

16 Gordon Institute of Business Science
5,8
6,4
5,9
3,7
2,7
4,9
4,1 2,0
5,6-1,3
-1,3
-0,5
3,72,9
1,8
1,6
2,9
4,0 3,1
5,2
4,9
6,1
3,8
6,7 6,6
1,7
2,2
1,4 4,2
2,5
2021-2030
Country
Median
Best
Worst
Growth Structure (% p.a.) 1,8 2,9 6,7 -1, 3
Population Growth (% p.a.) 1,3 1,4 3,8 -1,8
Per Capita Income (% p.a.) 0,5 1,7 5,8 -4 ,1
GDP/Capita (% p.a. 2010 - 2019) 0,2 1,8 7, 2 -7, 3
1,8% p.a
Six Factor Growth Structure
Figure 11:South Africa’s six-factor rank in comparison
Descriptor: Low growth, low volatility; driven by consumption spending and government spending; moderate-to-low investment contribution; NX supportive.
1999-2018
Growth Sequence,
Contributors &
Structure
Angola
Botswana
DRC
Ethiopia
Kenya
Malawi
Northern
Mozambique
Southern
Mozzambique
Namibia
Rwanda
Somalia
South Africa
South Sudan
Tanzania
Zambia
Zimbabwe
Growth
Stability
GDP Growth (% p.a.) 4,5 4,7 4,8 8,3 4,4 4,5 6,7 6,7 3,7 7, 8 2,9 2,8 5,7 6,8 6,4 3,9
Volatility 9% s.d.) 8,0 7,0 4,3 4,6 2,2 7,6 3,6 3,6 3,4 2,3 0,4 2,1 7, 0 1,7 4,0 1 9,5
Contributors (+/-)
Consumption 2,8 2,6 2,8 5,4 3,6 3,5 3, 2 3,2 3,5 5,3 2,2 1 ,9 0,7 3,5 2,3 2,7
Investment -0,2 1,0 1 ,9 3,7 1,1 1,5 1,4 1,4 0,6 2,9 0, 5 0,7 - 0,8 3,2 3,2 0,3
Government 0,4 0,8 0,3 0,7 0,5 0,4 2 ,7 2,7 0,9 1, 2 0,2 0,6 4,6 0,6 0 ,9 1,6
Export 1,5 1,8 2 ,1 0,7 0,6 1,3 3,9 3,9 1,4 1,6 0,0 0, 7 1,7 0,9 3,0 0,5
Imports 0,0 -1,5 -2,4 -2,2 -1, 3 -2,2 - 4,6 -4,6 -2 ,8 -3,2 0,0 - 1,1 -0,5 -1,4 -3,0 -1,2
Contributors (%)
Consumption 62,6 55,8 59,0 65 ,1 80,0 7 7, 5 48,3 48,3 94 ,7 67, 8 7 7,0 67,1 94 ,1 51,8 35,3 68,6
Investment -5,0 21,8 39, 8 44,2 24,7 33,6 21 ,0 21,0 16, 2 37, 2 16,5 23,5 9,5 4 7, 3 49,9 8,3
Government 9,4 16,3 6,2 8,9 11 ,9 8,2 40,3 4 0,3 24, 3 15,4 7, 5 23,2 2 7, 5 8,4 14,5 41,3
Export 33,0 3 8,1 44,4 8 ,1 12,6 28,3 58,2 58,2 38,7 20, 8 0,3 26,2 12,5 13,2 46,8 13,2
Imports 0,0 -32,0 -4 9,4 -26 ,3 -2 9,2 - 4 7, 5 - 67, 8 - 67, 8 -7 3,9 - 41 ,1 -1,3 - 4 0,1 -43,5 -20,7 -46,6 -31,4
Drivers
(%)
C + G 72,0 72 ,1 65,2 74 ,0 92 ,0 85,7 88,6 88,6 119,1 83,2 84,5 90,3 92, 3 60,2 49,9 110,0
I + NX 2 8,0 2 7,9 34,8 26,0 8 ,0 14,3 1 1,4 11,4 -1 9,1 16,8 15,5 9,7 7, 7 3 9,8 5 0,1 -10,0
Components
Investment as % GDP
(10 years) 34,8 29, 0 22 ,9 33,3 20, 3 22 ,9 41,0 41,0 2 3,6 25,4 2 0,1 19,9 9,5 33,5 36,4 10,8
Government as % GDP
(10 years) 17, 8 18,5 1 2 ,1 8,9 13,6 9, 5 24,6 24,6 24,8 14,6 8,7 20,7 2 7, 5 9,6 12,9 1 9,1
NX as % GDP
(10 years) -5,4 1,6 -5, 2 -13,8 -11,2 - 17,0 -32,8 -32,8 - 16 ,1 - 17, 2 -1,4 - 0,3 -31 ,0 -6,6 - 0,9 -13 ,0

Gordon Institute of Business Science 17
Botswana
Awake, awake … awake!
…
Together we’ll work and serve
This land, this happy land!
~ Chorus to “Fatshe leno la rona”, Botswana’s
national anthem, written and composed by
Kgalemang Tumediso Motsete and adopted at
independence in 1966
At 46 out of the 125 countries scored on the six-factor model,
Botswana can boast a six-factor growth structure of 3.7% that
is meaningfully higher than a population growth of 2.2%.
This suggests a time horizon of around 19 years for a doubling
of GDP and 48 years for a doubling of GDP per capita. This
contrasts with a figure of nearly 40 years for doubling the South
African economy and almost 150 years based on South Africa’s
per capita growth rate. However, it is worth considering that
Botswana’s growth applies to a substantially higher per capita
income ($7894 adjusted for purchasing power parity) than its
economically bigger neighbour ($6120).
Figure 12: Summary of Botswana’s six-factor rank
Six factor
Rank 46 /125 Botswana
Country
Median
Best
Worst
15
Savings &
Investment
Savings % GDP (10-year average) 31,4 21 ,9 65,0 - 52 ,9
Investment % GDP (10-year average) 32,6 23,2 44,9 8,7
49
Demography
Population Growth % (t-18) 1,9 1,4 5,2 -3,8
24
Policy &
Institutions
Budget Balance % GDP(10-year average) -1,0 -3 ,1 19, 7 -8,0
Money Supply Growth % (5-year average) 8,2 8,1 -8,6 80,0
Debt % GDP 15 ,1 50,6 8,4 2 38,0
Central Bank Policy (0-4) 3 3 4 0
56
Education
Schooling (Years) 9,3 8,7 1,4 14,2
54
Healthcare
Life Expectancy (Years) 69,3 7 3 ,9 84,2 52,8
Change in Life Expectancy % (20 years) 1 7, 3 5,8 25,0 1 ,1
Infant Mortality Rate (per 1,000 live births) 32,3 14,3 1,6 81,0
Change in Infant Mortality % (10 years) 41 ,0 -25,0 -55,7 41,0
114
Openness
Openness 147 74 1169
Change in Openness % (5 years) 2,8 0,0 -44,4 126,7

18 Gordon Institute of Business Science
2021-2030
Country
Median
Best
Worst
Growth Structure (% p.a.) 3,7 2,9 6,7 -1,3
Population Growth (% p.a.) 2,2 1,4 3,8 -1,8
Per Capita Income (% p.a.) 1,6 1,7 5,8 -4 ,1
GDP/Capita (% p.a. 2010 - 2019) 3,0 1,8 7, 2 -7, 3
Figure 13: Botswana’s six-factor rank in comparison
3,7% p.a
Six Factor Growth Structure
5,8
6,4
5,9
3,7
2,7
4,9
4,1 2,0
5,6-1,3
-1,3
-0,5
3,72 ,9
1,8
1,6
2,9
4,0 3,1
5,2
4,9
6,1
3,8
6,7 6,6
1,7
2,2
1,4 4,2
2,5
Descriptor: Strong growth structure, underpinned by investment component, but volatility driven up by large (volatile) NX sector.
1999-2018
Growth Sequence,
Contributors &
Structure
Angola
Botswana
DRC
Ethiopia
Kenya
Malawi
Northern
Mozambique
Southern
Mozzambique
Namibia
Rwanda
Somalia
South Africa
South Sudan
Tanzania
Zambia
Zimbabwe
Growth
Stability
GDP Growth (% p.a.) 4,5 4,7 4,8 8,3 4,4 4,5 6,7 6,7 3,7 7, 8 2 ,9 2,8 5,7 6,8 6,4 3,9
Volatility 9% s.d.) 8,0 7,0 4,3 4,6 2,2 7,6 3,6 3,6 3,4 2,3 0,4 2,1 7,0 1,7 4,0 1 9,5
Contributors (+/-)
Consumption 2,8 2,6 2,8 5,4 3,6 3,5 3, 2 3,2 3,5 5,3 2,2 1 ,9 0,7 3,5 2,3 2,7
Investment -0,2 1,0 1 ,9 3,7 1,1 1,5 1,4 1,4 0,6 2,9 0, 5 0,7 - 0,8 3,2 3,2 0,3
Government 0,4 0,8 0,3 0,7 0,5 0,4 2 ,7 2,7 0,9 1, 2 0,2 0,6 4,6 0,6 0 ,9 1,6
Export 1,5 1,8 2 ,1 0,7 0,6 1,3 3,9 3,9 1,4 1,6 0,0 0, 7 1,7 0,9 3,0 0,5
Imports 0,0 -1,5 -2,4 -2,2 -1, 3 -2,2 - 4,6 -4,6 -2 ,8 -3,2 0,0 - 1,1 -0,5 -1,4 -3,0 -1,2
Contributors (%)
Consumption 62,6 55,8 59,0 65 ,1 80,0 7 7, 5 48,3 48,3 94 ,7 67, 8 7 7,0 67,1 94 ,1 51,8 35,3 68,6
Investment -5,0 21,8 39, 8 44,2 24,7 33,6 21 ,0 21,0 16, 2 37, 2 16,5 23,5 9,5 4 7, 3 49,9 8,3
Government 9,4 16,3 6,2 8,9 11 ,9 8,2 40,3 4 0,3 24, 3 15,4 7, 5 23,2 2 7, 5 8,4 14,5 41,3
Export 33,0 3 8,1 44,4 8 ,1 12,6 28,3 58,2 58,2 38,7 20, 8 0,3 26,2 12,5 13,2 46,8 13,2
Imports 0,0 -32,0 -4 9,4 -26 ,3 -2 9,2 - 4 7, 5 - 67, 8 - 67, 8 -7 3,9 - 41 ,1 -1,3 - 4 0,1 -43,5 -20,7 -46,6 -31,4
Drivers
(%)
C + G 72,0 7 2 ,1 65,2 74,0 92,0 85,7 88,6 88,6 119,1 83,2 84,5 90,3 92,3 6 0,2 49,9 1 10,0
I + NX 28,0 2 7,9 34,8 26 ,0 8,0 14, 3 11,4 11,4 -1 9,1 16,8 15,5 9, 7 7,7 3 9,8 5 0,1 -10,0
Components
Investment as % GDP
(10 years) 34,8 29,0 22 ,9 33,3 20, 3 22 ,9 41,0 41,0 23,6 25,4 2 0,1 1 9,9 9, 5 33,5 36,4 10, 8
Government as % GDP
(10 years) 17, 8 18,5 1 2 ,1 8,9 13,6 9, 5 24,6 24,6 24,8 14,6 8,7 20,7 2 7, 5 9,6 1 2,9 1 9,1
NX as % GDP
(10 years) -5,4 1,6 -5,2 -13,8 -11, 2 -1 7, 0 -32,8 -32 ,8 - 16 ,1 -1 7, 2 -1,4 -0, 3 -31,0 - 6,6 -0 ,9 -13,0

Gordon Institute of Business Science 19
Botswana’s position of 15 out of 125 for the factor of saving and
investment suggests ample investment capital available to build
productive capacity. Like Somalia, one factor presents itself as
the obvious area for the most efficient application of policy levers
in Botswana. At 114 out of 125, Botswana’s openness factor is the
country’s only one outside of the top half – and just one place
outside the bottom 10 countries. This factor is explained by the
Figure 14: Botswana’s concentrated export basket (Source: Growth Lab at Harvard University)
Unlike Somalia’s standout factor of demography, which is deeply
embedded, Botswana faces a lever that policy can rapidly alter.
Flows of trade and capital and the movement of people and ideas
are more rapidly and inexpensively changed today than ever
before. This ingredient is Botswana’s needle mover.
Harmonising
African
economies
and trade
It may have gone under-reported by the world’s media amid
the COVID-19 dominance of airwaves, but the African Union
(2018) recently announced a bold agreement to embrace one
of the phenomena touted by the six-factor model. On 1 January
2021, the African Continental Free Trade Agreement (AfCFTA)
came into effect, where the goal: “Enhance competitiveness
of services through: economies of scale, reduced business
costs, enhanced continental market access, and an improved
allocation of resources including the development of trade-
related infrastructure” (African Union, p. 36). Or, in terms used
by the six-factor model, the goal is to: build connectedness,
openness, and economic integration. The AfCFTA creates the
largest free-trade area by number of participating countries since
the establishment of the World Trade Organization, including
more than 1.2 billion people, covering a combined GDP of some
$2.5trillion (International Monetary Fund, 2019).
Figure 15: Value of intra-continental trade by continent, measured as an average between
2015 and 2017 (Source: UNCTAD)
VALUE OF INTRACONTINENTAL TRADE
35
0
50
15
40
5
45
10
55
20
60
25
65
30
70
Just 2% of African trade is within the continent
Africa The
Americas
Asia Europe Oceania
The International Monetary Fund (2019) estimates that eliminating
tariffs to 90% of existing intra-Africa trade flows – that being the
most ambitious target under the AfCFTA – would boost regional
trade by approximately 16% of current volumes, over time.
country’s economic relationship with other economies being
explained overwhelmingly by a single element, namely diamonds.

20 Gordon Institute of Business Science
The heightened connectedness that AfCFTA foretells
will present opportunities and challenges to individual
companies. One powerful tool to aid executives in navigating
a more connected continent is the CAGE model developed
by Pankaj Ghemawat (Ghemawat & Altman, 2016). With
professorships at the IESE Business School in Barcelona
and the Stern School of Business at New York University,
Ghemawat maps this challenge using the “distance” between
two countries based on cultural, administrative, geographic,
and economic (CAGE) issues.
Literal distance, the geographic element or “G”, is self-
explanatory. A business is less likely to succeed in a new
country that is a longer flight distance away, more of a
climatic contrast, and which disrupts sleep patterns to speak
with another on the phone or via Zoom.
Administrative and cultural distance are ignored to one’s peril,
according to Ghemawat. In his MBA course, Globalisation
of Business Enterprise (2020), he cites myriad examples of
• Language
• Etnicity
• Religion
• Work Systems
• Tradition
• Values, social norms,
and dispositions
• Colonial ties
• Trade agreements
• Currency
• Legal system
• Government policies
• Political hostility
• Visa and work permit
requirements
• Corruption
• Per capita income
• Cost of labor
• Availability of human
resources
• Organisational capabilities
• Economic size
• Physical distance
• Common land border
• Time zones
• Climate
• Landlockedness
• Transportation
• Communication
Figure 16: The CAGE framework
(*) Prof. Pankaj Ghemawat - IESE
CAGE DISTANCE FRAMEWORK *
Cultural
Economic
Administrative
Geographic
Connectedness, AfCFTA, and CAGE
companies that entered foreign markets, confident of their
oerings, that end up losing vast sums because of some
cultural or administrative incompatibility. CAGE provides a
checklist of potential barriers for a business to analyse ahead
of any expansion.
Economic distance is one with greater nuance. While all
elements of C, A, and G are better when the dierence is
smaller, that is not the case with E. There is little to gain
from expansion into an economically identical market. More
enticing is a market where the cost of labour will lower costs
of production, or per capita income makes goods and services
more aordable or more highly desired. A tech start-up may
chase a labour force with better technological savvy, or a
miner may expand for access to mineral resources. Culturally,
administratively, geographically, and economically, Africa is a
vastly diverse place. While AfCFTA will bring African countries
closer by trade rules and institutions, this will make the
management of CAGE elements more important than ever.

Gordon Institute of Business Science 21
Business managers and executives often think of the world – and
target markets – in terms of market strategies. This is the world
of competitors, customers, and suppliers; of supply and demand;
and of formalised “rules of the game”. However, there is an
environment too frequently ignored that has just as much scope
for the creation and generation of competitive advantage. The
non-market environment is that of media, activists, citizens, non-
governmental organisations (NGOs), regulators, and governments.
In this world, it is non-market strategies that yield benefits.
Non-market strategies also have heightened relevance in Africa.
In developed countries where formal institutions are strong,
non-market strategies are largely institutionalised. The rules of
the game are well known and the outcomes generally are more
predictable. Think of lobbyists in Washington DC. In markets
where traditional, formal institutions are weak, as is the case
in much of Africa, the non-market environment is larger and
less formalised, and so has greater capacity for adaptation. As
AfCFTA connects more African countries at deeper levels, non-
market strategies will become more critical still.
At the heart of non-market strategies is the understanding
that companies do not always have to be mere subjects of their
environment. Regulators can be engaged; local communities can
be brought on board; governments can be partners; and activists
can be assets.
Figure 17: Graphical representation of a company situated within a
market environment and a non-market environment
Regulators
Media
Activists
NGOs
Governments
Competitors
COMPANY
COMPANY'S MARKET
ENVIRONMENT
COMPANY'S NONMARKET
ENVIRONMENT
CitizensSuppliersCustomers
Connectedness, AfCFTA, and non-market strategies
One iconic example of a non-market strategy in a major
African market is the way South Africa-based network
provider MTN has dealt with regulators and government
in Nigeria. The company faced political opposition and
several large, high-profile findings and allegations for
falling foul of regulations (Wõcke & Beamish, 2017), which
was hardly an issue to be addressed with market strategy.
MTN’s economics and oerings were successful, and
the company responded with two impactful non-market
strategies. The company listed in Nigeria, sending the
message of commitment to the country. It also established
an international advisory board. This body, which featured
South Africans and Nigerians, including the likes of former
presidents, provided a platform for advice and discussion
on potential challenges before reaching the stage of
potential fines (Onaji-Benson, 2019).
South Africa-based cement-maker PPC displayed similar
non-market acumen in its dealings in Rwanda. In addition
to a fruitful relationship with the host government, PPC
employed a “community relational political strategy to
train and empower local community members to get
the required tailor training, start their businesses and
register a cooperative to support them” (Onaji-Benson,
2019, p. 184). This was a non-market strategy in building
constituencies and legitimacy in a foreign context.

22 Gordon Institute of Business Science
References
African Union. (2018). Agreement establishing the African
Continental Free Trade Area. Retrieved from https://au.int/
sites/default/files/treaties/36437-treaty-consolidated_text_
on_cfta_-_en.pdf
The Economist. (2013, September 2). What was the great
divergence?
Ferguson, N. (2011, July). The 6 killer apps of prosperity [Video
file]. Retrieved from https://www.ted.com/talks/niall_
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Conclusion
Our research on African economies and the potential for
prosperity highlights six factors as key to economic growth,
namely: savings and investment; policy and institutions;
demography; education; healthcare; and openness. A focus on
these six factors by African economies is poised to facilitate and
catalyse the recovery from the pandemic setbacks. Our findings
further reiterate the role of policymakers, and business leaders
in the agency towards African prosperity. With an understanding
of what is needed for prosperity, we recommend that further
research and action must proffer steps towards how these
macro actions need to be effectively implemented for the overall
economic and social development of the continent.
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