.


International Journal of Economics and Financial 
Issues

ISSN: 2146-4138

available at http: www.econjournals.com

International Journal of Economics and Financial Issues, 2016, 6(4), 1507-1514.

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 2016 1507

Human Capital, Institutions and Innovation in Sub-Saharan 
Africa

Stephen Oluwatobi1, Oluyomi Ola-David2, Isaiah Olurinola3, Philip Alege4, Adeyemi Ogundipe5*

1Department of Economics and Development Studies, Covenant University, Nigeria, 2Department of Economics and Development 
Studies, Covenant University, Nigeria, 3Department of Economics and Development Studies, Covenant University, Nigeria, 
4Department of Economics and Development Studies, Covenant University, Nigeria, 5Department of Economics and Development 
Studies, Covenant University, Nigeria. *Email: ade.ogundipe@covenantuniversity.edu.ng

ABSTRACT

This study examined the impact of human capital and institutions on innovation in Sub-Saharan Africa (SSA) and clearly highlighted the relevance of 
the human factor in determining innovation outcomes in the Sub-Saharan African region. Using the system generalized method of moments, coupled 
with some descriptive analyses, it was found out that human capital, as well as an enabling institutional environment, affects innovation outcomes 
in SSA. On the contrary, innovation outcomes in the region did not benefit from foreign investment. The study, therefore, recommends that human 
capital capacity be cultivated and given the enabling environment to contribute to innovation outcomes. This is expected to attract innovation-centred 
investments into the region.

Keywords: Human Capital, Institutions, Innovation, Sub-Saharan Africa 
JEL Classifications: I25, O15, O31, O32, O43, O55

1. INTRODUCTION

The quality of human capital has been adjudged a crucial 
determinant of innovation in countries world over. In turn, 
innovation significantly impacts on the varying levels of growth 
and development (Romer, 1990; Romer, 1994; Tebaldi and 
Elmslie, 2013). Developed countries, which are at the frontier 
of technological advancement and innovation are characterized 
by a highly skilled labour force, which drive their production 
and innovation processes. On the other hand, countries with low 
human development indices usually lag behind in development 
and capacity to innovate due to human and physical capital 
deficits. Brain drain which connotes the movement of highly 
skilled labour and professionals from developing to developed 
countries also contributes to Africa’s human capital deficits. 
However, it is theoretical propositions as well as empirical 
indications posit that latecomer countries have greater chance of 
achieving higher rates of innovation growth as a result of lower 
effective costs of education, which enable them catch up (Ang 
et al., 2011).

Notably, African countries have been experiencing the highest 
levels of growth in the history of their development path 
(The Economist, 2013). However, this rapid growth rate is 
not accompanied by structural transformation which entails a 
significant shift of labour skills from low-productivity agricultural 
sectors to sectors with higher productivity potentials. Much of 
Africa’s growth is driven by resource booms and windfalls from 
resource-seeking foreign direct investment. The capital-intensive 
nature of resource seeking FDI provides meager opportunity 
for local capacity building, which efficiency-seeking and cost-
saving FDI have as advantage. Nevertheless, given the extent 
globalization and the proliferation of a knowledge economy, Africa 
could catch up with frontier economies by leapfrogging major 
channels of technology transfer - trade, investment and knowledge 
flows. Countries’ absorptive capacity, indicated by human capital 
and innovation capacity, however determine the extent of learning 
and leapfrogging that may occur.

Sub-Saharan Africa (SSA) has been termed the region with the 
least innovative output (Oluwatobi et al., 2014). As a latecomer 



Oluwatobi, et al.: Human Capital, Institutions and Innovation in Sub-Saharan Africa

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 20161508

in development, SSA demonstrates potential for accelerated 
economic growth and development. However, since innovation 
is not an end in itself, it cannot drive growth. The human capital 
component is essential in determining innovation outcomes, since 
even physical investments tend to accrue to regions with larger 
stock of human capital hence greater growth (Chi, 2008). Even 
though, SSA region has 60% of the fastest growing economies in 
the world, the dismal level of innovation and competiveness leaves 
much to be desired and raises a cause for concern for development 
experts. Most of the existing studies on the relationship between 
human capital and innovation have been based on non-African 
economies. Related regional studies focused on the human capital, 
institutions, economic nexus, while most others are country-
specific studies (Oluwatobi and Ogunrinola, 2011).

Going forward, it is pertinent to investigate the impact of human 
capital on innovation outcomes in SSA in order to ascertain the 
possibility of achieving innovation-driven growth in the region. 
Also, the study by Oyelaran-Oyeyinka and Barclay (2004), which 
is the closest to this study on SSA, examined human capital and 
the systems of innovation in the development of Africa. However, 
we highlight the shortcomings of the study in an attempt to 
carry on a current and more robust analysis. Firstly, the study 
by Oyelaran-Oyeyinka and Barclay (2004) did not capture 
innovation; rather it related human capital variables with growth 
and development. Secondly, the study adopted simple descriptive 
statistics and ordinary least squares as estimation techniques; 
these are incapable of addressing endogeneity and heterogeneity 
problems associated with cross country studies on the subject. 
Thirdly, the study spans the period 1960-2000; hence, its findings 
may no longer be current and useful for drawing meaningful 
conclusions. In addition to these, most studies on this subject 
looked at the relationship between human capital and innovation 
with little attention to institutions, which defines the environment 
that enables innovation. Besides, most of the studies have been on 
non-SSA countries (Chi, 2008; Ang et al., 2011; Kato et al., 2015).

This study, thus, fills these gaps by considering institutions in 
the relationship between human capital and innovation as well 
as paying attention to SSA in isolation of other regions. This is 
necessary to capture results that are distinct to the region given its 
peculiarity. In addition, the study employs the System Generalized 
Method of Moments (SGMM) estimation technique, which has 
the capacity to address endogeneity and heterogeneity problems. 
Next to this introductory section we present a brief overview of 
existing literature. Section three is a presentation of some stylized 
facts on human capital, institutions and innovation in SSA as well 
as in relation to other regions of the world. Section four describes 
the methodology employed for the study. The results from data 
analyses and estimations are presented I section five. The paper 
is concluded in section five with a summary of findings, their 
implications and policy recommendations.

2. LITERATURE REVIEW

The theoretical background behind the relationship between 
human capital and innovation has been established by the 
proponents of endogenous growth theory (Romer, 1990; Aghion 

and Howitt 1992; Romer, 1994; Aghion and Howitt, 1997). This 
theory emphasized the predetermination of innovation as against 
the neoclassical idea that suggested that innovation was exogenous 
and could not be explained (Solow, 1957). This foundation sets 
the pace for examining the impact of certain variables, such as 
human capital and institutions, on innovation. Tebaldi and Elmslie 
(2008, 2013) developed the baseline model to advance this theory 
specifically to establish the relationship among human capital, 
institutions and innovation. Several studies in literature have 
examined this relationship. However, most of them have been 
in advanced and emerging economies. Particularly, most of the 
studies are non-African studies. Some of them are reviewed in 
this section.

Kato et al. (2015) embarked on a study aimed at investigating how 
the human capital of founders affect the innovation outcome of 
their organizations. Their study is based on the premise that the 
value of human capital determines the level of investments made 
in R and D for innovation. They discovered that founders with 
greater human capital positively affects the innovation outcomes of 
their organization using probit model. They were able to pinpoint 
that it is the human capital as a result of training and experience 
that directly affects innovation while human capital as a result of 
educational background indirectly affects innovation outcomes 
through investment in R and D.

Some studies have validated that human capital is a major driver of 
innovation. Mariz-Perez et al. (2012) is one of such studies. Their 
goal was to find out the impact human capital has on the capacity 
for innovation since human capital is the potential for vale creation. 
Thus, they asserted that human capital is relevant for any economy 
to achieve competitive advantage sustainably. Their argument 
was based on the fact that human capital manifests knowledge 
and capacity as main features for developing innovation and 
commercializing it.

Some other studies distinguished themselves by examining the 
composition of human capital and investigating which component 
is responsible for innovation (Chi, 2008; Ang et al., 2011; Zhang 
and Zhuang, 2011). Their results confirmed that human capital 
with tertiary education play more significant role in affecting 
innovation that human capital with primary and secondary 
education. This suggests that higher levels of human capital are 
required to improve innovation outcomes. This does not mean that 
primary education and secondary education are irrelevant in the 
innovation process; it may, however, be an evidence of low level 
of development of an economy.

Ang et al. (2011) used a sample of 87 countries over a period of 
35 years to find out whether the composition of human capital 
affects innovation using the SGMM technique. They discovered 
that a higher intensity of human capital with primary and secondary 
education translates in imitation while a higher intensity of human 
capital with tertiary education facilitates innovation. They posited 
that developing economies characterized by more of human capital 
with only primary and secondary education, which translates in 
imitation instead of innovation. Thus, such economies imitate 
what advanced economies are doing as against generating new 



Oluwatobi, et al.: Human Capital, Institutions and Innovation in Sub-Saharan Africa

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 2016 1509

ideas, processes and products. This suggests that the composition 
of human capital has a direct impact on innovation growth.

Zhang and Zhuang (2011) also examined the impact of human 
capital composition on innovation; however, unlike Ang et al. 
(2011), who examined 87 countries, they examined China 
specifically using difference GMM. They found out that more 
developed provinces in China have more capacity to absorb human 
capital with tertiary education unlike less developed provinces 
that depend on primary and secondary education. This result is a 
validation of the result of Ang et al. (2011). Their findings suggest 
that economic growth is enhanced when innovation is enhanced; 
and innovation is enhanced when human capital is enhanced. Chi 
(2008) embarked on a similar study on the same scope. However, 
she used different methodology. Though she used GMM technique 
for estimation, it was for robustness purposes as the main technique 
used was two-stage least squares.

There is, therefore, an established relationship between human 
capital and innovation; but, this relationship is expected to 
translate in growth and development. Fleisher et al. (2010), 
therefore, tried to provide evidence from their investigation 
that variances in regional patterns of growth are as a result of 
variances in innovation, human capital and foreign investment. 
Using fixed effect, they discovered that the direct effect of human 
capital was traceable to innovation activities while its indirect 
effect was traceable to spillover effects from foreign investments. 
Their study revealed that the human capital investment not 
only leads to innovation growth, but also leads to a decline 
in inequality. This indicates the effect the relationship has on 
economic development. The findings of Basu and Mehra (2014) 
buttressed this. They posited that innovation reflects the capacity 
of human capital and discovered that wage inequality increases 
as a result of diversification of human capital in terms of skilled 
and unskilled human capital. Human capital investments will, 
therefore, improve the innovation capacity of unskilled human 
capital in order to reduce the level of inequality.

Developing economies can, therefore, address their development 
challenges by investing in human capital development in order 
cultivate the innovation capacity required to drive economic 
development. Teixeira and Fortuna (2010) conducted a study on the 
Portugese economy to examine this using cointegration technique. 
They discovered that investing in human capital capacity building 
activities develops the economy’s ability to identify, assimilate 
and value knowledge developed by more advanced economies. 
This gives developing economies the opportunity of learning fast 
to bridge the technology gap, address the challenge of inequality 
and promote domestic innovation.

These studies, therefore, validate that there is a relationship 
between human capital and innovation and innovation growth 
can be achieved by the enhancement of human capital. These 
have been the expience in advanced, emerging and non-African 
developing economies. This study therefore seeks to investigate 
whether similar outcomes are valid in SSA. Though there are 
related studies in SSA, they examined majorly the relationships 
between human capital, innovation and economic growth in Africa 

(Oyelaran-Oyeyinka and Barclay, 2004) while some others were 
country-specific (Oluwatobi and Ogunrinola, 2011).

3. SOME STYLIZED FACTS

Human capital capacity in SSA has been the lowest when 
compared with other regions of the world within the time period 
of this study. Table 1 shows that the region has the least human 
capital capacity through the period. Europe and Central Asia 
(ECA) topped the list. This was followed by Latin America and 
the Caribbean (LAC) after which Middle East and North Africa 
(MENA) and East Asia and the Pacific (EAP) followed. The 
trend clearly shows that advanced economies invest substantially 
in cultivating their human capital capacity. ECA, which topped 
the list, has experienced an increasing trend in its human capital 
capacity. SSA also experienced an increasing trend in its human 
capital capacity though its performance is low compared to the 
others. Nevertheless, the trend is an indication that the region is 
experiencing growth in its human capital capacity to contribute 
to the development process.

Assessing the trend of human capital in SSA, however, is not 
sufficient; for it is imperative to assess the trend of the enabling 
factors that allow the optimization of this capacity. Table 2, 
therefore, presents the trend of institutional quality in SSA in 
comparison with other regions of the world. Virtually all the 
regions have been almost on a downward trend in their ability 
to control corruption except LAC and ECA. Government 
effectiveness and regulatory quality were also on a downward 
trend for all the regions except LAC and ECA. SSA performance 
of institutional quality, thus, have been poor, which is an 
indication that human capital in the region lacks the enabling 
institutional environment to contribute to innovation outcomes 
and the development process.

This conclusion was reinforced by assessing the trend of 
innovation outcomes in the region in comparison with those of 
other regions of the world. This is presented in Table 3. It was 
observed that regions with the largest human capital capacity, as 
shown in Table 1, had the largest innovation output as shown in 
Table 3. Moreover, the region with the best performing institutional 
quality had the largest innovation output. This is a signal of the 
direct relationships among the variables. SSA, which had the least 
human capital capacity, has the least innovation output throughout 
the period of study as shown in Table 3.

Table 1: Human capital with tertiary education: SSA and 
the World
Region 1996 2001 2006 2007 2008 2009 2010 2011
SSA 3.71 4.63 5.87 6.10 6.43 6.71 7.21 7.56
EAP 11.25 17.24 24.37 25.24 26.08 27.72 29.11 30.11
ECA 38.52 47.23 55.95 56.82 57.50 58.49 60.04 60.32
MENA 17.24 21.09 24.43 25.95 27.91 28.66 30.48 31.34
SA 5.59 8.33 10.13 11.50 13.02 14.17 15.67 15.88
LAC 18.09 24.43 32.42 35.52 38.52 39.60 41.17 42.32
Source: Oluwatobi et al. (2014). SSA: Sub Saharan Africa, EAP: East Asia and the 
Pacific, ECA: Europe and Central Asia, MENA: Middle East and North Africa, 
SA: South Asia, LAC: Latin America and the Caribbean



Oluwatobi, et al.: Human Capital, Institutions and Innovation in Sub-Saharan Africa

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 20161510

These trends reveal clear patterns that not only show the 
relationship that exists among human capital, institutions and 
innovation in SSA but also indicate the performance of the 
region in comparison to other regions of the world. The result 
of the low performance of the region in terms of its innovation 
output, is buttressed by the performance of selected countries 
in SSA for this study. The global innovation index 2014, 
presented in Table 4, showed that none of the countries scored 
up to average.

On the other hand, the global competitiveness report, shows 
that the SSA region has potentials to improve its innovation 
performance given its level of competitiveness. Though not among 
the top ranking most competitive economies, selected countries 
in SSA are improving the level of competitiveness to catch up 
with advanced economies. From Table 5, 18 countries out of 
32 countries studied, scored above average.

4. METHODOLOGY

The foundation on which this study is conducted is based on 
the theoretical and empirical model developed by Tebaldi and 
Elmslie (2008, 2013) consistent with Romer (1990),…. on the 
interelationship among human capital, institutions, innovation 
and economic growth. Their model relates human capital and 
innovation as follows:

A=δAHAq[T(A)] (1)

Where A is a measure of innovation, HA is a measure of human 
capital and q is a measure of institution in relation to institutional 
quality and technical knowledge. This model, thus, is augmented 
to suit this study and capture two other variables; namely economic 
growth and spillovers from foreign investments. It is expected 
that innovation will be enhanced as the economy grows. It is 

also expected that spillovers from foreign investments improve 
innovation. The model for this study is therefore presented as 
follows:

Innovation =AH e Y Si,t i,t
ins

i,t i,t i,t
2 3 i,t 4 5

α α α α ε
 (2)

Table 2: Institutions: SSA and the World
Region Control of corruption Government effectiveness Regulatory quality

1996 2000 2005 2008 1996 2000 2005 2008 1996 2000 2005 2008
EAP −0.43 −0.6 −0.53 −0.57 −0.3 −0.48 −0.46 −0.53 −0.35 −0.61 −0.56 −0.69
ECA −0.7 −0.62 −0.52 −0.48 −0.58 −0.51 −0.37 −0.31 −0.59 −0.49 −0.32 −0.1
LAC −0.35 −0.18 −0.16 −0.12 −0.34 −0.15 −0.14 −0.1 −0.22 −0.07 −0.07 −0.12
MENA −0.46 −0.57 −0.55 −0.62 −0.45 −0.63 −0.63 −0.61 −0.64 −0.78 −0.73 −0.63
SSA −0.63 −0.58 −0.68 −0.62 −0.66 −0.72 −0.78 −0.78 −0.65 −0.64 −0.75 −0.7
World −0.03 −0.02 −0.02 −0.02 −0.04 −0.01 −0.01 −0.01 −0.05 −0.03 −0.02 −0.01
Source: Oluwatobi et al. (2014), SSA: Sub Saharan Africa, EAP: East Asia and the Pacific, ECA: Europe and Central Asia, MENA: Middle East and North Africa, LAC: Latin America 
and the Caribbean

Table 3: Innovation: SSA and the World
Region 1996 2001 2006 2007 2008 2009
SSA 3908.6 3860 4615.6 4952.4 5074.4 5080.1
EAP 89930.6 117690 161522.7 169109.4 182046.1 190578.9
ECA 240081.3 255860.3 282648.5 287422.2 291637 290424.4
MENA 9500.4 11452.6 15206.1 16628.2 17920.1 19167
SA 10266.1 11380.8 17784.2 19385.6 20372.6 21432.3
LAC 10503.8 16074 21729.6 23337 24743.1 24032.6
Source: Oluwatobi, Efobi, Olurinola and Alege (2014), SSA: Sub Saharan Africa, EAP: East Asia and the Pacific, ECA: Europe and Central Asia, MENA: Middle East and North Africa, 
SA: South Asia, LAC: Latin America and the Caribbean

Table 4: Global innovation index 2014
Countries Score (0-100) Global rank 

(Out of 143)
SSA rank

Benin 24.21 132 28
Botswana 30.87 92 6
Burkina Faso 28.18 109 14
Burundi 22.43 138 30
Cabo Verde 30.09 97 8
Cameroon 27.52 114 17
Cote d’Ivoire 27.02 116 18
Ethiopia 25.36 126 24
Gambia, The 29.03 104 11
Ghana 30.26 96 7
Guinea 20.25 139 31
Kenya 31.85 85 4
Lesotho 27.01 117 19
Madagascar 25.5 124 23
Malawi 27.61 113 16
Mali 26.18 119 20
Mauritius 40.94 40 1
Mozambique 28.52 107 12
Namibia 28.47 108 13
Niger 24.27 131 27
Nigeria 27.79 110 15
Rwanda 29.31 102 10
Sao tome and principe
Senegal 30.06 98 9
South Africa 38.25 53 3
Swaziland 25.33 127 25
Tanzania 25.6 123 22
Togo 17.65 142 32
Uganda 31.14 91 5
Zimbabwe 24.31 130 26



Oluwatobi, et al.: Human Capital, Institutions and Innovation in Sub-Saharan Africa

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 2016 1511

Where A captures technical knowledge, Hit measures human 
capital, Insit measures institutions, Yit measures economic growth, 
Sit measures knowledge spillovers from foreign investments and εit 
represents the stochastic error term. i and t represent the country 
and time identifiers respectively. The formulation of the model is 
consistent with theoretical and empirical postulations framed on 
the logic that human capital cultivation is significant to absorption 
of knowledge, creation of ideas, enhanced R and D output and 
innovation growth.

The model presented in equation 2 is re-presented in equation 3 as 
a linear function for the purpose of estimation. The transformation 
into a linear function for estimation is done using logarithm.

lnInnovationi,t=α0+α1lnInnovationi,t-1+α2 lnHit+α3Insit+α4lnYit+ 
α5lnSit+φi,t (3)

Where α0=lnA, φi,t=lnεi,t and lnInnovationi,t-1 is a signification that 
it is a dynamic panel model.

The SGMM is the estimation technique employed for this study. 
Some of the reasons responsible for the choice of this technique is that 
it helps to cater for unobserved heterogeneity and endogeneity biases 
associated with estimating dynamic panel models. It has proven to be 
more efficient than instrumental variables estimator in the presence 
of heteroskedasticity and can exploit stationarity restrictions.

5. RESULTS AND DISCUSSION

The results for this study are presented in Tables 6 and 7. The 
two tables indicate the relationship between human capital and 
institutions and innovation in SSA. Human capital, in Table 6, is 
indicated by secondary school enrolment, thus, capturing human 
capital with secondary education; while human capital in Table 7 
is indicated tertiary school enrolment, hence, capturing human 
capital with tertiary education. It is pertinent to examine what kind 
of human capital has the most significant impact in affecting the 
innovation outcomes of SSA.

The six indicators measuring institutional quality were each used 
independently. This was pertinent since they were highly correlated 
with each other from the multicollinearity test as indicated in the 
correlation matrix in the appendix (See Table A1). The relationship 
between human capital and innovation was therefore examined in 
the presence of each of these institutional variables.

The probability values of the Sargan test in each of the six models 
in Table 6 showed that there is no over-identifying restriction. 
Hence, the Roodman (2009) concern of too many instruments 
has been addressed. The Hansen test results as indicated by the 
probability values, further corroborates this. The AR(1) and AR(2) 
results for each of the models in the table also indicates that there 
is no autocorrelation. These results strengthen the validity of the 
results of this study. Hence, the results are useful for drawing 
conclusions.

From Table 6, human capital had a significant relationship with 
innovation in all the models except for where control of corruption 
was the indicator for institutional quality. Only regulatory quality, 
political stability and rule of law were statistically significant for 
all the indicators of institutional quality. From model 1, a change 
in human capital with secondary education by 1 percent leads to a 
contemporaneous change in innovation by 0.541%. This is a clear 
indication of the relevance of human capital in driving innovation 
in SSA. Regulatory quality had a much more significant impact 
on innovation in the model compared to human capital. The result 
showed that a unit change in regulatory quality will translate in a 
change in innovation by 0.586%.

The result from model 4 validates the human capital has a 
significant effect on innovation in SSA. Specifically, a change in 
human capital with secondary education by one percent leads to 
a change in innovation by 0.514%. This reflects a greater impact 
of human capital on innovation that political stability. The result 
shows that innovation outcome is altered by 0.499% as a result 
of a unit change in political stability. This means that… The 
result from model 5 corroborates previous results indicating the 
impact of human capital with secondary education on innovation 
outcomes in SSA. It showed that a change in human capital with 
secondary education by 1% translates into a change in innovation 
by 0.652%. Institutional quality, as indicated by rule of law, was 
responsible for 62% of the variations in innovation, thus, validating 
the relevant role institutions have in affecting the amount and 
quality of innovation in the SSA region.

Table 5: Global competitiveness index 2014/2015
Countrie Score (1-7) Rank 2014/15 

(out of 144)
SSA Rank

Benin
Botswana 4.15 74 4
Burkina Faso 3.21 135 23
Burundi 3.09 139 24
Cabo Verde 3.68 114 10
Cameroon 3.66 116 12
Central African Republic
Comoros
Cote d’Ivoire 3.67 115 11
Ethiopia 3.6 118 13
Gambia, The 3.53 125 18
Ghana 3.71 111 8
Guinea 2.79 144 25
Kenya 3.93 90 6
Lesotho 3.73 107 7
Madagascar
Malawi 3.25 132 21
Mali 3.43 128 20
Mauritius 4.52 39 1
Mozambique 3.24 133 22
Namibia 3.96 88 5
Niger
Nigeria 3.44 127 19
Rwanda 4.27 62 3
Sao Tome and Principe
Senegal 3.7 112 9
South Africa 4.35 56 2
Swaziland 3.55 123 16
Tanzania 3.57 121 14
Togo
Uganda 3.56 122 15
Zimbabwe 3.54 124 17
Source: World Economic Forum (2014), SSA: Sub Saharan Africa



Oluwatobi, et al.: Human Capital, Institutions and Innovation in Sub-Saharan Africa

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 20161512

The result shows that economic growth has a significant relationship 
with innovation. The variable was statistically significant at 1% 
level of significance for all the six models. Moreover, the result 
shows that innovation is highly sensitive to economic growth in 
SSA as indicated by the coefficients, which reflect that a change 
in economic growth leads to a more than proportionate change 
in Innovation in SSA. This means that innovation thrives as the 
economies in the region grow.

FDI Spillover, measured by net FDI inflows as a percentage of 
GDP, did not show a statistically significant relationship with 
innovation except in model 4, which indicates vary little impact of 
the variable on innovation. This means that the SSA region hardly 
benefits from knowledge spillovers from foreign investments in 
the region. This is not farfetched given that most FDI in the region 
are targeted at extracting available raw materials as well as taking 
advantage of the large market for their products and services.

These results are clear indications of the significant impact human 
capital with secondary education has on innovation in SSA. It 
was, hence, necessary to validate this result by examining the 
relationship between higher levels of human capital and the effect 
on innovation. The impact of human capital with tertiary education 
on innovation outcomes in SSA was therefore examined. The 
results of the investigation are presented in Table 7.

Human capital with tertiary education was statistically significant 
for all the six models in the table, thus, indicating that there is 
a significant relationship between highly skilled human capital 
and innovation in SSA. This validates the idea that highly-skilled 
human capital is relevant to innovation outcomes and will enable 
developing economies improve on their capacity to innovate and 
catch up with advanced economies. Hence, a 1% change in tertiary 
human capital will cause a contemporaneous change in innovation.

The coefficient of institutional quality indicated statistical 
significance for regulatory quality, political stability, rule of law 
and voice and accountability. A unit change in regulatory quality 
translate in a change in innovation by 0.506% while a unit change 
in political stability leads to a change in innovation by 0.406%. 
The coefficient of rule of law indicates that it is affects changes 
in innovation by 0.377% as it changes by a unit while a change in 
voice and accountability by one unit leads to a contemporaneous 
change in innovation by 0.613%.

The results showing the relationship between economic growth and 
innovation are consistent with those in Table 6. It was statistically 
significant at 1% level of significance and the coefficients 
show that a change in economic growth by 1% leads to more 
than proportionate changes in innovation, thus, validating that 
magnanimous impact the variable has on innovation in SSA and 

Table 6: SGMM estimation (human capital measured by secondary school enrolment)
Variables Dependent variable: Innovation

1 2 3 4 5 6
Lag of innovation 0.207

(0.142)
0.172

(0.154)
0.225

(0.175)
0.278

(0.183)
0.300*
(0.169)

0.393
(0.395)

Human capital (secondary) −0.541**
(0.239)

−0.178
(0.201)

−0.428*
(0.224)

−0.514***
(0.194)

−0.652**
(0.255)

−0.970*
(0.510)

Regulatory quality 0.586*
(0.355)

Corruption of control −0.345
(0.263)

Government effectiveness 0.264
(0.220)

Political stability 0.499**
(0.228)

Rule of law 0.617**
(0.274)

Voice and accountability 1.088
(0.760)

Economic growth 1.521***
(0.186)

1.540***
(0.192)

1.611***
(0.234)

1.758***
(0.270)

1.708***
(0.232)

1.604***
(0.357)

FDI spillover −0.0216
(0.0308)

0.0179
(0.0421)

−0.0109
(0.0279)

−0.0753*
(0.0395)

−0.0249
(0.0323)

−0.0618
(0.0521)

Year −0.0151
(0.0172)

0.00329
(0.0296)

−0.0107
(0.0200)

−0.0223
(0.0177)

−0.00895
(0.0169)

−0.0100
(0.0381)

Constant −1.161
(33.58)

−36.79
(60.12)

−11.43
(40.32)

7.820
(32.50)

−17.72
(33.89)

−13.98
(71.50)

Sargan P 0.188 0.133 0.168 0.488 0.469 0.241
Hansen P 0.235 0.212 0.221 0.458 0.404 0.114
AR (1) P 0.045 0.048 0.058 0.045 0.048 0.219
AR (2) P 0.165 0.239 0.132 0.148 0.123 0.217
Observations 253 253 253 253 253 253
Number of year 13 13 13 13 13 13
Source: Computed by Authors using Stata 12.1. Standard errors are in parentheses. ***,**,*Denotes P<0.01, P<0.05 and P<0.1 respectively, SGMM: System Generalized Method of 
Moments



Oluwatobi, et al.: Human Capital, Institutions and Innovation in Sub-Saharan Africa

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 2016 1513

indicating that innovation advances in the region as the economies 
in the region grow. This indicates the direct relation economic 
growth has with innovation in SSA.

The results show that FDI spillover did have any significant 
relationship with innovation in SSA, thus, buttressing the idea that 
foreign investments in the region are not impactful on innovation 
in the region.

6. CONCLUSION

This study examined the impact of human capital and institutional 
quality on innovation in SSA. The study ascertains that human 
capital capacity at the secondary and tertiary levels are relevant 
to drive innovation in the region. Moreover, the quality of 
institutions affects the output of innovation. These results, thus, 
validate the idea that human capital capacity, coupled with the 
enabling institutional environment, are requirements to facilitate 
innovation. The implication for SSA is that it can catch up with 
advanced economies and close the technology gap by investing 
in human capital capacity development and providing the 
enabling institutional environment that facilitates free enterprise 
and supports innovation. The study, therefore, recommends to 
policy makers that efforts be made to catch up by cultivating the 

human capital capacity at the secondary and tertiary levels as 
well as provide the enabling environment for such capacities to 
be engaged to enhance innovation outcomes. The human capital 
capacity generated, coupled with the enabling environment should 
translate in competent human capital pool that attracts foreign 
investments that are innovation-centred. Based on the results, this 
study recommends that part of the enabling environment should be 
to redefine terms with foreign investors to direct their investments 
in such a way that SSA benefits from knowledge spillovers that 
contributes to innovation outcomes in the region.

REFERENCES

Aghion, P., Howitt, P. (1992), A model of growth through creative 
destruction. Econometrica, 60(2), 323-351.

Aghion, P., Howitt, P.W. (1997), Endogenous Growth Theory. Cambridge, 
MA: MIT Press.

Ang, J.B., Madsen, J.B., Islam, R. (2011), The effects of human 
capital composition on technological convergence. Journal of 
Macroeconomics, 33, 465-476.

Basu, S., Mehra, M.K. (2014), Endogenous human capital formation, 
distance to frontier and growth. Research in Economics, 68, 117-132.

Chi, W. (2008), The role of human capital in China’s economic 
development: Review and new evidence. China Economic Review, 
19, 421-436.

Table 7: SGMM estimation (human capital measured by tertiary school enrolment)
Variables Dependent variable: Innovation

1 2 3 4 5 6
Lag of innovation −0.0530

(0.0793)
−0.0765
(0.104)

−0.0274
(0.0703)

−0.0860
(0.0751)

−0.0268
(0.0782)

0.0869
(0.0804)

Human capital (tertiary) −0.413***
(0.106)

−0.360**
(0.144)

−0.341***
(0.0929)

−0.394***
(0.105)

−0.467***
(0.0802)

−0.478***
(0.110)

Regulatory quality 0.506*
(0.260)

Corruption of control 0.319
(0.284)

Government effectiveness 0.213
(0.192)

Political stability 0.406***
(0.139)

Rule of law 0.377*
(0.203)

Voice and accountability 0.613***
(0.177)

Economic growth 1.234***
(0.184)

1.328***
(0.233)

1.340***
(0.153)

1.242***
(0.187)

1.365***
(0.196)

1.481***
(0.145)

FDI spillover −0.0383
(0.0559)

−0.00809
(0.0693)

−0.0293
(0.0553)

−0.0180
(0.0546)

−0.0397
(0.0477)

−0.0580
(0.0485)

Year 0.0809**
(0.0411)

0.0575
(0.0389)

0.0676**
(0.0323)

0.0554**
(0.0279)

0.0781**
(0.0318)

0.0808
(0.0516)

Constant −187.4**
(83.23)

−142.0*
(79.43)

−162.5**
(65.04)

−136.3**
(56.16)

−184.4***
(62.84)

−193.4*
(105.5)

Sargan P 0.444 0.130 0.290 0.228 0.282 0.115
Hansen P 0.248 0.133 0.276 0.311 0.175 0.423
AR (1) P 0.182 0.213 0.283 0.078 0.171 0.140
AR (2) P 0.444 0.232 0.347 0.657 0.505 0.346
Observations 191 191 191 191 191 191
Number of year 13 13 13 13 13 13
Source: Computed by authors using stata 12.1. Standard errors are in parentheses. ***,**,*Denotes P<0.01, P<0.05 and P<0.1 respectively, SGMM: System Generalized Method of 
Moments



Oluwatobi, et al.: Human Capital, Institutions and Innovation in Sub-Saharan Africa

International Journal of Economics and Financial Issues | Vol 6 • Issue 4 • 20161514

Fleisher, B., Li, H., Zhao, M.Q. (2010), Human capital, economic growth, 
and regional inequality in China. Journal of Development Economics, 
92(2), 215-231.

Kato, M., Okamuro, H., Honjo, Y. (2015), Does founders’ human capital 
matter for innovation? Evidence from Japanese start-ups. Journal of 
Small Business Management, 53(1), 114-128.

Mariz-Perez, R.M., Teijeiro-Alvarez, M.M., Garcia-Alvarez, T.M. (2012), 
The relevance of human capital as a driver of innovation. Elsevier 
Economy Notebooks, 35(98), 68-76.

Oluwatobi, S.O., Efobi, U.R., Olurinola, I.O., Alege, P.O. (2014), 
Innovation in Africa: Why institutions matter. South African Journal 
of Economics. Available from: http://www.onlinelibrary.wiley.com/
doi/10.1111/saje.12071/abstract.

Oluwatobi, S.O., Ogunrinola, I.O. (2011), Government expenditure on 
human capital development: Implications for economic growth in 
Nigeria. Journal of Sustainable Development, 4(3), 72-80.

Oyelaran-Oyeyinka, B., Barclay, L.A. (2004), Human capital and systems 
of innovation in African development. African Development Review, 
16(1), 115-138.

Roodman D. (2009), A note on the theme of too many instruments. 
Oxford bulletin of economics and statistics. 71(3) 135-150. Doi: 
10.111/J.1468-0084.00542X.

Romer, P. (1994), The origin of endogenous growth. Journal of Economic 

Perspectives, 8(1), 3-22.
Romer, P.M. (1990), Endogenous technological change. The Journal of 

Political Economy, 98(5), 71-102.
Solow R. M. (1957), Technical Change and the Aggregate Production 

Function. The Review of Economics and Statistics, 39(3) 312-320.
Tebaldi, E., Elmslie, B. (2008), Institutions, innovation and economic 

growth. Journal of Economic Development, 33(2), 27-53.
Tebaldi, E., Elmslie, B. (2013), Does institutional quality impact 

innovation? Evidence from cross-country patent grant data. Applied 
Economics, 45, 887-900.

Teixeira, A., Fortuna, N. (2010), Human capital, R&D, trade, long-
run productivity. Testing the technological absorption hypothesis 
for the Portugese economy, 1960-2001. Research Policy, 39, 
335-350.

The Economist. (2013), Investing in Africa, the hottest frontier: 
Strategies for putting money to work in a fast-growing continent. 
The Economist. Availanble from: http://www.economist.com/
news/finance-and-economics/21575769-strategies-putting-money-
work-fast-growing-continent-hottest/print. [Last retrieved on 
2013 May 28].

Zhang, C., Zhuang, L. (2011), The composition of human capital and 
economic growth: Evidence from China using dynamic panel data 
analysis. China Economic Review, 22, 165-171.

APPENDIX

Appendix Table

Table A1: Correlation matrix
Indicators Control of 

corruption
Government 
effectiveness

Political 
stability

Regulatory 
quality

Rule of law Voice and 
accountability

Control of corruption 1.0000
Government effectiveness 0.8322 1.0000
Political stability 0.6925 0.6178 1.0000
Regulatory quality 0.7548 0.8575 0.5791 1.0000
Rule of law 0.8526 0.8644 0.7820 0.8244 1.0000
Voice and accountability 0.7104 0.7409 0.7148 0.7078 0.8097 1.0000