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Entrepreneurial Activity and Its
Determinants: Findings from African
Developing Countries
Ondřej Dvouletý and Marko Orel
Abstract Entrepreneurship research in Africa has not received much attention from
scholars in the past. Therefore, we contribute to this body of knowledge from the
perspective of African developing countries. We demonstrate that most of the
African developing countries have not yet conducted Global Entrepreneurship
Monitor to study entrepreneurship in their country, and we show that the existing
datasets are very limited. Utilising the available data, we study entrepreneurial
activity and its determinants on a sample of 12 African countries over the years
2001–2016. Using the data from Global Entrepreneurship Monitor, we show that the
overall rate of entrepreneurship is higher compared to Europe (on average 31%).
Utilising other data from the World Bank, Transparency International and Heritage
Foundation, we estimate multivariate regression models to study determinants of
early-stage entrepreneurial activity. Although the number of available observations
limits our results, we find some empirical evidence showing the importance of GDP
per capita, unemployment rate, economic freedom index, corruption perceptions and
perceived opportunities as factors influencing the early-stage level of entrepreneurial
activity. Our study also offers several directions for future research, regarding both
research methods and other potential variables of interest.
1 Introduction
Business Report, South Africa’s leading business media, published a widely viewed
story on the continent’s continuous entrepreneurial transformation in December 2018.
The media report argued that with embracing and adopting pan-Africanism mindset
across the continent, most of 54 African countries are experiencing digital renaissance
that is increasing their connectedness and opening markets (Rey 2018). With entre-
preneurship being lower in Africa compared to other parts of the world and past
emphasis on government-controlled businesses that discouraged private investments
O. Dvouletý (*) · M. Orel
Department of Entrepreneurship, Faculty of Business Administration, University of Economics
in Prague, Prague, Czech Republic
e-mail: ondrej.dvoulety@vse.cz;marko.orel@vse.cz
©Springer Nature Switzerland AG 2019
V. Ratten et al. (eds.), Sustainable Entrepreneurship, Contributions to Management
Science, https://doi.org/10.1007/978-3-030-12342-0_2
9
in new businesses, Africa is finally seeing a change with Africans displaying one of the
highest entrepreneurial intents globally (Ratten and Jones 2018).
By overtaking structural challenges, it appears that entrepreneurship is seeing a (re)
birth all over the continent with bursting entrepreneurial activities being the potential
for solving Africa’s economic and social challenges by crafting jobs and having a hand
in GDP growth for Africa’s countries. Undeniably, African nations are moving from
traditional sources of income and are experiencing an increasing entrepreneurial
activity that is more often seen not only as sustainable job generation tool but as the
lead route to economic development (Chigunta 2017;Dvouletýetal.2018).
Several attempts have been made over the last two decades to conglomerate the
knowledge on entrepreneurial activities in Africa (e.g. Engelmann 1994; Frese and
de Kruif 2000; Kiggundu 2002; Sriram and Mersha 2010; Kshetri 2011; Munemo
2012; Kuada 2015; Röschenthaler and Schulz 2015; George et al. 2016b; Lashitew
and van Tulder 2017; Atiase et al. 2018; Ratten and Jones 2018) although compre-
hensive research on its determinants is still limited to some extent. One of the goals
of this chapter is, therefore, to signify that majority of developing countries in Africa
have not yet conducted GEM to study their entrepreneurial activities. Using existing
GEM surveys and other data resources, we conduct research on entrepreneurial
undertakings in 12 African countries and demonstrated the importance of several
determinants that influence the early-stage level of entrepreneurial activity.
The chapter is structured as follows. Starting with literature review, we investi-
gate existing literature on entrepreneurial activities in Africa that probes into the
examined topic. Following a literature review of the past researches of the field, we
build a panel of 52 African countries and search for data and variables of interest in
noted global databases. As out of panelled countries only 17 have conducted GEM
study at least once, following only a dozen of them done the study at least three
times, we narrow our sample accordingly.
Subsequently, we reflect average values across countries and available years,
focusing on entrepreneurial activity, and variables linked to business ecosystems and
organisational policies that are a bracket with start-up businesses. With the empirical
approach, we tend to enquire into the patterns influencing the start-up rates within
developing African countries by exploring cross-country variation in economic and
institutional determinants of new entrepreneurial activity. After reviewing results
and engaging into the discussion, we wrap the conclusion and propose a handful of
suggestions for future research. We are keen to believe that our contribution is vital
for the discussion on entrepreneurial activity in African developing countries and its
determinants and will be identified as ambitious attempt to provide a novel resource
for scholars, interested in the subject.
2 Literature Review
As most of the available literature focuses on entrepreneurial activities in Western
societies and Asian countries, research on African entrepreneurship is still fairly
limited by this date. Nevertheless, existing publications on entrepreneurial activities
10 O. Dvouletý and M. Orel
in Africa are stamped with positive and optimistic expectations towards growth and
expansion of entrepreneurship across the continent and can be viewed as the “Africa
rising”narrative (Mahajan 2011). Not only that more businesses are transforming
and are being easier to regulate, but newly emerging middle class across African
nations is also being recognised as keen on engaging into entrepreneurial activities
within their regions (Tvedten et al. 2014). This transitional process has sparked the
growing attention in Africa’s entrepreneurial potential and emerged different
research directions into the topic (Ratten and Jones 2018).
Indeed, it appears that an entrepreneurial mindset is becoming more acknowl-
edged throughout the African continent. George et al. (2016a) present their findings
that point towards individuals relying on their social relationship to enable entrepre-
neurial activities and thus seek to allocate the potential to generate a reasonable
income gain. While they sample Kenyan households to test, if disintegration of
social structure reduced entrepreneurial behaviour, Meagher (2005) analysed the
role of class, religion, ethnicity and gender in generating positive as well as negative
trends in the restructuring of African informal economies by drawing on empirical
studies across Africa. Social networks are commonly viewed as an essential factor
for entrepreneurial success in developing countries (Egbert 2009) and can contribute
to entrepreneurial success or failure of individuals from different age groups.
It comes to our understanding that throughout the continent, but especially in
countries of sub-Saharan Africa, youth unemployment remains high. While many
young individuals venture into one of the forms of self-employment in the informal
sector, Chigunta (2017) argues that entrepreneurship provides both pathways out of
poverty for some young people, although the majority of them face complex
challenges when starting and later on running a viable business. Spreading knowl-
edge throughout training and tutoring indeed plays a significant role in this case. As
young African entrepreneurs often face high costs of finding a favourable moment to
enter the business opportunity, entrepreneurial trainings prove to be more effective
than direct or indirect subsidies. The similar observation share also Efobi and Orkoh
(2018) who evaluated effects of entrepreneurship training programmes in Nigeria.
Brixiová et al. (2015) therefore test the role of tutoring young entrepreneurs by
surveying them in Swaziland and develop a model of business creation with skill
differences between adult and young entrepreneurs.
In this context, Kojo Oseifuah (2010) implies that the training of youth with an
emphasis on financial literacy and entrepreneurial skills may have significant effects
on the growth of youth entrepreneurship in South Africa. As these practical impli-
cations are limited to only one of the sampled African countries, knowledge sharing
through education has been positively linked with entrepreneurial activities of
African youth throughout the research work of various authors (Aladekomo 2004;
Awogbenle and Iwuamadi 2010; Nafukho and Helen Muyia 2010; Ajufo 2013;
DeJaeghere and Baxter 2014; Dzisi et al. 2018).
By associating the entrepreneurial productivity to start-up capital and skill set,
Brixiová and Kangoye (2016) contribute to the empirical literature on entrepreneur-
ship, entrepreneurial development and gender role in Africa. They show that while
Entrepreneurial Activity and Its Determinants: Findings from African... 11
business training may be positively linked with sales performance on men entrepre-
neurs, it has no markable effect on female entrepreneurs.
With the study of young female employment and entrepreneurship in sub-Saharan
Africa, Langevang and Gough (2012) found out that diverging trajectories of
different entrepreneurial fields can be attributed to globalisation that affects trades
differently and generates different opportunities. Accompanied by globalisation
accelerating the movement towards market liberalisation (Zahra et al. 2008), it
appears that Africa is in transition that is driven by the popularisation of entrepre-
neurship and change in behavioural patterns of its population (Edoho 2015). In a
similar light, Jones et al. (2018c) highlight that there is a need for further research in
contracting entrepreneurial behaviour between African nations and regions.
Khayesi et al. (2017) explore cultural determinates as possible facilitators and
barriers to entrepreneurship development in parts of Africa. They particularly note
that different strands of research on culture and entrepreneurship in African countries
rest disperse and have not been synthesised into an obtainable resource. Kuada
(2010) establishes a link between African culture and leadership operations and
their inferences in economic growth on the African continent.
One of the cornerstones that can be seen as blocking further liberalisation of
entrepreneurial activities in Africa is inefficient and slow bureaucracies that do not
tend to minimise compliance costs for entrepreneurs (Edoho 2015). Sriram and
Mersha (2010) note that promoting entrepreneurship and to increase the probability
of success of business require a systematic approach towards stimulating entrepre-
neurial ventures. However, in order to increase efficiency of relevant policies and
promote entrepreneurship and innovation, the governmental bureaucracies “must
be responsive to the needs of the entrepreneurial class”(Mbaku 1996: 105) and
respondent to pitfalls of corruption (Harsch 1993; Robson et al. 2009; Ngunjiri 2010;
Hope 2016).
After exploring previously published studies in the body of relevant literature, we
continue our analysis by constructing a dashboard dataset of African countries and
progress our exploration to root the data and variables of interest in acknowledged
databases.
3 Data and Sample
To study entrepreneurial activity in African developing countries, we have built a
panel dataset of 52 countries, based on the classification of the trade and develop-
ment conference of the United Nations (2018)
1
. Then we used the list of countries
1
These include Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon,
Central African Republic, Chad, Comoros, Congo, the Democratic Republic of the Congo, the
Republic of the Congo, Côte d’Ivoire, Djibouti, Egypt, Arab Rep., Eritrea, Ethiopia, Gabon,
Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali,
Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé and
12 O. Dvouletý and M. Orel
mentioned above to search for data and variables of interest in established interna-
tional databases such as the Global Entrepreneurship Monitor (2018), World Bank
Database (2018), Heritage Foundation (2018) and Transparency International
(2018). We aimed to collect data for the longest possible period; however, the first
available datasets of Global Entrepreneurship Monitor (GEM) were available from
2000 and onwards, and thus, our analysis includes the period 2001–2016. The list of
collected variables can be found in the following Table 1.
Unfortunately, there are many missing values in the collected indicators. Most of
the African developing countries have unfortunately not taken apart in the Global
Entrepreneurship Monitor (GEM) yet to explore their national entrepreneurial activity.
Out of the 52 developing countries, only 17 countries have conducted GEM study at
least once, and only 12 countries have done the study at least three times. We get a
similar picture when we inspect the number of available years in the remaining
indicators, including World Bank indicators (2018). Also, data representing the new
business density indicator [which is often used as a measure of new entrepreneurial
activity, see, e.g. Dvouletý (2018a) and Fritsch (2015) for a discussion] have many
missing values. More available years increase the reliability of any statistical analysis.
Looking at the missing values, we have decided to include in our study only those
12 countries (24% of the initial cross-country sample) that have at least 3 years of
GEM data available. These are, namely, Algeria, Angola, Botswana, Burkina Faso,
Cameroon, Ghana, Morocco, Nigeria, South Africa, Tunisia, Uganda and Zambia. We
summarise the collected variables in the following Table 2for the whole sample, and
then, we further comment briefly on other variables and their cross-country values in
the following text.
4 Descriptive Evidence
In this section, we comment on the average values across countries and available
years. We focus on the entrepreneurial activity and variables related to the business
environment and administrative procedures associated with business start-up. We
report all average values across countries in Table 3.
4.1 Entrepreneurial Activity
What is the overall entrepreneurship rate among the 12 African developing countries
during the analysed years 2001–2016? Global Entrepreneurship Monitor data (2018)
show that on average 20.4% of the 18–64 population are either nascent entrepreneurs
Príncipe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan,
Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe (United Nations 2018).
Entrepreneurial Activity and Its Determinants: Findings from African... 13
or owner-managers of new business, and on average 11% of the 18–64 population
were engaged in owning-managing established business and at the same time
receiving salaries, wages or any other payments for more than 42 months. Combin-
ing both types of entrepreneurial activity according to GEM, we may conclude that
the overall entrepreneurship activity was roughly 31.4%, which is much higher
compared to Europe, where the overall entrepreneurship rates are around 15%
depending on the time period and survey used (cf. Dvouletý 2018b).
Table 1 Definition of variables
Variable Description
Total early-stage
entrepreneurial activity
“Percentage of 18–64 population who are either a nascent
entrepreneur or owner-manager of a new business”(Global
Entrepreneurship Monitor 2018)
Established business
ownership rate
“Percentage of 18–64 population who are currently an owner-
manager of an established business, i.e., owning and managing a
running business that has paid salaries, wages, or any other
payments to the owners for more than 42 months”(Global
Entrepreneurship Monitor 2018)
New business density “The number of new limited liability corporations registered in the
calendar year per 1000 people ages 15–64”(World Bank 2018)
GDP per capita GDP per capita is gross domestic product divided by midyear
population. Data are in constant 2010 US dollars (World Bank
2018)
Unemployment rate (%) Unemployment, total (% of total labour force, national estimate)
(World Bank 2018)
Economic freedom index “A measure of country´s economic freedom (the higher rank, the
higher freedom) based on two quantitative and qualitative factors,
grouped into four broad categories, or pillars, of economic
freedom”(Heritage Foundation (2018)
Corruption perceptions
index
“A measure of country´s level of corruption (the higher rank, the
less corrupted)”(Transparency International 2018)
Start-up procedures “Start-up procedures to register a business (number)”(World Bank
2018)
Costs of start-up “Cost of business start-up procedures (% of GNI per capita)”
(World Bank 2018)
Start-up days “Time required to start a business is the number of calendar days
needed to complete the procedures to legally operate a business”
(World Bank 2018)
Fear of failure “Percentage of 18–64 population perceiving good opportunities to
start a business who indicate that fear of failure would prevent
them from setting up a business”(Global Entrepreneurship
Monitor 2018)
Perceived opportunities “Percentage of 18–64 population who see good opportunities to
start a firm in the area where they live”(Global Entrepreneurship
Monitor 2018)
Source: Global Entrepreneurship Monitor (2018), World Bank Database (2018), Heritage
Foundation (2018) and Transparency International (2018)
14 O. Dvouletý and M. Orel
Nevertheless, there are large differences across the African developing countries.
The new entrepreneurial activity measured by TEA was on average the highest in
Zambia (38%), followed by Nigeria (36.6%) and Ghana (32%). On the contrary, the
lowest was in South Africa (7%), Tunisia (7.6%) and Morocco (8.6%). Established
business ownership rate (EBOR) shows a similar picture. The highest EBOR was in
Ghana (33.1%), Uganda (26%) and Burkina Faso (24.5%). The lowest rates of
EBOR were reported for South Africa (2%), Algeria (4%) and Botswana (4.8%).
4.2 Economic Indicators and Business Environment
The presented book chapter includes only the developing countries in Africa, so it is
not surprising that the economic indicators are still lagging the developed countries.
On the top are, regarding GDP, South Africa, followed by Botswana and Algeria,
which contrast the poorest Uganda, Burkina Faso and Ghana. Economic freedom
index shows the highest numbers for Botswana which scores 69 points, contrary to
Cameroon that scored during the analysed period on average only 52.8 points. In
overall, the countries score 57.9 points, and these values have been increasing over
time (Heritage Foundation 2018).
Corruption still seems to be a significant problem of the countries. The least
corrupted countries score according to Transparency International (2018) close to
100 points. The average value of this indicator was however only 34.3, and the
lowest values were observed in Angola (19.4), Nigeria (21.4) and Uganda (24.8). On
the contrary, Botswana has a score of 59.9 and leads the group, followed by
South Africa having an average score of 45.7.
Table 2 Summary statistics (years 2001–2016)
Variable Mean SE Min Max
Observations
(N)
Total early-stage entrepreneurial activity
(TEA)
20.4 12.6 4.2 41.5 57
Established business ownership rate
(EBOR)
10.9 10.5 0.8 37.7 57
New business density 3.0 4.1 0.1 18.4 90
GDP per capita 2819.2 2085.3 412.2 7582.6 204
Unemployment rate (%) 10.8 7.3 1.9 29.8 204
Economic freedom index 57.9 6.1 24.3 72.0 199
Corruption perceptions index 34.3 12.2 10.0 65.0 195
Start-up procedures 9.2 3.1 3.0 17 158
Costs of start-up 64.3 145.9 0.2 1316.4 158
Start-up days 32.2 24.3 8.5 125 158
Fear of failure 27.3 9.9 10.4 63.7 57
Perceived opportunities 55.4 19.7 13.6 85.5 57
Source: STATA 14, own calculations
Entrepreneurial Activity and Its Determinants: Findings from African... 15
Table 3 Average values of variables across countries (years 2001–2016)
Variable/
country
Total early-stage
entrepreneurial
activity (TEA)
Established
business
ownership rate
(EBOR)
New
business
density
GDP
per
capita
Unemployment
rate (%)
Economic
freedom
index
Corruption
perceptions
index
Start-up
procedures
Costs
of
start-
up
Start-
up
days
Fear
of
failure
Perceived
opportunities
Algeria 9.9 4.1 0.5 4296.8 15.3 54.6 31.1 12.9 12.5 23.4 35.6 52.6
Angola 26.2 7.4 N/A 2956.5 15.1 45.0 19.4 9.4 349.2 68.1 44.1 66.9
Botswana 28.6 4.8 11.0 6192.6 18.4 69.0 59.9 9.8 4.2 74.4 19.0 61.9
Burkina
Faso
28.3 24.5 0.1 551.7 4.3 58.1 34.7 5.8 75.1 21.0 19.8 61.2
Cameroon 30.1 13.2 N/A 1309.1 4.7 52.8 23.1 9.1 98.7 29.4 23.2 64.6
Ghana 32.1 33.1 0.9 1287.6 5.0 59.2 39.3 8.7 36.0 14.3 17.8 74.8
Morocco 8.6 9.3 1.4 2646.4 10.2 58.4 35.7 6.9 12.0 13.3 34.2 42.6
Nigeria 36.6 14.3 0.7 2037.6 4.3 53.4 21.4 8.3 39.1 29.0 22.8 84.1
South Africa 7.0 2.0 8.1 6931.8 24.6 63.5 45.7 7.2 4.3 48.4 30.7 32.6
Tunisia 7.6 7.1 1.4 3771.7 14.6 58.6 44.5 9.0 6.8 11.0 28.0 33.5
Uganda 31.7 26.0 0.6 548.3 2.9 61.6 24.8 15.6 84.4 29.4 20.7 75.2
Zambia 38.0 10.0 1.1 1230 10.0 58.0 30.8 7.1 30.7 21.9 14.9 78.7
Whole
sample
20.4 10.9 3.0 2819.2 10.8 57.9 34.3 9.2 64.3 32.2 27.3 55.4
Source: STATA 14, own calculations
16 O. Dvouletý and M. Orel
Once we look at the data from Doing Business Statistics administered by the
World Bank (2018), we might get an insight into the administrative burden of
business start-up. We comment on the number of start-up procedures, costs of
start-up and number of days to legally establish an enterprise. Nevertheless, we
must admit that these indicators reflect the specific kind of enterprise (generally a
limited liability company of a specified size, see World Bank, 2018, for details), and
thus they cannot reflect administrative requirements for all types and forms of
entrepreneurship in a country, so we should be a bit cautious when interpreting
these data (although they might provide an informative insight on the general
situation of the country). Having looked at the data, on average 9 procedures
(ranging from 6 in Burkina Faso to 16 in Uganda) need to be completed by people
aiming to start up business legally. Individuals aiming to pursue self-employment
career also need to count with a timely process, which takes on average 32 days
(ranging from 11 in Tunisia to 74 in Botswana). It also seems that the process is quite
financially demanding as applicants need to pay on average 64% (ranging from 4%
in South Africa to 349% in Angola) of the gross national income (GNI) to the public
administration for the legal establishment of a company.
It is also worth noting that besides other issues, fear of failure is a strong factor
that might prevent individuals from pursuing entrepreneurship career (Vaillant and
Lafuente 2007). On average 27% of the 18–64 population, who see good opportu-
nities for business start-up (on average 55% of the 18–64 population), say that fear of
failure would prevent them from starting a business (Global Entrepreneurship
Monitor 2018)
5 Empirical Approach
Building on the presented descriptive evidence, we would like to explore more the
patterns influencing the start-up rates in the African developing countries. In line
with the previously published studies (e.g. Nikolaev et al. 2018; Roman et al. 2018;
Dvouletý 2017,2018a), we use for this purpose multivariate analysis of panel data.
We aim to explore cross-country variation in economic and institutional deter-
minants of the new entrepreneurial activity. We focus only on the new business
activity because we have two independent measures of it—one from the Global
Entrepreneurship Monitor (2018), total early-stage entrepreneurial activity (TEA),
and the World Bank (2018)’snew business density rate. We employ two measures of
new business activity, there are significant differences across countries, and the
variation within indicators is also substantial. Employing two independent measures
helps us to increase the reliability of the obtained results.
In light with the previous research on entrepreneurial activity (Bosma et al. 2018;
Urbano et al. 2018; Chowdhury et al. 2018; Stenholm et al. 2013; North 1990), we
assume that legislative, institutional and economic settings influence early-stage
entrepreneurial activity in African developing countries significantly.
Entrepreneurial Activity and Its Determinants: Findings from African... 17
According to our limited dataset and the fact that this is the first type of such an
empirical study focusing purely on African developing countries, we study the role
of fundamental economic and institutional determinants of early-stage entrepreneur-
ial activity. As for the economic indicators, we work with the unemployment rate
and gross domestic product (GDP) per capita. Institutional factors include index of
economic freedom and corruption perceptions, and entrepreneurship-specific deter-
minants include the level of perceived opportunities and fear of failure rate. The
following section presents the obtained empirical results.
6 Results and Discussion
We run our analysis in the STATA 14 software, and we estimate all models with the
robust standard errors that are dealing with the consequences of heteroskedasticity and
autocorrelation (Wooldridge 2010). Although the number of observations is limited,
we also include year dummies as there were quite significant differences in the values
of indicators over time. The obtained estimates are presented in Table 4.Table4
presents two pairs of econometric models for both types of early-stage entrepreneurial
activity. We conclude that presented models are statistically significant, and they meet
the standard econometric assumptions. We interpret the obtained estimates as follows.
First of all, we would like to honestly declare that the number of observations in
both specifications is very low (57 observations in Models 1 and 2 and 33 observa-
tions in Models 3 and 4), and thus, we need to be very cautious in the interpretation
of obtained estimates. The significance of obtained variables also differs across
specifications, and thus, we base our interpretation on the significant variables, but
we also comment on the sign of the insignificant variables if they indicate the same
direction of impact. Overall, most of the variables included in our analysis show a
similar impact of independent variables on both types of early-stage entrepreneurial
activity, which is a good sign.
The unemployment rate (Models 1 and 3) seems to negatively influence the level
of early-stage entrepreneurial activity although it is significant only for the indicator
TEA. The GDP per capita seems to be quite stable, significant and positive for the
new business density indicator (Models 3 and 4); nevertheless, in TEA Models
(1 and 2), there are contradictory and insignificant coefficients. We know from the
review of the literature by Roman et al. (2018) and Dvouletý (2017) that both
indicators can be either positive or negative depending on the stage of the business
cycle. However, GDP is more likely to be positively influencing early-stage activity
(more wealth, more business opportunities), whereas unemployment rate can indi-
cate economic decline (or recession stage of the economy) and thus higher levels of
necessity entrepreneurship (positive impact) or more business bankrupts (negative
impact).
Quite conclusive are indicators reflecting the development of institutions in
African developing countries. Both economic freedom index and corruption percep-
tions index were found to be positively significant for new business density variable
18 O. Dvouletý and M. Orel
and positive (but insignificant) for TEA indicator. Such an observation is quite in
line with what we know from the previous research in Europe and OECD countries.
Freytag and Thurik (2007) have explained that more economic freedom allows
individuals to engage in entrepreneurship easier and to more smoothly operate in
the economy in relation to all aspects of the country’s institutional settings (and its
openness towards doing business). Positive coefficient for corruption perceptions
variable also fits into the existing body of knowledge (Mohamadi et al. 2017;
Dvouletý and Blažková 2018), indicating that the less corrupted environment
(higher value of the index) is positively associated with the higher early-stage
entrepreneurial activity.
Finally, we comment on the impact of the two indicators obtained from the
Global Entrepreneurship Monitor (2018)—the level of perceived opportunities and
Table 4 Determinants of early-stage entrepreneurial activity in African developing countries
(years 2001–2016)
Model number Model (1) Model (2) Model (3) Model (4)
Independent/
dependent
variable
Total early-
stage
entrepreneurial
activity (TEA)
Total early-
stage
entrepreneurial
activity (TEA)
New business
density (new
registrations per
1000 people ages
15–64)
New business
density (new
registrations per
1000 people ages
15–64)
Unemployment,
total (% of total
labour force)
1.048** 0.000890
(0.358) (0.0910)
Log(GDP per
capita)
0.293 1.474 3.206** 3.793**
(3.289) (1.718) (1.001) (1.271)
Economic
freedom index
0.132 0.561***
(0.401) (0.102)
Corruption per-
ceptions index
0.158 0.266***
(0.104) (0.0391)
Perceived
opportunities
0.636*** 0.105***
(0.0847) (0.0251)
Fear of failure
rate
0.398 0.0685
(0.259) (0.0870)
Constant 36.57 0.821 51.81*** 38.78**
(35.10) (15.24) (9.089) (10.33)
Year dummies Yes Yes Yes Yes
Observations 57 57 33 33
R
2
0.674 0.840 0.910 0.908
Adjusted R
2
0.520 0.770 0.849 0.853
AIC 415.9 373.4 140.6 139.4
BIC 446.6 402.0 158.5 155.9
Models were estimated with robust standard errors. Estimated models include fixed effects for
years. Standard errors are reported in parentheses. Statistical significance: +p<0.10, *p<0.05,
**p<0.01, ***p<0.001
Source: STATA 14, own calculations
Entrepreneurial Activity and Its Determinants: Findings from African... 19
fear of failure rate. The obtained results have empirically supported an assumption
that the more opportunities people perceive in the country, the higher is the level of
early-stage entrepreneurial activity because people are more willing to exploit these
business opportunities (Roman et al. 2018; Bosma and Schutjens 2011). Finally, the
variable representing fear of failure rate was not found to be statistically significant,
although the coefficient was found to be negative across both specifications. The
negative coefficient was expected in line with the previous literature because the fear
of failure is a strong factor that might prevent individuals from pursuing entrepre-
neurship career (Bosma and Schutjens 2011; Vaillant and Lafuente 2007).
7 Conclusions and Suggestions for Future Research
Development of entrepreneurial activity and its determinants in a cross-country
setting has been widely studied in developed countries in the past. We already
know from Freytag and Thurik (2007), Dvouletý (2017) and Roman et al. (2018)
that the trends and determinants of entrepreneurship change over time and across
countries. We also know that those findings from developed countries lead the
entrepreneurship research. However, are these findings applicable and transferable
for other continents and countries that have not been studied yet? Honestly, we do
not know, and we need to find this out empirically. Unfortunately, calls for replica-
tion (see, e.g. Davidsson 2015,2016,2017) and expansion for research to the
countries that have not received particular attention yet do not receive many
responses from the empirical researchers in the field and especially from the editors
and reviewers of the leading entrepreneurship journals who still focus mainly on
theoretical contributions.
We believe that we need to support all efforts resulting in discussion findings
from the countries that have not received that much attention yet as they might enrich
our field. One of the under-researched regions is Africa. Jones et al. (2018a,b,c)
have tried to encourage more scholars to enter the debate, and they have published
three interesting special issues of the Journal of Small Business and Enterprise
Development to move the discussion further and to enrich academia. However, there
is still a lack of studies studying the overall level of entrepreneurial activity and its
determinants in Africa. Therefore, we contributed to this body of knowledge from
the perspective of African developing countries. We demonstrate that most of the
African developing countries have not yet conducted Global Entrepreneurship
Monitor to study entrepreneurial activity in their country. Only 17 of 52 countries
have conducted GEM study at least once. Also, data from the World Bank,
Transparency International and Heritage Foundation offer a quite limited number
of available years for the classical determinants of entrepreneurship. Thus we would
like to encourage policymakers, representatives of agencies (such as the World
Bank) and scholars to collect more datasets reflecting the situation in developing
African counties.
20 O. Dvouletý and M. Orel
In this book chapter, we provide readers with an overview of the existing data,
and we show the overall entrepreneurial activity in a sample of 12 African
developing countries during the years 2001–2016. Collected data from Global
Entrepreneurship Monitor (2018) report the activity as on average 31% of the
18–64 population, consisting of 20.4% of the 18–64 population who are either
nascent entrepreneurs or owner-managers of new business and of 11% of the
18–64 population who were engaged in owning-managing established business
and at the same time receiving salaries, wages or any other payments for more
than 42 months. Given the high proportions of early-stage entrepreneurial activity
and existing data on new business density from the World Bank (2018), we studied
determinants of early-stage entrepreneurial activity deeper.
Estimating multivariate econometric models, we found that most of the variables
included in our analysis show a similar impact of independent variables on both
types of early-stage entrepreneurial activity. Although the number of available
observations limits our results, we find some empirical evidence showing the
importance of GDP per capita, unemployment rate, economic freedom index, cor-
ruption perceptions and perceived opportunities as factors influencing the early-
stage level of entrepreneurial activity. We would like to highlight from the obtained
findings the positive impact of perceived opportunities and negative influence of the
corruption perceptions on early-stage entrepreneurial activity.
We believe that our conducted analysis serves as an inspiration for future
research. When having more available datasets, the researchers could employ
other measures of entrepreneurial activity, such as established business ownership
rate (EBOR) from Global Entrepreneurship Monitor, self-employment rates from
national labour force surveys or rates of the registered business activity. Other
specific measures of entrepreneurship, such as high-growth, necessity/opportunity
driven, should be considered in the forthcoming research (c. f. Dvouletý 2018a). An
empirical review of the literature by Roman et al. (2018) and Dvouletý (2017) also
suggests several other interesting determinants of entrepreneurial activity to be
studied in the future. They include the role of foreign direct investments, access to
credit, education structure of the population or the role of research and development
(R&D) expenditures, employees and institutions. More sophisticated econometric
methods suitable for a dynamic empirical analysis (such as general methods of
moments, GMM, and vector autoregressive models, VAR, with impulse response
functions) should also be employed by the scholars. Finally, we believe that scholars
should also continue analysing entrepreneurial activity in Africa at the lower levels
of administrative units, such as regions or cities.
Acknowledgement This work was supported by the Internal Grant Agency of the Faculty of
Business Administration, University of Economics in Prague under no. IP300040.
Entrepreneurial Activity and Its Determinants: Findings from African... 21
References
Ajufo, B. I. (2013). Challenges of youth unemployment in Nigeria: Effective career guidance as a
panacea. African Research Review, 7(1), 307–321.
Aladekomo, F. O. (2004). Nigeria educational policy and entrepreneurship. Journal of Social
Sciences, 9(2), 75–83.
Atiase, V. Y., Mahmood, S., Wang, Y., & Botchie, D. (2018). Developing entrepreneurship in
Africa: Investigating critical resource challenges. Journal of Small Business and Enterprise
Development, 25(4), 644–666.
Awogbenle, A. C., & Iwuamadi, K. C. (2010). Youth unemployment: Entrepreneurship develop-
ment programme as an intervention mechanism. African Journal of Business Management, 4(6),
831–835.
Bosma, N., & Schutjens, V. (2011). Understanding regional variation in entrepreneurial activity and
entrepreneurial attitude in Europe. The Annals of Regional Science, 47(3), 711–742.
Bosma, N., Sanders, M., & Stam, E. (2018). Institutions, entrepreneurship, and economic growth in
Europe. Small Business Economics, 51,1–17.
Brixiová, Z., & Kangoye, T. (2016). Gender and constraints to entrepreneurship in Africa: New
evidence from Swaziland. Journal of Business Venturing Insights, 5,1–8.
Brixiová, Z., Ncube, M., & Bicaba, Z. (2015). Skills and youth entrepreneurship in Africa: Analysis
with evidence from Swaziland. World Development, 67,11–26.
Chigunta, F. (2017). Entrepreneurship as a possible solution to youth unemployment in Africa.
Laboring and Learning, 10, 433–451.
Chowdhury, F., Desai, S., & Audretsch, D. B. (2018). Introduction and overview. In Corruption,
entrepreneurship, and social welfare (pp. 1–8). Cham: Springer.
Davidsson, P. (2015). Data replication and extension: A commentary. Journal of Business
Venturing Insights, 3,12–15.
Davidsson, P. (2016). The power of replication. In Researching entrepreneurship (pp. 247–284).
Cham: Springer.
Davidsson, P. (2017). A future of entrepreneurship research: Domain, data, theory, and impact. In
G. Ahmetoglu, T. Chamorro-Premuzic, B. Klinger, & T. Karcisky (Eds.), The Wiley handbook
of entrepreneurship (pp. 1–23). Hoboken: Wiley.
DeJaeghere, J., & Baxter, A. (2014). Entrepreneurship education for youth in sub-Saharan Africa: A
capabilities approach as an alternative framework to neoliberalism’s individualizing risks.
Progress in Development Studies, 14(1), 61–76.
Dvouletý, O. (2017). Determinants of nordic entrepreneurship. Journal of Small Business and
Enterprise Development, 24(1), 12–33.
Dvouletý, O. (2018a). How to analyse determinants of entrepreneurship and self-employment at the
country level? A methodological contribution. Journal of Business Venturing Insights, 9(2),
92–99.
Dvouletý, O. (2018b). Determinants of self-employment with and without employees: Empirical
findings from Europe. International Review of Entrepreneurship, 16(3), 405–426.
Dvouletý, O., & Blažková, I. (2018). Entrepreneurship and corruption: Do corruption perceptions
influence regional entrepreneurial activity? The 12th International Days of Statistics and
Economics, pp. 433–440.
Dvouletý, O., Gordievskaya, A., & Procházka, D. A. (2018). Investigating the relationship between
entrepreneurship and regional development: Case of developing countries. Journal of Global
Entrepreneurship Research, 8(1), 16.
Dzisi, S., Odoom, F. D., & Gligah, B. (2018). Entrepreneurship training and skills development in
Africa: Evidence from Koforidua Technical University, Ghana. International Journal of
Economics and Business Research, 15(4), 509–523.
Edoho, F. M. (2015). Entrepreneurialism: Africa in transition. African Journal of Economic and
Management Studies, 6(2), 127–147.
22 O. Dvouletý and M. Orel
Efobi, U., & Orkoh, E. (2018). Analysis of the impacts of entrepreneurship training on growth
performance of firms: Quasi-experimental evidence from Nigeria. Journal of Entrepreneurship
in Emerging Economies, 10(3), 524–542.
Egbert, H. (2009). Business success through social networks? A comment on social networks and
business success. American Journal of Economics and Sociology, 68(3), 665–677.
Engelmann, R. (1994). Entrepreneurship and Africa. In African development and public policy
(pp. 67–85). London: Palgrave Macmillan.
Frese, M., & de Kruif, M. (2000). Psychological success factors of entrepreneurship in Africa: A
selective literature review. In Success and failure of microbusiness owners in Africa
(pp. 31–53).Westport, CT: Quorum Books.
Freytag, A., & Thurik, R. (2007). Entrepreneurship and its determinants in a cross-country setting.
Journal of Evolutionary Economics, 17(2), 117–131.
Fritsch, M. (2015). Methods of analyzing the relationship between new business formation and
regional development. In C. Karlsson, M. Andersson, & T. Norman (Eds.), Handbook of
research methods and applications in economic geography (pp. 303–320). Cheltenham: Elgar.
George, G., Corbishley, C., Khayesi, J. N., Haas, M. R., & Tihanyi, L. (2016a). Bringing Africa in:
Promising directions for management research. The Academy of Management Journal, 59(2),
377–393.
George, G., Kotha, R., Parikh, P., Alnuaimi, T., & Bahaj, A. S. (2016b). Social structure, reasonable
gain, and entrepreneurship in Africa. Strategic Management Journal, 37(6), 1118–1131.
Global Entrepreneurship Monitor. (2018). Entrepreneurial attitudes and behavior data. Retrieved
October 26, 2018, from https://www.gemconsortium.org/data
Harsch, E. (1993). Accumulators and democrats: Challenging state corruption in Africa. The
Journal of Modern African Studies, 31(1), 31–48.
Heritage Foundation. (2018). Economic freedom index. Retrieved October 26, 2018, from https://
www.heritage.org/
Hope Sr., K. R. (2016). African political economy: Contemporary issues in development. London:
Routledge.
Jones, P., Maas, G., Dobson, S., Newbery, R., Agyapong, D., & Matlay, H. (2018a). Entrepre-
neurship in Africa, part 1: Entrepreneurial dynamics in Africa. Journal of Small Business and
Enterprise Development, 25(3), 346–348.
Jones, P., Maas, G., Dobson, S., Newbery, R., Agyapong, D., & Matlay, H. (2018b). Entrepre-
neurship in Africa, part 2: Entrepreneurial education and eco-systems. Journal of Small Busi-
ness and Enterprise Development, 25(4), 550–553.
Jones, P., Maas, G., Dobson, S., Newbery, R., Agyapong, D., & Matlay, H. (2018c). Entrepre-
neurship in Africa, part 3: Conclusions on African entrepreneurship. Journal of Small Business
and Enterprise Development, 25(5), 706–709.
Khayesi, J. N., Sserwanga, A., & Kiconco, R. (2017). Culture as a facilitator and a barrier to
entrepreneurship development in Uganda. Entrepreneurship in Africa, 15, 307.
Kiggundu, M. N. (2002). Entrepreneurs and entrepreneurship in Africa: What is known and what
needs to be done. Journal of Developmental Entrepreneurship, 7(3), 239.
Kojo Oseifuah, E. (2010). Financial literacy and youth entrepreneurship in South Africa. African
Journal of Economic and Management Studies, 1(2), 164–182.
Kshetri, N. (2011). Institutional and economic foundations of entrepreneurship in Africa: An
overview. Journal of Developmental Entrepreneurship, 16(01), 9–35.
Kuada, J. (2010). Culture and leadership in Africa: A conceptual model and research agenda.
African Journal of Economic and Management Studies, 1(1), 9–24.
Kuada, J. (2015). Entrepreneurship in Africa—A classificatory framework and a research agenda.
African Journal of Economic and Management Studies, 6(2), 148–163.
Langevang, T., & Gough, K. V. (2012). Diverging pathways: Young female employment and
entrepreneurship in sub-Saharan Africa. The Geographical Journal, 178(3), 242–252.
Lashitew, A. A., & van Tulder, R. (2017). Inclusive business in Africa: Priorities, strategies and
challenges. Entrepreneurship in Africa, 15, 71.
Entrepreneurial Activity and Its Determinants: Findings from African... 23
Mahajan, V. (2011). Africa rising: How 900 million African consumers offer more than you think.
Upper Saddle River: Pearson Prentice Hall.
Mbaku, J. M. (1996). Bureaucratic corruption in Africa: The futility of cleanups. Cato Journal, 16,
99.
Meagher, K. (2005). Social capital or analytical liability? Social networks and African informal
economies. Global Networks, 5(3), 217–238.
Mohamadi, A., Peltonen, J., & Wincent, J. (2017). Government efficiency and corruption: A
country-level study with implications for entrepreneurship. Journal of Business Venturing
Insights, 8,50–55.
Munemo, J. (2012). Entrepreneurship in developing countries: Is Africa different? Journal of
Developmental Entrepreneurship, 17(01), 1250004.
Nafukho, F. M., & Helen Muyia, M. A. (2010). Entrepreneurship and socioeconomic development
in Africa: A reality or myth? Journal of European Industrial Training, 34(2), 96–109.
Ngunjiri, I. (2010). Corruption and entrepreneurship in Kenya. Journal of Language, Technology &
Entrepreneurship in Africa, 2(1), 93–106.
Nikolaev, B. N., Boudreaux, C. J., & Palich, L. (2018). Cross-country determinants of early-stage
necessity and opportunity-motivated entrepreneurship: Accounting for model uncertainty.
Journal of Small Business Management, 56, 243–280.
North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge:
Cambridge University Press.
Ratten, V., & Jones, P. (2018). Bringing Africa into entrepreneurship research. In African Entre-
preneurship (pp. 9–27). Cham: Palgrave Macmillan.
Rey, S. (2018). How Africa wins through design-centered and learned entrepreneurship.Retrieved
December 5, 2018, from https://www.iol.co.za/business-report/opinion/how-africa-wins-through-
design-centered-and-learned-entrepreneurship-18364475
Robson, P. J., Haugh, H. M., & Obeng, B. A. (2009). Entrepreneurship and innovation in Ghana:
Enterprising Africa. Small Business Economics, 32(3), 331–350.
Roman, A., Bilan, I., & Ciumaș, C. (2018). What drives the creation of new businesses? A panel-
data analysis for EU countries. Emerging Markets Finance and Trade, 54(3), 508–536.
Röschenthaler, U., & Schulz, D. (Eds.). (2015). Cultural entrepreneurship in Africa. London:
Routledge
Sriram, V., & Mersha, T. (2010). Stimulating entrepreneurship in Africa. World Journal of
Entrepreneurship, Management and Sustainable Development, 6(4), 257–272.
Stenholm, P., Acs, Z. J., & Wuebker, R. (2013). Exploring country-level institutional arrangements
on the rate and type of entrepreneurial activity. Journal of Business Venturing, 28(1), 176–193.
Transparency International. (2018). Corruption perception index. Retrieved October 26, 2018, from
https://www.transparency.org/research/cpi/overview
Tvedten, K., Hansen, M. W., & Jeppesen, S. (2014). Understanding the rise of African business: In
search of business perspective on African enterprise development. African Journal of Economic
and Management Studies, 5(3), 249–268.
United Nations. (2018). United Nations conference on trade and development: Classification of
countries. Retrieved October 26, 2018, from http://unctadstat.unctad.org/EN/Classifications.html
Urbano, D., Aparicio, S., & Audretsch, D. (2018). Twenty-five years of research on institutions,
entrepreneurship, and economic growth: What has been learned? SmallBusiness Economics,1–29.
https://doi.org/10.1007/s11187-018-0038-0
Vaillant, Y., & Lafuente, E. (2007). Do different institutional frameworks condition the influence of
local fear of failure and entrepreneurial examples over entrepreneurial activity? Entrepreneurship
and Regional Development, 19(4), 313–337.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT
Press.
World Bank Database. (2018). Statistical database. Retrieved October 26, 2018, from https://data.
worldbank.org
Zahra, S. A., Rawhouser, H. N., Bhawe, N., Neubaum, D. O., & Hayton, J. C. (2008). Globalization
of social entrepreneurship opportunities. Strategic Entrepreneurship Journal, 2(2), 117–131.
24 O. Dvouletý and M. Orel
