What Determines the Geography of Entrepreneurship? A Comparative Study Between Sub-Saharan Africa and South-East Asia

Abstract

There is a clear disparity between different regions of the world regarding the type and number of entrepreneurs. These differences are most prominent between low-income regions like Africa and middle- to high-income regions such as South-East Asia. Thirty years ago, Asian and African countries were at similar stages of development, but today their difference in entrepreneurship and economic development is massive, which makes them intriguing cases to compare. To investigate the extent of this and explain why it happens, this study identifies the main influences on entrepreneurial activity, according to entrepreneurship ecosystem (EE) theory and knowledge spillover theory. Making use of multivariate analysis of variance, the most prominent factors responsible for the difference in entrepreneurship capacity in the regions were found to be technology development, political situation and the quality of public institutions. South-East Asia provides these to a sufficient degree thereby giving rise to a healthy EE, while Africa’s ability to build this infrastructure is still in its embryonic stage. This study’s efficacy is to inform on possible policies that low- and middle-income nations can follow to build entrepreneurship in their current economic situations, as well as to expand EE theory in the underexplored context of developing countries.

Keywords

Entrepreneurship entrepreneurship disparity economic geography entrepreneurial geography Africa Asia

Introduction

The value of entrepreneurship as a ‘driver of economic dynamism’ has long been recognised by governments across the world. Entrepreneurs have consistently proven to be the answer to unemployment and poverty, even going further to influence the growth of the economy, shape regional industry and contribute significantly towards the fiscal performance of their countries (Acs et al., 2014; Civera et al., 2020; Larsson et al., 2017). Having said that, there exists a disparity in the spatial positioning of entrepreneurship across the globe. Some regions have more entrepreneurs than others, while others have more technocentric start-ups than others. The overall contribution of entrepreneurship to the economy varies by region as well (Álvarez-García et al., 2018). The birth rate of firms varies strongly by continental regions, which begs the question: what are the determinants of these disparities? The differences appear finely divided across high-income and low-income countries, prosperous regions and poor regions, global North and global south (Minniti & Naudé, 2010). These relative geographic demarcations of entrepreneurial activity show that the determinants of these differences are regional. Many existing studies regarding entrepreneurship at a regional level are conducted in western developed countries, and cannot be generalised to emerging economies and even less to low-income countries. What then is the regional context of entrepreneurship in other regions?

South-East (SE) Asia is home to several countries with fast-growing entrepreneurial scenes. It presented an ease of doing business score of 63.3/100 in 2018 with countries including India, China and South Korea, having not only the largest populations of entrepreneurs but also the largest proportion of high-tech and internet start-ups (World Bank, 2020). On the other hand, sub-Saharan (SS) African scooped an ease of doing business score of 51.8/100 with the largest economies on the continent, South Africa and Nigeria, having the largest populations of entrepreneurs which are in fact comparatively smaller than their Asian counterparts. The entrepreneurs in Africa are mostly identified as replicative entrepreneurs that are necessity driven and do not expand (Naudé, 2017). It is apparent that the quantities and quality of innovative entrepreneurs is not evenly divided across regions.

The main research questions to be addressed in this empirical study are:

RQ1: What factors influence the amount of and level of entrepreneurship in a region?

RQ2: How do these factors play out differently in Asia and Africa?

This comparative study will examine the differences between SE Asia where there exists robust entrepreneurship institution, and SS Africa where entrepreneurship is still in an emergent stage towards becoming an economic stronghold. It was anticipated that identifying the differences in the regions would further explain why some regions are more entrepreneurially oriented than others. This study is significant between the Asia and Africa for the subsequent reasons:

It can identify the exact socio-cultural, political and environmental reasons for the development of entrepreneurship in two distinct environments;

It can shed some light on and suggest appropriate policies for fostering entrepreneurship in Asia and Africa;

The results of this study will be applicable to emerging economies as well as low-income countries that are grossly under-represented in entrepreneurship literature; and finally

It will further the research on entrepreneurial ecosystems and their role in diverse regional contexts and regional development as well as why they fail, a gap brought to light by some studies (Malecki, 2018; Schäfer & Mayer, 2019).

The article will go on to give a theoretical foundation and review of relevant literature in the next section; define the variables and draw up a conceptual model, calculate the model using SPSS v23 in the third section, and finally discuss the results and give the policy recommendations in the subsequent sections.

Theoretical Foundation

The Concept of Entrepreneurship

The concept of entrepreneurship is still a hotly contested topic with several working definitions in use. This study accepts the definition put forward by Dau & Cuervo-Cazurra (2014), that states that entrepreneurship is the creation of a new and stable business venture; emphasis being on the newness of the venture. The defining characteristics of entrepreneurs include risk-taking, endurance, commitment and integrity towards a cause other than money (Graham, 2010; Kuratko, 2009). More often than not this cause relates to business opportunities and the state of the market and how best the entrepreneurial venture can disrupt them. Innovation is the driving force of entrepreneurs, and is the primary difference between them and small business owners. Smith and Chimucheka (2014) explain that many micro-, small- and medium-size enterprises owners and managers are often misidentified as entrepreneurs when they in fact operate replicative businesses that more often than not are necessity-driven. Such enterprises often have low-capital and low-technology usage, they do not expand and cannot be classified as productive entrepreneurship (Brixiová et al., 2015). This study will focus solely on entrepreneurs involved in innovative actions that make use of existing resources for new wealth creation capacity (Drucker, 2014). Such entrepreneurs’ strategies are aimed at expansion into new markets, new products and diverse methods of production and organisational structure.

Entrepreneurs can also be divided into formal and informal businesses (Adusei, 2016). Formal businesses are legally registered under the law while informal ones are not. To overcome the possibility of data constraints regarding unregistered firms, only formal ones are considered in this study. Finally, Naudé (2011) points out the three main elements of entrepreneurship as resource coordination, new enterprise creation and innovation. This almost perfectly encapsulates all the aforementioned attributes by putting together the notions that it is a dynamic process requiring creative energy towards a vision, and implementing new ideas and novel solutions through the use of calculated risks, effective teams, skilful use of resources and tactical execution of the business plans.

Entrepreneurship Geography Theories

Given the conventional acknowledgement that entrepreneurship is a progression of cooperative, network-based activities, the theory of motivation states that entrepreneurial intention is encouraged or discouraged by a mix of both exogenous factors and endogenous factors (Audretsch et al., 2006). The scope of this study largely covers the exogenous factors and to lesser extent the endogenous factors. The knowledge spillover theory of entrepreneurship (KSTE) proposes that majority of entrepreneurial activity is either a spin-off, spillover or start-up, all of which have their genesis knowledge gained from another organisation. These are mainly universities, research centres and other incumbent firms (Audretsch & Lehmann, 2005; Siegel & Wright, 2015). Resultant ventures will evolve in similar or related industries making use of uncommercialised ideas, and for this reason tend to stay within spatial proximity of one another. This allows entrepreneurs to receive guidance, management and technical assistance as well as resource sharing from strong existing players, thus keeping a strong innovation process in motion. It also gives entrepreneurs access to highly skilled human capital as well as well-connected social capital (Elston et al., 2016). This theory has been used in formulating the framework of this study, because the agglomeration of innovative entrepreneurs is spatially isolated with national borders. Though not absolutely, it is to a large extent, and the regional locales of KSTE are quite visibly definitive (Glaeser, 2007). This therefore shows that KSTE is the lever of entrepreneurship, determining why entrepreneurs start-up their businesses, how they discover opportunities and how they are able to take advantage of them. Their proximity to research centres and incumbent firms means that within their spatial context they have access to research and development (R&D), funding and population employment experience, all of which boosts overall entrepreneurial activity and generates even more entrepreneurial opportunities (Qian & Acs, 2013; Tavassoli et al., 2017).

Closely related to this, Larsson et al. (2017) describe the phenomenon of local embeddedness, which is an intersection of sociology studies and economic geography research. It states that because entrepreneurship does not occur in a vacuum, it is affected by existing local environment (Arenius & Minniti, 2005). This concept leads to entrepreneurship ecosystem (EE) theory. An EE is a ‘self-organizing, adaptive, geographically bound community of multifaceted agents working at numerous aggregated levels, whose non-linear exchanges result in patterns of activities from which entrepreneurial ventures form over time’ (Roundy et al., 2018; Schäfer & Mayer, 2019). Following the domains proposed by Isenberg (2010), the EE goes beyond a simple social context but encompasses policy, finance, culture, support, human capital and markets in an absolute geographic location, whether industrial region, county, city, country or even continental regions, as is the case here. This is to say the environmental factors in which entrepreneurs find themselves determines the outcome of their entrepreneurial intention and entrepreneurial ability. Going further, because it has not been proven explicitly the EEs are exclusively restricted to national borders, therefore, ceteris paribus, internal institutional environments can ideally affect those of neighbouring countries, or spillover, thereby developing regional knowledge systems that over time through multi-lateral collaborations can evolve to become regional innovation systems described by Cooke (2005). Knowledge flows in inter-regional networks are important, especially for innovative activities between talented individuals, even if most of the connections are localised within national borders.

This theoretical view of EE having a regional spatial interaction was also considered in the formulation the framework for this study, as it can explain how countries in one region may all present with well-developed EEs that positively influence entrepreneurial activity, and developing countries that share regional borders will have correspondingly rudimentary level EE’s that can only facilitate low levels of entrepreneurial activity (Malecki, 2018).

Entrepreneurship in SE Asia

According to the availability of the data, a random sample of six countries from each region (giving a total sample of 12 countries) was chosen for this study and these were namely: South Africa, Ghana, Uganda, Botswana, Algeria and Ethiopia from Africa, and China, India, Republic of Korea, Malaysia, Thailand and Indonesia from SE Asia.

SE Asia which is part of the Asia-Pacific region is made up of mostly middle-income and high-income countries. This sub-region is a particularly intriguing case, because many of the countries have recently (last 30–35 years) attained this status (Jones & Lee, 2018). It is now considered one of the hotspots for thriving entrepreneurship in the world, from being farming economies, that were heavily reliant on agriculture, low technology and manufacturing.

The Republic of Korea (Korea henceforth) has experienced a rapid increase in entrepreneurial activity, thanks to the intentional and conscious efforts of the government to promote innovation (Moskovitch & Kim, 2008). These have been far-reaching in human resource, finance and cultural outlook for entrepreneurship. By evolving from a fast follower to a fast mover strategy, Korea has developed a thriving EE for its entrepreneurs (Bae, 2013).

China, an ‘economic miracle’ (P. C. Huang, 2009), has various ministries to support entrepreneurship and innovation initiatives. The growth from 0 to 6 million registered firms in 20 years has been made possible by the transition that allows more freedom for businesses in government regulations, financial systems, property rights and ownership structure. However, they also face a large amount of uncertainty as the ecosystem is still developing. The success of existing entrepreneurs has nurtured a positive outlook of entrepreneurship (He et al., 2019).

Malaysia has a thriving entrepreneurship scene. It has experienced a state-led industrialisation (Abdullah & Muhammad, 2008) that provides finances and physical infrastructure and programmes such as the National Entrepreneurship Policy and Malaysia Plans, to emphasise entrepreneurial activities and promote an entrepreneurial culture (Bank Negara Malaysia, 2016). According to Global Entrepreneurship Monitor (2019), of the conditions necessary for entrepreneurship, Thailand’s framework is mixed. While market openness, physical infrastructure and access to finance are all above average, entrepreneurial education, government policies and programmes are limited and in some cases are even punitive (Yanya et al., 2013).

Indonesia has initiated a three-pronged approach towards entrepreneurship (Mirzanti et al., 2015). At the micro-level, government ministries all work in concert towards a set entrepreneurship policy, making use of training programmes and business incubators. At the meso-level they have enacted laws to encourage and protect entrepreneurs, provide incentives for tech businesses and improve general ease of doing business. At the macro-level government and private sectors work together to provide entrepreneurs with soft loans, training and venture capital funding under the National Entrepreneurship Movement. India is also pursuing a government-led development of entrepreneurship through policies, to promote and reward entrepreneurship. These include government programmes to offer training and capital subsidies for start-ups (Ahuwalia 2002). Several campaigns have also been launched to change the cultural outlook of entrepreneurs, which traditionally is seen as less desirable than working in government job (Sinha, 2003).

Entrepreneurship in SS Africa

SS Africa is a diverse region and no two countries have the same entrepreneurship scene. Having said that, many countries share similar economic history of civil unrest, economic depression and structural adjustment programmes, and as a result many present with environments that are not entirely conducive for entrepreneurs. It is also a popular conception that African cultures are not supportive of entrepreneurship (Munene, 1997).

South Africa, one of the most developed economies on the continent, has several policies aimed at promoting micro, small and medium-size enterprises creation but not necessarily entrepreneurship. These include government efforts in finance provision, legislation reform and incentive (Geitlinger, 2016). Despite these, Berry et al. (2002) connote that the inability of entrepreneurs to transition through innovation cannot be explained. The EE is sluggish, difficult to navigate and doesn’t enable entrepreneurs to link up with the assistance they need. Entrepreneurial intention is also less prevalent in lower-income households and lower levels of educational attainment.

Ghana is one of the fastest-growing markets in Africa and its entrepreneurs find that it is fairly easy to open a business (Connors & Press-Williams, 2016). However, physical infrastructure is still quite rudimentary in provision of electricity and internet. Access to finance remains a challenge to an extent that makes it ‘impossible’ to receive start-up funding.

Uganda has one of the highest total entrepreneurship activity (TEA) in the world with a significant portion of that being opportunity driven. Cultural outlook of entrepreneurship is positive and its entrepreneurs have the lowest fear of failure in the world. Easier access to funding, education and training are the last strands needed to push efficiency and innovation and give entrepreneurs a thriving system in which to flourish (Global Entrepreneurship Monitor Uganda, 2004).

Botswana has had unprecedented government-led programmes (Assan, 2012) to improve entrepreneurship policies in education, training and financing. It provides a more conducive EE for its citizens than most other countries in the region, partly because it does not share the same history of recurring civil unrest and structural adjustment programmes. However, the entrepreneurial activities are being undermined by negative cultural outlook of entrepreneurship that considers it as a last resort when one cannot secure formal employment and is therefore undesirable. (Josiah & Themba, 2015)

Algeria exhibits a low number of entrepreneurs for its level of development. The government has ongoing entrepreneurship policy, supporting with finances and training. However, a clear gender disparity still exists with fewer women getting involved. There exists a negative cultural outlook towards entrepreneurship, which in turn discourages entrepreneurship. Ethiopia has an SME development plan that faces strong adversity; growth and innovation are almost negligible (Singh & Belwal, 2008). Unlike other countries, entrepreneurs have led a revolution and currently contribute over 80% of economic growth. Not only is there a dearth of government programmes and policies that support entrepreneurs, but the government is viewed as standing in the way on entrepreneurial development (Issays, 2005)

Summary of Literature

This study will identify the explicit factors that contribute towards the prevailing levels of entrepreneurship in the two heterogeneous regions, using the aforementioned countries as samples. The results of the study will contribute to the understanding of global entrepreneurship culture by what entrepreneurs with diverse backgrounds and cultural characteristics as well as different levels of entrepreneurship experience in their unique geographical contexts. The indicators found in literature to investigate are summarised in Table 1.

Table 1.

Literature Review of Indicators.

Type of Variables	Indicators (Abbreviation)	Literature
Independent variables: Socio-cultural, techno-economic environment	Birth potential of country (BP)	Dahl and Sorenson (2009, 2012); Kolympiris et al. (2015)
	Proximity to knowledge sources and related industries (KS)	Audretsch (2018); Audretsch et al. (2006); Autio et al., (2018)
	Access to finance (FN)	Ekpe et al. (2010); Reshetnikova (2018); Wellalage et al. (2019); Wu (2018)
	Size of labour force (LF)	Benjamin et al. (2010); Siegel and Wright (2015); Yeung (2009)
	Number of R&D employees (RDE)	Naudé (2017); Wilbanks (2013)
	Access to amenities (AM)	Atiase et al. (2018); Naudé (2011); Temtime and Pansiri (2004)
	Speed of internet (IN)	Batjargal (2007); Hisrich et al. (2002); Naudé (2017)
	Amount of electricity consumed (EL)	Atiase et al. (2018); Naudé (2017); Vernet et al. (2019)
	Ease of doing business (EoB)	Asongu et al. (2018); Walsh and Winsor (2019)
	Starting procedures (SP)	Ciccone and Papaioannou (2007); Klapper et al. (2007)
	Time to start a business (TS)	Ciccone and Papaioannou (2007)
	Protection of IPR (PIP)	Acs and Sanders (2008, 2012); Anthony and Adegbola (2017); Sarkar (2016)
	Socio-cultural support of entrepreneurship (SCS)	Brixiová et al. (2015); Halkias et al. (2011)
Mediating variables: Institutional environment	Government policies (GPY)	Adusei (2016); Atiase et al. (2018); Chowdhury et al. (2015); Wu (2018); Zou and Liang (2015)
	Government procedures (GPD)	Akinyemi and Adejumo (2018); Kshetri and Dholakia (2011); Madzikanda et al. (in press)
	Government programmes (GPG)	Bloom and Van Reenen (2010); Isenberg (2010)
	Corruption (CO)	Dreher and Gassebner (2013); Dutta and Sobel (2016); Méon and Weill (2010)
Dependent variables	Total entrepreneurial activity (TEA)	Bosma (2013); Iversen et al. (2008); Lepoutre et al. (2013)
	New business density (NBD)	Boudreaux et al. (2019); Chambers and Munemo (2019); Kasseeah (2016)

Source: The authors.

Note: IPR: Intellectual Property Rights.

The relationship of the factors is drawn up in the conceptual model in Figure 1.

Figure 1.

Source: The authors.

Research Method

Data and Methodology

In order to meet the main objective of the study, the country datasets of the aforementioned 12 countries were analysed in two groups representative of the two regions under comparison. All the data for relevant indicators were collected from secondary sources, Global Entrepreneurship Monitor (2019) and World Bank (2020) for the most recent data available. These were chosen because the data have been collated to a standard degree and are assumed comparable. It is acknowledged that these data may not always be accurate but this study considers them to be valid (Civera et al., 2020). These data were compiled, organised and used for statistical analysis. Firstly, a factor analysis was conducted to check for construct validity of each factor to check for the relevance of each, the results for which are in Table 2.

Table 2.

Results of Factor Analysis.

Factor	Eigen Value	Indicators	Factor Loading	Variable Definition
1	7.324	Birth potential	0.621	Socio-cultural factors influencing entrepreneurial activity
		Knowledge source	0.742
		Access to finance	0.728
		Labour force	0.610
		Time to start	0.813
		Protection of IP	0.582
		Socio-cultural support	0.549
2	4.415	Consumption of electricity	0.766	Techno-political factors influencing entrepreneurial activity
		Corruption	0.788
		Ease of doing business	0.527
		Starting procedures	0.644
		R&D employees	0.709
		Access to amenities	0.869
		Speed of internet	0.652
3	1.951	Government procedures	0.696	Institutional mediators of entrepreneurial activity
		Government programmes	0.644
		Government policies	0.860
4	1.475	Total entrepreneurial activity	0.508	Measure of entrepreneurial activity
		New business density	0.546

Source: The authors.

Note: IP: Intellectual property.

From the factor analysis, we extracted these variables and their respective operational definitions are given as follows:

Socio-cultural factors are the attributes regarding cultural outlook and affordances that each geographic region offer its local entrepreneurs (Walsh & Winsor, 2019).

Techno-political factors are the unique factors, that are as a result of the country’s level of development, that give advantages or disadvantages to local entrepreneurs (Castaño-Martínez et al., 2015).

Institutional mediators are the government or public service conditions by which entrepreneurs are either enabled or discouraged (Madzikanda et al., in press).

The measure of entrepreneurial activity will approximately give the amount of entrepreneurial activity that prevails in the current conditions (Bosma, 2013).

Once the efficacy of each is confirmed, multivariate analysis of variance (MANOVA) was performed to identify if any significant differences exist among the two groups, and to what extent these differences occur and finally a mean score analysis to determine the exact factors in each variable that account for the differences between the two groups. This multivariate test is the most appropriate because it has the power to detect even subtle differences between a combination of factors for two groups and has a better chance of discovering which factor is truly important.

Model Calculation

In order to determine whether MANOVA was an appropriate analytical method to investigate if the factors chosen have an impact on entrepreneurial activity in both SE Asia and SS Africa, a correlation analysis was performed. As the results in Table 3 show, MANOVA can be used, as most of the factors show significant interrelations amongst each other.

Table 3.

Correlations Among the Variables.

	Socio-cultural	Techno-political	Institutions	Entrepreneurial Activity
Socio-cultural	1.00	0.473*	0.862**	0.791**
Techno-political		1.00	0.792**	0.332*
Institutions			1.00	0.368**
Entrepreneurial activity				1.00

Source: The authors.

*p < .05, **p < .01, ***p < .001.

Differences Between the Regions

Prior to performing MANOVA, Box’s M test was done to test the homogeneity of variance between the two groups of countries. As the result of this was not significant, it was concluded that MANOVA was a suitable method, the result of which are tabulated in Table 4.

Table 4.

MANOVA Results.

SE Asia			SS Africa
Box’s M test	3.153		Box’s M test	4.581
Sig	0.950		Sig	0.230
		Mean Values
Socio-cultural	3.138		Socio-cultural	2.774
Techno-political	4.613		Techno-political	3.837
Institutions	4.102		Institutions	2.374
Entrepreneurial activity	3.414		Entrepreneurial activity	2.447

Source: The authors.

Note: *p < .05, **p < .01, ***p < .001.

SE Asia has higher mean values suggesting that the ecosystem components, measured here on a scale of sufficiency where 1 means insufficient and 5 means highly sufficient, are mostly sufficient to support high levels of entrepreneurial activity. To test the difference of the effect of these variables on each of the regions, Table 5 shows the differences of the effects.

Table 5.

Test of Between Subject Effects.

	Type III Sum of Squares	df	Mean Square	F	Sig.
Socio-cultural	1.836	7	0.003	1.836	0.242*
Techno-political	19.235	7	23.188	3.532	0.034**
Institutions	25.866	8	62.933	8.203	0.001**

Source: The authors.

Note: *p < .05, **p < .01.

As seen in Table 5, there was a statistically significant difference in importance towards entrepreneurship in terms influence, with technopolitical factors and institutions being of greater importance than socio-cultural ones at the α-level of significance of 0.05. This table also shows that techno-political factors and institutional factors differentiate the regions the most. Going further, the mean scores of the indicators of these variables were computed in order to determine which specific indicators differentiate the regions the most, thus allowing meaningful conclusions and recommendations to be drawn up. The results thereof are shown in Table 6.

Table 6.

Mean Score Analysis.

Indicator	Scale	ASIAMean	Std Dev.	AFRICAMean	Std Dev.
TP1	Number of R&D employees	6.988	1.352	4.934	1.360
TP2	Access to amenities	1.383	0.073	1.137	0.68
TP3	Use of internet	9.272	0.580	8.039	0.417
TP4	Corruption	4.236	0.360	4.384	0.479
TP5	Electricity	3.168	1.019	6.761	1.475
TP6	Ease of doing business	7.868	1.018	4.758	0.275
TP7	Number of start procedures	1.812	0.527	2.278	0.243
IM1	Government programmes	1.136	0.125	1.031	0.155
IM2	Government procedures needed	0.138	1.137	2.176	0.646
IM3	Government policies	1.099	0.091	0.896	0.097

Source: The authors.

Note: TP: Techno-political factors. IM: Institutional mediators.

The results in Table 6 show that of the techno-political factors, the number of R&D employees, the ease of doing business and the consumption of electricity show the largest differences between the regions. For the institutional factors, government procedures and government policies have the largest disparity. From this, it can be stipulated that these factors are largely responsible for the differences in EEs in the regions.

Results and Discussion

The results of the MANOVA test indicate that the SE Asian countries present larger mean variables for the variables than do the SS African ones. This is interpreted to mean the socio-cultural, techno-political and institutional factors chosen for the study are sufficient to support a well-established EE in Asia. This is in support of the propositions made by Bosma (2013), who emphasises the holistic look at the EE. Having been adjusted to account for differences in general population sizes through the log function, the African countries display above-average means. This is to say, although the EE is not well defined or established yet, the relevant factors are already working in concert towards it (Autio et al., 2014; Malecki, 2018).

Table 5 shows that the techno-political and institutional factors are the ones that differentiate the two regions the most. Though socio-cultural factors also differentiate them, statistically their influence is small. This suggests the poor cultural outlook and social support towards entrepreneurship in African societies, discussed by Munene (1997), is diminishing and there is greater support for entrepreneurship as a career choice. Techno-political factors are shown to be an imperative contributor to the differences in the EE in these regions by providing efficient production systems and more opportunities (Zhang & Dodgson, 2014). The institutional factors have a greater positive influence on entrepreneurship in Asia than in Africa (Assan, 2012). This is likely as a result of heavy involvement of the state in private sectors which in fact promotes entrepreneurship in countries such as Korea, China and India (Kshetri & Dholakia, 2011; Moskovitch & Kim, 2008)

From these two factors, it was derived in Table 6 that the most differentiating aspects are the number of R&D employees, ease of doing business, consumption of electricity, government procedures needed to start a firm and national government policies. The Asian countries highly enforce entrepreneurship education as well as education in science, technology, engineering and mathematics fields, so it follows that they have a correspondingly large number of R&D employees (Abdullah & Muhammad, 2008; Q. Huang et al., 2016). The African counterparts do not currently have this emphasis on scientific education, so the number of innovation employees is statistically less significant (Kew et al., 2013). However, in Uganda and Botswana several youth-oriented programmes are underway designed to encourage education in entrepreneurship and other specialised skills (GEM, 2020). Access to electricity and even internet conjures up the issue of the development of physical infrastructures that can support entrepreneurship activities. P. C. Huang (2009) and Bank Negara Malaysia (2016) show that in Malaysia and China and even Korea there are intentional efforts to improve physical infrastructures in order to promote innovation and enhance the performance of entrepreneurs. By contrast (Issays, 2005), the inability of governments to prioritize the building of physical infrastructure in the form of electricity, internet, transport and housing, leaves entrepreneurs with limited resources to work with (Connors & Press-Williams, 2016).

This point leads to examining the government procedures and policies. While the Asian countries experience substantial government involvement in entrepreneurship activities, the opposite is true for the African countries. A larger mean number of government procedures needed in the process of opening a firm, mean entrepreneurs in Africa face cumbersome administrative processes before they can begin their ventures, which are not only time consuming and frustrating but also waste money and deter entrepreneurs (Madzikanda et al., 2021). As told by Berry et al. (2002), government policies in support of ‘entrepreneurship’ exist in several African countries; however, in practice they only support small and mid-size business with little to no innovation and growth capacity. Other regressive policies include poor financial systems that limit financing options and legislation that often penalises entrepreneurial activities through high taxation.

Conclusions

Previous studies have almost always looked at these two regions separately making conclusions that are largely negative for SS Africa. This study analysed the differences between them to determine how SS Africa could achieve what SE Asia has. The results imply that firstly, the socio-cultural context of the two regions is different, but the disparity is not very large. The social and cultural contexts that the entrepreneurs face in the two regions is not very different, therefore it cannot significantly account for the differences in entrepreneurship levels. Second, the results show that technology capacity, political policies and the quality of the public institutions account for the largest differences and will therefore need the most attention on the side of SS Africa.

Based on these results, it can be concluded that disparity of entrepreneurial activity in the two regions can be attributed to socio-cultural context to a small extent and techno-political and institutional factors to a large extent. While both regions have similarly supportive cultures and social perspectives, their level of technology infrastructures and quality of leadership rendered by public institutions is poor in SS Africa or rather is still at a stage of infancy; hence, their ability to develop a strong entrepreneurial ecosystem, such as the ones in SE Asia, remains low for now.

Policy Implications

The results confirm that entrepreneurship has different roles to play in the two regions because of their developmental stage. While in SE Asia’s emerging middle-income and high-income countries it contributes largely towards gross domestic product and economic growth through innovation, in SS Africa’s middle-income and low-income countries it focuses on building production efficiencies and infrastructures to achieve wealth. This study recommends that governments of the southern African countries work deliberately towards thriving developing entrepreneurial ecosystems. This can be through building of physical infrastructure, favorable policies as well as provision of finances. Policies that reward and encourage entrepreneurship should be implemented at every level and physical infrastructure that can support transport, communications, manufacturing and science, technology, engineering and mathematics fields should be prioritised in spending. The authors believe such policies can allow SS African countries to achieve the accelerated success that SE Asia has witnessed in a similar time frame.

Limitations and Recommendations for Further Study

This study relies on secondary data for comparison which may not necessarily be accurate or representative of the regions. Further research may consider collecting primary data through questionnaires for analysis. Such data must control for factors such as age, gender and level of education to allow for a fair comparison between the regions.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID ID

Beverlley Madzikanda

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