The reliance of African countries on traditional industries, and persistently high unemployment rates have all contributed to the continent’s economic difficulties, and sparked interest in its economic growth. One of the main factors influencing productivity and achieving inclusive growth is perceived to be bridging the infrastructure gaps and encouraging entrepreneurial activities. However, limited evidence exists about the cross-country effects of infrastructure, entrepreneurship and economic growth in the African context. The purpose of this study is to assess the effects of infrastructures on the entrepreneurship-growth nexus.
The authors use both the static and dynamic panel regressions to examine the influence of infrastructure development and its components on the entrepreneurship-growth nexus in a sample of forty-two African countries between 2006 and 2022.
Empirical results reveal that infrastructures play a significant role in improving the impact of entrepreneurship on economic growth. Specifically, the findings show that water and sanitation infrastructure have the most significant impact on strengthening the effect of entrepreneurship on economic growth, followed by transport, electricity and information and communication technology infrastructure, respectively. This highlights how important but frequently disregarded water and sanitation infrastructure is for promoting economic growth and entrepreneurship.
This paper’s main policy implication is to integrate infrastructure and entrepreneurial development in policymaking because of their interdependent consequences, which are essential for promoting sustainable economic growth.
This study contributes to the current literature by examining the roles of infrastructure development and its components in strengthening the influence of entrepreneurship on economic growth in the African context.
1. Introduction
Interest in economic growth is growing especially swiftly in Africa because the continent relies mainly on traditional sectors, lacks economic diversification and has persistently high unemployment rates (African Development Bank – AfDB, 2024; Gokhool et al., 2024). Due to this challenging environment, Africa’s real GDP growth has recently slowed, dropping from 4.2% in 2021 following the COVID-19 rebound to the lowest of 2.8% in 2023 (World Bank, 2024). Historical growth rates have not been able to keep up with population growth; hence, per capita GDP has barely increased. There has not been a major structural change because most countries rely on traditional, low-productivity sectors like agriculture or low-skilled services for jobs and economic growth (AfDB, 2024).
In the past year, however, the majority of African nations have seen an upswing in growth, which has given the continent a new commercial vibrancy (Adomako et al., 2024). In 2024 and 2025, the continent’s real GDP is predicted to expand by an average of 3.8% and 4.2%, respectively. The continent continues to be the second-fastest-growing region after Asia in 2024, and it is home to 11 of the top 20 fastest-growing economies in the world (AfDB, 2024). Notwithstanding the continent’s massive infrastructure gaps in areas like energy and transportation, which have been a significant barrier to increasing intra-regional trade in Africa (Green, 2023; Gokhool et al., 2024). Infrastructures like water, sanitation and information and telecommunication technology (ICT) have been among the key factors contributing to the recovery (Ochieng et al., 2023).
Goals 6, 7 and 9 of the Sustainable Development Goals (SDGs) for 2030 (United Nations, 2015) include providing universal, dependable, sustainable and affordable access to infrastructure. Many African nations have prioritized this by diversifying their economies toward high-tech and creativity, which will reduce their reliance on extractive industries (Acheampong and Menyeh, 2023; Ajmi et al., 2024). Without question, having access to dependable, reasonably priced and environmentally friendly infrastructural facilities is a prerequisite for both economic growth and the provision of modern services. Evidence has also demonstrated that infrastructure significantly lowers the obstacles to communication, networking and information sharing, all of which are critical to entrepreneurial endeavors (Ma et al., 2021).
In addition, it is often acknowledged that entrepreneurship is a vital force behind economic expansion, promoting innovation, generating employment and improving productivity (Dvouletý, 2017; Matenda et al., 2023; Ordeñana et al., 2024). However, African nations have yet to fully embrace the potential of entrepreneurship to support long-term, sustainable economic growth and development. This is because a number of contextual factors, including infrastructure, frequently affect how strongly and consistently entrepreneurship and economic growth are related. By lowering operational hurdles, enhancing market accessibility and promoting the spread of innovation, infrastructure plays a crucial role in supporting entrepreneurial endeavors (Aggarwal and Joshi, 2024; Mugano and Dorasamy, 2024).
Scholars have looked into the factors that drive the development of entrepreneurial activities to actualize their potential effects on economic growth and create suitable policies (Omri, 2020; Dutta and Meierrieks, 2021; Sanga and Aziakpono, 2025). Others have examined the influence of infrastructure on entrepreneurship (Ajide, 2020; Pelz et al., 2023; Qian et al., 2024; Sun et al., 2024) and economic growth (Noah, 2021; Owusu-Manu et al., 2017; Syadullah and Setyawan, 2021). Infrastructure has also been related to many economic variables, including growth, finance and governance, among others (Cédric and Emmanuel, 2024; Noah and David, 2024a; Noah and David, 2024c) . However, despite its importance, the moderating role of infrastructure in the entrepreneurship-growth nexus remains underexplored in the existing literature.
In this way, our research fills in current empirical gaps and tackles important issues about how infrastructure, entrepreneurship development and economic growth interact in Africa. In particular, it explores the role of infrastructure in economic growth and whether entrepreneurship development promotes economic growth. The study also looks at how infrastructure development shapes the relationship between entrepreneurship and economic growth and evaluates whether these two areas work in tandem or in opposition to one another. We further investigate how each infrastructural component enhances the influence of entrepreneurship on economic growth in Africa, since each of these indicators does not have the same effect on entrepreneurship and economic growth. Notably, there are only a few concurrent examinations of these dynamics in the African setting in the research that currently exists, which is crucial for guiding regional entrepreneurship strategies. The combined consequences of infrastructural development, entrepreneurial and economic growth in Africa are thus being objectively evaluated in this study.
Also, this study used the panel-corrected standard error (PCSE) to address the problem of cross-sectional dependency, and the system generalized method of moments (SGMM) to address the problem of endogeneity as well as the robustness check. Considering the interdependence of countries in trade, economics and finance, ignoring slope heterogeneity and cross-sectional dependency in panel data may induce bias and inconsistency in the estimations in this research. To choose suitable estimating methods if cross-sectional dependency is detected in the panel data, it is required to test for it. Last but not least, in contrast to previous studies, this research highlights Africa’s distinct economic and technological environment, providing customized findings that represent the continent’s opportunities and limitations in using infrastructure development for economic growth and entrepreneurship. Section 2, which covers the literature review, is part of the remainder of this study. The study’s data and methodology are presented in Section 3. The findings and a discussion of the empirical findings are presented in Section 4. We offer the study’s conclusion and recommendations in Section 5.
2. Review of related literature
Entrepreneurship is a transformative force, it accounts for 90% of businesses globally and uses more than half of the world’s workforce, creating jobs, stimulating innovation and spawning more businesses (LaRock and Hemmy, 2025). The process of “creative destruction,” in which new industries arise and current firms are displaced, causes new enterprises to disrupt markets at the industry level (Schumpeter, 1942). Woolley (2017) asserts that new goods, services, or even process improvements brought about by entrepreneurial endeavors also cause market disruptions. Governments then profit from the tax money that is raised as a result of the sale of goods and services, corporate profits and employment income, all of which fund economic spending. Entrepreneurship benefits societal transformation in addition to the bottom line. Entrepreneurs are increasingly taking on significant social issues like human rights, education, poverty and the environment. Therefore, the competitiveness of nations, regions and cities is influenced by the performance of emerging businesses, which is crucial for economic well-being, innovation and social prosperity (Amran et al., 2023; Sharma et al., 2024). Moving from this descriptive recognition of the significance of entrepreneurship to its accurate incorporation into formal models of economic growth presents a theoretical difficulty. For example, neo-classical growth theory holds that technological advancement is the key to long-term growth and that output growth is a function of labor, capital and technological advancement (Dornbusch et al., 2011). Despite its strength, this paradigm frequently views technological advancement as an exogenous “black box.” A crucial opportunity is presented here: entrepreneurship can be seen as the main force behind this total factor productivity, particularly when paired with enabling infrastructure.
There have been attempts to include entrepreneurship in growth models. Schumpeter (1912) investigates the relationship between economic growth and entrepreneurship in his seminal work. He concludes that entrepreneurship fosters economic expansion by turning creative concepts into new products and services that create jobs and gross fixed capital development. Porter’s (1990) claim that entrepreneurship is “at the heart of national advantage” lends credence to this view. It is crucial to put innovations into practice. The contribution of entrepreneurship to economic advancement has been the subject of numerous recent debates. The ability of entrepreneurs to put ideas into practice and encourage competition is what drives economic progress. Both the female workplace and female entrepreneurship indicators have a beneficial effect on economic growth (Chikh-Amnache and Mekhzoumi, 2023). As economic growth is positively correlated with innovative entrepreneurship (Ordeñana et al., 2024), and as the emergence of women’s entrepreneurship indicates unrealized potential for progress and wealth in many nations, Tahir and Burki (2023) supported the conclusion that entrepreneurship significantly and favorably affects economic growth in the BRICS economies.
Despite this growing body of evidence on the entrepreneurship-growth nexus, a significant gap persists. It is still more descriptive than analytical to incorporate entrepreneurship into growth models. Research like Ordeñana et al. (2024), Tahir and Burki (2023), and others frequently cite the relationship between GDP growth and entrepreneurial indices without thoroughly analyzing the underlying mechanisms or resolving conflicting conclusions. Additionally, they emphasized self-employment as a proxy for entrepreneurship. For instance, how does Schumpeter’s theory of the market-disrupting force operate differently in developed and emerging economies? How much importance does capital accumulation have in relation to entrepreneurial activities at various stages of growth? A comparative critique that integrates various viewpoints into an analytical framework that can be tested is often absent from the literature.
This study offers a more nuanced theoretical model in an attempt to close this gap. Our approach is distinct not merely in acknowledging entrepreneurship’s role, but in analytically decomposing it into specific, measurable channels such as its interaction with infrastructure development to enhance total factor productivity and contrasting its impact under varying institutional conditions. Furthermore, our study differs in terms of the measurements of entrepreneurship. To ensure reliable estimations, this study uses a superior measure of entrepreneurship from the World Bank data set called “new entry density,” which captures the crucial component of entrepreneurial venturing and is accessible for the chosen African countries. This study seeks to offer a better, more predictive framework for comprehending economic growth in the 20-first century by going beyond a list of positive links to a rigorous analysis of how and when entrepreneurship propels growth. Therefore, given the evidence of a positive relationship between entrepreneurship and economic growth, our first hypothesis is as follows:
Entrepreneurship positively influences economic growth in Africa.
Efforts have also been made by scholars to examine the infrastructural effects on economic growth. Owusu-Manu et al. (2017) found a statistically significant correlation between Sub-Saharan Africa’s economic growth and infrastructure development. Chen (2023) backed this argument, stating that high-income and upper-middle-income nations with comparatively strong industrial bases benefit more from infrastructure development in terms of economic growth. The idea that seaport infrastructure and the economy are directly related is also supported by Sun and Kauzen (2023). Researching the relationship between taxation and economic growth, Pradhan (2021) found that infrastructure has a favorable and significant impact on economic growth across all income levels and subcategories.
Syadullah and Setyawan (2021) found that government spending on irrigation, ports and roads has a beneficial impact on economic growth. Timilsina et al. (2023) asserted that infrastructure improvements, particularly in the areas of telecommunications and energy generation capacity, provide significant, long-term benefits for economic growth. Nonetheless, in developing countries as opposed to developed ones, the influence of communication and energy infrastructure is greater than that of transportation infrastructure. Others have also examined the effects of specific infrastructure indicators on economic growth, like ICT (Adeleye and Eboagu, 2019; Awad and Albaity, 2022; David and Grobler, 2020; David, 2024; Noah and David, 2013), electricity, transport and others. These studies also contributed to the growth models by incorporating the influence of different infrastructure indicators alongside traditional determinants of economic growth. However, most of these studies concentrated primarily on specific components of infrastructure while giving limited consideration to the overall impact of aggregate infrastructure or the combined analysis of both in the same study. This study seeks to fill that gap by investigating the effects of infrastructure at both aggregate and disaggregated levels on economic growth in Africa. Considering the positive relationship between economic growth and Infrastructure, our second hypothesis can therefore be stated as:
Infrastructure development and its components positively influence economic growth in Africa.
One of the earliest studies to look at the connection between infrastructure and entrepreneurship was Audretsch et al. (2014). They proposed that while there is a positive correlation between startup activity and infrastructure generally, some types of infrastructure, like broadband, are more favorable to startup activity than others, including highways and railroads. The inauguration of high-speed trains in China has been shown to boost the entrepreneurial level of nearby cities (Qian et al., 2024). According to Ajide (2020), infrastructures are important for enhancing entrepreneurial development in Africa. Pelz et al. (2023) found that rural electrification alone did not result in changes in nonfarm entrepreneurship and nonfarm household employment outcomes in the two to four years after grid connection in Nigeria and Ethiopia. Gomes and Lopes (2022) showed that ICTs are a useful tool for increasing the capacity to create, gather, process and interpret information, which is essential for entrepreneurial activity.
Barnett et al. (2019) proposed that social networking and information acquisition play a mediating role in the impact of ICT utilization on entrepreneurship. Sun et al. (2024) confirmed that Internet use is linked to a smaller gender gap in entrepreneurship and that the Internet has a stronger gender gap-mitigating effect for informal than formal entrepreneurship. Public infrastructure investments are linked to the loss of jobs and businesses, whereas private infrastructure investments are positively and significantly linked to the creation of jobs and businesses (Bennett, 2019). According to Roig-Tierno et al. (2015), the growth of young, creative businesses can be positively impacted by the combination of supportive infrastructure. Ma et al. (2021) proposed that the primary way high-speed railroads promote entrepreneurial activity is through market potential, which is achieved through quicker information exchange and in-person contacts. Families with higher levels of education, those with more money and those living in big cities are more affected.
A more complex understanding of how infrastructure affects the connection between entrepreneurship and economic growth can be obtained by combining viewpoints from the theories of entrepreneurial ecosystems, institutions and capabilities. According to entrepreneurial ecosystem theory, the entrepreneurial environment is shaped by a number of interrelated elements, including money, human capital, infrastructure and supportive regulatory frameworks (Stam, 2015). According to this concept, infrastructure plays a crucial enabling role by expanding market accessibility, connection and information flow, all of which increase the efficiency of entrepreneurial endeavors in propelling economic expansion. By concentrating on the impact of both formal structures, such as laws, rules and governance, and informal societal norms on entrepreneurial activity, institutional theory provides an alternative perspective (North, 1990). Well-developed infrastructure frequently reflects institutional strength, lowering operational inefficiencies and increasing confidence in economic systems. Furthermore, as Sen (1999) explains, capacity theory emphasizes the significance of increasing people’s actual freedoms and opportunities. According to this perspective, infrastructure encourages entrepreneurship by expanding access to necessary services like digital tools, health care, education and transportation, especially for marginalized populations. By doing this, it promotes inclusive, long-term growth and increases economic participation.
Even though studies on infrastructure and economic growth are well-documented (Chen, 2023; Noah and David, 2024b; Noah and David, 2025; Pradhan, 2021), a critical look at the existing literature shows that there are very few studies on entrepreneurship and infrastructure development, most especially in Africa. The only exception that relates the infrastructure to entrepreneurship is Ajide (2020) and Pelz et al. (2023). While Ajide (2020) considered the major infrastructure indicators, the study adopted static panel regression generalized least squares, which is inadequate to address the problem of endogeneity and cross-sectional dependency. Pelz et al. (2023) not only conducted a country-specific study but also considered only the electricity infrastructure. The majority of the studies across the globe focus on the specific indicators of infrastructure, like telecommunications (Barnett et al., 2019; Gomes and Lopes, 2022; Sun et al., 2024) and transport (Audretsch et al., 2014; Ma et al., 2021; Qian et al., 2024).
These studies strengthen the evidence base by showing how infrastructure not only drives economic growth directly but also enhances entrepreneurship as an intermediary channel. They also highlighted how reliable infrastructure creates a supportive environment for entrepreneurial activities by reducing costs and improving access to markets. However, none of these studies have considered the moderating effects of infrastructure development and its components on the entrepreneurship-economic growth nexus. These are the gaps addressed in the present study by the following hypotheses:
Infrastructure development has a significant role in strengthening the impact of entrepreneurship on economic growth in Africa.
Moreover, not all indicators of infrastructure have the same effect on entrepreneurship. We develop the fourth hypothesis to investigate how each infrastructural element enhances the influence of entrepreneurship on economic growth in Africa:
Infrastructure indicators positively improve entrepreneurship’s impact on economic growth in Africa.
3. Data and methodology
Investigating the degree to which infrastructure development and its components impact the effect of entrepreneurship on economic growth in Africa across a panel of forty-two African nations between 2006 and 2022 is the primary objective of this study. Algeria, Benin, Botswana, Cape Verde, Central African Republic, Chad, Comoros, Congo Dem. Rep., Congo Rep., Cote d’lvoire, Egypt, Eswatini, Ethiopia, Gabon, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Paupa New Guinea, Rwanda, Sao Tome and Principle, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Tanzania, Togo, Uganda, Zambia and Zimbabwe, these nations are taken into consideration in this study based on the data available for the variable of interest.
Except for infrastructure data, which is sourced from the AfDB’s Africa infrastructure development index (AIDI) database, the remaining data are sourced from the world development indicators (WDI) of World Bank . The data sets for this study were generally consistent and aligned well for analytical purposes. Infrastructure data obtained from the AIDI concentrates on infrastructure performance across African nations. As stated in Table 1, the aggregate infrastructure data and its components are publicly available in the AIDI database. In contrast, the WDI provided a broader set of economic and social indicators. Although the sources differ in their primary focus, the structure, time coverage and country-level data presentation were largely uniform, making it feasible to merge the two data sets. Some minor harmonization steps, such as standardizing periods and aligning country codes, were required to ensure coherence. Nonetheless, both sources adhere to internationally accepted data collection standards, which support their reliability and compatibility for integrated analysis within the scope of this research.
Justification of variables in the model
| Variable | Justification | Supporting literature |
|---|---|---|
| Economic growth (dependent variable) | A standard measure of economic performance, reflecting productivity and living standards | Chen (2023), Ghazy et al. (2022) |
| Infrastructure development | Critical for productivity, trade and business efficiency | Awad and Albaity (2022), David (2024), David and Grobler (2020) |
| Entrepreneurship | Captures entrepreneurial activity and its impact on job creation and innovation | Noah and David (2025), Ghazy et al. (2022) |
| Labor | Skilled labor enhances productivity, while unskilled labor may hinder growth | Huang and Chen (2021), Ghazy et al. (2022) |
| Capital stock | Reflects investments in physical assets, driving technological progress | Awad and Albaity (2022), Adeleye et al. (2019) |
| Inflation | High inflation destabilizes economies, reducing investment and growth | Akinsola and Odhiambo (2017), Khan et al. (2022) |
In addition, the basis for choosing the 42 African nations for this study is guided by the availability of consistent and reliable data on key variables, such as the variables of interest. This scope, which spans the years 2006–2022, enables a thorough longitudinal analysis that documents economic transformations, policy modifications and structural patterns across several African economies. The results are more broadly applicable because the selected nations span a wide range of geographical areas, economic levels and phases of entrepreneurial and infrastructure development. This timeframe is also appropriate for assessing the moderating impacts of infrastructure on inclusive growth since it also represents a pivotal period in Africa’s development trajectory, characterized by increased infrastructure investment and entrepreneurship promotion.
The four main infrastructure indicators (electricity, transportation, telecommunications, water and sanitation) make up the infrastructure development index. The study relied on the secondary source data obtained from AfDB’s AIDI for infrastructure development and its indicators. According to the AfDB (2024), the four main infrastructure components (energy, transportation, telecommunications and water and sanitation) all have data accessible, each of which represents a different aspect of infrastructure development. The index is constructed using a four-step, systematic process. First, each component’s different measurement units are addressed using a normalizing procedure. To enable comparability, the minimum–maximum (min–max) scaling approach standardizes all values to lie within a range of 0–100.
Second, for every infrastructure type, a composite index is computed. When a component has more than one indication, a weighted average is calculated, with weights determined by taking the inverse of the standard deviation of each indicator. This lessens the impact of extremely erratic indicators on the index as a whole. Third, using the same weighted approach, the sub-indices of each of the four components are combined to create a comprehensive infrastructure index. Finally, the weighted average of the normalized national scores within each sub-region is used to create sub-regional infrastructure indices.
Regarding the measurement of the variables, GDP per capita (Constant US$) is used to measure the dependent variable, economic growth. Infrastructure and entrepreneurship development are the two variables of interest that have been previously justified. Specifically, entrepreneurship and infrastructure play vital roles in driving economic growth as they contribute directly to productivity and innovation. Reliable infrastructure, covering transportation systems, energy provision and digital connectivity, lowers transaction costs, enhances trade, and expands access to markets and services, thereby improving overall efficiency (Pradhan, 2021; Timilsina et al., 2023). At the same time, entrepreneurship stimulates job creation, fosters creativity and channels resources toward emerging opportunities that sustain long-term economic growth (Chikh-Amnache and Mekhzoumi, 2023; Ordeñana et al., 2024). When combined, these factors may provide a supportive environment for investment, strengthen competitiveness and promote inclusive economic growth. To quantify entrepreneurship, the new business entry density metric is used, which measures the number of registered enterprises per 1,000 working individuals. Consistent with previous research by Ghazy et al. (2022), the widely used measure offers comprehensive coverage across countries, periods and variables. Inflation, labor and capital stock are the control variables. The labor force participation rate, or the percentage of people aged 15 and over who are economically engaged, is used to measure labor. Production requires skilled labor, which is also a necessary component of growth. The economy is more likely to grow when there are more skilled workers using machinery for manufacturing; nevertheless, a high percentage of uneducated and untrained workers may hinder growth (Ghazy et al., 2022; Huang and Chen, 2021).
The gross fixed capital formation, which measures the stock of fixed investments and includes the net increase in physical assets over the measurement period, serves as a proxy for capital stock. According to Romer (1986) and Solow and Swan (1956), enterprises can acquire knowledge through capital accumulation, which can lead to increasing returns and stimulate economic growth (Adeleye et al., Nathaniel, 2019). Physical capital accumulation is a significant determinant of growth. The GDP deflator is used to proxy the inflation rate. There is a negative correlation between inflation and economic growth since high inflation destabilizes countries’ economic growth (Akinsola and Odhiambo, 2017). This position implies that, despite some evidence to the contrary, increasing inflation slows down growth (Khan et al., 2022). Therefore, Tables 1 and 2 summarise the justification of variables and data sources, and measurement, respectively.
Data sources and variable measurements
| Variable category | Variable name | Measurement/proxy | Unit | Data source |
|---|---|---|---|---|
| Dependent variable | Economic growth | GDP per capita (constant US$) | US$ (constant) | (WDI) |
| Variables of interest | Infrastructure development | African infrastructure development index (AIDI) | Composite index | AfDB’s AIDI database |
| - Electricity | Net generation | kWh per inhabitant | AfDB’s AIDI database | |
| - Transportation | Total paved roads (km per 10,000 inhabitants) and Total road network (per km2 of exploitable land area) | Subindex | AfDB’s AIDI database | |
| - Telecommunications | Total phone subscriptions (per 100 inhabitants); Number of Internet Users (per 100 inhabitants); Fixed (wired) broadband Internet subscribers (per 100 Inhabitants); and international internet Bandwidth (mbps) | Subindex | AfDB’s AIDI database | |
| - Water and sanitation | Improved water source (% of population with access); and improved sanitation facilities (% of population with access) | Subindex | AfDB’s AIDI database | |
| Entrepreneurship | New business entry density | New business regs. per 1,000 workers | WDI | |
| Control variables | Labor | Labor force participation rate | % of pop. ≥15 years | WDI |
| Capital stock | Gross fixed capital formation | % of GDP | WDI | |
| Inflation | GDP deflator | Annual % change | WDI |
The theoretical basis of this study is the Solow–Swan type of Neo-classical growth model and the Schumpeterian innovation theory, which is based on the justifications of the variables. These choices are commonly found in empirical literature due to their popularity and support for the inclusion of the interaction of entrepreneurship and infrastructure in the model. An aggregate production function serves as the foundation for the link between economic growth and which can be shown as follows:
where A stands for total factor productivity (TFP), K for capital, L for labor and Y for output. The Neoclassical growth equation used in this study is extended through “A” (level of technology – physical and digital infrastructure), which can be broadly interpreted as embodying productivity and efficiency in all of its ramifications, to ascertain the impact of infrastructure and entrepreneurship development on economic growth. Following the above discussion, the linear productivity growth model can be specified as:
where INF is the inflation rate, GDP is the economic growth, LFP is the labor force, GFC is the capital stock, EUR is the entrepreneurship and IDI is the infrastructure development. With these changes, equation (2) above can be rewritten to investigate whether infrastructure development and its components have an impact on or hinder the entrepreneurship-growth nexus. The infrastructure-entrepreneurship nexus is represented by the interaction of entrepreneurship with infrastructure and its components. The following is a representation of the model:
where EUR*IDI is the interacting term between entrepreneurship and infrastructure development and its components. Equation (3) represents the empirical growth model to be specified for estimation to achieve the objectives of this study. Theoretically and empirically, every variable’s coefficient should be positive, except for the inflation rate. It should be noted that the indications of the interaction terms’ coefficients assess whether the development of infrastructure and its components (transport, energy, ICT, water and sanitation) improves or distorts the relationship between entrepreneurship and economic growth. If the sign is positive, infrastructure will enhance the performance of entrepreneurship on economic growth, and vice versa. To ensure that entrepreneurial potential translates into inclusive and sustainable development, it is crucial to investigate how infrastructure (transport, energy, ICT, water and sanitation) affects entrepreneurship’s impact on economic growth. This is because strong infrastructure promotes business scalability, increases productivity and lessens regional disparities.
By enhancing connectivity and logistics, infrastructure lowers transaction costs, enabling business owners to work more productively and devote funds to expansion-oriented endeavors. Improved market access, made possible by transportation and ICT networks, increases consumer reach and business growth prospects. Reliable water and energy supplies reduce production risks, promoting stability in operations and investment. Innovation dispersion promotes competitive entrepreneurship by speeding up the adoption of new technology and business models, made possible by digital and transportation infrastructure. By fostering an atmosphere that encourages company expansion and productivity increases, these factors work together to reinforce the connection between entrepreneurship and economic growth.
Additionally, panel regression estimation techniques are used in this study. In particular, the panel-corrected standard error (PCSE) method is used. Considering the characteristics of our data sets (where the number of countries, n = 42 > years involved, T =17), subjection to unit root and cointegration tests, the PCSE approach yields the most accurate and dependable estimate. Above all, it enables us to account for cross-sectional dependency, heteroscedasticity and serial correlation (Beck and Katz, 1995; Reed and Webb, 2010). We also address the endogeneity issue and take into account the system generalized method of moments (SGMM) for robustness checks. Prior to the estimation of the models with the PCSE method, the study conducted a preanalysis including descriptive statistics and correlation, followed by a series of panel unit root tests like Levin, Lin and Chu (LLC); Phillips-Perron (PP)–Fisher; Augmented Dickey Fuller (ADF)–Fisher; and Im, Pesaran and Shin (IPS). The Kao cointegration test was also conducted to examine the long run relationship among the variables. Both E-views 12 and Stata 15 were used for the estimation of the data to achieve the objectives of the study.
4. Presentation of results
Table 3 presents the data overview and sources, and Table 4 presents the pairwise correlations between the variables. Details from Table 3 indicate that there is substantial variety in all the variables, with the GDP per capita displaying the most variation. Except for a few infrastructure indicators, Table 4 also demonstrates that there is no strong link between the independent variables in our analysis. Estimating each indicator independently takes care of this exceptional case for the infrastructure to avoid the problem of multicollinearity. Furthermore, entrepreneurship and economic growth, as well as the development of infrastructure and its components, are positively correlated. In the meantime, labor force participation is inversely correlated with economic growth. This may be because of the potential for significant unemployment and underemployment brought on by population growth, as stated by Ghazy et al. (2022) and Huang and Chen (2021). However, since correlation coefficients only indicate the degree to which the variables are linearly related to one another, the PCSE and system GMM approaches are used to determine the main effects.
Descriptive statistics
| Variables | Mean | Max. | Min. | SD | Observations | Source |
|---|---|---|---|---|---|---|
| GDP | 2,105.944 | 10,956.95 | 284.471 | 2,145.864 | 578 | WDI |
| Capital | 23.187 | 78.001 | 2.225 | 8.570 | 578 | WDI |
| Labor | 62.366 | 89.450 | 34.637 | 12.155 | 578 | WDI |
| Entrepreneurship | 1.354 | 20.091 | 0.005 | 2.628 | 578 | WDI |
| Inflation | 9.076 | 604.946 | −21.165 | 31.454 | 578 | WDI |
| Infrastructure | 23.169 | 89.912 | 1.564 | 19.938 | 578 | AIDI |
| Transport | 9.992 | 56.511 | 1.091 | 10.832 | 578 | AIDI |
| Electricity | 9.389 | 82.376 | 0.056 | 15.209 | 578 | AIDI |
| ICT | 8.841 | 58.904 | 0.001 | 10.984 | 578 | AIDI |
| Water and sanitation | 59.959 | 99.795 | 6.987 | 22.081 | 578 | AIDI |
Correlation analysis
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
|---|---|---|---|---|---|---|---|---|---|---|
| (1) GDP | 1.000 | |||||||||
| (2) Capital | 0.054 (0.193) | 1.000 | ||||||||
| (3) Labor | −0.271*** (0.000) | 0.078* (0.059) | 1.000 | |||||||
| (4) Entrepreneurship | 0.701*** (0.000) | −0.063 (0.131) | −0.026 (0.528) | 1.000 | ||||||
| (5) Inflation | −0.054 (0.198) | −0.148*** (0.000) | 0.077* (0.063) | 0.007 (0.859) | 1.000 | |||||
| (6) Infrastructure | 0.750*** (0.000) | −0.086* (0.039) | −0.346*** (0.000) | 0.541*** (0.000) | −0.006 (0.889) | 1.000 | ||||
| (7) Transport | 0.605*** (0.000) | −0.160*** (0.000) | −0.270*** (0.000) | 0.449*** (0.000) | 0.028 (0.502) | 0.757*** (0.000) | 1.000 | |||
| (8) Electricity | 0.743*** (0.000) | −0.093** (0.025) | −0.228*** (0.000) | 0.632*** (0.000) | −0.005 (0.909) | 0.815*** (0.000) | 0.530*** (0.000) | 1.000 | ||
| (9) ICT | 0.561*** (0.000) | −0.037 (0.372) | −0.181*** (0.000) | 0.444*** (0.000) | 0.014 (0.734) | 0.745*** (0.000) | 0.367*** (0.000) | 0.569*** (0.000) | 1.000 | |
| (10) Water and Sanitation | 0.752*** (0.000) | −0.052 (0.214) | −0.526*** (0.000) | 0.494*** (0.000) | 0.005 (0.905) | 0.841*** (0.000) | 0.685*** (0.000) | 0.647*** (0.000) | 0.604*** (0.000) | 1.000 |
Values in parentheses () are the p-values of the test statistic; “***”, “**” and “*” imply significance at 1, 5 and 10 %, respectively
Additionally, a panel series needs to be examined for stationarity and unit roots, as discontinuities can have a big impact on econometric estimates. Discontinuities can also significantly affect econometric results. Hence, it is important to check a panel series for stationarity or unit roots. To evaluate the integration order of the variables, we performed panel unit root tests. The findings of the Levin, Lin and Chu (LLC), PP–Fisher, ADF–Fisher and Im, Pesaran and Shin (IPS) stationarity tests are shown in Table 5. The presence of a unit root in the panel series is the null hypothesis for all stationarity tests. The findings demonstrate the order one, or I(1) integration for economic growth, entrepreneurship, infrastructure development, transportation, electricity and ICT infrastructure. On the other hand, water and sanitation infrastructure, labor force, capital and inflation are all integrated of order zero, or I(0). The stationarity test findings unequivocally demonstrate that the integration sequence of the panel series changes. The stationarity tests clearly show that the panel series’ integration sequences differ from one another.
Panel unit roots test results
| Series | Stationarity | PP-Fisher | ADF- Fisher | LLC | IPS | Decision |
|---|---|---|---|---|---|---|
| GDP | Level | 113.774** (0.024) | 70.918 (0.880) | −2.311*** (0.010) | 1.910 (0.972) | I(1) |
| First difference | 363.592*** (0.000) | 209.478*** (0.000) | −8.614*** (0.000) | −7.704*** (0.000) | ||
| EUR | Level | 71.811 (0.864) | 68.939 (0.911) | 0.393 (0.653) | 2.771 (0.997) | I(1) |
| First difference | 315.243*** (0.000) | 155.932*** (0.000) | −2.656*** (0.000) | −4.024*** (0.000) | ||
| GFC | Level | 86.373* (0.090) | 87.038* (0.082) | −2.049** (0.020) | −0.942* (0.173) | I(0) |
| First difference | 360.996*** (0.000) | 237.322*** (0.000) | −13.645*** (0.000) | −9.990*** (0.000) | ||
| LFP | Level | 170.019*** (0.000) | 99.344 (0.121) | −4.518*** (0.000) | −1.813** (0.035) | I(0) |
| First difference | 287.100*** (0.000) | 240.398*** (0.000) | −7.192*** (0.000) | −8.157*** (0.000) | ||
| INF | Level | 170.019*** (0.000) | 99.344 (0.121) | −4.518*** (0.000) | −1.813** (0.035) | I(0) |
| First difference | 569.917*** (0.000) | 212.550*** (0.000) | −8.625*** (0.000) | −8.058*** (0.000) | ||
| IDI | Level | 30.672 (1.000) | 36.667 (1.000) | −4.008*** (0.000) | 4.917 (1.000) | I(1) |
| First difference | 371.253*** (0.000) | 174.963*** (0.000) | −3.911*** (0.000) | −5.810*** (0.000) | ||
| TRA | Level | 129.264*** (0.001) | 95.701 (0.180) | −4.630*** (0.000) | −0.856 (0.196) | I(1) |
| First difference | 492.662*** (0.000) | 231.403*** (0.000) | −11.641*** (0.000) | −9.117*** (0.000) | ||
| ELE | Level | 71.253 (0.838) | 62.062 (0.965) | −3.005*** (0.001) | 2.113 (0.983) | I(1) |
| First difference | 486.955*** (0.000) | 241.958*** (0.000) | −10.457*** (0.000) | −9.082*** (0.000) | ||
| ICT | Level | 5.399 (1.000) | 9.597 (1.000) | 3.045 (0.999) | 9.343 (1.000) | I(1) |
| First difference | 264.796*** (0.000) | 159.770*** (0.000) | −9.538*** (0.000) | −5.749*** (0.000) | ||
| WSC | Level | 778.416*** (0.000) | 117.529*** (0.009) | −2.636*** (0.004) | 3.099*** (0.999) | I(0) |
| First difference | 816.795*** (0.000) | 321.922*** (0.000) | −6.212*** (0.000) | −12.054*** (0.000) |
Values in parentheses () are the p-values of the test statistic, “***”, “**” and “*” imply significance at 1, 5 and 10 %, respectively. GDP = GDP per capita; EUR = entrepreneurship; LFP = labor force; capital (GFC), INF = inflation rate; IDI = infrastructure development;, TRA = transport; ELE = electricity; ICT = information and telecommunication; and WSC = water and sanitation infrastructure
The cointegration test is therefore required to determine whether there is a long-term relationship between the variables once the unit root tests are completed. This is shown using the Kao–Engle Granger test in Table 6. Due to its greater capacity to accommodate regressors than the constrained Pedroni and Westerlund cointegration tests, this method is used. The findings of the Kao cointegration test, which show that each panel series is cointegrated, are supported by the statistic at the one percent significance level.
Kao–Engle–Granger panel cointegration results
| Test | Statistic | p-value | Conclusion (H0) |
|---|---|---|---|
| Modified Dickey–Fuller | −1.30* | 0.097 | Rejected |
| Dickey–Fuller | −1.770** | 0.038 | Rejected |
| Augmented Dickey–Fuller | −3.679*** | 0.000 | Rejected |
| Unadjusted modified Dickey–Fuller | −3.039*** | 0.001 | Rejected |
| Unadjusted Dickey–Fuller | −2.760*** | 0.003 | Rejected |
H0: No cointegration, “***”, “**” and “*” imply significance at 1, 5 and 10 %, respectively, and H0 is rejected
Table 7 displays the results of the cross-sectional dependency tests conducted using the Pesaran and Breusch–Pagan Lagrange Multiplier (LM) tests. At the one percent significance level, the findings confirm that there are cross-sectionally dependent components among the variables. The growing degree of economic interdependence among African nations exacerbates this. Ignoring this could lead to uneven and skewed study outcomes. As previously mentioned, this is among the reasons the PCSE technique was used in this work, which is suitable for addressing these and related issues.
4.1 Presentation of empirical results
Columns (1) through (10) of Table 8 reflect the PCSE’s conclusions about the long-term impacts of infrastructure development, its components, entrepreneurship and the control variables on economic growth. Results on whether entrepreneurship, infrastructure development and its components spur economic growth are presented in Columns (1) through (5). The results on whether the interactions of entrepreneurship with infrastructure development and its components enhance or modify its impact on growth are presented in Columns (6) through (10) as well.
PCSE results
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
|---|---|---|---|---|---|---|---|---|---|---|
| GFC | 0.329*** (5.550) | 0.535*** (7.186) | 0.262*** (6.211) | 0.331*** (3.955) | 0.382*** (5.072) | 0.365*** (4.936) | 0.464*** (5.810) | 0.305*** (5.601) | 0.227*** (2.667) | 0.384*** (4.712) |
| LFP | −0.298*** (−6.977) | −0.492*** (−11.23) | −0.417*** (−10.48) | −0.957*** (−16.93) | 0.352*** (5.049) | −0.637*** (−13.21) | −0.672*** (−14.47) | −0.526*** (−11.84) | −1.069*** (−15.65) | −0.639*** (−12.44) |
| INF | −0.001 (0.208) | −0.001 (−0.034) | −0.001 (−0.495) | −0.001*** (2.949) | −0.001** (2.376) | −0.001 (1.636) | −0.001 (1.229) | −0.001 (0.373) | −0.001*** (3.541) | −0.001*** (2.601) |
| EUR | 0.095*** (8.008) | 0.151*** (11.37) | 0.124*** (11.33) | 0.329*** (21.90) | 0.155*** (19.40) | |||||
| IDI | 0.829*** (32.94) | |||||||||
| TRA | 0.591*** (45.16) | |||||||||
| ELE | 0.400*** (45.26) | |||||||||
| ICT | 0.039*** (2.942) | |||||||||
| WSC | 1.542*** (14.48) | |||||||||
| EUR*IDI | 0.325*** (29.13) | |||||||||
| EUR*TRA | 0.304*** (36.04) | |||||||||
| EUR*ELE | 0.267*** (36.57) | |||||||||
| EUR*ICT | 0.114*** (5.035) | |||||||||
| EUR*WSC | 0.354*** (25.46) | |||||||||
| Constant | 2.259*** (18.38) | 2.887*** (22.87) | 3.407*** (42.37) | 4.468*** (28.62) | −0.650* (−1.845) | 3.490*** (24.85) | 3.571*** (24.88) | 3.644*** (35.15) | 4.697*** (30.69) | 3.242*** (20.16) |
| Multicollinearity | 1.29 | 1.27 | 1.24 | 1.10 | 1.36 | 1.06 | 1.06 | 1.06 | 1.05 | 1.07 |
| R-squared | 0.764 | 0.660 | 0.758 | 0.549 | 0.698 | 0.633 | 0.603 | 0.712 | 0.540 | 0.543 |
| Wald test | 1,772.61 | 4,641.98 | 5,621.89 | 1,236.05 | 682.33 | 1,366.09 | 2,327.48 | 2,484.40 | 581.13 | 1,023.12 |
Values in parentheses () are the t-values of the test statistic, “***”, “**” and “*” imply significance at 1, 5 and 10 %, respectively. GDP = GDP per capita; EUR = entrepreneurship; LFP = labor force; GFC = capital; INF = inflation rate; IDI = infrastructure development; TRA = transport; ELE = electricity; ICT = information and telecommunication, WSC = water and sanitation infrastructure; EUR*IDI = interactive term between entrepreneurship and infrastructure development; EUR*TRA = interactive term between entrepreneurship and transport infrastructure; EUR*ELE = interactive term between entrepreneurship and electricity infrastructure; EUR*ICT = interactive term between entrepreneurship and ICT infrastructure; EUR*WSC = interactive term between entrepreneurship and water and sanitation infrastructure
We also used the dynamic analysis (SGMM) method to look at the dynamic relationship and short-term effects, as shown in Table 9’s Columns (1)–(10), as part of a robustness analysis meant to confirm the possible effects of entrepreneurship, infrastructure development and the control variables on economic growth. Crucially, post-estimation testing verifies that the estimates obtained from the SGMM and PCSE models are robust. There is little discernible multicollinearity among the explanatory variables, as indicated by the comparatively low variance inflation factors (VIF) for economic growth models, which range from 1.05 to 1.36. This conclusion is supported by the correlation analysis. Furthermore, the R-square (R2) statistics, which range from 0.54% to 0.76%, and the Wald chi-square (X2) statistics, which are all significant at the 1% level, imply that the model estimations are valid and dependable for making decisions. Additionally, at the five percent significance level, the SGMM results show that there is a first-order serial correlation but no second-order serial correlation. Additionally, the Sargan test passes diagnostic tests by confirming the reliability of the estimating instruments.
SGMM results
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
|---|---|---|---|---|---|---|---|---|---|---|
| L.GDP | 0.954*** (33.14) | 0.922*** (74.72) | 0.932*** (87.15) | 0.963*** (59.97) | 0.965*** (103.1) | 0.952*** (106.7) | 0.935*** (88.93) | 0.932*** (108.6) | 1.001*** (94.18) | 0.951*** (98.06) |
| GFC | 0.0491*** (13.42) | 0.0537*** (12.76) | 0.0517*** (11.84) | 0.0487*** (9.574) | 0.0495*** (12.62) | 0.053*** (14.95) | 0.050*** (15.74) | 0.056*** (16.05) | 0.051*** (11.06) | 0.053*** (17.11) |
| LFP | 0.146** (2.296) | −0.038 (−0.974) | 0.128*** (3.514) | 0.099*** (2.790) | 0.132** (2.351) | 0.200*** (4.833) | 0.068** (2.136) | 0.141*** (3.490) | 0.252*** (4.746) | 0.181*** (5.052) |
| INF | −0.001*** (−9.266) | −0.001*** (−16.13) | −0.001*** (−9.176) | −0.001*** (−15.23) | −0.001*** (−8.703) | 0.001 (1.417) | 0.001 (1.148) | 0.001 (0.0962) | 0.001 (1.374) | 0.001* (1.889) |
| EUR | 0.016*** (7.697) | 0.006** (2.270) | 0.016*** (7.884) | 0.017*** (6.962) | 0.016*** (8.796) | |||||
| IDI | 0.002 (0.153) | |||||||||
| TRA | 0.081*** (7.504) | |||||||||
| ELE | 0.016*** (5.004) | |||||||||
| ICT | 0.001** (2.004) | |||||||||
| WSC | −0.001 (−0.082) | |||||||||
| EUR*IDI | 0.014*** (9.719) | |||||||||
| EUR*TRA | 0.020*** (7.703) | |||||||||
| EUR*ELE | 0.016*** (10.23) | |||||||||
| EUR*ICT | 0.001 (1.281) | |||||||||
| EUR*WSC | 0.017*** (10.66) | |||||||||
| Constant | −0.170** (−2.254) | 0.184* (1.884) | −0.0774 (−1.050) | −0.112* (−1.862) | −0.175 (−1.551) | −0.281*** (−3.346) | 0.0131 (0.192) | −0.108 (−1.262) | −0.513*** (−4.317) | −0.257*** (−3.158) |
| Wald test | 69,394.62 | 39,744.73 | 38,724.44 | 53,967.30 | 53,054.70 | 55,688.21 | 29,728.47 | 39,312.39 | 113,180.85 | 44,290.06 |
| AR1 | −2.900*** | −2.897*** | −2.887*** | −2.893*** | −2.911*** | −2.927*** | −2.903*** | −2.897*** | −2.919*** | −2.919*** |
| AR2 | −1.721 | −1.655 | −1.785 | −1.746 | −1.706 | −1.631 | −1.740 | −1.773 | −1.553 | −1.683 |
| Sargan | 33.164 | 32.853 | 31.891 | 32.705 | 31.784 | 31.399 | 33.342 | 32.314 | 33.151 | 31.563 |
Values in parentheses () are the t-values of the test statistic, “***”, “**” and “*” imply significance at 1, 5 and 10 %, respectively. GDP = GDP per capita; EUR = entrepreneurship; LFP = labor force ; GFC = capital; INF = inflation rate; IDI = infrastructure development; TRA = transport; ELE = electricity; ICT = information and telecommunication; WSC = water and sanitation infrastructure; EUR*IDI = interactive term between entrepreneurship and infrastructure development; EUR*TRA = interactive term between entrepreneurship and transport infrastructure; EUR*ELE = interactive term between entrepreneurship and electricity infrastructure; EUR*ELE = interactive term between entrepreneurship and ICT infrastructure; EUR*WSC = interactive term between entrepreneurship and water and sanitation infrastructure
According to the empirical results obtained from the PCSE analysis in Columns (1)–(5) in Table 8, the control variable coefficients indicate that the labor force is negative and statistically significant at the 1% level, while capital stock is positive and statistically significant. The coefficients of the inflation rate are also negative but statistically insignificant, except in the models for the ICT and water and sanitation infrastructure. This suggests that a rise in capital stock is equivalent to an increase in economic growth of 0.227%–0.535%. Additionally, this aligns with earlier research such as Adeleye and Eboagu (2019) and Noah and David (2025). The unexpected negative labor force coefficients also suggest that a decrease in the labor force is equivalent to an increase in economic growth of 0.298%–1.069%. This is also in line with some earlier research, such as Ghazy et al. (2022), Huang and Chen (2021). Additionally, the inflation rate’s negative coefficients suggest that a decrease in the inflation rate is equivalent to a 0.001% increase in economic growth.
Furthermore, these imply that both capital stock and the inflation rate align with the anticipated outcomes and are consistent with established growth theories, underscoring their critical roles in influencing economic growth. Capital accumulation has long been recognized as a driver of output expansion, while inflation, when maintained within a moderate range, can stimulate investment and economic activity in line with theoretical expectations. In contrast, the role of labor appears to diverge from both the prior assumptions and the propositions of conventional growth theories, suggesting a more complex relationship between labor input and economic growth in Africa. Interestingly, this contradiction is not entirely novel, as stated earlier, as some recent empirical studies have also reported similar outcomes, highlighting the need to reexamine the traditional assumptions about labor’s contribution to growth in Africa.
Considering the variables of interest, the coefficient associated with entrepreneurship demonstrates a positive and statistically significant relationship at the 1% level. This suggests that over time, the direct effects of entrepreneurship have a positive impact on economic growth. Accordingly, a rise in entrepreneurship is associated with an increase in economic growth of 0.095%–0.329%. Entrepreneurship improvement, which comprises an entrepreneur driven by necessity or an early-bird businessman, and opportunity or innovative-driven business, may be a result of growth in sectors like fintech, agribusiness, renewable energy and e-commerce. This result supports the study’s first hypothesis. This is also in line with the findings of Ordeñana et al. (2024), who claimed that only innovative and early-stage entrepreneurship plays a significant role in economic growth, while high-growth entrepreneurship is found to have no relationship with economic growth.
In light of how infrastructure development affects economic growth, its coefficients are likewise positive and statistically significant when compared to economic growth at the 1% level. This suggests that over time, the direct effects of infrastructure development have a positive impact on economic growth. In other words, economic growth increases by 0.829% when infrastructure development increases. Owusu-Manu et al. (2017) and Timilsina et al. (2023) concur that infrastructure has a direct impact on economic growth.
To examine the diverse impacts of infrastructure components, we specifically go into their disaggregated effects as well. The impact of various infrastructure components on economic growth may vary. By dissecting infrastructure development, these varied contributions can be comprehended. The findings demonstrate that, at the one percent level, the four infrastructure components (transport, electricity, ICT and water and sanitation infrastructure) have a positive effect on economic growth. This suggests that long-term economic growth is positively impacted by the direct effects of infrastructure related to transportation, electricity, ICT, water and sanitation. In other words, an increase in transport, electricity, ICT and water and sanitation infrastructure connections corresponds to a 0.591%, 0.400%, 0.039% and 1.542% increase in economic growth, respectively. This is in line with research conducted by Chen (2023), David (2024) and David and Grobler (2020), among others. These results also support the study’s second hypothesis.
The primary focus of this study centers on examining whether infrastructure development and its components augment or dampen the positive effect of entrepreneurship on economic growth. To determine this, we assess how entrepreneurship contributes to Africa’s economic growth when infrastructure development is leveraged. According to the findings in Table 8’s Column (6), the interactive variable that represents entrepreneurship and infrastructure development has a positive coefficient that is statistically significant at the 1% significance level. This suggests that a rise in entrepreneurship brought about by advancements in infrastructure development causes Africa’s economic growth to rise by 0.325%. In essence, the region’s entrepreneurship-growth relationship is strengthened by infrastructure development. This result implies that infrastructure development and entrepreneurship work hand in hand to create an atmosphere that supports economic expansion in Africa. This result supports the study’s third hypothesis.
In addition, these results align with theoretical expectations and are consistent with established growth models that highlight the importance of infrastructure as a critical driver of economic growth. Traditional growth theories, particularly those extending from the neoclassical and endogenous frameworks, stress that infrastructure not only enhances productivity but also facilitates the efficient allocation of resources across sectors. However, while earlier empirical studies have predominantly concentrated on the contributions of ICT and energy infrastructure to growth, the present study underscores the often-overlooked significance of water supply and sanitation. By drawing attention to these essential services, the results suggest that access to clean water and adequate sanitation not only improves public health outcomes but also enhances labor productivity, reduces economic losses linked to disease and fosters a more sustainable growth trajectory. This emphasis broadens the scope of infrastructure-growth literature, offering fresh insights into how different types of infrastructure contribute to long-term economic growth.
Table 8’s Columns (7) through (10) also show that the interactive variables that represent entrepreneurship and the infrastructure development components (transport, electricity, ICT, water and sanitation infrastructure) have positive and statistically significant coefficients at the 1% significance level. This suggests that improvements in ICT, transportation, electricity, water and sanitation infrastructure boost entrepreneurship, which, in turn, boosts economic growth in Africa by 0.304%, 0.267%, 0.114% and 0.354%. As a result, the region’s entrepreneurship-growth relationship is further supported by all of the infrastructure components, including transportation, electricity, ICT, water and sanitation. These results support the study’s fourth hypothesis and are in line with entrepreneurial ecosystems theory and Schumpeterian innovation theory. These findings, which are a noteworthy discovery and addition to the body of literature, suggest that the impact of entrepreneurship on economic growth in Africa is amplified by infrastructure development and its components. Unlike the previous studies, the present study also emphasizes the significant influence of water supply and sanitation in strengthening the impact of entrepreneurship on economic growth in Africa.
Table 9 further displays the results of the SGMM estimations, which demonstrate that growth is consistent across all models. In other words, ceteris paribus, an increase in the economic growth from the prior year adds, on average, between 0.922% and 1.001% to the current growth. With very few exceptions, the coefficients of every explanatory variable match the findings of the PSCE estimations.
4.2 Discussion of empirical results
Given our knowledge of the infrastructure gap in Africa, which limited the potential of entrepreneurship toward economic growth, the difficulties faced by entrepreneurs served as the impetus for our study. We postulated that expanding access to infrastructure such as transportation, electricity, ICT, water and sanitation should either reinforce or distort the beneficial effects of entrepreneurship on African economic growth. The empirical results support each of our hypotheses. The study’s conclusions offer important new information about how infrastructure development and entrepreneurship propel economic growth to African stakeholders, policymakers and development planners.
First, it becomes evident that infrastructure development plays a significant role in promoting economic growth, both directly and through its relationship to entrepreneurship. The results highlight the necessity of consistent infrastructure development. This suggests that a strong infrastructure network is a major enabler of entrepreneurship since it reduces business expenses, boosts productivity and makes it easier to access markets. Therefore, policies that combine the development of infrastructure with the encouragement of entrepreneurship can improve the conditions for economic growth in Africa. According to the interaction between entrepreneurship, infrastructure development, and economic growth, infrastructure contributes to the reinforcement of the relationship between entrepreneurship and growth. Consequently, improved economic growth may result from coordinated investment in both sectors. However, while infrastructure and its components demonstrate strong potential to complement entrepreneurship in driving growth, ensuring scalability will depend on supportive policies, adequate financing and effective monitoring frameworks.
Second, considering the individual effects of infrastructure indicators reveals that water and sanitation infrastructure has the most significant impact in strengthening the entrepreneurship-growth nexus, followed by transport, electricity and ICT infrastructure, respectively. These demonstrate that even though water and sanitation infrastructure are frequently disregarded when talking about entrepreneurship, it is essential to enhance the influence of entrepreneurship on economic development and growth. Businesses, especially those in manufacturing, agriculture and health care, must have access to clean water and adequate sanitary facilities. By boosting worker productivity, assisting water-dependent industries and promoting investment in rural areas, improved water and sanitation infrastructure supports entrepreneurship and growth.
In the African context, women often encounter higher safety concerns and reduced access to clean water and sanitation compared to men. These challenges can restrict their mobility, affect their health, and limit the time available for income-generating activities, thereby hindering their full participation in entrepreneurial ventures. Improved water and sanitation infrastructure can ease these burdens, creating safer, healthier, and more enabling environments for women to engage in business activities. Water and sanitation infrastructure also plays a significant role in strengthening the link between entrepreneurship and economic growth due to its support for health-dependent microenterprises, its empowerment of women-led businesses and its contribution to the development of rural agro-based ventures. All of which are vital drivers of inclusive economic progress, often constrained by inadequate access. In areas underserved by other forms of infrastructure, improvements in water and sanitation intersect with public health, gender equality and productivity, creating conditions that foster entrepreneurial activity and broaden economic participation. However, expanding such projects nationwide requires significant financial investment, raising questions about cost efficiency, budget prioritization and long-term sustainability. Integrating water and sanitation initiatives with broader entrepreneurship programs also presents institutional and coordination challenges, as it requires alignment across multiple sectors and levels of government.
Additionally, a well-developed transportation system is necessary for business success because it makes it easier to move labor, goods and services. Businesses in Africa have historically faced higher costs and restricted access to markets due to poor transportation conditions and inadequate connectivity. However, enhancing transportation infrastructure will increase market accessibility, make it easier to obtain raw materials and lower the costs of supply chains and logistics. The African Continental Free Trade Area (AfCFTA), which seeks to increase intra-African trade and entrepreneurship, is one example of a regional integration initiative that benefits from this.
Furthermore, a steady supply of electricity is essential for successful entrepreneurship, especially in the technology, manufacturing and service sectors. However, business operations have been hindered by power shortages in many African nations, which have resulted in high prices for alternative energy sources like diesel generators. Therefore, improved electrical infrastructure encourages industrialization and innovation, lowers production costs and minimizes operational disruptions. Some countries, like Ghana and Kenya, have made great efforts to implement renewable energy projects, like solar and wind power, which have improved businesses’ access to electricity, especially in rural and peri-urban areas.
ICT infrastructure is also essential for fostering entrepreneurship in the digital age because it gives companies access to global markets, financial services and information. The entrepreneurial landscape has changed as a result of Africa’s quick rise in internet penetration and mobile connectivity, which enables companies to use technology to innovate and grow. Improved ICT infrastructure encourages entrepreneurship, as demonstrated by the growth of fintech companies such as Mobile-Pesa (M-Pesa) in Kenya, Paystack in Nigeria and Flutterwave throughout Africa. These platforms have transformed small business financial transactions, allowing them to grow and become part of the global economy. However, limited digital skills, high costs of accessing services, unstable internet connectivity in rural regions, and restrictive regulations hindering technology adoption are likely key factors behind the minimal influence of ICT infrastructure on the relationship between entrepreneurship and economic growth. These challenges continue to constrain its effectiveness, despite ongoing technological advancements.
Theoretically, this study is anchored in the Solow–Swan neo-classical growth model and Schumpeterian innovation theory. Our empirical findings have significant implications for both frameworks, not only validating their core tenets but also demonstrating how their integration provides a more complete explanation of economic growth in the African context. The fundamental tenet of the Solow–Swan growth model, which identifies physical capital accumulation as a key factor in economic expansion, is substantially supported by the positive influence of capital stock. This finding reaffirms the continued relevance of the model’s production function, where increases in capital investment translate into higher output levels.
In contrast, the negative result for the labor variable introduces an important departure from the conventional neo-classical view, which assumes labor to be a consistently positive input. In the African context, the results indicate that simply expanding the labor force does not automatically generate growth, as productivity is constrained by factors embedded within the total factor productivity component (A). Labor’s contribution is dampened by structural inefficiencies such as underemployment, skills gaps and the predominance of the unorganized sector. These results emphasize that the Solow framework needs to be reinterpreted for developing countries, emphasizing that the quality and efficient allocation of labor, in addition to its quantity, determine its growth potential. To fully realize labor’s beneficial contribution to economic growth, institutional reforms that enhance education, skill development and labor market efficiency are consequently necessary.
Schumpeterian growth theory, which holds that innovation-driven entrepreneurship is essential to economic transformation, is validated by the positive and substantial effects of entrepreneurship on economic growth. A new theoretical dimension is added to studies of economic growth by the role of infrastructure as a moderating factor in the relationship between entrepreneurship and growth. The results support ideas from spatial economics and regional development theories by indicating that infrastructure development increases the output and effectiveness of entrepreneurial endeavors. This is consistent with theories that highlight the significance of economic geography (Krugman, 1991), according to which the development of infrastructure enhances connectivity, lowers transaction costs and permits businesses to reach a wider audience. Therefore, incorporating both the quality of infrastructure and entrepreneurship activity into the production function is crucial, as these factors complement and reinforce each other in driving economic growth in Africa.
Furthermore, the substantial impacts of the various infrastructure components (transport, electricity, ICT, water and sanitation) indicate that each type of infrastructure makes a distinct contribution to economic growth. This emphasizes the need to move beyond a one-size-fits-all model and adopt a disaggregated approach to researching the role of infrastructure in economic growth and development. The results offer empirical support for recent research that calls for sector-specific infrastructure investments that are suited to various economic environments. Thus, our results do not merely support the chosen theories in isolation; they argue for a synthesized theoretical model. We demonstrate that sustainable growth in Africa is best explained by a framework where neo-classical capital accumulation is necessary but insufficient unless complemented by Schumpeterian entrepreneurial innovation, the potency of which is itself determined by the quality and specificity of foundational infrastructure.
5. Conclusion and recommendation
This study confirms the impact of infrastructure development and entrepreneurship on economic growth in 42 African economies between 2006 and 2022. Using PCSE and SGMM estimations for dynamic relationship and robustness checks, it contributes to the literature by examining the relationship between infrastructure development and its components, as well as the nexus between entrepreneurship and growth. The study also looks at the direct and indirect effects of entrepreneurship, infrastructure development, its components and control variables on economic growth over time. The results of the study show that entrepreneurship has a long-run positive impact on economic growth. The findings also demonstrate that infrastructure development and its components have a beneficial, long-term impact on economic growth. The benefits of entrepreneurship in Africa are increased by the moderating effects of infrastructure development on the relationship between economic growth and entrepreneurship. The study also demonstrates how infrastructure accelerates the relationship between entrepreneurship and growth. Additionally, the control variable results indicate that capital stock has a positive effect on economic growth in Africa, whereas the labor force and inflation rate have a negative effect.
The empirical results point to a number of crucial policy avenues for promoting economic expansion. First, because entrepreneurship has a significant positive influence on growth, it needs to be actively encouraged through targeted incentives such as tax rebates, start-up grants and expedited business registration procedures. By creating industry-specific incubation programs and research and development (R&D) financing, policymakers should pay special attention to high-growth industries like fintech, agribusiness and renewable energy. Also, the results highlight the necessity for policymakers to give infrastructure development top priority, especially in the areas of transportation, energy, ICT and water and sanitation, since these industries greatly boost economic growth. To improve the quality and accessibility of infrastructure, the enactment of appropriate economic policies, such as greater public investment and public-private partnerships (PPPs), should be encouraged.
Infrastructure components’ diverse contributions to entrepreneurship and economic growth are highlighted by the disaggregated analysis. Each indicator contributes differently to promoting entrepreneurship activities and economic growth. Therefore, the implementation of sector-specific infrastructure policies should be the focus of efforts. Policies pertaining to infrastructure and entrepreneurship should be established together rather than separately, as evidenced by their interaction impacts. For example, training in digital skills is necessary for broadband programs, and startup incubators should be located in industrial zones. To promote synergy, energy infrastructure must match the demands of the tech sector. With this strategy, entrepreneurship is made possible by infrastructure, and the demand from entrepreneurs drives additional infrastructure advancements.
At the macroeconomic level, such synergy aids structural transformation, helping countries move up global value chains. Infrastructure investments yield higher long-term returns when they spur entrepreneurial activity beyond direct impacts. Policymakers should assess projects not just on physical outputs but also on their potential to unlock innovation. National strategies should treat infrastructure as platforms for entrepreneurship, not just public goods. To maximize their synergistic impacts, development initiatives must take a comprehensive strategy, combining entrepreneurial programs with infrastructure upgrades, particularly water/sanitation (growth impact) and transportation.
Given the substantial influence of capital stock, efforts should also be geared towards improving the efficiency of the financial system, upholding property rights and lowering administrative obstacles to capital accumulation to foster an environment that is conducive to investment. To promote long-term economic growth, financial institutions should also make credit more widely available to companies, especially those in capital-intensive industries. However, efforts should be made to increase labor productivity by funding high-quality and accessible education, career training and skill-development initiatives that meet industry demands. This will go a long way to reduce underemployment, bridge skill gaps and move workers from the unorganized to the formal sector, thereby improving labor productivity. Finally, the results of inflation show that sector-specific inflation management policies are necessary. As a result, there should be implementation of prudent fiscal and monetary policies to maintain price stability while permitting targeted assistance for industries that are susceptible to inflationary pressures.
The study’s relevance to larger entrepreneurial ecosystems is limited because it uses new business entry density as the only proxy for entrepreneurship, ignoring scaling enterprises, innovation-driven startups, early-stage entrepreneurial activity, perceived opportunities and informal ventures.
Acknowledgements
The authors acknowledge the management of North-West University, South Africa, for providing an enabling environment for the conduct of this study. The authors also sincerely appreciate the constructive comments and valuable suggestions of the anonymous reviewers, which significantly contributed to improving the quality of the manuscript.
