The effect of financial inclusion on economic growth: the role of human capital development

Abstract

This research aims to investigate how financial inclusion affects the expansion of the economy in sub-Saharan Africa. It was conducted through a quantitative research design to analyse the interactive effects of financial inclusion and human capital on this growth. The research was conducted in 47 African countries between 2010 and 2020. The study found that financial inclusion and economic growth were positively correlated using a Driscoll Kray error estimate approach. Financial inclusion has a beneficial effect on economic growth through human capital. To boost economic growth in sub-Saharan Africa, the agenda for financial inclusion must be continuously improved, and human capital development must be furthered.

Keywords:

• Financial inclusion
• human capital
• economic growth
• sub-Saharan Africa
• panel regression

Reviewing Editor:

• George Mudimu, Development Sciences, Marondera University of Agricultural Sciences and Technology, Marondera, Zimbabwe

Subjects:

• African Studies
• Development Studies
• Economics and Development
• Regional Development
• Sustainable Development

Introduction

Opinion leaders and experts have recently focused on financial inclusion and its impact on the world economy. Financial inclusion (FI) is a tool financial institutions and policymakers use to support the development of the financial sector and guarantee long-term, equitable, and sustainable economic growth (Collard, 2010). Only 20% of individuals in the poorest developing countries save with legal financial institutions, and 65% of adults still require access to a transaction account (Pazarbasioglu et al., 2020). According to the Global Findex research, just 33% of individuals in sub-Saharan Africa (SSA), the least banked region globally, held a bank account with a formal financial institution in 2017 (Demirguc-Kunt et al., 2018). The adoption of mobile money is primarily responsible for the recent increase in the percentage to 71% (Demirgüç-Kunt et al., 2018). According to statistics, many countries have decided to make FI an official aim to assist their development, which includes economic growth (Sahay et al., 2015). The main objectives of the UN-established Millennium Development Goals are to achieve sustainable development and enhance global welfare with a focus on equality and respect for human rights to foster social, economic, and environmental progress.

Through FI, economic players can expand access to financial services and encourage economic growth in underserved market segments. Some scholars claim a strong covariant relationship between FI and a country’s economic growth (Beck et al., 2007; Sarma & Pais, 2011). A strong and resilient financial system is the foundation of any nation’s economic expansion, advancement, and development. Economic performance is sluggish, and income inequality results from an exclusive financial system. Low FI in Africa has had a detrimental impact on the continent’s economic performance and increased poverty levels despite economic growth promoting productivity and equitable development. Pearce (2011) claims that FI is now generally acknowledged and has taken precedence in many nations’ policies. Establishing a sturdy base for a country’s financial infrastructure is crucial to support its economic expansion and advancement (Sharma, 2016; Huang et al., 2023; Ali et al., 2021). Different policies have been put in place by policymakers in sub-Saharan African countries to improve the accessibility of financial services for those who are excluded from the financial industry. To reduce poverty, these policies classify savings, insurance, money transfers, loans, and pensions as financial products. Scholars, legislators, policymakers, official and informal financial stakeholders, and the general public all support FI. It is an essential prerequisite for economies. Furthermore, to lower poverty and encourage greater economic involvement, the World Bank and the G20 have raised FI in developing countries (Rewilak, 2017).

The empirical study produced conflicting results about how FI affects economic growth. For example, research has shown that, regardless of the nation, there is a positive correlation between FI and economic growth (Dixit & Ghosh, 2013; Mohan, 2006; Onaolapo, 2015; Sharma, 2016; Swamy, 2014). Positive wealth creation and long-term, sustainable economic growth are made possible by inclusive finance (Dahiya & Kumar, 2020). However, occasionally, it might only have a favourable effect (Mehrotra & Yetman, 2015). Financial stability can be adversely affected, and distressed finance can arise from FI attained through rapid credit growth or unregulated fund intermediation–growth relationship (Mehrotra & Yetman, 2015). From an economic perspective, FI might be seen as a means of gaining extra revenue or as having the potential to result in an inefficient use of resources, which would impede economic growth (Sharma, 2016; Thathsarani et al., 2021). Inverse relationships between FI and economic growth have been shown in prior research (Chiwira et al., 2021; Menyelim et al., 2021; Nwisienyi & Obi, 2020). Even though research on the connection between FI and economic growth has advanced, prior studies used a range of proxy variables to cover numerous FI indicators. When calculating the FI index, the three facets of banking usage, penetration, and availability, have all been taken into account (Sarma, 2008). Evans et al. (2000) assessed the development of human capital as a part of economic growth, but they did not consider the relationship between it and FI. Nonetheless, it seems that the literature has mostly ignored the way that FI and the development of human capital interact to influence economic growth in sub-Saharan Africa. We argue that human capital will greatly enhance Africa’s economic development when paired with FI.

This study employs the theories of endogenous growth and human capital to comprehend how a knowledge-based economy will have positive externalities and spillover effects to boost economic growth. Thus, the theoretical foundation for this research is the endogenous growth hypothesis (Romer, 1986), which maintains that acquiring new knowledge is the mechanism through which endogenous growth is realised. The finance-growth literature has used this idea considerably (Aghion et al., 1999; Macek, 2015; Schilirò, 2019).

There are two objectives for this paper. We first investigate the relationship between FI and economic growth. We then assess how FI and human capital interact to affect economic growth. Our study adds to the body of knowledge in many ways. Firstly, it is noteworthy for being one of the few international studies emphasising FI and how it fosters economic growth. Our study adopts a more comprehensive cross-country methodology, whereas previous empirical studies mostly focused on specific economies to evaluate the association between FI and economic growth (Lenka & Sharma, 2017; Nwafor & Yomi, 2018; Sharma, 2016). Thirdly, we enhance the current study by Ahmad et al. (2021) with human capital development, another critical factor that may substantially impact the relationship between FI and economic growth in Sub-Saharan African countries. Both intellectually and experimentally, it is recognised that FI is important in promoting economic growth. After much discussion, there is currently a consensus that FI may support growth, poverty alleviation, and overall financial development as policy goals. Greater access to financial services raises the standard of living for the disadvantaged (Hannig & Jansen, 2010). Formal financial intermediaries are still necessary for the majority of the world’s impoverished. Sub-Saharan Africa’s underdeveloped economic structure and dearth of reliable financial institutions impede economic progress, even if African nations are beginning to recognize the necessity of FI structures. Our results would offer credible and persuasive evidence to regulators, politicians, and scholars in the discussion of FI’s effect on economic growth. We also point out that the link might be strongly impacted by the degree of FI and the economic development (for both developed and developing countries). Even though the World Bank (2019) has classed several under-study countries as developing countries, these countries are actually in the moderate to upper-income range, which makes them an excellent topic for analysing how FI and human capital issues are handled in these settings. The cultural effects of nations that either occupied or conducted business with Africa have left African countries with distinctive historical backgrounds, which makes the African environment an intriguing one for research like this. The weak financial systems in Sub-Saharan Africa need to be strengthened to accommodate the most marginalized.

The paper is organised as follows: Following the introduction, Section 2 provides a quick overview of relevant theories and empirical findings from the literature. Section 3 explains the research method. The data and empirical findings from this study are analysed and discussed in Section 4, and concluding remarks are given in Section 5.

Literature review

According to the Endogenous Growth Theory, the economy’s expansion depends on factors like human capital, innovation, knowledge, learning, and technical advancement. According to this idea, examples of internal or endogenous sources of growth include FI, human capital, and technical advancement. Although it dates back to the Schumperian period, Romer (1986) popularized this viewpoint. Thus, how growth is realised endogenously is new information. Productivity growth and per capita growth are positively impacted by the new knowledge created inside the economic system. Growth-promoting factors include specialisation-based labour division and growing returns to factors. The hypothesis states that positive externalities and spillover effects will support economic growth in a knowledge-based economy. The most important feature of the models is their capacity to allow investment and saving policies to influence a nation’s long-term growth rates (Macek, 2015).

The idea of economic growth still dominates the literature on well-being and the enhancement of human existence. This is because it represents the ultimate objective of world leaders and legislators. Any nation’s economic expansion does signal the advancement and improvement of its quality of life. (Stein, 2019). According to Schumpeter (1947), economic growth is the rise in a nation’s output of commodities and services. The gross domestic product (GDP) is a widely used metric to compare the value of goods and services generated over time. Empirical evidence has demonstrated that numerous factors influence the rate of economic growth. These comprise FI (Kim et al., 2018; Sapovadia, 2018), financial development (Levine, 1997), and financial system indicators (Batrancea et al., 2022).

FI and entrepreneurship are increasingly using the human capital theory (Unger et al., 2011). According to the hypothesis, people with more information have stronger cognitive abilities, which increases their potential for productivity and efficiency in developing activities (Becker, 1964; Mincer, 1974). It is, therefore, the result of both human involvement and intrinsic skills. According to Ahmad et al. (2022), educated individuals are often more adept at using financial services and natural resources than illiterate and unskilled individuals. This study clarifies how FI contributes to economic progress in Africa by integrating human capital as a moderating factor. The success of FI programs depends on human capital components, such as people’s capacity to participate in and manage financial market activities. It is suggested that human capital is a moderator that is essential for enhancing FI’s impact on economic growth. Thus, it follows that FI by itself cannot guarantee economic growth. Thus, this study includes human capital in the suggested model to improve economic growth.

FI has been characterised in many ways in the body of current literature. These definitions, however, are only applicable if low-income and disadvantaged groups can easily access and afford the financial markets’ services. Thus, according to Sarma (2008) and Park and Mercado (2015), these procedures facilitate, provide, and assist individuals in society in using formal financial services. Both voluntary and involuntary FI and exclusion are possible (World Bank, 2014). The percentage of individuals and companies that use financial services and products is used to estimate voluntary FI. It can also be defined as a process by which the service provider provides financial products to all public members at reasonable costs, in appropriate locations, and without discrimination (Bakar & Sulong, 2018). Measurement for involuntary FI or exclusion is based on perceived obstacles to participation in the formal financial system for individuals who cannot do so. These could include usage and access barriers (Camara & Tuesta 2014).

Enhancing FI is a powerful tool that can provide accessible and reasonably priced financial services to marginalised populations sustainably and responsibly (Duvendack & Mader, 2020). This access not only offers people the chance to become more empowered but also holds the potential to significantly reduce poverty. To ensure a substantial improvement in FI, numerous initiatives are being implemented at all governmental levels in both developed and developing nations. The challenge lies in the need for more consensus over FI’s contribution to the phenomena of economic growth (Bello, 2022; Duvendack & Mader, 2020). Several factors have intervened in moderating the relationship between FI and economic growth to generate and solidify their connection. Some well-known moderators are the availability of credit and the geographic reach of banks (Chakravarty & Pal, 2013); financial development (Gleasure & Feller, 2016); mobile technology (Andrianaivo & Kpodar, 2012); and human capital (Ahmad et al., 2022).

Human capital is not just a crucial element but the driving force of economic development and advancement. The literature has conclusively demonstrated how finance contributes to economic growth. One of the most important resources for adding value to other resources, be they natural, technological, or service-related is human capital. A rise in competition can cause countries to develop relatively faster when they have sufficient pools of high-quality human resources (Rahmi, 2020). The curse of sub-Saharan Africa’s natural resources must be broken through human capital development, which is reflected in education and skill development and has the potential to boost productivity (World Development Indicators (WDI), 2020). As a result, there are numerous studies on the benefits of education and how it affects economic growth. The literature has emphasised how important human capital is to FI for it to have the desired effect. Boachie and Adu-Darko (2022) discovered that higher school enrollment and sustained education in northern Ghana were caused by increased FI through savings groups in Ghana. There are some restrictions on this study. Time series data, which are not universally applicable, were used in this study. Again, Arora (2012) emphasises the need to take into account the potential negative effect that low human capital development and high illiteracy levels can have on people’s ability to reap the desired benefits associated with FI efforts. Aurora used 21 developing Asian countries from 2000 to 2010 in a cross-sectional analysis. To produce endogenous growth, Chou and Chinn (2001) investigated the connection between financial development and human capital. They created a human capital-based theoretical model. Human capital development is the outcome of the model’s creation of innovation and financial development. Thus, according to their model, financial innovations represent wider ranges of financial products and intermediaries by making it easier for households to invest their savings, which in turn promotes the development of human capital. Rahmi (2020) examines the impact of FI and other variables on human capital investment to strengthen the case for a relationship between FI and human capital (See also Attanasio, 2015). In 2019, Rahmi (2020) used data from 800 households in the Indonesian province of Aceh to perform a pooled regression analysis. They discover that, among other things, FI and income levels have a positive impact on human capital. Nevertheless, these studies were limited to Asian nations and do not adequately represent sub-Saharan Africa. Additionally, they did not employ human capital development as a moderating factor.

Saydaliyev et al. (2022) examined the indirect effects of remittance inflows through economic growth in wealthy and emerging countries influenced by FI and human capital between 2007 and 2018, using the dynamic panel data technique. The results demonstrated that FI and human capital are the main drivers of economic growth in developing countries that receive remittances. More specifically, while human capital growth directly supports robust economic activity, increased FI efficiently absorbs and directs remittance inflows into legal and productive economic activities. Similarly, Huang et al. (2023) evaluated the impact of FI and income inequality on human capital in SSA countries using panel data from 36 SSA countries between 2004 and 2019. According to the research, high FI and a reduction in income inequality improve human capital.

The relationship between FI’s contribution to economic growth and human capital in the region is examined by Thathsarani et al. (2021) using secondary data from eight South Asian countries between 2004 and 2018. The FI index was produced using a Granger causality test on panel data using principal component analysis with vector error correction models. The study concluded that although FI has a great short-term impact on economic growth, it has a negative impact on South Asian countries’ development of their human capital. They also noted that domestic credits impact short-term growth and the economy’s human capital development in the private sector. However, none of these studies considered human capital development as a moderating factor.

The relationship between FI and economic growth in SSA countries was empirically assessed by Adedokun and Ağa (2021) from 2004 to 2017 using the generalised technique of moments and Dumitrescu-Hurlin’s causality test. The results show a short-term causal relationship between FI and economic growth with FI significantly boosting economic growth in SSA countries. Similarly, Stein and Yannelis (2020) investigated the impact of FI on human development in Sub-Saharan Africa. The study uses a panel data approach and the Generalized Method of Moments (GMM) method. The results show that FI promotes human development.

Human capital development has become even more significant in the financial industry with the recent explosion of digital financial technologies. Numerous studies have identified new financial technology innovations as drivers of economic growth, such as cashless payments, online banking, and automated teller machines (ATMs) (Attanasio, 2015; Teixeira & Queirós, 2016). Various researchers (Danvila-del-Valle et al., 2019; Donate et al., 2016) looked at the factors related to the expansion of human capital. Since information and communication technology (ICT) provides people with access to technological knowledge, skill enhancement, and educational opportunities, it has been acknowledged as one of the critical factors influencing human capital development (Kanungo & Gupta, 2021).

There are numerous theoretical explanations for why human capital is so necessary. The most popular theories are the Mankiw et al. (1992) enhanced Solow Model and the endogenous growth theory (Romer, 1990). Using the theory of change as a foundation, Duvendack and Mader (2019) link human capital to the FI-economic growth nexus. The creation of a new financial innovator variable and the determination of the long-term impact on economic growth through the use of human capital and easily accessible financial goods both bolstered the case for the endogenous growth model (Gourène & Mendy, 2017). Consequently, human capital development is intrinsically related to FI, which brings together the finance industry and information technology. The literature has shown a strong correlation between FI and investments in human capital (Peia & Roszbach, 2015). There is also a discussion of the two-way causal relationship between these two variables (Macek, 2015).

Previous research has shown a positive correlation between FI and human capital development (Roberts-Mahoney et al., 2016). An analysis conducted from 2005 to 2014 on frontier nations revealed a relationship between low FI and low human capital development (Nwafor & Yomi, 2018). Sima et al. (2020) analysed the connection between human capital and first-order and level variations in production. The findings demonstrate a statistically significant and positive correlation. Some researchers (Shafuda & De, 2020; Villela & Paredes, 2022) found an inverse relationship between human capital and economic growth, unlike Akinlo and Oyeleke (2020) who found a positive relationship. Donate et al. (2016) use a model that incorporates elements of new and classical theories of economic growth to highlight the different effects of human capital depending on a country’s developmental stage. According to them, if productivity in accumulating knowledge is high enough, the model offered by Uzawa-Lucas (1965–1988; Lucas, 1988) may explain the development mechanisms. However, the Grossma and Helpman (1994) model can be described as an economy with many products and a high level of research and development spending if technological advancement is considered an endogenous element. During the initial stages of development, physical capital plays a major role in driving income growth per capita as knowledge is acquired through continuous education and training, leading to higher stages of development.

Kpodar and Andrianaivo (2011) examined how FI and ICT affected economic growth in various African economies between 1988 and 2007. They investigated several ICT indicators, including local call rates and the penetration of mobile and fixed phones, to evaluate their effect on economic growth. To lessen endogeneity issues, the study employed the GMM estimator system. Their results validated the hypothesis that ICT, especially the widespread use of mobile phones, plays a major role in the economic development of African countries. The benefits of increased mobile phone usage for development are partly attributable to improved FI. The impact of FI and mobile phone advancements on economic growth are combined, particularly in countries where mobile financial services are widely used. Building on this background, Evans (2018) investigated the relationship between mobile phones, the internet, and FI in Africa. The study’s empirical analysis employed the fully modified ordinary least squares approach and Granger causality tests. The findings show that FI has spread throughout Africa due to rising internet usage and mobile payments, which are essential elements of FI. Additionally, the causality test reveals a one-way causal relationship between FI and mobile device and internet use. Therefore, the results demonstrate that FI is facilitated by mobile phones and the internet. Oruo (2013) examined the connection between FI and Kenya’s economic growth. The result showed a strong correlation between FI and economic growth, especially regarding the banking sector’s branch networks, mobile money users, and accounts. Onaolapo (2015) analysed the impact of FI on Nigeria’s economic growth and found a direct link between FI and economic growth. The author also gave examples of how FI significantly improved bank branch networks, small business lending, and loans to rural areas, all of which aided in the fight against poverty and made financial transactions easier. Huang et al. (2023) used panel data spanning 36 countries from 2004 to 2019 to investigate the effects of FI and income inequality on human capital in SSA countries. According to their findings, human capital improvements correlate with increased FI and decreased income inequality. Using panel data from 74 countries between 2004 and 2020, Hussain et al. (2023) empirically examined the relationship between FI and carbon emissions using the Environmental Kuznets Curve. Their findings supported the hypothesis that an inverse U-shaped relationship exists between carbon emissions and financial system inclusivity. Except for standalone economies, this relationship was noted in developed, emerging, and frontier economies. Between 2009 and 2019, Abdelghaffar et al. (2023) assessed the relationship between FI and human development in nations that fall into various income categories. By using the generalised method of moments, they showed that FI has a bigger effect on human development in low- and lower-middle-income countries than in high- and upper-middle-income countries. Ali et al. (2021) investigation aimed to ascertain whether FI fosters economic expansion in the Islamic Development Bank’s member nations. Their Granger causality analysis, which employed data from 45 countries between 2000 and 2016, demonstrated a unidirectional and bidirectional causal relationship between FI indicators and economic growth. Their results showed that the FI index positively impacted economic growth in the Islamic Development Bank’s member nations. Although the aforementioned studies look at the connections between FI, human capital, and economic growth, none look at how human capital influences the relationship between FI and growth.

Chatterjee (2020) used a panel data analysis to examine how FI, or the diffusion of information and communication technology, affects African economic growth. The results show that FI can improve per capita growth, both alone and in conjunction with internet and mobile access. However, there is room for considerable improvement in the role of ICT indicators in developing countries in promoting growth and FI (Chatterjee, 2020). Similarly, Agyekum et al. (2022) demonstrated a favourable correlation between FI and African economic growth initiatives.

The reviewed papers indicate that while certain studies focus specifically on African countries, others are non-African. Some studies defined the concept using an index of digital FI, while others used indicators related to ICT. Several empirical studies support the idea that FI spurs economic growth, but this theory may only occasionally hold. Some obstacles to economic growth are lack of a solid financial system, effective financial instruments, and insufficient financial policy. The beneficial impact of FI on economic growth is limited to specific economic systems. There are certain restrictions on these studies. A limited number of studies have employed time series data, which is not universally applicable, and other studies have neglected to consider endogeneity. By considering the moderating effect of human capital development in these studies, this study seeks to close the gap.

According to recent research, the impact of FI and human capital on economic growth is exciting. An overall assessment of the literature indicates that the development of scholarly work in this field has gone through several stages, beginning with conceptual development and working through problems and challenges unique to the economy before developing an index to measure FI. Because of the fragility and development of the African financial market, the literature on FI and economic growth has become increasingly critical.

Data and econometric model

This study undertakes a rigorous examination of the relationship between FI and economic growth in 47 Sub-Saharan African countries, leveraging the crucial role of human capital. The nations under study have implemented a range of coordinated operations, such as facilitating trade, supporting small and medium-sized businesses, and mobilising resources within their member nations. Our study utilises dynamic panel data from the World Development Indicators spanning the years 2010–2020, ensuring a robust and comprehensive analysis. The sample is selected based on data accessibility.

In this study, real GDP per capita is based on 2020 constant prices and expressed in US dollars as per the literature and used as a measure of economic growth. Variables that affected GDP growth were included as controls. In the 2014 Global Financial Development Report, the World Bank defined FI as the proportion of the population that uses financial services. Following Kim et al. (2018), the measures of FI in this paper are automated teller machines per 100,000 adults (ATM), borrowers from commercial banks per 1,000 adults (BOR) to GDP, depositors of commercial banks per 1000 adults (DOCB), and commercial bank branches per 100,000 adults (CBB). Earlier researchers have used these indicators to depict the usage and accessibility aspects of FI based on physical points of service (Ozili, 2022; ul Ain et al., 2020). Human capital development is measured by secondary school enrollment (SSE) (Chou & Chinn, 2001). SSE establishes the foundation for lifelong learning and human capital development by incorporating basic education up to the primary school level. The percentage of total enrollment, regardless of age, to the population in that age group that officially corresponds to the stated educational level is known as the SSE ratio (World Development Indicators (WDI), 2022). To promote long-term economic growth and reduce poverty in Africa, readily available, high-quality secondary education may be essential (Chatterji et al., 2019; Kim, 2019). It accomplishes this by unleashing productivity gains, particularly in the informal economy, and providing a growing labour force with relevant skills (Abdelghaffar et al., 2023; Crama et al., 2023). Secondary education makes the transition from primary to postsecondary education possible. Determining the quality and degree of influence at the secondary and tertiary levels is crucial in meeting the country’s increased need for a skilled labour force (Crama et al., 2023). According to Tlali and Sepiriti (2023), secondary education fosters self-discipline, moral and spiritual principles, individual and collective accountability, and the ability to model positive behaviours. The study’s variables were chosen using a backward elimination method.

Based on neoclassical theory, past studies indicate that a few factors influence economic growth. Therefore, we include controls for economic growth in this research using the methodology of Cherif and Dreger (2016). These controls include international trade as measured by trade openness (TROP), a commonly used indicator that was proposed by Harrison (1996), and inflation (annual %) for consumer prices (INF). Economic growth is anticipated to be directly impacted by TROP. The inflation variable, which calculates the annual percentage change in the consumer price index, is expected to have a negative effect on economic growth as a gauge of macroeconomic (in)stability. Gross fixed capital creation as a percentage of GDP serves as a proxy for investment rates, which are predicted to positively impact economic growth. Government spending, which measures final government consumption expenditure, determines government size when expressed as a percentage of GDP. It is anticipated that government spending will directly affect economic growth. The percentage of employed individuals in the 15–60 age range is utilised as a stand-in for labour force participation, which is predicted to favour economic expansion. FI drives financial activity and is contingent on the volume of individuals utilising financial services. We predict that FI and human capital development will support economic growth because educated people frequently use natural resources and financial services more effectively than unskilled and illiterate people (Ahmad et al., 2022). Following Dreger et al. (2016) and Azuh et al. (2020), logarithms were used to transform each variable. These variables were expressed as a percentage of GDP.

Model specification

Analyzing the dynamics of the African economy to determine whether there is a growth–finance nexus or a finance–growth nexus is crucial for understanding the underlying relationship between FI and economic growth and the role of human capital. This study uses the secondary school enrolment variable as the stock of human capital, utilizing Ibrahim’s (2018) endogenous model. Principal Component Analysis (PCA), a method commonly used in economics and finance studies, is used to construct the FI index (FII) (Le et al., 2019, 2020; Nguyen et al., 2021). This method tries to reduce the number of dimensions in multidimensional data by incorporating several aspects of FI. By rearranging the data to condense a large number of original variables into smaller factors or components, the goal is to extract as much information as possible from the original dataset. We need to find components M = [M1, M2,…, Mn], which are linear combinations of the initial variables x = [x1, x2,…, xn], to obtain the maximum variance. As mentioned, our study primarily focuses on four key elements in the SSA sample. ATMs per 100,000 adults are used to estimate the first dimension or FI1. BOR per 1000 adults is used to measure the second dimension or FI2. The DOCB per 1000 adults makes up the third dimension or FI3. The number of CBB per 100,000 adults serves as the indicator for the fourth dimension, or FI4. Earlier studies (Hussain et al., 2023; Ajide et al., 2022; Ali et al., 2021; Le et al., 2019) have used these dimensions. Using the PCA method, we created the following model to answer our research question and create a composite index for FII: $$\mathit{\text{GD}}{P}_{i,t}={\beta }_{1}F{I}_{i,t}+{\beta }_2\mathit{\text{HC}}{D}_{i,t}+{\beta }_3\mathit{\text{GOVEX}}{P}_{i,t}+{\beta }_4\mathit{\text{INF}}{L}_{i,t}+{\beta }_5\mathit{\text{TRO}}{P}_{i,t}+{\beta }_6\mathit{\text{LF}}{P}_{i,t}+{\beta }_7\mathit{\text{FC}}{F}_{i,t}+{\theta }_i+{\mu }_t+{\vartheta }_{i,t}$$

Therefore, the model given in Equation (1) is expanded to include the interaction term (HCit*FIit) in order to measure the indirect effect of FI on economic growth via the human capital channel. …………………(2) $${\mathit{\text{GDP}}}_{i,t}={\gamma }_1{\mathit{\text{FI}}}_{i,t}+{\gamma }_2{\mathit{\text{HCD}}}_{i,t}+{\gamma }_3{\mathit{\text{GOVEXP}}}_{\mathit{\text{it}}}+{\gamma }_4\left({\mathit{\text{HCD}}}_{i,t}\mathrm{*}{\mathit{\text{FI}}}_{\mathit{\text{it}}}\right)+{\gamma }_5{\mathit{\text{INFL}}}_{i,t}+{\gamma }_6{\mathit{\text{TROP}}}_{i,t}+{\gamma }_7{\mathit{\text{LFP}}}_{i,t}+{\gamma }_8{\mathit{\text{FCF}}}_{\mathit{\text{it}}}+{ϵ}_i+{\delta }_t+{\epsilon }_{i,t}$$…………………(2)

Where:

${\mathit{\text{GDP}}}_{i,t}$= Real GDP per capita in the country i at time t

${\mathit{\text{LFP}}}_{i,t}$= Labour force in the country i at time t

${\mathit{\text{FCF}}}_{\mathit{\text{it}}}$ = Stock of physical capital in the country i at time t

${\mathit{\text{HCD}}}_{i,t}$ = Stock of human capital in the country i at time t

${\mathit{\text{FI}}}_{i,t}$ = Financial inclusion indicators in the country i at time t

${\mathit{\text{GOVEXP}}}_{\mathit{\text{it}}}$ = Government expenditure in the country i at time t

INFL_it = inflation in the country i at time t

TROP_it = Trade openness in the country i at time t

t_i = Time effect in the country i

ϑ_t = Country fixed effect at time t

Ɛ_it = Error term in the country i at time t.

Based on ${\gamma }_1$ and γ₂, the direct effects of FI and human capital are investigated; meanwhile, γ₄ is used to assess the interactive term’s indirect effects. Based on previous research, we anticipate that there will be a positive correlation between FI and human capital. However, since learning and teaching must be effective and efficient if the ratio is low, the SSE is predicted to have a negative impact on growth. However, an interactive term involving FI and human capital is anticipated to have a positive effect.

Panel models are used in these kinds of studies because they minimize estimation biases from combining data sets into a single time series. Nonetheless, fundamental difficulties arise with standard panel estimations, such as the pooled ordinary least squares, fixed, and random effects models. Because it imposes and assumes homogenous intercept and slope parameters for all cross-sections while ignoring their heterogeneity, the pooled OLS, for example, is frequently a very restrictive model and permits the error term to correlate with some regressors (Asteriou & Hall, 2011; Gujarati & Porter, 2009). Conversely, the fixed effects (FE) model assumes country-specific intercepts but common variance and slope estimators. Dummy variables can be used to observe the cross-sectional and time effects, particularly in two-way FE models. Nevertheless, the loss of degrees of freedom in the FE estimation approach poses a serious risk.

The results of the Breusch-Pagan heteroskedasticity test indicated a p-value of less than 0.001, which suggests that heteroskedasticity is present, and the null hypothesis of constant variance was rejected. Predictions will therefore be skewed and inconsistent if they are based on their regression estimates. Moreover, first-order autocorrelation is implied by the significant Wooldridge test result for autocorrelation at 5%. This suggests that there is a time correlation between the residuals. The residuals exhibit cross-sectional correlation at the 5% significance level, according to Pesaran’s test of cross-sectional independence. According to Hoechle (2007), heteroskedasticity, cross-sectional dependence, and first-order autocorrelation suggest that Driscoll and Kraay standard errors should be used when estimating the model to prevent estimation bias. When there is cross-sectional dependence, Driscoll and Kraay’s standard errors (DKE) are preferred (Hoechle, 2007). These standard errors demonstrate good calibration when cross-sectional dependence is seen in the regression residuals. Therefore, DKE were used to estimate the model. Important economic variable observations and their correlations were analysed using descriptive analysis.

Results

This section presents the empirical findings in detail and investigates the connections between different financial indicators, human capital, and economic growth. Table 1 provides variable descriptive statistics. Table 2 displays the direction of the associations between the variables and their correlation.

Table 1. Descriptive statistics.

Variables	Obs	Mean	Std	Min	Max
LGDPPC	460	3.232	0.420	2.432	4.202
LGOVEXP	449	1.168	0.185	0.555	1.608
LINFL	418	0.604	0.449	−0.882	2.177
LTROP	455	1.809	0.294	−0.105	2.542
LLFP	412	1.813	0.0822	1.647	1.974
LSSE	250	1.687	0.208	1.115	2.039
FII	239	−3.74e-10	1.000	−2.555	1.776

Table 2. Pearson correlation matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	VIF
(1) LGDPPC	1
(2) FII	0.829***	1					1.97
(3) LLFP	−0.479***	−0.293***	1				1.32
(4) LTROP	0.329***	0.399***	0.110**	1			1.84
(5) LINFL	−0.095*	−0.143**	0.122**	−0.343***	1		1.15
(6) LGOVEXP	0.246***	0.480***	0.041	0.252***	−0.052	1	2.22

*** p < 0.01; **p < 0.05; *p < 0.1.

NB: GDPPC: GDP per capita(log); GOVEXP: government expenditure; INFL: Inflation; TROP: trade openness; LFP: labour force participation; SSE: secondary school; FII: financial inclusion index.

The regression variables’ descriptive statistics are shown in Table 1. In Africa, 52% of students enroll in secondary school on average, demonstrating high literacy and human capital. Nevertheless, because only 13.3% of 100,000 adults have access to an ATM on average, the FI in ATM tellers per 100,000 adults is extremely low. On the other hand, a mean value of approximately 73% indicates that adults have very high loan access. The average inflation rate in Africa is approximately 9.15%, whereas the average government consumption expenditure as a percentage of GDP is approximately 15.4%. The large range between the lowest and maximum figures for inflation and government spending indicates significant regional variations throughout Africa. Again, 74.6% of GDP is contributed by average trade openness, indicating that trade openness in Africa is high. This percentage will increase to 74.6% of GDP between 2010 and 2020. Additionally, the average labour force participation rate suggests that 65.6% of the population is of working age, suggesting that the majority of people in Africa are young.

Table 2 reports Pearson’s correlation, where the coefficient varies from 0.027 to 0.48, indicating the absence of multicollinearity. There is no evidence of multicollinearity using the variance inflation factor because the VIFs are below 10. While Table 2 shows a significant direct relationship between FII, TROP, and GOVEXP and economic growth, LFP and INFL negatively relate to economic growth. Therefore, an increase in FII leads to an improvement in economic growth.

Regression output

According to the post-estimation test results, all of the tests are significant, with probability values of 0.000, which is less than the acceptable 0.05 significance level. To choose between the random and fixed-effects models, the Hausman test for FE is used. The result rejected the null hypothesis, favouring random effects and suggesting that the FE is appropriate. Furthermore, a p-value of less than 0.001 was obtained from the Breusch-Pagan heteroskedasticity test, indicating the rejection of the constant variance null hypothesis and the existence of heteroskedasticity. Predictions will, therefore, be skewed and inconsistent if they are based on their regression estimates. Additionally, first-order autocorrelation is present because the Wooldridge test for autocorrelation is significant at 5%, suggesting that the residuals correlate over time. According to Pesaran’s test of cross-sectional independence, the residuals exhibit cross-sectional correlation at a 5% significance level. According to Hoechle (2007), heteroskedasticity, cross-sectional dependence, and first-order autocorrelation suggest that Driscoll and Kraay standard errors should be used when estimating the model to prevent estimation bias. For this reason, Driscoll and Kraay standard errors were used to estimate the model.

Table 3 shows the impact of FI and human capital on economic growth in Africa from 2010 to 2020. Models 3 and 4 show the FE as the robustness test results, whereas models 1 and 2 represent DKE with the moderating effect. Conclusions, however, are predicated on the standard errors of Driscoll Kraay. The regression model is deemed fit and dependable based on the significant p-values of the F-statistics (p < 0.0001). The FE regression model is warranted due to the Hausman statistics’ significant p-value (p < 0.0053). Lastly, the Durbin-Wu Hausman’s insignificant p-value suggests that the instruments are valid and the regression model is free of endogeneity problems (p < 0.0205).

Table 3. Effect of financial inclusion and human capital on economic growth.

Variable	DKE (1)	DKE (2)	FE (1)	FE (2)
FII	0.335*** (00231)	−0.139 (0.101)	0.00025 (0.0163)	−0.126** (0.0525)
LSSE		0.623** (0.0975)		0.0907 (0.107)
FII*LSSE		0.212*** (0.0599)		0.0924*** (0.0272)
LLFP	−0.680*** (0.0831)	−0.673*** (0.0984)	0.0041 (0.117)	−0.597*** (0.196)
LTROP	0.285** (0.120)	0.246** (0.0760)	0.0281 (0.0234)	0.0398** (0.014)
LINFL	−0.0733** (0.0330)	−0.354 (0.0323)	−0.0048 (0.0063)	−0.00238 (0.00552)
LGOVEXP	−0.357*** (0.102)	−0.484*** (0.0662)	−0.0625 (0.0430)	0.129** (0.0426)
Year	−0.00819** (0.0029)	−0.002821 (0.00175)	0.00601*** (0.0004)	0.00614** (0.0019)
Countrylet	−0.00571*** (0.000836)	0.00262* (0.00187)	−0.385** (0.0349)	−0.419* (0.195)
Constant	21.07** (5.748)	9.099** (3.425)	0(0)	0(0)
Observation	200	107	200	107
R-squared	0.744	0.888
F-statistics	8210.46		376951.96
Prob > F	0.0000		0.000
No of groups	27	18	27	18
DKE	Yes	Yes
FE			Yes	Yes

Standard errors in parentheses.

*** p < 0.01; **p < 0.05; *p < 0.1.

NB: SSE: secondary school enrolment; ATM: ATM teller per 100,000 adults; BOR: borrowers from commercial banks per 1000 adults.

Effect of financial inclusion and human capital on economic growth

Table 3 shows how FI and human capital will affect economic growth in Africa between 2010 and 2020. Models 1 and 2 depict DKE with the moderating effect, whereas models 3 and 4 display the robustness test results as the FE. Still, our conclusions are based on Driscoll Kraay’s standard error model. The F-statistics’ significant p-values (p < 0.0001) demonstrate how well-fit and dependable the regression model is. Regression with FE is warranted due to the Hausman statistics’ significant p-value (p < 0.0053). Ultimately, Durbin-Wu Hausman’s insignificant p-value (p < 0.0205) suggests no endogeneity problems with the regression model and that the instruments are reliable. While the FI index is significant at a 5% level, human capital, as measured by secondary school enrollment, has a significant positive impact on economic growth at a 1% significance level. Consequently, when formulating FI strategies, policymakers must prioritise accelerating human development. For instance, developed countries are usually thought of as having high incomes. Compared to other income brackets, their economy is considered more mature due to their advanced infrastructure, high GDP, robust social welfare systems, high levels of industrialisation, and political stability. Since these nations have advanced levels of human development, the impact of FI is less noticeable than it is in underdeveloped nations. Rich and upper-middle-class countries have already exceeded a critical financial literacy barrier and have lower poverty rates, greater employment rates, and better access to healthcare. On the other hand, low-income countries are often classified as underdeveloped, developing, emerging, or newly industrialised. These nations face challenges brought on by their underdeveloped economies and low levels of human development, such as shorter life expectancies, higher infant mortality rates, poorer educational standards, environmental and climate change problems, and poorer health conditions. Previous empirical research (Fang & Chen, 2017; Qadri & Waheed, 2013; Tandrayen Ragoobur & Narsoo, 2022; Abdelghaffar et al., 2023; Huang et al., 2023; Hussain et al., 2023; Ali et al., 2021) supports the idea that human capital and economic growth are positively correlated. Higher human capital quality nations stand to gain more from the financial sector. Numerous experts, such as financial analysts in these nations, are capable of selecting options efficiently and effectively. They can innovate to promote economic growth and are more adept at utilising opportunities and resources (Sarwar et al., 2021). Underdevelopment of human capital can result in financial resource misappropriation, which would be detrimental to economic growth. The results of earlier studies support this conclusion (Chiwira et al., 2021; Menyelim et al., 2021; Nwisienyi & Obi, 2020). Given the strong positive correlation between school enrollment and economic growth, human capital would foster labour productivity, encourage innovation, and lessen income inequality in society, all of which would enhance economic growth. Because FI is more affordable and easier to use for individuals and businesses than traditional financial services, which are still costly and complex, it contributes to the growth of the national economy. Low-cost financing gave rise to vulnerable and low-income people who organized manufacturing and other services in rural areas and increased output. At the local level, this value-added correlates with state and national output growth, resulting in strong macro-level growth that eventually drives economic growth.

The findings demonstrate that governments in sub-Saharan Africa need to establish an appropriate financial infrastructure to improve the financial sector’s penetration and remove access obstacles. Furthermore, by providing financial services to the excluded population, Demirguc-Kunt et al. (2018) contend that FI expedites the accumulation of assets and establishes credit, contributing to growth as FI increases. In order to promote financial development and economic growth, FI would guarantee efficiency. The outcome supports earlier research by Hannig and Jansen (2010), who found a strong and positive influence on economic growth. Innovations like FI and human capital can potentially boost economic growth, which is in line with the endogenous growth theory. Nonetheless, Table 4’s analysis of the negative impact of commercial bank borrowers per 1000 adults on economic growth shows that, as the number of borrowers rises, credit risk also increases, impeding the development of the financial system for growth. On the other hand, at a 1% level, government spending and the labour participation rate have a substantial inverse relationship with economic growth. Surprisingly, increased labour participation would not boost productivity or economic growth. This finding contradicts that of Tsani et al. (2013), who found that female labour force participation directly and significantly influences economic growth in the South Mediterranean region.

Table 4. Effect of financial inclusion and human capital on economic growth.

Variable	DKE (1)	DKE (2)	FE (1)	FE (2)
LBOFCB	0.0048 (0.0436)	−2.5685*** (1.0503)	−0.0174 (0.0371)	0.64601*** (0.1933)
LBOFCB*LSSE		1.7009*** (0.5989)		−0.3963*** (0.1097)
LATM	0.5168*** (0.0620)	−0.1975 (0.6832)	0.0107 (0.0394)	−0.3931** (0.1908)
LATM*LSSE		0.3186 (0.3307)		0.2277* (0.1214)
LDOCB	0.0930*** (0.0229)	1.7249*** (0.5842)	0.0412 (0.0328)	−0.7438*** (0.2517)
LDOCB*LSSE		−0.9713*** (0.3609)		0.4679*** (0.1567)
LCBB	−0.2817 (0.0265)	1.3236*** (0.4658)	−0.0216 (0.0623)	1.2173*** (0.4411)
LCBB*LSSE		−0.8607*** (0.2817)		−0.6770*** (0.2523)
LLFP	0.7019*** (0.0691)	−0.1283 (0.1299)	0.0472 (50069)	−0.6910*** (0.2628)
LTROP	0.2995*** (0.1114)	0.3976*** (0.0421)	0.0412 (0.0545)	0.0434609 (0.0404)
LIFL	−0.0791*** (0.0302)	−0.0573 (0.0362)	−0.0389 (0.0093)	0.0005 (0.0060)
LGOVEXP	−0.3246*** (0.1060)	−0.6905*** (0.0616)	−0.0521 (0.0780)	0.0719 (0.0658)
YEAR	−0.0082*** (0.0027)	−0.0100*** (0.0030)	0.0052*** (0.0017)	0.0076*** (0.0018)
Countrylet	−0.0056*** (0.0009)	−0.0015 (0.0017)
Constant	20.4495*** (5.352684)	22.9972*** (6.3408)	−7.5358*** (3.5317)	−10.848*** (3.4574)
Observation	200	107	200	107
R-squared	0.746	0.888	24.62	0.765
F-statistics			376951.96	18.97
Prob >F			0.000	0.000
No of groups	27	18	27	18
DKE	Yes	Yes
FE			Yes	Yes

Standard errors in parentheses.

*** p < 0.01; **p < 0.05; *p < 0.1.

For the entire sample, the government’s expenditure to GDP ratio is negative and significant, indicating that the GDP per capita increases as government spending increases. Trade openness positively impacts economic growth, suggesting that more international trade would boost productivity and efficiency and spur economic expansion. Trade openness has varying effects on GDP per capita growth in sub-Saharan African nations. As a result, the government causes private investment from individuals and businesses to be crowded out. Our results support the findings of Hassan et al. (2011), who found a positive correlation between economic growth and fixed capital formation. As predicted, inflation has a negative impact on economic growth (Ekinci et al., 2020). Elevated inflation impedes economic operations, undermining sustainable economic expansion.

The moderating effect of human capital and financial inclusion on economic growth

To examine how increased human capital may influence the impact of FI on economic growth, Table 3 Model 2 incorporates the moderating effect of both FI and human capital. The combination of FI, economic growth, and school enrollment has a significant positive interactive effect. As a result, FI and school enrollment work together to promote economic growth. Consequently, FI has a positive impact on economic growth through human capital. This supports the endogenous theory, which holds that human capital is developed domestically within nations, and previous studies (Beck et al., 2011). Therefore, raising the calibre of human capital in SSA boosts FI and economic expansion. Credit risk is decreased by human capital, which promotes growth and FI.

According to the results, a one-unit increase in the FI variable increases economic growth by 0.212% when there is an improvement in human capital. The study shows that higher FI correlates with higher human development and is associated with higher human development. The study backs up the claims made by Beck et al. (2007, 2011) that access to financial services promotes faster development. Financial technology is an essential tool for advancing economic growth and decreasing poverty. Like its influence in many other industries, FI, which includes digital technology, is unique in the financial services sector because it makes higher revenue generation possible. Promising growth in certain African markets indicates that digital financial services have the potential to further and expedite FI globally, especially in developing countries.

Components of financial inclusion index

This section considers the individual FI components to reexamine the research questions as further robustness tests. The independent variables’ variance inflation factors have an average value of 3.56, which is below 10, suggesting no multicollinearity among them. A combination of LATM and LSSE positively impacts economic growth. This relationship is explained by the recognition of technology as the main force behind the development of human capital, enabling people through the application of their technological know-how and proficiency (De Grip & Sauermann, 2012). Furthermore, by extending financial technology, ATMs improve FI (Thathsarani et al., 2021).

Additionally, there is a strong positive correlation between economic growth and BOFCB, which stands for borrowers from commercial banks (model 2). As per the findings of Allen et al. (2016), the primary objective of FI is to facilitate the transfer of unbanked individuals into the banking sector through loans and other financial services. This endeavour promotes growth and human development by lowering vulnerability, facilitating investment and savings opportunities, and encouraging the creation of jobs (Thathsarani et al., 2021). Furthermore, the Commercial Bank Branch Count (CCB), which quantifies the number of commercial bank branches per 100,000 individuals, exhibits a noteworthy correlation with economic growth due to the increased accessibility of sufficient financial services in SSA nations. This is a matter of great importance to pertinent authorities (Ahmed, 2013; Menyelim et al., 2021). Researchers claim that although some SSA banking systems have not been able to support new economic initiatives well enough, especially when it comes to growing the banking network (Taddese et al., 2023), other SSA banking systems are improving financial access for the unbanked population.

The average number of depositors at commercial banks and access to financial services, as represented by BOFCB, CCB, and DOCB, have a significantly positive relationship with economic growth, which is consistent with ATM results. According to Allen et al. (2016), the results validate that increased access to financial services raises people’s living standards and creates more opportunities for saving and investing.

Conclusion

This study examined the relationship between FI and economic growth and the moderating effect of human capital using the FE (within) estimators and the standard errors of Driscoll and Kraay. The mechanism by which FI promotes economic growth is human capital. While the literature has recognised the impact of FI on economic growth, empirical research on this issue is still in its infancy and focuses on a few nations. This article aims to shed more light on the relationship between FI and global economic growth. The results of this study confirm the positive correlation between FI and economic growth, given the robustness of our empirical evidence to alternative estimation approaches.

We contribute theoretically by elucidating how human capital development affects the connection between FI and economic growth. This is accomplished by taking advantage of their fundamental traits and acquired skills, which promote FI and growth. By doing this, we respond to the request made by Ahmad et al. (2021) for additional research to be undertaken to understand better how human capital affects the relationship between FI and economic growth by drawing on endogenous growth theory. Again, we support the claim made by the endogenous growth theory that increased productivity is directly correlated with increased innovation and human capital investment from public and private sector organizations by offering solid empirical data from sub-Saharan Africa. As a result, regulators and shareholders may notice that fostering human capital development through their unique abilities and competencies can result in an active role and successfully improve FI and economic growth. The SSA government should create complementary plans to expand FI by assisting financial markets in lowering barriers to entry and boosting the penetration of financial services while preserving profitability and economic expansion. Policies can be put in place to assist financial institutions in providing services in remote locations and at reasonable prices, which will promote economic growth. To have a positive impact on the growth of society and the country as a whole, they must promote the financial system’s accessibility to significant population segments. Managers should invest in human capital to increase employment opportunities, boost economic growth, and increase productivity. To address the shortcomings in the financial system, managers, governments, and policymakers must create suitable reforms and policies that address the issues both domestically and regionally. Therefore, the specific policy recommendations centre on putting formalised policies and reforms related to human capital into practice. This involves improving education and training resources to support FI, encourage economic growth, and ease the expansion of regional trade.

This research however, has some limitations, just like other studies. Although the sample size was considered appropriate, we could have conducted a more thorough analysis and been able to generalise our results with a larger sample. Since the growth model lacks institutional controls, nations with various frameworks and institutions may aim for varying degrees of FI and, consequently, influence the strength of the relationship between FI and economic growth. There are numerous ways to expand this research. First, by expanding the sample size, a theoretical framework can be created to assess the overall effects of FI. Second, the results of this study can be confirmed using alternative proxies for FI and human capital.

Author contributions

Christopher Boachie: The conception or design of the work; analysis, and interpretation of data for the work. Drafting the work and reviewing it critically for important intellectual content. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Eunice Adu-Darko: Data preparation and final approval of the version to be published. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Acknowledgment

The authors received no direct funding for this research. No funds, grants, or other support was received for conducting this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data for the study is obtained from publicly available sources and can also be made available upon request.

Additional information

Notes on contributors

Christopher Boachie

Christopher Boachie has a PhD in finance and a background in finance, economics, corporate social responsibility, and chartered accountancy. His research interests are in corporate governance, financial performance, microfinance, banking, ethics, development finance, and corporate social responsibility. He has worked at Goldfields and IE Insurance and is currently at Central University.

Eunice Adu-Darko

Eunice Adu-Darko is a development finance economist. She has a PhD in Finance. Her particular research interest is in financial sector development, institutional framework of economies and organisations, productivity, economic growth, microfinance and the finance of the extractive industries. Eunice is an academic, a researcher and currently, the head of the banking and finance department at central university.