Formal financing of small and medium scale enterprises in Nigeria: A path to economic development?

Onyele, Kingsley Onyekachi; Ikwuagwu, Eberechi; Umezurike, Innocent Article Formal financing of small and medium scale enterprises in Nigeria: A path to economic development? The Journal of Entrepreneurial Finance (JEF) Provided in Cooperation with: The Academy of Entrepreneurial Finance (AEF), Los Angeles, CA, USA Suggested Citation: Onyele, Kingsley Onyekachi; Ikwuagwu, Eberechi; Umezurike, Innocent (2025) : Formal financing of small and medium scale enterprises in Nigeria: A path to economic development?, The Journal of Entrepreneurial Finance (JEF), ISSN 2373-1761, Pepperdine University, Graziadio School of Business and Management and The Academy of Entrepreneurial Finance (AEF), Malibu, CA and Los Angeles, CA, Vol. 27, Iss. 1, pp. 1-34, https://doi.org/10.57229/2373-1761.1508 This Version is available at: https://hdl.handle.net/10419/319787 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ The Journal of Entrepreneurial Finance The Journal of Entrepreneurial Finance Volume 27 Issue 1 2025 Article 3 May 2025 Formal Financing of Small and Medium Scale Enterprises in Formal Financing of Small and Medium Scale Enterprises in Nigeria: A Path to Economic Development? Nigeria: A Path to Economic Development? Kingsley Onyekachi Onyele Rhema University Nigeria Eberechi Ikwuagwu Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria Innocent Umezurike Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria Follow this and additional works at: https://digitalcommons.pepperdine.edu/jef Part of the Entrepreneurial and Small Business Operations Commons, and the Finance and Financial Management Commons Recommended Citation Recommended Citation Onyele, Kingsley Onyekachi; Ikwuagwu, Eberechi; and Umezurike, Innocent (2025) "Formal Financing of Small and Medium Scale Enterprises in Nigeria: A Path to Economic Development?," The Journal of Entrepreneurial Finance : Vol. 27: Iss. 1. DOI: https://doi.org/10.57229/2373-1761.1508 Available at: https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 This Article is brought to you for free and open access by the Graziadio School of Business and Management at Pepperdine Digital Commons. It has been accepted for inclusion in The Journal of Entrepreneurial Finance by an authorized editor of Pepperdine Digital Commons. For more information, please contact bailey[email protected]. Formal Financing of Small and Medium Scale Enterprises in Nigeria: A Path to Formal Financing of Small and Medium Scale Enterprises in Nigeria: A Path to Economic Development? Economic Development? Cover Page Footnote Cover Page Footnote We express our gratitude towards the authors whose scholarly works have significantly aided in the fruition of our article. This article is available in The Journal of Entrepreneurial Finance: https://digitalcommons.pepperdine.edu/jef/vol27/ iss1/3 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs Formal Financing of Small and Medium Scale Enterprises in Nigeria: A Path to Economic Development? Eberechi Ikwuagwu Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria [email protected] Kingsley Onyele Rhema University Nigeria, Aba, Abia State, Nigeria [email protected] Innocent Umezurike Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria [email protected] Abstract: This investigation assessed the influence of formal financing for small and medium-sized enterprises (SMEs) on the economic advancement of Nigeria, utilising annual time series data ranging from 1992 to 2022. Employing the Cobb-Douglas framework, control variables, including capital formation and labour, which could influence economic development, were incorporated into the empirical model to mitigate bias. Following initial tests for stationarity, it was determined that all variables achieved stationarity upon the first difference, a finding that validated the Vector Error Correction Mechanism (VECM) application. The analysis revealed that the credit extended to SMEs by commercial banks and the loans provided by microfinance banks exhibited a negative and significant influence on economic development, as measured by GDP per capita, in both the long and short term. The negative coefficients associated with the credit from commercial banks to SMEs and the loans from microfinance banks indicate that these financial institutions have not yet catalysed the requisite leap in Nigeria's economic development, probably due to the negative effects of unstable interest rates on lending. Both gross capital formation and labour demonstrated a significant impact on GDP per capita; however, the effect of labour was found to be negative. Consequently, it was concluded that financing for SMEs through commercial banks’ credit and microfinance bank loans had a negative and significant effect on Nigeria's economic development. The study recommended that policymakers and regulatory bodies should empower and facilitate formal financial institutions, such as commercial banks and microfinance banks, to extend financial services to SMEs, thereby enhancing their productivity. Keywords: SMEs financing; commercial banks; microfinance banks, economic development; VECM 1 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs 1. Introduction The significance of small and medium enterprises (SMEs) in the realisation of economic development, particularly within a developing nation such as Nigeria, cannot be overstated. SMEs serve as both the foundation and the essential building blocks in the pursuit of substantial and sustainable economic growth and development. They function as the primary driving force behind the achievement of industrial advancement. This is fundamentally attributable to their considerable potential in facilitating the diversification and expansion of industrial production, and in fulfilling the fundamental objectives of development. In the context of a sustainable economy, SMEs have been emphasised as instrumental in fostering a favourable economic turnaround and in complementing the efforts of the prevailing medium and large-scale industries (Batrancea et al., 2022). The acknowledgement of the pivotal roles of SMEs as catalysts and engines of growth has led to an increased focus on specialised education regarding the methodologies and strategies necessary to establish and maintain a genuinely viable private sector predominantly characterised by SMEs. The economic contributions of these enterprises are evident in their capacity to mobilise dormant financial resources, conserve foreign exchange, utilise local raw materials, serve as specialised suppliers to larger corporations, enhance variety and choice for consumers, mitigate monopolistic tendencies, provide a source of innovation, nurture new industries, and, most importantly, create employment opportunities (Olowookere et al., 2021). The theoretical framework that connects finance and economic growth, as posited by McKinnon (1973) and Shaw (1973), underscores the imperative for financial liberalisation aimed at augmenting changes in actual savings, which subsequently diminish interest rates and enhance investment and capital formation (Olorunfemi & Ganiyu, 2016). Consequently, if SMEs can successfully attract a greater proportion of savings to bolster their capital base at a low-income level, where challenges such as low propensity to save and information asymmetry prevail, a significant number of SMEs in developing nations would generate sufficient income to meet their basic needs, as the majority of their earnings merely suffice to satisfy physiological requirements (Onyeiwu et al., 2020). Schumpeter (1973) underscores the critical importance of credit for small enterprises in financing innovations, thereby promoting output growth. This elucidates the necessity for sustained credit support for SMEs to attain their full potential. Nevertheless, this assertion may not hold for numerous developing nations, including Nigeria, due to the asymmetric financial opportunities confronting small business operators. Nigeria’s government has been keen on developing SMEs; as such, many microfinance agencies have been set up to promote the growth of SMEs in Nigeria. According to Ikpor et al. (2017), in 1962, the Nigerian Industrial Development Bank (NIDB) was established, and the Rural Banking Initiative (RBI) was set up in 1977. Also set up was the Agricultural Credit Guarantee Scheme Fund (ACGSF), a program that guarantees credit loans to farmers from deposit money banks (DMBs) or commercial banks and the Nigerian Agricultural Cooperative Bank to enhance lending to agriculture and SMEs. To cushion off the negative impact of structural adjustment programs in the mid-1980s, the government set up the National Economic Reconstruction Fund (NERFUND) to give SMEs a concessionary long-term loan of five to ten (5–10) years (Ogujiuba et al., 2013). To further boost the source of finance to SMEs, the government in 1991 set up a community banking scheme to enhance rural development and to provide start-up capital to smallholders. Also, the people’s bank and Family Economic Advancement Programme (FEAP) were established in 1997. In 2002, the government merged the NERFUND and NIDB with the Bank of Industry to provide loans at an interest rate of 10 percent to the industrial sector and SMEs. Many other intervention schemes were established to provide long-term or specialised funds for the promotion of SMEs in Nigeria. According to Tonuchi et al. (2021), these include the Microfinance Initiative (MFI) set up in 2005. In 2010, CBN, in her quest to provide adequate funds for SMEs, set up ₦200 billion in intervention funds to finance SMEs that engaged in manufacturing. Also, ₦300 billion in airline and off-grid power funds were earmarked to support SME clusters. Of recent, the establishment 2The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs of NIRSAL Micro-finance Bank for SMEs is a major booster to SMEs activities in the country. However, these projects were characterised by non-repayment of loans and failure to achieve the stated objectives. This is because the policies were fostered by the government in the form of credit allocation, subsidies, and interest ceilings. These may have led to the deterioration of credit available to SMEs in Nigeria. To cushion the effect of the COVID-19 pandemic that bedevilled the world in 2020, which by extension has caused global hardship for SMEs, the federal government, through the CBN, introduced an N50 billion Targeted Credit Facility (TCF) as a stimulus package to support households and micro, small, and medium enterprises (MSMEs) that are affected by the coronavirus pandemic. It has been observed that commercial banks, along with the previously established merchant banks that maintained liquidity levels surpassing regulatory requirements, have exhibited a hesitance to finance SMEs (Masato & Troilo, 2015). Although microfinance institutions (MFIs) have experienced significant expansion in numerous countries, the magnitude of their credit offerings remains constrained, rendering their support insufficient for many medium-scale projects. Furthermore, the interest rates associated with micro-credits are considerably elevated, attributable to substantial administrative expenses concerning their operational scale (Onyele & Onyekachi-Onyele, 2020). Reports indicate that SMEs contribute approximately 75 percent of all entrepreneurial activities constituting Nigeria’s gross domestic product, with 21 percent attributed to micro-enterprises and 4 percent associated with large complex organizations. Additionally, Nigeria is recognised for its substantial entrepreneurial dominance due to its capacity to consolidate skilled and semi-skilled labour (Olaniyi & Adekanmbi, 2022). Over the years, numerous studies have been conducted to assess the influence of SME financing on economic growth and development within Nigeria. However, many of these investigations utilised primary data, which, at best, reflect the perspectives of respondents yet suffer from considerable inconsistencies in estimation methodologies. Moreover, the majority of these studies predominantly concentrated on the effects or impacts of SME financing and economic growth without adequately addressing the influence of SME financing on components of economic development, such as per capita income and human development. Furthermore, prior studies largely neglected to ascertain the presence or absence of causality between these variables, which is equally crucial. Consequently, the objective of this study is to empirically analyse the impact of SME financing on economic development in Nigeria and to delineate the nature of causality between these variables. This study utilised annual time series data from 1992 to 2022. For future researchers, this study aims to catalyse further investigation, particularly in Nigeria, where SMEs remain relatively under-researched. At the individual level, this study enhanced understanding and appreciation of the significance of developing human capital in pursuit of economic development. For the government, it provided a framework conducive to policy formulation and implementation. Above all, this study will contribute to the existing body of literature and address the existing gaps in knowledge. The subsequent sections of this study are organised as follows: Section two reviewed the pertinent literature, section three elaborated on the methodology, section four presented data analysis and interpretation of findings, and section five offered conclusions and recommendations. 2. Review of Related Literature 2.1. Conceptual framework Small enterprises are recognised for their enhanced adaptability in the face of challenging and evolving circumstances, primarily due to their often minimal capital intensity, which enables them to modify product lines and inputs at a relatively low cost. The sub-flexibility, adaptability of the sector, and regenerative characteristics have rendered it a focal point for the industrial development strategies of numerous nations, particularly in developing regions (World Bank, 2019). These enterprises serve as an 3 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs effective mechanism to foster indigenous entrepreneurship, augment employment opportunities per unit of expenditure, and facilitate local technological advancement. Their extensive distribution provides a proficient method of mitigating rural-urban migration and optimising resource utilisation (Ogujiuba et al., 2013). Additionally, they play a vital role in strengthening inter-industrial connections by producing intermediate goods utilised in large-scale industries. The lack of access to relatively affordable and efficient financial sources has been identified as the primary impediment to their contribution to economic advancement (Ochonogor, 2020). A prevalent concern is that the banking system within this sector, which is expected to be the principal financier of SMEs, is inadequately supporting new economic initiatives, particularly with the expansion of SMEs and the agricultural sector. It has been observed that commercial and previously established merchant banks, which maintained liquidity levels exceeding regulatory requirements, have exhibited reluctance in financing SMEs. Although microfinance institutions (MFIs) have experienced vigorous growth in several countries, the magnitude of their credit remains constrained, rendering their support insufficient for numerous medium-sized projects. Furthermore, the interest rates associated with micro-credits are exceedingly high due to significant administrative expenses relative to their operational scale. The primary focus of this study arises from the observation that proprietors of small-scale enterprises lack adequate financing to sustain their business operations. This predicament can be attributed to the low levels of income. It is a well-established fact that SMEs encounter financial obstacles. Figure 1. Conceptual Framework The researchers’ conceptual framework is illustrated in Figure 1. Numerous studies have identified financial constraints as the predominant barrier to the growth of SMEs and economic development in developing nations, including Nigeria. For example, Adelaja (2003) contended that the absence of access to institutional finance has consistently constituted a pervasive issue for SME development in Nigeria. The challenge of financing SMEs has garnered substantial research attention from scholars. Their findings reveal four recurring issues in SME financing: the cost of capital, risk, inappropriate terms associated with bank loans, and a lack of equity capital. Over the years, the government has enacted various policies and introduced schemes aimed at financing SMEs. Nevertheless, it is concerning to observe that SMEs continue to face significant funding shortages, and the financing challenges persist. Mahmoud (2005) has concluded that these financial difficulties restrict the SMEs Financing Formal Credit Extension Commercial Bank’s Credit Microfinance Bank’s Credit Economic Development 4The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs developmental potential of SMEs within the economy. There exists considerable doubt regarding the capacity of SMEs to contribute to economic development in light of their limited access to funds. 2.2. Formal SMEs financing in Nigeria: stylized facts The declining rate of funding for SMEs continues to be a significant constraint that impedes its potential contribution to economic growth and development. Specifically, there exist four principal challenges impacting SMEs in Nigeria, namely, an inhospitable business environment, inadequate funding, deficient managerial competencies, and limited access to contemporary technology. There is a conspicuous reliance by SMEs on financial institutions for the procurement of funding, business expansion, and the acquisition of the latest technologies necessary to maintain competitiveness and foster economic growth. Additional constraints arise from infrastructural inadequacies, excessive taxation, and an unfavourable macroeconomic climate (Gumel & Bardai, 2021). Among these challenges, the scarcity of financial resources occupies a pivotal role. According to the Organisation for Economic Co-operation and Development (OECD), there exists a profound historical context regarding community finance, microfinance, and governmentsupported SME financing arrangements aimed at delivering financial services to the SME sector independent of the commercial banking framework (OECD, 2018). Nevertheless, the performance of these initiatives remains questionable. The criticism surrounding this performance has prompted the government to enhance the financial sector through its long-term strategic framework: The Financial System Strategy (Ohamara, 2017). The financing of SMEs in Nigeria is significantly hindered by a dearth of data on SME operations, regulatory frameworks, the lack of credible collateral, and incomplete credit histories. Policies regarding SMEs encounter persistent challenges due to inadequate data necessary to comprehend the issues and operations of existing government initiatives targeting SMEs (Onyeiwu et al., 2021). The current credit information system fails to correspond with the requirements of the SME sector. To effectuate a transformative narrative, the Nigerian government and the organised private sector must establish a platform wherein privately owned credit bureaus, alongside the Credit Risk Management System (CRMS), can adequately encompass the SME subsector. The Central Bank of Nigeria (CBN) has intensified pressure on financial institutions, particularly commercial banks, to enhance their lending capacities to SMEs (Olowookere et al., 2021). The heightened risk coupled with the pre-existing incidence of loan defaults dissuades banks from extending credit to small-scale enterprises. Banks have shown a preference for incurring penalties to the CBN rather than disbursing loans to these SMEs (Mordi et al., 2014). They tend to focus on target sectors where interest yields a more substantial margin, accompanied by a commensurate level of risk characterised by global economic shocks. Previous endeavours to promote SME financing have yielded mixed outcomes; however, the Nigerian authorities persist in advancing new initiatives and policies for this sector. Figure 2 illustrates the trends in credit allocated by commercial banks to SMEs and loans dispensed by microfinance banks in Nigeria from 1992 to 2022. The figure indicates that credit from commercial banks to SMEs surpassed that of microfinance loans between 1992 and 2007. Nevertheless, from 2008 to 2022, the loans provided by microfinance banks began to rise significantly, overtaking the credit extended by commercial banks to SMEs. The abrupt increase in microfinance loans in 2008 can be attributed to the Financial Inclusion Taskforce, which was officially launched in February 2005 to oversee progress on financial inclusion and to make appropriate recommendations, thereby enhancing the operational capabilities of microfinance banks (Kama & Adigun, 2013). The subsequent increase in loans from microfinance banks in 2012 is ascribed to the Central Bank of Nigeria's (CBN) endorsement of the National Financial Inclusion Strategy (NFIS) in 2012 (CBN, 2018). 5 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs Figure 2. Trend SMEs financing in Nigeria 2.3. Economic development in Nigeria The expansion in the production of goods and services constitutes a fundamental determinant of economic performance. Distributing total production across the population illustrates the extent to which a nation's total output can be allocated among its citizens. The increase in real GDP per capita reflects the rate of income growth per individual within the population. As a singular composite measure, it serves as a potent summary indicator of economic development (World Bank, 2018). Economic development pertains to the augmentation in the number of individuals within a nation's population, characterised by sustained growth transitioning from a rudimentary, low-income economy to a sophisticated, high-income economy. Its scope encompasses the processes and policies through which a nation enhances the economic, political, and social welfare of its populace. Economic development in Nigeria represents a comprehensive concept that entails the enhancement of the quality of life and well-being of the nation’s citizens. Several factors influencing Nigeria's economic development include: a) The oil sector: A principal contributor to Nigeria's GDP; b) Agriculture: A cornerstone of Nigeria's economy; c) Services sector: Encompassing telecommunications and financial technology; d) Manufacturing and industrial development: A crucial element within Nigeria's economic framework; e) Political stability and governance: An essential factor influencing Nigeria's economic progress. Despite possessing the largest economy and population on the African continent, Nigeria presents limited opportunities to a substantial portion of its citizenry. Individuals born in Nigeria in 2020 are anticipated to possess a productivity level that is only 36% of their potential, contingent upon full access to education and healthcare, resulting in the 7th lowest human capital index globally. Insufficient job creation and entrepreneurial avenues hinder the integration of the 3.5 million Nigerians entering the labour market annually, compelling numerous workers to seek employment abroad for improved prospects (Ikwuagwu & Onyele, 2023). The poverty rate is projected to have escalated to 38.9% in 2023, with an estimated 87 million Nigerians subsisting below the poverty threshold—representing the world’s second-largest impoverished population, after India. 0.00 50,000.00 100,000.00 150,000.00 200,000.00 250,000.00 300,000.00 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Commercial Banks Microfinance Banks 6The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs Moreover, Ezeaku et al. (2017) assessed the influence of SME financing on the growth of the manufacturing sector in Nigeria, utilising annualised data from 1981 to 2014. A cointegrating relationship was identified through the Engel and Granger residual-based approach, which provided evidence of a long-run relationship between SME credit and the growth of manufacturing output in Nigeria. The findings indicated that SME financing has positively influenced the growth of the manufacturing sector. In a related study, Oleka et al. (2014) evaluated the extent to which microfinance banks have facilitated the financing of SMEs in Nigeria. This study encompassed a decade from 2003 to 2013. The data employed consisted of both primary and secondary information, collected via questionnaires and annual reports from 300 randomly selected small and medium-scale enterprises that had accessed funds from microfinance banks in Nigeria. The results provided compelling evidence that access to microfinance significantly bolstered the growth of SMEs in Nigeria. Moreover, Olowe et al. (2013) examined the influence of microfinance on the growth of SMEs within the Nigerian context. A simple random sampling methodology was employed to select a total of 82 operators of SMEs, which constituted our sample size. The analysis of the data was conducted using the Pearson correlation coefficient and multiple regression analysis. The findings from this study indicated that the financial services acquired from MFBs exerted a positive and significant influence on the growth of SMEs in Nigeria. Furthermore, the results indicated that elevated interest rates, the requirement for collateral security, and the frequency of loan repayment adversely affected the expansion of SMEs in Nigeria. 2.6. Gap in Empirical Literature The culmination of various empirical and theoretical discourses regarding the impact of formal financing on the economic development of SMEs in Nigeria presents a diverse array of outcomes as reported by different scholars in the field. The discourse surrounding the influence that small and medium-scale enterprises have on economic growth and development, particularly as facilitated by financing from commercial banks and microfinance institutions, remains inconclusive. It is noteworthy that the disparate results emerging from these various discussions can be attributed to the proxy indicators and data utilised in the analysis of the study outcomes. The lending rate, capital formation, and labour force are essential components of this discussion about small and medium-scale enterprises, given their substantial costs and critical importance to the sustainability of SMEs in Nigeria. The lending rate reflects the costs associated with formal credit access for businesses. Capital formation signifies the pivotal role of infrastructure in Nigeria, which is vital for the operational success of numerous small enterprises. The aforementioned critical variables have been excluded from the research models proposed by preceding studies. The present research endeavours to address this gap by incorporating the omitted variables into the research model to elucidate the relationships among the variables of interest. The existing gap substantiates the necessity for further inquiry into this subject. Consequently, there is an imperative to undertake this research to enhance the depth of literature on this topic and potentially influence economic decision-making in this domain positively. 3. Research Methodology This section reaffirms the research methodology employed in the study through a concise exposition of the research design, data collection instruments, the data collection procedure, and the data analysis technique. For this investigation, the selected research design is the ex post facto research design. This chosen design denotes a systematic approach whereby the researcher is unable to manipulate the data owing to its prior occurrence. 13 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs The selection of secondary data aligns with the choice of information in light of temporal constraints. Moreover, this option was favoured over other potential alternatives due to its accessibility and reliability, which are further substantiated by the verifiable sources from which the data was derived. The data for this study were derived from secondary sources encompassing the period from 1992 to 2022, specifically extracted from the CBN Statistical Bulletin and the World Development Indicators (WDI). The collected secondary data encompasses various economic indicators, including economic development quantified by GDP per capita, as well as commercial bank credit to SMEs and microfinance banks, which serve to evaluate the financing of SMEs, in addition to gross capital formation and labour force statistics, which function as control variables. The selection of secondary data is predicated upon the understanding that such data cannot be procured via primary sources due to the necessity for extensive temporal collection. Since the data was sourced from CBN and WDI, which are institutions of high reputation, it is assumed that the data used were collated by well-trained and educated personnel that ensured data quality standards as published on their respective websites. As such, the issue of potential limitations such as data accuracy, completeness, and the impact of external factors over the long period were well managed. The data used for the study captured SMEs in all economic sectors in Nigeria. Hence, the population rather than the sample was used for the analysis. This research is fundamentally based on the Cobb-Douglas production function. The CobbDouglas production function represents a specific functional form of the production function and is extensively utilised to depict the technological relationship between multiple inputs, particularly physical capital and labour, and the resultant output that can be generated from these inputs. Over the past several decades, numerous scholars have employed these models to explore various indicators that contribute to economic growth and development. Moreover, traditional elements such as capital and labour have experienced significant advancements in the academic literature as a consequence of this evolution (Brown, 2017). In its most conventional form concerning the production of a singular good utilising two factors, the function is expressed by equation (1): 𝑌 =𝑓(𝐿,𝐾)=𝐴𝐿𝛽𝐾𝛼 (1) Y = total production (the real value of all goods produced in a year) L = labour input K = capital input (a measure of all machinery, equipment, and buildings; the value of capital input divided by the price of capital) A = total factor productivity 0< 𝛼 < 1 and 0< 𝛽 < 1 are the output elasticities of capital and labor, respectively. Capital and labour are the two "factors of production" of the Cobb–Douglas production function. The choice of this framework is owed to its simplicity in application and the availability of time series data in Nigeria. In line with the Cobb-Douglas production framework, Onyeiwu et al. (2020) specified the model represented by equation (2): 𝐿𝐴𝑆𝐺𝐷𝑃𝑡= 𝛽0+ 𝛽1𝐺𝐶𝐹+ 𝛽2𝐶𝑆𝑀𝐸+𝛽3𝐿𝑅+𝛽4𝐸𝐷 (2) Where: LASGDP =Natural Logarithm of the aggregate of SME contribution to Gross Domestic Product GCF = Gross capital formation. CBSMEs = Commercial Bank Credit to SMEs. LR = Lending Rate. ED = Electricity Distribution. This study modified equation (2) to capture a more elaborate model for economic development and SMEs financing with control variables (LNGCF and LNLAB) as specified in equation (3). 14The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs 𝐿𝑁𝐺𝐷𝑃𝑃𝐶𝑡= 𝛽0+ 𝛽1𝐿𝑁𝐶𝐵𝑆𝑀𝐸𝑠 + 𝛽2𝐿𝑁𝑀𝐵𝐿+𝛽3𝐿𝑁𝐼𝑁𝑇+𝛽4𝐿𝑁𝐺𝐶𝐹+𝛽5𝐿𝑁𝐿𝐴𝐵+µ (3) Where: LNGDPPC is the natural log of GDP per capita; LNCBSMEs measure the natural log of credit to SMEs by commercial banks; LNMBL is aggregate loans obtained from microfinance banks; LNINT indicates the natural log of the lending interest rate; LNGCF denotes the natural log of gross capital formation, while LNLAB measures the natural log of labour. According to the economic priori of the signs of parameters, it is expected that 𝛽1,𝛽2,𝛽4,𝑎𝑛𝑑 𝛽5> 0, while 𝛽3 < 0. The model articulated in equation (3) may be represented in the vector error correction model (VECM) framework, wherein LNGDPPC may not instantaneously revert to its long-run equilibrium levels, thereby capturing the rate of adjustment between the short-run and long-run levels within the error correction equation as delineated in equation (4), where ∆ signifies the change in the dependent variable LNGDPPC alongside the independent variables LNCBSMEs, LNMBL, LNINT, LNGCF, and LNLAB. 𝐿𝑁𝐺𝐷𝑃𝑃𝐶𝑡= 𝛽0+ ∑𝛽1𝛥𝐿𝑁𝐺𝐷𝑃𝑃𝐶𝑡−1 + 𝑝 𝑖=1 ∑𝛽2𝛥𝐿𝑁𝐶𝐵𝑆𝑀𝐸𝑠𝑡−1 + 𝑝 𝑖=1 ∑𝛽3𝛥𝐿𝑁𝑀𝐵𝐿𝑡−1 + 𝑝 𝑖=1 ∑𝛽4𝛥𝐿𝑁𝐼𝑁𝑇𝑡−1 𝑝 𝑖=1 +∑𝛽5𝛥𝐿𝑁𝐺𝐶𝐹𝑡−1 + 𝑝 𝑖=1 ∑𝛽6𝛥𝐿𝑁𝐿𝐴𝐵𝑡−1 + 𝑝 𝑖=1 𝛽7𝐸𝐶𝑇𝑡−1 + µ𝑡 (4) This study utilised the VECM methodology. The statistical characteristics of the series were scrutinised, recognising that a majority of economic time series data exhibit non-stationarity over time. This necessitates an examination of the statistical properties of the variables in question. The statistical assessments conducted for our series include the Augmented Dickey-Fuller (ADF) test, employed to ascertain the stationarity level; the Johansen cointegration test, utilised to evaluate the long-term relationship among the model variables; and Granger causality tests, which ascertain the causal transmission mechanism among the variables, both endogenous and exogenous. A significant proportion of economic data exhibit unit roots (i.e., non-stationarity), which leads to the issue of spurious regression. To mitigate this problem, the study undertakes a stationarity test for the time series data utilising the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The optimal lag length for the ADF test will be determined by the Schwarz information criterion (SIC). In instances where a unit root is present in the data, the corresponding time series will be classified as nonstationary. The formal ADF test procedure can be articulated as presented in equation (5): 𝛥𝑋𝑡= 𝑎]𝜏+𝛽𝑋𝑡−1 +∑𝛿𝑗𝛥𝑋𝑡−1 +µ𝑡 (5) 𝑝 𝑗=1 Where 𝑡 is the time, 𝑝 is the lag order, and 𝛥𝑋𝑡 is the first difference of the time series data. If 𝛽 is considerably negative in the ADF result, the null hypothesis that variable(x) is non-stationary 𝐻0:𝛽 =0is rejected. The Philip-Perron (PP) test, conversely, shall be utilised owing to its additional merit over the Augmented Dickey-Fuller (ADF) test, as it has been modified to eliminate the presumption that the error terms are serially independent, incorporating serial correlation through the implementation of the NeweyWest (1994) covariance matrix. In the PP test, the order of integration for our variables is determined based on the test that encompasses both the intercept and time trend. Consequently, we can delineate the general form of the test utilising the following equation: 15 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs 𝑋𝑡=𝑎1+𝑏2𝑋𝑡−1 + 𝑎3(𝑡+𝑇 2)+µ𝑡 (5) Where, T is the number of observations in the model and 𝑎1, 𝑎2, and 𝑎3 are the regression's coefficients. Here, we also test the alternative, which asserts the contrary, against the null hypothesis, which states that the series has a unit root. The null hypothesis was evaluated at the traditional 1% and 5% levels of significance, and if the variables are found to be non-stationary at level, they will be converted to first difference to become stationary. When the stationarity is confirmed, indicating that all variables are ascertained to possess the same order of integration, we proceed to investigate the presence of cointegration among the included variables. The objective herein is to ascertain whether a long-term relationship exists between nonstationary data that are cointegrated in the same order. This concept was introduced by Johansen (1988) and further elaborated by Johansen and Juselius (1990). The cointegration test is executed to scrutinize whether our variables exhibit a long-term equilibrium relationship. As posited by Engle and Granger (1987), the cointegrated series must possess an Error Correction Model (ECM) representation. This is the rationale behind the popularity of cointegration analysis, as it furnishes a formal framework for the examination and estimation of both short-run and long-run relationships among economic variables. Furthermore, the ECM methodology offers a remedy to the potential issues associated with regression. 𝛥𝑌𝑡−1 =Ӷ0+Ӷ1𝛥𝑌𝑡−1 + Ӷ2𝛥𝑌𝑡−2 + …+ Ӷ𝑘−1𝛥𝑌𝑡−𝑘 + 𝛱𝑌𝑡−𝑘 +𝜀𝑡 (6) The deterministic trend of the model is represented by Ӷ0, the random error term is represented by 𝜀𝑡 and 𝑌𝑡 represents the ap × 1 vector of non-stationary variables (at level). The error correction terms (ECT), denoted by 𝛱, give details about how the VECM has been adjusted to its long-term equilibrium. As posited by Engle and Granger (1987), the cointegrated series must possess an ECM representation. This is the reason that cointegration analysis has gained prominence, as it provides a formal framework for the examination and estimation of both short-run and long-run relationships among economic variables. In addition, the ECM methodology offers an adjustment to the potential issues associated with regression. The Granger causality test, as articulated by Amblard and Michel (2011), stipulates that a time series variable (designated as y) is Granger caused by another time series variable (designated as x) if the future values of y can be more accurately predicted by incorporating the past values of both ‘y’ and ‘x’ compared to predictions based solely on the past values of ‘y.’ This study presents a straightforward bivariate model that tests whether y is Granger caused by x through the subsequent equation: 𝑦𝑡=𝑎0+ ∑ƴ11,𝑖𝑦𝑡−𝑗 + ∑ƴ12, 𝑝 𝑗=1 𝑝 𝑗=1 𝑖𝑥𝑡−𝑗 +𝜀𝑡 (7) From the equation 𝑎0 is the constant and 𝜀𝑡 represent the error term. As elucidated by the Granger representation theorem, Granger causality stipulates that at least one of the various adjustment coefficients must possess a non-zero value for a long-term relationship among the variables to be sustained. In its conventional formulation, the results of Granger causality demonstrate two-way feedback relationships. 16The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs 4. Empirical Results 4.1. Descriptive statistic Due to the implementation of a log-linear econometric model specification, the extreme values for all selected variables exhibit minimal variation, with the exception of the LNMBL and LNGCF variables. The standard deviations reported in relation to the mean, as illustrated in Table 1, indicate that the data points, represented in the natural logarithm of the variables, are not markedly dispersed from the mean. Regarding the skewness and kurtosis section in Table 1, the conventional guideline asserts that a reported value of less than 3 is classified as platykurtic. Observing the reported kurtosis statistic in Table 1 reveals that all variables, with the exception of the LNINT variable, are indeed platykurtic. Adhering to the established guideline, the kurtosis statistic for the LNINT variable exceeds 3, thereby allowing the researcher to characterize the LNINT variable as leptokurtic (indicative of a high peaked distribution). The reported skewness statistic falls within the range of -0.2 and 0.4, thereby indicating with authority that the distribution is approximately symmetric. The examination of normal distribution is forthcoming, and the reported Jarque-Bera statistic in Table 1 is sufficiently supported by the p-values, all of which are insignificant at the 5% level, thereby suggesting normal distribution. The aforementioned results lead to the acceptance of the null hypothesis, thereby concluding that all variables presented in Table 1 conform to a normal distribution. Table 1. Descriptive Statistic LNGDPPC LNCBSMEs LNMBL LNINT LNGCF LNLAB Mean 7.079441 10.35039 9.799578 2.867567 8.692251 17.68150 Median 7.537117 10.61752 10.25783 2.867082 8.927350 17.71112 Maximum 8.071204 11.72749 12.47850 3.394508 11.08563 17.96979 Minimum 5.598524 9.282464 4.911183 2.440879 5.982950 17.33200 Std. Dev. 0.778641 0.734736 2.168155 0.192852 1.327513 0.193934 Skewness -0.434006 0.106683 -0.437620 0.086333 -0.146834 -0.213615 Kurtosis 1.628538 1.739759 1.972512 4.127125 2.365320 1.887472 Jarque-Bera 3.402705 2.110236 2.353128 1.679457 0.631702 1.834480 Probability 0.182437 0.348151 0.308336 0.431828 0.729168 0.399621 Sum 219.4627 320.8621 303.7869 88.89459 269.4598 548.1265 Sum Sq. Dev. 18.18846 16.19510 141.0269 1.115756 52.86871 1.128317 Observations 31 31 31 31 31 31 Source: Authors’ computation using EViews 10 4.2. Unit root tests As a prerequisite to the cointegration analysis, we undertake a unit root test utilizing the two principal methodologies of the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to ascertain the stationarity of our variable. The results of the ADF and PP tests are delineated in Table 2. 17 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs Table 2. Unit Root Tests Variable Lag length ADF statistics PP statistics Remark LNGDPPC 7 -0.957005 -1.108759 D(LNGDPPC) 7 -7.021640 -6.697349 I(1) LNCBSMEs 7 -1.494513 -1.494513 D(LNCBSMEs) 7 -5.424138 -5.423566 I(1) LNMBL 7 -3.257904 -3.340962 D(LNMBL) 7 -6.834485 -10.03599 I(1) LNINT 7 -1.127362 -1.745226 D(LNINT) 7 -6.834485 -7.649792 I(1) LNGCF 7 -2.564645 -2.689021 D(LNGCF) 7 -4.016228 -3.977679 I(1) LNLAB 7 -2.498267 -1.437917 D(LNLAB) 7 -3.581226 -3.183396 I(1) Critical values 1% -4.296729 -4.309824 5% -3.568379 -3.574244 10% -3.218382 -3.221728 *** represents stationary at 1% level of significance; ** represents stationary at 5% level of significance; * represents stationary at 10% level of significance Level represents Logarithms of variables; ‘D’ represents that the variable has been differenced. The findings indicate that all variables are non-stationary at the level. However, upon transformation of the variables to their first difference, employing both intercept and deterministic trend, they attain stationarity. Consequently, the variables are deemed to be integrated of one order, specifically I(1), leading to the application of Johansen cointegration test and VECM. 4.3. Johansen cointegration test The results of the Johansen cointegration test, predicated on the trace test, are presented in Table 3 (Panel A). The test based on trace statistics evaluates the null hypothesis asserting the absence of cointegration among the variables. We reject the null hypothesis if the test statistic surpasses the critical values of the trace tests at either the 1% or 5% significance level. Table 3 (Panel B) elucidates the outcomes of the Johansen cointegration test based on the maximum eigenvalue. This test (maximum eigenvalue) is conducted under the null hypothesis concerning the number of cointegrating equations (r) against the alternative hypothesis positing an increase in the number of cointegrating equations by one (r + 1). The null hypothesis is not rejected if the test statistic remains below the critical value established for the maximum eigenvalue test. In Table 3 (Panel A), the trace test, which is considerably more stringent, indicated the presence of no fewer than three (3) cointegrating equations at a significance level of 5%. Consequently, the null hypothesis of the absence of cointegration was rejected, as the trace test statistic values of 133.0034, 86.07811, and 51.61595 exceed the 5% critical values of 95.75366, 69.81889, and 47.85613, respectively. Thus, the trace statistics confirmed the existence of three cointegrating relationships at the 5% level of significance. 18The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs The maximum eigenvalue test presented in Table 3 (Panel B) suggests the existence of three (3) cointegrating equations at the 5% significance level. Therefore, it is permissible to reject the null hypothesis concerning the absence of cointegrating vectors, given that the eigenvalue statistics of 46.92524, 34.46216, and 30.69059 surpass the 5% critical values of 40.07757, 33.87687, and 27.58434, respectively. In light of these findings, it may be deduced that there are three (3) significant long-run relationships among the variables, as established by the trace and maximum eigenvalue statistics. Recognizing that the variables may exhibit both short-run and long-run effects, the vector error correction model (VECM), which differentiates these effects, was subsequently estimated. Table 3. Johansen Co-integration Test Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob.** Panel A: Unrestricted Cointegration Rank Test (Trace) None * 0.801727 133.0034 95.75366 0.0000 At most 1 * 0.695276 86.07811 69.81889 0.0015 At most 2 * 0.652953 51.61595 47.85613 0.0213 At most 3 0.362014 20.92536 29.79707 0.3623 At most 4 0.234775 7.891618 15.49471 0.4770 At most 5 0.004530 0.131661 3.841466 0.7167 Trace test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Panel B: Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.801727 46.92524 40.07757 0.0073 At most 1 * 0.695276 34.46216 33.87687 0.0425 At most 2 * 0.652953 30.69059 27.58434 0.0193 At most 3 0.362014 13.03374 21.13162 0.4491 At most 4 0.234775 7.759958 14.26460 0.4036 At most 5 0.004530 0.131661 3.841466 0.7167 Max-eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Source: Authors’ computation using EViews 10 4.4. Vector error correction mechanism (VECM) The cointegration among the variables facilitates the estimation of the VECM. The results of the estimated VECM are encapsulated in Table 4. The cointegration test substantiates the existence of a longrun relationship and serves to disaggregate the long-run effects from the short-run impacts. The VECM elucidates how the short-run disequilibrium relationships among the estimated variables are adjusted 19 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs towards the long-run equilibrium trajectory. The coefficients of the variables represent the long-term elasticities of the normalized cointegrating vectors. The standard error statistics are presented in parentheses, while the t-statistics are enclosed in brackets. The coefficients pertaining to commercial banks’ credit to SMEs (LNCBSMEs) and microfinance banks’ loans (LNMBL) are both negative and statistically significant. The anticipated signs for both variables were expected to be positive; however, they are observed to be negative, potentially attributable to elevated interest rates, the crowding-out effect of government borrowings, or inadequate penetration of capital into economically productive activities. This observation aligns with the tenets of the adverse selection theory, which posits that banks are often uncertain in their ability to identify creditworthy borrowers from the existing pool, leading them to raise interest rates to mitigate the risk of selecting suboptimal borrowers. Consequently, high-risk borrowers are inclined to accept such facilities, which ultimately heightens the rate of loan defaults and diminishes per capita output. Table 4. Long-run Estimates Normalized Cointegrating Coefficients LNCBSMEs(-1) LNMBL(-1) LNMBL(-1) LNINT(-1) LNGCF LNLAB C -0.174364 -0.311302 -0.311302 0.796269 1.877698 -12.70356 203.7908 (0.05897) (0.06926) (0.06926) (0.22111) (0.24312) (1.03163) [-2.95697] [-4.49447] [-4.49447] [ 3.60119] [ 7.72340] [-12.3140] Source: Authors’ computation using EViews 10 The long-run cointegrating equation extracted from Table 4 is as delineated by equation (8). 𝐿𝑁𝐺𝐷𝑃𝑃𝐶 =203.7908− 0.174364LNCBSMES − 0.311302LNMBL + 0.796269LNINT + 1.877698LNGCF− 12.70356LNLAB (8) The discovery of a long-run equilibrium relationship among the variables, as evidenced by the Johansen cointegration analysis, necessitated the implementation of the VECM. Through this methodology, both the long-run equilibrium and the short-run dynamic relationships pertinent to the variables under investigation are elucidated. The VECM estimates presented in Table 5 demonstrate that LNGDPPC exhibits a negative response to both LNCBSMEs and LNMBL. This result signifies that the established influence is statistically significant at lag 1. It is observable that an increase of 10% in LNCBSMEs and LNMBL corresponds to a decrease in LNGDPPC of approximately 2.0% and 2.3%, respectively. Furthermore, LNINT exerts a negative effect on LNGDPPC, while a positive effect is noted for LNGCF. The error correction term (ECT) satisfied the stipulated conditions. The presence of a negative sign and the statistical significance of the error correction coefficients are essential prerequisites for any disequilibrium to be rectified. In this context, the coefficient of ECT (-1) within the model is -0.452356, accompanied by a probability value of 0.0001. This coefficient signifies that the rate of adjustment between the short-run dynamics and the long-run equilibrium in the initial model is 45.2%. Consequently, the ECT is poised to effectively rectify any discrepancies of the short-run dynamics to its long-run equilibrium on an annual basis within the model. Hence, the outcome indicates that the ECT rectifies the disequilibrium from the preceding periods at a rate (or speed) of 45.2% annually. Citing the output of the VECM presented in Table 5, the adjusted R-squared value reported is approximately 77.9%, suggesting a robust linear correlation between LNGDPPC and the chosen 20The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs independent variables. Reinforcing the aforementioned assertion, the F-statistic (F= 8.330309, pvalue=0.000170), which is evidently significant at the 1% level, posits that the independent variables (LNCBSMEs, LNMBL, LNINT, LNGCF, and LNLAB) collectively elucidate the variations in the dependent variable (LNGDPPC). The F-statistic delineated in Table 5 further substantiates the significance of the model employed in this study. The Durbin-Watson (DW) statistic suggests the absence of significant serial correlation, supporting this claim with an established guideline that stipulates values of the DW within the range of 1.5-2.5 are generally considered normal. Table 5. Vector Error Correction Mechanism (VECM) Coefficient Std. Error t-Statistic Prob. ECT(-1) -0.452356 0.085691 -5.278905 0.0001 D(LNGDPPC(-1)) 0.749743 0.115547 6.488638 0.0000 D(LNGDPPC(-2)) 0.420534 0.110834 3.794267 0.0020 D(LNCBSMEs(-1)) -0.198851 0.046405 -4.285120 0.0004 D(LNCBSMEs(-2)) -0.229393 0.036348 -6.311032 0.0000 D(LNMBL(-1)) -0.076037 0.030283 -2.510828 0.0249 D(LNMBL(-2)) 0.065740 0.030193 2.177322 0.0471 D(LNINT(-1)) -0.478650 0.128875 -3.714070 0.0023 D(LNINT(-2)) 0.149387 0.090049 1.658958 0.1194 D(LNGCF(-1)) 0.116912 0.128626 0.908931 0.3788 D(LNGCF(-2)) 0.570244 0.139644 4.083544 0.0011 D(LNLAB(-1)) -2.565558 1.006730 -2.548406 0.0232 D(LNLAB(-2)) -1.972869 1.423667 -1.385766 0.1875 Constant 0.002147 0.032811 0.065440 0.9487 R-squared 0.885522 Mean dependent var 0.068507 Adjusted R-squared 0.779221 S.D. dependent var 0.131691 S.E. of regression 0.061878 Akaike info criterion -2.420451 Sum squared resid 0.053604 Schwarz criterion -1.754349 Log likelihood 47.88632 Hannan-Quinn criter. -2.216817 F-statistic 8.330309 Durbin-Watson stat 2.277238 Prob(F-statistic) 0.000170 Source: Authors’ computation using EViews 10 4.5. Validity tests To enhance the reliability of the estimated model, the researcher undertakes a comprehensive diagnostic examination addressing issues of serial correlation, heteroskedasticity, and normal distribution. The results delineated in Table 6 indicate that the Breusch-Godfrey Serial Correlation LM Test confirms the absence of serial correlation in the VECM model. This finding corroborates the Durbin-Watson (DW) statistic result presented in Table 5. The subsequent test for heteroskedasticity reveals that the model exhibits homoskedasticity. These results are favorable and confirm that our overall findings are non-spurious and consequently reliable. The outcomes of the Ramsey test provide evidence of the normal distribution of the model. Furthermore, the Recursive CUSUM and CUSMSQ plots illustrated in Figures 4 and 5 affirm the stability of the model. 21 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs Table 6. Validity tests Test Obs* R-squared Prob. Chi-square F-statistic t-Statistic Prob. Breusch-Godfrey Serial Correlation LM Test 2.146112 0.3420 0.639345 - 0.5406 Heteroskedasticity Test: Breusch-Pagan-Godfrey 21.39154 0.2601 1.618498 - 0.2330 Jarque-Bera Normal distribution - - - 0.707691 0.7019 Source: Authors’ computation using EViews 10 -12 -8 -4 0 4 8 12 09 10 11 12 13 14 15 16 17 18 19 20 21 22 CUSUM 5% Significance -0.4 0.0 0.4 0.8 1.2 1.6 09 10 11 12 13 14 15 16 17 18 19 20 21 22 CUSUM of Squares 5% Significance Figure 4. CUSUM plot Figure 5. CUSUMSQ plot 4.6. Granger causality tests Through the execution of this examination, the pairwise associations among the estimated variables are determined. Consequently, Table 7 is provided as follows. Table 7. Pairwise Granger Causality Test Pairwise Granger Causality Tests Date: 01/02/25 Time: 03:28 Sample: 1992 2022 Lags: 2 Null Hypothesis: Obs F-Statistic Prob. LNCBSMES does not Granger Cause LNGDPPC 29 5.95507 0.0079 LNGDPPC does not Granger Cause LNCBSMES 3.80754 0.0302 LNMBL does not Granger Cause LNGDPPC 29 0.01348 0.9866 LNGDPPC does not Granger Cause LNMBL 3.66655 0.0408 Source: Authors’ computation using EViews 10 The causal relationship between LNCBSMES and LNGDPPC exhibited a bidirectional nature. 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Finance, competitiveness and innovation. https://documents1.worldbank.org/curated/en/099012324094532203/pdf/P1523071e2033 b07b1889c1901b0a386567.pdf World Development Indicators (WDI). https://databank.worldbank.org/source/worlddevelopment-indicators 30The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs Appendix 1: Data Presentation GDPPC (GDP per capita); CBSMEs (commercial bank’s credit to SMEs); MBL (microfinance bank’s loans); INT (interest rate on lending); GCF (gross capital formation); LAB (labour force) Year GDPPC CBSMEs MBL INT GCF LAB 1992 477.08 20400.00 135.80 29.80 396.61 33665896.00 1993 270.03 15462.90 654.50 18.32 559.15 34567536.00 1994 320.83 20552.50 1220.60 21.00 744.09 35509739.00 1995 407.28 32374.50 1129.80 20.18 1153.47 36476502.00 1996 460.32 42302.10 1400.20 19.74 1494.75 37359834.00 1997 478.58 40844.30 1618.80 13.54 1697.77 38306242.00 1998 467.94 42260.70 2526.80 18.29 1948.65 39308944.00 1999 496.03 46824.00 2958.30 21.32 2098.54 40378165.00 2000 565.31 44542.30 3666.60 17.98 2404.82 41439754.00 2001 586.83 52428.40 1314.00 18.29 2473.47 42444294.00 2002 733.54 82368.40 4310.90 24.85 3078.78 43376700.00 2003 786.80 90176.50 9954.80 20.71 3846.23 44463288.00 2004 992.75 54981.20 11353.80 19.18 4723.72 45568178.00 2005 1250.41 50672.60 28504.80 17.95 5772.64 46768185.00 2006 1652.15 25713.70 16450.20 17.26 7948.12 47945348.00 2007 1876.41 41100.40 22850.20 16.94 6997.62 49185874.00 2008 2227.79 13512.20 42753.06 15.14 7535.27 50482323.00 2009 1883.89 16366.49 58215.66 18.99 9177.08 51791905.00 2010 2280.11 12550.30 52867.50 17.59 9183.06 53143752.00 2011 2504.88 15611.70 50928.30 16.02 9897.20 54535983.00 2012 2728.02 13863.46 90422.25 16.79 10281.95 53686203.00 2013 2976.76 15353.04 94055.58 16.72 11478.08 52794910.00 2014 3200.95 16069.27 112110.15 16.55 13593.78 53696560.00 2015 2679.55 12949.48 187247.34 16.85 14112.17 54557234.00 2016 2144.78 10747.89 196194.99 16.87 15104.18 55285972.00 2017 1941.88 10747.89 194024.94 17.56 16908.13 57856159.00 2018 2125.83 44822.84 207963.32 19.33 24550.24 60517054.00 2019 2334.02 123932.10 262630.00 15.53 35863.98 63226720.00 2020 2074.61 62510.17 179377.51 12.32 41253.55 62242961.00 2021 2065.75 83740.00 187661.03 11.48 58293.95 62700036.50 2022 2184.42 94460.00 195944.54 12.34 65227.13 63706286.72 Source: CBN Statistical Bulletin and the World Development Indicators (WDI) 31 Onyele et al.: Formal Financing of SMEs in Nigeria: A Path to Economic Development?Published by Pepperdine Digital Commons, 2025 The Journal of Entrepreneurial Finance Published by Pepperdine Digital Commons, 2025 Ikwuagwu et al.: Formal Financing of SMEs Appendix 2: Natural log of Data LN = natutal log Year LNGDPPC LNCBSMEs LNMBL LNINT LNGCF LNLAB 1992 6.17 9.92 4.91 3.39 5.98 17.33 1993 5.60 9.65 6.48 2.91 6.33 17.36 1994 5.77 9.93 7.11 3.04 6.61 17.39 1995 6.01 10.39 7.03 3.00 7.05 17.41 1996 6.13 10.65 7.24 2.98 7.31 17.44 1997 6.17 10.62 7.39 2.61 7.44 17.46 1998 6.15 10.65 7.83 2.91 7.57 17.49 1999 6.21 10.75 7.99 3.06 7.65 17.51 2000 6.34 10.70 8.21 2.89 7.79 17.54 2001 6.37 10.87 7.18 2.91 7.81 17.56 2002 6.60 11.32 8.37 3.21 8.03 17.59 2003 6.67 11.41 9.21 3.03 8.25 17.61 2004 6.90 10.91 9.34 2.95 8.46 17.63 2005 7.13 10.83 10.26 2.89 8.66 17.66 2006 7.41 10.15 9.71 2.85 8.98 17.69 2007 7.54 10.62 10.04 2.83 8.85 17.71 2008 7.71 9.51 10.66 2.72 8.93 17.74 2009 7.54 9.70 10.97 2.94 9.12 17.76 2010 7.73 9.44 10.88 2.87 9.13 17.79 2011 7.83 9.66 10.84 2.77 9.20 17.81 2012 7.91 9.54 11.41 2.82 9.24 17.80 2013 8.00 9.64 11.45 2.82 9.35 17.78 2014 8.07 9.68 11.63 2.81 9.52 17.80 2015 7.89 9.47 12.14 2.82 9.55 17.81 2016 7.67 9.28 12.19 2.83 9.62 17.83 2017 7.57 9.28 12.18 2.87 9.74 17.87 2018 7.66 10.71 12.25 2.96 10.11 17.92 2019 7.76 11.73 12.48 2.74 10.49 17.96 2020 7.64 11.04 12.10 2.51 10.63 17.95 2021 7.63 11.34 12.14 2.44 10.97 17.95 2022 7.69 11.46 12.19 2.51 11.09 17.97 32The Journal of Entrepreneurial Finance, Vol. 27, Iss. 1 [2025], Art. 3 https://digitalcommons.pepperdine.edu/jef/vol27/iss1/3 DOI: 10.57229/2373-1761.1508