The Impact of Financial Development on Manufacturing Sub-Sector Growth in Nigeria, 1991–2024

THE IMPACT OF FINANCIAL DEVELOPMENT ON MANUFACTURING SUB-SECTOR GROWTH IN NIGERIA, 1991–2024 Author: Omojuwa O. Michael, Omitogun O., & Onanuga A. Toyin Department of Economics, Olabisi Onabanjo University, Ago-Iwoye, Nigeria Corresponding Author’s Email: ojotopejoshua@gmail.com Abstract This study examines the impact of financial development on manufacturing sub-sector growth in Nigeria over the period 1991-2024, drawing on the endogenous growth framework. The study employs the Financial Development Index (FDI) compiled by the International Monetary Fund (IMF) as a composite measure of financial development, capturing depth, access, efficiency, and stability dimensions of the financial system annual time-series data sourced from the World Bank Development Indicat ors, the IMF Database, and the Central Bank of Nigeria (CBN) Statistical Bulletin. The study adopts the Autoregressive Distributed Lag (ARDL) model to analyse the data. The findings reveal that financial development exerts a positive and statistically significant effect on manufacturing sub-sector growth in the short run, but a negative and significant effect in the long run, suggesting that expanded financial services have not been consistently channelled into productive industrial financing. All control variables interest rate, inflation, and exchange rate exert negative and significant effects on manufacturing sub-sector growth. The study contributes to the literature by employing a multidimensional composite index of financial development, extending the analytical period beyond prior studies, and uncovering the temporal asymmetry in the finance-manufacturing nexus. The study therefore, recommends that there is need to align financial sector expansion with industrial development priorities, reduce borrowing costs, and stabilise macroeconomic fundamentals to sustain manufacturing growth. Keywords: Financial development, manufacturing sub-sector growth, ARDL Model JEL Classification: G20, L60, O11 Introduction The manufacturing sector occupies a pivotal position in the growth trajectories of both developed and developing economies. Globally, the sector contributed approximately 15.2% of world GDP in 2023, reflecting a gradual decline from 16.5% recorded in 2021 (OECD, 2025). Within developing economies, manufacturing-led industrialisation remains a cornerstone of structural transformation, enabling nations to transition from agrarian dependence towards value-added production, employment generation, and technological advancement (United Nations Industrial Devel opment Organisation [UNIDO], 2024). For Sub-Saharan Africa, the pattern is equally instructive: manufacturing output as a share of GDP declined from 11.55% in 2021 to 11.18% in 2023 (World Bank, 2024), highlighting structural vulnerabilities in the region’s industrial base. Financial development, broadly understood as the expansion and deepening of financial institutions and markets, has long been theorised as a prerequisite for sustained manufacturing growth. The endogenous growth framework, pioneered by Romer (1986) and extended by Lucas (1988) and Pagano (1993), posits that a well-functioning financial system enhances capital allocation efficiency, mobilises savings, and mitigates transaction and information costs, thereby stimulating productive investment and fostering sectoral growth. The theoretical nexus between financial development and manufacturing output is further reinforced by King and Levine (1993), who demonstrate that financial intermediation positively influences capital accumulation and productivity. Despite this theoretical consensus, empirical evidence on the finance-manufacturing nexus remains inconclusive, particularly in the context of Sub-Saharan African economies, necessitating continued and rigorous investigation. In Nigeria, the manufacturing sector’s contribution to GDP has been characterised by prolonged volatility and persistent underperformance. From a contribution of 19.49% in 1991, the sector’s share declined sharply to 6.55% in 2010 before partially recovering to 15.36% in 2023 (World Bank, 2024). This trajectory reflects decades of structural challenges, including overdependence on petroleum revenue, inadequate infrastructure, high production costs, volatile macroeconomic conditions, and limited access to affordable industrial credit (Imoughele & Okoro, 2021, 2023). Successive governments have implemented wide-ranging financial sector reforms including the Structural Adjustment Programme (1986), bank consolidation exercises, and interest rate liberalization with the implicit objective of deepening financial markets and directing credit towards productive sectors, including manufacturing. However, empirical studies examining the outcome of these reforms present conflicting evidence. Whilst some scholars establish a positive nexus between financial development and manufacturing output (Batool et al., 2024; Ekwunife et al., 2024), others document negative or mixed effects (Amalu et al., 2021; Eduno, 2021; Seyfullayev & Seyfullayev, 2023; Shang et al., 2019). This empirical discord suggests that the direction and magnitude of the finance - manufacturing relationship are sensitive to the choice of financial development proxies, the econometric methodology employed, and the specific country context under examination. Against this backdrop, the present study makes a distinctive contribution to the extant literature in three key respects. First, whilst prior studies have predominantly proxied financial development using single-dimensional indicators such as private sector credit, broad money supply, or banking sector deposits (Afolabi et al., 2022; Amalu et al., 2021; Eduno, 2021; Egbetunde et al., 2019), this study employs the IMF’s composite Financial Development Index (FDI), which simultaneously captures depth, access, efficiency, and stability dimensions of both financial institutions and financial markets. This multidimensional approach addresses a critical measurement gap identified in the literature (Muhammad, 2019; Olaniyi & Odhiambo, 2023) and provides a more accurate and policy-relevant assessment of financial development’s influence on manufacturing growth. Second, by extending the analytical period to 2024, the study incorporates post - pandemic economic dynamics and recent financial sector developments that have substantially altered Nigeria’s industrial landscape, thus surpassing the time scope of comparable studies (Aminu et al., 2019; Ekwunife et al., 2024). Third, the study directly addresses the unresolved question of temporal asymmetry in the finance-manufacturing nexus by distinguishing between short-run and long-run effects within a robust ARDL framework. The remainder of this paper is structured as follows: Section 2 presents the conceptual and empirical literature review; Section 3 outlines the methodological framework; Section 4 presents and discusses the empirical results; and Section 5 offers conclusions and recommendations. Literature Review Manufacturing sub-sector growth refers to the sustained increase in output, productivity, employment, and investment within industries that transform raw materials into finished goods (Adesina, 2021). It constitutes a critical component of economic development, particularly in emerging economies, as it drives industrialisation, enhances value addition, and fosters technological advancement. According to UNIDO (2024), manufacturing-led growth is central to sustained economic development, offering increasing returns through innovation, productivity spillovers, and employment generation. For Nigeria, revitalising the manufacturing sector is essential for reducing import dependence, creating jobs, and achieving sustainable economic diversification. Financial development, on the other hand, refers to the process of expanding and improving the depth, accessibility, and efficiency of financial intermediation, encompassing financial institutions, instruments, and markets (Mishkin, 2015; World Bank, 2016). The IMF (2023) conceptualises financial development through a composite index that integrates four principal dimensions: financial depth (the size of financial institutions and markets relative to GDP), financial access (the ability of individuals and firms to access financial services), financial efficiency (the effectiveness with which financial intermediaries allocate capital), and financial stability (the resilience of financial systems to shocks). The IMF Financial Development Index (FDI) synthesises these dimensions into a single metric, providing a comprehensive and internationally comparable measure of financial development status. This mult idimensional conceptualisation is critical because reliance on a single indicator such as private sector credit or money supply risks capturing only a narrow facet of the financial system’s role in supporting productive activities. The conceptual gap in ea rlier studies, which largely treat financial development as unidimensional, motivates the adoption of the composite FDI in the present study. Conversely, several studies have been carried out, such as Batool et al. (2024) investigated the relationship betw een financial development and Pakistan’s manufacturing sector over the period 1974 -2023. Employing two -way Granger causality tests and cointegration analysis, the study found bidirectional cointegration between financial development and manufacturing outpu t in both the short and long run. The study concluded that strengthening the financial sector through targeted fiscal policies is vital for sustaining manufacturing growth in Pakistan. Whilst methodologically rigorous, the study’s context -specificity limit s its direct applicability to Nigeria, where institutional frameworks and financial market structures differ considerably. Ekwunife et al. (2024) examined the impact of financial development on manufacturing sector performance in Nigeria from 1986 to 2021. Employing the Error Correction Model (ECM) and ARDL model, the study established that financial intermediation positively and significantly influences manufacturing sector output, whilst financial liberalisation exhibits a negative but significant effect. The study’s use of single-dimensional proxies for financial development intermediation and liberalization represents a notable methodological limitation, as it fails to capture the multidimensional nature of financial development. Seyfullayev and Seyfulla yev (2023) explored the relationship between financial development and economic growth in Azerbaijan’s manufacturing sector over the period 2005-2021. The Johansen cointegration test and Toda -Yamamoto causality approach revealed no long-run cointegration o r short-run causal relationship between financial development and manufacturing growth. The authors concluded that Azerbaijan’s financial market remains fragile, with inefficiencies exerting an inverse effect on industrial performance. Whilst the Azerbaijan context differs from Nigeria, the finding that financial development does not automatically translate to manufacturing growth resonates with this study’s long-run findings. Also, Manisha and Aneja (2023) investigated the impact of financial sector develo pment on total factor productivity (TFP) in India’s manufacturing sector over the period 1998 –2017. Employing the ARDL model, cointegration analysis, and Granger causality tests, the study established a positive long-run association and a unidirectional ca usal relationship running from financial development to TFP. The findings support a beneficial long -term finance –manufacturing relationship in India, which contrasts with this study’s Nigerian findings, possibly reflecting differences in financial system maturity and policy coherence. Afolabi et al. (2022) investigated the effects of specific financial sector development indicators on Nigeria’s manufacturing sector from 1991 to 2020, employing the ARDL technique with data from the Central Bank of Nigeria. T he findings revealed a positive relationship between money supply and manufacturing sector output. However, loans to the private sector and the prime lending rate significantly impacted the manufacturing sector positively. Energy cost and exchange rate had little effect. The study’s findings underscore the heterogeneity of financial development proxies in determining manufacturing outcomes, validating the adoption of a composite index in this study. Amalu et al. (2021) assessed the impact of financial development on Nigeria’s manufacturing sector output between 1986 and 2019, using credit to the private sector relative to GDP and market capitalisation relative to GDP as proxies. Employing the ARDL bounds test and ECM, the study found that credit to the priva te sector positively and significantly impacts manufacturing output, whilst market capitalisation exerts a significant negative effect. The study confirms cointegration but reports a slow speed of adjustment to equilibrium, suggesting that short-run gains may not translate into sustained long-term growth. The limited time scope (ending in 2019) leaves post-pandemic developments unaddressed. Eduno (2021) examined the influence of financial sector development on manufacturing output in Nigeria from 1980 to 20 18, employing the ARDL model and using credit to the private sector (CPS/GDP), broad money supply (M2/GDP), and interest rate spread as financial development proxies. The findings revealed that financial indicators significantly affected manufacturing outp ut in the short run. However, gross fixed capital formation had no significant long-run impact, suggesting that capital accumulation alone does not guarantee sustained industrial growth without complementary financial support. The study’s reliance on a nar row set of monetary indicators constrains its policy implications. Egbetunde et al. (2019) investigated the relationship between financial development and industrial sector output in Nigeria over the period 1970–2016, employing the ARDL model. The results revealed that all variables were cointegrated, and causality runs from financial development to industrial output. The study established a positive long-run relationship, highlighting the critical role of financial deepening through credit provision, monet ary expansion, and capital market activities. However, the study’s period does not capture the significant reforms that occurred after 2016, limiting the contemporary relevance of the findings. Moreover, Muhammad (2019) examined the relationship between ba nking sector growth and manufacturing sector performance in Ghana from 1983 to 2014, employing the ARDL approach. The study found that financial development measured by broad money supply to GDP significantly boosts manufacturing output in both the long an d short run. However, credit -based financial development showed no significant effect. The scholar confirmed that the direction and impact of financial development on manufacturing output are determined by the choice of financial development indicator, a f inding that directly validates the present study’s use of a composite index. Shang et al. (2019) investigated the relationship between financial development and China’s manufacturing sector structural upgrading between 2006 and 2018 using panel data regres sion. The study found that financial efficiency positively correlates with manufacturing structural upgrading, whilst financial agglomeration exerts a negative effect. The study’s distinction between the “quality” and “quantity” dimensions of financial development aligns with the multidimensional approach adopted in this study, reinforcing the argument that improvements in financial quality, rather than mere expansion, drive manufacturing growth. Mesagan et al. (2018) examined the relationship between Nigeria’s industrial performance and financial sector growth between 1981 and 2015, using private sector credit and money supply as proxies. The ARDL model revealed that in the short run, money supply and private sector credit negatively affected manufacturing valueadded, whilst positively (though insignificantly) affecting output and capacity utilisation. In the long run, both indicators exerted favourable effects on output. These contrasting short- and long-run effects partially corroborate the temporal asym metry identified in this study. However, a critical synthesis of these studies reveals two persistent gaps that this study seeks to bridge. First, existing studies have employed a wide variety of financial development proxies including private sector credi t, money supply, market capitalisation, interest rate spreads, and banking system liquidity without adopting a comprehensive composite index. This measurement fragmentation produces inconsistent and incomparable findings, making it difficult to derive defi nitive policy conclusions. Second, the empirical findings on the direction of the finance-manufacturing nexus remain unresolved: some studies report a positive relationship (Batool et al., 2024; Egbetunde et al., 2019; Manisha & Aneja, 2023; Muhammad, 2019), whilst others document negative or mixed effects (Amalu et al., 2021; Afolabi et al., 2022; Eduno, 2021; Mesagan et al., 2018; Seyfullayev & Seyfullayev, 2023). This study directly addresses both gaps by employing the multidimensional IMF Financial Development Index, extending the sample period to 2024, and applying the ARDL bounds testing approach to distinguish between short-run and long-run dynamics in the Nigerian context. Methodology This study is anchored on the endogenous growth theory, as formali sed by Romer (1986) and subsequently extended by Lucas (1988), Pagano (1993), and Rajan and Zingales (1998). The theory posits that long-run economic growth is determined by internal factors including capital accumulation, human capital investment, and inn ovation rather than exogenous technological change. Crucially, Pagano (1993) integrates the financial sector into the endogenous growth framework by demonstrating that financial intermediation influences economic growth through three channels: the proporti on of savings channelled into productive investment (θ), the social return on investment (A), and the savings rate (s). This framework directly links financial development to manufacturing sub-sector growth by emphasising that an efficient financial system reduces transactio n costs, mobilises savings, and allocates capital to its most productive uses, thereby stimulating industrial output. Following Pagano’s (1993) formalisation, the steady -state growth rate can be expressed as g = Aθs − δ, where g is the growth rate, A is th e marginal productivity of capital, θ is the efficiency of financial intermediation, s is the savings rate, and δ is the depreciation rate. This equation implies that financial development can influence manufacturing growth through improvements in A (alloc ative efficiency), θ (financial sector efficiency), or s (savings mobilisation). Adamopoulos (2010) and subsequent scholars, including Egbetunde et al. (2019), have operationalised this framework empirically by relating manufacturing sub-sector output (Msg ) to financial development indicators. The present study adopts and extends this framework by utilising a comprehensive composite index of financial development (the IMF FDI) that simultaneously captures all three channels of financial influence on manufacturing growth. Model Specification Following the theoretical framework and adapting the empirical model of Afolabi et al. (2022), the functional form of the model is specified as: 0 1 2 3 4 (1)t t t t t tMSG FDI LIT INF EXR           Where MSGt denotes manufacturing sub-sector growth as a percentage of GDP in period t; FDI ᵗ is the IMF Financial Development Index; LIT t is the lending interest rate; INFt is the inflation rate (measured by the Consumer Price Index); EXR t is the exchange rate; δ₀ is the constant; β₁ to β₄ are the parameters to be estimated; and t is the stochastic error term, assumed to be normally distributed with zero mean and constant variance. The key modification over prior studies (Afolabi et al., 2022; Amalu et al., 2021) lies in replacing multiple single-dimensional proxies for financial development with the IMF composite FDI, which integrates financial depth, access, efficiency, and stability into a single, theoretically grounded indicator. Definition of Variables, Data Sources, and A Priori Expectations The dependent variable, manufacturing sub-sector growth (MSG), is measured as manufacturing value-added as a percentage of GDP (World Bank, 2024). This metric captures the relative economic contribution of the manufacturing sector and ser ves as a standard proxy for manufacturing sub-sector performance in the Nigerian empirical literature (Afolabi et al., 2022; Amalu et al., 2021). The primary independent variable, the Financial Development Index (FDI), is drawn from the IMF Financial Devel opment Database (IMF, 2024). It ranges from 0 to 1, with higher values denoting greater financial development. Unlike prior studies that use single proxies such as private sector credit or money supply, the composite FDI captures all dimensions of financia l system performance, thereby overcoming the measurement limitation identified by Muhammad (2019). Further, three control variables are incorporated to account for macroeconomic conditions that may influence manufacturing output. The lending interest rate (LIT), sourced from the CBN Statistical Bulletin (2024), represents the cost of capital, with high rates expected to constrain industrial investment and output (β ₂ < 0). The inflation rate (INF), proxied by the Consumer Price Index and sourced from the Wor ld Bank (2024), is expected to exert a negative effect on manufacturing growth by raising input costs and eroding purchasing power (β₃ < 0 or > 0). The exchange rate (EXR), sourced from World Bank Development Indicators (2024), captures currency dynamics; depreciation increases the cost of imported raw materials, potentially reducing manufacturing competitiveness (β₄ < 0). Annual time-series data spanning 1991 -2024 (34 observations) were employed, covering a period that encompasses major financial sector reforms, economic crises, and post-pandemic recovery, thereby providing a comprehensive analytical window. Conversely, the ARDL model as an estimator was adopted and other pre-estimation techniques such as stationary test, and is well-suited for the study’s 34 annual observations (Pesaran et al., 2001). The bounds test determines long-run cointegration by comparing the F -statistic against critical bounds, while the ECM captures short-run dynamics and adjustment speed. Diagnostics include VIF, Jarque -Bera, Bre usch-Godfrey LM, ARCH, and CUSUM tests. Data Presentation and Discussion of Results Table 1: Descriptive Statistics Result Statistic MSG FDI LIT INF EXR Mean 12.7513 0.1974 18.3735 18.2381 1995.2060 Median 11.9350 0.2000 17.7000 13.1300 2059.5500 Maximum 20.9300 0.2700 31.6000 72.8400 2585.7000 Minimum 6.5500 0.1200 11.5000 1.4700 1390.5000 Std. Dev. 4.3489 0.0336 4.2519 15.9044 445.7112 Skewness 0.3997 -0.3749 0.8361 2.1692 -0.1713 Kurtosis 1.8428 2.7986 4.2805 6.8998 1.3495 Jarque-Bera 2.8023 0.8539 6.2840 48.2112 4.0257 Probability 0.2463 0.6525 0.0432 0.0000 0.1336 Observations 34 34 34 34 34 Source: Researcher’s computation (2026). Table 1 result reveals that the manufacturing sub-sector growth (MSG) records a mean of 12.75% of GDP, with a m edian of 11.94%, indicating a slight positive skew (0.40) and a platykurtic distribution (kurtosis = 1.84). The standard deviation of 4.35 denotes considerable variability in Nigeria’s manufacturing output over the study period, reflecting the sector’s susceptibility to macroeconomic volatility and policy inconsistency. The Jarque-Bera statistic of 2.80 (p = 0.246) confirms that MSG is normally distributed, satisfying assumptions for inferential analysis. The Financial Development Index (FDI) has a mean val ue of 0.197, indicating a low -to-moderate level of financial development relative to the theoretical maximum of 1.0. The narrow standard deviation of 0.034 reflects the relative stability of Nigeria’s financial development over the period, notwithstanding structural reforms. The mild negative skewness ( -0.37) and mesokurtic kurtosis (2.80) are consistent with approximate normality (Jarque -Bera: 0.85, p = 0.653). The lending interest rate (LIT) averages 18.37%, underscoring persistently high borrowing costs in Nigeria’s credit market. The inflation rate (INF) displays the highest variability (std. dev. = 15.90) and a mean of 18.24%, reflecting Nigeria’s chronic inflationary pressures driven by fuel subsidy removals, currency depreciation, and food supply disruptions. The exchange rate (EXR) averages N1,995.21 per US dollar, with a standard deviation of 445.71, capturing the significant currency depreciation experienced across the study period. Table 2: Correlation Matrix Result Variable FDI LIT INF EXR FDI 1.0000 LIT -0.7547 1.0000 INF -0.5952 0.4547 1.0000 EXR 0.7589 -0.7062 -0.3948 1.0000 Source: Researcher’s computation (2025) Table 2 presents the correlation matrix for the independent variables. No correlation coefficient exceeds the threshold of 0.8 in absolute value, which serves as the conventional benchmark for problematic multicollinearity (Gujarati & Porter, 2009). The strongest correlations are observed between FDI and LIT ( -0.755) and between FDI and EXR (0.759), both of which fall below t he critical threshold. These relationships reflect the macroeconomic interconnectedness of financial development with interest rate and exchange rate dynamics in Nigeria. Table 3: Variance Inflation Factor (VIF) Result Variable Coefficient Variance Uncentered VIF Centered VIF FDI 671.3477 142.6959 3.9053 LIT 0.0285 53.8132 2.6588 INF 0.0013 3.8598 1.6391 EXR 3.03E-06 67.0715 3.0986 Source: Researcher’s computation (2026) Table 3 presents the results of the Variance Inflation Factor (VIF) test, which provides a more definitive assessment of multicollinearity than the correlation matrix. Critically, all centred VIF values are below 5, with the highest centred VIF being 3.906 for FDI. This confirms that multicollinearity does not pose a threat to the relia bility or efficiency of the parameter estimates. The low VIF values across all variables validate the robustness of the subsequent ARDL estimation and ensure that the coefficients are not inflated or unstable due to inter-variable linear dependence. Table 4: Unit Root Result Source: Researcher’s computation (2026) Table 4 results reveal that the variables exhibit a mixed order of integration of I(0) and I(1). With the mixed integration and the fact that none of the variables are integrated of order two, I(2), the appropriate estimation technique for the dataset is the ARDL (Autoregressive Distributed Lag) Model. The model is suitable for I(0) and I(1) variables. Lag Length Selection Lag length selection is a prerequisite for conducting a Bounds Test or an ARDL model. On that note, the researcher conducted separate lag length selection on each model because each model includes different explanatory variables. With the model structure, the time-series properties of these regressors differ, meaning the number of lags required to capture their dynamics will also differ. The results of the lag selection criteria ar e shown in Table 5 Table 5: Lag Length Selection for the Model Source: Author’s Computation (2026) As indicated in Table 5, reveal that, out of the lag length selection criteria, LR, FPE, AIC, and HQ, revealed that lag 3 specificati ons are optimal for the model to ensure balanced model fit and predictive accuracy. Therefore, lag three was selected for further modeling. Variables Model Specification ADF @ Level ADF @ 1st Difference Order of Integration Msg None [-0.5477] (-1.9534) [-2.4392] (-1.9521) I (1) FDI Constant, Linear Trend [-3.1091] (-3.5578) [-5.4498] (-3.5629) I (1) LIT Constant, Linear Trend [-5.4239] (-3.5684) - I (0) INF Constant, Linear Trend [-1.0401] (-3.6032) [-5.8456] (-3.6032) I (1) EXR None [0.9335] (-1.9517) [-2.6166] (-1.9517) I (1) Lag Length Selection for Model 0 -493.0585 NA 3875528. 32.1973 32.4749 32.2878 1 -343.4514 231.6497 2665.3370 24.8678 26.8107 25.5011 2 -297.8130 52.9994 1899.5790 24.2460 27.8541 25.4221 3 -228.1240 53.9528* 519.6284* 22.0725* 27.3459 23.7915* Table 6: ARDL Bounds Test for Cointegration Result Significance Level I(0) Lower Bound I(1) Upper Bound F-Statistic Decision 10% 2.68 3.53 5% 3.05 3.97 4.425 Cointegrated 2.5% 3.40 4.36 1% 3.81 4.92 Researcher’s computation (2025). Table 6 reveal the ARDL bounds cointegration test result, with the computation of F-statistic of 4.425 which exceeds of cours e both the lower [I(0) = 3.05] and upper [I(1) = 3.97] critical bounds at the 5% level of significance. This suggests that there is a cointegration in the model and of course, it equally implies, the variables of the study have long-run relationship. Table 7: ARDL Short -Run and Long-Run Estimates Financial Development and Manufacturing Sub-Sector Growth Variable Coefficient Std. Error t-Statistic Prob. D(MSG(-1)) 0.5612 0.1363 4.1164 0.0017 D(MSG(-2)) 0.9325 0.1641 5.6835 0.0001 D(FDI(-1)) 46.8356 11.4079 4.1056 0.0017 D(FDI(-2)) 31.5003 11.6810 2.6967 0.0208 D(LIT) 0.1880 0.0729 2.5801 0.0256 D(LIT(-1)) 0.4851 0.0914 5.3107 0.0002 D(LIT(-2)) 0.1701 0.0769 2.2098 0.0492 D(INF(-1)) -0.0930 0.0259 -3.5929 0.0042 D(EXR) -0.0102 0.0034 -3.0123 0.0118 ECM(-1) -0.9429 0.1517 -6.2146 0.0001 Adjusted R² = 0.8183; Durbin-Watson = 2.38 Long-Run Estimation Variable Coefficient Std. Error t-Statistic Prob. FDI -65.3374 23.8045 -2.7447 0.0191 LIT -0.3921 0.1687 -2.3252 0.0402 INF 0.0866 0.0269 3.2155 0.0082 EXR -0.0108 0.0017 -6.2153 0.0001 @TREND 0.3408 0.0836 4.0752 0.0018 Panel A of Table 7 presents the short-run ARDL estimates for Model. The lagged dependent variables, D(MSG( -1)) and D(MSG( -2)), are positive and statistically significant (coeffi cients = 0.561 and 0.933; p < 0.05), indicating persistence in manufacturing sector performance and supporting the notion that prior industrial momentum reinforces contemporary output growth. Both the first and second lagged differences of financial develo pment, D(FDI( -1)) and D(FDI( -2)), exert positive and statistically significant effects on manufacturing sub-sector growth (coefficients = 46.84 and 31.50; p < 0.05). This finding is consistent with the short-run expectation that improved financial access, depth, and efficiency facilitate credit availability and investment in the manufacturing sector, stimulating output expansion in the near term. The result corroborates the findings of Ekwunife et al. (2024) and Afolabi et al. (2022), who also report a positive short-run finance-manufacturing relationship in Nigeria. Among the control variables, the current lending interest rate D(LIT) and its first and second lags are positive and significant, suggesting that interest rate dynamics exhibit complex short-run effects, possibly reflecting the interplay of monetary policy transmission and credit market behaviour. The lagged inflation rate D(INF( -1)) exerts a negative and significant effect ( -0.093; p = 0.004), consistent with the notion that inflationary pressur es erode purchasing power and increase production costs, thereby suppressing manufacturing output. The current exchange rate D(EXR) also exerts a negative and significant effect ( -0.010; p = 0.012), suggesting that currency depreciation immediately raises the cost of imported raw materials and capital goods, constraining manufacturing activity. The model demonstrates strong explanatory power, with an adjusted R-squared of 0.818 and a Durbin -Watson statistic of 2.38, indicating no evidence of serial autocorrelation in the residuals. The error correction term, ECM( -1), is negative and highly significant ( -0.943; p = 0.0001), confirming the existence of a stable long-run cointegrating relationship and the system’s capacity to self -correct following short-run disequilibria. The magnitude of the ECM coefficient implies that approximately 94.3% of any deviation from long-run equilibrium is corrected within one year, indicating a remarkably rapid adjustment process. This high speed of adjustment may reflect the sens itivity of Nigeria’s manufacturing sector to financial conditions, exchange rate movements, and monetary policy signals, which tend to resolve relatively quickly in the context of a small open developing economy with volatile financial markets. Panel B of Table 6 presents the long-run ARDL coefficients. Contrary to the positive short-run effect, the Financial Development Index (FDI) exerts a negative and statistically significant long-run effect on manufacturing sub-sector growth (coefficient = - 65.337; t = -2.745; p = 0.019). This finding implies that a unit increase in the FDI leads to a 65.34 percentage point reduction in manufacturing sub-sector growth as a share of GDP over the long term. This result, whilst counter -intuitive from a theoretical perspect ive, aligns with a strand of the empirical literature documenting adverse long-run finance - manufacturing relationships in developing economies (Amalu et al., 2021; Seyfullayev & Seyfullayev, 2023). The plausible explanations for this finding are multifacet ed: first, financial development in Nigeria may have channelled funds predominantly towards the non-tradable services sector rather than the manufacturing sector, consistent with the financialisation hypothesis (Orhangazi, 2008). Second, expanded financial services may have facilitated increased importation of manufactured goods, crowding out domestic manufacturing production. Third, the high interest rate environment, even as the financial system expands, may have offset the potential productivity gains fr om improved financial intermediation. These findings are broadly consistent with Shang et al. (2019), who distinguished between the “quality” and “quantity” dimensions of financial development, noting that an expansion in the quantity of financial services does not necessarily translate into productive industrial outcomes. The lending interest rate (LIT) exerts a negative and significant long-run effect (coefficient = -0.392; p = 0.040), confirming that persistently high borrowing costs constrain long -term investment in the manufacturing sector and reduce its GDP contribution. This finding is consistent with theoretical expectations and aligns with the conclusions of Egbetunde et al. (2019) and Amalu et al. (2021), who similarly report adverse interest rate effects on industrial output. The inflation rate (INF) exhibits a positive and significant long-run coefficient (0.087; p = 0.008). Whilst this appears counterintuitive, it may reflect a nominal growth effect: sustained inflation in Nigeria’s economy may h ave inflated the nominal value-added of manufacturing output relative to GDP over the long run, a phenomenon also observed by Afolabi et al. (2022) in certain model specifications. The exchange rate (EXR) exerts a negative and significant long-run effect ( -0.011; p = 0.0001), consistent with the conclusion that persistent currency depreciation undermines manufacturing competitiveness by raising the naira cost of imported inputs, capital goods, and machinery, thereby reducing the sector’s GDP share over the long horizon. Post-Estimation Diagnostics Post-estimation diagnostic tests confirm the validity and reliability of the ARDL estimates. The Jarque -Bera test for normality of residuals yields a statistic of 1.489 (p = 0.475), confirming that residuals are no rmally distributed. The Breusch -Godfrey serial correlation LM test produces a statistic of 4.190 (p = 0.050), indicating no significant evidence of serial autocorrelation. The ARCH test for heteroscedasticity yields a statistic of 1.039 (p = 0.492), confir ming homoscedastic residuals. The CUSUM and CUSUM of Squares tests indicate that all parameters remain within the 5% significance bounds throughout the estimation period, confirming structural stability. These diagnostic outcomes collectively validate the robustness and reliability of the empirical estimates. Discussion and Comparison with Prior Studies The short-run positive effect of financial development on manufacturing sub-sector growth corroborates the findings of Batool et al. (2024), Ekwunife et al. (2024), and Afolabi et al. (2022), who document favourable short-run finance–manufacturing linkages in various contexts. In the short run, improved financial access and credit availability appear to stimulate industrial activity, consistent with the endog enous growth framework’s prediction that financial intermediation mobilises savings for productive investment. However, the negative long-run effect diverges from studies that report consistently positive finance-manufacturing relationships (Egbetunde et a l., 2019; Manisha & Aneja, 2023; Muhammad, 2019), whilst aligning with the mixed and adverse findings reported by Amalu et al. (2021), Eduno (2021), and Seyfullayev and Seyfullayev (2023). The temporal asymmetry positive short-run but negative long-run effects identified in this study represents a novel and important contribution to the Nigerian empirical literature. This pattern suggests that whilst financial sector expansion initially stimulates manufacturing activity through improved credit availability, the long-run structural misalignment between financial development and industrial policy characterised by high lending rates, speculative financial activities, and credit directed away from the manufacturing sector ultimately constrains the sector’s growt h. This finding directly validates Muhammad’s (2019) assertion that the indicator choice fundamentally determines the observed relationship, as the composite FDI in this study captures dimensions of financial development that single indicators fail to refl ect. The negative long-run effect of all macroeconomic control variables (interest rate and exchange rate) reinforces the conclusion that Nigeria’s manufacturing sector is acutely sensitive to macroeconomic instability, a finding consistent across the lite rature (Afolabi et al., 2022; Amalu et al., 2021; Imoughele & Okoro, 2023). Conclusion and Recommendations This study has examined the impact of financial development on manufacturing sub-sector growth in Nigeria over the period 1991 -2024, employing the AR DL bounds testing approach and utilising the IMF’s composite Financial Development Index as a multidimensional proxy for financial development. The empirical evidence establishes a clear long-run cointegrating relationship among financial development, manufacturing sub-sector growth, lending interest rate, inflation, and exchange rate. The findings reveal a critical temporal asymmetry: financial development exerts a positive and statistically significant effect on manufacturing sub-sector growth in the shor t run, suggesting that immediate improvements in financial access, depth, and efficiency stimulate industrial activity. However, in the long run, financial development exhibits a negative and significant effect, indicating that the cumulative expansion of Nigeria’s financial sector has not been effectively channelled into productive industrial financing. The negative long-run effects of lending interest rate and exchange rate further underscore that macroeconomic instability characterised by high borrowing costs and persistent currency depreciation constitutes a structural binding constraint on Nigeria’s manufacturing competitiveness and growth potential. The policy implications of these findings are substantial and multidimensional. First, policymakers shou ld prioritise the alignment of financial sector expansion with industrial development objectives. Specifically, the Central Bank of Nigeria should design and enforce directed credit programmes, development finance interventions, and concessional lending facilities that explicitly target the manufacturing sector at affordable interest rates, thereby translating financial deepening into productive industrial outcomes. Second, structural reform of Nigeria’s interest rate environment is imperative. The persist ently high lending interest rates documented in this study averaging 18.37% over the sample period significantly elevate the cost of industrial capital, discourage long -term investment, and ultimately suppress manufacturing output. Monetary policy framewor ks should aim to sustainably reduce real lending rates through improved monetary policy transmission and enhanced competition in the banking sector. 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