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The Sectoral Distribution of Commercial Banks' Credit and
Economic Growth in Nigeria
*Wilfred, Chukwuebuka Ebube; Onuegbu, Ebere Clementina; Alake, Samuel Olaitan; Ogbonna,
Vitalis
Department of Accountancy, University of Nigeria, Enugu Campus, College of Postgraduate Studies
*Correspondence Author
DOI :
https://doi.org/10.51583/IJLTEMAS.2026.150600254
Received: 11 May 2024; Accepted: 16 May 2024; Published: 01 August 2026
ABSTRACT
The study examined the sectoral distribution of commercial banks' credit and economic growth in Nigeria.
The specific objectives include to: Examine the effect of commercial bank’s loans and advances to agricultural
sector on economic growth in Nigeria. Ascertain the effect of commercial bank’s loans and advances to
mining and quarrying sector on economic growth in Nigeria. The study adopted pre-test estimation of
descriptive statistics, unit root and bound tests to ensure that the data set is stationary and fit for the analysis.
Auto-Regressive Distributed Lag Model (ARDL) method of analysis was adopted for the estimation. The data
utilized for the analysis were extracted from CBN statistical bulletin. The study revealed that commercial
bank’s loans and advances to agricultural sector for the period of this study has positive and insignificant
impact on gross domestic product in Nigeria, commercial bank’s loans and advances to mining and quarrying
sector for the period of this study has positive and insignificant impact on gross domestic product in Nigeria.
Hence, sectoral distribution of commercial bank’s loans, advances have not contributed significantly to the
Nigerian economic growth. Thus, the study concluded that sectoral distribution of commercial bank’s credit
had positive and insignificant impact on economic growth of Nigeria. The study recommends that monetary
authorities should deregulate interest rate to make funds available for agricultural sector and mining and
quarrying sector to expand their business, increase productivity and boast the economy at the end.
Government should devise measures to closely monitor the utilization of credits given to every sector of the
Nigerian economy in order to enthrone accountability and ensure that the projects for which the credits were
obtained are carried out.
Keywords: Commercial Banks' Credit, Economic Growth, Auto-Regressive Distributed Lag Model
INTRODUCTION
Historically, Nigeria’s growth has been tied to its status as Africa’s largest oil producer. However, in recent
years, the structure of the economy has shifted significantly towards services. Oil sectors contributes less than
10% to the total GDP, it accounts for roughly 80% of government revenue and over 90% of export earnings
(NBS,2023). Services (Telecommunications, Finance, and Real Estate) and Agriculture are the true engines
of growth. In Q3 2025, the non-oil sector contributed 96.6% to the GDP (Trading Economics, 2025). The
contribution of the banking system towards the growth of an economy is primarily credited to the role it plays
in savings mobilization and allocation of resources to deficit sectors of the economy (Nwakoby and
Ananwude, 2016). Access to credit enables enterprises to enhance their productive capacity and their potential
to grow (Mathias & Inedu,2022). Commercial bank loans and advances constitute a major source of capital
for the real sector of the economy, enabling the starting and sustaining of businesses. These banks act as
intermediaries between surplus and deficit spending units, accepting deposits from customers and channeling
part of these deposits into sectors such as manufacturing, mining and quarrying, real estate and construction,
transport and communication and agriculture (Mathias & Inedu,2022). Commercial banks are financial
intermediaries that accept deposits from surplus units and provide loans and advances to deficit units within
the economy. These loans and advances play a pivotal role in economic development by providing the capital
needed for businesses to invest, expand, and sustain their operations. Businesses, particularly in sectors such
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as manufacturing, mining and quarrying, real estate and construction, transport and communication and
agriculture, utilize these funds to purchase factor inputs (labour, land, and capital), thereby contributing to the
production of goods and services. Loans and advances can take various forms, including overdrafts, term
loans, trade credits, and specialized financing arrangements. The effectiveness of these loans in promoting
economic growth largely depends on factors such as interest rates, repayment terms, and the borrowers’
capacity to utilize the funds productively (Mathias and Inedu,2022). However, the effects of bank loans and
advances on economic growth are significantly influenced by how these funds are utilized and the terms of
repayment. Nuri and Ibrahim (2019) highlighted that the development of any sector is closely tied to funding
from the financial system. Similarly, Greenwood and Jovanovic (1990) argued that financial intermediaries
improve resource allocation, provide better information, and foster growth. The availability of credit can
influence saving rates, investment decisions, technological innovations, employment levels, aggregate
demand, and, ultimately, long-term economic growth.
Sectoral allocation of credit refers to the distribution of loans and advances to specific sectors of the economy,
such as manufacturing, mining and quarrying, real estate and construction, transport and communication and
agriculture. This allocation is critical because different sectors have varying levels of productivity, labour
intensity, and capital requirements. For instance, the agricultural sector is highly labour-intensive and often
requires long-term credit facilities due to the seasonal nature of production, while the manufacturing sector
may demand shorter credit cycles for inventory and capital equipment. The efficiency and productivity of
credit allocation depend on factors such as government policies, institutional frameworks, and the capacity of
borrowers to utilize the funds effectively (Mathias & Inedu,2022). Results of previous studies on the effect
of commercial banks’ credit allocated to agricultural sector on economic growth in Nigeria are mixed. For
example, the studies by Ubesie, et al. (2019) and Nteegah (2017) indicate that commercial banks’ credit
allocated to agricultural sector had no significant effect on economic growth in Nigeria and the studies by
Akujuobi and Nwezeaku (2015) and Oladapo and Adefemi (2015) shows that commercial banks’ credit
allocated to agricultural sector had a significant positive effect on economic growth in Nigeria. Hence, there
is no specific agreement on the extent to which sectoral distribution of commercial bank’s loans, advances
impact on economic growth in Nigeria. More so, the focus of this study is on the influence sectoral distribution
of commercial bank’s loans, advances have on economic growth. The variables used includes commercial
banks’ credit distributed to agricultural, manufacturing, construction, mining and quarrying, and services
sectors and utilized gross domestic product to measure for economic growth in Nigeria. Thus, this study
explores sectoral distribution of commercial bank’s loans, advances and economic growth in Nigeria from
2008-2024.
Model Specification
Adejayan and Sulaiman (2017) examined the Impact of sectoral distribution of Commercial banks' loans and
advances on economic growth in Nigeria. Adejayan and Sulaiman (2017) adopted commercial banks' loans
and advances to Manufacturing sector (CMAN, representing Production sector), commercial banks' loans and
advances to Export sector (CEXP, representing General Commerce sector) and commercial banks' loans and
advances to Transport and Communication sector (CTC, representing Service sector) was used to capture
sectoral distribution of commercial banks' loans and advances to different sectors of the economy, while Real
Gross Domestic Product (RGDP) was used to capture economic growth. This study was modified to reflect
the following variables such as, commercial bank’s loans and advances to agriculture (CBLA), commercial
bank’s loans and advances to mining and quarrying (CBLMQ), while real gross domestic product (RGDP)
was adopted as measure for economic growth.
RGDP = (CBLTA, CBLMQ)
The study captured the relationship that exists between them in the equation below.
Where commercial bank’s loans and advances to agriculture is CBLA, commercial bank’s loans and advances
to mining and quarrying is CBLMQ, while real gross domestic product is RGDP.
Description of Variables
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Dependent Variables
Real gross domestic product: Real gross domestic product is a macroeconomic measure of value of
economic output adjusted for price changes.
Independent Variable
Commercial bank’s loans and advances to agriculture: commercial bank’s loans and advances to
agriculture are loans and advances granted to the agriculture sector by commercial banks is adopted as an
independent variable.
Commercial bank’s loans and advances to mining and quarrying: commercial bank’s loans and advances
to mining and quarrying is adopted as an independent variable.
Pre-Test Analysis
Descriptive Statistics
This test will examine the mean, the median and the standard deviation, and other statistical properties of the
dataset.
Unit Root Tests
To check the stationarity of this study to avoid generating a spurious result, this study adopted Augmented
Dicky Fuller (ADF) unit root tests at the 5 percent significance level to determine the order of integration of
the variables under consideration.
Co-integration Tests
Co-integration test was carried out to ascertain whether there could be long-run equilibrium relationship
between the dependent variables and the independent variables.
Method of Data Analysis
This study employed the autoregressive distributed lag (ARDL) model proposed by Pesaran et al. (2001). The
ARDL approach is based on the ordinary least square (OLS) estimation of a conditional unrestricted error
correction model (UECM) for cointegration as developed by Pesaran et al. (2001). It is mainly used to
establish whether one variable is dependent on another or a combination of other variables. It entails
establishing the coefficient(s) of regression for a sample and then making inferences on the population. The
simple regression equation is stated thus; Y = b
0
+ b
1
X
1
+ µ. Where: Y = the variable been predicted, b
0
= the
intercept, b
1
= the slope, X = the variable used to predict Y, µ = the error term. The intercept (b
0
) is the value
of the dependent variable when the independent variable is equal to zero while the slope of the regression line
(b
1
) represents the rate of change in Y as X changes. Because Y is dependent on X, the slope describes the
predicted values of Y given X.
RESULTS AND INTERPRETATION
Table 4.1 Descriptive Statistics
RGDP
CBLMQ
CBLA
Mean
122255.1
2951.177
828.2653
Median
102575.4
2171.370
525.9500
Maximum
277493.8
8528.590
2854.510
Minimum
39954.21
846.9400
106.3500
Std. Dev.
68399.20
2271.431
805.0583
Skewness
0.819298
1.483456
1.332204
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Kurtosis
2.781492
3.975761
3.656502
Jarque-Bera
1.935695
6.909561
5.333798
Probability
0.379900
0.031594
0.069467
Sum
2078336.
50170.01
14080.51
Sum Sq. Dev.
7.49E+10
82550376
10369902
Observations
17
17
17
Source: Researcher’s Eview 9, 2026.
Note: CBLA = Commercial bank’s loans and advances to agricultural sector, CBLMQ = Commercial bank’s
loans and advances to mining and quarrying sector, RGDP= Real gross domestic product.
Table 4.1 present the descriptive statistics. The table explains the aggregative averages of the mean, median
and standard deviation, a measure of spread and variation which were used for consistency and robustness
checks of the results. The skewness, kurtosis and Jarque-Bera probability values demonstrated the series
normality test. The minimum row showed the lowest values of each variable and the maximum row gave the
highest values of each variable. All variables are positively skewed. Among the variables only real gross
domestic product exhibited platokurtic behavior while commercial bank’s loans and advances to agricultural
sector and commercial bank’s loans and advances to mining and quarrying sector exhibited leptokurtic
behavior
Table 4.2 Augmented Dickey-Fuller (ADF) Unit Root Test
Variables
ADF Statistic.
Critical value @ 5%
Order of integration
RGDP
-3.129735
-3.081002
I(1)
CBLA
-3.900267
-3.828975
I(0)
CBLMQ
-3.508059
-3.081002
I(1)
Source: Researcher’s Eview9, 2026
Table above presents the summary results of the ADF unit root tests. The result in table 4.4 indicates that
commercial bank’s loans and advances to mining and quarrying sector and gross domestic product are
stationary at order 1 while commercial bank’s loans and advances to agricultural sector is stationary at level.
Table 4.3 ARDL Bounds Test
Test Statistic
Value
k
F-statistic
33.19861
2
Critical Value
Bounds
Significance
I0 Bound
I1 Bound
5%
3.79
4.85
Source: Researcher’s Eview9, 2026
From the table above, a bound test cointegration was carried out. The bound test result revealed that f-statistics
of 33.19861 is greater than lower bound of 3.79 and upper bound of 4.85 test under 5% level of significance.
Hence, this study establishes that there is a presence of cointegration and further establishes the existence of
long run relationship between the dependent and independent variables in the model.
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Table 4.4: ARDL Cointegrating and Long Run Form
Variables
Coefficient
Std. error
t-statistics
p-value
D(CBLA)
6.397807
14.028981
0.456042
0.6572
D(CBLMQ)
3.673137
4.206382
0.873229
0.4012
CointEq(-1)
-0.047568
0.074105
-0.641892
0.5341
Long Run Coefficients
CBLA
134.499489
214.852941
0.626007
0.5441
CBLMQ
77.219439
175.714756
0.439459
0.6688
C
75361.875217
66142.8945
14
1.139380
0.2787
Source: Researchers’ Eview 9, 2026.
The negative coefficient of the error correction term (-0.047568) indicates that the study conforms to
theoretical exposition of the error correction modeling with the negative value of the error correction term.
The speed of adjustment from the disequilibrium is corrected at the speed of 4% yearly. The Auto-Regressive
Distributed Lag Model (ARDL) results of the first objective revealed that commercial bank’s loans and
advances to agricultural sector for the period of this study for both short and long run had positive and
insignificant impact on gross domestic product in Nigeria. The coefficient of commercial bank’s loans and
advances to agricultural sector are 6.39 and134.4 for short run and long run equation respectively; indicating
that the increase in gross domestic product is due units of increases in commercial bank’s loans and advances
to agricultural sector. Again this was confirmed by the p-value > 0.05 level of confidence. The Auto-
Regressive Distributed Lag Model (ARDL) results of the second objective revealed that commercial bank’s
loans and advances to mining and quarrying sector for the period of this study for both short and long run had
positive and insignificant impact on gross domestic product in Nigeria. The coefficient of commercial bank’s
loans and advances to mining and quarrying sector are 3.67 and 77.2 for short run and long run equation
respectively; indicating that the increase in gross domestic product is due units of increases in commercial
bank’s loans and advances to agricultural sector. Again this was confirmed by the p-value > 0.05 level of
confidence.
DISCUSSION OF FINDINGS
The findings of the research were interpreted in line with the objectives:
Objective I: Examine the effect of commercial bank’s loans and advances to agricultural sector on
economic growth in Nigeria.
The result obtained from test of hypothesis one revealed that commercial bank’s loans and advances to
agricultural sector for the period of this study has positive and insignificant impact on gross domestic product
in Nigeria. The coefficient of commercial bank’s loans and advances to agricultural sector is 0.09; indicating
that a unit increase in gross domestic product is due to 9% increases in commercial bank’s loans and advances
to agricultural sector. This finding is in consonance with the research of Ozor, (2018) studied commercial
banks’ credit to the agricultural sector on economic growth of Nigerian Economy. Autoregressive Distributed
Lag (ARDL) approach was adopted for estimation. The study found that significant relationship exists
between Nigeria’s commercial banks’ credit to agricultural sector and economic growth. The study further
revealed that under most of the models, commercial banks’ credit key explanatory variables were statistically
insignificant contrary to apriori expectations. On the basis of these findings, the study therefore concluded
that the impact of commercial banks’ credit to the agricultural sector on the economy is mixed and largely
insignificant in Nigeria
. However, this study is in contrary with the study of Courage and Aisien (2019)
analyzed the effects of commercial bank credit to the real sector on Nigeria's economic growth from 1981 to
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2015 using co-integration and error correction mechanisms. The study found that commercial bank credit to
the agricultural sectors significantly affects economic growth in both the short and long term.
Objective II: Ascertain the effect of commercial bank’s loans and advances to mining and quarrying
sector on economic growth in Nigeria.
The result obtained from test of hypothesis three reveal that commercial bank’s loans and advances to mining
and quarrying sector for the period of this study has positive and insignificant impact on gross domestic
product in Nigeria. The coefficient of commercial bank’s loans and advances to mining and quarrying sector
is 0.84; indicating that a unit increase in gross domestic product is due to 84% increases in commercial bank’s
loans and advances to mining and quarrying sector…. The outcome of this finding is consonance with the
study of Kurotamunobaraomi and Giami (2024) investigated the relationship between sectoral credit
allocation of deposit money banks and economic growth in Nigeria from 2003 to 2022 using ordinary least
squares (OLS) regression model. They found, an insignificant positive relationship between banks‟ credit
distributed to mining and quarrying sector and GDP in Nigeria.
CONCLUSION
The study evaluated sectoral distribution of commercial bank’s credit and economic growth in Nigeria. The
objectives include to: Examine the effect of commercial bank’s loans and advances to agricultural sector on
economic growth in Nigeria. Ascertain the effect of commercial bank’s loans and advances to mining and
quarrying sector on economic growth in Nigeria. The study adopted pre-test estimation of descriptive
statistics, unit root test and bounds test to ensure that the data set is stationary and fit for the analysis. The
Auto-Regressive Distributed Lag Model (ARDL) method of analysis was adopted for the estimation. The
study revealed that commercial bank’s loans and advances to agricultural sector for the period of this study
has positive and insignificant impact on gross domestic product in Nigeria, Commercial bank’s loans and
advances to mining and quarrying sector for the period of this study has positive and insignificant impact on
gross domestic product in Nigeria. Hence, sectoral distribution of commercial bank’s credit has not
contributed significantly to the Nigerian economic growth. Thus, the study concluded that sectoral distribution
of commercial bank’s credit had positive and insignificant impact on economic growth of Nigeria. The study
recommends that monetary authorities should deregulate interest rate to make funds available for agricultural
sector and mining and quarrying sector to expand their business, increase productivity and boast the economy
at the end.
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economic growth in Nigeria. International Journal of Economics, Commerce and Management, 3(5),
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3. Courage, O., & Aisien, E. (2019). Commercial bank credit to the real sector and economic growth in
Nigeria. International Journal of Economics and Financial Issues, 9(2), 88–97.
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72.
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