Effects of Macroeconomic Indices on NonPerforming Loans in Nigeria Banks
Article History
Received: 25.10.2020; Revision: 04. 11.2020; Accepted: 19. 11.2020; Published: 21. 11.2020
Author Details
Dr. Adepoju Adeoba Asaolu^{1} and Oluchi Jacinta Adibe^{2}
Authors Affiliations
^{1}Department of Banking and Finance, Faculty of Management Sciences, University of Benin, BeninCity, EdoState, Nigeria
^{2}Achievers University, Owo, OndoState, Nigeria
Abstract: This study examines the effects of macroeconomic indices on nonperforming loans in Commercial banks in Nigeria using time series data during 19922019. Analysis was done using OLS, Johansen Cointegration Test and Vector Error Correction Model. Nonperforming loan is the dependent variable while inflation, real exchange rate, lending rate and real gross domestic product (RGDP) growth are the explanatory variables. Our long run analyses show that lending rate and inflation have positive relationship with the dependent variable (NPL) while the RGDP shows significant negative relationship with NPL. Meanwhile, all other variables have nonsignificant positive relationship with nonperforming loans in Commercial Banks in Nigeria at the short run. The study recommends that the monetary authorities should be more flexible and deliberate in setting business friendly Monetary Policy Rate (MPR) which invariably regulates the lending rate, this is because lending rate is a core part of bank’s cost profile and efficiency is critical to their performances. Stabilizing lending rate is achievable when investors buoy up savings (cheap funds) to dilute cost of fund and borrowing for investment purposes.
Keywords: Macroeconomic indices, Lending rate, Inflation, Real exchange rate.
INTRODUCTION
The banking sector plays vital roles in economic development both in all classes of economy because of the salient position it occupies in the mobilisation of savings and distribution of credit. Commercial banks have provision and extension of credit facilities (loans and advances) to credit worthy customers who are also perceived to be having the capacity and wherewithal to meet repayment obligations.
Bank loans are regarded as risk assets because the monies advanced as loans by the banks belong to depositors and it is the major source of bank earnings through net interest income (Asekome and Agbonkhese, 2014). It is also through the process of lending that banks provide productive investments which invariably contribute to the growth and development of the economy and the promotion of the welfare of the people (Unugbro, 2007). However, in the process of extending these credit facilities to customers, banks are exposed to various risks chief among which is the credit risk expressed as loss resulting from failure of borrowers to repay a loan or meet contractual obligation and making it difficult for depositors’ monies to be available as and when such is demanded.
Nonperforming loans are those that borrowers fail to repay according to earlier agreed schedule of payment of interest and principal for at least 90 days issued prudential guidelines to licensed banks and recommended their classification into three categories which are substandard, doubtful and lost (Unugbro, 2007). The increase in the level of nonperforming loans in the banking sectors becomes a serious threat to banks’ existence due to its drastic effects on bank profitability, capital erosion and impairment of liquidity, poor asset quality, loss of public confidence and bank failure (Aigbovo and Igbinosa, 2014).
In
Nigeria, increase in nonperforming is one of the most crucial
liquidity challenges facing the Commercial Banks. The proportion of
nonperforming loans to total loans and advances in the banking
system was believed to be the major caused of financial crisis in
the 1990s and 2000s which were experienced both in developing and
developed countries (Badar and Javid, 2013). For example, in
Nigeria, the total loans granted by banks increased by 736.8 percent
from N1.9
trillion in 2005 to N15.9
trillion in 2019. However, the banking industry witnessed a
significant downturn in the quality of its assets as nonperforming
loans rose remarkably by 99.1 percent from N368.76
billion as at end December 2005 to N3.2
trillion as at end December 2013 but later the value of
nonperforming loan declined to N1.1
trillion by December 2019. Consequently, the ratio of nonperforming
loans to total loans of the banks increased to 7.92 per cent in 2006
to 14.84 per cent in 2017 and later decline to 6.03 percent in 2019.
This
decline could be attributed to increase in loan recoveries and loan
writeoffs and the decision of banks to diversify their loan
portfolio into other variable sectors such as manufacturing and
export (Achara, 2019). According to the report from National Bureau
of statistics (NBS), nonperforming loans in Nigeria dropped to a
4year low of N1.44
trillion in Q2 2019 from N1.93
trillion. This suggests that in one year, banks recovered N96.22
billion nonperforming loans (Bamidele, 2019). We expect the level
of nonperforming loan to increase after covid19 because most
economic activities were negatively affected and grounded due to the
initial lockdown, this reduced the capacity of organisations to meet
up with their loan obligations with many of them already requesting
for restructuring of their credit facilities.
Identifying the macroeconomic indices that influence nonperforming loans is necessary for developing economy like Nigeria, as the higher the risk coefficient associated with macroeconomic factors such as inflation, exchange rate and lending rate, among others, the lower the bank positive disposition to granting credit facilities to customers (Asekome and Agbonkhese, 2014).
Macro environment typically affects balance sheets of banks, which, in turn, affect capacity to honour debt obligations.
Hence,
proxies for the macroeconomic environment tend to have some form of
relationship with nonperforming loans (Kure, Adigun and Okedigba,
2017). In Nigeria the real gross domestic product has improved from
N46,
012.52billion in 2008 to N59,
929billion by December 2012, It also increase from N63,
219.72billion in 2013 to N71,
387.83 by December 2019 reflecting economic growth and a decline
in inflation to 8.48% in December 2013 to an increases to 11.4% in
December 2019. The rate of exchange has increased tremendous from
N169.68
to $1 by December 2014 to N306.95
to $1 by December 2019 (CBN, Bulletin 20122019). This increase
adversely affects the economy as Nigeria is net importer. This
negative effect leads to increase in loan default and nonperforming
loans. Foreknowledge of the extent to which macroeconomic factor
affects nonperforming loans of Commercial banks would assist
regulatory authorities in the formulation and implementation of
policies geared towards efficiency and effectiveness of the banking
sector in credit management and the economy as a whole. For this
purpose, the study seeks to examine the effects of macroeconomic
factors on nonperforming loans in Commercial banks in Nigeria.
The rest of the study is organised as follows. Section 2 reviewed conceptual frameworks, theoretical and empirical literatures; Section 3 the methodology of the study; Section 4 analysis, result presentation and interpretation. Section 5 conclusion and recommendation.
LITERATURE REVIEW
Conceptual Framework
Concept of nonperforming loan
Nonperforming loans are called such due to the inability of the obligors to meet up with loan repayments or loans contrary preterms and conditions. A loan is considered nonperforming when the creditors form the opinion that the borrower is no longer in position to repay either the principal or interest charges or both after 90 days or more. This also occurs when such a default becomes lingering and persistent (Unugbro, 2007).
Caprio and Klingebiel (1999) define nonperforming loans as a sum of borrowed money that a debtor has not made payments for at least 90 days contrary to schedule. The provision of CBN’s has recommended the classifications of nonperforming loans as substandard, doubtful and lost.
Onyiriuba (2009) identified some of the causes of nonperforming loans in the banking sector as inability to monitor loan utilisation and the performance achieved by the borrower, insider abuse and dealings perpetrated by bank officials and lack of credit analysis capabilities in several lending areas into which the bank ventures, among others.
Unugbro on his part list implications of nonperforming loans on financial institution to include among other things, evocation of negative public image which cast doubts on the collective integrity of the management team and limitation in availability of loanable fund.
The
quantum of nonperforming loans in Nigeria has been a major source
of concerned as it threatens the country’s financial sector. This
has culminated in the signing into law the Amendment Act, 2019 of
Asset Management Corporation of Nigeria (AMCON) on 7^{th}
August, 2019. The new law grants the Corporation more power to
enforce recovery of debt from prominent Nigerians and corporations
such as the power to place any bank account or any other account
related to a bank account of a debtor of an eligible financial
institution under surveillance (Bamidele, 2019). The current report
of National bureau of statistic 2019 shows that total gross loans in
Nigeria banks stood at N15.9trillion
as at the end of December 2019 and nonperforming loans to the total
loans ratio declined to 6.03 percent. This reduction in
nonperforming can be attributed to the recent directive given by
Central Bank of Nigeria to Commercial banks for the immediate
suspension of interests on nonperforming loans to oil markets as
the failure of Federal Government to pay fuel subsidy to oil
marketers has worsened their situation which in turn increase
nonperforming loans, this is in addition to the recent amendment of
AMCON law and every other measure put in place by the Central Bank
(Bamidele, 2019).
Concept of Macroeconomics
These are events that affects the course or direction of a given large scale economy. The macro environment typically affects balance sheets of business agents, the capacity to honour debt obligations. Hence, proxies for the macroeconomic environment tend to have some form of relationship with nonperforming loans (Kure, Adigun and Okedigba, 2017). A macroeconomic factor can be positive, negative or neutral. Positive macroeconomic factor comprise of events that ultimately stimulate economic stability and expansion within a country. Some positive factors can lead to reduction in the volume of nonperforming loans while some can lead to increase in the volume of nonperforming loan. Negative macroeconomic factors comprise of events that threaten the national or global economy while neutral macroeconomic factors are some economic changes that are neither positive nor negative. Macroeconomic factors include inflation, gross domestic product, exchange rate, price level, unemployment, interest rate, national income and so on. This paper makes use of inflation, real exchange rate, lending rate and real gross domestic product (RGDP) growth as our macroeconomic factors.
Concept of Gross Domestic Product (RGDP) growth
This
is
the total monetary or market value of all the final goods and
services produced within a country in a given period of time. It is
a sophisticated measure of the value of economic activity. Real
Gross Domestic Product is the production of goods and services
valued at constant prices. High GDP growth implies that the economy
is performing well, and incomes of its citizens are increasing.
Growing revenues demonstrate that loans will be paid. Annual GDP
growth will unreservedly assure that bank lending would function
effectively (Anjom and Karim, 2015). In Nigeria the real gross
domestic product has improved from N46,
012.52billion in 2008 to N59,
929billion by December 2012, It also increase from N63,
219.72billion in 2013 to N71,
387.83 by December 2019 reflecting economic growth (CBN, Bulletin
2019).
Concept of Inflation
Inflation refers to general rise in the level of prices for goods and services with a consequence of marked drop in the purchasing power of currency. Inflation can have a positive or negative effect on the economy; the effect of inflation on NPLs may either be positive or negative.
Higher inflation reduces borrowers’ repayment capacity and raises NPLs, whereas real value of debt service tends to decline with higher inflation, thereby driving down NPLs (Klein, 2013). The aim of monetary authority is to tame inflation by reducing it to a single digit with monetary tightening since 2011, after a steady decline in inflation to 8.48% in December 2013; it increases to 11.4% in December 2019 and it has been over 13% in 2020. This level of increase usually affects the borrower ability to repay their debt as high inflation rate decreases the overall operating efficiency of the firms and the economy resulting to increase in the growth of nonperforming loans in Commercial banks.
Concept of Exchange rate
Exchange
Rate is the price or measure of a nation’s currency in term of
other currencies. Real exchange rate compares a nation’s currency
value against the weighted average of a basket of major currencies.
Currency depreciation may have a negative or a positive effect on
nonperforming loans (NPLs). Currency depreciation in a country with
flexible exchange rate regimes and a large amount of lending in
foreign currency, may have a positive effect on accumulation of NPLs
(Fofack, 2005). The rate of exchange is increasing tremendously from
N169.69
to $1 by December 2014, to N306.95
to $1 by December 2019 and now N379
to $1 as at November 3, 2020.This increase adversely affects the
economy as Nigeria as a net importer. This negative effect leads to
increase in loan default and nonperforming loans in the banking
industry.
Concept of lending rate
Lending rate is the cost attached to the principal by a lender to a borrower for the use of assets. Our finding reveal that high lending high lending rate leads to an increase in nonperforming loan as borrowers often find it difficult to repay their loan. Nigeria Banks increases their maximum lending rate from 15% to 20% and above ( note, this is exclusive of other flat charges) and this high rate mostly affects the borrower’s ability to repay their debt, which in turn lead to increase in nonperforming loans.
Theoretical Framework
Information Asymmetric and Moral Hazard Theory
Information asymmetry theory was first applied by Akerlof (1970). It states that bank may find it difficult to decipher between good and bad borrowers because some borrower may falsify their account in order to obtain credit facility from bank and this action lead to adverse selection and moral hazard problems. Moral hazards on the other hand occur when a banks customer provides details that is misleading about its financial statements or his or her credit capacity, or has a hidden incentive to take risks that are unusual in an attempt to earn a profit. A prospective borrower may not enter into the contract with the bank in good faith, thereby given misleading information about his or financial status or credit capacity. Moral hazard may result to information asymmetry between banks customer and the bank which makes it hard to distinguish between credit worthy customer and noncredit worthy customer (Richard (2011), this has also led to accumulation of nonperforming loans (Bofondi & Gobbi, 2003). The theory is important to this study due to the fact that effective and efficient financial systems and financial intermediation requires accurate information about borrowers and the venture the credit are used for. More so, the moral hazard theory stated that the higher the nonperforming loan's the lower the financial performance and the higher the assets quality the higher the financial performance of banks and vice versa (Okoh, Inim, and Idachaba, 2019).
The Adverse Selection Theory
This theory was propounded by Akerlof (1970) and later expanded by Rothschild and Stiglitz (1976), it describes a situation where the probability of loan default increases with rising interest rate and the quality of borrowers worsens as the cost of borrowing rises (Musara and Olawale, 2012). The theory is founded on the assumption that banks are not certain in selecting creditworthy borrowers from a pool of loan seekers with different credit risk exposures exante. Thus, financial intermediaries are more likely to lend to highrisk borrowers who are not concerned about the harsh lending conditions and are prone to loan default (Ezeoha, 2011).
Empirical Review
Okoh, Inim and Idachaba (2019) investigate the effects of nonperforming loans on the financial performance of Commercial banks in Nigeria; using multiple regression techniques to analyse data from 1985  2016. The study shows that NonPerforming Loans to Total Loans ratio and Cash Reserve Ratio had statistically negative significant effect on Return on Asset (ROA). These result shows that a high level of nonperforming loans would reduce the financial performance of commercial banks in Nigeria.
In his study, Atoi (2018) applied restricted dynamic GMM and a panel vector autoregressive framework to estimate the macroeconomic and bank specific drivers of nonperforming for licensed and International banks in Nigeria from quarter 2, 2014 to quarter 2, 2017. His findings reveal that NPLs drivers vary across the two categories of banks, but weighted average lending rate is a vital drivers of NPLs on both banks. Mazreku, Morina, Spinteri and Grima (2018) examine the determinants of the level of nonperforming loans in Commercial Banks of transition countries. The study employed Pooled OLS, fixed and random effect estimation as well as complex dynamic panel data method for autoregressive lagged and the result shows that GDP growth and inflation are both negatively and significantly correlated with the level of nonperforming loans while unemployment is positively related to nonperforming loans.
Koju, Koju and Wang (2017) evaluate the macroeconomic and bank specific determinants of nonperforming loans (NPL) in the Nepalese banking system using both static and dynamic panel estimation approaches. The findings show that NPLs have significant positive relationship with the export to import ratio, inefficiency, and assets size and a negative relationship with the GDP growth rate, capital adequacy, and inflation rate. The results of the empirical study indicate low economic growth as the primary cause of high NPLs in Nepal and suggest that efficient management and effective financial policies are required for a stable financial system and economy.
Kure, Adigun and Okadigba (2017) examine the determinants of nonperforming loans and its feedback on the macro economy. The study employed the Pool Mean Group (PMG) estimator and a Panel Vector Autoregressive (PVAR) Lag method to analyse quarterly data spanning 20072016. The result revealed a negative relationship between economic growth and nonperforming loans, suggesting that improvement in the production environment can lower the growth of nonperforming loans. The study further ascertains moderate impact of NPLs on the economy: decline in credit and bank assets, increase in risk taking by banks and reduction in economic growth.
Umoren, Nwosu, Udoh and Apan (2016) investigate the relationship between nonperforming loans and Manufacturing subsector productivity in Nigeria, time series data from 19802016 were analysed using exponential trend, Granger causality test, and simple regression model. Descriptive and inferential analyses revealed that, NPLs exhibited a negative exponential growth rate of 5.89%; while manufacturing subsector productivity declined at the rate of 6.60% during the study period. The result of the analysis indicated an inverse significant relationship between NPLs and Manufacturing subsector productivity in Nigeria. The study by Morakinyo and Sibanda (2016) investigate the dynamics of nonperforming loans and economic growth in Nigeria using Autoregressive distributed lag model using quarterly data spanning 1998 to 2014. To analyse endogenous growth model and found that NPLs level and bank credits to the economy has a negative but significant impact on economic growth. The study also applied an error correction mechanism to establish a slow response to equilibrium in the next period, once the system was distorted. Also, Idewele (2016) examines the determinants of nonperforming loans in Nigeria using ordinary least square multiple regression to analyse time series from 19812014. The study revealed that gross domestic product is not a significant determinant of bad debt ratio and poor credit management contributed significantly to nonperforming loans in Nigerian banks. Ugoani (2015) evaluate the effects of nonperforming loans on Bank profitability in Nigeria using descriptive and regression statistical method. The result confirm that nonperforming loans has a negative influence on bank profitability.
Aigbovo and Igbinosa (2014) examine the determinants of macroeconomic drivers of nonperforming loans in the Nigeria banking sector. The study employs Engle and Granger, two stage Cointegration estimation techniques and the associated Error Correction Mechanism to estimate the multivariate model with time series data from 19802012. The result of the study revealed that economic variables has not adequately impacted on nonperforming loans of banks in Nigeria due to the level of economic and financial development in the country and complexity in implementing banking reforms that hinders these reforms from achieving the desired results. Akinlo and Mofoluwaso (2014) evaluated the determinant of nonperforming loans in Nigeria using descriptive statistic, Augmented Dickey fuller Unit root test, Johansen Cointegration and Error correction model with data spanning 19812011. The result shows that increase in real GDP tend to reduce nonperforming loans both in the short run and long run, exchange rate and credit to private sector tend to increase NPLs, lending rate has increasing effects on NPLs and stock market index has a negative effect on NPLs. Asekome and Agbonkhese (2014) employed econometric technique of Ordinary Least Square to determine the effects of macroeconomic indicators on risk assets creation in Nigeria. The result revealed that all the variable were in tandem with the theoretical expectation except gross domestic product and considering the tvalue, all the variables were statically significant as well except capacity utilization of industries. Also, Chude and Chude (2014) examine implication of nonperforming loans on Economic growth in Nigeria. The study was analysed using OLS, Augmented Dickeyfuller unit test and Johansen Cointegration method on time series time spanning 19922009, the result shows that there is a long run relationship between NPLs and economic growth and also a significant relationship between inflation rate and NPLs.
In an International Monetary Fund Working Paper, Klein (2013) investigates the nonperforming loans (NPLs) in Central, Eastern and SouthEastern Europe (CESEE) covering 1998–2011. The study reveals that the NPLs level can be ascribed to both macroeconomic conditions and banks’ specific factors, even though the banks’ specific factors was found to have a relatively low explanatory effect on NPLs. It further reveal that NPLs were found to respond to macroeconomic conditions, such as GDP growth, unemployment, and inflation which means it affects the economic recovery of the region.
Mohammad, Ammara, Abrar and Fareeha (2012) examined economic determinants of nonperforming loans using correlation and regression analysis to analyse the impact of selected independent variables and the result reveals that interest rate, energy crisis, unemployment, inflation and exchange rate has a significant positive relationship with the nonperforming loans of Pakistan banking sector, while GDP growth rate has a significant negative relationship with the nonperforming loans of Pakistan banking sector.
Khemraj and Pasha (2009) tested empirically a fixedeffect panel data model for the determinants of nonperforming loans in the Guyanese banking sector. The study found standard macroeconomic and bank specific factors to be relevant in the evolution of nonperforming loans. Among the macroeconomic variables considered were annual inflation rate, real effective exchange rate and GDP growth rate. The authors also found banks, with relatively higher interest rates and excessive lending, incur higher levels of nonperforming loans.
METHODOLOGY
The study investigate the effect of macroeconomic indices on nonperforming loans in commercial banks in Nigeria using annual time series data from 1992 to 2019 obtained from the Central Bank of Nigeria (CBN) statistical bulletin and National Bureau of Statistics (NBS) reports. The model was estimated by employing the econometric techniques of Ordinary Least Square Method (OLS), Augmented DickeyFuller (ADF) unit root test, Johansen cointegration test and Vector Error Correction Mechanism (VECM). Unit root test was carried out using the Augmented DickeyFuller (ADF) in order to determine the stationarity of variables. Ordinary Least Squares method is adopted to investigate the long run relationship between variables. The Vector Error Correction Model is also adopted to examine the speed of adjustment, that is, the rate at which the dependent variable adjust to changes in the independent variables in the long run. The Johansen cointegration test is used to test cointegration and convergence between the variables.
Model Specification
The model is based on the modification of the empirical models of Aigbovo and Igbinosa (2014). The model specifies the dependent variable Non performing loan is a measure of loan loss provision to total loan ratio (LPTLR) as a function of lending rate, inflation rate, real effective exchange rate and real GDP growth representing the independent variables. The model is specified as follows:
NPL = F (LR, INF, REXR, RGDPG)…..…………………………………………. (1)
The econometric form of equation (1) is represented as:
NPL = ᵦ_{0 + }ᵦ_{1 LR + }ᵦ_{2 INF+ }ᵦ_{3 REXR +} ᵦ_{4 RGDPG+ ᶒ }…………………… (2)
Where:
NPL_{ = }Non performing loan is a measure of loan loss provision to total loan ratio (LPTLR)
LR = Lending rate
INF= Inflation
REXR = Real Exchange rate
RGDPG = Real Gross Domestic Product Growth
ᵦ_{0 }=_{ }The intercept; the expected value of NPLR when all the explanatory_{ }variables assume zero as value.
ᵦ_{1, }ᵦ_{2, }ᵦ_{3, }ᵦ_{4 = }The slope coefficient or the parameters for the independent variables
_{ᶒ = }Error term or stochastic variable
Our apriori expectations are ᵦ_{1 }ᵦ_{4 >} 0 (nonnegativity assumptions)
ANALYSIS, PRESENTATION AND RESULT INTERPRETATION
The study seeks to investigate the effect of macroeconomic indices on the Nonperforming loans in Commercial banks Nigerian banking sector. The analysis was conducted using inferential statistic such as the Augmented DickeyFuller (ADF) Unit Root test, Johansen Co integration test Ordinary Least Square Method (OLS), and the Error Correction Mechanism (ECM).
In order to avoid the occurrence of spurious regression parameter, the Augmented Dickey Fuller test was employed to test the presence or otherwise of unit root in the series.
Table 1. Unit Root Test
Variables 
ADF Test @ LEVEL 
Critical values @ 5% 
ADF Test @ First Difference 
Critical values @ 5% 
ADF Test @ Second Difference 
Critical values @ 5% 
Remarks 
NPL 
3.362899 
3.56068 
4.827006 
3.595026 
 
 
1(1) 
INF 
2.043160 
3.587527 
3.951301 
3.603202 
 
 
1(1) 
LR 
6.648284 
3.587527 
 
 
 
 
1(0) 
REXR 
2.635033 
3.587527 
4.771311 
3.595026 
 
 
1(1) 
RGDPG 
2.548293 
3.595026 
2.017185 
3.595026 
4.838260 
3.622033 
1(2) 
Source: Authors’ computation (2020)
The results of the test presented in Table 1 above show that at level lending rate (LR) was stationary; that is, it was integrated of order zero. Three variables, namely; nonperforming loans (NPL), inflation and real exchange rate were stationary at first difference. That is, they were integrated of order one while real Gross domestic product growth was stationary at second difference; that is, it were integrated of order two. This means that the calculated ADF statistics values in absolute term were greater than their respective critical values at 5% level.
Therefore, the study rejects the null hypothesis of presence of unit root and accepts the alternative hypothesis that there is no unit root in the data series.
Seeing that the variables have different order of integration from the unit root result above, the method of cointegration would no longer be Augmented Engle Granger cointegration method. Rather, the study will adopt Johansen multivariate cointegration investigating method which is a system equation.
According to the rule, all that is required to ensure cointegration is at least one cointegration equation.
Table 2. Johansen Multivariate Cointegration Test result

TRACE STATISTIC 
MAXEIGEN STATISTIC 

Hypothesized No of CE(s) 
Trace Statistic 
Critical Value @ 5% 
MaxEigen Statistic 
Critical Value @ 5% 
r = 0* 
127.7338 
69.81889 
61.04545 
33.87687 
r ≤ 1* 
66.68834 
47.85613 
40.62378 
27.58434 
r ≤ 2* 
26.06457 
29.79707 
14.11219 
21.13162 
r ≤ 3* 
11.95238 
15.49471 
10.26998 
14.26460 
r ≤ 4 
1.682397 
3.841466 
1.682397 
3.841466 
(*) denotes rejection of the hypothesis at 5%
Source: Authors’ computation (2020)
For the purposes of reasonable policy making, the relationship between nonperforming loan and macroeconomic indices variables in the long run is very important. If variables have a causal relationship that allows them to move in perfect harmony in the long run, the confidence level of the consistency of the formulated policy will be robust. It was against this backdrop that the cointegration test was conducted, so as to determine if there is a cointegration among the variables. From the test statistic of trace and maximumeigen values below, result shows that there is two and two cointegrating equation among the variables. This, therefore, gives the basis to reject the null hypothesis of no cointegration among variables at 5% level. This confirms the existence of long run relationship between nonperforming loans and macroeconomic variables in Nigeria.
Table 3. Ordinary Least Square Regression
Dependent Variable: NPL 

Method: Least Squares 

Date: 08/13/20 Time: 14:45 

Sample: 1992 2019 

Included observations: 28

Source: Authors’ computation (2020)
The result presented above revealed that not all the variables satisfy the apriori expectation with respect to their sign. The result above reveals that the coefficient of RGDP growth has a negative and significant effect on nonperforming loans which implies that a unit increase in growth of real gross domestic product will lead to a decrease nonperforming loans by 0.000191. Similarly, the coefficient of REXR exerts a negative and insignificant effect on NPLs which implies that a unit increase in REXR will lead to a decrease in NPLs in Nigeria. Meanwhile, the coefficient of inflation and lending rate are positive and statistically significant at one percent which implies that a unit increase in inflation and lending rate will lead to an increase in NPLs by 0.128353 and 1.337969 percent respectively.
The coefficient of determination is approximately 82% which implies that 82% systematic variations in NPL are attributed to the explanatory variables in the model while the remaining 18% is due to Gaussian White noise. When this adjusted to its degree of freedom, it becomes 80%. The Fstatistic was 26.30574 and it is statistically significant one percent. This shows that there is a simultaneous relationship between nonperforming loan and macroeconomic variables in Nigeria.
Since the order of integration is not the same, it means that the study is not permitted to adopt a linear equation modelling such as ECM, because literature says for ECM to be adopted, series must be integrated of the same order. Now that they are integrated of different order, it means that the only way forward is to adopt a system equation method. The study, therefore decides to adopt VECM since it is theoretically justified. The result is presented below in table 3 and the model of interest is model 1, which is the model that carries the dependent variable D(NPL). The Vector Error Correction Model was estimated to analyse the systematic disequilibrium adjustment process and the short run effect among the variables
Table 4. Vector Error Correction Model
Variables 
Coefficients 
Standard Error 
tStatistics 
CointEq1ECM 
0.025149 
0.10983 
0.22899 
D(NPL(1)) 
0.138154 
0.26382 
0.52366 
D(INF(1)) 
0.165293 
0.11738 
1.40818 
D(LR(1)) 
0.150710 
0.50761 
0.29690 
D(REXR(1)) 
0.016061 
0.02404 
0.66817 
D(RGDP(1)) 
0.000378 
0.00079 
0.47959 
Constant 
0.212505 
1.68842 
0.12586 
Rsquared 
0.588438 


Adjusted Rsquared 
0.240193 


Fstatistic 
1.689723 


Source: Authors’ computation (2020)
The CointEq1ECM results above shows that the coefficient of the vector error correction term satisfies apriori expectation. This means that it will be effective to correct any deviations from the longrun and short run dynamics. All the variables are positively sign and conform to the a priori expectation of the theory but not statistically significant. The coefficient of CointEq1ECM is 0.025149, indicating that, the speed of adjustment to long run equilibrium is 2.51% when any past deviation will be corrected in the present period. This implies that the present value of NPLs adjust slowly to changes in independent variables. This reveals that the CointEq1 (ECM) has an effective correcting property as the short run and the long run dynamic will be corrected in the long run. The model of interest is the NPLs and it implies that a unit change in past period in NPLs with a coefficient of 0.138154 will cause the current NPLs to increase by 0.138154 units, thou positive but not statistically significant. The result reveals that the coefficient of Inflation is positively related to NPL, this implies that a unit increase in one period past in inflation (1)) with a coefficient 0.165293 will lead to an increase in NPLs by 0.165293 units in the current year. Lending rate is positively related to NPLs, its coefficient of 0.150710 implies that a unit increase in LR (1)) will lead to an increase in NPLs by 0.150710 units in the current year. Real exchange rate and real gross domestic product growth are positively related to nonperforming loan which implies that a unit increase in one period past in REXR(1)) and RGDP (1)) with coefficient of 0.016061 and 0.000378 will cause NPLs to increase by 0.016061, 0.000378 units respectively in the current year. The coefficient of determination (R^{2}) is approximately 59% which implies that approximately 59% systematic variations in NPLs are attributed to the explanatory variable in the model while the remaining 41% is attributed to internal factors in the banks. The F statistic was 1.689723. This confirms that the model is of good fit to investigate the effect of macroeconomic indices on nonperforming loans in Nigeria. From the result, also, it could be seen that the intercept (Constant) has a negative coefficient value of 0.212505.
This shows that even if all the explanatory variables in the model were held constant or equal to zero, NPLs will be reduced by 0.212505 units. This result of this study revealed that macroeconomic indices impacted positively but not significantly on nonperforming loans in Commercial banks in Nigeria.
Conclusion
The study concludes that lending rate and inflation are the vital macroeconomic indices that influence nonperforming loans in Commercial banks in Nigeria in the long run, the real gross domestic product also has significant negative effect on nonperforming loan in the long run. Meanwhile, all the macroeconomics variables have nonsignificant positive relationship with nonperforming loans in Commercial Banks in Nigeria at the short run. This implies that inflation, macroeconomics indices have impacted positively on nonperforming loans in Commercial banks in Nigeria. Therefore the study recommends that;
The monetary authorities should be more flexible and deliberate in setting business friendly Monetary Policy Rate (MPR) which invariably regulates the lending rate, this is because lending rate is a core part of bank’s cost profile and efficiency is critical to their performances. Stabilizing lending rate is achievable when investors buoy up savings (cheap funds) to dilute cost of fund and borrowing for investment purposes.
Also, the monetary authority should be more determined to tame the consumer price index (CPI) that is inflation; high rate undermines borrowers’ performance on their loans and could stifle their purchasing power. This is achievable via stabilizing exchange rate and money supply.
The government should review on more regular basis policies that stimulate economic and financial stability in the economy.
A corollary of above point is for the Central Bank of Nigeria (CBN) to regularly make policies that would further stabilize the banking sector; this in effect will reduce the impact of nonperforming loans on the bank’s bottom line. Applicable measures include strengthening the banks’ internal risk management process of identification, measurement and risk monitoring.
Reference
Achara, C. (2019). NonPerforming loan hit 4years Low as Bank recover N496b. An Article publish on Nairametrics Stock Select Newsletter.
Aigbovo, O., & Igbinosa, O.B. (2014). Determinant of Nonperforming Loans in Nigeria Banking Sector: Evidence from Macroeconomic factors. Journal of Economics and development Studies. 2 (2) Dec, 2014.
Akerlof, G. A (1970). The market for "lemons": Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488500.
Akinlo, O. & Mofoluwaso, E. (2014). Determinants of nonperforming loans in Nigeria. IBFR Accounting and Taxation, 6(2), 2128.
AMCON. (2016). Hard Times await AMCON Debtor. Press Release (AMCON), http://www.amcon.com.ng/Mediacenter/HardTimesAwaitAMCONDebtors.
Anjom, W., & Karim, A. M. (2016). Relationship between nonperforming loans and macroeconomic factors with bank specific factors: a case study on loan portfolios–SAARC countries perspective. ELK Asia Pacific Journal of Finance and Risk Management, 7(2), 129.
Asekome, M. O.. & Agbonkhese, A.O. (2014). Macroeconomic Indicators and Commercial Banks’ Risk Assets Creation in Nigeria. European Scientific Journal, May Edition 10 (13).
Atoi, N.V. (2018). Nonperforming loan and its effects on Banking Stability: Evidence from National and International Licensed Banks in Nigeria. CBN Journal of Applied Statistic,.9(2) 4374.
Badar, M., Javid, A. Y., & Zulfiquar, S. (2013). Impact of macroeconomic forces on nonperforming loans: An empirical study of commercial banks in Pakistan. wseas Transactions on Business and Economics, 10(1), 4048.
Bamidele, S.A. (2019). NonPerforming loan hit 4years Low as Bank recover N496b. An Article publish on Nairametrics Stock Select Newsletter
Bofondi, M., & Gobbi, G. (2003). Bad Loans and Entry in Local Credit Markets. Rome: Bank of Italy Research Department.
Caprio, G., & Klingebiel, D. (1999). Episodes of systemic and borderline financial crises, mimeo, World Bank.
Central Bank of Nigeria (2009). Statistical Bulletin, Vol. 20.
Central Bank of Nigeria (CBN) (2010). Prudential Guidelines for Deposit Money Banks in Nigeria.
Central Bank of Nigeria (CBN) (2016). Prudential Guidelines for Deposit Money Banks in Nigeria.
Central Bank of Nigeria: Statistical Bulletin (2015, 2016, 2017, 2018 and 2019 versions).
Chude, N. P. & Chude, D. I. (2014). The Implication of NonPerforming Loans On Nigeria’s
Dickey, D.A., & Fuller, W.A. (1981). Likehood Ratio Statistics for Autoregressive Time series with a Unit Roots. Econometric, 49 (4),10571072.
Economic Growth (19922009), Journal of Business and Management, 16(2), 611.
Ezeoha, A. E. (2011). Banking consolidation, credit crisis and asset quality in a fragile banking system. Journal of Financial Regulation and Compliance, Emerald Group Publishing, 19(1), 3344.
Fofack, H. (2005). Nonperforming Loans In SubSaharan Africa: Causal Analysis And Macroeconomic Implications, World Bank Policy Research, Working Paper 3769.
Idewele, O.I. (2016). Determinants of Nonperforming Loans in Nigeria. International Journal of Business and Management Studies. (2), 413428.
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169210.
Khemraj, T., & Pasha, S. (2009). The Determinants of NonPerforming Loans: An Econometric Case Study Of Guyana. Munich Personal Repec Archive.
Klein, N. (2014). NonPerforming Loans in CESEE: Determinants and Impact on Macroeconomic Performance. Journal of Banking and Finance, 234249.
Koju, L., Koju, R. & Wang, S.(2017). Macroeconomic and BankSpecific determinants of NonPerforming Loans: Evidence from Nepalese Banking System. Journal of Central Banking Theory and Practice,,111138. UDK: 336.71(541.35). DOI: 10.2478/jcbtp20180026.
Kure, E., Adigun, M., & Okedigba, D. (2017). NonPerforming Loans in Nigerian Banks: Determinants and Macroeconomic Consequences. Central Bank of Nigeria Economic and Financial Review. 55(3) September 2017.
Mazreku, I., Morina, F.V., Spinteri, J.S., & Grima, S. (2018). Determinant of the level of Nonperforming Loans of Commercial Banks of Transition Countries. European Research Journal, 21(3), 313.
Morakinyo, A. E., & Sibanda, M. (2016). NonPerforming Loans and Economic Growth in Nigeria,
Muhammad, F., Ammara, S. Abrar, H.C., & Fareeha, K. (2012) Economic Determinants of Non Performing Loans: Perception of Pakistan Bankers. European Journal of Business and Management 4 (19) 8799
Musara, M. & Olawale, F. (2012). Perceptions of startup small and medium sized enterprises (SMES) on the importance of business development services providers (bds) on improving access to finance in South Africa. Journal of Social Science, 30 (1) pp. 3141.
National Bureau of Statistic report of (2019) and various issues
Okoh, G., Inim, E.V. & Idachaba, O.I. (2018). Effect of NonPerforming Loans on the Financial Performance of Commercial Banks in Nigeria. American International Journal of Business and Management Studies. 1 (2).
Onyiriuba, L. (2009). Principles and the Practice of Bank Lending. Publish in Nigeria by NFS Data Bureau Limited, Lagos.
Richard, E. (2011). “Factors That Cause NonPerforming Loans in Commercial Banks in Tanzania and Strategies to Solve Them”, Journal of Management Policy and Practice. 12(7). 50 – 57.
Rothschild, M., & Stiglitz, J. (1976). Equilibrium in competitive insurance markets: An essay on the economics of imperfect information. Quarterly Journal of Economics, 90 (4), 629649.
SPOUDAI Journal of Economics and Business, 66 (4), 6181.
Ugoani, J.N.N. (2015). Nonperforming Loans Portfolio and its effect on Bank profitability in Nigeria. Independent Journal of Management and Production. (7).
Umoren, A.A., Nwosu, A.C., Udoh, E.J., & Apan, B.S. (2016). NonPerforming Loans in the Nigerian Banking System and Manufacturing Subsector Productivity. International Journal of Social Science and Economic Research. 1 (7).
Unugbro, A.O. (2007). Bank Management. Mindex Publishing, Benin City, Nigeria.