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International Journal of Civil Engineering and Technology (IJCIET)

Volume 10, Issue 02, February 2019, pp. 2329-2347, Article ID: IJCIET_10_02_232

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=02

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

CREDIT TO THE PRIVATE SECTOR AND

ECONOMIC GROWTH IN THE PRESENT

TECHNOLOGICAL WORLD: EMPIRICAL

EVIDENCE FROM NIGERIA

Idowu Akin (Postgraduate Student)

Covenant University, Nigeria

Ochei Ailemen Ikpefan

Professor of Finance, Covenant University, Nigeria

Isibor Areghan

Lecturer, Covenant University, Nigeria

ABSTRACT

In the present technological world, an online real-time technology is needed to

facilitate credit to the private sector. Absence of continuous credit to the private sector

has hindered sustainable development in developing countries such as Nigeria. This

study therefore empirically examined the impact of credit to the private sector on

economic growth in the present technological world in Nigeria using time series data

from the period of 1986 to 2016. Dependent variable was GDP growth rate

(GROWTH), as proxy for Economic Growth. Credit to the Private Sector (PSCR) was

the main explanatory variable, while other explanatory variables were; Broad Money

Supply (M2), Real Interest Rate (RINT), Labour Rate (LABR), Gross Fixed Capital

Formation (GFCF). Augmented Dickey Fuller (ADF) unit root test was used to test for

the stationarity properties and order of integration of the data used in the study, the

result revealed that Real interest rate was stationary at levels, while all other variables

were found to be stationary at their first difference. The Vector Autoregressive (VAR)

econometric technique of estimation was employed to detect the effect of Credit to the

Private Sector on complete time path of Nigerian economic growth and vice versa.

Research findings revealed that the response of GROWTH to most of the shocks

(impulses) were positive except for Interest Rate while GROWTH appeared to be

unresponsive to the Interest Rate shocks. Under the Credit to the Private Sector bloc,

the first 2 lags of PSCR being significant at the 5 percent and 1 percent level

respectively are found to be significant predictors of the dependent variable (PSCR).

Furthermore, the estimation result shows that factors like LABF (the three lags) and

RINT (third lag) are equally significant determinants of PSCR.The study therefore

Idowu Akin, Ochei Ailemen Ikpefan and Isibor Areghan


recommends that government should increase credit to the private sector to boost the

sector so that banks and other financial institutions can increase lending to the

Nigerian economy.

Keywords: Economic Growth, Credit to the Private Sector, Vector Autoregression

Model, Management of Credit, Technology

Cite this Article: Idowu Akin, Ochei Ailemen Ikpefan and Isibor Areghan, Credit to

the Private Sector and Economic Growth in the Present Technological World:

Empirical Evidence from Nigeria, International Journal of Civil Engineering and

Technology, 10(02), 2019, pp. 2329–2347

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=02

1. INTRODUCTION

The private sector investors in any country are expected to have moved from analog to digital

system in all aspects of their operation including communication with their bankers. This is

important so as to smoothen their financial operations and avoid delays in their financial

dealings with their bankers. Where there is absence of digitalization in the private sector, there

will be lower output and performance because of inconsistent and delay in the supply of credit

from financial institutions on request.

One of the ponderous benchmarks for evaluating performance of an economy is through

the production levels over a specific measure of chronology. Economic growth, as a

macroeconomic policy objective is a key indicator of how healthy or not, an economy is. When

it comes to improving the general living standards and reducing poverty levels, most especially,

in developing countries, economic growth is one of such important tools utilised. Economic

growth poses as an indispensable factor for economic development. Economic growth

transforms societies by lowering inequality levels; creates job opportunities leading to higher

demand for labour; and drives human development by increasing the ability of people to pay

for necessary goods and services. It is indomitably presumed sure-enough that the preeminent

antecedents arousing economic aggrandizement are capital, labour and technology which is

exogenously determined (Okwo, Mbaijaku, and Ugwunta, 2012).

The financial intermediation function which involves mobilising financial resources from

the surplus sectors and channelling it to productive sectors of the economy makes finance a

crucial discuss in achieving economic growth. Financial institutions such as the deposit money

banks are responsible for the facilitation of financial transactions that ignites the taking-part

rate of the private sector in economic growth and development. Banks in Nigeria are the key

players in the financial intermediation space and are majorly responsible for financial

intermediation activities in the Nigerian financial system. A financial system is not just a

system for facilitating payments or extension of credit facilities, it is the core of a market driven

economy that consists of several inter-related parts which are critical to effective resource

allocation (Isibor, Ojo, and Ikpefan, 2017). Economies with well-developed financial systems

have been observed to have greater likelihood of achieving rapid economic growth than

financial systems that are less-advanced. The size of these financial systems, which strongly

correlates with level of national income, enables individuals and households to reconcile their

everyday exigencies while business firms and enterprises are also able to access funds to

increase their production. A financial system that is alive and kicking will also yield resource

allocation efficiency, astronomical technological advancement and accumulation of rapid

human and physical capital. Several financial sector reforms have taken place in Nigeria over

the years, the most prominent being the Structural Adjustment Program (SAP) in 1986. These

Credit to the Private Sector and Economic Growth in the Present Technological World: Empirical

Evidence from Nigeria


reforms have so far had significant impact on the country’s level of financial development and

have showed how important finance is to economic growth. The Nigerian financial system

consists of Deposit Money Banks (DMBs), Merchant Banks, Microfinance Banks (MFBs),

Primary Mortgage Bank (PMGs), Bureaux-de-Change (BDCs), Finance Companies,

Development Finance Institutions (DFIs), Discount Houses, Non-Interest Bank, Insurance

Companies, Capital Market Operators and Self-Regulatory Organisations (SROs). National

Economic Reconstruction Fund (NERFUND), National Social Insurance Fund (NSTIF),

Nigeria Deposit Insurance Commission (NDIC), Securities and Exchange Commission (SEC),

National Pension Commission (PENCOM) and National Insurance Commission (NAICOM).

Credit as a whole, is a vital link to money redistribution as household and individual

consumption is financed, production is facilitated and capital is formed, which will invariably

result to the facilitation of economic activities. By so doing, it is expected that as economic

conditions shrink, demand for credit will follow suit as businesses will reciprocate by cutting

down output levels and households will reduce their consumption patterns, thereby causing the

demand for credit to diminish. Martin and Douglas (2013) opined that the booster of economic

activities is credit as it allows businesses to obtain loans for expansion of production and

households to purchase homes and other assets then pay back at agreed instalments. It also

enables governments to engage in building more infrastructural projects.

Private sector credit refers to the provision of financial wherewithal to the private sector by

financial institutions, such as through loans, acquisition of non-equity securities, and trade

credits and other accounts receivable that originate a claim for repayment. Commercial banks,

Merchant Banks, Non-Interest banks as well as Other Financial Institutions (OFIs) are the

components of financial institutions. Deposit money banks accept deposit liabilities and give

out credit facilities to those in need of it. Stock markets do not give out credit facilities, but

they are a channel through which people become part owners of companies by acquiring shares

of publicly quoted companies.

Evidences drawn from more recent empirical findings on the subject of finance and growth

tend to agree with the Schumpeterian postulation that finance is a necessary condition for

growth, though a few contrary empirical evidences still exists. Studies show that efficient

provisioning of loans and advances by banks and other financial institutions have valid and

indicative impact on levels of productivity and it also generates employment chances while an

underdeveloped private sector credit system hampers economic growth. Emmanuel, Abiola and

Anthony (2015) observed that economic research all over the world has been inconclusive on

the subject of finance and growth. Notwithstanding, they noted that, there were more results in

favour of the affirmative interconnection between credit and growth than the contrary.

In terms of economic growth stimulation graduating to attendant development, credit to the

private sector far outweighs credit to the public sector; which includes credit to government

business-related companies, public establishments and also credit to central banks, even though

credit to the private sector may sometimes include a considerable relative amount of credit to

state-owned or partially state-owned companies. According to the World Bank Index (2014),

the engine of productive growth is private markets as they tend to create more productive jobs

and generate greater incomes. Private sector investments can help to improve the basic services

and conditions that get poor people empowered – by improving health, education and

infrastructure while government only complements by way of regulation, provision of service

and funding. Therefore, in this study basically, our focus shall be on credit emanating from all

financial set up to the private sector, which constitutes forthright provisioning of loans and

advances to the private sector of the Nigerian economy.



2.1. THEORETICAL FRAMEWORK

2.1.1. Supply-leading hypothesis

This hypothesis which was pioneered by Schumpeter (1911) suggests financial institutions as

being mechanisms for the expansion of productive magnitude of an economy. It argues that

finance precedes growth. Asserting further that the financial institutions mobilize funds,

efficiently allocates such funds, mitigates the problem of information failure, checks the

progress of firms’ production, regulates risk factors and focus on transactions cost reduction,

just to mention a few; In accordance with the proposition, economic growth is positively

impacted by all these factors. King and Levine (1993) posit that financial institutions

aggrandize the agglomeration of capital and also positively alter the productive capacity of

production antecedents; they opined that the above-mentioned dual functions were essential in

economic growth stimulation. Other key proponents of supply-leading hypothesis include

Gurley and Shaw (1967), McKinnon (1973), Shaw (1973) and Fry (1988). They all argued in

support of financial development as having a positive effect on economic growth. King and

Levine (1993), in their study of finance and growth found an assertive link between the

variables. The study also found that even as finance precedes growth, it relented in giving clear

information on the causality direction.

2.1.2. Demand-following hypothesis

This hypothesis suggests that growth that acts as a stimulant for the advancement of the

financial sector and not vice versa. It postulates a causal relationship intervening finance and

growth. This hypothesis states that growth is what generates amplifies requisition for financial

services. The debate on the demand-following hypothesis originated from the works of

Robinson (1952) that altercated growth as not being exerted by any causal impact, instead,

finance goes ahead of economic growth as a turn-out of a surge in request for financial services.

As an economy advances, more financial institutions spring up, thereby leading to an upsurge

in request for financial products and services. Goldsmith (1969) analysed data from thirty-five

countries for the period ranging from 1860 to 1963. Findings revealed financial and economic

advancement as being correlated positively over the specified period. He stressed that financial

development, to a large extent, performs better in the beginning phase of economic

advancement when nations have crouched stratum of earnings. Findings from the study also

revealed that as countries continued to develop, the size of financial institutions grew. Some

proponents of the demand-following hypothesis are McKinnon (1973), Jung (1986), Lucas

(1988), Kar and Pentecost (2000), Omotor (2007), Ndlovu (2013) whose findings all support

the demand-following phenomenon.

2.1.3. Endogenous Growth Theory

To underpin this study, the endogenous growth theory as propounded by Romer (1986) and

Lucas (1988) was analysed. The endogenous growth theory was propounded following the

weaknesses noticed in the Solow-Swan growth model which centred on the assumption that

technological change was exogenously determined. The endogenous growth theory holds that

a powerful financial sector advances economic growth and also has it that economic growth

rate in the long run can be impacted by policy measures. Romer (1986) and Lucas (1988) both

emphasized human capital as an important element in explaining growth. It buttresses how the

workforce with greater knowledge, education and training can help to increase the rate of

technological advancements. By reason of the endogenous growth theory, constant returns to

scale and diminishing returns of individual functions in the Solow model is given up.




Therefore, the core implication of the endogenous theory is that financial sector policies which

encompass competitiveness, innovation and change will promote economic growth.

2.2. EMPIRICAL FRAMEWORK

Diverse empirical findings on the relationship between financial development and economic

growth are presented below. They include cross-country studies and single country reviews.

Atif, Jadoon, Zaman, Ismal and Seemad (2010) investigated the effect of financial

development and trade openness on GDP growth in Pakistan; 1980 to 2009. Financial

development and trade openness were found to Granger cause economic growth over the

specified period. Similarly, Ndlovu (2013) empirically investigated the connection between

financial sector development and economic growth in Zimbabwe between the period of 1980

and 2006. The study used five main variables and three control variables to test. He found a

demand-following development in the financial sector and economic growth correlation in

Zimbabwe. His empirics averred that States must look for ways to protect their indigenous

interests without adversely hindering growth because “big” Government is possible to cause a

drawback to economic growth. Ndlovu also posited that globalisation and creation of jobs are

necessary to activate economic movement.

Mikhail (2015) carried out a comparative study on domestic private credit and per capita

real GDP for 24 countries that are categorised under Organisation for Economic Cooperation

and Development (OECD) during 1989 to 2013. He tested for stationarity of the variables and

employed Granger causality tests as well as fully modified ordinary least squares (FMOLS) in

his investigation. He empirically established that, for OECD advanced nations, there were zero

comprehensive causal connection between credit depth and growth of the economy while he

found that link to be quite supply-preceding for countries that did not show any form of

connection. The researcher therefore dissuades policymakers from overtly depending on

“bank-based financial-development” as the only momentum to drive economic growth. Nkoro

and Uko (2013) investigated the financial sector development and economic growth in Nigeria.

Their results showed an assertive correlation between financial sector development and

economic growth in Nigeria. Mamman and Hashim (2013) also conducted a research on the

impact of private sector credit and growth on the Nigerian real sector. The researchers observed

that government should optimize the flow of credit to the private sector as a turnout of its

forthright shock in the formation and subsequent propagation of growth.

Osuji and Chigbu (2012) studied the impact of financial development on Nigeria’s

economic growth; 1960 to 2008. Granger Causality test, Co-integration analysis and Error

Correction Method (ECM) were employed. Co-integration and Granger tests results showed

that there was co-integration between the independent variables with the dependent variable;

GDP. They concluded that the government should ensure quality regulation of the finance

space to capacitate them to make available the required resources for promotion and

advancement of the Nigerian economy. Cevik and Rahmati (2013) empirically investigated the

causal relationship between financial development and economic growth in Libya during the

period of 1970 to 2010. The empirical analysis yield did not correlate with the estimation

research method and the specification of the model. However, it indicated that there exists no

long-run relationship between financial intermediation and output growth.

Akano & Kazeem (2014) investigated the shock of total bank credit on growth of the

Nigerian economy by using ordinary least square (OLS) and co integration analysis. They

found out that aggregate bank credit and inflation rate showed affirmative correlation with

economic growth. Adelakun (2010) also investigated the relationship between financial

development and economic growth in Nigeria using the Ordinary Least Square method of



estimation. The result revealed that there is a substantial positive relationship between financial

development and economic growth in Nigeria. The research recommended an advancement of

the financial sector including pursuing diversification of financial products.

Emecheta and Ibe (2014) investigated the relationship between bank credit and economic

growth in Nigeria between 1960 and 2011 by using reduced Vector Autoregression (VAR)

approach. Their findings reveal that bank credit and economic growth had positive significant

relationship during the period under review. Adekunle, Salami and Adedipe (2013) carried out

a research on the impact of financial sector development on Nigeria’s economic growth by

making use of the Ordinary Least Square (OLS) regression method. Conclusions from the study

indicate that the link between financial sector development and the real sector is weak and

therefore cannot propel growth required to achieve vision 20:2020.Resultsrevealed that there

was a positive impact on gross fixed capital formation by commercial bank credit. The study

therefore recommended that monetary authorities should be make efforts to effectively manage

the banks maximum lending. This policy thrust will most likely lead to the enhancement of

capital formation through increased investment activities which is needed for real sector

investment and industrial growth.

Al-Malkawi, Hazem and Abdullah (2012) empirically examined financial development and

economic growth relationship in a small open economy of United Arab Emirates (UAE).The

study used the Autoregressive Distributed Lag (ARDL) Bound Testing approach to co-

integration in their analysis. The results showed a negative but statistically significant

relationship between financial development and economic growth. The direction of causality

for both variables was bi-directional. On the whole, their evidence neither gave credence to the

demand-following hypothesis nor to the supply-leading hypothesis in United Arab Emirates

(UAE). Anthony (2012) employed Distributed Lag-Error Correction Model (DL-ECM) and

Distributed Model to empirically investigate the determinants and impact of bank savings and

bank credits in Nigeria. The Distribution Lag Model results revealed that only four out of the

five explanatory variables were statistically significant while the DL-ECM results showed that

all the explanatory variables were statistically significant. To accelerate growth through

savings enhancement, the author recommended that government should direct their efforts

towards improving per capita income in the country.

Mba (2015) investigated the effect of financial liberalization on economic growth in

Nigeria for the period of 1986 and 2011. His method of estimation was long-run estimates from

Ordinary Least Square (OLS).Findings from the study revealed that output growth in Nigeria

was negatively impacted by financial liberation. The author posited that credit to private sector

is being diverted towards buying and selling of consumables rather than channelling it towards

productive activities which translate into increased output. Results from the co-integration

analysis revealed that a long run relationship exists among the variables used in the study.

Basically, the study made recommendations that commercial banks should shift focus from

lending to government and selected borrowers to genuine private investors.

Ebiringa and Duruibe (2015) employed the use of vector autoregression (VAR) model to

analyse development of financial system and economic growth relationship in Nigeria. Results

obtained showed that causality does not run from indicators of financial system development

to economic growth. This implies that financial institutions play less significant role towards

the output growth when we talk of credit delivery to the less privileged in Nigeria. However,

in the short-run, it was found that there was a positive impact of financial development on

economic growth. The study suggested that, in order to adequately support growth, the

financial system needs to be properly strengthened so that they can offer innovative financial




products and services accompanied with formulation and implementation of sound monetary

policies.

3. MODEL SPECIFICATION

In an attempt to investigate the shock of credit on Nigeria’s economic growth, a model of

economic growth (output) as a function of Credit to the Private Sector in addition to other

control variables was formed. The selected control variables for this study, which also cause

variations to GDP other than Credit to the Private Sector alone, are Broad Money Supply, Real

Interest Rate, Labour and Gross Fixed Capital Formation. The model applied in this study is a

slight modification of the endogenous growth theory discussed in the literature review section

above.

Therefore, 𝐺𝑅𝑂𝑊𝑇𝐻𝑡 = 𝑓(𝑃𝑆𝐶𝑅𝑡, 𝑀2𝐺𝐷𝑃𝑡, 𝑅𝐼𝑁𝑇𝑡, 𝐿𝐴𝐵𝐹𝑡 , 𝐺𝐹𝐶𝐹𝑡) (3.1)

Expressing the model in an implicit form, we have;

𝐺𝑅𝑂𝑊𝑇𝐻𝑡 = 𝐴. 𝑃𝑆𝐶𝑅𝑡𝛽1

. 𝑀2𝐺𝐷𝑃𝑡𝛽2

. 𝑅𝐼𝑁𝑇𝑡𝛽3

. 𝐿𝐴𝐵𝐹𝑡𝛽4

. 𝐺𝐹𝐶𝐹𝑡𝛽5

(3.2)

Equation (3.2) is transformed into an explicit form to comprise the stochastic term and is

stated as: 𝐺𝑅𝑂𝑊𝑇𝐻𝑡 = 𝛽0 + 𝛽1𝑃𝑆𝐶𝑅𝑡 + 𝛽2𝑀2𝐺𝐷𝑃𝑡 + 𝛽3𝑅𝐼𝑁𝑇𝑡 + 𝛽4𝐿𝐴𝐵𝐹𝑡 + 𝛽5𝐺𝐹𝐶𝐹𝑡 +𝜀𝑡(3.3)

Where:

GROWTH : Growth Rate of Gross Domestic Product

PSCR : Credit to Private sector.

M2GDP : Broad Money Supply to GDP ratio

RINT : Real Interest Rate.

LABF : Labour Force

GFCF : Gross Fixed Capital Formation.

ε t : Stochastic Term

Subscript t : estimated period of time i.e. 1986 to 2016

Furthermore,

𝛽0 + 𝛽1𝑃𝑆𝐶𝑅𝑡 + 𝛽2𝑀2𝐺𝐷𝑃𝑡 + 𝛽3𝑅𝐼𝑁𝑇𝑡 + 𝛽4𝐿𝐴𝐵𝐹𝑡 + 𝛽5𝐺𝐹𝐶𝐹𝑡 represents the

regression function

𝛽0 represents the intercept of the regression function

𝛽1, 𝛽2, 𝛽3, 𝛽4𝑎𝑛𝑑 𝛽5 are parameters to be estimated which represents the slope of the

Population Regression Line.

To be estimated, equation 3.3 has to be transformed to include natural logarithm which will

reduce the likely presence of heteroscedasticity in the estimation. It is presented as follows:

𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡 = 𝛽0 + 𝛽1𝑙𝑛𝑃𝑆𝐶𝑅𝑡 + 𝛽2𝑙𝑛𝑀2𝐺𝐷𝑃𝑡 + 𝛽3𝑙𝑛𝑅𝐼𝑁𝑇𝑡 + 𝛽4𝑙𝑛𝐿𝐴𝐵𝐹𝑡 + 𝛽5𝑙𝑛𝐺𝐹𝐶𝐹𝑡 +

𝜀𝑡

(3.4)

In order to achieve the stated objectives of the study, time series data will be analyzed with

the aid of STATA statistical analysis software package, version 13. The Augmented Dickey-

Fuller unit root test will be employed to determine the unit root status of the variables in the

study. Preceding the unit root test will be the Vector Autoregression model of estimation. This

technique was chosen because the VAR model has proved to be a useful tool for describing the



dynamic behaviour of economic and financial time series and also for forecasting and policy

analysis. Following this is the Impulse response and Forecast-Error Variance Decomposition

analysis. F-test and standard error test will be used to test for the reliability of the predictors

and the statistical significance of the regression model.

Equation 3.4 above could be specified in a VAR (1) framework as follows:

𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡 = 𝛽10 + 𝛽11𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 + 𝛽12𝑙𝑛𝑃𝑆𝐶𝑅𝑡−1 + 𝛽13𝑙𝑛𝑀2𝐺𝐷𝑃𝑡−1 + 𝛽14𝑙𝑛𝑅𝐼𝑁𝑇𝑡−1 + 𝛽15𝑙𝑛𝐿𝐴𝐵𝐹𝑡−1 + 𝛽16𝑙𝑛𝐺𝐹𝐶𝐹𝑡−1 + 𝜀1𝑡(3.5)

𝑙𝑛𝑃𝑆𝐶𝑅𝑡 = 𝛽20 + 𝛽21𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 + 𝛽22𝑙𝑛𝑃𝑆𝐶𝑅𝑡−1 + 𝛽23𝑙𝑛𝑀2𝐺𝐷𝑃𝑡−1 + 𝛽24𝑙𝑛𝑅𝐼𝑁𝑇𝑡−1 + 𝛽25𝑙𝑛𝐿𝐴𝐵𝐹𝑡−1 + 𝛽26𝑙𝑛𝐺𝐹𝐶𝐹𝑡−1 + 𝜀2𝑡(3.6)

𝑙𝑛𝑀2𝑡 = 𝛽30 + 𝛽31𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 + 𝛽32𝑙𝑛𝑃𝑆𝐶𝑅𝑡−1 + 𝛽33𝑙𝑛𝑀2𝐺𝐷𝑃𝑡−1 + 𝛽34𝑙𝑛𝑅𝐼𝑁𝑇𝑡−1 + 𝛽35𝑙𝑛𝐿𝐴𝐵𝐹𝑡−1 + 𝛽36𝑙𝑛𝐺𝐹𝐶𝐹𝑡−1 + 𝜀3𝑡 (3.7)

𝑙𝑛𝑅𝐼𝑁𝑇𝑡 = 𝛽40 + 𝛽41𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 + 𝛽42𝑙𝑛𝑃𝑆𝐶𝑅𝑡−1 + 𝛽43𝑙𝑛𝑀2𝐺𝐷𝑃𝑡−1 + 𝛽44𝑙𝑛𝑅𝐼𝑁𝑇𝑡−1 + 𝛽45𝑙𝑛𝐿𝐴𝐵𝐹𝑡−1 + 𝛽46𝑙𝑛𝐺𝐹𝐶𝐹𝑡−1 + 𝜀4𝑡 (3.8)

𝑙𝑛𝐿𝐴𝐵𝐹𝑡 = 𝛽50 + 𝛽51𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 + 𝛽52𝑙𝑛𝑃𝑆𝐶𝑅𝑡−1 + 𝛽53𝑙𝑛𝑀2𝐺𝐷𝑃𝑡−1 + 𝛽54𝑙𝑛𝑅𝐼𝑁𝑇𝑡−1 + 𝛽55𝑙𝑛𝐿𝐴𝐵𝐹𝑡−1 + 𝛽56𝑙𝑛𝐺𝐹𝐶𝐹𝑡−1 + 𝜀5𝑡(3.9)

𝑙𝑛𝐺𝐹𝐶𝐹𝑡 = 𝛽60 + 𝛽61𝑙𝑛𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 + 𝛽62𝑙𝑛𝑃𝑆𝐶𝑅𝑡−1 + 𝛽63𝑙𝑛𝑀2𝐺𝐷𝑃𝑡−1 + 𝛽64𝑙𝑛𝑅𝐼𝑁𝑇𝑡−1 + 𝛽65𝑙𝑛𝐿𝐴𝐵𝐹𝑡−1 + 𝛽66𝑙𝑛𝐺𝐹𝐶𝐹𝑡−1 + 𝜀6𝑡(3.10)

3.2. Optimal Lag Length

In practice, too many lags in a VAR model erode degrees of freedom and increase the

possibilities of multicollinearity. To forestall this occurrence, the Akaike’s Information

Criterion (AIC), Schwarz’s Information Criterion (SIC) and some other lag length selection

criteria would be used to determine the number of lags to be constituted in the model. The

information criterion is generally used in analysing economic time series to establish the

appropriate distributed lag length.

4. UNIT ROOT TEST

Table 4-1 Augmented Dickey Fuller Unit Root Test at Levels

Variable

ADF t-

Stat

Value

Critical Values

Remark 1% 5% 10%

LN_GROWTH -1.895 -4.380 -3.600 -3.240 Non-

Stationary

LN_GFCF -2.676 -4.343 -3.584 -3.230 Non-

Stationary

LN_LABF -3.269 -4.343 -3.584 -3.230 Non-

Stationary

LN_RINT -4.067 -4.343 -3.584 -3.230 Stationary

LN_M2GDP -2.727 -4.343 -3.584 -3.230 Non-

Stationary

LN_PSCR -3.386 -4.343 -3.584 -3.230 Non-

Stationary

Source: Researcher’s compilation

For a variable to be stationary, the value of the ADF t-Stat Value must be greater than the

10% Critical Values. In Table 4-1, the results of Unit Root and Order of Integration at levels




showed that only Real Interest Rate (LN_RINT) was stationary at levels at 10% critical values

and level of significance as the value of the ADF t-Stat Value was greater than the 10% Critical

Value figure.

In Table 4-2 below, the Unit Root Test and Order of Integration results at first difference

shows that GDP growth rate, Gross Fixed Capital Formation, Labour Force, Broad Money

Supply to GDP ratio and Private Sector Credit were all stationary at first difference at 10%

critical values and level of significance as the value of the ADF t-Stat Value was greater than

the 10% Critical Value figure..

Table 4-2 Augmented Dickey Fuller Unit Root Test at First Difference

Variables

ADF t-

Stat

Value

Critical Values

Remark Order of

Integration 1% 5% 10%

DL_GROWTH -5.916 -4.380 -3.600 -3.240 Stationary I(1)

DL_GFCF -5.374 -4.352 -3.588 -3.233 Stationary I(1)

DL_LABF -4.566 -4.352 -3.588 -3.233 Stationary I(1)

DL_M2GDP -4.234 -4.352 -3.588 -3.233 Stationary I(1)

DL_PSCR -3.681 -4.352 -3.588 -3.233 Stationary I(1)

Source: Researcher’s compilation

4.2. VECTOR AUTOREGRESSION (VAR) ANALYSIS

4.2.1. Optimal Lag Selection

Before proceeding with the VAR analysis, the problem that arises next is in the determination

of an optimal lag length of the variables. This will be resolved using the information criterion

technique. As stated in section 3.2, too many lags erode the degree of freedom. This causes

statistically insignificant coefficients, increases the possibility of multicollinearity and lowers

the efficacy of the test to point out unit roots. On the other hand, too few lags may lead to

specification errors and this means that the regression will behave like a white-noise process.

In order to prevent this, it is appropriate to obtain the optimal number of lags for the estimation.

The optimal number of lags would be determined based on the Akaike Information criterion.

The lag with the highest value would be picked. The result of this process is presented in the

table below.

Table 4-3 Selection-order criteria

Lags Log of

Lags

Likelihood

Ratio

Degree

of

freedom

Probability

Final

Prediction

Error

Akaike

Information

Criterion

Hannan-

Quinn

Information

Criteria

Schwarz-

Bayesian

Information

Criteria

0 26.0465 4.3e-09 -2.22738 -2.18646 -1.93059

1 67.9237 83.754 36 0.0000 2.9e-09 -2.88041 -2.59395 -.802876

2 468.764 801.68 36 0.0000 5.5e-26* -43.4182 -42.8862 -39.5599

3 3648.16 6358.8 36 0.0000 - -393.351 -392.614 -388.008

4 3654.33 12.353 36 1.0000 - -394.037 -393.3 -388.695

5 3709.03 109.39* 36 0.0000 - -400.114* -399.378* -394.772*

Source: Researcher’s compilation using STATA 13



The Akaike Information criterion in Table 4-3 above suggested optimal lag length to be 5

as lag 5 has the highest Akaike Information criterion value. However, proceeding with 5 lags

in the estimation still revealed the presence of multicollinearity which warranted the estimation

software to drop some of the variables used. As a result of this, the study proceeded with

manual selection of the third lags based on the fact that it has the highest Likelihood Ratio

figure.

4.2.2. Vector Autoregression Stability Condition

The Autoregressive (AR) Roots table is used to examine the stability of the estimated VAR

model using its roots and modulus. A modulus value less than one imply that the model is

stationary or stable, if found to be stable, the VAR model is considered to be stationary. An

unstable VAR i.e. one whose modulus value is greater than one, makes further analysis which

includes impulse response and forecast-error variance decomposition invalid and unreliable.

This implies that the VAR model cannot be applied for forecasting and policy making purposes.

The necessary and sufficient condition for a stable VAR model is when all the inverse roots

are less than one or equals to one. Table 4-4 shows the Eigenvalue Stability Condition.

Table 4-4 Eigenvalue Stability Condition


From Table 4-4 above, it can be seen that the VAR satisfies stability condition as all the

inverse roots, which is the second figures of the eigenvalue stability condition and with the

figures ending with i, are less than one or equals to one. This therefore means that the VAR

model is suitable for forecasting and for policy making purpose.




4.2.3. Vector Autoregression (VAR) Results

Sample: 1990 - 2016 No. of obs = 27

Log likelihood = 124.6974 AIC = -.7923972

FPE = 1.42e-07 HQIC = .8345105

Det(Sigma_ml) = 3.92e-12 SBIC = 4.678914

Equation Parms RMSE R-sq chi2 P>chi2

----------------------------------------------------------------

dlgrowth 19 3.39959 0.7543 82.90268 0.0000

dlgfcf 19 .277181 0.6583 52.01483 0.0000

dlm2gdp 19 .126692 0.6782 56.90273 0.0000

dlpscr 19 .108341 0.8750 188.9545 0.0000

dllab 19 .20135 0.4961 26.58186 0.0872

dlint 19 .116579 0.8830 203.7596 0.0000

----------------------------------------------------------------

------------------------------------------------------------------------------

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

dlgrowth |

dlgrowth |

L1. | -.0558315 .2235439 -0.25 0.803 -.4939695 .3823066

L2. | .5264611 .2068896 2.54 0.011 .1209649 .9319574

L3. | -.297902 .1620832 -1.84 0.066 -.6155793 .0197753

|

Dlgfcf |

L1. | -.188563 2.51427 -0.07 0.940 -5.116442 4.739316

L2. | -6.342889 2.433436 -2.61 0.009 -11.11234 -1.573443

L3. | 3.210257 2.329302 1.38 0.168 -1.35509 7.775605

|

dlm2gdp |

L1. | 8.179992 6.760304 1.21 0.226 -5.06996 21.42994

L2. | 15.02057 7.342328 2.05 0.041 .6298689 29.41127

L3. | 11.58083 6.8791 -1.68 0.092 -25.06362 1.901959

|

Dlpscr |

L1. | 5.189372 6.000662 0.86 0.387 -6.57171 16.95045

L2. | -10.8758 4.521272 -2.41 0.016 -19.73733 -2.014273



L3. | 9.223411 4.012542 2.30 0.022 1.358973 17.08785

|

dllab |

L1. | -5.85955 3.051744 -1.92 0.055 -11.84086 .1217586

L2. | 4.351756 4.770351 0.91 0.362 -4.99796 13.70147

L3. | -7.867525 3.819791 -2.06 0.039 -15.35418 -.3808714

|

Dlint |

L1. | 2.144764 3.304972 0.65 0.516 -4.332862 8.622391

L2. | -.3023762 3.365886 -0.09 0.928 -6.899392 6.29464

L3. | 3.93137 3.127108 1.26 0.209 -2.197648 10.06039

|

_cons | -1.563653 1.289605 -1.21 0.225 -4.091232 .9639259

-------------+----------------------------------------------------------------

dlm2gdp |

dlgrowth |

L1. | -.0124685 .0083308 -1.50 0.134 -.0287966 .0038596

L2. | -.0251558 .0077102 -3.26 0.001 -.0402675 -.0100442

L3. | -.0012893 .0060404 -0.21 0.831 -.0131281 .0105496

|

Dlgfcf |

L1. | -.0578276 .0936992 -0.62 0.537 -.2414748 .1258195

L2. | .1317419 .0906868 1.45 0.146 -.0460009 .3094847

L3. | -.1421114 .086806 -1.64 0.102 -.3122481 .0280253

|

dlm2gdp |

L1. | .3398599 .2519361 1.35 0.177 -.1539257 .8336455

L2. | .0420193 .2736263 0.15 0.878 -.4942785 .578317

L3. | .2810251 .2563632 1.10 0.273 -.2214376 .7834878

|

dlpscr |

L1. | -.2360184 .2236265 -1.06 0.291 -.6743184 .2022815

L2. | .1472165 .1684941 0.87 0.382 -.1830259 .4774589

L3. | -.3614446 .1495353 -2.42 0.016 -.6545284 -.0683608

|

dllab |

L1. | .2404951 .1137293 2.11 0.034 .0175899 .4634004

L2. | .077471 .1777766 0.44 0.663 -.2709646 .4259067




L3. | .1801346 .1423521 1.27 0.206 -.0988703 .4591395

|

Dlint |

L1. | .0197237 .1231663 0.16 0.873 -.2216778 .2611253

L2. | .0848307 .1254364 0.68 0.499 -.1610201 .3306815

L3. | -.1390556 .1165378 -1.19 0.233 -.3674656 .0893543

|

_cons | .14642 .0480597 3.05 0.002 .0522248 .2406152

-------------+----------------------------------------------------------------

dlpscr |

dlgrowth |

L1. | -.0057074 .0071241 -0.80 0.423 -.0196703 .0082556

L2. | -.0164995 .0065933 -2.50 0.012 -.0294222 -.0035768

L3. | .0006676 .0051654 0.13 0.897 -.0094564 .0107916

|

dlgfcf |

L1. | .3314608 .0801269 4.14 0.000 .174415 .4885067

L2. | .1063133 .0775508 1.37 0.170 -.0456834 .2583101

L3. | -.1505596 .0742322 -2.03 0.043 -.2960519 -.0050672

|

dlm2gdp |

L1. | .3127689 .2154431 1.45 0.147 -.1094918 .7350295

L2. | .12162 .2339915 0.52 0.603 -.3369949 .5802349

L3. | -.1130574 .219229 -0.52 0.606 -.5427383 .3166235

|

dlpscr |

L1. | .4659417 .1912342 2.44 0.015 .0911296 .8407538

L2. | -.429012 .1440877 -2.98 0.003 -.7114187 -.1466053

L3. | -.1408774 .1278751 -1.10 0.271 -.391508 .1097531

|

dllab |

L1. | -.2897172 .0972556 -2.98 0.003 -.4803346 -.0990998

L2. | .7521624 .1520256 4.95 0.000 .4541977 1.050127

L3. | -.2225796 .1217323 -1.83 0.067 -.4611706 .0160114

|

dlint |

L1. | .0189683 .1053256 0.18 0.857 -.1874661 .2254028

L2. | -.0919036 .1072669 -0.86 0.392 -.3021429 .1183356



L3. | -.2283136 .0996573 -2.29 0.022 -.4236383 -.0329888

|

_cons | .1269121 .0410982 3.09 0.002 .0463611 .2074631

Where DLGROWTH represents GDP growth rate stationary at first difference in the unit

root result in Table 4-2, DLPSCR represents credit to the private sector also stationary at first

difference in the unit root result in Table 4-2, DLINT is real interest rate stationary at first

difference in the unit root result in Table 4-2, DLGFCF is gross fixed capita formation

stationary at first difference in the unit root result in Table 4-2, DLM2GDP represents broad

money supplied to the economy also stationary at first difference in the unit root result in Table

4-2, and DLLAB represents labour force in the economy stationary at first difference in the

unit root result in Table 4-2.

4.2.4. Interpretation of VAR Results

The probability values (P>|z|) from the result would be examined to check the significance of

each variables against their dependent variables. The value of the (P>|z|) must be less than 1

for the three lags to show the significance of each variable.

4.2.4.1. Dlgrowth Bloc

Under DLGROWTH, the second and third lags of M2GDP were observed to be positively

significant at 5 percent levels of significance (95% confidence level) respectively. The

implication of this is that a percentage increase in M2GDP ratio will result to a more than

proportionate change in DLGROWTH. The reason for this lagged and significant relationship

is that most times, economic policy changes do not always reflect immediately in the economy,

its effect only manifests after a period of time. DLPSCR was also found to be a crucial

determinant of DLGROWTH in this study. The significant impact on DLGROWTH was found

to be exerted by the second and third lag of DLPSCR. This variable was found to be significant

at 5 percent level of significance. The economic implication of this is that, an increase in credit

to the private sector has significant impact on growth of the economy. The second lag of

DLGFCF was also found to be significant at the 5 percent level, signalling the importance of

gross fixed capital formation in the growth of the economy. LLAB in this study was also found

to be a crucial determinant of DLGROWTH, the first and third lag being significant at 10

percent level of significance. The first, second and third lag of DLINT were found to be

insignificant with figures of 0.516, 0.928, and 0.209 not less than 0.1.

4.2.4.2. Dlm2gdp bloc

In the DLM2GDP bloc, the second lag of DLGROWTH was found to be significant for current

values of financial depth. The economic interpretation/implication of this is that past values of

the growth rate in past years can be a good predictor of the current values of DLM2GDP.

DLPSCR is also found to moderately affect the values of DLM2GDP ratio. This means that

DLPSCR is a good predictor of DLM2GDP as the value of the third lag was significant at 5%

level of significance. Furthermore, while DLLAB was also found to be significant at 5% level

at the first lag, other variables like DLGFCF and DLRINT were found to be insignificant for

the values of DLM2GDP.

4.2.4.3. Dlpscr bloc (Credit to the Private Sector)

The DLPSCR bloc examined the effect of all other endogenous variables on itself. The second

lags of DLGROWTH have been found to be significant at the 5 percent level. The reason

behind this is that, as an economy expands, there is every tendency that demand for credit by




the private sector would increase. Hence economic growth is a driver of private sector demand

for credit. Similarly, the result shows that the first and third lags of DLGFCF also significantly

affect DLPSCR. This follows from the fact that an increase in DLGFCF makes demand for

credit by the private sector to increase although in less proportionate terms. Also, the first 2

lags of DLPSCR being significant at the 5 percent level were found to be significant predictors

of the dependent variable (DLPSCR). Furthermore, the estimation result shows that factors like

LLAB (the three lags) and DLINT (third lag) are equally significant determinants of DLPSCR.

4.2.5. Impulse-Response Function (IRF) Analysis

In a vector autoregressive system, this provides a framework for examining the relationship

amongst the variables within the system. The effect of those shocks will then be traced out on

the endogenous variables. According to Gyanti and Porter (2009), The IRF traces out the

response of the dependent variable to shocks in the error terms. In other words, the IRF

examines the responsiveness of the dependent variables (endogenous variables) in a VAR

model when a shock is applied to the error term. In the analysis below, the response of the key

variable of the study are examined within 95 percent confidence interval. The shaded area along

the graph line was used to do the analysis. If the shaded area is much, the there is a positive

impulse-response among the variables and if its short, then a minimal impulse-response occurs

among the variables.

Figure 4-1 Impulse-response Graphs


Where: Impulse Variable refers to the source of the shock

Response Variable refers to the variable affected by the shock.

The response of DLGROWTH to most of the shocks (impulses) is positive as the graph

line shows an upward movement in all the graphs of DLGROWTH to other variables and also

0

.5

1

0

.5

1

0

.5

1

0

.5

1

0

.5

1

0

.5

1

0 5 0 5 0 5 0 5 0 5 0 5

varbasic, dlgfcf, dlgfcf varbasic, dlgfcf, dlgrowth varbasic, dlgfcf, dlint varbasic, dlgfcf, dllab varbasic, dlgfcf, dlm2gdp varbasic, dlgfcf, dlpscr

varbasic, dlgrowth, dlgfcf varbasic, dlgrowth, dlgrowth varbasic, dlgrowth, dlint varbasic, dlgrowth, dllab varbasic, dlgrowth, dlm2gdp varbasic, dlgrowth, dlpscr

varbasic, dlint, dlgfcf varbasic, dlint, dlgrowth varbasic, dlint, dlint varbasic, dlint, dllab varbasic, dlint, dlm2gdp varbasic, dlint, dlpscr

varbasic, dllab, dlgfcf varbasic, dllab, dlgrowth varbasic, dllab, dlint varbasic, dllab, dllab varbasic, dllab, dlm2gdp varbasic, dllab, dlpscr

varbasic, dlm2gdp, dlgfcf varbasic, dlm2gdp, dlgrowth varbasic, dlm2gdp, dlint varbasic, dlm2gdp, dllab varbasic, dlm2gdp, dlm2gdp varbasic, dlm2gdp, dlpscr

varbasic, dlpscr, dlgfcf varbasic, dlpscr, dlgrowth varbasic, dlpscr, dlint varbasic, dlpscr, dllab varbasic, dlpscr, dlm2gdp varbasic, dlpscr, dlpscr

95% CI fraction of mse due to impulse

step

Graphs by irfname, impulse variable, and response variable



the shaded areas or regions were long. This means that a 1 percent standard deviation shock to

DLM2GDP can result in an increase of 1 percent change in the growth rate for all the periods

in consideration. With respect to impulse from DLPSCR, the response of DLGROWTH is

minimal as the graph line was parallel instead of upward movement and also the shaded region

was low. Also, for DLGROWTH, the response of DLM2GDP is highly positive based on the

high-shaded area in the graph. Also, for DLPSCR, DLM2GDP’s response however, is minimal

though positive due to the fact that the shaded region is minimal. For DLPSCR, a shock to

DLGROWTH, other things being equal, would result in a minimal response from DLPSCR as

the shaded area in the graph is low. The same is also true for DLPSCR’s response to

DLM2GDP shocks.

5. CONCLUSION

This study attempted to re-examine the significant impact of private sector credit on Nigeria’s

economic growth using annual time series data from 1986 to 2016. The Vector Auto-regression

technique of estimation was employed. Variables used in the study were GDP growth rate

(GROWTH), as proxy for Economic Growth. Credit to the Private Sector (PSCR), Broad

Money Supply (M2), Real Interest Rate (RINT), Labour Rate (LABR), and Gross Fixed Capital

Formation (GFCF). In analysing credit and growth relationship in Nigeria, conclusions drawn

is that credit is a very important factor to consider as it was found to have significant

impingement on growth of the Nigerian economy. It has potential to accelerate development

of various key sectors of the economy such as the agriculture sector, industrial/food production

sector, manufacturing sector, building and construction, horticulture, tourism, information and

communications technology, transportation, etc.

6. RECOMMENDATIONS

The government should increase credit to the private sector to boost the sector so

that banks and other financial institutions can increase lending to the Nigerian

economy.

The Central Bank of Nigeria should use monetary policy instruments like the open

market operations to mop up excess liquidity in the economy as too much money

in the system may result in negative GDP growth.

The Central Bank of Nigeria should endeavour to control the rising interest rate so

as to boost bank lending to the private sector, thus increasing domestic investment.

Government should invest more in the educational sector so as to boost human

capital development and also to bring about efficiency of labour in the economy.

Investment in technology is needed by financial institutions for quick response so

as to boost credit to the private sector.

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