Bank and Non-Bank Financial Deepening and Economic Growth: The Nigerian Experience (1981-2010)

Bank and Non‐Bank Financial Deepeningand Economic Growth: The Nigerian

Experience (1981–2010)MARTINS IYOBOYI�

The paper aimed at empirically investigating the impact of financialdeepening on the growth of an economy, with particular emphasis onNigeria over the period 1981–2010. Financial deepening was segregatedto capture both bank and non‐bank financial variables, because both areimperative to economic activities. The study therefore combined a proxy ofbanking sector development with stock market development to capture theinfluence of financial deepening on growth. The analysis is based on thebound testing approach to cointegration. The empirical results confirmcointegrated relationship between economic growth and financialdeepening. The study also showed that, in the period of study, whilethere is bidirectional causality between bank financial deepening andeconomic growth, causality runs from economic growth to non‐bankfinancial deepening. The results of the investigation are in favour of thefinance‐growth cum growth‐finance hypothesis. For the period understudy, Nigeria’s economic growth is sensitive to changes in financialdeepening, past level of growth and the openness of the economy. It istherefore imperative that policies which seek to deliberately increasefinancial depth be vigorously pursued, in order to stimulate growth andconsequently further deepen the financial sector of the economy.

(J.E.L.: E44; O11; O16).

1. Introduction

Economists have for long been interested in studying the relationshipbetween the financial sector and the growth of the economy forpractical reasons. Three major strands of thought can be delineated ineconomic literature regarding financial deepening and economic growth.These are:

(i) Financial deepening is a causal factor in economic growth that is there is aunidirectional relationship between financial deepening and economic growth.

�Department of Economics and Development Studies, Federal University Dutsin‐Ma, P.O.Box 5001, Katsina, Nigeria. E‐mail: [email protected], [email protected]

Economic Notes by Banca Monte dei Paschi di Siena SpA,

vol. 42, no. 3-2013: pp. 247–271

© 2013 The AuthorEconomic Notes © 2013 Banca Monte dei Paschi di Siena SpA. Published by John Wiley &Sons Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA02148, USA.

(ii) There is bidirectional causality between financial deepening and economic growth.(iii) Financial deepening is not a causative factor in economic growth.

Financial deepening has been observed by scholars to work through anumber of mechanisms. Bencivenga and Smith (1991) and De Gregorio andGuidotti (1995) maintain that financial deepening promotes the conversionof savings into investment in addition to improving corporate governance.According to Greenwood and Jovanovich (1990) and Levine (2004),specialization is improved while information and transaction costs arereduced in an environment of financial deepening. Various terms are usedby scholars interchangeably when the focus is on the analysis of thefinancial sector. These include financial deepening, financial development,finance and the like. In the present study, however, the terms ‘financialdeepening’ and ‘financial development’ are used to refer to the samephenomenon.

It is interesting to note that there is as yet no consensus on the place offinancial deepening on economic growth. This is because several economistsreject the view that financial deepening has any causal role to play in theprocess of growth (Ireland, 1994). Some scholars contend that financialdeepening is nothing more than a by‐product of economic growth, to theextent that when an economy experiences expansion, the financial sectormerely responds in a rather passive manner to meet the demands of financialservices occasioned by the growth in the real sector. This view may not beunconnected with the studies that indicate that the growth in per capitaincome of some economies brought about greater increases in the growth offinancial assets than by growth in national product, as has happened in Japan,Western Europe and the United States of America. Their overwhelmingconclusion is that a country’s financial system becomes deepened as nationalwealth increases. Another discordant tune comes from other economists,some of them notable, who consider financial deepening as irrelevant toeconomic growth (Lucas, 1988; Stern, 1989).

It must be maintained that any analysis of the impact of financialdeepening on the growth process which does not account for the direction ofcausality is at best inadequate. Consequently, significant coefficientsobtained from cross‐country growth regressions should not be taken toimply causality running from financial development to economic growth orvice versa. For empirical studies which seek to establish the direction ofcausality between financial deepening and economic growth, the resultshave largely been mixed (see Calderon and Lin, 2003; Abu‐Bader and Abu‐Qarn, 2008). Consequently, studies which have tended to show the pattern ofcausality between financial deepening and economic growth on selectedcountries have generally differed in their results and conclusions, whileevidence suggesting a unidirectional link from financial deepening to growthis not strong enough as to be conclusive (Al‐Yousif, 2002).

248 Economic Notes 3-2013: Review of Banking, Finance and Monetary Economics

© 2013 The AuthorEconomic Notes © 2013 Banca Monte dei Paschi di Siena SpA.

Analysis that combines the impact of financial deepening in the bankingand non‐banking sectors of an economy on growth is imperative. Stockmarket development provides one of such avenues to explore if growth innon‐banking financial sector can generate economic growth. However, theresults on studies conducted on the casual relationship between stock marketdevelopment and economic growth have been mixed. Many studies havefound bidirectional link between stock market development and economicgrowth. They include Luintel and Khan (1999) and Gursoy and Muslumov(1999). The study conducted by Gursoy andMuslumov (1999) is interestingin that results indicate the existence of a bidirectional causal relationshipbetween stock market development and economic growth, with someevidence that developing countries tend to have a stronger associationbetween stock market development and economic growth.

A study on the impact of bank and non‐bank financial deepening oneconomic growth in Nigeria is motivated by a variety of reasons. Nigeria isthe most populated Black Country in the world, the second largest economyin Africa, a major OPEC‐member oil producer and the political andeconomic powerhouse of the West‐African sub‐region. Consequently thecountry has huge potentials in many sectors of the economy including oiland gas, agriculture, tourism, manufacturing and telecommunications. Itfollows that the economy can leverage on its sizeable and youthfulpopulation, its relatively highly educated workforce, in addition to its naturalresources endowments, a result that can help planners not only in the countrybut other developing and emerging countries.

Nigeria’s economic and financial trend in recent times can be aptlycaptured by various macroeconomic indicators for the period 1980–2010 asshown in Table 1.

Growth in gross domestic product (GDP) has been robust over the years,helped largely by the surge in crude oil sales. Broad money supply (M2) hasbeen increasing steadily from the 1980s due to the expansion in economic

Table 1: Some Macroeconomic Indicators

YearNominalGDP

Broad moneysupply (M2)

Total bank creditto the economy

Marketcapitalization

Total importsand exports

1981 47,619.7 16,161.7 16,268.50 5100 23,862.91985 67,908.6 26,277.6 32,680.30 6,600 18,783.41990 267,550.0 68,662.5 57,674.90 16,300 155,604.01995 1,933,211.6 318,763.5 474,361.40 180,400 1,705,789.12000 4,582,127.3 1,036,079.5 667,621.70 472,300 2,930,745.72005 14,572,239.1 2,814,846.1 2,256,411.7 2,900,100 10,047,391.12010 29,205,783.0 11,525,530.3 896,2973.1 9,918,200 19,146,463.1

Sources: Statistical Bulletin of the Central Bank of Nigeria (CBN), the Nigerian Stock Exchange Fact Bookand the Securities and Exchange Commission.Note: All the figures are in N0 Million.


M. Iyoboyi: Bank and Non‐Bank Financial Deepening and Economic Growth 249

activities. From 16,161.7 million naira in 1981, broad money supply hassteadily increased through the years to 11,525,530.3 million naira in 2010.Total bank credit to the economy has also shown upward movements sincethe 1980s. From 16,268.50 million naira in 1981 to 667,621.70million nairain 1990, and 8,962,973.1 million naira in 2010, growth in bank lending hasbeen tremendous andmight have led to growth in national output. In the non‐banking sector, especially the capital market, the country has experiencedmassive increase in stock market capitalization from a modest 5100 millionnaira in 1981 to 9,918,200 million naira in 2010.

In Nigeria’s financial landscape, there are a number of regulatoryinstitutions. These are the Federal Ministry of Finance, the Securities andExchange Commission (SEC; the apex institution in the capital market), theCentral Bank of Nigeria (CBN; the apex institution in the money market),National Insurance Commission, Nigerian Deposit Insurance Corporationand the National Pensions Commission. Although financial reforms dateback to 1952 when the Banking Ordinance was enacted, the countryembarked on a Structural Adjustment Programme (SAP) in 1986 as amechanism for deepening the financial system as well as to respond to thechallenges posed by technological innovation, financial and systemic crisis,in addition to globalization and the need to strengthen ethical standards. Themajor thrust of SAP in relation to the financial systemwas a policy shift fromdirect control to a financial system which is based on market forces in termsof risk and monetary management, as well as asset holding capabilities ofoperators. Other reforms in the financial sector include the bankconsolidation policy in 2005 and insurance capitalization policy in 2007.

The aim of this paper is to empirically analyse the causal association andlong‐term relationship between bank and non‐bank financial deepening andeconomic growth in the Nigerian economy, within an autoregressivedistributed lag (ARDL) framework. The study is motivated by theimperative of incorporating both bank and non‐bank sources of growthwithin a single framework, as against previous studies which focused on oneat the exclusion of the other.

Following the introduction, the rest of the paper is structured as follows.Section 2 deals with literature and theoretical considerations. Data andmethods are covered in Section 3. Section 4 comprises empirical results anddiscussions. In Section 5, the study is concluded.

2. Literature Review and Theoretical Considerations

2.1. Financial Deepening and Economic Growth

There are several studies which lend support to a positive relationshipbetween financial deepening and economic growth, with strong belief infinancial deepening as a causative factor in a nation’s growth trajectory



(Shaw, 1973; Roubini and Sala‐i‐Martin, 1992; Levine and Zervos, 1996;Demirguc‐Kunt and Levine, 2001; Bordo and Jonung, 2003; Aghionet al., 2005). Aziz and Duenwald (2002) maintain that economic growth ispromoted by financial development through private savings, investment andtrade liberalization.

The view that financial deepening is sine qua non to the growth processin not without premises. Several economists including Hicks (1969), Fry(1978) and Moore (1986) contend that growth is induced in an environmentof financial efficiency. This is underscored on the view that well‐functioningfinancial system gives leverage to liquidity expansion, savings mobilizationand consequently capital accumulation, with the result that financial‐deficient sectors are galvanized through resource injection or transfer fromfinancial‐surplus sectors. Thus the existence of a well‐functioning financialmechanism engenders the ready availability and channelling of financialservices to where productivity can bemaximized in the real sector. This viewis also supported by several empirical studies such as those of De GregorioandGuidotti (1995), which show statistically significant estimates of proxiesemployed for financial deepening. Rajan and Zingales (1998) argue thateconomic growth is facilitated by financial development by reducing thecosts of external finance to firms.

That financial deepening is imperative to the growth process has a longhistory. Schumpeter (1912), for example, argues in favour of financialdeepening in which a well‐functioning financial system helps entrepreneursgarner funds to facilitate production of innovative products. This is supportedby the empirical findings of Guiso et al. (2004) which link the spread ofentrepreneurship and economic growth to diversities among local financialdevelopment. Hicks (1969) goes as far as maintaining that the financial sectorwas instrumental to the Industrial Revolution in England through access toloanable funds employed in the execution of capital projects.

How financial deepening is transmitted to growth has been extensivelydissected in the literature, particularly from endogenous growth theory. First,the rate of savings is believed to be increased, thereby directly impacting thequantum of investments. Second, the productivity and quality of investmentcan be increased and third, it has the tendency of reducing the costs oftransactions which make possible greater amounts of funds available forinvestment or production (Greenwood and Jovanovich, 1990). Ansari(2002) concludes that financial sector development redirects credits fromless efficient sectors to efficient sectors, which has the tendency ofimproving entrepreneurship and economic growth.

2.2. Empirical Literature Linking Financial Development to Economic Growth

Some of the empirical literatures linking financial development toeconomic growth present interesting results. Zaman et al. (2010), while



investigating the impact of financial development and trade openness onGDP growth in Pakistan and employing the bound testing approach tocointegrated, find cointegrated relationship between economic growth, tradeopenness and financial development in both the long‐run and short‐run andthat financial development Granger‐causes economic growth in the period ofstudy.

Darrat (1999), while investigating the causal link between the degree offinancial deepening and economic growth in Saudi Arabia, Turkey and theUnited Arab Emirates, using multivariate Granger causality tests within anerror‐correction framework, finds results in support of the hypothesis thatfinancial deepening is a causal factor in economic growth. Nieuwerbughet al. (2005) investigate the long‐term relationship between economicgrowth and financial market development in Belgium and find strongevidence indicating that stock market development induces economicgrowth in the period of study. Tharavaniji (2007) finds that in contrast toless‐developed capital market economies, countries with higher capitaldeepening are faced with less contraction in their business cycles andconsequently less susceptible to economic downturns. Bolbol et al. (2005)indicate that capital market development has contributed to the economicgrowth of Egypt. A study conducted by Chee et al. (2003) on Malaysiashows that stock market Granger‐causes economic growth, in addition tohaving a positive impact. Hamid and Sumit (1998) investigate therelationship between stock market development and economic growth for21 emerging economies and find results suggesting a positive relationshipbetween several indicators of stock market performance and economicgrowth. Aziz andDuenwald (2002), while studying the relationship betweengrowth and financial development in China, find that financial deepening isan important impact factor on growth through private savings, investmentand trade liberalization.

Ndebbio (2004) in a study on selected sub‐Saharan African countriesconcludes that financial deepening positively affects per capita growth ofoutput. Esso (2009) examines the cointegrating and causal relationshipbetween financial development and economic growth among West Africancountries. Using the ARDL approach, the results are in favour of a positivelong‐run relationship between financial development and economicgrowth in Cote d’Ivoire, Guinea, Niger and Togo, and a negative one inCape Verde and Sierra Leone, with financial deepening inducing growthonly in Cote d’Ivoire and Guinea. Muhammad and Umer (2010) test thesupply‐leading and demand‐following hypotheses on a study on Pakistan.A unidirectional causality running from real per capita income tofinancial development was found. Nzotta and Okereke (2009) in theirstudy of the impact of financial deepening on the Nigerian economyconclude that although financial deepening positively affects Nigeria’seconomic growth, its rate is low.



2.3. Stock Market Development and Economic Growth

A lot of debate, both empirical and theoretical, has been going onregarding the role and impact of stock market development on economicgrowth. What appears to dominate the debate and discussions is that thecontribution of stock market is reflected in its capacity to mobilize savingsthrough helping savers diversify their portfolio, allowing investors avoidunsystematic risks, both of which can increase the efficiency of capital(Pagano, 1993; Levine and Zervos, 1998). An important function of stockmarkets which is not usually performed by banks is that of financing riskyprojects, in which case the stock market is seen as an indispensable tool inrisky‐project financing, and thus a veritable source of business opportunitiesand innovation.

Scholars have observed the crucial role of developed stock market interms of liquidity and productivity shock reductions, which can assist firmsto take advantage of opportunities and dynamics of the businessenvironment. Thus, the efficiency of a firm is more assured in anenvironment of well‐developed stock market because it can obviate a firm’ssudden collapse due to insufficient capital. Liquidity shocks are thought tobe better managed by businesses and individuals due to the greater leverageafforded by a well‐functioning stock market in terms of portfoliodiversification and or selection. Stock market deepening has the additionaladvantage of improving investments in non‐liquid assets, thereby aiding thepotential and actual productivity of an economy. It is believed by scholarsthat developed stock market has the capacity of accelerating bankinginstitutions’ activities, especially in emerging or developing economies(Kahn and Sendahji, 2000).

Stock prices and the health of an economy have been linked in theliterature. According to Schwert (1990), stock prices do reflect futureactivities in the real sector, in that generally they indicate what the real sectorholds in the future in current information, thus signalling the general healthof an economy. In addition, the demand for investment and consumptiongoods can result from changes in the prices of stocks due to the wealth effect.

The nexus between capital market and financial deepening is welldocumented. While the capital market is a means of investmentdiversification, it offers firms the leverage to diversify their sources offinance. Information asymmetry is minimized in a stock exchangeenvironment which acts to promote higher bank credits which consequentlyraise the level of investment and enables the financial system tilt towardsrobustness in terms of depth and sophistication.

Part of the growth of the financial sector in Nigeria may be linked to theintroduction of the SAP in 1986, with its prong of privatization, an exercisethat exposed investors to the role of the stock market (Alile, 1996). Studieson the Nigerian economy have been varied in terms of methods used and



results obtained. Nurudeen (2009) on a study conducted on Nigeria finds apositive relationship between stock market development and economicgrowth. Adamu and Sanni (2005) employed Granger‐causality test andregression analysis to examine the impact on economic growth of stockmarket in Nigeria and find a one‐way causality between growth in GDP andmarket capitalization and a two‐way causality between growth in GDP andmarket turnover. Nyong (1997) developed an aggregate index of capitalmarket development comprising the ratio of market capitalization to GDP,the ratio of total value of transactions on the stock exchange to GDP, thevalue of equities transaction relative to GDP and listings, which wereutilized to measure the impact of financial deepening on Nigeria’s economicgrowth. He finds that capital market deepening has a negative and significantcorrelation with long‐run growth. Osinubi and Amaghionyeodiwe (2003)find a positive relationship between the stock market development andeconomic growth in Nigeria.

3. Materials and Methods

The paper investigates the causal and long‐run relationship betweeneconomic growth and bank and non‐bank financial deepening in Nigeria. Toachieve this, the paper adopts the following procedures. First, thecharacteristics of the time series were investigated. Second, the causalrelationship between the regressors and regressand is examined. Third, thepaper employed the ARDL bounds testing approach to cointegrationproposed by Pesaran et al. (2001) to test the long‐run relationship betweenthe variables used in the specified model.

3.1. Sources of Data

The present investigation covers the period 1981–2010. The study usedsecondary data obtained from various issues of the Statistical Bulletin of theCBN, the Nigerian Stock Exchange (NSE) Fact Book, in addition to thedatabase of the SEC.

Two measures of financial deepening were employed in the analysis,one based on banks and the other on non‐bank sources. These are:

1. Total banking credit to the economy as a proportion of GDP. This is a measure offinancial depth in the banking sector.

2. Stockmarket capitalization as a proportion ofGDP. This is ameasure of capital marketdevelopment, indicating the degree of financial depth of non‐bank financialinstitutions.



Economic growthwas proxied using annual values of nominal GDP thatis GDP at current basic prices. Openness was proxied by the proportion ofthe sum of imports and exports over nominal GDP.

3.2. Model Specification and Data Estimation Procedure

On the basis of theory, the specification of the growth equation forNigeria may be written in log‐linear form as follows:

Log GDPt ¼ a0 þ a1Log GDPt�1 þ a2Log BFDt þ a3Log NBFDt

þ a4Log OPNt þ mtð1Þ

where a0 is the intercept term, GDP is gross domestic product, BFD is bankfinancial deepening, NBFD is non‐bank financial deepening, OPN is degreeof openness and mt is a white‐noise disturbance term.

To empirically analyze the long‐run relationships and dynamicinteractions between economic growth and the variables chosen, thefollowing procedure was adopted.

First, the properties of the times series employed in the investigation areexamined in order to determine their order of integration. Three unit roottests were explored. These are the Ng–Perron, Phillips–Perron (PP) and theKwiatkowski–Phillips–Schmidt–Shin (KPSS) unit root tests. Second, theexistence of a cointegrating relationship was investigated. There aredifferent econometric methods in the analysis of long‐run and dynamicinteractions between two or more time‐series variables. They include thetwo‐step procedure of Engle and Granger (1987), the Full InformationMaximum Likelihood‐based approach of Johansen (1988, 1996), Phillipsand Hansen (1990) and Johansen and Juselius (1990), all of which requirethat the variables under investigation be integrated of order one. Suchapproaches cannot proceed without first testing the variables for stationarity,with the consequence that some level of uncertainty may not be inevitable.Moreover, the procedure is not adequate when small samples are consideredin an investigation, resulting in unsatisfactory small sample properties. Thechoice of a better alternative such as the bounds testing approach tocointegration proposed by Pesaran et al. (2001) is justified, in that it hascertain econometric advantages over the traditional approaches. In the firstplace, the procedure is applicable irrespective of whether the underlyingregressors are purely I(0), purely I(1) or fractionally cointegrated. In thesecond place, its statistical properties in small samples are superior to othertests of cointegration in that the latter are not robust when subjected to smallsample sizes. Lastly, the estimation of the long‐ and short‐run parameters of amodel using ARDL approach is ensured.

In the present study, two steps were followed to test the long‐runrelationship between the variables. First, the Wald test was conducted to



determine the existence of any long‐run relationship. Thereafter, the long‐run coefficients of the specifiedmodel were estimated, followed by the short‐run coefficients using the error correction representation of the ARDLspecification, in order to establish the speed of adjustment to equilibrium.

The ARDL model of the specification in Equation (1) is presented asfollows:

DLogðGDPÞt ¼ a0 þXk

i¼1

a1iDLogðGDPÞt�i þXk

i¼0

a2iDLog BFDt�i

þXk

i¼0

a3iDLog NBFDt�i þXk

i¼0

a4iDLog OPNt�i þ mtð2Þ

where D is the first‐difference operator and k is the lag length.Accordingly, the unrestricted error correction model (ECM) which

follows the order of ARDL specification of growth for the above ARDLmodel is presented in Equation (3):

DLogðGDPÞt ¼ a0 þXk

i¼1

a1iDLogðGDPÞt�i þXk

i¼0

a2iDLog BFDt�i

þXk

i¼0

a3iDLog NBFDt�i þXk

i¼0

a4iDLog OPNt�i

þXk

i¼0

c1Log GDPt�1 þ c2Log BFDt�1

þ c3Log NBFDt�1 þ c4Log OPNt�1 þ mtð3Þ

where the parametersai: i ¼ 1, 2, 3, 4 are the short‐run dynamic coefficients,while the parametersci: i ¼ 1, 2, 3, 4 function as the long‐run multipliers ofthe underlying ARDL model. Thus while Equation (1) is a specificationof the model on theoretical basis, Equation (2) represents an ARDLspecification while Equation (3) denotes an unrestricted ECM representationof the ARDL model.

A priori, growth is expected to be positively correlated with both bankand non‐bank financial deepening indicators. Growth is also dependent onpast levels, so that the inclusion of the lagged value of the regressand isconsistent with literature.

The presumptive signs of the variables are stated as follows:

a1 > 0; a2 > 0; a3 > 0; a4 > 0

To test for the long‐run relationship among the variables, theWald test isconducted by imposing restrictions on the estimated long‐run coefficients.



The null and alternative hypotheses are stated as follows:

Ho : a1 ¼ a2 ¼ a3 ¼ a4 ¼ 0 against the alternative hypothesis;

H1 : a1 6¼ a2 6¼ a3 6¼ a4 6¼ 0

3.3. Causality Tests Procedure

The choice of Granger procedure is informed by its relative simplicity(Granger, 1986). The procedure adopted in the investigation of the causalrelationship among the variables is in three stages. First, the Ng–Perron, PPand KPSS unit root tests are carried out with a view to determining the orderof integration of the variables. Second, a test for the existence of a long‐runrelationship between growth and financial deepening originating from banksand non‐bank financial institutions is implemented. Last, the standardGranger‐type causality tests augmented with a lagged error‐correction termare executed. It should be noted that if cointegration exists among thevariables, there will be Granger causality in at least one direction inEquations (4–7). Should the Granger causality test be conducted in firstdifference while employing the method of vector autoregression (VAR) andin the presence of cointegration, it will be appropriate to capture the long‐runrelationship among variables by including an error correction term in theVAR. Using an ECM to investigate Granger causality would involve, in thecase of short‐run causality, checking the statistical significance of the laggeddifferences of the variables, while in the long run, causality is determined bythe statistical significance of the error‐correction term.

In the presence of cointegration based on the bounds test, the Grangercausality tests should be done under vector error correction model (VECM)when the variables under consideration are cointegrated. By doing so, theshort‐run deviations of series from their long‐run equilibrium path are alsocaptured by including an error correction term. Tests for Granger causality inthis study are made on the VECMs of long‐run cointegrating vectors ascaptured in the follows specifications:

DðGDPÞt ¼ b0GDP þ b1GDPðGDPÞt�1 þ b2GDPðBFDÞt�1þ b3GDPðNBFDÞt�1 þ b4GDPðOPNÞt�1

þXp

i¼1

b5GDPDðGDPÞt�i þXq

i¼0

b6GDPDðBFDÞt�i

þXr

i¼0

b7GDPDðNBFDÞt�i þXr

i¼0

b8GDPDðOPNÞt�i

þ utGDPð4Þ



DðBFDÞt ¼ b0BFD þ b1BFDðGDPÞt�1 þ b2BFDðBFDÞt�1þ b3BFDðNBFDÞt�1 þ b4BFDðOPNÞt�1

þXp

i¼1

b5BFDDðGDPÞt�i þXq

i¼0

b6BFDDðBFDÞt�i

þXr

i¼0

b7BFDDðNBFDÞt�i þXr

i¼0

b8BFDDðOPNÞt�i

þ utBFDð5Þ

DðNBFDÞt ¼ b0NBFD þ b1NBFDðNBFDÞt�1 þ b2NBFDðGDPÞt�1þ b3NBFDðBFDÞt�1 þ b4NBFDðOPNÞt�1

þXp

i¼1

b5NBFDDðNBFDÞt�i þXq

i¼0

b6NBFDDðGDPÞt�i

þXr

i¼0

b7NBFDDðBFDÞt�i þXr

i¼0

b8NBFDDðOPNÞt�i

þ utNBFDð6Þ

DðOPNÞt ¼ b0OPN þ b1OPNðOPNÞt�1 þ b2OPNðGDPÞt�1þ b3OPNðBFDÞt�1 þ b4OPNðNBFDÞt�1

þXp

i¼1

b5OPNDðOPNÞt�i þXq

i¼0

b6OPNDðGDPÞt�i

þXr

i¼0

b7OPNDðBFDÞt�i þXr

i¼0

b8OPNDðNBFDÞt�i

þ utOPNð7Þ

where the variables are as previously defined and in logs.

4. Results and Discussion

Tables 2A and 2B show the results of the unit root tests conducted. PanelA shows the Ng–Perron test statistics for the variables in their levels and firstdifferences. Panel B shows the results of the PP and the KPSS unit root tests.The results of the tests suggest that the variables are integrated either inlevels or at first differences. It is particularly noteworthy that while the PPunit root rests indicate that all the variables are integrated of order 1, the testresults from the KPSS indicate that all the variables are stationary at levels.In the Ng–Perron results, there is a mix of I(0) and I(1). In any case, applyingthe ARDL procedure for the chosen model is appropriate in that theintegration of the variables are either at levels or first differences, while thereis absence of integration of order 2, i.e. I(2).



Table

2A:Resultof

UnitRootTests:Ng–PerronUnitRootTests(W

ithInterceptandaLinearTrend)

Variable

MZa

MZt

MSB

MPT

Level

1stDifference

Level

1stDifference

Level

1stDifference

Level

1stDifference

GDP

�4.82197

�13.3717

�1.49249

�2.58176

0.30952�

0.19308�

18.5264�

6.83663�

BFD

�28.4247

��3

3.7893

�3.71773

��4

.10316

0.13079

0.12143

3.50315

2.73614

NBFD

�3.55642

�13.8835

�1.32420

�2.61359

0.37234�

0.18825

25.4615�

6.68233

OPN

�10.7145

�11.9172

�2.29340

�2.44101

0.21405�

0.20483

8.60519�

7.64660

Source:Extracted

from

regression

output

usingEview

s7.

Note:

� denotesrejectionofthenullhypothesisatthe0.01

significancelevel.The

nullhypothesisat0.01

and0.05

significancelevel,respectiv

ely.The

nullhypothesisisthattheseries

contains

aunitrootor

isnon‐stationary.T

herejectionof

thenullhypothesisisbasedon

Ng–

Perron(2001)

criticalvalues.The

MZa,MZt,MSBandMPTtestsaremodified

versions

ofthePhillips

(1987),P

hillips

andPerron(1988)

ZaandZttests,theBhargava(1986)

R1statistic

andthepointoptim

alstatistic

ofElliot,R

othenbergandStock

(1996).T

helag

lengthsareselected

basedon

SIC

criterion.

Table2B

:Resultof

UnitRootTests:PPandKPSSUnitRootTests

Variables

Level

Firstdifference

Order

ofintegration

PP†

KPSS†

PP‡

KPSS‡

PP†

KPSS†

PP‡

KPSS‡

GDP

�0.144825

0.698574

��2

.111055

0.121367

��

�4.603372�

0.125628

�4.519856�

0.127446

I(0)

forKPSSandI(1)

forPP

BFD

�1.561014

0.497528

��2

.011646

0.224201

��5

.237681�

0.262772

�6.566591�

0.280326

I(0)

forKPSSandI(1)

forPP

NBFD

�0.620965

0.488843

��1

.968607

0.170131

��5

.384819�

0.191315

�6.023650�

0.127106

I(0)

forKPSSandI(1)

forPP

OPN

�2.149826

0.407930

��

�3.199878

0.144788

��

�8.462503�

0.150422

�8.593706�

0.149689

I(0)

forKPSSandI(1)

forPP

Notes:PP,Phillips–Perronteststatistic;K

PSS,K

wiatkow

ski–Phillips–Schmidt–Shinteststatistic;P

P†andKPSS†,unitroottestswith

constant;P

P‡andKPSS‡,unitroottestswith

constant

andtrend.

� ,��

and

��indicate

statistical

significanceat

the1,

5and10

percent

levels,respectiv

ely.



Table3:

Granger

CausalityTestResults

Nullhypothesis

F‐statistics(p‐value)

Decision

Conclusion

1Lag

2Lags

(1)NBFD

andGDP

NBFD

does

notGranger

Cause

GDP

2.81785(0.1052)

1.24516(0.3066)

Accept

Unidirectional

GDPdoes

notGranger

Cause

NBFD

5.63335(0.0253)

3.20740(0.0591)

Reject

(2)BFD

andGDP

BFD

does

notGranger

Cause

GDP

7.25668(0.0122)

3.14030(0.0623)

Reject

Bidirectio

nal

GDPdoes

notGranger

Cause

BFD

2.48135(0.1273)

3.74352(0.0391)

Reject

(3)OPN

andGDP

OPN

does

notGranger

Cause

GDP

0.06423(0.8019)

0.03220(0.9684)

Accept

Unidirectional

GDPdoes

notGranger

Cause

OPN

4.17715(0.0512)

2.12792(0.1419)

Reject(at1lag)

(4)BFD

andNBFD

BFD

does

notGranger

Cause

NBFD

1.10157(0.3036)

0.52695(0.5974)

Accept

Independent

NBFD

does

notGranger

Cause

BFD

0.32140(0.5756)

0.06918(0.9334)

Accept

(5)OPN

andNBFD

OPN

does

notGranger

Cause

NBFD

0.78530(0.3836)

0.82011(0.4529)

Accept

NBFD

does

notGranger

Cause

OPN

0.00643(0.9367)

0.11911(0.8883)

Accept

Independent

(6)OPN

andBFD

OPN

does

notGranger

Cause

0.36273(0.5522)

1.13371(0.3392)

Accept

Independent

BFD

does

notGranger

Cause

OPN

2.81831(0.1052)

1.05868(0.3632)

Accept

Source:Extracted

from

regression

output

usingEview

s7.



The results of the directions of causality between the variables in theinvestigation are presented in Table 3.

The results of Granger Causality tests (of 1 and 2 lags) in Table 3 suggestthat bidirectional causality runs from bank financial deepening to economicgrowth and from economic growth to bank Financial Deepening. In otherwords, there is mutual causality between bank financial deepening andeconomic growth. The results also indicate that economic growth Granger‐causes non‐bank financial deepening and that this causality is unidirectional.The results reject the hypothesis that economic growth does not Grangercause openness, as causality flows from economic growth to openness.There is no causality between Bank Financial Deepening and non‐bankfinancial deepening, between openness and non‐bank financial deepeningand between openness and bank financial deepening. Overall, the resultstend to indicate that financial deepening is a cause of growth and that growthis a cause of financial deepening. This conclusion is not different when otherlag lengths were considered (the results are not reported to conserve space),as what appeared to be glaring is that there is at least causation running fromone of the proxies of financial deepening in either the bank or non‐bankingsector to growth. It follows that the hypothesis of bidirectional causalitybetween finance and growth can be employed for Nigeria. Also noteworthyis the direction of causality running from economic growth to openness,indicating the attraction which openness (e.g. globalization) tends to inducewhen there is improvement in the country’s GDP. Thus higher growth tendsto stimulate a country’s integration with the world economy.

We next proceed to the test of long‐run relationship (i.e. cointegration).Three steps are followed in the ARDL bounds test. First, ordinary leastsquare is applied to Equation (2) to test for the existence of a cointegratinglong‐run relationship based on the Wald (F‐statistics). The optimal laglength for estimating the long‐term coefficients is selected using the SchwarzInformation Criterion (SIC).

The results of the bounds cointegration test are reported in Table 4.

Table 4: Bounds Test for Cointegration Analysis

Computed Wald (F‐statistic): 5.31 K ¼ 4

Critical value Lower bound value Upper bound value

0.01 3.74 5.060.05 2.86 4.010.10 2.45 3.52

Source: Pesaran et al. (2001), Table CI (iii), Case 111: Unrestricted intercept and no trend. k is the numberof regressors in the ARDL model.



The lower bound critical values assume that the explanatory variablesare integrated of order I(0), while the upper bound critical values assume thatthe variables are integrated of order I(1). Consequently, if the computed F‐statistic is less than the lower bound value, the null hypothesis is not rejected.On the other hand, if the computedF‐statistic is greater than the upper boundvalue, the null hypothesis is rejected. However, if the computed F‐statisticfalls between the lower and upper bound values, the results are inconclusive,and further evidence is necessitated from the ECM.

Results in Table 4 suggest that the application of the bounds F‐test usingARDL modelling approach indicates that there is a long‐run relationshipbetween economic growth, bank financial deepening, non‐bank financialdeepening and openness. This is because the computed Wald (F‐statistic) of5.31 is higher than the upper bounds of the critical values at the 1, 5 and 10per cent levels, respectively. Based on this, it is plausible to expect astatistically significant ECM coefficient as a further verification about thecointegration of the underlying variables. This is because a statisticallysignificant ECM coefficient is conclusive about the cointegration of theunderlying variables and consequently establishes the existence of a long‐run relationship among them.

The next stage is to apply the ARDL model to estimate Equation (3) forthe long‐run elasticities and short‐run parameters. The results are shown inTables 5A and 5B.

The estimated long‐run results in Table 5A show that all the explanatoryvariables are correctly signed with the exception of non‐bank financialdeepening. As expected, one period lagged growth, bank financialdeepening and openness have positive relationship with economic growthand are statistically significant at the 1 per cent level. Thus, past values ofgrowth, bank financial deepening (originating from total bank credit to thedomestic economy), in addition to openness (in terms of the proportion oftotal trade to GDP), have very significant effect on growth, as can be seen by

Table 5A: Long‐Run and Short‐Run Estimates: Long‐Run Estimated Coefficients Basedon ARDL Model (1,1,0,0): Dependent Variable: (GDP)

Variable Coefficient t‐values p‐values

Constant �1.301672� �5.745197 0.0000GDPt‐1 1.053299� 42.31455 0.0000BFD 0.401253� 5.801639 0.0000NBFD �0.120189�� 1.901102 0.0694OPN 0.374246� 3.763360 0.0010

Source: Extracted from regression output using Eviews7.Notes: Diagnostic statistics: R2 ¼ 0.99; adjusted R2 ¼ 0.99; F‐statistic ¼ 2803.643 [0.000000];JB ¼ 1.522451 [0.467094]; BG (x2, 2) ¼ 2.358453 [0.3075]; ARCH (x2, 1) ¼ 0.106394 [0.7443];ARCH (x2, 2) ¼ 0.469711 [0.7907]; White Heteroskedasticity (x2, 4) ¼ 21.85715 [0.0816]; RamseyRESET ¼ 0.733093 [0.4007].



their very low probability values. Consequently, a change in lagged growthvalues results in an increase in economic growth. A change in bank financialdeepening will lead to a positive change in economic growth. Non‐bankfinancial deepening, although statistically significant at the 10 per cent levelis incorrectly signed, suggesting that positive variations (changes) inNigeria’s economic growth in the long run are not likely to be induced bychanges in the non‐bank financial sector. Openness has a very significantinfluence on economic growth, judging by the statistically significantcoefficient or low p‐value. It indicates that there are gains originating fromthe integration of Nigeria to the global economy.

The diagnostic statistics for the long‐run estimates are satisfactory. Theadjusted R2 is 0.99, implying that 99 per cent of variation in economicgrowth is explained by the lagged values of GDP, bank financial deepening,non‐bank financial deepening and openness. Thus, the goodness‐of‐fit,encapsulated in the adjusted coefficient of determination shows that theestimated model has high predictive capability. The F‐statistic is statisticallysignificant, as indicated by the p‐value of 0.000000, showing jointsignificance of estimated coefficients. The p‐value of the Jarque‐Bera(JB) statistic is reasonably high, in which case we do not reject the normalityassumption. The Breusch‐Godfrey (BG) serial correlation LM test forautocorrelation is satisfactory, as the statistic shows acceptance of the nullhypothesis of no serial autocorrelation. The ARCH and White hetero-skedasticity results suggest that the null hypothesis of homoskedasticity isaccepted. The regression specification error test as captured by the RamseyRESET is quite satisfactory as the F‐statistic is not statistically significant,indicating that the model is correctly specified.

Table 5B: Long‐Run and Short‐Run Estimates: Error Correction Representation Based onARDL Model (1,1,0,0)

Variable Coefficient SE t‐values (prob.)

Constant 0.029337 0.021297 1.377531 (0.1829)DGDPt�1 0.757748� 0.208273 3.638249 (0.0015)D (BFD) �0.177721�� 0.092827 �1.914530 (0.0693)D (BFD)t�1 0.325668� 0.083826 3.885057 (0.0009)D (NBFD) �0.186986�� 0.087116 �2.146409 (0.0437)D (OPN) 0.301473� 0.088087 3.422462 (0.0026)ECM (�1) �0.898325� 0.303587 �2.959036 (0.0075)

Source: Extracted from regression output using Eviews7.Notes: Diagnostic statistics: R2 ¼ 0.74; adjusted R2 ¼ 0.67; F‐statistic 10.16605 [0.000026];BG ¼ 4.358747 (0.1131); JB ¼ 2.437350 [0.295622]; ARCH (x2, 1) ¼ 0.582020 [0.4455]; ARCH(x2, 2) ¼ 1.284499 [0.5261]; Ramsey RESET ¼ 0.419264 [0.5247].�, �� and �� indicate significance at the 1, 5 and 10 per cent level, respectively. Probability values are inbrackets. BG and ARCH denote Breusch‐Godfrey Serial Correlation LM and ARCH test, respectively, totest for the presence of serial correlation and ARCH effect. JB and RESETstand for Jarque‐Bera NormalityTest and Ramsey Regression Specification Error Test, respectively. All the variables are in logs.



Table 5B presents the results of the error correction representation of theselected ARDL model. An examination of all the variables shows that theyare correctly signed except non‐bank financial deepening. All the variablesare highly statistically significant at either the 1, 5 or 10 per cent level. Theimplication of this is that economic growth in Nigeria is highly responsive tochanges in bank financial deepening, openness and past volume of output orincome (represented by the GDP). Specifically one‐period lagged value ofbank financial deepening is positively related to growth while current valueis not. This may be due to the fact that investment takes time to betransformed into goods and services and a time lag in investment isinevitable, so that current volume of goods and services can only beexplained by past financial resources employed in production. Oneinteresting result of the estimated model in both the long and short run isthat both coefficients of non‐bank financial deepening are inversely relatedto economic growth in Nigeria. The result is hardly surprising judging by thedevelopments in the country’s capital market which have suffered variousforms of systemic crisis over the years. The sudden collapse of stock marketprices, insider abuse and the general erosion of confidence in the operationsof the capital market in recent times tend to indicate that stock marketdevelopment in the country is not economic growth oriented.

As expected, the coefficient of the error correction model (ECM)mechanism is negative, and is statistically significant, judging by its value of�0.89. It indicates that a deviation in growth from equilibrium is correctedby asmuch as 89 per cent the following year. In other words, when economicgrowth is above or below its equilibrium level, it adjusts by approximately89 per cent within the first year to ensure full convergence to its equilibriumlevel.

The ECM value also lends support to the existence of a long‐runrelationship (i.e. cointegration) between economic growth and the variablesemployed in themodel, thereby lending credence to the test for cointegrationconducted.

The diagnostic statistics for the short term are satisfactory. The overall fitof the estimated ECM model is adequate. The adjusted R2 value of 0.67shows that the independent variables employed in the model jointlyaccounted for 67 per cent of the total variation in growth. All the variablesare jointly statistically significant, as indicated by the F‐statistic. The modelsatisfies the diagnostic BG serial correlation LM test. The JB statistic is2.437350 while the probability of obtaining the value, on the basis of thenormality assumption, is 30 per cent, implying that we cannot reject the nullhypothesis of normally distributed error term. The BG statistic indicatesabsence of serial autocorrelation, lending credence to the statisticalestimates. The null hypothesis of heteroskedasticity is rejected at the 1per cent level of significance, as the computed Chi‐square value is lowerthan the related tabulated value at the appropriate degree of freedom. In



addition, the Ramsey RESET statistic indicates that the model is correctlyspecified.

4.1. Stability of Estimated Coefficients

To determine the stability of the estimated coefficients of the growthequation for Nigeria, the cumulative sum of recursive (CUSUM) andcumulative sum of squares of recursive residuals (CUSUMSQ) tests,developed by Brown et al. (1975), were applied to the residuals generatedfrom Equation (3). The CUSUM and CUSUMSQ tests are shown inFigures 1 and 2.

The CUSUM plot does not cross the 5 per cent critical lines, implyingthat the stability of estimated coefficients of the economic growth equationfor Nigeria exists over the entire sample period. Except for the periodbetween 2002 and 2008, the CUSUMSQ plot does not cross the 5 per centcritical lines, implying that for the period 2002 and 2008, there might havebeen instability in the system. This is not difficult to understand, consideringthat there were major restructuring of the financial sector at the time. Inparticular, there were reforms in the financial sector including the bankconsolidation policy in 2005 and insurance capitalization policy in 2007,both of which might have led to temporary instability in the system.

-15

-10

-5

0

5

10

15

90 92 94 96 98 00 02 04 06 08 10

CUSUM 5% Significance

Figure 1: CUSUM Test

Note: The straight lines represent critical bounds at 5 per cent significance level.Source: Extracted from regression output using Eviews7.



5. Conclusion and Policy Implication

This paper has investigated the impact of financial deepening onNigeria’s economic growth using time series data from 1981 to 2010. Thebound testing approach proposed by Pesaran et al. (2001) was employed.The empirical analysis demonstrates that, bank financial deepening leads toincreases in economic growth. The results show that there is a long‐runrelationship between economic growth, bank and non‐bank financialdeepening and openness. The results of the investigation are in favour of thehypothesis that there is bidirectional and feedback relationship betweeneconomic growth and financial deepening, although financial deepeningfrom the non‐bank sector does not seem to significantly influence economicgrowth in Nigeria. This is likely to be due to the inability of the variousreforms in the capital market to improve long‐term growth throughproductive investment, especially when underscored on very poorinfrastructural base, insider abuse, general low confidence in the operationof the stock market and the like.

The results of Granger causality tests suggest that there is mutualcausality between bank financial deepening and economic growth. Theresults also indicate that economic growth Granger‐causes non‐bankfinancial deepening and that this causality is unidirectional. Causality alsoflows from openness to economic growth. The results also suggest that

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

90 92 94 96 98 00 02 04 06 08 10

CUSUM of Squares 5% Significance

Figure 2: CUSUM Squares Test

Note: The straight lines represent critical bounds at 5 per cent significance level.Source: Extracted from regression output using Eviews7.



higher growth tends to stimulate a country’s integration with the worldeconomy. For the period under study, Nigeria’s economic growth is sensitiveto changes in the variables employed.

However, a caveat is warranted. It must be said that the results of thestudy are based on the proxies employed for bank and non‐bank financialdeepening. Although similar conclusionswere reachedwhen other measuresof non‐financial deepening (such as total value of securities traded and totalvalues of equities) were used (the results are not presented), different resultsmight well be the case if other proxies (should the data be available) are used.In any case, the study combines a proxy of banking sector development withthat of stock market development.

It is important to stress that the findings of this study may be country‐specific, in line with the view held in the literature that the relationshipbetween financial deepening and economic growth cannot be generalizedacross countries. Thus, using the results of this study to generalize on othersmay be inadvisable.

The findings have useful policy implications. For Nigeria’s economicgrowth to improve and be maintained, there is need to deepen the financialsector. Specifically, growth in the financial sector must be attuned to growthin the real sector of the economy, as a way of reducing unemployment,inflation and poverty.



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Non‐technical Summary

The role of finance in stimulating growth and development is generallyrecognized. The aim of the paper was to find out the degree to which thefinancial sector of the Nigerian economy has promoted growth. The studyperiod is 1981–2010, using both bank and non‐bank financial variables. Theresults of the study showed that there is a long‐run relationship betweeneconomic growth and bank and non‐bank financial variables relating to theNigerian economy. In addition, there is evidence suggesting that financialdevelopment and economic growth reinforce each other. Part of therecommendation in the paper is that emphasis should be placed bygovernment on enacting policies and implementing same so that theeconomy of Nigeria can be stimulated and the many problems which thecountry faces can be adequately addressed.



Documents

Bank and Non-Bank Financial Deepening and Economic Growth: The Nigerian Experience (1981-2010)