Bank Financing and Industrial Growth: Issues and

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Volume 2 Issue 2 2017

Amity Journal of Economics

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Amity Journal of Economics2(2), (53-66)

©2017 ADMAA

Bank Financing and Industrial Growth: Issues and Considerations

Shobande, Olatunji AbdulUniversity of Lagos, Akoka, Yaba, Lagos, Nigeria

AbstractThis paper empirically examines the relationship between bank financing and industrial growth in

Nigeria between 1981 and 2016. To this end, the multivariate framework based on the Vector Error Correction Model and Johannsen Co-integration techniques were adopted. Both the long run and short run estimation established that bank financing has a statistically long-term relationship with industrial growth. Furthermore, the result shows that stability through nominal exchange rate and macroeconomic uncertainty anchored on the premise of inflationary pressure are critical constraints to industrial growth in Nigeria. From the policy perspective, policy makers need to champion the course of financial and policy reformation to ensure a functional financial system that promotes domestic credit, especially to industrial sector, to meet the quest for industrialization.

Key Words: Banking, Financing, Growth, Industrial Growth

JEL Classification: G21, E43, E44, E51

Paper Classification: Research Paper

IntroductionThe place and importance of the industrial sector in the economic prosperity of a nation cannot

be overemphasized. As it were, the sector plays critical role in the enhancement of economic transformation, value addition and increase in the potential for long-term growth. The recorded success of the Asian Tigers is largely attributed to the rapid development in their industrial sector, a movement that turned around the fortune and prosperity of their nations. Today, Asia is seen as the factory of the world economy.

Apparently, the major theoretical ground for establishing sound financial system in any nation relies on the use of multi-faceted strategy that promotes nurtures and supports the disbursement and allocation of funds, as well as monitors range of activities to speed up the rate of industrialization. While this function has not been adequately performed, many financial institutions have blamed access to finance for poor industrial growth and the inability of most small industries to defend bankable projects (Demirguc-Kunt et al. 2017). Varied studies claimed that industrial sector performance in Nigeria is far from financing issues. For instances, Shobande (2017) believes there is no reason to believe that emerging micro industry have no access to capital

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rather, the issue lies with the fact that the majority in this industry do not have feasibility studies (that are good enough) to defend most of their proposed businesses.

In view of the challenges and opportunities facing industrial sector and the need for the financial sector to restore its potentials as driver of the economy; the study evaluates the main implication of bank financing on industrial growth in Nigeria. The purpose of this study is to: (a) contribute to a better understanding of the evolving context in which industrial performance can speed up growth process, if properly financed; (b) analyze the macroeconomic environment where the sector operates and their ability to cope with existing policy in the face of dynamic macroeconomic uncertainty; (c) the uniqueness of this study is reflected methodological approach anchored on the Vector Error correction model that enables us determine the speed of convergence. The full analysis centered on Nigeria economy as effort to complement previous study on the financial theory and industrial economic studies at national, regional and global levels. This study will serve as policy ingredient to broad audience including regional organizations, academic scholars alike and policy analysts who may find the study useful.

Historical Perspective In Nigeria, the contribution of the industrial sector, particularly to Gross Domestic Product

(GDP) is relatively less than 10%, while the level of capacity utilization is relatively less than 45% (Akingumola, 2011). The collapse of the world oil market in the early 1980 which was accompanied by a sharp decline in the exchange rate earning, the low level of power supply as well as credit constraint in the aspect of financing the industrial sector are major factors that have led to the decline. A flashback at the performance of the manufacturing sub-sector shows that as on 1960, the sub-sector accounted for only 3.8% of industrial growth in the country. This rose to 5.3% in 1966 and 6.35% in 1969. On the average, the sub-sector was responsible for about 5% of the country’s GDP during the 1960s. During the early part of the 1970s, there was decline in the share of manufacturing, as it dropped from 6.35% to 3.6% in 1970 and 3.33% in 1974. However, the contribution of the sub-sector in the latter part of the decade improved with its share standing at 8.79% in 1979. The average contribution, which stood at 4.8% in the 1970s, was slightly lower than its corresponding average in the previous decade.

The rising trend in the latter part of the 1970s was sustained, especially in the early 1980s. It is on record that the manufacturing sub-sector attained its historic peak GDP contribution of 9.9% during this decade, precisely in 1983. After this period, its contribution fluctuated between 5.2% and 8.74%. The average annual contribution for 1980 was 8.19%, which doubled the corresponding figure in 1970. The 1990s was characterized by persistence fall in the contribution of the sub-sector. Specifically, the contribution ranged between 5.54% in 1990 and 4.89% in 1999 with an overall average share for the decade standing at 5%. In 2010, the contribution of manufacturing to total value added stood at 2.2% compared to 17% of the world, 21% for developing economies, 10% for Africa as well as less developed countries and 9% for major petroleum and gas exporting developing economies.

Undoubtedly, lack of funds have made it difficult for firms to make investments in modern machines, information technology and human resource development, which are crucial in reducing production cost, raising productivity and improving competitiveness. Low investment can be traced largely to bank’s unwillingness to make credit available to manufacturers, owing partly to the mismatch between the short-term nature of bank funds and medium to long term nature of funds needed by industries (Dangote, 2001). The Bank of Industry (BoI) has made attempt to rescue the industrial sector by providing easy access to funds with the hope of building

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the economy and accelerating the speed of industrialization. Efforts by the Central Bank of Nigeria (CBN) to use persuasive strategy, as a means of getting the attention of banks to support the sector have proved abortive.

Review of LiteratureThere are two strong theoretical approaches in existing literature that provide clear

understanding of the basis for cheap financing for industrial growth. The first is the bank-based view, which suggests that providing a sound financial system, through mobilization of savings from surplus units to deficit units, would speed up the rate of industrial growth. The second approach favors resource mobilization through the capital market to support industrial process by selling shares, stocks and bond as well as other valuable securities (Levine, 2004 and Beck et al, 2004). In literature, the source of finance available to a firm seeking industrial growth can be classified as internal and external. The external is often recognized as the source of fund that can be mobilized by the firm outside the organization through debt and equity. The equity fund is often mobilized through the efficiency of the market-based hypothesis that allows financial resource to be mobilized from institutional investors, rational economic agents and mutual funds. The second instrument using debt can be raised through floating of corporate bond or sourcing of finance/funds from the banks (Shobande and Oke, 2016).

The above theoretical exposition clearly shows that access to finance for industrial growth can be classified into two strands of literature: the first is the market-based approach that is designed based on the Anglo- American model, which suggested a strong financial market with less emphasis on the bank financing model. The second is the bank financing model which can be traced to the Japanese model that gives support to bank financing and strengthens the quest for a viable financial system for speeding up industrial growth. While, the market-based model is often regarded as the endogenous source of financing, since it is relatively savings, impersonal financing and retained earnings, the bank financing model is often recognized as the exogenous industrial financing model since its source of fund is determined by cost of capital as well as stability of the financial system (inflationary pressure and exchange fluctuation) (Shobande, 2017).

The basic point of divergence between the market based and bank financing model is caused by the recent development in the capital market, which gave rise to a wide spread universal banking system. Beyond this divergence, is the ever-increasing nature of risk exposure as well as recognizing the need for diversification of the financial structure to accommodate geographical spread and socio-economic reach, which supports the quest for financial inclusion strategies? Rising from the above debate emerged the new strategies for financing for industrial growth: (a) the bank financing strategy to act as a provider of short term source of financing, (b) the development of financial institutions to act as a provider of medium term to long term fund, (c) the small industries supplementing funds through resource mobilization from the capital market.

On the empirical strands, the theoretical disagreement also finds its root in the controversial findings among numerous scholars, which investigated this relationship. While the point of disagreement can be traced to the environment in which various studies were carried out, the choice of econometric instrument as well as the proxies used on the variables of interest could also be a factor (Shobande, 2016). Empirical studies on the impact of small industrial financing on economic growth documented mixed results as revealed in Syed et al (2012), Olawale and Garwe (2010), Akingunola (2011), and Asta and Zaneta (2010) among others. In Pakistan, Syed et al (2012) examined the impact of SMEs sector on economic development using descriptive statistics, paired samples and Pearson product moment correlation analysis. The study revealed that SMEs play

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a significant role in the economic progress of the Pakistan economy. In South Africa, Olawale and Garwe (2010) examined the obstacles of new SMEs using the principal component analysis. The results revealed that apart from the constraints of credit availability, the inability of SMEs to understand market dynamics in which they operate also contributed to a stunted growth experience in the country during the period reviewed. In Nigeria, Akingunola (2011) examined SMEs and economic growth using econometric regression analysis. The study affirmed that there exists a positive significant relationship between SMEs financing and growth which is determined by the level of investment of these firms. The result of the studies by Olawale and Garwe (2010) and Syed et al (2012) are consistent with Asta and Zaneta (2010) who both examined the growing importance of SMEs and their influence on economic development in Lithuana economy. The study acknowledged that special attention must be given to the processes, tendencies and perspectives of enhancing SMEs performance through improved business environment, economic and social effectiveness, as well as integrated based financial decision modeling to improve the contribution of small industries to overall industrial growth in the country.

Empirical studies in support of SMEs financing and investment through a bank-based model also documented a mixed results as in Beck, Demirguc-Kunt and Maksimovic (2005); Omah, Durowoju Adeoye and Elegunde (2012); Ahiawodzi and Adade (2012); Obamuyi (2011); Chiou, Wu and Huang (2011); Amonoo, Acquah and Asmah (2003); Aliyu and Bello (2000); Nwosa and Oseni (2013); Shobande and Oke (2016); Blackwell and Winters (2000) and Beck et al (2004) among others. In terms of cross country studies, Chiou, Wu and Huang (2011) examined how diversified operation of banks impact on their loans to SME’s growth for a panel data set of 28 banks. The result indicated that total assets on SMEs on loan is positive at one per cent. Also, the significant level and credit guarantee also have positive impact to loan through the assistance of credit growth for cross section countries guarantees scheme. This result by Chioi, Wu and Huang (2010) is contrary to Beck, Demirguc-Kunt and Maksimovic (2005) view which earlier used the standard growth regression model to estimate the relative size of SMEs sector in terms of employment and found a positive relationship but not robust impact on industrial and economic development.

In terms of regional studies, Omah, Durowoju Adeoye and Elegunde (2012) studied the impact of post-bank consolidation on the performance of SMEs in Nigeria with special reference to Lagos. The authors used a sample of 50 drawn from supra-population of Ikeja Local Government area of Lagos. The applied mean, standard deviation and coefficient of variation revealed that there is no link between post bank consolidation and industrial growth through SMEs financing in Lagos. This result by Omah, Durowoju Adeoye and Elegunde (2012) was contrary to the finding of Ahiawodzi and Adade (2012) investigation on the effect of access to credit on growth of SMEs in the Ho Municipality of Volta region of Ghana using combination of survey and econometric analysis. While the econometrics results of Ahiawodzi and Adade (2012) show that access to credits exerts a significant positive effect on SMEs in Ho Municipality, the survey presented sectorial analysis of the behavioral performance of these SMEs towards industrial growth. In Nigeria, Obamuyi (2011) compared the performance of loans granted to SMEs by banks with that of micro-credit institutions with specific reference to Ondo state, using the descriptive statistics. The study revealed that average repayment rate from banks was 93.2% while 34.06% for micro-credit schemes. The result further suggested that financing by banks performed on higher level than micro-credit schemes.

In terms of country-specific studies, some controversial and confusing results were observed. For instance, in Ghana, Amonoo, Acquah and Asmah (2003) sought to establish whether there is a relationship between interest rates and the demand for credit as well as interest rates and loan repayment by the poor and the SMEs performance on industrial growth. Their result showed a

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negative relationship between interest rates and the demand for credit as well as interest rates and loan repayment. Their study further suggested that lowering interest rate would increase the demand for credit in the bank. This result contradicts Aliyu and Bello (2013) stand in their examination of the contribution of commercial banks to growth of SMEs in Nigeria between 1980 and 2009. Using the ratio and trend analysis, the results of the study revealed that commercial bank financing has a positive relationship with SMEs growth but with a declining ratio with government persuasive approach through Central Bank directives to abolish the mandatory credit allocations. The result presented by Aliyu and Bello (2013) is consistent with Nwosa and Oseni (2013) who both investigated the impact of bank loans to SMEs on industrial sector through the manufacturing sector output between 1992 and 2010. The result deduced that bank loan to SMEs has a positive significant relationship to industrial growth.

Shobande and Oke (2016) examined the linkages among SMEs financing, diversification and long-term growth in Nigeria using quarterly data between 1995 and 2014. The study observed that there is a long run relationship between SMEs financing, diversification and long-term growth. The authors further suggested the need for easy access to credit at a cheaper cost, if SMEs growth would further contribute to attainment of long run growth. The finding of Shobande and Oke (2016) is contrary to the conclusion by Blackwell and Winters (2000) who found an inverse relationship for the case of lines of credit to small firms, between local banking and average interest rate. Beck et al (2004) analysis using a database containing information about obstacles in accessing credit and financing pattern in 74 countries conclude that movements of banking concentration show difficulties in accessing credit decrease as firm increases. Ming-Wen (2010) used a dataset covering 37 countries comprising developed and developing countries and examined the contribution of SMEs to economic growth between 1960 and 1990. The study finds that small businesses are beneficial to growth prosperity than larger ones. The conclusion of Ming-Wen (2010) is contrary to Anthony and Arthur’s (2008) investigation on the role of SMEs on growth of per capita income in the United States, using dataset from firms in the formal manufacturing sector with fewer than 10, 20, 100 or 250 employees. The result revealed a negative relationship between small firm’s growth and economic prosperity.

Also, Onyeiwu (2012) examined the effect of SMEs financing on economic growth in Nigeria using quarterly data between 1994 and 2008. The study revealed that SMEs financing is crucial for improvement in economic performance during the review period. The result of Onyeiwu (2012) is consistent with Duru and Lawal (2012) who both accessed the impact of financial sector reform on the growth of small firms and suggested that government should create enabling environment to further enhance the contribution of these industries to economic growth.

Empirical studies in support of integrating informal sectors into the economy through adequate financing of entrepreneurship and their relative impact on industrial growth also documented divergent results - Olukayode and Somoye (2013), Fairle (2011), Kreft and Sobel (2005), Rasool et al (2013), Onakaya et al, (2013) and Abdul-Kemi and Idris (2014), etc. In the celebrated work of Olukayode and Somoye (2013) who both evaluated the impact of financing on entrepreneurship growth in Nigeria using endogenous growth model, the results show that finance, interest rate, real gross domestic product, unemployment and industrial productivity have statistical significance on entrepreneurship development. This study is consistent with Fairle (2011) who earlier investigated the relationship between entrepreneurship economic condition and great recession using the regression analysis; the results point to consistent picture of slack labour market outweighing the negative influence on business creation, which in turn, affected their contribution to industrial growth. Kreft and Sobel (2003) investigated the causal relationship between public policy, entrepreneurship and growth using the granger causality techniques. They

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noted that the presence of entrepreneurship activity can and would accelerate industrial growth by drawing venture funding.

Rasool et al (2012) examined the drivers of entrepreneurship linking with economic growth and employment generation using the panel data analysis and the stepwise lease square techniques. They explained that research and development activities play crucial role in entrepreneurship potential to speed up industrial growth and economic performance. Abosede and Onakaya (2013) examined the impact of entrepreneurship development and the quest for inclusive growth using the estimated regression analysis model. The findings revealed that apart from the bottleneck of access to capital constraining effective and efficient contribution of entrepreneurship to industrial growth, the limited managerial capacity was also observed. This is consistent with the study of Abdul-Kemi and Idris (2014) which reviewed the role of entrepreneurship and economic development in Nigeria using Autoregressive Integrated Moving Average (ARIMA) model. The study provided evidence that entrepreneurship financing has significant impact on industrial growth and economic performance in Nigeria.

Despite the potential importance of the question of convergence, empirical evidence on such question remains scanty. There are clear signals that there seems to be empirical confusion concerning the extents to which existing literature can explain the correlation between bank financing and industrial growth especially in terms of access to cheap cost of capital to speed up the rate of industrial growth through the financial sector. Some of the studies were more descriptive in nature without quest to test the nature, kind and pattern of the relationship. In addition, majority of the researches were conducted over a decade ago, as such they may not reflect the recent and current scenarios.

Theoretical Architecture and Methodology

Theoretical Architecture The theoretical foundation for this work is driven by the celebrated work of Levine (2011) and

Beck et al, (2004) who followed the work of bank-based financing theoretical position to identify the implication of cheap access to bank financing on the quest for industrial growth. There is no gain-saying the facts that sound financial architecture and engineering of a country have implications for its speed of industrialization. The study model used also drew from the works of Amonoo, Acquah and Asmah (2003). These authors extensively explained the motive behind bank financing and asserted its importance for industrial growth among other factors. These works are considered appropriate for this study while other factors of interest were included.

Research MethodologyDrawing from Beck et al (2004), which were adapted, by Amonoo, Acquah and Asmah (2003)

and modified by Levine (2011) the model this study is as specified:

(1)

Where; IoPc = industrial growth value added (% of GDP); SME = Bank Domestic Credit to SMEs, INT = Domestic Interest rate (Monetary policy), X = Control variables, ∝= Constant, ϑ1= Slope, and t = Time.

However, the empirical models adopted from the above equation (1) is modified taking into consideration focus of this study, money supply (MOS), expected inflation rate (INF) proxies as consumer price index to incorporate macroeconomic uncertainty in the model, real exchange rate

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(Nexch) to capture the impact of external influence on industrial output while the influence of fiscal policy is captured by tax rate proxies as TaxR.

Data Description Industrial growth: it is measured as industrial value added (% of GDP).

Internal factor

Interest rate: Interest rate spread (lending rate minus deposit rate, %)

Domestic Money Supply: The variable is defined as broad money as percentage of GDP, which is considered as proxy for overall monetary stance. As discussed in a seminar work by using IS-LM framework (Bernanke and Blinder, 1988).

Inflation rate: This study introduces the inflation rate based on the consumer price index between 1981 and 2016 in the estimation as a control variable to verify the hypothesis of whether there is connection between credit growth and macroeconomic uncertainty.

Tax rate: The variable is introduced to check the impact of fiscal policy on industrial growth through tax charge or tax incentives.

External factor

Nominal Exchange rate: As discussed in Barro et al (2011), a fall in the value of nominal exchange rate of a country expresses an appreciation of domestic currency and thus results in domestic credit.

Estimation Techniques The estimation techniques adopted are based on the Johansen Cointegration techniques and

Vector Error Correction Model. The justification of this is based on the results of our unit test. The unit root test using Augmented Dickey Fuller test and Philip Perron all suggested the techniques stated above. The implication of this technique is that it allows us to access the long and short run relationship among the variables considered. Also, it provides avenue to determine the speed of convergence of the variables to their respective positions.

Econometric Analysis and Discussion

The methods used to confirm the orders of integration are ADF and PP and Ng-Perron. These are presented in the Tables 1 and 2.

Table 1: Results of ADF and PP Unit Root Test (Without Trend): 1981-2016

Variable ADF Test Statistics PP Test Statistics

Data First Difference Data Data First Difference Data

IoPC -1.986356 -5.414337 -2.162344 -5.455549

SME -2.687333 -3.360255 -2.441037 -8.967042

INT -1.534458 -4.622635 -1.248818 -12.41097

MoS -0.683318 -3.271863 -0.324128 -2.122043

TaxR -0.746988 -6.304865 -0.676672 -6.328695

INF -1.875288 -7.142305 -1.864380 -17.54507

Rexch -2.055964 -4.329568 -2.241578 -4.237600

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Mac-Kinnon Critical Value

1% -3.639407 -3.646342 -3.639407 -3.646342

5% -2.951125 -2.954021 -2.951125 -2.954021

10% -2.614300 -2.615817 -2.614300 -2.615817

Note: *,**, *** show significant at 1%, 5% and 10% level respectively.

Both ADF and PP tests confirmed that all the variables are stationary at their first difference. All the variables are stationary at 1% level of significance in both tests except money supply which is stationary at 5% level of significance. Therefore, these results suggest that all five variables are integrated in the same order.

Table 2 :Results of NG-Perron Unit Root Test (Without Trend): 1981-2016

Variables MZa MZt MSB MPT

Data

IoPC -5.23656 -1.59848 0.30525 4.72979

SME -4.21549 -1.25882 0.22113 2.40292

INT -3.25748 -1.27492 0.39138 27.9457

MoS -3.53775 -1.10508 0.31237 6.88651

TaxR -1.60688 -0.62750 0.39051 10.9398

INF -3.27490 -1.88724 0.17238 5.48017

Rexch -3.83239 -1.36936 0.35731 6.40159

First Difference Data

IoPC -14.5846 -2.69960 0.18510 1.68297

SME -16.3493 -2.85761 0.17478 1.50420

INT -16.8776 -2.90347 0.17203 1.45711

MoS -17.5201 -2.92110 0.16673 1.53836

TaxR -16.2506 -2.85035 0.17540 1.50816

INF -32.9119 -4.05655 0.12325 2.76901

Rexch -15.4737 -2.78152 0.17976 1.58334

Critical Valuesa

1% -13.8000 -2.58000 0.17400 1.78000

5% -8.10000 -1.98000 0.23300 3.17000

10% -5.70000 -1.62000 0.27500 4.45000

Note: *,**, *** show significant at 1%, 5% and 10% level respectively.

Asymptotic critical value taken from Ng-Perron : Modified Philips-Perron test. : Modified PP t-test. MSB: Modified Sargan-Bhargava test. MPT: Modified Point Optimal test. Ng-Perron unit root test also confirmed that all seven variables are stationary at their first difference. However, therefore, this result also suggests that all seven variables are integrated in the same order, i.e. I (1). Once we establish the order of integration, the study process requires the estimation of the long-run relationships among the variables included. However, before estimating this relationship, we need to identify the optimal lag length of the model. The lag length selection results are provided in the Table 3.

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Table 3 :Results of Optimal Lag Length Selection Results

Lag LogL LR FPE AIC SC HQ

0 -538.6216 NA 541970.5 33.06798 33.38542 33.17479

1 -359.9013 270.7884 223.6162 25.20614 27.74566* 26.06061

2 -284.4791 82.27874* 69.53437* 23.60479* 28.36641 25.20693*

Note: * indicates lag order selected by the criterion. LR: sequential modified likelihood ratio test statistic, FPE: final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion and HQ: Hannan Quinn information criterion test statistics

Using Vector Autoregressive model, lag length selection criteria except SC advocate that to use 2 lags as an optimal lag length for this study. Therefore, we included 2 lags in our model. The results of Johansen and Julius co-integration test are shown in Table 4.

Table 4: Results of Johansen and Julius Cointegration Test (1981--2016)Part 1: Trace Statistics

Null Hypothesis Alternative Hypothesis

Test Statistic Critical Value (5%)

Probability**

H_0:r≤0 H_1:r>0 239.1*** 125.6154 0.0000

H_0:r≤0 H_1:r>1 147.7*** 95.75366 0.0000

H_0:r≤0 H_1:r>2 100.1*** 69.81889 0.0000

H_0:r≤0 H_1:r>3 55.720** 47.85613 0.0077

H_0:r≤0 H_1:r>4 28.44645 29.79707 0.0710

H_0:r≤0 H_1:r>5 13.98086 15.49471 0.0835

H_0:r≤0 H_1:r>6 3.315147 3.841466 0.0686

Part 2: Maximal Eigen Value Statistics

Null Hypothesis Alternative Hypothesis

Test Statistic Critical Value (5%)

Probability**

H_0:r=0 H_1:r=1 91.34*** 46.23142 0.0000

H_0:r=1 H_1:r=2 47.684** 40.07757 0.0058

H_0:r=2 H_1:r=3 44.390** 33.87687 0.0020

H_0:r=3 H_1:r=4 27.27386 27.58434 0.0547

H_0:r=4 H_1:r=5 14.46560 21.13162 0.3281

H_0:r=5 H_1:r=6 10.66571 14.26460 0.1718

H_0:r=6 H_1:r=7 3.315147 3.841466 0.0686

Part 3: Normalized Cointegrating Vector

IoPC SME INT MoS TaxR INF Rexch

1.000000 0.34177 0.10249 0.79120 -0.005671 0.001647 -0.011579

1.000000 0.28674 0.24031 -0.347356 0.121704 -14.31061

0.47988 1.82415 0.01882 1.935792 0.873253 52.64346

0.31820 0.55160 1.60140 0.749164 0.466660 21.16819

Note: ** show significant at 5% level

Trace test statistics identified four co-integrating relations in the system of equation at 5% level of significance. Null hypothesis is hence rejected at rank 0, 1, 2 and 3 but failed to reject null hypothesis at rank 4. Maximum Eigen value test identified three co-integrating relation, hence,

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null hypothesis is rejected at rank 0,1 and 2 but failed to reject null hypothesis at rank 3. This indicates the existence of long-run correlation between the variables. However, in ECM, three co-integrating relations are used by using trace statistics.

Table 5 shows the results of ECM and first panel of this table shows the short-run. Industrial output Per Capita have some short-run relations with its first lag, first and second lag of Money Supply and Real Exchange Rate. Second panel shows the long-run relationship while the third indicates the Error Correction Coefficients.

Table 5 : Results of Short Run Vector Error Correction Model

Panel 1: Short-Run Relationship

Variables IoPC SME INT MoS TaxR INF Rexch

D(IoPCt-1 0 . 4 2 1 5 5 0 (0.20380)

1 2 . 4 6 9 3 3 (15.9473)

4 8 . 0 2 8 3 8 (32.9319)

-0 .325992 (0.38750)

-38 .96851 (22.5517)

-137 .7587 (115.219)

-2 .942535 (1.37501)

D(IoPCt-2 0 . 0 3 1 2 0 0 (0.31899)

1 7 . 6 4 8 9 1 (17.9248)

-19 .83712 (37.0154)

-0 .187755 (0.43555)

-50 .41094 (25.3481)

6 5 . 4 7 8 5 9 (129.506)

-1 .705452 (1.54551)

D(SME t-1 -0 .001252 (0.00343)

-0 .044662 (0.19263)

-1 .166809 (0.39779)

-0 .003279 (0.00468)

0 . 1 4 9 6 5 6 (0.27241)

0 . 9 0 8 7 4 6 (1.39176)

-0 .050007 (0.01661)

D(SME t-2 0 . 0 0 1 4 5 9 (0.00399)

0 . 0 3 0 8 8 8 (0.22410)

-0 .203378 (0.46278)

-0 .003756 (0.00545)

-0 .001696 (0.31691)

-1 .794605 (1.61913)

-0 .025255 (0.01932)

D(INT t-1 -0 .000419 (0.00282)

0 . 3 2 0 3 3 0 (0.15839)

-0 .025817 (0.32708)

-0 .002137 (0.00385)

-0 .001036 (0.22398)

0 . 3 1 5 6 7 3 (1.14436)

0 . 0 1 2 1 9 9 (0.01366)

D(INT t-2 0 . 0 0 1 2 2 5 (0.00153)

0 . 1 9 5 0 3 3 (0.08622)

-0 .348771 (0.17805)

-0 .001044 (0.00210)

-0 .061661 (0.12193)

-0 .222052 (0.62293)

0 . 0 1 0 2 2 6 (0.00743)

D(MoS t-1 0 . 5 1 0 4 5 9 (0.22172)

4 3 . 3 6 4 6 4 (12.4591)

3 0 . 3 9 0 4 2 (25.7287)

0 . 5 4 7 1 3 6 (0.30274)

-30 .53525 (17.6189)

-152 .0268 (90.0171)

1 . 3 6 5 0 2 8 (1.07425)

D(MoS t-2 0 . 3 5 4 7 5 7 (0.16132)

1 2 . 9 8 8 6 5 (20.3033)

-94 .98288 (41.9273)

0 . 2 0 8 6 7 7 (0.49335)

4 5 . 3 1 4 6 2 (28.7117)

4 9 . 1 6 2 1 2 (146.691)

-0 .703975 (1.75059)

D(TaxR t-1 0 . 0 0 0 4 9 4 (0.00298)

0 . 1 4 8 2 4 8 (0.16728)

-0 .621091 (0.34543)

0 . 0 0 9 6 0 5 (0.00406)

0 . 0 0 3 9 2 4 (0.23655)

0 . 8 3 0 7 4 5 (1.20857)

-0 .027067 (0.01442)

D(TaxR t-2 0 . 0 0 2 2 9 2 (0.00335)

0 . 0 9 8 6 8 7 (0.18844)

-0 .671418 (0.38913)

0 . 0 0 4 9 8 7 (0.00458)

0 . 1 8 6 1 1 6 (0.26648)

1 . 1 2 5 4 5 3 (1.36145)

-0 .005193 (0.01625)

D(INF t-1 0 . 0 0 1 5 6 1 (0.00121)

0 . 0 0 7 5 4 8 (0.06789)

-0 .246827 (0.14019)

0 . 0 0 0 5 3 2 (0.00165)

-0 .097796 (0.09600)

0 . 0 3 9 0 7 0 (0.49048)

-0 .011499 (0.00585)

D(INF t-2 0 . 0 0 0 5 3 6 (0.00106)

-0 .018051 (0.05961)

-0 .189728 (0.12309)

-0 .000362 (0.00145)

-0 .139355 (0.08429)

0 . 0 7 7 7 0 0 (0.43067)

-0 .000507 (0.00514)

D(Rexch t-1 0 . 0 5 5 3 3 5 (0.09113)

-6 .195533 (5.12072)

1 1 . 8 4 3 4 7 (10.5745)

-0 .165363 (0.12443)

-9 .131565 (7.24140)

2 9 . 8 3 5 3 5 (36.9971)

0 . 1 9 0 4 2 8 (0.44152)

D(Rexch t-1 -0 .031160 (0.08081)

-3 .978399 (4.54088)

2 1 . 1 2 6 1 6 (9.37711)

0 . 0 0 7 6 2 8 (0.11034)

-11 .37513 (6.42142)

7 . 8 6 4 2 6 4 (32.8078)

0 . 5 4 4 3 3 0 (0.39152)

Constant 0.040090 -13.02763 16.12764 0.034330 -3.123277 22.17085 -0.127104

Panel 2: Long-run Relationship (From Cointegrating Vector)

DIoPC = -6.177468 - 0.005671DTaxR(-1) + 0.00164DINF(-1) - 0.011579DRexch(-1)DSME= 55.07301 - 0.347356DTaxR(-1) -14.31061DRexch(-1)DINT= -308.4790 + 1.935792DTaxR(-1) + 0.873253DINF(-1) + 52.64346DRexch(-1)DMoS= -148.2539 + 0.749164DTaxR + 0.466660DINF + 21.16819DRexch

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Panel 3: Speed of Adjustment

Coint-Eq1 EC1(t-1) -0 .933617 (0.41272)

3 0 . 1 5 5 9 9 (25.4396)

- 0 . 4 0 4 8 (0.1339)

0.571200 (0.61815)

1 0 6 . 3 6 6 6 (35.9751)

-231 .0602 (183.801)

0 . 1 4 0 5 8 3 (2.19345)

Coint-Eq2 EC2(t-1) 0 . 0 0 1 4 8 4 (0.00391)

-0 .954032 (0.21968)

1 . 3 0 4 4 2 8 (0.45365)

-0 .002556 (0.00534)

-0 .262410 (0.31066)

-0 .204513 (1.58719)

0 . 0 0 9 7 9 1 (0.01894)

Coint-Eq3 EC3(t-1) 0 . 0 0 1 6 2 0 (0.00457)

-0 .396845 (0.25688)

-0 .441297 (0.13048)

0.002799 (0.00624)

0 . 2 9 6 2 4 2 (0.36327)

-0 .997744 (1.85599)

-0 .038371 (0.02215)

Coint-Eq4 EC4(t-1) -0 .004955 (0.00969)

0 . 9 0 4 9 3 1 (0.54467)

3 . 4 5 2 8 6 0 (1.12477)

-0 .008498 (0.01323)

-0 .493216 (0.77024)

0 . 0 6 9 5 8 4 (3.93524)

0 . 0 8 4 3 7 6 (0.04696)

Note: *, **, *** show significant at 1%, 5% and 10% level respectively.

The second panel of the table identifies four co-integrating relations which confirm the long-run relationship among the regressions. The first co-integrating vector shows the negative correlation between Industrial Output Per Capita and Tax Rate, positive correlation with Inflation Rate and negative relation with Real Exchange Rate. The negative relation of Industrial Output and Tax rate can be due to disincentive effect of tax on the industry’s capacity in large scale production. General increase in commodity price levels up the industry’s ability to employ more capital goods whereby serving as in-built incentive for firms. Real exchange constitute major problem of the industry since basic raw inputs are imported. This is expected to have negative impact on the industry.

The third panel of table shows the coefficients of speed of convergence to equilibrium which explain how the above model is adjusted towards long-run equilibrium. Negative and significant error correction coefficient (-0.933) of IoPC (1st elements of Coint-Eq1), indicates that 93% disequilibrium is corrected each year which implies that Industrial Output Per Capita moves downward towards long run equilibrium trail. In the same equation (Coint-Eq1), coefficients of Interest Rate is significant which implies that there is long run equilibrium in the system. The significant and negative error correction coefficient (-0.95) of Domestic Credit to SMES (2nd elements of Coint-Eq2), reveal that 95% disequilibrium is corrected by each year indicating that capital moves downward towards long run equilibrium path. However, in this equation (Coint-Eq2) other variables are not statistically significant. The significant and negative error correction coefficient (-0.44), Interest Rate (3rd elements of Coint-Eq3) indicates a 44% disequilibrium is corrected each year. However, in this equation also (Coint-Eq3), other variables are not statistically significant at any level.

Conclusion and Policy Implication As stated earlier, it is not an overstatement to suggest that the issue related to the effects of

bank financing, its evolution over time and its position relative to industrial inclusiveness now occupy a vital role in academic and policy debate on financial theory as catalyst for industrial growth and development strategies. This study contributes to this literature by estimating the long run cointegration equilibrium on bank financing and industrial growth, using annual series data sourced from the Central Bank of Nigeria, (CBN) which was analysed, using the Vector Error Correction Model. The results of the cointegration test based on the Johansen techniques suggest that the variables are mutually cointegrated, which suggests that long run relationship exists among the bank financing components and industrial growth fundamentals. The results of the short run dynamics show that change in the previous (lagged one) period of the variables has a negative impact on industrial growth, while change in the (lagged two) period of the fundamental

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variables has a positive impact on industrial growth. Thus, it is recommended that macro-economic balance while providing access to cheap finance through manipulation of interest rate by the monetary authorities would speed up/accelerate the rate of industrial growth in Nigeria.

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Author’s Profile

Shobande Abdul Olatunji is a Doctoral researcher at the Department of Economics, Nnamdi Azikiwe University, Anambra State, Nigeria. He holds a master’s Degree (M.Sc.) in Economics (Distinction) from the University of Lagos (Unilag), Nigeria, graduating with distinction. He had his First Degree, Bachelor of Science (B.Sc) in Economics too from Caleb University, Nigeria, graduating with First Class Honour, coming top of his class. Shobande has a decade experience in the Banking Industry in Nigeria with good understanding of the Nigeria Economic System. He serves as a Special Consultant to the Central Bank of Nigeria (CBN) on macroeconomics Modelling. He is presently a Lecturer at the Department of Economics, Caleb University, Lagos, Nigeria. Apart from his academic excellence, Shobande has a strong research in Financial Econometrics, Firm Dynamics, Monetary Policy, Macroeconomic Modelling, Computation General Equilibrium, Stochastic Frontier Analysis and Dynamic Stochastic General Equilibrium Modelling. He has published over twenty articles in Learned Journals and participated in several local and international conferences. He is an active member of various international and national professional bodies. He is a Fellow of the Institute of Chartered Economist of Nigeria; Member, Nigeria Economic Society; Associate Member, Nigerian Institute of Management; Associate Member, African Econometric Society among others. Combining his academic excellence and interest in research, Shobande has authored five books in his area of economics. These books include Microeconomics (A Modified Techniques), Macroeconomics (A New Dimension), Advance Econometrics (A Matrix Approach), Managerial Economics (A Problem-Solving Approach) and Research Strategies & Tactics (A Simplified Approach).

Documents

Bank Financing and Industrial Growth: Issues and