ISSN: 2276-8157 http://www.irdionline/catr_article

International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016

BANK CONSOLIDATION AND PERFORMANCE OF THE NIGERIAN ECONOMY

Baghebo, Michael and Okumoko Tubo P. Department of Economics

Niger Delta University, Wilberforce Island, Yenagoa Bayelsa State, Nigeria

ABSTRACT From the 1970s to the early 2000s, Nigeria put in place reform policy measures to ensure strong, reliable and competitive banking sector, and for banks to perform better and contribute to the economy; and to ensure consistency with international standards. Despite the policy measures, the system was still characterized by capital inadequacy and lack of public confidence and low savings in the banking system. This led to the consolidation policy reform announced in July 6, 2004 for all deposit money banks to meet a minimum paid-up capital of N25 billion on or before the deadline of December, 2005. At this time (after the consolidation) a well capital adequate and functional banks; with banking sector stability were expected. But that was not the case. Banks in Nigeria still run into liquidity problem with symptoms of failure and instability in the sector. The sector’s efficient financial intermediation role is in doubt; alongside with its quantity effect (increase in savings and investment) and quality effect (increase in productivity) of the economy. The consolidation policy trust of the government to stimulate market liberalization in order to create a favourable and enabling environment to promote resource allocation efficiency; savings mobilization; capital base expansion; promotion of investment and growth through market-based interest rates with widespread access to bank credit and relatively easy for entrepreneurs to get a loan for investment is still far from expectations. Therefore the study empirically examines the relationship between banks’ consolidation and performance of the Nigerian economy from 1970-2013 using the Analysis of Variance (ANOVA) technique. The findings revealed that Banks’ consolidation in Nigeria has a significant impact on banks’ credit to the economy and eceonomic growth. It concludes that banks’ consolidation significantly facilitates and promotes banks’ credit to the economy and economic growth in Nigeria. It is recommended that another round of consolidation policy should be carried out and monetary policy rate and cash reserve ratio are more effective measures and should be used to influence the level of banks’ credit to the economy and the economic growth rate. Keywords: Banks Consolidation, Nigerian Economy, and Analysis of Variance

INTRODUCTION A well performing economy offers the prospect for the reduction of poverty. It increases the productive capacity of the economy, which leads to increase provision of goods and services, and the creation of jobs and income for the people. Also, it is an effective way to bring about higher living standards in the economy by providing the goods and service required by the people; and also brings economic power and prestige (Ohale and Onyema, 2002). One way to foster performance of any economy is through efficient financial intermediation. The financial sector is a critical component of the economy. How well it functions is a key factor in determining how the rest of the economy functions. The financial sector remains a large part of the economy and a major employer. Its impact on the overall economy is measured through its direct contribution to employment and GDP. A well functioning financial system provides primarily three types of services to the rest of the economy. These are credit provision; liquidity provision; and risk management services. With respect to credit provision, the financial system facilitates transformation of savings from surplus sector to deficit sectors. The importance of credit provision to the larger economy is based on the fact that credit conditions affect economic growth through investment. On the other hand, liquidity affects direct transaction costs of getting into and out of an investment, and length of time it takes to execute a transaction, potentially forcing purchases or sales to be spread over days or weeks; whereas risk management by financial institutions helps to protect businesses and individuals against many forms of financial risk. Correspondence Author: Baghebo, Michael: E-mail: mikephd3c@gmail.com

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Bank Consolidation and Performance of the Nigerian Economy A well functioning financial system promotes economic growth through different channels. Important among them are quantity effect (increase in savings and investment) and through quality effect (increase in productivity). Higher economic growth, increased investment and greater financial deepening result partly from increased savings. While grater financial deepening propels economic growth by improving the productivity of investment (Waheed, 2009). However for a well functioning financial system, the banking sector plays the major role. It is for this reason that countries continue to consolidate their banking sectors. Banking sector consolidation has become prominent in most of the emerging markets, as financial institutions become more competitive and resilient to shocks. It also resulted by corporate restructuring and operations to curb challenges of the global banking system. Bank consolidation focused on further liberalization of banking business; ensuring competition and safety of the system; mobilization of savings; expansion of the capital base; and proactively positioning the banking industry to perform it role of intermediation and playing a role in economic growth and development. In Nigeria, before the major banking sector consolidation in 2005, the system was characterized by: Generally small-sized fringe banks with very high overhead costs; low capital base averaging less than N1.4 billion; heavy reliance on government patronage; undercapitalization and/or insolvency; persistent illiquidity; poor asset quality; unprofitable operations, etc. It is believed that banking sector consolidation through money deposit banks recapitalization policies would engender effective utilization of public funds for profitability and economic productivity in the banking sector, thus, enhancing the credibility of the financial sector in Nigeria. It is in recognition of this that various banking sector reforms have been carried out in Nigeria. These include the introduction of Prudential Guidelines in 1990, increased minimum paid-up capital requirements of Deposit Money Banks from N50 million in 1992 to N500 million in 1998, and N2 billion in 2002. However, the “King” of all reforms was announced on July 6, 2004 for all deposit money banks to meet a minimum paid-up capital of N25 billion on or before the deadline of December, 2005. In 2009 the CBN replaced eight (8) chief executives/directors of suspected banks and pumped in N620 billion of liquidity into the banks. Also, the Asset Management Corporation of Nigerian (AMCON) Bill was passed into law by the National Assembly. Banking sector reforms in Nigeria are embarked mainly to achieve several objectives. These include market liberalization in order to promote resource allocation efficiency; savings mobilization; capital base expansion and; promotion of investment and growth through market-based interest rates. Other objectives are: improvement of the regulatory and surveillance framework; encouraging healthy competition in the provision of services and laying the basis for inflation control and economic growth. The link between bank consolidation and the performance of the economy is on the bases that bank consolidation will improve the banking sector performance by ensuring competition and safety of the system; expansion of the capital base; and mobilization of savings. The improved banking system will then promote resource allocation efficiency by making more credit available to the economy, which in turn propels investment and economic growth. This will again improve the performance of the economy. The soundness of the financial system is one of the fundamentals for measuring the financial health of the economy. A healthy financial system fosters efficient resource allocation, investment and thus economic growth. It is however determined by a strong and well functioning banking sector. To ensure strong, reliable and competitive banking sector, and for banks to perform better and contribute to the economy; and to ensure consistency with international standards, reform policy measures have been put in place in Nigeria over the years. These include the introduction of Prudential Guidelines in 1990, increased minimum paid-up capital requirements of Deposit Money Banks from N50 million in 1992 to N500 million in 1998, and N2 billion in 2002. Consequent upon the policy measures, the number of deposit money banks which stood at 14 in 1970, increased to 29 (107%) in 1986 and 66 (128%) in 1993, but dropped to 54 (-18.2%) in 1998 as a result of bank failures, and rose to 89 in 2004. The system was still characterized by capital inadequacy and lack of public confidence and low savings in the banking system. However, in order to ensure capital adequacy of the banks and restore public confidence in the system, the “mother” of all consolidations was announced on July 6, 2004 for all deposit money banks to meet a minimum paid-up capital of N25 billion on or before the deadline of December, 2005.

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016 Resulting from the consolidation, again, the 89 banks through mergers and acquisition or bank failure were reduced to 25 universal banks. This was further reduced to 24 banks at the end of December 2007. At this time (after the consolidation) a well capital adequate and functional banks; with banking sector stability were expected. But that was not the case. Banks kept on giving symptoms of failure and there was instability in the sector. This once more led CBN to replaced eight (8) chief executives/directors of suspected banks and pumped in N620 billion of liquidity into the banks in 2009. It has however been worrisome that despite the consolidation, banks in Nigeria still run into liquidity problem. The sector compared to her counterparts in South Africa, Yugoslavia etc is still below optimal level. Banking sector confidence has not yet fully restored. The sector’s efficient financial intermediation role is in doubt; alongside with its quantity effect (increase in savings and investment) and quality effect (increase in productivity) of the economy. This therefore puzzles one about the impact bank consolidation has on the banking sub-sector and its consequences on the Nigeria’s economy. Available statistics showed that the total bank credit had a positive trend in the post consolidation era though not consistent. It improved from N1133 billion in 2004 to N6170 billion in 2008 and dropped to N5881 billion in 2009. The ratio of aggregate credit to aggregate deposits was 55.5% in 2004, 60.5% in 2006, and was flat at about 71% between 2007 and 2008 and further increased to 77% in 2009. The levels however showed increase, but were below the maximum recommended ratio of 80%. Also, banks Aggregate performance improved marginally on gross earnings from their pre consolidation performance level. Non-performing credits worsened in the post consolidation era. It grew from N316 billion in 2004 to N357 billion in 2005 representing an average of N337 billion in the pre consolidation era as compared to N222 billion in 2006, N388 billion in 2007, N464 billion in 2008 and N620 billion in 2009. The industry total loans stood at N8.15 trillion in 2012, an increase of 12.10% over the N7.27 trillion reported in 2011 (Okafor 2012). This trend once again draws attention to the impact it has on the Nigerian economy. Thus raising questions like how does the economy performs after the consolidation exercise; does it perform differently from the pre-consolidation era, etc. In addition, the objectives of the government for the consolidation hinge on stimulating market liberalization in order to create a favourable and enabling environment to promote resource allocation efficiency; savings mobilization; capital base expansion and; promotion of investment and growth through market-based interest rates. By now one should expect that just like most economies, there should be a widespread access to bank credit and relatively easy for entrepreneurs to get a loan for investment. But this is still far from expectations. There are still problems of lack of easy access to formal finance, among others. This also necessitates the need to examine the impact of bank consolidation on loans to enterprises in Nigeria. Empirical Literature This section review related empirical studies in the area of study. There is no doubt a lot of studies has been done in Nigeria. For example, in a study, Olowofeso, et al (2015) examined the dynamics of deposit money banks (DMB) credit and the role of consolidation in credit growth in Nigeria using vector error correction model and Granger causality test. Their study used a quarterly data from 1999Q1 – 2013Q2. The authors found a positive relationship between post-consolidation credit supply growth and the real gross domestic product. The authors further pointed out that despite the one sided positive causality found from credit supply to economic growth the total contribution of the consolidated credit growth to real activity was not significant. In another study Yakubu (2015) examined the effect of the Nigerian banking consolidation on the country’s Economic Growth. The study covered the periods 1980 to 2010. The author adopted Modified Wald (MWALD) test of granger causality. It was revealed that banking consolidation caused economic growth through loans and advances made by the banks, as expected. However, interest rate does not cause loans and advances. Aruomoaghe & Olugbenga (2014) investigated whether the banking industry is really financing capital investment thereby contributing immensely to the development of the economy. The study period spans through 1981–2012. The authors found that the banks had contributed much in financing capital investments and stock market development in Nigeria.

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Bank Consolidation and Performance of the Nigerian Economy Baghebo & Stephen (2014) has also examined the impact of monetary policy on selected macroeconomics variables such as gross domestic product, inflation, and balance of payment in Nigeria. The period of their study spans through 1980-2011. Ordinary least square (OLS) regression analysis and the error correction method were adopted for the data analysis. The authors found that the provision of investment friendly environment in the Nigerian economy increases the growth rate of GDP. However Emori, et al (2014) in their study established the impact of bank capital, aggregate investment, loans and advances, bank profitability on the performance of the Nigerian economy. The period of their study spans through 1986-2011 and multiple regression was used for analysis. It was revealed that bank capital was a determinant of banks performance and banks’ investment had a positive impact on the economy. Also Olayinka & Farouk (2014) examined the impact of consolidation on the performance of banks in Nigeria. The study used a period of 12 years from 2000 to 2011. The authors studied 22 banks in which four (4) banks were drawn using stratified sampling technique. They concluded from their study that consolidation had significant positive impact on the performance of banks in Nigeria. Abdul-Qadir (2013) the impact of board composition on the performance of banks considered healthy by the central bank of Nigeria in the post-consolidation era. The study used a sample of 12 banks covering the period 2006-2010. The multiple regressions (ANOVA), was employed. The absence of a significant relationship and impact that was not attributable to the mechanisms of corporate governance. Agbada and Osuji (2013) studied the efficacy of liquidity management and banking performance in Nigeria aftermath of several banking reforms, rescue mission by the Central bank of Nigeria (CBN) and the attendant Merger and Acquisitions. Data obtained were first presented in tables of percentages and pie charts and were empirically analyzed by Pearson product-moment correlation coefficient (r). Findings from the empirical analysis indicated that there was significant relationship between efficient liquidity management and banking performance. However, Ibe (2013) investigated the impact of liquidity management on the profitability of banks in Nigeria. Three banks were randomly selected to represent the entire banking industry in Nigeria. The proxies for liquidity management include cash and short term fund, bank balances and treasury bills and certificates, while profit after tax was the proxy for profitability. Elliot Rothenberg Stock (ERS) stationary test model was used to test the run association of the variables under study while regression analysis was used to test the hypothesis. The result of this study showed that liquidity management was indeed a crucial problem in the Nigerian banking industry. Nwankwo (2013) analyzed the impact of pre and post bank consolidation on the growth of Nigerian economy using T-test. It was found that that post bank consolidation had significant positive effect on the growth of Nigeria economy; pre bank consolidation had positive and insignificant effect on economic growth. The author concluded from the results of the study that merger and acquisition growth strategy results in superior economic growth and that pre bank consolidation is not significant to economic growth. Obamuyi (2013) examined the extent to which banks in Nigeria had performed their intermediation functions of deposit mobilization and granting of loans and advances and the effects on their performance, from 2006 to 2011. The author used seven selected banks out of the 24 existing banks as sample. Descriptive statistics of trend analysis, percentage growth and averages were adopted. The results of the study revealed that banks with high deposits and loans performed better in terms of profitability than banks with low deposits and loans. Ogboi and Unuafe (2013) examined the impact of credit risk management and capital adequacy on banks financial performance in Nigeria Using a time series and cross sectional data from 2004-2009. Panel data model was adopted. Their findings revealed that sound credit risk management and capital adequacy impacted positively on bank’s financial performance with the exception of loans and advances which was found to have a negative impact on banks’ profitability. Okpala (2013) also investigated the association between consolidation and business valuation and their effect on capital and liquidity level of banks in Nigeria. A total of twenty-two (22) banks with randomly selected staff strength of 220 were selected for his study.

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016 Person product moment correlation “r” was employed. The results of his study revealed that consolidation exercise increased bank capital without liquidity due business valuation methods used. He pointed out that liquidity played no significant role in the performance of banks in Nigeria. Osuka & Richard (2013) also examined the major determinants of the financial performance of Deposit Money Banks in Nigeria. The study used regression and established that; asset quality, capital adequacy and employee motivation apart from profits are key success factors in the financial performance of Banks. Umaru (2013) used a panel data consisting of 11-year time series covering six stratified randomly selected consolidated banks in Nigeria. The study adopted Chow test procedure. It was revealed that policy had ‘real’ and significant impact on bank intermediation, portfolio management and performance. Aduda and Kingoo (2012) investigated the relationship between e-banking and performance of Kenya banking system. The study used both descriptive and inferential statistics in analyzing the data. In general the study revealed that e-banking had strong and significance marginal effects on returns on asset in the Kenyan banking industry. The author concluded from the results of the study that there existed positive relationship between e-banking and bank performance. Also, Adesina (2012) carried out a study on the performance of the Nigerian banks in the post – 2005 consolidation: through the CAMEL rating system for the period of 2006-2010, using fifteen banks. The banks were ranked based on their performance. By his findings, WEMA Bank Plc was not so successful in financial performance by consistently ranking among the last 7 performing banks based on all the Group Ranking on the CAMEL parameters for the study period (2006 -2010). The study revealed that GTB Plc was on the top on the basis of overall performance, followed by diamond bank. Alabede (2012) investigated the determinants of Nigerian banks’ performance from 1999 to 2010, using multiple regressions. The study provided evidence indicating that in the presence of the effect of global financial condition, only assets quality and market concentrations were significant determinants of the Nigerian banks’ performance. Barros & Caporale (2012) examines the Nigerian banking consolidation process using a dynamic panel for the period 2000-2010. The Arellano and Bond (1991) dynamic GMM approach is adopted for the study. The authors found that the Nigerian banking sector has benefited from the consolidation process, and specifically that foreign ownership, mergers and acquisitions and bank size decrease costs. Okafor (2012) evaluated the performance of Nigerian banks before and after consolidation. The period 2004-2005 was designated the pre consolidation era, while 2006 – 2009 was deemed the post consolidation period. The findings of the study indicated that consolidation has improve the performance of Nigerian banking sector in terms of asset size, deposit base and capital adequacy. Omowunmi (2012) examined the effects of bank deregulation on bank performance in Nigeria. The study employed the Ordinary Least Square (OLS) technique. The author found that the deregulation of the banking sector had positive and significant effect on bank performance. Mogboyin, et al (2011) investigated the response of flow of credit from the banking sector to reforms and consolidation programmes in the banking sector of Nigeria. The study used cross-sectional data from 89 pre-consolidation banks and 25 post-consolidation banks in Nigeria. The Engle Granger and error corrections approaches were used. The results of the study revealed that consolidation induced changes in the banking structure in terms of size and capitalization positively influence bank lending performance in the Nigerian banking industry. Similarly, Oladejo & Oladipupo (2011) conducted a study to explore various implications of capital regulation on the performance of the Nigerian banks. The study adopted an exploratory methodology and submitted that though reforms of banks becomes necessary, there was a limit to which banks should be regulated on the issue of capital adequacy. The study argued that consolidation arising from the recapitalization of banks brought about lots of problems that may mar the aim of the reform if not properly approached.. On the other hand, Joshua (2010) carried out a comparative analysis of the impact of mergers and acquisitions on financial efficiency of banks in Nigeria.

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Bank Consolidation and Performance of the Nigerian Economy Three banks were selected for the study. The author found that the post-mergers and acquisitions’ period was more financially efficient than the pre-mergers and acquisitions period. On the other hand a number of foreign studies existed in the area of study. These studies include the study of Azizi and Sarkani (2014) studied the financial performance of Mellat Bank using CAMEL model. Also the relationship between model variables and the financial performance of the bank was studied and examined using two linear and multiple regressions as well as OLS method. Results of the study indicated that there was a positive significant relationship between the indices of liquidity, quality of management and earnings with financial performance. However, no relationship was seen between capital adequacy and assets quality with bank financial performance. Ally (2013) analyzed the financial performance of commercial banks in Tanzania from 2006 to 2012. Financial ratios were employed to measure the profitability and liquidity of banks; in addition Analysis of Variance (ANOVA) was used to test the significance differences of profitability means among peer banks groups. The study found that overall bank financial performance increased considerably in the first two years of the analysis. The results also showed that there was no significant means difference of profitability among peer banks groups in terms of return on asset. Basu, et al (2004) studied the effects of bank consolidation on bank performance for more than 100 banks from Argentina. The period of study was between 1995-2000. From their study, the authors found a positive and significant effect of bank consolidation on bank performance. Nonetheless all the studies that considered bank consolidation and bank performance are bank specific. They did not considered the entire banking sector of the economy. On the other hand, studies that focused on bank consolidation and the performance of the economy (or economic growth) have either used a non-econometrics technique (descriptive techniques) or have failed to consider the performance of the economy before and after the bank consolidation policy to establish if the performance of the economy is different from the pre-consolidation era. Thus this study departs from previous studies, and adds value to the existing literature for it will compare the performance of the Nigerian economy before and after the consolidation policy. This therefore will establish if the post-consolidation performance of the economy is different from the pre-consolidation era. Theoretical Framework The Non-Performing Loan Hypothesis Concerning the “non-performing loan hypothesis”, Bebeji (2013) writes; “when banks are in poor condition ridden by high level of non-performing loans, the willingness for the banks to expand loans is decreased, which implies that loan growth will not be consistent with expansion of deposits. This situation aptly describes the scenario of Nigerian banks prior to consolidation. However, with consolidation now in place, financial intermediation is an important activity in the economy, because it allows funds to be channeled from people who might otherwise not put them to productive use to people who will. In this way financial intermediation help promote a more efficient and dynamic economy using the financial intermediaries mechanisms”. The Information Asymmetries Argument “Information asymmetry” is mainly concerned about bank/borrower and the bank/lender relation. This theme of theory believes that the borrower is probably to have more and better information than the lender about the risks of a project for which they are given funds. This generates to the problems of moral hazard and adverse selection. Thus, reduce the efficiency of the transfer of funds from surplus to deficit units (Gwilym, 2011). Borrowers are closely monitored by banks. ‘Monitoring of a borrower by a bank’ means information collection before and after a loan is granted, in addition to screening of loan applications, study the borrower’s ongoing creditworthiness and making sure that the borrower coherent to the terms of the contract (Gwilym, 2011). When using direct financing, borrowers must give a satisfactory amount of information to prospective investors/lenders. The screening conducted by banks (delegated monitoring) proffer a means to improve the information asymmetry problems in the subsequent direct financing (Gwilym, 2011).

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016 “The basic idea behind the theory of delegated monitoring is that not all savers (bank depositors and other creditors) have the time, inclination or expertise to monitor institutional borrowers for default risk. They therefore engage in indirect finance (through using an intermediary) rather than direct finance. Monitoring of borrowers involves increasing returns to scale, which reinforces the view that it is most efficiently performed by specialist intermediaries. There is a resulting problem that the information provided by the monitor may not be reliable, and the monitor must have incentives to perform properly” (Gwilym, 2011). Bank lending primarily can be distinguished into transactions-based lending (financial statement lending, asset based lending, credit scoring, etc.) and relationship lending. In the former class information that is relatively easily available at the time of loan origination is used. While in the latter class, data gathered over the course of the relationship with the borrower is used (Scholtens and Wensveen 2003). The Theory of Bank Runs ‘Bank runs’ could be defined as a sudden series of demands or great withdrawals on a bank. It is a sudden large demand on a bank. long-term financial assets such as loans, through short-term deposits is the source of potential fragility of banks, since they are open to the possibility that a great number of depositors will coincidentally decide to withdraw their funds for reasons other than liquidity needs. The liquidity transformation function of banks makes them vulnerable to runs (that is, the possibility that many depositors simultaneously seek to repurchase their claims out of fear that the bank will default if they wait). Bank runs would not be a problem if they were confined to banks that were already (pre-run) insolvent (Gwilym, 2011). In this way, Gwilym, (2011) adds, “a run can in itself cause a bank to default that would not otherwise have defaulted. If enough other depositors are running; it becomes each depositor’s best strategy to run themselves. Any event that causes depositors to anticipate a run also makes them anticipate insolvency. It thus does in fact cause a run and so the outcome validates the anticipation. The possibility of a run makes intermediation more costly in terms of depositors needing to monitor banks more closely and banks needing to maintain more reserves. Whereas a bank run relates to an individual bank, a panic refers to a simultaneous run on several banks. If runs are contagious, they will lead to a panic.” A bank panic was the case of the banks in Nigeria, before the 2005 bank consolidation exercise, when the banking public lost confidence on banks and bank failure became a common concept even to the primary and secondary school children. MATERIALS AND METHOD The data for all the variables in this study are annual time series data obtained from the Central Bank of Nigeria Statistical Bulletin 2007 and 2013. Model Specification The study adopts analysis of covariance (ANCOVA) as its method of analysis. The analysis of covariance is relevant in studying the performance of economic variables after a perceived policy change to account for structural breaks. It is an alternative to the Chow test. It involves estimating a regression model with at least a dummy explanatory variable and its interaction with the quantitative variables believed to have influence on the dependent variable. The models to capture the various objectives are presented below The model stating the impact of bank consolidation on deposit money bank’s credit to the Nigerian economy is specified as:

퐵퐶퐸 = 푓(퐿푅,퐶푅푅,푀푃푅, Di)...(1) where: BCE = Banks’ credit to the entire economy LR = liquidity ratio CRR = Cash reserve ratio MPR = Monetary policy rate Di = Dummy variable proxy for consolidation policy (D = 0 for periods before the 2005

consolidation and 1 for periods after the consolidation) The econometrics model is stated as:

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Bank Consolidation and Performance of the Nigerian Economy

푙푛퐵퐶퐸 = 휑 + 휑 퐿푅 + 휑 퐶푅푅 +휑 푀푃푅 + 휆 퐷 + 휆 퐿푅 ∗ 퐷 + 휆 퐶푅푅 ∗ 퐷 + 휆 푀푃푅 ∗ 퐷 +푢 ...(2) Where: all variables remain as previously defined 푙푛= natural logarithm operator 휑1, 휑2 & 휑3 = common slope coefficients of LR, CRR and MPR, respectively 휆11, 휆12 & 휆13 = differential slope coefficients of LR, CRR and MPR, respectively u2 = random error term. Banks’ credit to the entire economy (BCE) is logged to bring it to common ratio with LR, CRR, and MPR which are already in rates. The reason for taken the natural logarithm is that logged series effectively linearize exponential trends, because the log function is the inverse of an exponential function. The model to investigate the impact of bank consolidation on economic growth in Nigeria is specified in its functional form is as:

푅퐺퐷푃 = 푓(퐿푅, 퐼,푀푃푅, Di)...(3) Where:

RGDP = Real GDP Proxy for economic growth LR = liquidity ratio I = investment MPR = Monetary policy rate Di = Dummy variable proxy for consolidation policy (D = 0 for periods

before the 2005 consolidation and 1 for periods after the consolidation) The econometrics model is stated as:

푙푛푅퐺퐷푃 = 휃 + 휃 퐿푅 + 휃 퐼푛퐼 + 휃 푀푃푅 + 휆 퐷 + 휆 퐿푅 ∗ 퐷 + 휆 퐼 ∗ 퐷 + 휆 푀푃푅 ∗ 퐷 +푢 ...(4)

Where: all the variables remain as previously defined 푙푛= natural logarithm operator 휃 , 휃 & 휃 = common slope coefficients of LR, I & MPR, respectively 휆21, 휆22 & 휆23 = differential slope coefficients of LR, I & MPR, respectively. u2 = random error term Banks’ credit to the entire economy (BCE) and investment (I) are logged to bring them to common ratio with LR and MPR which are already in rates. The reason for taken the natural logarithm is that logged series effectively linearize exponential trends, because the log function is the inverse of an exponential functions. The common slope coefficients measure the impact of the explanatory variables, on the dependent variable over time. On the other hand, the differential slope coefficients measure the amount by which the impact of the corresponding explanatory variables on the dependent variables has changed as a result of the 2005 bank consolidation. In other words, the periods of 2005 bank consolidation differ from those before 2005 bank consolidation by 휆i. If 휆i ˃ 0, then the period after consolidation has a larger coefficient than the period before the policy; and the impact of the consolidation policy is positive. Whereas if 휆i ˂ 0 the reverse is the case and if 휆i = 0, then there is no difference between the coefficients for both periods and the policy has no (zero) impact on the dependent variable(s). Data Presentation and Analysis of Results This is a time series study. Thus time series variables are used. The variables selected for the study are banks credit to the economy, real GDP proxy for economic growth, liquidity ratio, cash reserve ratio, monetary policy rate and gross fixed capital formation proxy for investment. However, it is a common practice to plot the log of time series to get a glimpse of the growth rate and trend of the series in a study like this and usually the first step in the analysis of time series. Thus we plot the series, and are presented below:

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016

Figure 5.1: Trend of the Logarithm of Real GDP

Figure 5.2: Trend of the Logarithm of Banks Credit to the Economy

56

78

910

Loga

rithm

of R

eal G

DP

1970 1980 1990 2000 2010Time

68

1012

14Lo

garit

hm o

f ban

ks' c

redi

t to

the

Eco

nom

y

1970 1980 1990 2000 2010Time

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Bank Consolidation and Performance of the Nigerian Economy

Figure 5.3: Trend of Liquidity Ratio

Figure 5.4: Trend of Monetary Policy Rate

2040

6080

100

Liqu

idity

Rat

io

1970 1980 1990 2000 2010Time

510

1520

25M

onet

ary

polic

y R

ate

1970 1980 1990 2000 2010Time

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016

Figure 5.5: Trend of Cash Reserve Ratio

Figure 5.6: Trend of the Logarithm Gross Capital Formation

010

2030

Cas

h R

eser

ve R

atio

1970 1980 1990 2000 2010Time

24

68

10Lo

garit

hm o

f Gro

ss C

apita

l For

mat

ion

1970 1980 1990 2000 2010Time

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Bank Consolidation and Performance of the Nigerian Economy

Figure 5.7: Trend of liquidity Ratio, Cash Reserve Ratio, Monetary Policy Rate; and the Logarithms of Real GDP, and Banks Credit to the Economy

The first impression from the figures is that all the variables have fluctuating trending. They also portray a similar trend movement. Figure 5.7 reveals that the logarithms of real GDP and banks credit to the economy, and monetary policy rate have very similar movement indicating the possibility of monetary policy rate to cause the other two variables to move the same direction with it; while cash reserve ratio and liquidity ratio show similar fluctuating trend which on the other hand means that the policies concerning banks liquidity and cash reserve have been similar during the period of the study. The fluctuations in general could be due to policy changes over the years especially during the period of the study. For instance, CBN monetary policy rate changes almost on yearly basis even before and after the consolidation policy. It has been fluctuating, with the highest monetary policy rate recorded at 26 percent in 1993 and the lowest at 4 percent in 1977. Similar almost yearly bases policy changes of the liquidity ratio and cash reserve ratio have occurred which causes the respective trends to appear fluctuating. Policy change in respect to the monetary policy rate, liquidity ratio and cash reserve ratio have caused the up and down movement in real GDP and banks’ credit to the economy as shown in the figures. The implication of this is that except the data is made stationary and the trend is removed, the variables of similar trend will show a similar relationship in estimation and could be misleading. We further examine the summary statistics of all the variables and the results are presented in tables 5.1a and 5.1b.

Table 5.1a: mean, Standard Deviation Maximum Values and Minimum Values of the Variable Variables Mean Standard

Deviation

Minimum

value

Maximum

value

LRGDP 6.906503 1.659319 5.426271 10.35923

LBCE 10.22549 2.418632 5.862779 14.79038

MPR 10.82818 5.218501 3.5 26

CRR 7.829545 6.060531 1 32

LR 49.95455 12.80092 29.1 94.5

LGFCF 5.113838 2.361304 1.84055 9.298534

Source: Author’s Computation

020

4060

8010

0

1970 1980 1990 2000 2010Time

Logarithm of Real GDP Logarithm of banks' credit to the EconomyMonetary policy Rate Cash Reserve RatioLiquidity Ratio

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016

Table 5.1b: Skewness and Kurtosis

Variables Pr(Skewness) Pr(Kurtosis) Joint adjusted

chi2(2)

joint p-value

LRGDP 0.0018 0.7730 8.46 0.0145

LBCE 0.5149 0.1334 2.85 0.2405

MPR 0.1620 0.7618 2.17 0.3376

CRR 0.0000 0.0002 24.07 0.0000

LR 0.0076 0.0300 9.85 0.0073

LGFCF 0.0136 0.1921 7.00 0.0303

Source: Author’s Computation

The liquidity ratio moves faster compared to the other variables, as indicated by the high liquidity ratio mean value in table 5.1a. This is followed by bank credit to the economy and monetary policy rate. Cash reserve ratio reduces to a minimum value of 1 while its maximum value is 32. In table 5.1b on the other hand, the probability values for Log of gross capital formation, liquidity ratio, cash reserve ratio, and real GDP are significant at 5 percent while the log of banks’ credit to the economy and monetary policy rate are not significant at 5 percent. Thus we accept the hypothesis of normality for banks’ credit to the economy and monetary policy rate and conclude that they are normally distributed while the hypothesis is rejected for the case of Log of gross capital formation, liquidity ratio, cash reserve ratio, and real GDP, therefore these variables are not normally distributed. In addition, all the variables are positively skewed except cash reserve ratio which neither positively nor negatively skewed. Similarly, real GDP, cash reserve ratio, liquidity ratio and gross fixed capital formation do not have fat tails but banks’ credit to the economy and monetary policy rate have fat tails as indicated by the respective probability values. In the next section, all the variables are tested for stationarity.

Data Analysis Test for multicollinearity The variables in the specified two models were subjected to multicolinearity test. This test is necessary in order to avoid the consequences of multicolinearity and to obtain unique estimates of the regression parameters. The test results are presented as shown below:

Table 5.2: Variance inflation factors (VIFs) for all the variables in eq (2) Variables VIF 1/VIF

MPR

CRR

LR

1.18 0.848245

1.13 0.881630

1.17 0.856793

Mean VIF 1.16

Source Author’s Computation

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Bank Consolidation and Performance of the Nigerian Economy Table 5.3: Variance inflation factors (VIFs) for all the variables in eq (4)

Variables VIF 1/VIF

MPR

GFCF

LR

2.67 0.374443

2.88 0.346810

1.21 0.825142

Mean VIF 2.26

Source Author’s Computation The variables included in the two regression equations are all free from the problem of multicolinaerity. As shown in tables 5.2 and 5.3, the Variance Inflation Factors (VIFs) of all variable in the two regression equations are very low. They are all below the conventional 10. This indicates that the inclusion of Monetary Policy Rate (MPR), Cash Reserve Ratio (CRR), and Liquidity ratio in our regression equation one will not affect the uniqueness of the regression parameter estimates. Thus the above variables shall be included as explanatory variables in our regression equation to explain the dependent variable. Similarly, MPR, LR, and Gross Fixed Capital Formation (GFCF) shall be included in the regression equation two to explain the dependent variable. Augmented Dickey Fuller Unit Root Test Again, to avoid the problem of spurious results that usually occurs due to non-stationary data, we conduct test for stationarity.

Table 5.4: Results of Unit Root Test using Augmented Dickey Fuller Test Procedure Variable At level First Difference

No

Constant

Constant Constant

& trend

No

constant

Constant Constant

& trend

Lag

length

at level

Lag

length

at 1st

diff.

BCE 0.109 -1.946 -1.341 -6.917*** -6.878*** -7.085*** 1 0

RGDP -0.688 -1.726 -1.706 -6.246*** -6.184*** -6.150*** 1 0

MPR -0.308 -1.821 -1.750 -3.967*** -3.966*** -4.042** 1 2

CRR -1.082 -2.619* -2.914 -3.842*** -3.795*** -3.731** 3 2

LR -1.136 -3.590** -3.628** -6.771*** -6.715*** -6.671*** 1 0

GFCF -1.437 -1.767 -1.172 -6.290*** -6.304*** -6.483*** 1 0

Where: ***, **, and * denotes significance at 1%, 5% and 10% respectively and the rejection of the null hypothesis of

presence of unit root. The optimal lag lengths were chosen according to Akaike’s FPE tests.

Source: Authors Computation The Augmented Dickey Fuller test results revealed that the variables are all stationary at first difference at 5% level except Liquidity Ratio (LR) which is stationary at levels. This result also indicates the possibility of cointegration of the variables. Therefore in the next section, a test for cointegration is carried out.

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016 Johansen Cointegration Test The study conduct a cointegration test to establish if a long run relationship exists among the variables of interest. To achieve this, we use the trace statistics to compare with the 5% critical value. The results are as follows:

Table 5.5a: Results of Johansen test for cointegration between BCE, MPR, CRR and LR Maximum

Rank

Eigenvalue Trace Statistics 5% critical value

0 . 46.2587 39.89

1 0.45833 20.5084* 24.31

2 0.33176 3.5776 12.53

3 0.08162 0.0017 3.84

4 0.00004 - -

Source: author’s computation Table 5.5b: Results of Johansen test for cointegration between RGDP, MPR, LR and GFCF

Maximum

Rank

Eigenvalue Trace Statistics 5% critical value

0 . 57.5088 54.64

1 0.38927 36.7985 34.55

2 0.30768 11.3550* 18.17

3 0.19227 2.3870 3.74

4 0.05525 - -

Source: author’s computation The tests show the existence of cointegration between the dependent variable and the independent variables in all the two regression equations. The first equation shows the existence of one cointegrating equation as shown in table 5.5a, while table 5.5b revealed the existence of two cointegrating equations. This result therefore led to the estimation of error correction models for the two regression equation. The error correction results are presented in the next section. Error Correction Results The existence of cointegration is an indication that long run relationship exists among the variables. Therefore we run an error correction model for the two equations. The results are given in equations (5) and (6). BCE = -163452.8 + 15862.0MPR -12363.2CRR + 3385.7LR -.3690964ECM . . (5) (-0.5) (0.80) (-0.86) (0.59) (-3.15) and RGDP = 0.039 + 0.013MPR -0.00072LR + 0.823GFCF - 0.287ECM . . (6) (0.60) (0.64) (-0.10) (9.97) (-2.65) Note that figures in parenthesis are the respective t-statistics. The error correction results in equation (5) indicates that banks credit to the entire economy, monetary policy rate, cash reserve ratio and liquidity ratio adjust to equilibrium in the long run. 36.91% of the error is being corrected every year. On the other hand, real GDP, monetary policy rate, cash reserve ratio and investment converge to equilibrium in the long run, with adjustment speed of 28.68% annually. The error correction coefficients for the two models appeared significant.

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Bank Consolidation and Performance of the Nigerian Economy

DISCUSSION OF RESULTS To examine the impact of bank consolidation policy on banks credit to the economy, we regress BCE on Monetary Policy Rate (MPR), Cash Reserve Ratio and Liquidity Ratio. The regression results are presented in table 5.6. For the period before the consolidation policy, the result revealed that MPR and CRR have a positive relationship with BCE, while LR has negative relationship.

Table 5.6: Estimation results for objective one

Dependent

Variables

coefficients Robust

Standard Errors

t-statistics P-value

MPR 0.3369253 0.0804503 4.19 0.000

CRR 0.0748218 0.0414757 1.80 0.080

LR -0.0254681 0.0153581 -1.66 0.106

Di -4.982586 2.85018 -1.75 0.089

DiMPR 1.363026 0.4834727 2.82 0.008

DiCRR -0.4893345 0.1685458 -2.90 0.006

DiLR -0.1230929 0.0806802 -1.53 0.136

Constant 7.184974 1.38978 5.17 0.000

R2 0.6594

Adjusted R-squared 0.5913

F-statistics F( 7, 35) 9.68

Durbin-Watson statistics d-statistic ( 8, 43) 0.8363288

Breusch-Godfrey LM Chi-square Statistics 17.206 (0.0000)

Ramsey RESET F-stat (3, 32) 1.51 (0.2630)

LM heteroskedasticity Chi-square Statistics 0.324 (0.5694)

Source: Author’s computation 퐵퐶퐸 = 7.185 + 0.3367푀푃푅 + 0.0745퐶푅푅 − 0.025퐿푅 − 4.9823퐷푖 + 1.363퐷푖푀푃푅− 0.489퐷푖퐶푅푅 −0.123퐷푖퐿푅 A percentage change in MPR and CRR increased BCE by 0.3369 and 0.0748 percent respectively. On the other hand, a percentage change in LR reduces the BCE by 0.0255 percent. During the period of the bank consolidation policy proper, BCE was affected negatively. BCE reduced by over 100 percent. After the policy however, the sign for CRR changed. A percentage change in CRR reduces BCE by 0.4893%. This result came out in accordance to expectation. Similar signs with the period before bank consolidation were observed after the policy for the variables MPR and LR. But the impact on BCE was lager after the policy. It revealed that after the policy, a percentage change in MPR increases BCE by over 100%, while a percentage change in LR reduces BCE by 0.123%. The reduction in BCE caused a percentage increase in LR after the policy is 0.098% higher. This change is characterized by the policy. This showed that the liquidity ratio and the cash reserve ratio after the policy has not benefited the economy in terms of credit availability. Likewise the cash reserve ratio. Though LR is not statistically significant before and after the policy, but the CRR is statistically significant after the consolidation and insignificant before the policy. The MPR on the other hand is significant both before and after the consolidation.

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016 The differential intercepts and slopes were together statistically significant as the F-statistics with 7 and 35 degree of freedom of 9.68 is greater than the 2.84 critical value at 5%. The study therefore rejects the hypothesis that banks’ consolidation has no significant impact on the performance of the Nigerian economy and concludes that the 2005 bank consolidation policy has a significant impact on banks credit to the entire economy of Nigeria. That is, in all the variables we reject the test hypotheses except liquidity ratio which has no differential impact on the performance of the economy before and after the policy. The findings in this study are similar to the findings of Joshua (2010) in Nigeria who found that banks’ consolidation has significant impact on banks’ efficiency and the capital market performance. The Durbin-Watson statistics of 0.8363288 indicates a zone of indecision. Thus the presence or absence of autocorrelation could not be ascertained. That notwithstanding, the Breusch-Godfrey LM Chi-square Statistics of 17.206 (0.0000) showed strong evidence of serial correlation. The results were however robust, since the standard errors were corrected for arbitrary level of serial correlation. The Ramsey RESET test statistics of 1.51 (0.2630), which is statistically insignificant implies the rejection of the hypothesis that the model is incorrectly specified. Thus the model in this study is correctly specified. Finally, the LM heteroskedasticity Chi-square Statistics of 0.324 (0.5694) means that the model is free from heteroskedasticity. The impact of the bank consolidation on real GDP was examined. The results are presented in table 4.6. The results revealed that before the consolidation policy, Monetary Policy Rate (MPR), Liquidity Ratio (LR) and Investment (gross fixed capital formation, GFCF) positively impacted on economic growth in Nigeria. After the consolidation however, LR turned out to impact economic growth negatively.

Table 5.7: Estimation results for objective two

Dependent

Variables

coefficients Robust

Standard Errors

t-statistics P-value

MPR 0.0133662 0.0128279 1.04 0.305

GFCF 0.7928894 0.2582914 3.07 0.004

LR 0.013798 0.0070174 1.97 0.057

Di 0.7732662 0.7515631 1.03 0.311

DiMPR 0.0540029 0.0411841 1.31 0.199

DiGFCF 0.0005754 0.0025342 -3.23 0.822

DiLR -0.0285287 0.0146544 -2.95 0.060

Constant -0.6327841 .3283695 -1.93 0.062

R2 0.7170

Adjusted R-squared 0.6587

F-statistics F( 7, 35) 12.31

Durbin-Watson statistics d-statistic ( 8, 43) 1.937419

Breusch-Godfrey LM Chi-square Statistics 0.038 (0.8445)

Ramsey RESET F-stat (3, 32) 0.56 (0.3160)

LM heteroskedasticity Chi-square Statistics 0.024 (0.8770)

Source: Author’s Computation

30

Bank Consolidation and Performance of the Nigerian Economy

푅퐺퐷푃 = −0.6327841 + 0.0133662MPR + 0.7928894GFCF + 0.013798LR + 0.7732662Di+ 0.0540029DiMPR + 0.0005754DiGFCF− 0.0285287DiLR

For the period before the consolidation, a percentage change in MPR, GFCF, and LR leads to an increase in real economic growth by 0.0134%,0. 7929% and 0.0138% respectively. Even at the period of the consolidation proper, the economic growth rate witnessed a 0.7732% increase resulting from the policy. During the period after the policy, it is revealed that a percentage change in MPR and GFCF boost economic growth by 0.0540 and 0.0005 percent. There is an improvement in economic growth percentage by 0.0406 percent due to percentage change in MPR after the consolidation. But the case of GFCF is different as economic growth reduces by 0.7929percent compared to the period before the consolidation policy. On the other hand, 0.0285 percent reduction was witnessed for every percentage point change in gross capital formation. This however negates our expectation. Again, the differential intercepts and slopes are jointly statistically significant as the F-statistics with 7 and 35 degrees of freedom of 12.31 is greater than the critical value of 2.18. Based on this, the study generally rejects the hypothesis and concludes that the bank consolidation policy has a significant impact on economic growth in Nigeria. Specifically however, the hypotheses that gross capital formation and liquidity ratio has no significant impact on economic growth after the consolidation policy is rejected while the hypotheses that monetary policy rate has no significant impact on economic growth after the consolidation is accepted. This finding is consistent with the findings of Nwankwo (2013) in Nigeria who analyzed the impact of pre and post bank consolidation on the growth of the Nigerian economy and found that post bank consolidation had a significant effect on the growth of the Nigerian economy; an insignificant effect was found for the period before the consolidation policy. Also Yakubu (2015) in Nigeria had similar findings with the findings of this study. They found that banking sector consolidation causes economic growth. Durbin-Watson statistics d-statistic of 1.937419 showed no autocorrelation, likewise the Breusch-Godfrey LM Chi-square Statistics of 0.038 (0.8445) showed an evidence that there is no serial correlation. The Ramsey RESET F-statistics showed a value of 0.56 (0.3160). Based on this insignificant value, we accept the null hypothesis and conclude that the model for this study is correctly specified. Finally, an insignificant LM heteroskedasticity Chi-square Statistics of 0.024 (0.8770) is an indication of the acceptance of the null hypothesis. Thus there is no problem of heteroskedasticity. That is, the variables of the study have constant (equal) variance. SUMMARY OF FINDINGS From the results obtained, the study came up with the following findings: i. Bank consolidation in Nigeria has a significant impact on banks’ credit to the entire economy. ii. The consolidation also has a significant impact on economic growth in Nigeria. iii. There is a long run relationship between banks’ credit to the entire economy and monetray policy,

cash reserve ratio, and liquidity ratio. The variables converge to equilibrium with a speed of 36.9 percent annually.

iv. Also there is a long run relationship between economic growth and monetary policy rate, liquidity ratio, and investment in Nigeria. 28.68 percent of the error is corrected every year to adjust to equilibrium.

v. The monetary policy rate, liquidity ratio, and cash reserve ratio explains 65.94% of total variation in banks credit to the entire economy, while monetary policy rate, liquidity ratio, and investment on the other hand accounts for 71.70 percent of total variation in economic growth in Nigeria.

CONCLUSIONS From the findings, the study concluded that bank consolidation has significant impact on banks’ credit to the entire economy and economic growth in Nigeria. It is also concluded that there is a long run relationship between banks’ credit to the entire economy and monetray policy, cash reserve ratio, and liquidity ratio in Nigeria. Finally, there is a long run relationship between economic growth and monetary policy rate, liquidity ratio, and investment in Nigeria.

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International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016 RECOMMENDATIONS i. Another round of consolidation policy should be carried out in order for banks and the entire

banking sector to come out stronger. The sector compared to some other country’s banking sectors such as Yugoslavia and South Africa is still weak.

ii. The use of monetary policy rate and cash reserve ratio as policy tools to influence the level of banks’ credit to the economy and the economic growth rate is highly recommended. This is because the study revealed that they are significant determinants of credit to the economy and GDP growth in the post consolidation era.

iii. Finally, the credibility of the banking sector authority is also relevant. Thus the study recommends the use highly qualified personnel for the formulation of banking sector policies.

REFERENCES Adesina, K.S. (2012). A comparative performance of the Nigerian banking sector in the post-2005

consolidation: Through the CAMEL rating system. International journal of business and social sciences, 3(13), 122-136.

Aduda, J. & Kingoo, N. (2012). The Relationship between Electronic Banking and Financial Performance

among Commercial Banks in Kenya. Journal of Finance and Investment Analysis, 1(3), 99-118. Agbada, A. O. & Osuji, C. C. (2013). The efficacy of liquidity management and banking performance in

Nigeria. International Review of Management and Business Research, 2(1), 223-233. Alabede, J. O. (2012). The intervening effect of global financial condition on the determinants of bank

performance: evidence from Nigeria. Accounting and Finance Research, 1(2), 9-14. Ally, Z. (2013). Comparative Analysis of Financial Performance of Commercial Banks in Tanzania.

Research Journal of Finance and Accounting, 4(19), 225-394. Aruomoaghe, J. A. & Olugbenga, E. (2014). Financing capital investments in Nigeria: the role of the

banking industry. Asian Economic and Financial Review, 4(8), 1123-1130.. Azizi, M. & Sarkani, Y. A. (2014). Review Financial Performance of Mellat Bank According to Camel

Model Spectrum. Journal of Multidisciplinary Research, 3(1), 4-12. Baghebo, M. & Stephen, E. T. (2014). Monetary policy and economic growth in Nigeria. Asian Economic

and Financial Review, 4(1), 20-32. Barros, C. P. & Caporale, G. M. (2012). Banking consolidation in Nigeria. Mais Working Papers No. 99.

Available at: http://pascal.iseg.utl.pt/~cesa/index.php/menupublicacoes/working-papers Basu, R., Druck, P., Marston, D., & Susmel, R. (2004). Bank Consolidation and Performance: The

Argentine Experience. IMF Working Paper No. 04/149. Bebeji, A. (2013). Consolidation and asset quality of banks in Nigeria. International Journal of Business and

Management Invention, 2(2), 12-20. Eferakeya, I. & Alagba, O. S. (2015). Regulatory-induced consolidation through mergers and acquisitions

and its implication on banks performance in Nigeria. International Journal of Business and Industrial Marketing, 1(1), 6-15.

32

Bank Consolidation and Performance of the Nigerian Economy Emori, E., Nkamare, S., & Nneji, I. (2014). The impact of banking consolidation on the economic

development of Nigeria. Research Journal of Finance and Accounting, 5(16), 113-119. Gwilym O.A. (2011). financial intermediation. Undergraduate study in Economics, Management, Finance

and the Social Sciences. The London School of Economics and Political Science. Available at: www.londoninternational.ac.uk [Accessed: 21st July, 2015].

Ibe, S. O. (2013). The Impact of Liquidity Management on the Profitability of Banks in Nigeria. Journal of

Finance and Bank Management, 1(1), 37-48. Joshua, O. (2010). Comparative analysis of the impact of mergers and acquisitions on financial efficiency of

banks in Nigeria. Journal of Accounting and Taxation, 3(1), 1-7. Kwanbo, M. L. & Abdul-Qadir, A. B. (2013). Board Composition, Executive duality and Performance of

Banks in the Post-Consolidation Era in Nigeria. International Journal of Academic Research in Economics and Management Sciences, 2(1), 109-122.

Mogboyin, O., Asaolu, T. O., & Ajilore, O. T. (2011). Bank consolidation programme and lending

performance in Nigerian banking system: An empirical analysis with panel data. International Journal of Applied Economics and Finance, 3, 1-9.

Nwankwo, O. F. (2013). Impact of pre and post bank consolidation on the growth of Nigeria economy.

International Journal of Business and Management, 8(13), 73-82. Obamuyi, T. M. (2013). Analysis of the Deposits and Lending Behaviors of Banks in Nigeria. International

Journal of Engineering and Management Sciences, 4(1), 46-54. Ogboi, C. & Unuafe, O. K. (2013). Impact of Credit Risk Management and Capital Adequacy on the

Financial Performance of Commercial Banks in Nigeria. Journal of Emerging Issues in Economics, Finance and Banking, 2(3), 10-28.

Ohale, L. & Onyema, J. I. (2002). Foundations of macroeconomics. Owerri: Springfield Publishers. Okafor, R.G. (2012). Performance evaluation of Nigerian commercial banks: Before and after consolidation.

IJEMR, 2(2), 2249 – 2585. Okpala, K. E. (2013). Consolidation and business valuation of nigeria banks: what consequence on liquidity

level. International Journal of Business and Social Science, 4(12), 312-320. Oladejo, M. O. & Oladipupo, O. A. (2011). Capital Regulation and the Performance of the Nigerian banks:

Need for Review. Journal of Emerging Trends in Economics and Management Sciences, 2(3), 215-224.

Olayinka, T. T. & Farouk, M. A. (2014). The impact of consolidation on the performance of banks in

Nigeria. Global Advanced Research Journal of Management and Business Studies, 3(10), 479-485. Olowofeso, O. E., Bada, A. S., Adeboye, A. A., Adejo, V. T., Bassey, K. J., & Dzaan, K. S. (2015).

Investigating the dynamics of bank credit in Nigeria: the role of bank consolidation. CBN Journal of Applied Statistics, 6(1a), 133-151.

33

International Journal of Social Science and Sustainable Development, Volume 6, Number 1, 2016 Omowunmi, O. F. (2012). The Effect of Bank Deregulation on Bank Performance in Nigeria. International

Journal of Advances in Management and Economics, 1(5), 31-36 Osuka, B. O. & Richard, O. C. (2013). Determinants of Financial Performance of Quoted Banks in Nigeria:

A Study of Selected Deposit Money Banks International Journal of Education and Research, 1(10), 120-139.

Scholtens, B.and Wensveen D.V. (2003). The theory of financial intermediation: an essay on what it does

(not) explain. SUERF – The European Money and Finance Forum. Umaru, I. G. (2013). Isolating the ‘Pure’ Effect of the 2004/2005 Bank Consolidation Policy on Performance

of Banks in Nigeria, Using the Chow Test. Accounting and Finance Research, 2(4), 47-59. Waheed, A. (2009). Financial sector reforms and economic growth: A time series data analysis for Pakistan.

Japan Institute Of Development Economics Working Paper No.452. Yakubu, Y. (2015). Banking consolidation and economic growth in Nigeria: dynamic chain transmission

evidence. IOSR Journal of Humanities and Social Science, 20(5), 27 ASCN/2016/093/J.O. Noah/ascnjournal@yahoo.com/FRB/25/04/2016