An Econometric Investigation in to Financial Intermediation, Domestic Investment and Economic Growth...

Njimanted Godfrey Forgha, Molem Christopher Sama, Nkwetta Ajong Aquilas

Journal of Finance and Economics

An Econometric Investigation in to Financial Intermediation, Domestic Investment and Economic Growth in Cameroon

Njimanted Godfrey Forgha1, Molem Christopher Sama2, Nkwetta Ajong Aquilas2,

1Head of Division for Tertiary Science and Technologies, University of Bamenda, N.W.R, Cameroon

2Department of Economics and Management, University of Buea, S.W.R, Cameroon

Abstract

The finance-growth causalities have been widely investigated in the literature on finance and growth. Though it is generally accepted that finance affects economic growth and vice versa through capital formation, this channel has not received much attention in recent empirical works. It is on this ground, that this study is designed to examine the interactions between financial intermediation, domestic investment and economic growth in Cameroon. Using time series data from 1975 to 2014 inclusive obtained from World Development Indicators, and using the Vector Autoregression approach, results reveal that financial intermediation doesn’t have a significant effect on domestic investment in Cameroon. We found no causality from financial intermediation to domestic investment in Cameroon except in the case of commercial bank credit to GDP ratio. There was also no causality running from domestic investment to financial intermediation in Cameroon. Findings also revealed the significant effects of domestic investment, broad money, ratio of narrow money to broad money, Financial Intermediation Development index2 and Financial Intermediation Development index3 on Cameroon’s economic growth while the effects of domestic credit to private sector, central government claims and commercial bank credit to GDP ratio are insignificant.

Cite this article:

  • Njimanted Godfrey Forgha, Molem Christopher Sama, Nkwetta Ajong Aquilas. An Econometric Investigation in to Financial Intermediation, Domestic Investment and Economic Growth in Cameroon. Journal of Finance and Economics. Vol. 4, No. 1, 2016, pp 1-9. http://pubs.sciepub.com/jfe/4/1/1
  • Forgha, Njimanted Godfrey, Molem Christopher Sama, and Nkwetta Ajong Aquilas. "An Econometric Investigation in to Financial Intermediation, Domestic Investment and Economic Growth in Cameroon." Journal of Finance and Economics 4.1 (2016): 1-9.
  • Forgha, N. G. , Sama, M. C. , & Aquilas, N. A. (2016). An Econometric Investigation in to Financial Intermediation, Domestic Investment and Economic Growth in Cameroon. Journal of Finance and Economics, 4(1), 1-9.
  • Forgha, Njimanted Godfrey, Molem Christopher Sama, and Nkwetta Ajong Aquilas. "An Econometric Investigation in to Financial Intermediation, Domestic Investment and Economic Growth in Cameroon." Journal of Finance and Economics 4, no. 1 (2016): 1-9.

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1. Introduction

The role of financial intermediaries in stimulating the economic growth of a country cannot be overemphasized. This is explained by the attention this subject has received in the finance-growth literature over the past decades in most advanced countries of the world. The observation of the link between financial intermediation and growth dates back from [1], who argued that financial intermediaries do provide services which are very essential for technological innovation, economic growth and economic development. Some of these services include mobilization of savings, the management of risk, evaluation of projects, monitoring of managers and the facilitation of transactions [2]. Ref. [3, 4], cited in [5] have stressed that a well-developed and functioning financial system enhances economic growth. The theoretical outcomes reached by [6] and [7] are in line with those of [1].

Ref. [8] also emphasized that financial intermediaries and financial markets are at the core of the processes that provide liquidity and mechanisms that allow firms and households make to payments. Financial institutions also facilitate allocation of resources to their best uses through mobilization of financial resources and their reallocation from the surplus to the scarce sectors of an economy. This increase in capital accumulation and efficient reallocation of economic resources, guaranteed by an efficient and a well-functioning financial system has long-lasting effects on growth, given the spillovers from the productivity of other factors. Ref. [9, 10, 11, 12, 13], in [14] also reiterated that financial intermediaries facilitate the flow of finance from the surplus to the deficit sectors of the economy so as to attract investments that create economic growth.

Many studies have investigated the effects of financial intermediary development on economic growth; with most of the findings pointing to the positive effect of financial intermediation on economic growth. While most empirical works have focused on providing cross-country evidence, using cross-sectional and panel data, a few others have concentrated on country-specific evidence. Amongst those that have employed cross-sectional and panel data, are the studies carried out by [15] for European Union member states, [16] for selected middle-income and high-income countries, [17] for Taiwan, Korea and Japan, [18, 19] and [5] for a group of selected countries in Africa and [20] for Southern African countries. Some country-specific studies include the works of [21] and [2] in China, [22] in South Africa, [23, 24, 25, 26, 27] and [14] all in Nigeria, [28] and [29] in Ghana, [30] in Botswana, [31] and [32] in Cameroon.

Though finance has been identified as one of the drivers of economic growth, the debate on whether finance causes growth or growth causes financial development or they cause each other is still on-going in the finance-growth literature. There is no clear-cut conclusion on the nature of the relationship between finance and growth. Ref. [33], using GMM find a negative relationship between banking intermediation and economic growth with evidence from MENA countries. It was also discovered by [34] for some developed and developing that after a certain threshold, financial development only harms growth. From a study carried out in Nigeria, [35] concluded that bank credit has a negative effect on economic growth. Similar conclusions on the impact of financial development on the economic growth of a country were reached by [36].

Another issue in the finance-growth literature that has generated heated debate is that of selecting an appropriate indicator for financial intermediation development. There is no generally accepted indicator for measuring financial development; implication being that different transmission channels from financial development to economic growth would depend on the indicator of financial development that is used. Consequently, different measures of financial development adopted are most likely therefore to produce different results. While many studies have adopted a very limited number of indicators, a few have tested the growth effects of several indicators. Disagreements in the finance-growth relationship also exist from the point of view of the financial policy appropriate to permit the financial sector spur the expected growth. The bone of contention here is between financial sector liberalization (classical view) and government intervention in the financial sector (Keynesian and neo-Keynesian perspectives).

Though, these disagreements have been addressed in different ways using different estimation techniques like correlation analyses, the Ordinary Least Squares (OLS), Generalized Method of Moments (GMM), Autoregressive Distributed Lag Models (ARDL), Granger Causality, Cointegration and Error Correction Modelling techniques, the issue remains far from being settled. This has triggered continued enquiry on the topic. The current study uses the Vector Autoregressive (VAR) approach because the tools employed by this technique are helpful in understanding the interrelationships existing among economic variables and formulating a more structured economic model. This technique therefore enables us to understand the dynamic relationship between financial intermediation, domestic investment and economic growth.

Despite the many studies which have investigated the finance-growth relationship, evidence seems to exist only for countries of the developed, with few studies actually addressing the African peculiarity. Even in Africa, a very limited number of studies have focused on Cameroon. Cameroon is said to be highly under-represented in cross-country studies and country-specific studies. Moreover, in panel studies, where the data for many countries is pooled, some country-specific features which would have enabled a better understanding of the issue under consideration are lost. Countries do differ in terms of their level of political freedoms, rule of law and property rights protection. This and other country-specific characteristics cause significant differences on the role played by financial markets and institutions on economic growth from country to country. All of these have created a knowledge gap yet to be filled, as the nature of the relationship between finance and growth in Africa may still be misunderstood. While it is obvious that financial intermediation affects economic growth through capital formation, this channel has not been widely considered in the finance-growth literature. Thus, the attempt in this study to empirically establish the channel through which finance affects growth is value that has been added to the finance-growth literature. This study is expected to close this gap in the literature. Another significant contribution of this study is the attempt to use many indicators as well as creation of indexes of financial intermediation indexes for use in a single study (model) so as to test the relative strength of each indicator. Very few studies have established this. Many studies on finance and growth have focused on the financial system as a whole, very few focusing on financial intermediaries. This study is an important contribution in the literature, especially in Cameroon whose financial sector is about 85% dominated by financial institutions.

Since independence in 1960, the economy of Cameroon experienced significant growth from 1960 to 1977, driven by agriculture. Agriculture employed about 80% of the labour force, provided 85% of exports, contributing about 34% to Gross Domestic Product (GDP). The annual real GDP growth averaged 4.8% within the period. From 1960, the banking sector was at its infancy until the banking sector reforms of 1973. The banking sector grew from the start of 1978, thanks to banking reforms of 1973 up till 1985 when it witnessed an accumulation of unpaid debts. More banks accumulated so many losses due to fraud and mismanagement, crimes that were neglected by monetary authorities [32]. From 1960 to 1985, the financial sector was under repression from the government; all banks were owned and controlled by the government and credits were directed to the sectors deemed necessary by government, interest rates were controlled, high reserve requirements and other restrictions on financial institutions existed.

In 1975, offshore oil discovery produced a new primary export commodity by the late 1970s. From 1978 to 1985, growth was realized from the production of oil, with the growth rate as high as 12% [37]. Agriculture’s share in GDP declined to 28% in the late 1970’s while the share of oil to GDP rose to 17% of GDP. The oil boom enabled the Cameroon government to continually run surpluses with little external borrowing. Government highly depended on oil revenue.

However, in 1986, economic and banking crisis set in because of the fall in the world market prices of its main agricultural exports and poor economic management policies. Some domestic factors which resulted to defaults in payment, bad debts and severe liquidity problems in many banks included the mismanagement of public funds, repressive state policies, incompetence of credit managers, poor quality in the supervision of the banking system and credit issuing process [32]. In the late 1980’s, several banks went bankrupt and were closed. This period of crisis coincided with the sharp appreciation of the real exchange rate of Franc while the US dollar depreciated by 40% against the FCFA and the terms of trade deteriorated by 47% [31]. These caused a drastic collapse of the economy that performed well in the past decades. The GDP fell from 8 to -5 % per year [37], exposing the weaknesses of an economy that seemed to be well-managed and immune to external shocks. Based on the above, the financial sector was then liberalized as one of the suggestions of the Structural Adjustment Programme (SAP) of 1989 on the proposition of the Bretton Woods institutions [32].

The liberalization of the financial sector under the SAP improved banking sector performance (in terms of rise in liquidity) which improved the growth rate. Under the SAP, the financial sector was restructured; some banks were liquidated and recapitalized, monetary and financial policies were altered. The banking commission for the bank of Central African States (COBAC) was formed to regulate all banking activities. As such, interest rates were deregulated, directed credit schemes were removed, and banks were privatized, the money market was created and the capital account liberalized [39]. With the creation of the Douala Stock Exchange market in 2001, financial repression ended. Financial liberalization was meant to revamp the financial sector and get the economy back on track. Liberalization was aimed at increasing savings and investment, hence stimulating economic growth.

However, as liquidity increased from 53 % between 1997 and 2000 to 100 % in 2006, banks become cautious and reluctant in financing economic activities. Statistics from the World Bank’s World Development Indicators (WDI) reveals that during the period before financial liberalization (1961-1985) the average annual growth in gross capital formation was 12%. Between 1986 and 2000 when the financial sector was undergoing repression, the average annual growth in gross capital formation was about -4% meanwhile from 2001 to 2012; annual growth averaged approximately 10% [45].

Despite the financial reforms geared towards enhancing bank performance, generating gross capital formation hence stimulating economic growth, it is still not clear whether the desired outcomes are met, as GDP rates have persistently remained below double digits (the rate that is expected to drive the Cameroon economy to emergence). The GDP growth rate was 4.59% in 2012, 5.56% in 2013 and 5.59% in 2014 [45]. It is on the basis of the above issues that we raise the following research questions;

• To what extent does financial intermediation affect domestic investment in Cameroon?

• What is the extent to which domestic investment affects GDP growth in Cameroon?

• What is the nature of causality between financial intermediation, domestic investment and GDP growth in Cameroon?

The main objective pursued in this study is to examine the effects of financial intermediation on domestic investment and then evaluate how these have translated to economic growth in Cameroon. To achieve the main objective of the study, the specific objectives which the study intends to pursue are to;

• Determine the extent to which financial intermediation affects domestic investment in Cameroon.

• Evaluate the extent to which domestic investment affects GDP growth in Cameroon

• Examine the nature of the causality between financial intermediation, domestic investment and GDP growth in Cameroon.

• Make some necessary recommendations on how financial intermediation can stimulate economic growth in Cameroon.

The rest of this paper is therefore organized as follows; in section two, the relevant empirical literature is reviewed. In the third section, the analytical methodology used in estimating the empirical model is explored. The results/findings of the study are presented in the fourth section. In section five, we discuss the findings. In section six, the policy suggestions and conclusion are made.

2. Literature Review

Ref. [20] examined the role of financial intermediation on the economic growth of member countries of Southern African Development Community using pooled data and realized that financial development is positively correlated with the real per capita GDP growth rate. Reference [40] had a similar result after applying the traditional cross-section, instrumental variable and the dynamic panel techniques. Using panel data for 40 developing countries from 1975-1995, [18] investigated the role played by private capital flows and financial development in determining economic growth and came out with the result that private capital inflows foster higher economic growth (especially in countries with domestic financial sector is well-developed). In China, [2] confirmed a positive correlation between finance and growth. Ref. [21] also established a positive relationship between financial intermediation and economic growth using provincial data in China from 1985 to 1998, using the recently developed GMM dynamic panel techniques.

Reference [19] observed the existence of bi-directional causality between finance and growth for 31 African countries using the dynamic panel GMM technique, with data from 1970 to 2005. Reference [26] employing time series data from 1970 to 2010 and employing the Engle-Granger procedure, found that financial intermediation has a positive impact on economic growth in Nigeria. Ref. [5] also fund some evidence to suggest that a positive and bi-directional causal relationship exists between financial development and growth, using the dynamic GMM model with panel data from 1981-2010 for 24 African countries.

Reference [25] investigated the role of banks in capital formation and economic growth in Nigeria from 1980-2009 using the OLS estimation technique and found that commercial banks have a vital role to play in economic growth in Nigeria. Reference [41] posit that private capital inflows and domestic credit exert a positive influence on investment in developing countries while [41] held that domestic investment in Nigeria is not adequately being stimulated by the financial sector. Adopting total bank deposit and total bank credit as the indicators of financial development, [14] also found a positive effect of financial intermediation on economic growth in Nigeria using time series data from 1992-2011 and employing the OLS technique.

Using correlation analysis, [27] observed a moderate positive relationship between financial intermediation and economic growth in Nigeria. Reference [29] determined whether the measure of financial development matters for evaluating the role of financial development on economic growth in Ghana. While credit to private sector as share of GDP and total domestic credit is seen to induce economic growth, the stock of broad money to GDP is not growth-inducing. It was concluded that though finance promotes economic growth, this is highly dependent on the proxy used for measuring financial development. Reference [5] evaluated whether finance promotes growth in Botswana using the Fully Modified OLS regression and Pairwise Granger Causality as the tools for analysis, and with time series data from 1981-2010, the study concludes that finance may promote growth via the expansion of the size of the financial intermediary sector.

3. Materials and Methods

This study covers a period of 40 years running from 1975 to 2014 inclusive. This period is appropriate for this study because it coincides with the pre-reform, reform and post-reform periods in the financial sector of Cameroon. The data relevant for this study was also available within this period. The fact that the explanatory variables have already taken effect on the dependent or observed variable necessitates the use of the ex-post facto research design.

In order to capture the effects of the growth trend of financial intermediation on economic growth via domestic investment, an empirical model is designed. In this model, the proxies used in measuring financial intermediation are incorporated in to the endogenous neoclassical growth model which sees growth purely from within than from without. The model is of the form Y=AK, where Y is real aggregate output growth, A is the total factor productivity and K is the real aggregate capital stock (a composite of human and physical capital). This is not different from the theoretical models adopted by [16, 17, 18, 29, 31] amongst others in modelling the relationship between the level of financial intermediation and economic growth. On the basis of this, the empirical model for this study is specified as;

(2.1)

A priori expectations; .

In the specification shown by equation (2.1), t is time, i is ith indicator or index of the level of financial intermediation development. The rest of the variables are defined as follows;

Y is real output growth, proxied by annual real GDP growth. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes minus any subsidies not included in the value of the products.

X is a vector of fundamental control variables of growth as justified by theory among which includes Labour Force (L), Capital Stock (K); proxied by Gross Fixed Capital Formation as a % of GDP and consumer price index (INFLA).

FID is a vector of proxies for indicators of financial intermediation development comprising of;

i) Domestic Credit to Private Sector (DCPS) by banks as percentage of GDP which is defined as financial resources provided to the private sector by other depository corporations (apart from the central bank) such as through advances, purchases of non-equity securities, trade credits and other accounts receivable, that establish a claim for repayment. This indicator measures the level of financial services.

ii) Claims on Central Government (CGOV) as percentage of GDP comprises all loans to central government institutions net of deposits. This indicator also measures the level of financial services.

iii) Broad money supply (M3) as a % of GDP, defined as the sum of currency outside commercial banks; demand deposits; time, savings, and foreign currency deposits of resident sectors except central government; bank and travellers’ checks; and other securities like certificates of deposit and commercial paper. This indicator measures size of the financial intermediary.

iv) Ratio of Narrow Money to Broad Money (M1/M3). Narrow money (M1) includes currency, i.e. banknotes and coins, as well as balances which can immediately be converted in to currency or used for cashless payments, i.e. overnight deposits.

v) Bank Deposits to GDP (CBD/GDP) which includes total value of demand, time and saving deposits at domestic deposit money banks as a share of GDP. Deposit money banks include commercial banks and other financial institutions that accept transferable deposits, such as demand deposits.

The study uses secondary data from World Development Indicators (WDI).

These indicators of financial intermediation and other fundamental determinants of growth have been carefully selected on the basis of some empirical studies such as those of [5, 14, 19, 25, 26, 29] and [40]. Total labour force (L) is logged to allow its coefficient to be interpreted as elasticity. The rest of the variables are non-log since they are already in the form of percentages and ratios that can permit their coefficients be interpreted as elasticity.

Empirical studies have shown that whether finance is good or bad for growth depends on the indicator used to proxy for financial intermediation development. Given that one indicator may not adequately proxy for financial intermediary development and that multicollinearity may occur if many indicators are used in one model, we employed Principal Component Analysis (PCA) to create composite indexes of financial intermediary development which account for most of the variations in the original dataset, while controlling for any multicollinearity. These indexes are composite in that each is a combination of all indicators identified.

The method of Principal Components (PC) summarizes the variations in a correlated multi-attribute to a set of uncorrelated components (also referred to as principal components), each forming a particular linear combination of the original variables. The extracted uncorrelated components are estimated from the eigenvectors of the covariance or correlation matrix of the original variables. Thus, PCA extracts the smallest number of components which account for most of the variance in the original multivariate data without losing information. This implies that the first few PC’s contain most of the variations in the original dataset while successive components account for the rest of the variance. The interpretation of the principal components is based on selecting those variables which are most strongly correlated with each of the components, whether positively or negatively.

Empirical studies have revealed that the nature of the causality between financial intermediation and economic growth is not clear. Since financial intermediation affects growth through capital formation, this implies the nature of the link between finance, investment and economic growth is not also clear. Taking this in to cognizance, estimating a model in which some variables are a priori assumed dependent whereas others are independent will not fully capture the situation on ground. In this paper, the exogeneity of variables in the model is not determined a priori. Such a dynamic behavioural pattern between the level of financial intermediation, domestic investment and economic growth can be better understood within the framework of the VAR technique, developed by Sims in 1980. The tools employed by VAR methodology; Impulse Response Function, Variance Decomposition and Granger Causality are helpful in understanding interrelationships among economic variables and in formulating a more structured economic model. However, this study makes use of only the VAR Granger Causality test. The level of financial intermediation has longrun implications on the rate of economic growth. Our current method of analysis does not allow us test for this.

The VAR model is an n-equation and an n-variable linear model in which each variable in the model is explained by its lagged values plus current and past values of the remaining n-1 variables. This model treats all variables symmetrically without making reference to the distinction between dependent and independent variables. It is one of the most successful, flexible and easy to use models for analysing multivariate systems of equations, extended from univariate time series models. Generally, a VAR system can be expressed in the following form;

(2.2)

is a vector of endogenous variables at time t, Ai (i=1, 2,…, p) are coefficient vectors, is the number of lags in the system and is a vector of residuals. For the purpose of this study, we estimate a VAR system shown by the following equations;

(2.3)
(2.4)
(2.5)

The B's, A's and Z's are the coefficients to be estimated, and are the uncorrelated white-noise disturbances with standard deviations and respectively. These white-noise residuals, also referred to as the impulse or the shock elements are the unexplained movements of the variables reflecting exogenous shocks if any. Since X and FID are composites representing vectors of determinants of growth and financial intermediation development respectively, this implies the VAR system in this study constitutes more than three variables, three equations. However, the key variables of interest in this study are financial intermediation development, domestic investment and economic growth.

Significance tests such as the adjusted R2, t-tests, and F-statistic are used to test the reliability of the estimates. Adjusted R2 measures the percentage of the total variation in the explained variable accounted for by the joint variation of the explanatory variables in the model. The t-statistic test is used in testing for the significance of estimated regression coefficients. The F-test is employed to test for the significance of the adjusted R-squared [43]. Autocorrelation was detected using the VAR residual serial correlation test of the Langragian Multiplier (LM) test. The degree of multicollinearity between explanatory variables was tested using the correlation matrix table. We also used the VAR residual heteroskedasticity test to test for the presence of heteroskedasticity in the VAR models. The VAR Granger causality test was adopted to test for the causality between the proxies and composite indexes of financial intermediation, domestic investment and economic growth. We used the Wald test to determine the joint significance of regression coefficients.

Two growth equations are estimated in this study; one constituting of the indicators of financial intermediation, domestic investment and other fundamental determinants of growth, the other having just the indexes of financial intermediation, investment and fundamental growth determinants.

Before carrying out the VAR analyses, the stationarity tests were carried out on the variables. A time series is stationary if it has a constant mean and variance over time and the covariance value between the two time periods depends on their lag, and not on the actual time at which their covariance is computed [44]. The mean and variance are therefore said to be time invariant. The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were used to test for the stationarity of the variables taking in to consideration the nature of the drift. These tests were carried out under the null hypothesis of no unit root () against the alternative of a unit root (). The test computes the tau statistic τ at 1%, 5% and 10% levels of significance which is compared with its critical values. If the calculated values of ADF and PP test statistic are more negative than the test critical values at a chosen percent significant level, the null hypothesis of no unit root is rejected otherwise it is accepted. The PP unit root test is a non-parametric test also called confirmatory test.

4. Results

From the six indicators used to proxy for financial intermediation development, we created three composite indices, FIDindex1, FIDindex2 and FIDindex3 using the method of principal components. The results of the principal components from which the three indices have been created is shown on Table 1.

From the results presented on Table 1, it is observed that the first three indices created from the first three components explain approximately 94% of the variations in the original dataset, with the remaining 6% of the variations explained by the rest of the components. The indexes created from the first, second and third principal components account for about 62.8%, 22.5% and 8.8% of the total variations in the original data respectively. We have therefore been capable of reducing the financial intermediation development indicators while preserving up to 94% of the information in the data. In addition, the correlation coefficients between the three indexes are zero (not presented), implying that they can be used in the same model. To interpret each principal component, the correlations between the original variables and the components are computed (Table 2), with the correlation coefficients of 0.38 and above considered as significant for the components under consideration.

The results of Table 2 show that the index created from the first principal component, FIDindex1 is significantly correlated with all financial intermediation development indicators except M1/M3, and these explain about 63% of the total variation in the original dataset. The second index FIDindex2 is significantly correlated with M1/M3 and CBD/GDP, accounting for about 22% of the variance. The third index FIDindex3 also significantly correlates with DCPS, CGOV and M1/M3. Before carrying out the VAR analysis, the ADF and PP tests (Table 3) were used in testing for unit roots on all the variables, including the composite indexes.

Table 3. Augmented Dickey-Fuller and Phillips-Perron Unit Root Test Results

The unit root test results of Table 3 show that while RGDPG and INFLA are stationary at their levels for the ADF and PP tests, all other variables are stationary after first difference except FIDindex2 which is stationary at level for the ADF test. All unit root tests are statistically significant. The correlation matrix table results (not presented) reveal weak association between determinants of economic growth, acting as control variables and the indicators of financial intermediation development and between growth determinants and the indexes of financial intermediation. This signifies absence of multicollinearity in the estimated VAR models. The VAR results capturing the effects of financial intermediation development on domestic investment are presented on Table 4.

From the results presented on Table 4, it is observed that real gross domestic product growth (RGDPG), total labour force (TLF), gross fixed capital formation (K), inflation (INFLA), domestic credit to the private sector (DCPS), central claims on the government (CGOV), broad money (M3), ratio of narrow to broad money (M1/M3) and bank deposits to GDP (CBD/GDP) in the lagged one year and lagged two year period have insignificant effects on current year gross domestic fixed capital formation in Cameroon. However, while DCPS, M1 in both lags, CGOV in the lagged two year period, M1/M3 in the lagged one period and CBD/GDP in the lagged two year period has negative effects on gross domestic fixed investment in Cameroon, the effects of CGOV in the lagged one year period, M1/M3 in the lagged two year period and CBD/GDP in the lagged one year period is positive.

Table 4. Results of the Domestic Investment Equation

The p-value of the Wald test statistic (0.2621) on the coefficients of financial intermediation indicators DCPS, CGOV, M3, M1/M3 and CBD/GDP is insignificant indicating that these indicators jointly do not significantly affect gross fixed capital formation. Moreover, the VAR Granger causality test results (not shown due to space) reveal that apart from CBD/GDP, no causality runs from the indicators of financial intermediation to gross domestic fixed capital formation in Cameroon. The value of adjusted R-squared (0.322283) suggests that about 32% of the variations in investment are accounted for by the variations in RGDPG, TLF, K, INFLA, DCPS, CGOV, M3, M1/M3 and CBD/GDP in the lagged one year and lagged two year periods. The p-values of the B-G LM test (0.3836) and White test (0.1176) are both insignificant, suggesting that autocorrelation and heteroskedasticity are absent in the domestic investment model. To assess the effects of these indicators and domestic investment on growth, we estimated a VAR model and the results are presented on Table 5.

The results presented on Table 5 shows that INFLA, DCPS, CGOV, M3 and CBD/GDP in the lagged one and two year periods and RGDPG and K in the lagged one and two year periods respectively have insignificant effects on the economic growth of Cameroon. Also, RGDPG, TLF, M1/M3 in the lagged one year period and TLF and K in the lagged two year periods have significant effects on economic growth in Cameroon. The Wald test (not presented), carried out on the joint significance of the coefficients of these variables in the lagged one year and lagged two year periods reveal that RGDPG, TLF, K, M3 and M1/M3 each jointly significantly affect economic growth in Cameroon. This is because the p-values of their Chi-square values are respectively, 0.0000, 0.0000, 0.0501, 0.0108 and 0.0002 which are all significant.

Table 5. Results of the Economic Growth Equation

The results presented on Table 5 shows that INFLA, DCPS, CGOV, M3 and CBD/GDP in the lagged one and two year periods and RGDPG and K in the lagged one and two year periods respectively have insignificant effects on the economic growth of Cameroon. Also, RGDPG, TLF, M1/M3 in the lagged one year period and TLF and K in the lagged two year periods have significant effects on economic growth in Cameroon. The Wald test (not presented), carried out on the joint significance of the coefficients of these variables in the lagged one year and lagged two year periods reveal that RGDPG, TLF, K, M3 and M1/M3 each jointly significantly affect economic growth in Cameroon. This is because the p-values of their Chi-square values are respectively, 0.0000, 0.0000, 0.0501, 0.0108 and 0.0002 which are all significant.

On the other hand, INFLA, DCPS and CBD/GDP in the lagged one year and lagged two year period each jointly do not significantly affect economic growth in Cameroon. These results are confirmed by the VAR Granger Causality test results (not presented) which show that causality flows from TLF, K, M3 and M1/M3 to RGDPG in Cameroon within our period of study. Apart from M1/M3, no causality runs from financial intermediation to RGDPG. The value of adjusted R-squared (0.7608) shows that about 76% of the variations in RGDPG are accounted for by the variations in RGDPG, TLF, K, INFLA, DCPS, CGOV, M3, M1/M3 and CBD/GDP. The p-value of F-statistic is 0.000049 showing that the adjusted R-squared is significant at 1%. While the p-value of the B-G LM test (0.0019) reveals that we cannot accept the hypothesis of no serial correlation, the p-value of the White test (0.1667) shows that we cannot reject the hypothesis of no heteroskedasticiy in our growth model. We now estimated a VAR model composing of the artificial indexes of financial intermediary development, domestic investment and growth determinants and the results are presented on Table 6.

Table 6. Results of the Economic Growth Equation with Indexes of Financial Intermediation

From Table 6, it can be observed that RGDP, TLF, K and FIDindex2 in the lagged one year period and FIDindex2 and FIDindex3 in the lagged two year period each significantly affect economic growth in Cameroon within the study period. The Wald test results (which are not presented due to space) reveal that RGDPG, TLF, K, FIDindex2 and FIDindex3 each jointly significantly affect RGDPG in Cameroon. Therefore, causality flows from TLF, K, FIDindex2 and FIDindex3 to RGDP in Cameroon within the period of study. No causality was seen from FIDindex1, FIDindex2 and FIDindex3 to domestic investment (K) or from K to FIDindex1, FIDindex2 and FIDindex3 in Cameroon (VAR Granger causality tests are not presented due to space). The value of adjusted R-squared (0.734007) shows that about 73% of the variations in growth are accounted for by the changes in RGDPG, TLF, K, FIDindex1, FIDindex2 and FIDindex3. Autocorrelation and heteroskedasticity are absent in this model since the B-G LM Test (0.1643) and the White Test (0.2701) are insignificant.

5. Discussion

The main objective of this study was to investigate the relationship between financial intermediation, domestic investment and economic growth in the case of Cameroon. The main empirical finding of this study that financial intermediation has does not have any significant effect on domestic investment in Cameroon is in conformity with the work of [41] in Nigeria whose findings reveal that the financial sector is not stimulating private investment and hence growth to the expected level. This is however contrary to the works of [25], [14] who found that banks have a vital role to play in promoting capital formation and hence economic growth in Nigeria. Reference [41] also found that both private capital inflows and domestic credit exert a positive effect on investment in developing countries.

The inability of the banking sector to stimulate domestic investment in Cameroon can be attributed to the fact that the banking sector has remained underdeveloped, with many banks facing the problem of excess liquidity in the phase of liberalized but highly regulated interest rates, high interest rate spreads and the increased costs of transaction. These, to an extent have limited the flow of funds required for capital formation from surplus sectors to deficit sectors. Another possible reason could be the flow of financial resources to unproductive investments, a feature that is common place within the Cameroon context. The insignificance of central government claims on gross fixed investment in Cameroon is partly justified by the virus of embezzlement that is much evident in the Cameroon public sector.

Notwithstanding the insignificant effects of the level of financial intermediation development on gross fixed investment in Cameroon, it is observed that gross fixed investment (K) and indicators of financial intermediation like broad money supply (M3), ratio of narrow money to broad money (M1/M3), FIDindex2 and FIDindex3 have significant effects on Cameroon’s economic growth. The implication of this result is that the channel through which financial intermediation affects economic growth is yet to be fully understood. This is because finance does not have any significant effect on investment, whereas some of its indicators as well as investment have a significant effect on economic growth. This suggests that variables other than financial variables are stronger in influencing the level of domestic investment and hence economic growth in Cameroon. The result that finance significantly affects economic growth confirms the results of previous works such as those of [2, 14, 18, 20, 29, 40], and [27].

6. Policy Suggestions and Conclusion

This study examines the relationship between financial intermediation, domestic investment and economic growth in Cameroon over 40 years using the VAR technique of estimation. Results showed the absence of causality from financial intermediation indicators to domestic investment in Cameroon but revealed the flow of causality from some indicators of financial intermediation development and domestic investment to growth in Cameroon. Based on the findings of this study we recommend that banks should ensure that all the credit granted to the private sector is channelled to more productive investments and high-yielding ventures. The banks could do this by monitoring closely the use of credits meant for investment.

The Cameroon government should also strictly monitor and control the funds set aside for investment projects and ensure that projects are effectively implemented. This would be very difficult to achieve with the low purchasing power of Cameroonian citizens. The government is hereby called up to mobilize, within the country, some strategies to stimulate growth through financial intermediation by increasing the current welfare of Cameroonians.

The current standards of living could be increased by improving the level of infrastructural development, which principally includes road network, farm to market roads, and reduction in the taxes on car importation according to their ages as a means to eliminating dumping, enhancing creativity through technical education and educational support funds by committing not less than 15% of the real GDP of the country on research for development.

Youths and development should be the major concern of the government of Cameroon. In this direction, the prospects of reducing unemployment to less than 2%, elimination of the growth of public sector which is highly characterized by corruption and the reduction of structural rigidities which highly account for high rate of insecurity are also recommended.

To increase the flow of credit to the private sector, the banking authorities should relax some of the conditions for obtaining loans for investment. Moreover, banks should give priority to real investment creating loans.

The findings of this study suggest that the amount of domestic investment created by financial intermediaries is inadequate to stimulate investment which would in turn spur economic growth. This might be because the study did not take in to consideration the activities of non-bank financial institutions. For further research therefore, we recommend that non-bank financial institutions should be incorporated in to ongoing finance-growth studies. This is especially true, as in most developing countries, this sector has a large number of players. Also, given that there is yet no generally accepted indicator of financial intermediary development, studies which exhaustively consider the determinants of financial intermediation are still strongly recommended.

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