A Thorough Analysis of the Bank Lending Channel of Monetary Transmission in the CEMAC area

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1 A Thorough Analysis of the Bank Lending Channel of Monetary Transmission in the CEMAC area Samba Michel Cyrille, PhD in Economics Assistant Lecturer at the University of Yaounde 2 P.O. Box YAOUNDE- CAMEROON sambamichelcyrille@yahoo.fr michelcysa@gmail.com ABSTRACT This paper addresses the relevance of the bank lending channel in the transmission of monetary policy in the CEMAC area. Testing for the existence of a bank lending channel usually raises an identification problem which renders the test less efficient. Therefore, in this paper, we employ a structural vector error correction model (SVECM) which enables us to disentangle the loan demand and supply effects of monetary policy moves. Using aggregate data of the CEMAC area, we find evidence that is consistent with a bank lending channel, although the very low credit ratio in the region. The magnitude of the effect is rather high and might be explained by banks characteristics in the region. Therefore, we argue that the central bank in the CEMAC zone should take advantage of this situation in order to implement relevant policies according to the situation of the business cycle. Also, commercial banks role could be strengthened if actions are taken in order to reduce their excess reserve holdings at the central bank, for those excess reserve holdings are thought to hamper monetary policy actions in the area. Keywords: Bank lending channel, Monetary Policy Transmission, Vector error correction model, CEMAC, Loan demand, Loan supply 1. INTRODUCTION The bank lending channel hypothesis postulates the existence of a channel for monetary policy transmission through bank credit. This channel is independent 1 of the traditional money channel, which takes into consideration the effects of changes in real interest rates on economic activity (Jimborean, 2009). It ascribes a special role to banks in the monetary policy transmission mechanism, stipulating that monetary policy tightening can affect not only the demand for loans( through the interest rate channel), but also the supply of loans which in turn, further affects investment and consumption. In other words, monetary policy affects not only the borrowers, but also the banks. The underlying theoretical mechanism is the following: a monetary contraction shrinks both banks reserves and deposits. The theory indicates that in the aftermath of a monetary policy tightening, banks lending policy is liable to change. In the literature, two hypotheses as proposed by Oliner and Rudebusch(1995), are crucial for the bank lending theory: (i) the imperfect substitution between credit and other assets on banks balance sheets, and (ii) the imperfect substitution between bank credit and other forms of financing on firms balance sheets. According to Jimborean(2009), these forms of imperfect substitution cause monetary policy to impact on economic activity in two stages: Society for Business Research Promotion 8

2 in the first stage, the imperfect substitution of bank assets determines a contraction in banks supply of loans when there is a monetary policy tightening. When banks face a decrease in liquidity, they reduce their loan supply instead of selling the bonds they have in their portfolios. Alternatively, rather than reducing lending, they could issue bonds or collect deposits from households or the corporate sector. Financial market imperfections, such as adverse selection and moral hazard (imperfect substitution between credit and bonds on the asset side and between bonds and deposits on the liability side); limit the ability of some banks to borrow in the financial market. Once the credit supply has diminished (due to imperfect substitution between bank credit and other forms of external funding on firms balance sheets), investment spending decreases, and so does economic activity (the second stage) too. The early empirical literature on the existence of a bank lending channel focused on the estimation of reduced-form equations of credit supply using aggregate data. The basic approach was pioneered by Bernanke and Blinder (1992) who found that restrictive monetary policy impulses lead to a decline in both aggregate loans and economic activity. Nevertheless, this strand of literature was criticized on the grounds that it is difficult to identify credit supply responses, given that monetary shocks simultaneously affect the demand for loans (Romer and Romer, 1990). As argued by Hulsewig et al. (2002), the result of Bernanke and Blinder (1992) does not sufficiently prove that the central bank is indeed able to affect the supply of intermediate loans. In principle, these results can also be explained solely by the interest rate channel. Consequently, there might be a positive correlation between bank lending and economic activity even if the bank lending channel does not operate at all( Kashyap and Stein, 2000; European Central Bank, 2000). Failure to disentangle loan demand and loan supply effects leads to an overestimation of the impact of monetary policy on the supply of loans. Therefore, several approaches have been used to cope with the identification problem. This has been done by operating a shift from aggregate data to disaggregate data in order to account for heterogeneity in the response of both banks and firms to changes in monetary policy. Information on bank characteristics such as capitalization, size and liquidity, is used to account for heterogeneity (e.g., Ashcraft, 2006; Kashyap and Stein, 1995, 2000; Peek, Rosengren and Tootell, 2003; Angeloni, Kashyap, Mojon and Terlizzese, 2002, for the Euro-area countries; Jimborean, 2009; Matousek and Sarantis, 2008, for the New EU member states; Cetorelli and Goldberg, 2008, for the United States). The main result of this class of research is that the impact of monetary policy on lending is stronger particularly for small banks with less liquid balance sheets, i.e. banks with lower ratios of securities to assets, than for large and liquid banks. Firm size is also considered as a natural proxy for information asymmetry. Gertler and Gilchrist (1993a, 1994), Gilchrist and Zakrajsek(1998), use quarterly panel data of nonfinancial firms in the United States and conclude that, following a monetary contraction, bank credit to small firms is reduced more than bank credit to large firms. However, Oliner and Rudebush(1995) using data for the U.S. manufacturing sector, find no evidence that a monetary shock changes the composition of bank and nonbank debt for either small firms or Society for Business Research Promotion 9

3 large firms. Rather, they argue that the main effect of a monetary contraction is to redirect all type of credit from small firms to large firms. Overall, the use of disaggregate data aiming at resolving the identification problem has not led to uniform results (Hulsewig et al., 2002). Therefore, a new strand of literature has favored the use of aggregate\ data and relied on the estimation of vector error correction models (VECMs). Within this framework the supply and demand for loans can be identified by testing for the presence of multiple cointegrating relationships and exclusion, exogeneity and homogeneity restrictions on the cointegrating relationships. Loan supply and demand can therefore be modeled jointly, rather than in a one-equation reduced-form setting. The use of Structural VECMs in the bank lending literature is growing fast, as a number of authors have relied on this methodology to solve the identification problem. (e.g. Hulsewig et al., 2002; Kakes, 2000; Calza et al., 2006; De Mello and Pisu, 2010). The current paper addresses the relevance of the bank lending channel in the transmission of monetary policy in the CEMAC area. We use a structural vector error correction model (SVECM) which accounts for endogeneity and nonstationarity of the time series. Our results show that although the CEMAC area is characterized by a low financial development, there is still evidence of a bank lending channel of monetary policy. The rest of the paper is as followed: section 2 describes our data and the time series properties, while in section 3, we focus on the empirical methodology and the results obtained. Section 4 concludes. 2.DATA AND TIME SERIES PROPERTIES The following analysis of the loan market in the CEMAC area is based on aggregate quarterly data taken from the IFS CD-ROM of the IMF. For harmonization purposes, we set our sampling period as from the first quarter of 1990 to the last quarter of The year 1990 saw the adoption by the central bank of the zone, of a new strategy of monetary policy. This new framework; the Monetary Programming; aimed at correcting the shortcomings of the previous monetary strategy, implemented since the early 1960s.Data on nominal variables were obtained by summing country level data of the six countries, while those on CPI are simple averages of individual countries CPI.. Specifically, our VECM analysis includes loans to the private sector by banks. Although the credit-to-gdp ratio in the CEMAC area is still relatively low (see Table 1 for more details), loans provided by banks are the main source of financing for the private sector due to the actual imperfections in the two stock markets of the area. Hulsewig et al (2002) explain that banks may be restricted in their loan decisions by their amount of disposable capital. They argue that the supply side factors of the loan market are covered by the banks equity position. We therefore include bank capital accounts (capital) in our VECM. The real side of the economy is mirrored as usual by real GDP (RGDP) taken as a proxy for credit demand factors. We also include the inflation rate (π), based on the logarithm of the CPI. The central bank s operating target is described by the rate offered by the central bank to second-tier banks for their refinancing process (Interest). We use the maximum debtor rate as the loan market rate in the CEMAC area (lending). All nominal variables were Society for Business Research Promotion 10

4 deflated by the CPI. We tested for the presence of units roots in our data. Results of these tests are reported in Table 2. A part from the inflation rate which is stationary in level, all the other variables are integrated of order one i.e I (1). 3. METHODOLOGY AND ESTIMATION RESULTS As De Melo and Pisu(2010), we consider a simple aggregate model of loan supply(l S ) and loan demand(l D ). Loan demand depends on macroeconomic conditions, proxied by economic activity(y) and inflation (π), as well as the lending rate (r L ) offered by banks. The supply of loans depends on the sources of funds available to banks, including capital(c), the borrowing rate (r P ) paid by banks for external funds and inflation, which affects the real rate of return on credit operations. For de Melo and Pisu(2010), this simple model allows for the identification of loan supply and demand, thus avoiding the well-known identification problems that arise in the estimation of reduced-form credit supply equations. The model can be written as:, and (1) The new strand of literature on the bank lending channel of monetary policy( Hulsewig et al., 2002; Kakes, 2000; and demelo and Pisu, 2010) states that if the presence of two cointegrating relationships cannot be rejected by the data, identification of the supply and demand functions depends on the estimated sign of the lending rate, which should be negative in the demand equation and positive in the supply equation, and the sign of the borrowing rate, which should be negative in the supply equation. Identification also requires r restrictions for each vector, where r is the number of cointegrating vectors. Therefore, as it is usual in the literature, we test for two exclusion restrictions: bank capital should not enter the demand equation (while being positively signed in the supply equation), and economic activity should not enter the supply equation (while positively signed in the demand equation). The rationale for those exclusions, as well as the inclusion of the borrowing and lending rates in the loan supply equation are found in de Melo and Pisu(2010). Hulsewig et al. (2002) also argue that the demand for loans can in principle be positively and negatively linked to real activity, y. Co-integration and identification tests. We test the model above in a VECM setting including six variables: credit stock, inflation, real GDP, the central bank operating rate, the lending rate and bank capital. It should be recalled that including the central bank discount rate as the policy rate in the VECM leads to the assumption that innovations of the policy rate indicate unexpected monetary policy shocks. We define all variables in logarithmic form, except for the interest rates. The VECM can be defined as usual, as for Y= [l, c, y, π], where L is the lag operator, and ε is an error term. The rank of, which can be written as =αβ, where α and β are p r matrices, and p is the number of variables in Y, is denoted by r. β is a vector of cointegration relationships and α is the loading matrix defining the adjustment speed of the variables in Y to the long-run equilibria defined by the cointegration relationships. We also Society for Business Research Promotion 11

5 include a dummy representing the devaluation of the local currency the Franc CFA; which occurred on January We introduce also petroleum average crude price (Oil) as an exogenous variable in the model. The optimal lag length was selected on the basis of the Schwarz (SC) criterion along with misspecification tests for the error terms. This led us to choose two lags in our VECM. Table 3 shows the results of the Johansen s trace test. The null hypothesis is of a cointegration rank of at most r (i.e., the VECM has at most r cointegrating relationships). On the basis of this test, the null is rejected for r=0 and r 1 (at the 5% level), suggesting the presence of two cointegrating relationships. Test statistics of the unrestricted vector autoregression model show that although the errors are not normally distributed, there is no presence of autocorrelation among them. The two estimated unrestricted cointegrating vectors are reported in Table 4 along with results of the weak exogeneity test. According to the signs of the relevant parameters, it appears that vectors β 1 and β 2 could be interpreted as demand and supply relationships, respectively. The bottom of Table 4 indicates also that at least three variables are weakly exogenous. According to Johansen (1995), a variable can be treated as weakly exogenous if its coefficients of all errors correction terms are zero, implying that the respective equation in the first difference does not contain information about the long-run parameters β. The likelihood ratio tests on join restrictions on α have been carried without imposing restrictions on β. The hypothesis of weak exogeneity test could not be rejected for capital and real GDP. To identify the supply and the demand equations, we imposed the following exclusion and exogeneity restrictions on the cointegrating parameters: If the null hypothesis is not rejected, loan demand is unaffected by bank capital and the policy rate, loan supply is unaffected by activity, and capital and activity are weakly exogenous. The null hypothesis could not be rejected at classical levels on the basis of a LR test (, p-value= 0.134). We therefore obtained the parameters of the demand and supply equations after normalizing the unrestricted cointegrating vectors in loans as follows (t-statistics are in brackets): (8.34) (2.45) (8.03) (0.574) (-12.81) (3.40) (11.65) Society for Business Research Promotion 12

6 Our parameters in equation (2) show that economic activity is a powerful determinant of the demand for bank loans in the CEMAC area. Other studies have also estimated the income elasticity of loan demand to be greater than one, including Calza et al.(2006), for the Europe area(1.48); Kakes(2000), for the Netherlands(1.75), Fase(1995), also for the Netherlands(2-2.5) and de Melo and Pisu(2010) for Brazil(2.164). Moreover, as expected, there is a positive relation between loan demand and inflation. However, the loan rate positively affects loan demand. A plausible explanation is that in the absence of alternative sources of external finance which is combined with the high concentration of the banking industry, borrowers are willing to pay even a high interest rate in order to finance their consumption and investment spending. As for the supply equation (equation 3), our estimates show that the supply of loans is positively related to the lending rate and negatively related to the policy rate. Both effects are highly significant. As argued by de Melo and Pisu(2010), this provides prima facie evidence of the existence of a bank lending channel in the CEMAC area, since monetary policy moves affect the supply of loans. These results show that although there is evidence of a low credit ratio in the CEMAC area, the scope of monetary policy is not limited. The policy rate elasticity of credit supply is derived through the product of the estimated coefficient on that rate (-3,368) and the sample mean of the policy rate (8.228), which results in an elasticity of Therefore, as the policy rate rises by 1%, loan supply by banks fails by around 27%. This is to show that a tightening of policy in the central bank reinforces the reluctance of commercial bank to provide loan to the private sector. With less liquid balance sheets (absence of securities) banks are heavily hit by a decrease of their liquidity following a tightening of monetary policy. Consequently, they reduce their loan supply. As expected, bank capital affects positively loan supply, but the effect is rather insignificant. Finally, there is a positive link between inflation and loan supply in the CEMAC area. 4. CONCLUSION This paper analyzes the relevance of a bank lending channel operating alongside the conventional interest rate channel in the monetary transmission mechanism process in the CEMAC area. For this purpose, we make use of recent developments on this issue to identify loan supply and loan demand, a recurrent difficulty arising in studies which use aggregate data in reduced-form equations. It has been demonstrated that sticking on aggregate data, but using structural VECMs help to disentangle the loan supply and demand effects of monetary policy moves. Using this innovative methodology, we show that although credit ratios are still low in the CEMAC area, the scope of monetary policy is not limited. Analyzing the loan market in the CEMAC area, we show that banks in the region play a vital role in the monetary policy transmission process, by passing the monetary actions of the central bank to the private agents. Bank characteristics (size, liquidity and capitalization) might explain the magnitude of the effect. Therefore, the central bank should take advantage of this situation by implementing proper policies according to the business cycle situation. At the meantime, actions aiming at restraining banks excess holdings at the central bank might also help strengthening the bank lending channel in the CEMAC area. Society for Business Research Promotion 13

7 REFERENCES Angeloni,& al.(2002). Monetary transmission in the Euro area: Where do we stand? ECB Working Paper, No.114. European Central Bank, Frankfurt. Ashcraft, A.B. (2006). New evidence on the lending channel. Journal of Money, Credit, and Banking, 38, Bernanke, B.S., and Blinder, A.S (1992). The Federal Fund Rate and the Channels of Monetary Transmission. American Economic Review, vol.82, pp Calza, A., Manrique, M., and Souza, J.(2003). Aggregate Loans to the Euro Area Private Sector. European Central Bank Working Paper series No.202. Cetorelli, N., and Goldberg, L.S.(2008). Banking Globalization, Monetary Transmission and the Lending Channel. NBER Working Paper, No National Bureau of Economic Research, Cambridge, MA. De Melo, L., and Pisu, M.(2010). The bank lending channel of monetary transmission in Brazil: A VECM approach. The Quarterly Review of Economics and Finance, Vol.50, pp European Central Bank (2000). Monetary Policy Transmission in the Euro Area. Monthly Bulletin, July, pp Fase,M.M.G.(1995). The Demand for Commercial Bank Loans and the Lending Rate. European Economic Review, 39(1), pp Gertler, M. and Gilchrist, S. (1993) The Role of Credit Market Imperfections in the Monetary Transmission Mechanism: Arguments and Evidence. Scandinavian Journal of Economics 95(1), Gertler, M. and Gilchrist, S. (1994), Monetary Policy, Business Cycles, and the Behaviour of Manufacturing Firms, Quarterly Journal of Economics, vol.109, pp Gilchrist, S. G. and E. Zakrajsek(1995). The importance of Credit for Macroeconomic Activity: Identification through Heterogeneity. In: Peek, J. and Rosengreen, (eds.), Is Bank Lending Lending Important for the Transmission of Monetary Policy? Federal Reserve Bank of Boston Conference Series, Vol.39, Boston, pp Hulsewig, O., Winker, P., and Worms, A.(2002). Bank Lending Transmission of Monetary Policy: A VECM Analysis for Germany. Unpublished Manuscript. University of Wurzburg, Wurzburg. Jimborean,R.(2009). The role of Banks in the monetary policy transmission in the new EU member states. Economic Systems 33, pp Johansen, S.(1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models: Oxford University Press. Kakes, J.(2000). Identifying the mechanism: Is there a bank lending channel of monetary transmission in the Netherlands? Applied Economics, 7, pp Kashyap,A.K., and Stein, J.C.( 1995). The Impact of Monetary Policy on Bank Balance Sheets. Canergie-Rochester Conference Series on Public Policy, 42. pp Kashyap, A.K., and Stein, J.C.(2000). What do a Million Observations on Banks Say about the Transmission of Monetary Policy? The American Economic Review, Vol.90, No.3, pp Matousek,R., and Sarantis, N.(2009). The Bank Lending Channel and Monetary Transmission in Central and Eastern European Countries. Journal of Comparative Economics, 37, pp Oliner, S.D., and Rudebush, G.D.(1995). Is There a Bank Lending Channel for Monetary Policy? Federal Reserve Bank of San Francisco Economic Review, No.2, pp Peek, J., Rosengren, E.S., and Tootell, G.M.B.(2003). Identifying The Macroeconomic Effect of Loan Supply Shocks. Journal of Money, Credit, and Banking, 35, pp Romer, C., and Romer, D. (1990). New Evidence on the monetary transmission mechanism. Brookings Paper on Economic Activity, 1, pp FOOTNOTES 1. Contrary to the view expressed by Jimborean(2009), Bernanke and Gertler(1995) rather consider the credit channel as a set of factors that amplify and propagate the conventional interest rate effects. Therefore, the credit channel is an enhancement mechanism, not a truly independent or parallel channel. Society for Business Research Promotion 14

8 2. CEMAC is the french acronym for Communauté Economique et Monétaire de L Afrique Centrale. It is formed by six countries including Cameroon, Central African Republic, Chad, the Republic of Congo, Equatorial Guinea and Gabon Table 1: CEMAC countries Selected Financial Sector Indicators ( ) M2/GDP Bank Credit to Private sector/gdp Bank Deposits/GDP Lending-Deposit Spread rate Cameroon Central A. Rep Chad Congo Equatorial Guinea Gabon Average Table 2: Augmented Dickey-Fuller and Phillips-Perron Unit Root Tests Variable ADF t-statistic Order PP t-statistic Order RGDP(y) *** *** 1 Inflation(π) *** *** 0 Loans( l ) ** *** 1 Capital(c) *** *** 1 Lending (r L ) *** *** 1 Interest (r P ) *** *** 1 *** Denote significance at the 1% level and the rejection of the null hypothesis of non stationarity. Critical values are obtained from MacKinnon 1996 Table 3: Johansen s trace test results Variables Null Hypothesis Eigenvalue Trace Statistic 5% p-value y,π,l,c, r L, r P r=0 r 1 r 2 r 3 r 4 r Trace test indicates 2 cointegrating equations at the 0.05 level. Table 4: Unrestricted cointegration vectors Credit Capital Interest Lending RGDP Inflation Weak Exogeneity a 4.78 [0.091] 2.00 [0.36] [0.0000] 8.72 [0.012] 1.54 [0.46] [ ] a The test statistics are distributed as χ 2 with 2 degrees of freedom. P-values are reported in brackets. Society for Business Research Promotion 15