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Bank interest margins in OECD countries
North American Journal of Economics and Finance 19 (2008) 249–260
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North American Journal of Economics and Finance
Bank interest margins in OECD countries Kim Hawtrey a,∗, Hanyu Liang b a b
Department of Economics, Management and Accounting, Hope College, Holland, MI 49424, United States BCS Capital Sydney 2000, Australia
a r t i c l e
i n f o
Article history: Received 19 February 2007 Received in revised form 26 May 2008 Accepted 22 July 2008 Available online 8 August 2008 JEL classification: E4 Keywords: Bank Interest margin
a b s t r a c t This paper extends the literature on bank interest margins by providing empirical evidence using panel data covering the banking sector of fourteen OECD countries. Each country’s banking sector is treated as a single representative firm viewed as a national riskaverse dealer setting loan and deposit rates to balance the random arrivals of loan requests and deposit supplies. We find that national industry margins are influenced by market power, operational cost, risk aversion, interest rate volatility, credit risk, volume of loans, implicit interest payments and quality of management. © 2008 Elsevier Inc. All rights reserved.
1. Introduction Through the process of taking deposits and making loans, banks help the economy allocate funds from savers to borrowers in a more efficient manner (Brock & Suarez, 2000). The reward earned by the banking sector during this process depends largely on the average interest margin, which is commonly defined as interest revenue minus interest expense, per dollar of assets. This paper seeks to explain the factors that determine interest margins. Regularly updating our knowledge of interest margin determinants is valuable for a number of reasons, including monitoring changing trends in bank efficiency through time, and evaluating whether bank margins are providing effective price signals to market players. In addition, margin behaviour potentially has significant policy implications: for instance, Claeys and Vander Vennet (2004) argue that high interest margins may reflect an inadequate regulatory banking environment. Bank interest margins can be analysed on four levels. At the most ‘micro’ level, margins are determined at the highly disaggregated level of business division within each bank. Next, the ‘meta’ approach uses the banking conglomerate as the unit of analysis. Third is the ‘macro’ approach which employs
∗ Corresponding author. Tel.: +1 616 395 7915. E-mail address: [email protected] (K. Hawtrey). 1062-9408/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.najef.2008.07.003
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the combined industry data in a given country. And fourth there is the ‘continental’ approach where banking sectors in a given economic region of the globe are considered in aggregate. This paper uses the third approach. We model interest margins at the level of each country’s banking sector, treating the banking sector in terms of a single representative agent, and are interested in the determination of industry margins on a national basis. Our industry-wide approach complements another recent study of bank interest margins conducted at the individual bank level which stressed the differences between institutions (Maudos & Guevara, 2004). Our paper builds on that work by emphasising differences between systems, not institutions. By conducting tests at the nationally aggregated level, our work makes the contribution of providing a vital second opinion to existing studies conducted at the micro level. Our view is that the differences between countries’ banking sectors are as pertinent as differences between individual banks within that country. A particular benefit of our approach is that it takes seriously the view of the productive nature of the banking firm (Lerner, 1981), emphasizing cross-border differences in the production costs associated with the process of intermediation. While production costs may be fairly similar for banks within a given country (common wages rates, property values etc) they can be expected to show wider variability between countries. Moreover, from an econometric perspective, it also allows the role of fixed effects across panel data to be tested. Since financial deregulation in the late 1970s, banks within each of the countries studied here have seen significant rationalisation of their domestic banking industries via the market for corporate control. The effect of this more competitive environment has been that players within each market largely mimic one another and often act ‘as one’, and therefore the approach of treating each national banking sector as a single agent is both reasonable and interesting. Econometrically, we effectively consider each industry is like a national ‘team’, competing globally but with its own cost structure and regulatory framework. The paper is based on the standard well-specified general model of bank interest margins. Based on the dynamic dealership model developed by Ho and Saunders (1981) and its extensions, the representative bank is viewed as a dynamic risk-averse dealer aiming to maximize expected utility, setting loan and deposit rates to balance the random arrivals of loan requests and deposit supplies.1 This study employs single stage estimation to explain the pure margin as well as the unit-specific components in the regression. The empirical estimation covers banks of fourteen OECD countries over the period 1987 to 2001, the most recent cross-country data available. We employ a data set that is reasonably consistent across countries in regard to its institutional definitions and accounting classifications. Market power, operational cost, risk aversion, volatility of the interest rate, credit risk, economies of scale, implicit interest payments, quality of management and opportunity cost are the factors tested for determining banks’ interest margins. The rest of the paper is organized as follows: Section 2 reviews the relevant literature on determinants of bank interest margins. Section 3 outlines the methodology and discusses the data. Section 4 develops the empirical results, and Section 5 concludes the study and discusses the implications. 2. The bank interest margin literature The study of bank interest margins can be traced back to 1945, when Samuelson explained how increasing the level of the interest rate affected the banking system (Samuelson, 1945). A landmark study of the determinants of bank margins by Ho and Saunders model (1981) analysed margins as an extension of the hedging hypothesis and expected utility approach. In the model, a bank is assumed to be a risk-averse dealer in the loan and deposit market where the loan requests and deposit supplies arrive non-synchronously and the bank is assumed to maximize its expected utility of terminal wealth. The net spread equation: s=a+b=
˛ 1 + RI2 Q 2 ˇ
(1)
1 An alternative to the dynamic dealer approach is the firm-theoretical model, in which banks are assumed to set the deposit and loan rate simultaneously, such as in Zarruck and Madura (1992) and Wong (1997).
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derived by the authors indicates the pure margin determinants where a is the margin on deposits, b the margin on loans, ˛/ˇ measures market power, R is the bank’s risk aversion, I2 is the variance of the interest rate on deposits and loans, and Q measures the size of bank transactions. Empirical tests of the above model have yielded conflicting results. Empirical studies notably include Angbazo (1997), Saunders and Schumacher (2000), Gischer and Juttner (2002), Maudos and Guevara (2004) and Hanweck and Ryu (2005). Saunders and Schumacher (2000) conducted an international study of banks’ net interest margin determination over the period 1988 to 1995, and found that implicit interest payments, opportunity cost, the capital-to-asset ratio, market power and rate volatility are each positively related to the net interest margin. However, due to the difficulties of specifying risk aversion and the size of transactions, these two variables were omitted from the model. Gischer and Juttner (2002) investigated the impact of global competition on interest rate margins and in contrast to Angbazo (1997), argued that improvements in managerial effectiveness should act to narrow banks’ interest margins. Using a sample period in the 1990s, they found that banks’ interest margins are negatively related to the degree of global competition, fees-to-interest income ratio and cost structure, and positively related to gross income volatility and market power applied. In Maudos and Guevara (2004), operating cost was included and a direct estimation of market power applied in an empirical study covering five European countries’ banking firms from 1993 to 2000. The results suggested that market power, operational cost, risk aversion, interest rate risk, credit risk, implicit interest payments, opportunity cost and quality of management are all positively related to the net interest margin, which is consistent with the theoretical model developed in Ho and Saunders (1981). The size of bank transactions was found to be inversely related to bank spread, which conflicted with theoretical priors but which we argue need not be surprising because transaction size is likely to be a proxy for scale economies. In the present paper, in contrast to Maudos and Guevara we predict margins to decline as bank scale increases, on account of the standard cost economies of scale effect. Hanweck and Ryu (2005) develop a dynamic bank behavior model which suggests banks of differing sizes react differently to credit, interest-rate, and term-structure shocks when altering net interest margins. Their empirical study using quarterly data from 1986 to 2003 found that banks appear to be more sensitive to credit risks than to interest-rate changes, which is in sharp contrast to Angbazo (1997). A group of studies have looked at selected countries, with varying results. Catao (1998) studies Argentina and finds that operating costs, problem loans, exchange rate risk and the cost of liquidity are all positively related to banks spreads. Barajas, Steiner and Salazar (1999) estimate the elements that explain a high bank interest margin during the pre-liberalization (1974–1988) and post-liberalization (1991–1996) periods in Colombia; the interest rate spread is specified as a function of mark power, volume of loans, wage rate and problem loans. Brock and Suarez (2000) estimate the factors that affect interest margins across six Latin American countries (Argentina, Bolivia, Chile, Colombia, Mexico, and Peru), where the capital-to-asset ratio has no significant effect on interest margins but liquidity ratio and cost ratio are each found to be positively related to margins. Ramful (2001) examines the determinants of interest rate margin in the Mauritian banking sector and finds that operating expenses, doubtful debts and market share affect interest margins positively. Afanasieff, Lhacer and Nakane (2002) use a panel regression of 142 Brazilian banks and find that size of bank, opportunity cost and operating cost are positively related to interest margins but a set of macroeconomic variables such as the market interest rate, the volatility of the market interest rate, inflation rate and output growth heavily affect margins as well. Our work is motivated by the inconclusive nature of the empirical literature surveyed above. We seek to shed light on the apparently contradictory results in earlier studies by providing a comprehensive cross-country study. A prior cross-country study was conducted by Demirguc-Kunt and Huizinga (1999) using data for eighty industrial and developing countries over the period 1988 to 1995, however their particular concern was exogenous influences such as macroeconomic indicators, tax rates and the degree of international ownership, all of which were found to be positively related to interest margins. Complementing their work, the cross-country results reported here put the focus is on internal influences on bank margins.
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3. Data and methodology 3.1. The data The data for this study are mostly obtained from OECD (Organisation for Economic Cooperation and Development) annual publication Bank Profitability: Financial Statements of Banks (2003). This publication provides uniform information on financial statements of banks in OECD member countries for the period 1987–2001. This is the most recent date for which consistent banking industry data are available across such a range of countries. The annual data over the period 1987 to 2001 include fourteen selected OECD countries: Austria, Belgium, Denmark, Finland, France, Germany, Italy, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and United States. The accounting framework used in the database for presenting the statistics is in accordance with the recommendations of the OECD Task Force on Bank Profitability. The coverage defines ‘bank’ as all institutions which conduct ordinary banking business, primarily taking deposits from the public at large and providing loans for a wide range of purposes. The institutional coverage is generally uniform, however is based on national criteria and on account of structural and regulatory differences between national systems, differences occur in some cases. In particular, the coverage for Finland includes not only deposit banks narrowly defined but also credit institutions such as finance companies and mortgage companies; France includes municipal financial institutions and some specialized institutions; Germany includes credit cooperatives and regional giro institutions; Spain includes credit cooperatives; Portugal includes two bank-like credit institutions; and the US includes federally-insured savings and loans institutions. The statistics for the remaining countries - Austria, Belgium, Denmark, Italy, Norway, Sweden, Switzerland, UK - relate to banks as traditionally understood, institutions granted a banking licence by the relevant government financial system authority. The data extracted from the OECD statistics include the following: net interest income, net interest expense, total assets, total revenue, total costs, operating expenses, loans, securities, other assets, noninterest income, cash, and central bank balances. Definitions of the explanatory variables and how they were constructed using these raw data are outlined in Section 3.4. For each country, the data were pre-aggregated by the OECD. Daily or monthly interest rates (according to availability) have been obtained for each country and sorted into annual averages from 1987 to 2001. 3.2. Econometric approach We use panel data, which has the advantage that certain effects which may not be observable in pure time series data can be detected and measured (Gujarati, 2003). The present study presents an opportunity to compare the merits of alternative panel regression approaches as applied to the analysis of bank interest margins. We test a number of panel data models including the pooled regression model (PRM), fixed effects model (FEM), random effects model (REM) and Generalised Least Squares (GLS). In the Pooled Regression Model all coefficients are constant across time and individuals. In general, the pooled model is: yit = ˇxit + uit
i = 1 . . . N, t = 1 . . . T
(2)
where uit is independently and identically distributed (i.i.d). A limitation of pooled regression is that the specific nature of each cross section is ignored. This can be addressed using a Fixed Effects Model (FEM), where the specification includes a unit specific component: yit = ˛ + ˇxit + zit + uit
i = 1 . . . N, t = 1 . . . T
(3)
where z is unobserved, and x and z are correlated. However a drawback of the FEM specification may be that the time invariant effects and their coefficients fall out and cannot be identified. To take into account the individual specific components, we can use the Least Squares Dummy Variable (LSDV) methodology. This means z can be interpreted as an intercept of observation i by including a dummy
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variable for each cross-section unit. That is, we have: yit = ˛1 + ˛2 D2 · · ·˛N DN + · · · + ˇxit + uit
(4)
In the LSDV approach, unobserved time effects are obtained by including time dummies and the variables are homogeneous across cross-sections but differential through time. Both cross-section and time dummy variables may be included if the intercept varies over individuals as well as time. Alternatively, time invariant factors can be incorporated using the Random Effects Model (REM). Consider the model again: yit = ˛ + ˇxit + zi + uit
i = 1 . . . N, t = 1 . . . T
(5)
In (5) ˛ represents the mean value of all the cross-sectional intercepts and the error component zi represents the deviation of the individual intercept from the mean value. The individual error components are assumed to be uncorrelated with each other and are not autocorrelated across cross-section units. Therefore, the random error zi is homogeneous over time but different across cross sections. An advantage of the REM model is that time-invariant factors are included in the regressions. We find it necessary to employ General Least Squares estimation (GLS). If autocorrelation is present in a panel analytic model, the panel data estimator is no longer the best linear unbiased estimator (BLUE). To account for this problem, common tests of serial correlation in the residuals such as Durbin-Watson, Breusch-Godfrey or Box-Pierce-Ljung may not be sufficient, because in panel estimation autocorrelation may be present not only from one period to another but also across each cross section. In addition, heteroskedasticity can affect the efficiency of the estimation. The GLS Estimator can account for these two problems. Unlike static models where all relationships are between variables at the same point in time, GLS is a simple dynamic panel model which describes how phenomena develop over time. The GLS estimator is largely used in estimations with heteroskedastic and/or autocorrelated residuals. For practical reasons, the FGLS (Feasible General Least Squares) estimator which is asymptotically efficient is utilized in this paper. 3.3. Dependent variable We follow the model established by Ho and Saunders (1981), as amended by Maudos and Guevara (2004), applied to treat the whole banking sector of a given country as a single decision maker. It is assumed the representative bank plays a risk-averse dealership role in the market by offering to take deposits (at interest rate rD ) and at the same time meet demand for loans (at interest rate rL ). At the beginning of the period the bank sets interest rates for the decision period, which then remain constant until the end of the period. The bank sets its interest rates as a margin to the current cost of funds in the money market (r). This can be expressed as follows: rD = r − a
(6)
rL = r + b
(7)
s = rL − rD = a + b
(8)
The bank applies the margin s which anticipates compensation for possible future risks of volatility of interest rates on the short-term money market and credit risk on loans. We estimate an equation in which the dependent variable is the average Net Interest Margin (NIM), defined as the difference between the representative bank’s interest income and interest expense expressed as a ratio to average total assets. 3.4. Explanatory variables Based on theory and the literature on bank interest margins, the explanatory variables employed in our estimation are:
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3.4.1. Market Structure (LG) The Lerner Index, which can be characterised as the negative inverse of demand elasticity, is used as a proxy of market power. A high Lerner Index indicates a strong degree of monopoly in the banking market, while in a highly competitive market the sector has less capacity to set high margins resulting in a low Lerner index. The difference between price and average cost divided by price is used as a proxy of the Lerner Index. Hence, the equation is: Lerner =
TR − TC p − AC = p TR
(9)
where p is TR/TA, TR is total revenue, TC is total cost and TA is total assets.2 3.4.2. Operating cost (OC) Operational cost is measured by the ratio of operating expenses to average total assets. Banks experiencing high operating costs will apply high interest spreads. 3.4.3. Degree of risk aversion (RA) It is not easy to find an ideal specification of the bank’s degree of risk aversion. McShane and Sharpe (1985) use the bank’s capital ratio as a proxy, measured as the ratio of shareholders’ funds to total assets of the bank. Maudos and Guevara (2004) also adopt the same indicator. However this measure can be artificially affected by accounting conventions such as asset revaluations and the treatment of various contingent accounts and risk management of a bank relates to risk of the overall portfolio. As well, banks operating in low risk segments of loan markets may still choose a low capital ratio on account of a relatively high risk preference, suggesting that the risk exposure of a low capital ratio institution need not necessarily exceed that of a high capital ratio institution if the high capital ratio is offset by a more risky loan portfolio. In this study we employ an alternative measure, securities plus other assets divided by volume of loans (where ‘other assets’ are assets after excluding loans, securities, inter-bank deposits, cash and balance with central bank). Everything else being equal, risk averse bank managers tend to impose an extra bank interest margin as a compensation of taking systematic risk. Therefore, degree of risk aversion is expected to be positively related to banks’ net interest margins. 3.4.4. Interest rate volatility (V) There is a variety of measures available to proxy interest rate volatility, based on previous studies. We use the standard deviation of daily ten year interest rates as the measure of interest rate risk, where the rate is the ten year yield on central government bonds. We also tested three month money market rates but found it made no substantial difference to the results. 3.4.5. Credit risk (CR) According to BIS (1999), credit risk is defined as ‘the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms’. One approach involves the capitalto-assets ratio being utilized as a measure of credit risk. However, according to Basel II, the capital requirement is based on risk adjusted assets for which the data are not available, and if using raw asset data, the results related to this variable could be doubtful (Gischer & Juttner, 2002). The volume of loans to average total assets ratio is used as our measure of credit risk since ‘loans are the largest and most obvious source of credit risk for most banks’ (BIS, 1999). This is the same measure used by Maudos and Guevara (2004). If banks charge additional interest margins to compensate for exposure to expected and unexpected credit risk, it will principally be in relation to their loan book. Therefore, banks’ interest margins are expected to be positively related to credit risk.
2 The Lerner Index is preferred here to the Herfindahl index (defined as the sum of the squares of the market shares of each individual firm) since the more static measure of market power may not capture the degree of competition. The Lerner Index captures more information about the actual price-setting behaviour of banks in relationship to their cost structures than the size of banks whether measured in terms of deposits, relative size of balance sheets or income generated (Gischer & Juttner, 2002).
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3.4.6. Interaction between credit risk and market risk (IN) Interaction between credit risk and market risk is measured as product of credit risk and the standard deviation of the interest rate. Two alternative interest rates are used: the three month rate and ten year rate respectively. As indicated earlier, however, the value of this variable may be too small to be a factor materially affecting the dependent variable significantly. 3.4.7. Scale effects (LN) According to the study of Ho and Saunders (1981), the size of bank transactions should have a positive impact on banks’ interest margins. Maudos and Guevara (2004) also state that the bank will apply a greater margin on sizable transactions. The reason is that, assuming everything else being equal, the larger the size of a transaction, the larger the potential loss will be. However, in the real world, it is known that larger customers normally enjoy narrower margins, and that larger banks can offer narrower margins to clients than small banks. Compared to smaller transactions, larger transactions reduce the frequency of operations and spread administrative overheads across a larger base, which reduces a bank’s operating expenses per dollar of revenue. A hurdle faced by researchers is the difficulty in obtaining data of average real loan and deposit transaction sizes. McShane and Sharpe (1985) assume the size of the transaction is invariant across trading banks and time. Maudos and Guevara (2004) specify the size of transactions as the natural logarithm of volume of loans. Our approach is to use the same variable but we see it as capturing something different: scale economies. Recall from the theoretical model the expression for unit costs: the term (C(L)/L + C(D)/D)/2 is the average operational cost of loans and deposits. It is positively related to the bank’s interest margin, which implies that banks recording higher operational costs will impose an extra interest margin. Accordingly, the logarithm of the volume of loans is employed in this paper upon the reasoning that an increased volume of loans should result in a reduction of unit costs, which achieves economies of scale and results in narrower margins. Hence, a negative sign is expected. 3.4.8. Implicit interest payments (I) In order to cover the cost of the banking services, banks impose extra interest margins which are the so-called ‘implicit’ interest payments. Based on Ho and Saunders (1981), Angbazo (1997), Saunders and Schumacher (2000) and Maudos and Guevara (2004), this factor is denoted by the difference between operating expenses and non-interest income divided by average total assets. Banks’ interest margins should fall as implicit interest benefits decline. Inspection of the data shows that overall, the implicit interest payments to total assets ratios have been decreasing steadily over the period across the countries in our sample, which implies that previously ‘free’ banking services have been transformed into a more transparent fee charging mode. 3.4.9. Opportunity cost of bank reserves (O) According to the regulatory and prudential rules set by BIS, reserve or liquidity requirements and minimum capital requirements should be applied by the national supervisory authority. The opportunity cost of keeping these reserves is the additional interest rate that can be obtained in the open financial market. Therefore, the larger the amount of reserves, the greater the opportunity cost. The opportunity cost of bank reserves is represented by cash plus balances with central bank, expressed as a percentage of average total assets. 3.4.10. Managerial efficiency (Q) According to Angbazo (1997) as well as Maudos and Guevara (2004) the higher the quality of management of a bank, the higher the interest margins that will be imposed by the bank, on the grounds that a high quality of administration implies a high-yield and low-cost composition of assets and liabilities. Gischer and Juttner (2002) suggest that improved quality of management should narrow down the interest margin due to efficiency, but the empirical results of their study do not support the viewpoint. Claeys and Vander Vennet (2004) use the inverse of total overhead costs to total assets to measure efficiency and find it affects interest margins negatively in developed countries. The loansto-employee ratio is utilized by Brock and Franken (2003) and is found to have a negative impact on
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interest margins in Chile. In this paper, operational expenses as a ratio of gross income are used as the proxy of quality of management. 3.5. Testing equation Based on the analysis above, our estimating equation is: NIMit = c1 + c2 (Market structure) + c3 (Operating cost) + c4 (Risk aversion) + c5 (Interest rate risk) + c6 (Credit risk) + c7 (Interaction) + c8 (Economies of scale) + c9 (Implicit interest payments) + c10 (Opportunity cost) + c11 (Managerial efficiency) Simply, Nit = c1 + c2 (LGit ) + c3 (OCit ) + c4 (RAit ) + c5 (Vit ) + c6 (CRit ) + c7 (INit ) + c8 (LNit ) + c9 (Iit ) + c10 (Oit ) + c11 (Qit ) + uit
(10)
where N = (Total Interest Income − Total Interest Expenses)/(Average Total Assets) LG = (Total Revenue − Total Cost)/(Total Revenue) OC = (Operating Expenses)/(Average Total Assets) RA = (Securities + Other Assets)/(Average Total Assets) V = s.d. of daily yield of government bond with 10 year maturity (SD10Y) CR = Loans/(Average Total Assets) IN = CR × SD10Y LN = Loans (logarithm of) I = (Operating Expenses − Non-interest Revenues)/(Average Total Assets) O = (Cash and Balance with Central bank)/(Average Total Assets) Q = (Operating Cost)/(Gross Income) 4. Results Following McShane and Sharpe (1985), Angbazo (1997) and Maudos and Guevara (2004), a singlestage approach is employed in this study. That is, the model incorporates in the regression in one step both variables explaining the pure margin such are market structure, scale and risk aversion, as well as the unit-specific variables like managerial efficiency, implicit interest payments, opportunity cost and credit risk. Annual country-based data over the period 1987 to 2001 are used in the estimation. Several alternative methods of panel regression are tested, for comparison purposes. Moreover, we estimate the equation with different types of measures of interest risk to check the robustness of the results. 4.1. Econometric issues Our basic test employed pooled least squares estimation with cross-section and period fixed (FEM) effects. We tested the effect of including or excluding the cross-section and period fixed effects because in this type of model it is important to evaluate model fit under alternative assumptions regarding fixed effects across countries. Introducing cross-section and period fixed effects increases the Durbin-Watson statistic from around 0.65 to 1.29. Furthermore, the smaller values of standard error of regression, sum squared residuals, AIC, Schwarz criterion also imply improvement of the model fit. We therefore prefer the model with cross-section and period fixed effects included. Also, in this data sample fourteen countries have been included, which is less than the number of time effects, and therefore a cross-section SUR weighted least squares regression (the Parks estimator) has been run which allows the residuals to be both cross-sectionally heteroskedastic and contemporaneously correlated. The Parks estimator is applicable when the number of periods exceed the number of cross section members.
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A concern that arose in our initial estimates was a relatively low value of Durbin-Watson statistic, indicating the possible presence of autocorrelation in the residuals. The Durbin-Watson statistic is formed simply by computing the first-order residual correlation on the stacked set of residuals. Therefore, it was initially unclear whether the residuals were cross-sectionally autocorrelated: the pooled OLS estimator might not be not BLUE if that were the case. In panel data the estimator is BLUE if the untransformed errors, uit , are uncorrelated and homoskedastic. When the assumption of homoskedasticity is violated, OLS is still unbiased but no longer efficient. However, White’s heteroskedasticity-consistent procedure is robust to heteroskedasticity (Woodlridge, 2002). In this case, estimating White’s heteroskedasticity-consistent procedure and/or feasible GLS specifications can account for this problem. The model was therefore re-estimated with Robust Standard Error (RSE) and/or GLS weights. Although a limitation of the procedure is that the formula only has asymptotic justification, computing robust standard errors will assure correct inference. By introducing White cross-section standard errors which are robust to contemporaneous correlation as well as different error variances across countries, the initial model was re-estimated. We conducted a test for equality of the variances between series in the original model and determinedly rejected the null hypothesis of equal variance of the residuals across countries, providing strong evidence of the presence of groupwise heteroskedasticity. As an additional check on our model, we also tested random cross section effects, in place of fixed effects, an alternative that is less restrictive on the data. The panel procedure allowed us to deal with random effects using Feasible Generalised Least Squares (FGLS) techniques which account for various patterns of correlation between the residuals. The results were essentially unchanged, and indeed it must be said that from a theoretical point of view, the benefits of introducing random effects are open to debate. Traditionally, the emphasis has been on whether zi is correlated with explanatory variables or not. If zi is correlated with explanatory variables the fixed effects model can be consistent, whereas if zi is uncorrelated with explanatory variables a random effects model will be appropriate. This is largely the difference in the two approaches, and is essentially an empirical question to do with model fit. Moreover, whilst Gujarati (2003) shows that if the number of time series data is large relative the number of cross-section units then random effect models may be more efficient than fixed effect models, that is not the case here. In any case, the fixed effect model performs better in this study than the random effect model. Table 1 shows the preferred model. It is a pooled estimation model with robust standard errors using GLS weights with fixed cross-section effects. The specification in employs a heteroskedasticrobust variance estimator (White cross-section standard errors). The Durbin-Watson test indicates no serial autocorrelation. In addition, Table 1 incorporates period weights which allow for period heteroskedasticity. It is found the cross-section SUR weighted least squares estimator introducing individual effects works well. Also, we dropped cross section effects and period effects respectively and found that the model with only cross section effects (using white cross-section standard errors and period GLS weights) performs best. 4.2. Parameter estimates The model variables generally present the expected sign and are highly significant. All the explanatory variables are positively related to banks’ interest margins except the log of the volume of loans (LN) and managerial efficiency (Q). This outcome is consistent with the theoretical priors. Notably, the coefficients are all significant at 5% level except opportunity cost. The Lerner Index (LG), as a proxy of the market power, is positively related to net interest margins, and is statistically significant. This result is consistent with our expectation that banks with monopoly power can charge a higher loan rate and offer a lower deposit rate. The impact of operational cost (OC) measured by the ratio of the operating expenses to the average total assets is also positive and statistically significant, which implies that banks may impose an extra interest margin to cover the high operational cost. The measure of risk aversion (RA) has the expected positive relationship with banks’ interest margin, which confirms that the risk averse bank manager tends to apply an extra interest margin. Interest risk (V) measured by annual standard deviation of daily interest rate presents the positive sign which is expected. Credit risk (CR) expressed as loan to average total assets ratio affects
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Table 1 Pooled estimation with fixed cross-section effects using GLS weights Variable LG OC RA V CR LN I O Q AUT BEL DEN FIN FRA GER ITA NOR SPA SWE SWI UK POR USA
Coefficient
Std. Error
t-Statistic
Prob.
0.029295 0.249085 0.001143 0.000840 0.007308 −0.002872 0.938153 0.018099 −0.030460 0.051699 0.052860 0.055749 0.048655 0.057741 0.057331 0.055114 0.055780 0.054368 0.055454 0.054011 0.055321 0.050159 0.061839
0.000777 0.008737 0.000191 5.01E−05 0.000458 0.000144 0.006080 0.000908 0.000443 0.001683 0.001699 0.001929 0.001665 0.001926 0.001988 0.001910 0.001897 0.001829 0.001999 0.001878 0.001856 0.001659 0.002168
37.71750 28.50775 5.983170 16.76414 15.96681 −19.95543 154.2968 19.92382 −68.73203 30.70932 31.10374 28.90530 29.22982 29.98473 28.83589 28.85880 29.40781 29.72782 27.73553 28.75376 29.80105 30.23037 28.52082
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Weighted Statistics R-squared Adjusted R-squared S.E. of regression F-statistic Prob(F-statistic)
0.999853 0.999836 0.958400 56656.90 0.000000
Mean dependent var S.D. dependent var Sum squared resid Durbin-Watson stat
−10.95675 74.73763 168.0910 1.965171
Note to table: Sample period: 1987–2001 annual data. Observations: 15. Cross-sections included: 14. Total pool (unbalanced) observations: 206. Method of estimation: Pooled GLS with fixed cross-section effects. IN omitted because insignificant. Fixed effects (cross-section) estimated coefficients: Austria (AUT) −0.004500, Belgium (BEL) −0.003257, Denmark (DEN) 0.001476, Finland (FIN) −0.009040, France (FRA) 0.003937, Germany (GER) 0.004545, Italy (ITA) 0.000543, Norway (NOR) 0.002080, Spain (SPA) −0.000826, Sweden (SWE) 0.001356, Switzerland (SWI) −0.000353, United Kingdom (UK) 0.000741, Portugal (POR) −0.007282, United states (USA) 0.010580. Fixed effects (period) estimated coefficients: 1987 −0.001815, 1988 −0.000857, 1989 −2.99E−05, 1990 0.000433, 1991 0.001242, 1992 0.000904, 1993 −2.62E−05, 1994 −0.000828, 1995 2.45E−05, 1996 −0.000205, 1997 −5.53E−05, 1998 −0.000314, 1999 −0.000862, 2000 0.000785, 2001 0.001034.
interest margins positively and significantly, which suggests that increased credit risk may cause an increase of banks’ interest margins. Interestingly, the log of the volume of loans (LN) imposes a negative effect on bank interest margins and is statistically significant at the 1% level, which confirms our assumption that increased volume of loans may result in a reduction of unit cost, which achieves economies of scale. Moreover, the payment of implicit interest (I) still shows a highly explanatory capacity, which confirms the importance of this variable. Implicit interest payments (I) here are noteworthy, as they have such a highly significant affect on interest margins. Opportunity cost (O) measured by cash and balance with central bank to average total assets ratio presents an expected positive sign and is statistically significant. Managerial efficiency (Q) is proxied by operating expenses to gross income ratio. The negative signs in both tables indicate that the lower the quality of management the narrower margin the bank may charge. This outcome is consistent with Angbazo (1997) and Maudos and Guevara (2004) but in contrast to Gischer and Juttner (2002). The standard deviations of daily yield of government bonds with ten years maturity are employed to measure interest risk (V). As an alternative, we also tested the model using the standard deviation of the monthly yield on government bonds with ten years maturity as the proxy of interest rate volatilities. Moreover, we tested the model using interest rates with three months maturity (both
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daily and monthly standard deviations, respectively). We found throughout our tests that the results were not particularly sensitive to the choice of interest rate. Accordingly, we have employed the daily yield of ten year bonds and not reported the results for the various other measures of interest rate volatility. Surprisingly, the effect of the interaction between interest risk and credit risk (IN) was found to be insignificant in the model. Neither including nor excluding this variable improves the other factor’s significance, and does not affect the adjusted R-squared value and other tests, but does improve the significance of the interest risk (V). Hence, multicollinearity between V and IN may be involved, so it is not reported in Table 1. 5. Conclusions and implications This study has analysed the empirical determinants of banking industry interest margins using a representative bank approach. Based on the model developed by Ho and Saunders (1981) the industry in each country is assumed to behave like a single risk-averse dealer in the loan and deposit market where the loan requests and deposits supplies arrive non-synchronously. The empirical analysis follows a single stage model and includes fourteen OECD countries’ banking sectors over the period 1987 to 2001. The empirical findings are consistent with our theoretical analysis. Scale effects, measured by the log of industry loan volume, are found to have a negative and significant effect on margins. The empirical analysis also shows that managerial efficiency, proxied by operating expenses to gross income ratio, has a negative coefficient, which means banks experiencing managerial efficiency are able to obtain higher interest loans and lower cost deposits. This outcome is consistent with Angbazo (1997) and Maudos and Guevara (2004) but in contrast to Gischer and Juttner (2002). We find that market power, operating cost, risk aversion, volatility of interest rate, credit risk, opportunity cost and implicit interest payments are all positively related to banks’ interest margins. The intensified competition of domestic banking markets has acted to reduce bank interest margins. Implicit interest payments (I) captures the transfer of ‘free’ services from implicit interest payments into explicit fees and charges. Operational cost (OC) is the next factor which is found to influence the bank margins significantly, which is consistent with the findings of Maudos and Guevara (2004). A 1% increase of operational cost will lead to a 0.1% to 0.2% increase of banks’ interest margins. The industry in most countries has experienced a decrease in operating expenses largely due to the increasing use of phone banking, internet banking, ATMs and EFTPOS. The effect of opportunity cost, unfortunately, is ambiguous in our empirical study. Theoretically, opportunity cost should explain the banks’ interest margins positively. Unfortunately, in most of our robust standard error and/or GLS weights estimators, the effect of opportunity cost appears to be insignificant. Pooled estimation, fixed effect estimation, random effect estimation and FGLS estimation were employed by turn in the regression analysis. According to the regression output, the fixed effect model performs better here than the random effect model and the re-estimation of the basic fixed effects model using GLS weights with fixed cross-section effects performs best and generates the preferred specification in this study. All the coefficients of the explanatory variables present the expected sign and are statistically significant at 1% level. The preferred estimates also incorporate robust standard errors which ensure the correct inferences from the estimation. In terms of directions for future research, a number of variables relevant to interest margins representing fruitful avenues for further work have not been included in this study due to restricted availability of data, or the limitations stemming from the degrees of freedom of the model, or the potential for multicollinearity between variables. One avenue for further work concerns influences external to the banking industry, Taxation effects have to date be incorporated into the model: the results Demirguc-Kunt and Huizinga (1999) provide a hint that banks’ interest margins may respond to corporate income tax rates. Another little researched aspect involves returns on competing investment instruments. According to Koch and McDonald (2003), theory says that competitive pressures on both the cost of bank funds and yields on earning assets should affect margins. A general equilibrium model that nests bank interest margins in the context of a spectrum of financial market returns represents an interesting path for future research.
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References Afanasieff, T., Lhacer, P., & Nakane, M. (2002). The determinants of bank interest spread in Brazil, Centre for Latin American Monetary Studies. Money Affairs, 15(July-December (2)), 183–207. Angbazo, L. (1997). Commercial bank net interest margins, default risk, interest rate risk and off-balance sheet banking. Journal of Banking and Finance, 21, 55–87. Barajas, A., Steiner, R., & Salazar, N. (1999). Interest spreads in banking in Colombia, 1974–1996. IMF Staff Papers, 40(2), 196–224. Bank for International Settlements (BIS). (1999). Credit risk modelling: Current practices and applications. Switzerland: Basel Committee. Brock P. & Franken H. (2003, September). Measuring the determinants of average and marginal bank interest rate spreads in Chile, 1994–2001 (Working Paper). University of Washington. Brock, P. L., & Suarez, L. R. (2000). Understanding the behavior of bank spreads in Latin America. Journal of Development Economics, 63, 113–134. Catao, L. (1998). Intermediation spreads in a dual currency economy: Argentina in 1990s (IMF Working Paper, WP98/90). Washington: IMF. Claeys, S., & Vander Vennet, R. (2004). Determinants of bank interest margins in Central and Eastern Europe: A comparison with the West (mimeo). Ghent University. Demirguc-Kunt, A., & Huizinga, H. (1999). Determinants of commercial bank interest margins and profitability: Some international evidence. World Bank Economic Review, 13(2), 379–408. Gischer, H., & Juttner, D. J. (2002). Global competition, fee income and interest rate margins of banks. In Financial management association conference. Gujarati, D. (2003). Basic econometrics (4th ed.). New York: McGraw Hill. [638-652]. Hanweck, G., & Ryu, L. (2005). The sensitivity of bank net interest margins and profitability to credit, interest-rate, and term-structure shocks across bank product specializations (Working Paper 05-02 FDIC Working Papers). New York: Federal Deposits Insurance Corporation. Ho, T., & Saunders, A. (1981). The determinants of bank interest margins: Theory and empirical evidence. Journal of Financial and Quantitative Analysis, 16, 581–600. Koch, T. W., & McDonald, S. (2003). Bank management (5th ed.). Thomson Southwestern Publishers. Lerner, E. M. (1981). The determinants of banks interest margins: Theory and empirical evidence. Journal of Financial and Quantitative Analysis, XVI(4), 601–602. Maudos, J., & Guevara, F. (2004). Factors explaining the interest margin in the banking sectors of the European Union. Journal of Banking and Finance, 28(9), 2259–2281. McShane, R. W., & Sharpe, I. G. (1985). A time series–cross section analysis of the determinants of Australian trading bank loan–deposit interest margins: 1962–1981. Journal of Banking and Finance, 9, 115–136. Ramful, P. (2001, May). The determinants of interest rate spread: empirical evidence on the Mauritian banking sector. Research Department, Bank of Mauritius. Samuelson, P. (1945). The effects of interest rate increases on the banking system. American Economic Review, (March). Saunders, A., & Schumacher, L. (2000). The determinants of bank interest rate margins: An international study. Journal of International Money and Finance, 19, 813–832. Wong, K. P. (1997). On the determinants of bank interest margins under credit and interest rate risks. Journal of Banking and Finance, 21, 251–271. Woodlridge, J. (2002). Econometric analysis of cross-section and Panel Data. MIT Press. Zarruck, E. R., & Madura, J. (1992). Optimal bank interest margin under capital regulation and deposit insurance. Journal of Financial and Quantitative Analysis, 27, 143–149.
Contents lists available at ScienceDirect
North American Journal of Economics and Finance
Bank interest margins in OECD countries Kim Hawtrey a,∗, Hanyu Liang b a b
Department of Economics, Management and Accounting, Hope College, Holland, MI 49424, United States BCS Capital Sydney 2000, Australia
a r t i c l e
i n f o
Article history: Received 19 February 2007 Received in revised form 26 May 2008 Accepted 22 July 2008 Available online 8 August 2008 JEL classification: E4 Keywords: Bank Interest margin
a b s t r a c t This paper extends the literature on bank interest margins by providing empirical evidence using panel data covering the banking sector of fourteen OECD countries. Each country’s banking sector is treated as a single representative firm viewed as a national riskaverse dealer setting loan and deposit rates to balance the random arrivals of loan requests and deposit supplies. We find that national industry margins are influenced by market power, operational cost, risk aversion, interest rate volatility, credit risk, volume of loans, implicit interest payments and quality of management. © 2008 Elsevier Inc. All rights reserved.
1. Introduction Through the process of taking deposits and making loans, banks help the economy allocate funds from savers to borrowers in a more efficient manner (Brock & Suarez, 2000). The reward earned by the banking sector during this process depends largely on the average interest margin, which is commonly defined as interest revenue minus interest expense, per dollar of assets. This paper seeks to explain the factors that determine interest margins. Regularly updating our knowledge of interest margin determinants is valuable for a number of reasons, including monitoring changing trends in bank efficiency through time, and evaluating whether bank margins are providing effective price signals to market players. In addition, margin behaviour potentially has significant policy implications: for instance, Claeys and Vander Vennet (2004) argue that high interest margins may reflect an inadequate regulatory banking environment. Bank interest margins can be analysed on four levels. At the most ‘micro’ level, margins are determined at the highly disaggregated level of business division within each bank. Next, the ‘meta’ approach uses the banking conglomerate as the unit of analysis. Third is the ‘macro’ approach which employs
∗ Corresponding author. Tel.: +1 616 395 7915. E-mail address: [email protected] (K. Hawtrey). 1062-9408/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.najef.2008.07.003
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the combined industry data in a given country. And fourth there is the ‘continental’ approach where banking sectors in a given economic region of the globe are considered in aggregate. This paper uses the third approach. We model interest margins at the level of each country’s banking sector, treating the banking sector in terms of a single representative agent, and are interested in the determination of industry margins on a national basis. Our industry-wide approach complements another recent study of bank interest margins conducted at the individual bank level which stressed the differences between institutions (Maudos & Guevara, 2004). Our paper builds on that work by emphasising differences between systems, not institutions. By conducting tests at the nationally aggregated level, our work makes the contribution of providing a vital second opinion to existing studies conducted at the micro level. Our view is that the differences between countries’ banking sectors are as pertinent as differences between individual banks within that country. A particular benefit of our approach is that it takes seriously the view of the productive nature of the banking firm (Lerner, 1981), emphasizing cross-border differences in the production costs associated with the process of intermediation. While production costs may be fairly similar for banks within a given country (common wages rates, property values etc) they can be expected to show wider variability between countries. Moreover, from an econometric perspective, it also allows the role of fixed effects across panel data to be tested. Since financial deregulation in the late 1970s, banks within each of the countries studied here have seen significant rationalisation of their domestic banking industries via the market for corporate control. The effect of this more competitive environment has been that players within each market largely mimic one another and often act ‘as one’, and therefore the approach of treating each national banking sector as a single agent is both reasonable and interesting. Econometrically, we effectively consider each industry is like a national ‘team’, competing globally but with its own cost structure and regulatory framework. The paper is based on the standard well-specified general model of bank interest margins. Based on the dynamic dealership model developed by Ho and Saunders (1981) and its extensions, the representative bank is viewed as a dynamic risk-averse dealer aiming to maximize expected utility, setting loan and deposit rates to balance the random arrivals of loan requests and deposit supplies.1 This study employs single stage estimation to explain the pure margin as well as the unit-specific components in the regression. The empirical estimation covers banks of fourteen OECD countries over the period 1987 to 2001, the most recent cross-country data available. We employ a data set that is reasonably consistent across countries in regard to its institutional definitions and accounting classifications. Market power, operational cost, risk aversion, volatility of the interest rate, credit risk, economies of scale, implicit interest payments, quality of management and opportunity cost are the factors tested for determining banks’ interest margins. The rest of the paper is organized as follows: Section 2 reviews the relevant literature on determinants of bank interest margins. Section 3 outlines the methodology and discusses the data. Section 4 develops the empirical results, and Section 5 concludes the study and discusses the implications. 2. The bank interest margin literature The study of bank interest margins can be traced back to 1945, when Samuelson explained how increasing the level of the interest rate affected the banking system (Samuelson, 1945). A landmark study of the determinants of bank margins by Ho and Saunders model (1981) analysed margins as an extension of the hedging hypothesis and expected utility approach. In the model, a bank is assumed to be a risk-averse dealer in the loan and deposit market where the loan requests and deposit supplies arrive non-synchronously and the bank is assumed to maximize its expected utility of terminal wealth. The net spread equation: s=a+b=
˛ 1 + RI2 Q 2 ˇ
(1)
1 An alternative to the dynamic dealer approach is the firm-theoretical model, in which banks are assumed to set the deposit and loan rate simultaneously, such as in Zarruck and Madura (1992) and Wong (1997).
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derived by the authors indicates the pure margin determinants where a is the margin on deposits, b the margin on loans, ˛/ˇ measures market power, R is the bank’s risk aversion, I2 is the variance of the interest rate on deposits and loans, and Q measures the size of bank transactions. Empirical tests of the above model have yielded conflicting results. Empirical studies notably include Angbazo (1997), Saunders and Schumacher (2000), Gischer and Juttner (2002), Maudos and Guevara (2004) and Hanweck and Ryu (2005). Saunders and Schumacher (2000) conducted an international study of banks’ net interest margin determination over the period 1988 to 1995, and found that implicit interest payments, opportunity cost, the capital-to-asset ratio, market power and rate volatility are each positively related to the net interest margin. However, due to the difficulties of specifying risk aversion and the size of transactions, these two variables were omitted from the model. Gischer and Juttner (2002) investigated the impact of global competition on interest rate margins and in contrast to Angbazo (1997), argued that improvements in managerial effectiveness should act to narrow banks’ interest margins. Using a sample period in the 1990s, they found that banks’ interest margins are negatively related to the degree of global competition, fees-to-interest income ratio and cost structure, and positively related to gross income volatility and market power applied. In Maudos and Guevara (2004), operating cost was included and a direct estimation of market power applied in an empirical study covering five European countries’ banking firms from 1993 to 2000. The results suggested that market power, operational cost, risk aversion, interest rate risk, credit risk, implicit interest payments, opportunity cost and quality of management are all positively related to the net interest margin, which is consistent with the theoretical model developed in Ho and Saunders (1981). The size of bank transactions was found to be inversely related to bank spread, which conflicted with theoretical priors but which we argue need not be surprising because transaction size is likely to be a proxy for scale economies. In the present paper, in contrast to Maudos and Guevara we predict margins to decline as bank scale increases, on account of the standard cost economies of scale effect. Hanweck and Ryu (2005) develop a dynamic bank behavior model which suggests banks of differing sizes react differently to credit, interest-rate, and term-structure shocks when altering net interest margins. Their empirical study using quarterly data from 1986 to 2003 found that banks appear to be more sensitive to credit risks than to interest-rate changes, which is in sharp contrast to Angbazo (1997). A group of studies have looked at selected countries, with varying results. Catao (1998) studies Argentina and finds that operating costs, problem loans, exchange rate risk and the cost of liquidity are all positively related to banks spreads. Barajas, Steiner and Salazar (1999) estimate the elements that explain a high bank interest margin during the pre-liberalization (1974–1988) and post-liberalization (1991–1996) periods in Colombia; the interest rate spread is specified as a function of mark power, volume of loans, wage rate and problem loans. Brock and Suarez (2000) estimate the factors that affect interest margins across six Latin American countries (Argentina, Bolivia, Chile, Colombia, Mexico, and Peru), where the capital-to-asset ratio has no significant effect on interest margins but liquidity ratio and cost ratio are each found to be positively related to margins. Ramful (2001) examines the determinants of interest rate margin in the Mauritian banking sector and finds that operating expenses, doubtful debts and market share affect interest margins positively. Afanasieff, Lhacer and Nakane (2002) use a panel regression of 142 Brazilian banks and find that size of bank, opportunity cost and operating cost are positively related to interest margins but a set of macroeconomic variables such as the market interest rate, the volatility of the market interest rate, inflation rate and output growth heavily affect margins as well. Our work is motivated by the inconclusive nature of the empirical literature surveyed above. We seek to shed light on the apparently contradictory results in earlier studies by providing a comprehensive cross-country study. A prior cross-country study was conducted by Demirguc-Kunt and Huizinga (1999) using data for eighty industrial and developing countries over the period 1988 to 1995, however their particular concern was exogenous influences such as macroeconomic indicators, tax rates and the degree of international ownership, all of which were found to be positively related to interest margins. Complementing their work, the cross-country results reported here put the focus is on internal influences on bank margins.
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3. Data and methodology 3.1. The data The data for this study are mostly obtained from OECD (Organisation for Economic Cooperation and Development) annual publication Bank Profitability: Financial Statements of Banks (2003). This publication provides uniform information on financial statements of banks in OECD member countries for the period 1987–2001. This is the most recent date for which consistent banking industry data are available across such a range of countries. The annual data over the period 1987 to 2001 include fourteen selected OECD countries: Austria, Belgium, Denmark, Finland, France, Germany, Italy, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and United States. The accounting framework used in the database for presenting the statistics is in accordance with the recommendations of the OECD Task Force on Bank Profitability. The coverage defines ‘bank’ as all institutions which conduct ordinary banking business, primarily taking deposits from the public at large and providing loans for a wide range of purposes. The institutional coverage is generally uniform, however is based on national criteria and on account of structural and regulatory differences between national systems, differences occur in some cases. In particular, the coverage for Finland includes not only deposit banks narrowly defined but also credit institutions such as finance companies and mortgage companies; France includes municipal financial institutions and some specialized institutions; Germany includes credit cooperatives and regional giro institutions; Spain includes credit cooperatives; Portugal includes two bank-like credit institutions; and the US includes federally-insured savings and loans institutions. The statistics for the remaining countries - Austria, Belgium, Denmark, Italy, Norway, Sweden, Switzerland, UK - relate to banks as traditionally understood, institutions granted a banking licence by the relevant government financial system authority. The data extracted from the OECD statistics include the following: net interest income, net interest expense, total assets, total revenue, total costs, operating expenses, loans, securities, other assets, noninterest income, cash, and central bank balances. Definitions of the explanatory variables and how they were constructed using these raw data are outlined in Section 3.4. For each country, the data were pre-aggregated by the OECD. Daily or monthly interest rates (according to availability) have been obtained for each country and sorted into annual averages from 1987 to 2001. 3.2. Econometric approach We use panel data, which has the advantage that certain effects which may not be observable in pure time series data can be detected and measured (Gujarati, 2003). The present study presents an opportunity to compare the merits of alternative panel regression approaches as applied to the analysis of bank interest margins. We test a number of panel data models including the pooled regression model (PRM), fixed effects model (FEM), random effects model (REM) and Generalised Least Squares (GLS). In the Pooled Regression Model all coefficients are constant across time and individuals. In general, the pooled model is: yit = ˇxit + uit
i = 1 . . . N, t = 1 . . . T
(2)
where uit is independently and identically distributed (i.i.d). A limitation of pooled regression is that the specific nature of each cross section is ignored. This can be addressed using a Fixed Effects Model (FEM), where the specification includes a unit specific component: yit = ˛ + ˇxit + zit + uit
i = 1 . . . N, t = 1 . . . T
(3)
where z is unobserved, and x and z are correlated. However a drawback of the FEM specification may be that the time invariant effects and their coefficients fall out and cannot be identified. To take into account the individual specific components, we can use the Least Squares Dummy Variable (LSDV) methodology. This means z can be interpreted as an intercept of observation i by including a dummy
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variable for each cross-section unit. That is, we have: yit = ˛1 + ˛2 D2 · · ·˛N DN + · · · + ˇxit + uit
(4)
In the LSDV approach, unobserved time effects are obtained by including time dummies and the variables are homogeneous across cross-sections but differential through time. Both cross-section and time dummy variables may be included if the intercept varies over individuals as well as time. Alternatively, time invariant factors can be incorporated using the Random Effects Model (REM). Consider the model again: yit = ˛ + ˇxit + zi + uit
i = 1 . . . N, t = 1 . . . T
(5)
In (5) ˛ represents the mean value of all the cross-sectional intercepts and the error component zi represents the deviation of the individual intercept from the mean value. The individual error components are assumed to be uncorrelated with each other and are not autocorrelated across cross-section units. Therefore, the random error zi is homogeneous over time but different across cross sections. An advantage of the REM model is that time-invariant factors are included in the regressions. We find it necessary to employ General Least Squares estimation (GLS). If autocorrelation is present in a panel analytic model, the panel data estimator is no longer the best linear unbiased estimator (BLUE). To account for this problem, common tests of serial correlation in the residuals such as Durbin-Watson, Breusch-Godfrey or Box-Pierce-Ljung may not be sufficient, because in panel estimation autocorrelation may be present not only from one period to another but also across each cross section. In addition, heteroskedasticity can affect the efficiency of the estimation. The GLS Estimator can account for these two problems. Unlike static models where all relationships are between variables at the same point in time, GLS is a simple dynamic panel model which describes how phenomena develop over time. The GLS estimator is largely used in estimations with heteroskedastic and/or autocorrelated residuals. For practical reasons, the FGLS (Feasible General Least Squares) estimator which is asymptotically efficient is utilized in this paper. 3.3. Dependent variable We follow the model established by Ho and Saunders (1981), as amended by Maudos and Guevara (2004), applied to treat the whole banking sector of a given country as a single decision maker. It is assumed the representative bank plays a risk-averse dealership role in the market by offering to take deposits (at interest rate rD ) and at the same time meet demand for loans (at interest rate rL ). At the beginning of the period the bank sets interest rates for the decision period, which then remain constant until the end of the period. The bank sets its interest rates as a margin to the current cost of funds in the money market (r). This can be expressed as follows: rD = r − a
(6)
rL = r + b
(7)
s = rL − rD = a + b
(8)
The bank applies the margin s which anticipates compensation for possible future risks of volatility of interest rates on the short-term money market and credit risk on loans. We estimate an equation in which the dependent variable is the average Net Interest Margin (NIM), defined as the difference between the representative bank’s interest income and interest expense expressed as a ratio to average total assets. 3.4. Explanatory variables Based on theory and the literature on bank interest margins, the explanatory variables employed in our estimation are:
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3.4.1. Market Structure (LG) The Lerner Index, which can be characterised as the negative inverse of demand elasticity, is used as a proxy of market power. A high Lerner Index indicates a strong degree of monopoly in the banking market, while in a highly competitive market the sector has less capacity to set high margins resulting in a low Lerner index. The difference between price and average cost divided by price is used as a proxy of the Lerner Index. Hence, the equation is: Lerner =
TR − TC p − AC = p TR
(9)
where p is TR/TA, TR is total revenue, TC is total cost and TA is total assets.2 3.4.2. Operating cost (OC) Operational cost is measured by the ratio of operating expenses to average total assets. Banks experiencing high operating costs will apply high interest spreads. 3.4.3. Degree of risk aversion (RA) It is not easy to find an ideal specification of the bank’s degree of risk aversion. McShane and Sharpe (1985) use the bank’s capital ratio as a proxy, measured as the ratio of shareholders’ funds to total assets of the bank. Maudos and Guevara (2004) also adopt the same indicator. However this measure can be artificially affected by accounting conventions such as asset revaluations and the treatment of various contingent accounts and risk management of a bank relates to risk of the overall portfolio. As well, banks operating in low risk segments of loan markets may still choose a low capital ratio on account of a relatively high risk preference, suggesting that the risk exposure of a low capital ratio institution need not necessarily exceed that of a high capital ratio institution if the high capital ratio is offset by a more risky loan portfolio. In this study we employ an alternative measure, securities plus other assets divided by volume of loans (where ‘other assets’ are assets after excluding loans, securities, inter-bank deposits, cash and balance with central bank). Everything else being equal, risk averse bank managers tend to impose an extra bank interest margin as a compensation of taking systematic risk. Therefore, degree of risk aversion is expected to be positively related to banks’ net interest margins. 3.4.4. Interest rate volatility (V) There is a variety of measures available to proxy interest rate volatility, based on previous studies. We use the standard deviation of daily ten year interest rates as the measure of interest rate risk, where the rate is the ten year yield on central government bonds. We also tested three month money market rates but found it made no substantial difference to the results. 3.4.5. Credit risk (CR) According to BIS (1999), credit risk is defined as ‘the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms’. One approach involves the capitalto-assets ratio being utilized as a measure of credit risk. However, according to Basel II, the capital requirement is based on risk adjusted assets for which the data are not available, and if using raw asset data, the results related to this variable could be doubtful (Gischer & Juttner, 2002). The volume of loans to average total assets ratio is used as our measure of credit risk since ‘loans are the largest and most obvious source of credit risk for most banks’ (BIS, 1999). This is the same measure used by Maudos and Guevara (2004). If banks charge additional interest margins to compensate for exposure to expected and unexpected credit risk, it will principally be in relation to their loan book. Therefore, banks’ interest margins are expected to be positively related to credit risk.
2 The Lerner Index is preferred here to the Herfindahl index (defined as the sum of the squares of the market shares of each individual firm) since the more static measure of market power may not capture the degree of competition. The Lerner Index captures more information about the actual price-setting behaviour of banks in relationship to their cost structures than the size of banks whether measured in terms of deposits, relative size of balance sheets or income generated (Gischer & Juttner, 2002).
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3.4.6. Interaction between credit risk and market risk (IN) Interaction between credit risk and market risk is measured as product of credit risk and the standard deviation of the interest rate. Two alternative interest rates are used: the three month rate and ten year rate respectively. As indicated earlier, however, the value of this variable may be too small to be a factor materially affecting the dependent variable significantly. 3.4.7. Scale effects (LN) According to the study of Ho and Saunders (1981), the size of bank transactions should have a positive impact on banks’ interest margins. Maudos and Guevara (2004) also state that the bank will apply a greater margin on sizable transactions. The reason is that, assuming everything else being equal, the larger the size of a transaction, the larger the potential loss will be. However, in the real world, it is known that larger customers normally enjoy narrower margins, and that larger banks can offer narrower margins to clients than small banks. Compared to smaller transactions, larger transactions reduce the frequency of operations and spread administrative overheads across a larger base, which reduces a bank’s operating expenses per dollar of revenue. A hurdle faced by researchers is the difficulty in obtaining data of average real loan and deposit transaction sizes. McShane and Sharpe (1985) assume the size of the transaction is invariant across trading banks and time. Maudos and Guevara (2004) specify the size of transactions as the natural logarithm of volume of loans. Our approach is to use the same variable but we see it as capturing something different: scale economies. Recall from the theoretical model the expression for unit costs: the term (C(L)/L + C(D)/D)/2 is the average operational cost of loans and deposits. It is positively related to the bank’s interest margin, which implies that banks recording higher operational costs will impose an extra interest margin. Accordingly, the logarithm of the volume of loans is employed in this paper upon the reasoning that an increased volume of loans should result in a reduction of unit costs, which achieves economies of scale and results in narrower margins. Hence, a negative sign is expected. 3.4.8. Implicit interest payments (I) In order to cover the cost of the banking services, banks impose extra interest margins which are the so-called ‘implicit’ interest payments. Based on Ho and Saunders (1981), Angbazo (1997), Saunders and Schumacher (2000) and Maudos and Guevara (2004), this factor is denoted by the difference between operating expenses and non-interest income divided by average total assets. Banks’ interest margins should fall as implicit interest benefits decline. Inspection of the data shows that overall, the implicit interest payments to total assets ratios have been decreasing steadily over the period across the countries in our sample, which implies that previously ‘free’ banking services have been transformed into a more transparent fee charging mode. 3.4.9. Opportunity cost of bank reserves (O) According to the regulatory and prudential rules set by BIS, reserve or liquidity requirements and minimum capital requirements should be applied by the national supervisory authority. The opportunity cost of keeping these reserves is the additional interest rate that can be obtained in the open financial market. Therefore, the larger the amount of reserves, the greater the opportunity cost. The opportunity cost of bank reserves is represented by cash plus balances with central bank, expressed as a percentage of average total assets. 3.4.10. Managerial efficiency (Q) According to Angbazo (1997) as well as Maudos and Guevara (2004) the higher the quality of management of a bank, the higher the interest margins that will be imposed by the bank, on the grounds that a high quality of administration implies a high-yield and low-cost composition of assets and liabilities. Gischer and Juttner (2002) suggest that improved quality of management should narrow down the interest margin due to efficiency, but the empirical results of their study do not support the viewpoint. Claeys and Vander Vennet (2004) use the inverse of total overhead costs to total assets to measure efficiency and find it affects interest margins negatively in developed countries. The loansto-employee ratio is utilized by Brock and Franken (2003) and is found to have a negative impact on
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interest margins in Chile. In this paper, operational expenses as a ratio of gross income are used as the proxy of quality of management. 3.5. Testing equation Based on the analysis above, our estimating equation is: NIMit = c1 + c2 (Market structure) + c3 (Operating cost) + c4 (Risk aversion) + c5 (Interest rate risk) + c6 (Credit risk) + c7 (Interaction) + c8 (Economies of scale) + c9 (Implicit interest payments) + c10 (Opportunity cost) + c11 (Managerial efficiency) Simply, Nit = c1 + c2 (LGit ) + c3 (OCit ) + c4 (RAit ) + c5 (Vit ) + c6 (CRit ) + c7 (INit ) + c8 (LNit ) + c9 (Iit ) + c10 (Oit ) + c11 (Qit ) + uit
(10)
where N = (Total Interest Income − Total Interest Expenses)/(Average Total Assets) LG = (Total Revenue − Total Cost)/(Total Revenue) OC = (Operating Expenses)/(Average Total Assets) RA = (Securities + Other Assets)/(Average Total Assets) V = s.d. of daily yield of government bond with 10 year maturity (SD10Y) CR = Loans/(Average Total Assets) IN = CR × SD10Y LN = Loans (logarithm of) I = (Operating Expenses − Non-interest Revenues)/(Average Total Assets) O = (Cash and Balance with Central bank)/(Average Total Assets) Q = (Operating Cost)/(Gross Income) 4. Results Following McShane and Sharpe (1985), Angbazo (1997) and Maudos and Guevara (2004), a singlestage approach is employed in this study. That is, the model incorporates in the regression in one step both variables explaining the pure margin such are market structure, scale and risk aversion, as well as the unit-specific variables like managerial efficiency, implicit interest payments, opportunity cost and credit risk. Annual country-based data over the period 1987 to 2001 are used in the estimation. Several alternative methods of panel regression are tested, for comparison purposes. Moreover, we estimate the equation with different types of measures of interest risk to check the robustness of the results. 4.1. Econometric issues Our basic test employed pooled least squares estimation with cross-section and period fixed (FEM) effects. We tested the effect of including or excluding the cross-section and period fixed effects because in this type of model it is important to evaluate model fit under alternative assumptions regarding fixed effects across countries. Introducing cross-section and period fixed effects increases the Durbin-Watson statistic from around 0.65 to 1.29. Furthermore, the smaller values of standard error of regression, sum squared residuals, AIC, Schwarz criterion also imply improvement of the model fit. We therefore prefer the model with cross-section and period fixed effects included. Also, in this data sample fourteen countries have been included, which is less than the number of time effects, and therefore a cross-section SUR weighted least squares regression (the Parks estimator) has been run which allows the residuals to be both cross-sectionally heteroskedastic and contemporaneously correlated. The Parks estimator is applicable when the number of periods exceed the number of cross section members.
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A concern that arose in our initial estimates was a relatively low value of Durbin-Watson statistic, indicating the possible presence of autocorrelation in the residuals. The Durbin-Watson statistic is formed simply by computing the first-order residual correlation on the stacked set of residuals. Therefore, it was initially unclear whether the residuals were cross-sectionally autocorrelated: the pooled OLS estimator might not be not BLUE if that were the case. In panel data the estimator is BLUE if the untransformed errors, uit , are uncorrelated and homoskedastic. When the assumption of homoskedasticity is violated, OLS is still unbiased but no longer efficient. However, White’s heteroskedasticity-consistent procedure is robust to heteroskedasticity (Woodlridge, 2002). In this case, estimating White’s heteroskedasticity-consistent procedure and/or feasible GLS specifications can account for this problem. The model was therefore re-estimated with Robust Standard Error (RSE) and/or GLS weights. Although a limitation of the procedure is that the formula only has asymptotic justification, computing robust standard errors will assure correct inference. By introducing White cross-section standard errors which are robust to contemporaneous correlation as well as different error variances across countries, the initial model was re-estimated. We conducted a test for equality of the variances between series in the original model and determinedly rejected the null hypothesis of equal variance of the residuals across countries, providing strong evidence of the presence of groupwise heteroskedasticity. As an additional check on our model, we also tested random cross section effects, in place of fixed effects, an alternative that is less restrictive on the data. The panel procedure allowed us to deal with random effects using Feasible Generalised Least Squares (FGLS) techniques which account for various patterns of correlation between the residuals. The results were essentially unchanged, and indeed it must be said that from a theoretical point of view, the benefits of introducing random effects are open to debate. Traditionally, the emphasis has been on whether zi is correlated with explanatory variables or not. If zi is correlated with explanatory variables the fixed effects model can be consistent, whereas if zi is uncorrelated with explanatory variables a random effects model will be appropriate. This is largely the difference in the two approaches, and is essentially an empirical question to do with model fit. Moreover, whilst Gujarati (2003) shows that if the number of time series data is large relative the number of cross-section units then random effect models may be more efficient than fixed effect models, that is not the case here. In any case, the fixed effect model performs better in this study than the random effect model. Table 1 shows the preferred model. It is a pooled estimation model with robust standard errors using GLS weights with fixed cross-section effects. The specification in employs a heteroskedasticrobust variance estimator (White cross-section standard errors). The Durbin-Watson test indicates no serial autocorrelation. In addition, Table 1 incorporates period weights which allow for period heteroskedasticity. It is found the cross-section SUR weighted least squares estimator introducing individual effects works well. Also, we dropped cross section effects and period effects respectively and found that the model with only cross section effects (using white cross-section standard errors and period GLS weights) performs best. 4.2. Parameter estimates The model variables generally present the expected sign and are highly significant. All the explanatory variables are positively related to banks’ interest margins except the log of the volume of loans (LN) and managerial efficiency (Q). This outcome is consistent with the theoretical priors. Notably, the coefficients are all significant at 5% level except opportunity cost. The Lerner Index (LG), as a proxy of the market power, is positively related to net interest margins, and is statistically significant. This result is consistent with our expectation that banks with monopoly power can charge a higher loan rate and offer a lower deposit rate. The impact of operational cost (OC) measured by the ratio of the operating expenses to the average total assets is also positive and statistically significant, which implies that banks may impose an extra interest margin to cover the high operational cost. The measure of risk aversion (RA) has the expected positive relationship with banks’ interest margin, which confirms that the risk averse bank manager tends to apply an extra interest margin. Interest risk (V) measured by annual standard deviation of daily interest rate presents the positive sign which is expected. Credit risk (CR) expressed as loan to average total assets ratio affects
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Table 1 Pooled estimation with fixed cross-section effects using GLS weights Variable LG OC RA V CR LN I O Q AUT BEL DEN FIN FRA GER ITA NOR SPA SWE SWI UK POR USA
Coefficient
Std. Error
t-Statistic
Prob.
0.029295 0.249085 0.001143 0.000840 0.007308 −0.002872 0.938153 0.018099 −0.030460 0.051699 0.052860 0.055749 0.048655 0.057741 0.057331 0.055114 0.055780 0.054368 0.055454 0.054011 0.055321 0.050159 0.061839
0.000777 0.008737 0.000191 5.01E−05 0.000458 0.000144 0.006080 0.000908 0.000443 0.001683 0.001699 0.001929 0.001665 0.001926 0.001988 0.001910 0.001897 0.001829 0.001999 0.001878 0.001856 0.001659 0.002168
37.71750 28.50775 5.983170 16.76414 15.96681 −19.95543 154.2968 19.92382 −68.73203 30.70932 31.10374 28.90530 29.22982 29.98473 28.83589 28.85880 29.40781 29.72782 27.73553 28.75376 29.80105 30.23037 28.52082
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Weighted Statistics R-squared Adjusted R-squared S.E. of regression F-statistic Prob(F-statistic)
0.999853 0.999836 0.958400 56656.90 0.000000
Mean dependent var S.D. dependent var Sum squared resid Durbin-Watson stat
−10.95675 74.73763 168.0910 1.965171
Note to table: Sample period: 1987–2001 annual data. Observations: 15. Cross-sections included: 14. Total pool (unbalanced) observations: 206. Method of estimation: Pooled GLS with fixed cross-section effects. IN omitted because insignificant. Fixed effects (cross-section) estimated coefficients: Austria (AUT) −0.004500, Belgium (BEL) −0.003257, Denmark (DEN) 0.001476, Finland (FIN) −0.009040, France (FRA) 0.003937, Germany (GER) 0.004545, Italy (ITA) 0.000543, Norway (NOR) 0.002080, Spain (SPA) −0.000826, Sweden (SWE) 0.001356, Switzerland (SWI) −0.000353, United Kingdom (UK) 0.000741, Portugal (POR) −0.007282, United states (USA) 0.010580. Fixed effects (period) estimated coefficients: 1987 −0.001815, 1988 −0.000857, 1989 −2.99E−05, 1990 0.000433, 1991 0.001242, 1992 0.000904, 1993 −2.62E−05, 1994 −0.000828, 1995 2.45E−05, 1996 −0.000205, 1997 −5.53E−05, 1998 −0.000314, 1999 −0.000862, 2000 0.000785, 2001 0.001034.
interest margins positively and significantly, which suggests that increased credit risk may cause an increase of banks’ interest margins. Interestingly, the log of the volume of loans (LN) imposes a negative effect on bank interest margins and is statistically significant at the 1% level, which confirms our assumption that increased volume of loans may result in a reduction of unit cost, which achieves economies of scale. Moreover, the payment of implicit interest (I) still shows a highly explanatory capacity, which confirms the importance of this variable. Implicit interest payments (I) here are noteworthy, as they have such a highly significant affect on interest margins. Opportunity cost (O) measured by cash and balance with central bank to average total assets ratio presents an expected positive sign and is statistically significant. Managerial efficiency (Q) is proxied by operating expenses to gross income ratio. The negative signs in both tables indicate that the lower the quality of management the narrower margin the bank may charge. This outcome is consistent with Angbazo (1997) and Maudos and Guevara (2004) but in contrast to Gischer and Juttner (2002). The standard deviations of daily yield of government bonds with ten years maturity are employed to measure interest risk (V). As an alternative, we also tested the model using the standard deviation of the monthly yield on government bonds with ten years maturity as the proxy of interest rate volatilities. Moreover, we tested the model using interest rates with three months maturity (both
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daily and monthly standard deviations, respectively). We found throughout our tests that the results were not particularly sensitive to the choice of interest rate. Accordingly, we have employed the daily yield of ten year bonds and not reported the results for the various other measures of interest rate volatility. Surprisingly, the effect of the interaction between interest risk and credit risk (IN) was found to be insignificant in the model. Neither including nor excluding this variable improves the other factor’s significance, and does not affect the adjusted R-squared value and other tests, but does improve the significance of the interest risk (V). Hence, multicollinearity between V and IN may be involved, so it is not reported in Table 1. 5. Conclusions and implications This study has analysed the empirical determinants of banking industry interest margins using a representative bank approach. Based on the model developed by Ho and Saunders (1981) the industry in each country is assumed to behave like a single risk-averse dealer in the loan and deposit market where the loan requests and deposits supplies arrive non-synchronously. The empirical analysis follows a single stage model and includes fourteen OECD countries’ banking sectors over the period 1987 to 2001. The empirical findings are consistent with our theoretical analysis. Scale effects, measured by the log of industry loan volume, are found to have a negative and significant effect on margins. The empirical analysis also shows that managerial efficiency, proxied by operating expenses to gross income ratio, has a negative coefficient, which means banks experiencing managerial efficiency are able to obtain higher interest loans and lower cost deposits. This outcome is consistent with Angbazo (1997) and Maudos and Guevara (2004) but in contrast to Gischer and Juttner (2002). We find that market power, operating cost, risk aversion, volatility of interest rate, credit risk, opportunity cost and implicit interest payments are all positively related to banks’ interest margins. The intensified competition of domestic banking markets has acted to reduce bank interest margins. Implicit interest payments (I) captures the transfer of ‘free’ services from implicit interest payments into explicit fees and charges. Operational cost (OC) is the next factor which is found to influence the bank margins significantly, which is consistent with the findings of Maudos and Guevara (2004). A 1% increase of operational cost will lead to a 0.1% to 0.2% increase of banks’ interest margins. The industry in most countries has experienced a decrease in operating expenses largely due to the increasing use of phone banking, internet banking, ATMs and EFTPOS. The effect of opportunity cost, unfortunately, is ambiguous in our empirical study. Theoretically, opportunity cost should explain the banks’ interest margins positively. Unfortunately, in most of our robust standard error and/or GLS weights estimators, the effect of opportunity cost appears to be insignificant. Pooled estimation, fixed effect estimation, random effect estimation and FGLS estimation were employed by turn in the regression analysis. According to the regression output, the fixed effect model performs better here than the random effect model and the re-estimation of the basic fixed effects model using GLS weights with fixed cross-section effects performs best and generates the preferred specification in this study. All the coefficients of the explanatory variables present the expected sign and are statistically significant at 1% level. The preferred estimates also incorporate robust standard errors which ensure the correct inferences from the estimation. In terms of directions for future research, a number of variables relevant to interest margins representing fruitful avenues for further work have not been included in this study due to restricted availability of data, or the limitations stemming from the degrees of freedom of the model, or the potential for multicollinearity between variables. One avenue for further work concerns influences external to the banking industry, Taxation effects have to date be incorporated into the model: the results Demirguc-Kunt and Huizinga (1999) provide a hint that banks’ interest margins may respond to corporate income tax rates. Another little researched aspect involves returns on competing investment instruments. According to Koch and McDonald (2003), theory says that competitive pressures on both the cost of bank funds and yields on earning assets should affect margins. A general equilibrium model that nests bank interest margins in the context of a spectrum of financial market returns represents an interesting path for future research.
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