This paper examines how banks respond to the monetary policy ofthe European Central Bank (ECB) according to their characteristicsand, in particular, to their market power, using banking micro-datafrom Eurozone countries over the period from 1999 to 2011. Ourresults suggest that banks with market power, which is proxiedby the Lerner index, have a credit supply that is less sensitive tomonetary policy shock. The market structures (aggregated meas-ures) in which the banks operate have a similar effect. Therefore,increased competition enhances the effectiveness of monetary pol-icy transmission through the bank lending channel. We find alsothat over the period from 2008 to 2011, this channel has beenstrengthened, nevertheless the negative effect of market power onmonetary effectiveness has remained.
In the current context of crisis recovery, one of the key issues of concern is the ability of financialintermediaries to finance new employment and to provide new opportunities for stable and sustain-able growth. Market structures and, in particular, banking competition may affect these goals. Indeed,competition may directly alter the growth of the credit supply, but may also indirectly affect it throughthe transmission effects of monetary policy (the bank lending channel).
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There is no consensus regarding the influence of financial intermediaries in monetary transmission.This influence has been partially ignored for a long time by the authorities in charge of monetary policy,the traditional channel of interest rates being independent from financial intermediaries. However,Bernanke and Blinder (1988) have suggested that monetary policy has additional effects on this chan-nel because a change in the policy rates by the central bank will affect the cost and credit availability toa greater extent than a mere drop in the risk-free rate. This channel, called the “credit channel”, is com-monly divided into two branches: the balance sheet channel and the bank lending channel (Bernankeand Gertler, 1995). According to the first channel, a change in interest rates will affect the value ofassets and the cash-flow of borrowers, and through this, their solvency, which itself determines therisk premium paid. The bank lending channel argues that the monetary policy directly affects theloan supply. According to Bernanke and Blinder (1988), the implementation of a restrictive monetarypolicy causes a funding shock,1 which in turn reduces the willingness of banks to lend and thereforethe supply of loans (Lensink and Sterken, 2002). However, a monetary tightening could lead the banksto increase the share of financing through financial markets, in particular by issuing certificates ofdeposits (Romer et al., 1990). In this context, a monetary policy shock would be cushioned. However,this cushioning would remain imperfect because the resources obtained in the markets will be moreexpensive (Stein, 1998). Moreover, the cost and availability of funds for any bank will depend on its bal-ance sheet strength and therefore its perceived creditworthiness. Thus, similarly to other borrowers,the bank creditworthiness, which defines the external premium paid, will be dependent on monetarypolicy. That being said, the bank lending channel and the balance sheet channel have commonalities.In both cases, the central mechanism is the external finance premium and its relationship with theborrower fundamentals. The only difference is based on types of borrowers and externalities pro-duced. The financial accelerator, which stems from the balance sheet channel, explains the monetarypolicy shock’s effects on households and businesses, while the bank lending channel focuses on theeffects on banks (Bernanke, 2007).
A number of studies have attempted to identify the monetary policy effects on the credit supply.One of the difficulties is the distinction between potential supply and demand effects. This distinc-tion is particularly difficult to make in the context of aggregated data. For this reason, the literaturehas opted for the use of disaggregated data. The seminal article identifying a bank lending channelfrom disaggregated bank data is attributed to Kashyap and Stein (1995). Consistent with the theory,the authors observe that the nature and strength of the banks’ balance sheets modify the impact ofmonetary policy shock. They find that small sized banks react more strongly to a monetary policyshock. Beyond size (Kashyap and Stein, 2000; Altunbas et al., 2002; Altunbas et al., 2009), the liter-ature has shown that capitalization (Kishan and Opiela, 2000; Van den Heuvel, 2002; Gambacorta,2005; Altunbas et al., 2009) and liquidity may affect a bank’s response to a change in monetary policy(Kashyap and Stein, 2000; Ehrmann et al., 2003; Altunbas et al., 2009).
Among the substantial literature on the bank lending channel, very few studies have focused onthe effects of bank structures and competition. The bank lending channel, however, could be highlydependent. Our hypothesis is that a bank with higher market power will have a greater ability to hedgeagainst a monetary shock due to greater access and better conditions in the financial markets. More-over, a bank with market power would have a buffer to smooth the shocks by reducing temporarily itsmargin. From an empirical point of view, some studies have linked bank competition with monetarypolicy effects on the credit channel (Adams and Amel, 2005; Gunji et al., 2009; Olivero et al., 2011a,b).However, these studies have some limitations. In particular, they use aggregate data to identify thebank lending channel or to represent banking competition.
In this article, we will analyze the bank’s market power effect on the credit supply and the responseto a monetary policy shock in the Eurozone during the period from 1999 to 2011 using individualbanking data. Our study is one of the first to analyze this relationship for the Eurozone and is a newcontribution to the literature arising from the work of Kashyap and Stein (2000).
Our results confirm that market power has a direct effect on the credit supply by allowing highercredit growth on average but also indirectly through the bank lending channel. Banks with a greater
1 In Bernanke and Blinder context, a decrease of the available reserves to the banking system.
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market power, which is estimated by the Lerner index, have a loan supply that is less sensitive to mone-tary policy shocks, confirming our preliminary theoretical expectation. Markets structures (aggregatedmeasures) in which banks operate have a similar effect. While it appeared that the small size bankswith low liquidity and inadequate capital were more sensitive to a monetary policy shock, the intro-duction of market power led to a loss of statistical significance of capitalization. The ability to hedgeand raise new resources from markets is more dependent on market power and therefore on the banks’ability to be profitable than capitalization. These results are robust to the addition of new variables(diversification, market depth, off-balance sheet activities, and risk) that could explain the growth ofthe credit supply. Some of these variables also interact with monetary policy. Off-balance sheet activ-ities tend to restrict the monetary policy shock effect. The proximity to the recent financial crisis ledus to inquire about the asymmetry of the bank lending channel. We find that the latter strengthenedsince the crisis. Monetary policy became more efficient over the period from 2008 to 2011, whichcan be explained by the use of unconventional monetary policies (Gambacorta and Marques-Ibanez,2011). Our results maintain this possibility. The massive liquidity supply during the crisis would haveallowed banks to grant more credit (Giannone et al., 2012). However, this effect does not affect theinfluence of market power: the banks with significant market power are less sensitive to monetaryshocks.
This study is organized as follows. In the second section, we present a review of the literature on thebank lending channel and the impact of competition on this channel. This focus precedes a descriptionof competition development in the Eurozone. The data and the empirical specifications readdressedin Sections 4 and 5, respectively. We discuss our primary results in Section 6 and other results and therobustness tests in Section 7. Finally, the conclusion summarizes our findings.
2. Literature review
2.1. The credit channel
Since the seminal contribution of Friedman and Schwartz (1963) revealing the influence of mon-etary policy on the real economy, many studies have attempted to distinguish the different channelsof monetary transmission. Theoretically, Bernanke and Blinder (1988) revealed the existence of atransmission channel via the supply of credit. The transmission of monetary policy passes throughboth a traditional liquidity effect, characterized by a shift in LM, called the “monetary channel”, andthe effect of credit availability. However, Romer et al. (1990) call these conclusions into question.Unlike Bernanke and Blinder (1988), they consider a bank’s ability to diversify its funding sources.Indeed, a bank can issue securities in the bond and money markets and thereby neutralize the effectof a reserve decrease. The authors reject the existence of a credit channel. In contrast, Stein (1998)considers multiple sources of funding and confirms the existence of a bank lending channel.
These theoretical questions have given rise to empirical expectation. Bernanke and Blinder (1992)corroborate their theoretical conclusion by observing the existence of a relationship between thefederal fund rate and the bank credit issued. These finding have difficulties to confirm the lendingview because they do not distinguish supply from demand of loans. To resolve this ambiguity, Kashyapet al. (1993) show that a phase of monetary restriction results in a decrease of bank lending but alsoan increase of commercial paper issuance. This tends to ensure that the shift in loan volume is due toa change in supply rather than a change in demand of loans.
The approach of Kashyap and Stein (2000) contrasts with previous works. These authors use bank-ing micro-data provided by the Call Report. These data allow them to examine the average response ofa bank following a monetary policy shock. Their results, compiled from more than one million obser-vations over the period from 1976 to 1993, are evidence of a very significant effect from monetarypolicy shocks on the credit supply. They confirm the existence of a credit channel of high importance,for which the effects are likely to spread to the real economy. Their study, as well as many othersrepeating the same dynamic empirical specification (Kashyap and Stein, 2000; Ehrmann et al., 2003;Ashcraft, 2006; Altunbas et al., 2009; Gambacorta and Marques-Ibanez, 2011), also emphasizes thatsome bank features can affect this channel. From this literature, the idea has emerged that the size,capitalization and liquidity of banks could enhance the effect of a monetary policy shock. Small-sized
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banks, whose funding is based largely on deposits and capital, more brutally modify their loan supplyfollowing a monetary shock (Kashyap and Stein, 2000; Altunbas et al., 2002, 2009). The less liquidbanks would also be the same (Kashyap and Stein, 2000; Ehrmann et al., 2003; Altunbas et al., 2009).These banks could not use the cash or transferable securities to hedge the shock and are thereforeforced to ration their loan supply (Altunbas et al., 2009). Finally, poorly capitalized banks may havedifficulty financing on the markets and may be statutorily restricted from taking new risks withoutraising new capital (Kishan and Opiela, 2000; Van den Heuvel, 2002; Gambacorta, 2005; Altunbaset al., 2009). However, some results are more mixed. Ehrmann et al. (2003) show, for the countriesof the European Union, that only liquidity plays a significant role in the monetary transmission. Forsix OECD countries, Brissimis and Delis (2009) find no evidence to support the existence of a creditchannel.
Beyond size, liquidity and capitalization, few studies have looked for other factors that could influ-ence the bank lending channel. According to Altunbas et al. (2010), the risk channel must also betaken into account when analyzing the effects of monetary transmission. A less risky bank will havea greater ability to refinance itself in the market and will, therefore, be less dependent on monetarypolicy. Loutskina and Strahan (2009), Altunbas et al. (2009) and Gambacorta and Marques-Ibanez(2011) argue and uncover that securitization activities development reduce the effectiveness of mon-etary policy. Indeed, switching from a logic of “originate and hold” to “originate and distribute”, byremoving loans from the balance sheet the bank will reinforce liquidity and reduce the regulatoryrequirements for capital (Altunbas et al., 2009). Similarly, the share of non-interest activities would bea factor influencing monetary transmission (Gambacorta and Marques-Ibanez, 2011). Finally, somestructural criteria would also operate on the credit channel intensity. The monetary policy wouldbe strengthened in economies with fragile banking systems or poorly developed and deep financialmarkets (Cecchetti, 1999).
More recent studies have also indicated the possible effects of the crisis on the credit channel.Gambacorta and Marques-Ibanez (2011) find that there has been a significant improvement in mone-tary transmission during the recent period. We can explain this shift by a sharp drop in interest ratesas well as the implementation of unconventional monetary policies.
2.2. Monetary transmission and competition
Beyond these various characteristics, the literature has shown that bank competition could alsoaffect monetary policy.
Theoretically, few studies have addressed the issue without limiting it to the credit channel. Forinstance, Stiglitz and Greenwald (2003, Part I, chapter 4) compare the effect of monetary policy in acompetitive system with its effect in a system of limited competition, considering a mean-varianceapproach. They demonstrate that an increase in interest rates will have a lesser effect in a competi-tive system. From Monti-Klein’s model, reinterpreted as a model of imperfect competition (Cournot),Freixas and Rochet (1997, Section 3.2) show that the effect of interbank rates on deposits and loansoffered depend on the number of players on the market (i.e., competition). They find that an increasein the number of banks mitigates the effect of interbank rates on lending rates. Thus, the scope of mon-etary policy strengthens with market concentration. Alencar and Nakane (2004) analyze the effect ofmonetary policy in a perfectly competitive and monopolistically competitive regime and observe aftersimulations that increased competition leads banks to display a greater sensitivity to interest rates,thus contradicting the results of Freixas and Rochet (1997).
The introduction of the euro has raised some questions regarding the involvement of banking struc-ture diversity on the effectiveness of monetary policy. Cecchetti (1999) notes that the impact of theEuropean monetary policy will differ from one country to another because of heterogeneous marketstructures. He finds that the countries with many small banks (e.g., Germany), are more sensitive tomonetary policy shock in euro area. Adams and Amel (2005) use aggregate data, but in the UnitedStates, to show that the effect of monetary policy on the credit channel is stronger in competitivemarkets, namely, in urban markets, than in the more concentrated banking markets that characterizerural areas in the United States. Conversely, Gunji et al. (2009) found that competition analyzed bythe H-statistic reduces the intensity of the bank lending channel. The works of Olivero et al. (2011a,b)
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raises the question of the influence of competition on monetary transmission using bank micro datain Latin America and Asia. The authors find opposite results depending on whether competition isproxied by concentration indices or the Panzar and Rosse H-statistic. When they use concentrationindices (Olivero et al., 2011a), they find that competition improve the effectiveness of monetary policy.In a recent study, from bank micro data, Fungácová et al. (2013) observe a similar effect for Eurozone.
3. Evolution of competition in the Eurozone
Important singularities remain between the banking markets of different Eurozone memberstates relating to economic, political and historical features. However, although heterogeneous, theseeconomies have concurrently been confronted in recent decades with common transnational devel-opments that have transformed their national banking markets and allowed them to converge.
The financial deregulation undertaken during the ‘80s, ending the control of interest rates, the com-partmentalization of activities and the influence of the state in the administration of loans, allowed fora tremendous increase in competition. Concomitantly and related to the process of financial deregula-tion, the European Union gradually established a framework for real banking integration. This processcomplies with the principle of the European Union countries as stated in the Treaty of Rome in 1957.Two articles of the latter (85 and 86) are intended to protect and promote competition indiscrimi-nately within the European Economic Community. However, these rules did not initially apply to thebanking sector. The special status given to the banking sector can be explained by the doubts regardingthe social benefits of deregulation in this sector. This position was partly the result of observationsmade following the 1929 crisis, which suggested that banking competition had an amplifying effect onthe crisis. Therefore, the European banking markets remained highly compartmentalized and concen-trated until the ‘70s. Following the ideological reversal of the ‘80s, these doubts were dispelled. Bankswere granted the opportunity to provide financial services in all European states, which promotedtransnational competition and the contestability of banking markets. A number of studies showedthat the deregulation process, coupled with the strengthening of European banking integration, ledto a marked increase in competition in the ‘80s. However, this process ended rapidly. Competitionappeared to stagnate or even decline over the‘90s. De Guevara et al. (2005) found no decrease in mar-ket power, estimated by the Lerner Index during this period. Maudos and de Guevara (2007) evenconclude that there was a decline in competition in many European countries.
The period from 1999 to 2011, which is the period of our study, was marked by significant structuralchanges following the introduction of the euro in 11 countries in 1999 and Greece in 2002 and theadoption of the FSAP2 in 1999. Moreover, the financial crisis that started in 2007 and the “GreatRecession” that followed have undoubtedly led to a change in the competition in the banking industry.During the crisis, states indeed offered significant support to major institutions to avoid bankruptcy,while the small players accepted their responsibility. Furthermore, to limit the restructuring costsduring the crisis, states in many cases promoted and coordinated mergers and acquisitions, whichresulted in an increase in market concentration. In a recent contribution, Weill (2013) shows thatbank competition did not increase during the 2000s, but it converged across European countries.
In the coming months, the effects of the crisis could continue to be felt. A new phase of consolidationis possible: some entities are vulnerable to takeover bids. The crisis also had a more indirect effect onmarket competition by producing a change in prudential rules, including the introduction of Basel III.The increase of capital and liquidity standards will necessarily create a cost for banks, which will notbe fully transferable to the consumer. Basel III as well as some national or supranational regulationprojects (Volcker rule, Vickers report, French banking law, Liikanen report) also plan to limit the scopeof activities. In all likelihood, this limitation would lead to a change in the market power of someinstitutions. These past and future changes of Eurozone competition require a detailed analysis ofeffects of these evolutions on the transmission mechanism.
2 FSAP is the Financial Service Action Plan.
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4. Data and estimations
We analyze the behavior regarding the loan process in the Eurozone following monetary pol-icy change. Our study requires the use of micro-data that we extract from the Bankscope databaseprovided by the Bureau Van Dijk.3
The selected bank sample covers all Eurozone members in 2003 (Austria, Belgium, Finland, France,Germany, Greece Ireland, Italy, the Netherlands, Portugal, and Spain), with the exceptions of Luxem-bourg, whose banking system has significant singularities. The sample does not include all of the banksin these different economies. We only selected the banks with total assets exceeding one billion eurosduring the study period and those with over 3 years of available data. We also excluded the bankswhose credit activity represented a marginal part of the balance sheet (less than 20%, see Oliveroet al., 2011b). Our final database includes 4962 observations covering the period from 1999 to 2011and a total of 675 banks.4 Table A1 in Appendix A details these statistics and the geographical locationof the selected banks.
The study of the bank lending channel links credit growth and changes in monetary policy. Thefirst step is to determine these two indicators. We therefore calculated the logarithmic growth rateof loans between t and t + 1. The shock of monetary policy is transcribed by the difference in a proxyvariable for the key interest rates of the European Central Bank. The latter cannot, in fact, be useddirectly because it has a low volatility (De Bondt, 2005) and, by definition, excludes the influence ofother instruments that would be likely to use the central bank.
Consistent with the literature, we use money market rates as a proxy for monetary policy imple-mentation. Because our study focuses on the Eurozone, we have several options. We can use the EONIA(Euro OverNight Index Average) as well as EURIBOR. According to De Bondt (2005), both are directlyinfluenced by the conditions of the refinancing operations and the marginal facilities granted by thecentral bank. Although highly correlated over the entire period of our study, these two indicators tendto disconnect during periods of crisis. EONIA was more volatile during the 2007–2008 financial crisis.We opt, therefore, to use the 3 months EURIBOR as a proxy for monetary policy and only use the EONIAfor robustness purposes.
Beyond these variables, balance sheets and individual profit and loss accounts easily allow us toisolate a number of specific characteristics for each bank that may influence the bank lending channel.The literature has argued that three bank characteristics would influence the reaction of banks’ to amonetary policy shock: capitalization, liquidity and size (Gambacorta, 2005).
We measure capitalization by the capital to asset ratio. This ratio has the advantage of being avail-able for all banks in our sample and for the entire period taken into account, in contrast with moreadvanced risk indicators such as the risk-adjusted capital ratios. Moreover, despite the simplicity ofthis ratio, Blundell-Wignall and Roulet (2012) note that the degree of leverage is empirically morelikely to account for the risk and the ability of banks to absorb losses. Bank liquidity is obtained usingthe ratio of liquid assets to total assets. We use the definition of liquid assets given by Bankscope.5
Finally, size is calculated by taking the logarithm of total assets.In this study, we introduce a new variable, which will be our primary interest variable: market
power. Market power is indeed likely to influence loan supply directly but also indirectly via thecredit channel. To measure market power, we use the Lerner index. The latter is commonly used toestimate competition and has the advantage of being an individual and dynamic measurement ofmarket power (Brämer et al., 2013), which distinguishes it from many alternative indicators (Stat-H,Boone, etc.). The Lerner index measures the banks’ ability to raise their prices above their marginal
3 Due to unavailability of high frequency (quarterly) data, we use annual information of the Eurozone banks. Gambacorta(2005) notes from the case of Italian banks that the use of higher frequency does not significantly improve the results.
4 For the year 2010, our database covers more than 99% of bank assets in the selected countries. Furthermore it is importantto mention that we use consolidated data for banks’ balance sheets.
5 We consider the following to be liquid: trading securities and at FV through income, loans and advances to banks, reverserepos and cash collateral, cash and due from bank.
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cost. Formally, the Lerner index is defined as the relative difference between price and marginal cost,expressed as a percentage of price, corresponding to the inverse of the price elasticity of demand.
Lerneri,t = Pi,t − Cmi,tPi,t
= −1e
(1)
where Pi,t is the price of total assets of bank i at time t, Cm is the bank’s marginal cost, and e is theelasticity of price demand. Thus, the ratio takes a value between zero and one. The index is zero inpure and perfect competition (high or infinite demand elasticity), the price being equal to the marginalcost. In contrast, in low competitive markets, the increased market power of the banks increases theLerner index to 1 because of the ability to raise prices above marginal cost (in a monopoly position,the elasticity of demand is low). In other words, the index decreases as the degree of competitionincreases.
The major drawback of the index is that it requires price and marginal cost estimates for the bankin question, which can be difficult to obtain. The conventional approach in the literature, however,is to estimate the price using the ratio of total income (interest income and non-interest income) tototal assets and the marginal cost from a translog function. Through this approach, and following themethodology detailed in Appendix B, we obtain our proxy for market power.
We believe that the four bank characteristics mentioned above (capitalization, liquidity, size andmarket power) can directly affect the loan supply, while also indirectly affecting it by influencingthe bank lending channel. To report this second effect, we created an interaction of these four bankvariables with the monetary policy indicator. The interaction variable obtained with market powerfor instance, shows the effect of the latter on monetary policy effectiveness. This interaction variableconstitutes our main variable of interest.
We seek to analyze the influence of individual market power on the bank lending channel, butbeyond market power, the competitive environment in which the banks operate could influence thebanks’ response to a monetary policy shock. We use the Herfindahl index to estimate the competitiveenvironment of the Eurozone member state.
The granted loans do not depend only on elements relating to the supply. Demand is crucial. Toaccount for demand, we use two proxies: the economy’s growth rate and inflation.
It should also be noted that all bank characteristics have been normalized. Thus, the average ofindicators is zero. This means that the sum of the interaction terms is zero for the average bank.The normalization allows us to interpret the coefficient of monetary policy as “the average effect ofmonetary policy on loans for an average bank” (Altunbas et al., 2009).6
A number of other variables will be used in this study primarily for robustness purposes. First, wewill verify our results by adding variables related to the supply. Risk-taking, diversification or evencompetition in financial markets appear to be able to influence the loan supply. The level of risk taking,estimated from the Z-score, could indeed affect the growth of the loan supply. In terms of a positiveeffect, a bank close to bankruptcy may have an incentive to boost its lending activities to survive(“gamble for resurrection”). However, the effect could be negative. Indeed, bank regulation aims toprevent such behavior by binding the loan supply of the riskiest banks. The ratio of non-interest incometo total income, frequently used as a proxy for diversification, has an ambiguous effect. The virtuesof diversification appear to be in favor of a positive effect by stabilizing bank earnings. However, thenon-interest activities attractiveness during the growth period could divert the bank from its lesstraditional profitable activities. The diversification can also occur through off-balance-sheet activities,which we verify. For its part, the integration of financial markets is theoretically capable of reducingthe influence of the bank lending channel by offering more financing alternatives to banks.
5. Empirical model
Our empirical specification is similar to that of Kashyap and Stein (2000), Ashcraft (2006) orAltunbas et al. (2009). The model allows the following to be tested: the extent to which the loan
6 Our results are not dependent on normalization.
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supply of a bank reacts to a change in monetary policy and to which bank characteristics influence thetransmission.
�ln(loans)i,t = ˛i + ˇ�ln(loans)i,t−1 +1∑j=0
˚j�iMt−j + ϑCAPi,t−1
1∑j=0
ij�iMt−jCAPi,t−1
+ LIQi,t−1 +1∑j=0
�j�iMt−jLIQi,t−1 + �SIZEi,t−1 +1∑j=0
vj�iMt−jSIZEi,t−1 + �LERNERi,t−1
+1∑j=0
ςj�iMt−jLERNERi,t−1 +1∑j=0
ıj� ln (GDPN)k,t−j +1∑j=0
ϑj�Ik,t−j + εi,t (2)
with i = 1, . . ., N representing banks; k = 1, . . ., 11 representing the country of origin of the bank; t = 1,. . ., T representing the date of observation extending from 1999 to 2011; and, finally, j representingthe number of lags.
We use a dynamic model to account for the inertia related to loan growth. Thus, the change inloans (�ln(loans)i,t) is explained by its lagged value (�ln(loans)i,t−1), by the monetary shocks(�iMt−j),by macroeconomic variables (namely GDP growth and inflation were both intended to control thedemand evolution), by lagged bank-specific variables to limit the simultaneity bias (LERNERi,t−1,CAPi,t−1, LIQi,t−1, SIZEi,t−1), and, finally, by the interaction terms between monetary policy and spe-
cific bank variables (∑1
j=0j�iMt−jCAPi,t−1, ...). These variables allow the differentiated responses ofbanks to monetary policy shock to be explained. Our main regression also includes individual fixedeffects and relies on the assumption that the macroeconomic variables (inflation and GDP growth)can account for time effects. Indeed, we cannot include time dummy variables directly in the modelbecause they are collinear with our variable of monetary policy. To verify this assumption, we includea time fixed effect in a second step, forcing us to exclude our monetary policy variable. Aware of thepotential issues of heterogeneity in the Eurozone, we then in the same vein integrate a country specificeffect in place of bank fixed effects.
To obtain a consistent and efficient estimation of our model, we use the GMM (“Generalized Methodof Moments”) system estimator, as proposed by Arellano and Bover (1995) and Blundell and Bond(1998). This method is more efficient than the single first difference GMM and allows: including thelagged dependent variable as an explanatory variable (dynamic modeling), taking in considerationindividual specific effects, and, finally, controling the endogeneity of explanatory variables. In ourcase, it is reasonable to assume that the specific variables related to banks and the macroeconomicvariables are endogenous or predetermined. Thus, for example, bank size may increase by the meregrowth of loans granted.
The instruments used are the lagged values of the dependent variable as well as the lagged values ofall explanatory variables. To verify the relevance of our instruments, we test the lack of the correlationof our instruments with the error term by using the Hansen over-identification test, and we ensurethat there is no serial correlation of order 2 in the equation in first difference by using the Arellanoand Bond autocorrelation test. Note that in all of our regressions we use the two-step procedure andobtain robust standard errors using the Windmeijer finite sample correction.
6. Results
In this section, we present the results of our empirical analysis regarding the market power effecton monetary policy transmission (Table 1). However, to demonstrate the specific effect of marketpower and its influence on other variables, we first transcribe a specification similar to Ehrmann et al.(2003) in the first column, where only the three specific bank variables commonly used (size, liquidity,capitalization) are considered. In the second column, we add the individual Lerner index in level andin interaction with the monetary policy rate to this first specification. Then, we control our baseline
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Table 1Regression results: main estimations.
Dependent variable:�loans
(1) Benchmarkmodel
(2) Market power (3) Time dummies (4) Countrydummies
Time dummies No No Yes No YesCountry dummies No No No Yes YesNo. of bank/no. of obs 675 4962 675 4962 675 4962 675 4962 675 4962Hansen test (p-value) 0.321 0.111 0.134 0.121 0.096AR(1)/AR(2) (p-value) 0 0.224 0 0.643 0 0.992 0 0.345 0 0.249
(1) Benchmark model similar to Ehrmann et al. (2003). (2) Our baseline model which includes market power indicator. Columns (3), (4) and (5) include respectively time dummies, countrydummies and the both together. Regressions are estimated by means of GMM estimators. Constant term included but not reported. Robust (Windmeijer) standard errors.
* Statistically significant at the 10% level.** Statistically significant at the 5% level.
*** Statistically significant at the 1% level.
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model by including time and geographical dummies in columns (3) and (4) and the both together incolumn (5).
The first major and common result to all estimations made confirms the existence of a bank lend-ing channel in the Eurozone. Thus, an expansionary monetary policy (restrictive), characterized by adecline (increase) in interest rates in the interbank market, has a positive (negative) effect on the creditsupply. The effect is also highly significant. Because we have verified the first fundamental result, wecan report the effect of the bank-specific variables on the transmission mechanism.
The benchmark model in column (1) shows that bank characteristics can affect the intensity of thebank lending channel. Indeed, capitalization, size and to a lesser extent liquidity interact significantlywith the monetary policy shock. The increase of these characteristics would reduce the effectivenessof monetary policy. This finding confirms many previous studies that highlighted how slightly cap-italized, small-sized and less liquid banks are more dependent on the credit channel. In particular,these results confirm the recent work of Altunbas et al. (2009) on the Eurozone. Well-capitalized,liquid and large-sized banks are better able to buffer their lending activities against monetary policyshocks for the following reasons: (1) well capitalized banks in a context of binding risk-based capitalrequirement can expand loans without raising new capital and have better access to markets due toasymmetric information (Van den Heuvel, 2002); (2) liquid banks can absorb monetary tighteningby drawing down cash and selling securities (Kashyap and Stein, 2000) and (3) large banks are moreaccustomed to financial markets because they are structurally less financed by deposits and equity(Kashyap and Stein, 1995).
We argue in this study that market power can also influence the transmission mechanism. Theresults in columns (2)–(5) of Table 1 show that the coefficient of the interaction terms between Lernerand monetary policy shock are very significant and positive.
This result indicates that banks with higher market power are less sensitive to changes in the inter-bank rates. Therefore, market power reduces the effectiveness of monetary policy. The first explanationis that market power increases the access to financial markets (i.e. to alternative sources of funding)and offers better financial conditions, which reduce the effects of a monetary policy shock. The secondexplanation is that margins and profitability, due to market power, create a “buffer” against shocks, inparticular, monetary policy shock. For instance, when the interbank rates increased in 2007 and 2008,the banks with market power were able to maintain their lending activities thanks to their “buffer”.In contrast, the banks with low market power are more vulnerable to monetary and macroeconomicfluctuations. These banks do have a limited ability to hedge against monetary policy shocks. An adjust-ment by the quantity of loans is the most viable solutions: already compressed margins preventingprice adjustment.
We note also that the bank characteristics that interact with monetary policy evolved with marketpower. Indeed, by comparing columns (1) and (2), we find that the significance of the interaction effectsbetween monetary policy shocks and capital has been reduced in the second interaction term andeliminated in the first in favor of the interaction effects with market power. Therefore, market poweris a channel that provides a better explanation for the transmission of monetary policy shocks. Theability to hedge and raise new resources on the markets is not necessarily dependent on capitalizationbut on market power. The ability to be profitable is more important than to absorb losses.
Beyond the effect on monetary policy, bank-specific variables can directly influence credit growth.First, it appears that size, capitalization and liquidity affect credit growth. A high level of capitaliza-tion or liquidity allows banks to easily meet lending opportunities without experiencing statutoryconstraints. These factors enhance the banks’ credibility and reputation, allowing them to collect newresources quickly and easily. Normally, only capital and liquidity should act on the bank creditwor-thiness. However, informational asymmetries and state distortions and, in particular, too big to failinsurance contribute to the size becoming a crucial factor.
Regarding our interest variable, as we expected, market power, estimated by the Lerner index,strengthens the ability of banks to expand their loan supply. The reasons are various. First, low com-petition substantially reduces the cost of other resources and, in particular, those deposits. Thus, theaverage cost of resources is lower, and the investment environment of the bank expands. Banks cantake the opportunity to expand their market share by allocating low-margin loans, which would notbe possible for a bank with less market power. Banks with market power are also relatively more
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capitalized and of a significant size, which allows them to have the positive effects already describedabove. Finally, individual market power is a proxy for bank efficiency. Thus, banks with market powernaturally increase their loan supply faster by eliminating less efficient banks from the competition.
Finally, as expected, it appears that demand, jointly proxied by the GDP growth rate and inflation,has a very significant effect on credit growth. Economic growth increases the viable projects andimproves the opportunities for firms, leading to an increase in credit issued. In addition, the pro-cyclicality of prudential standards accentuates this effect (Repullo and Suarez, 2009).
7. Extensions and robustness tests
7.1. Asymmetric effect and crisis effect (Table 2)
A common finding by many studies is that the credit channel may be asymmetrical, reacting dif-ferently to the rise and fall of the rates (Gambacorta, 2003). The competition effect is also likely to beasymmetrical. A bank with low market power could not only be forced to pass on the negative shocksquickly so as not to lose market share but could also be inert to a rate increase. Nevertheless, we donot find evidence of such an asymmetric effect.
Gambacorta and Marques-Ibanez (2011) highlight another type of asymmetry. They note that theinfluence of the bank business model on credit supply and the overall effectiveness of monetary pol-icy could be affected by the crisis due to the crisis intensity and the exceptional monetary policymeasures implemented. Indeed, together with a drastic drop in interest rates, central banks carriedout unconventional policies to revive lending and, through lending, the economy (Borio and Disyatat,2010). Unfortunately, no direct measure can report the effect of unconventional policies on the creditsupply. However, it is likely that our “crisis” dummy variable in Table 2 during the period from 2008 to2011 captures the effects of unconventional policies. During this period, the central bank has indeedintroduced many policies considered to be unorthodox. First, we find that our crisis variable had a verysignificant negative effect on the credit supply. The result is explained by the crisis effects. However,the interaction of our monetary policy variable with our crisis variable suggests that monetary policyhas become more effective. This finding could confirm that unconventional policies have enhancedthe transmission channel during this period. However in spite of the significant effects of the finan-cial crisis, we find that the crisis has not significantly altered the influence of market power on thetransmission mechanism.
Another concern arises from the fact that our sample comprises on the heterogeneous countriesand the results could be influenced by the geographical asymmetries of the selected economies.7
Indeed the financial and European sovereign crises highlighted one of the major shortcomings of theEurozone: its heterogeneity. The divergence within the Eurozone is particularly evident between thecore and “peripheral” economies. Consequently, as the impact of monetary policy could differ betweenthe north and the south of Eurozone particularly due to the differences in the scale of the crisis,8 webuild two subsamples corresponding to the two areas.9
The results of the two subsamples in columns (3) and (4) of Table 2 confirm the existence oflending channel in the south and north of the Eurozone. However the magnitude of this channel differssignificantly. This channel is nevertheless more relevant for the south economies (i.e. monetary policyshocks impact more the loan supply of the southern banks). These later have consequently moredifficulties to substitute their liabilities with other external sources of funding. This can be explainedby the fact that on average the southern banks are small and have been more impacted by the crisis(i.e. they became not sufficiently capitalized) beside the lower depth and liquidity of capital marketsin these countries.
7 We thank the anonymous referee for pointing out that.8 Al-Eyd and Pelin-Berkmen (2013) underline a fragmentation of Eurozone particularly following the European sovereign
crisis.9 We consider as South economies: Greece, Italy, Spain and Portugal. The other countries constitute the subsample “north”.
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Table 2Regression results: crisis effects and subsamples (north and south Eurozone).
Time dummies No No No NoCountry dummies No No No NoNo. of bank/no. of obs 675 4962 675 4962 675 4962 675 4962Hansen test (p-value) 0.102 0.138 0.803 0.099AR(1)/AR(2) (p-value) 0 0.700 0 0.400 0 0.861 0 0.459
Columns (1) and (2) consider crisis specific effects. Regressions are estimated by means of GMM estimators. Constant termincluded but not reported. Robust (Windmeijer) standard errors.
* Statistically significant at the 10% level.** Statistically significant at the 5% level.
*** Statistically significant at the 1% level.
However, even if the two groups face heterogeneous transmission mechanisms, it appears thatcompetition plays the same role in improving (significantly) the effectiveness of monetary policy.Thus, the lack of competition is a hurdle to monetary transmission both in the south and the north ofthe Eurozone, which confirms our main findings.
7.2. Competitive environment (Table 3)
The national competitive environment could have a different influence from individual marketpower on the loan supply. Banks may indeed be sensitive to both their own condition-estimated byan individual measure of market power- and to the overall condition of their market as well as theirnearest neighbors. This control is important because the banking industry is a network industry. Wetranscribe this idea by taking into account the market structure.
Our results, reported in Table 3, show that the national market structure strongly influences the loansupply. The more concentrated the system is, the more rapidly the credit supply will expand. Beyondthis potentially important result in view of economic policy, it appears that competitive structuressignificantly and positively interact with monetary policy shocks. To illustrate, a highly concentratedsystem will also be less sensitive to a monetary tightening. These results clearly indicate that the mon-etary policy transmission in the Eurozone is heterogeneous because of the differences in the market
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Time dummies No No NoCountry dummies No No NoNo. of bank/no. of obs 675 4962 675 4962 675 4962Hansen test (p-value) 0.094 0.333 0.252AR(1)/AR(2) (p-value) 0 0.700 0 0.198 0 0.659
(1) We introduce in our baseline model HHI. Column (2) substitutes Lerner Index by HHI. Column (3) adds to (1) an three-way interaction term of Lerner, HHI and monetary policy indicator change. Constant term included but not reported. Robust(Windmeijer) standard errors.
* Statistically significant at the 10% level.** Statistically significant at the 5% level.
*** Statistically significant at the 1% level.
structure. These results call for a strengthening of banking integration at the European level and theharmonization of market structures. Column (3) of the table specifies these earlier results. Indeed,it reports a specification with a three-way interaction term between individual Lerner index, marketstructure and change in monetary policy rate. The latter term shows that the effect of banking competi-tion on monetary policy transmission varies significantly with the market structure and consequentlywith the countries. Nevertheless, on average, market power continues to reduce monetary policy effec-tiveness. The more concentrated the system is, the lower the negative effect of market power. Thus,the lack of competition is less an impediment for monetary policy transmission in high-concentratedmarkets like the Finnish and the Dutch banking markets.
7.3. Others variables (Table 4)
In the first column of Table 4, the baseline model has been estimated with EONIA in place of theEuribor3months. The results are very consistent with our initial specification and therefore confirmthat market power affects monetary transmission through the bank lending channel.
In a second robustness requirement, we have included other variables in our initial specificationthat the literature has indicated may influence the loan supply and the bank lending channel. Wehave added a diversification index, the Z-score, the share of off-balance sheet activities and the marketcapitalization on GDP. No significant change is detected in our interest variable, as the effects of market
Time dummies No No No No NoCountry dummies No No No No NoNo. of bank/no. of obs 675 4962 675 4962 675 4287 675 4962 675 4962Hansen test (p-value) 0.142 0.309 0.583 0.477 0.094AR(1)/AR(2) (p-value) 0 0.662 0 0.642 0 0.638 0 0.387 0 0.698
Column (1)-changes the policy rate considered in our baseline model. Columns (2)–(5) introduce new variables respectively diversification, Z-score, off-balance sheet and capitalization.Regressions are estimated by means of GMM estimators. Constant term included but not reported. Robust (Windmeijer) standard errors.
* Statistically significant at the 10% level.** Statistically significant at the 5% level.
*** Statistically significant at the 1% level.
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power on the transmission mechanism and on credit supply remain the same. However, some resultsare interesting.
Bank diversification would have a negative effect on the loan supply (column (2)). The more variedthe banks’ incomes are, the less important the growth of the loan supply. We can explain this resultby the fact that a more diversified bank will have greater allocation opportunities. However, incomediversification appears to be unable to protect a bank against monetary policy shocks, as the interactionterms are insignificant (not shown). Beyond the positive effect on the loan supply, off-balance sheetactivities appear to interact strongly with monetary policy (column (3)). Off-balance sheet activitiestend to reduce the effectiveness of monetary policy. These activities include securitization, for whichAltunbas et al. (2009) have shown positive effects on supply but negative effects on the bank lendingchannel. Our results confirm these previous findings and are also consistent with the theory. A bankthat securitizes would not worry about the quality of its loans, which could enable it to quickly increaseits supply. However, a bank committed to this activity would be both more liquid and better capitalized,because it is less constrained by liquidity and capital requirements, which reduces the influence ofmonetary policy.
The Z-score index, defined above, has a significantly strong effect on the dependent variable. Theriskiest banks would have a more binding supply (column (4)). The result is consistent with the beliefthat the increased risk leads to a reduction of the regulatory capital to weight asset, which decreasesthe ability to take on new risks.
Finally, in column (5), we test the effect of financial integration. Indeed, a bank may hedge itselfmore easily in deep and liquid markets. To concisely approach this idea, we introduced the marketcapitalization to GDP ratio to our baseline regression, which is positively correlated with the loansupply.
Column (2) adopts a dynamic view unlike (1). Regressions are estimated by means of fixed effects estimators. Constant termincluded but not reported. Heteroscedasticity consistent standard errors.
* Statistically significant at the 10% level.** Statistically significant at the 5% level.
*** Statistically significant at the 1% level.
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7.4. Methodological control (Table 5)
Table 5 presents the estimation of our baseline model using estimators other than the GMM,namely, the fixed effects estimator. In our case, because of the dynamic nature of the model butalso because of endogeneity issues, the GMM is needed. However, the fixed effects model can verifythe consistency of our results. We used two different model specifications: one being similar to ourbasic model and the other being a not dynamic model, as fixed effects models are usually inconsis-tent with such a specification. The results of our estimations remain consistent with those previouslydemonstrated.
8. Conclusion
The potential effect of market power on the bank lending channel has so far been generally ignored.Yet, just as with size, capitalization or liquidity, market power could hamper monetary transmissionand, in particular, the bank lending channel. Our hypothesis is that a bank with higher market powerwill have a greater ability to hedge against a monetary shock due to greater access and better conditionsin the financial markets. Because market power would allow substituting more easily liabilities withother sources of funding, the effects of monetary policy change on the dynamic of credit will lessimportant and he monetary policy will be considered as less effective.
In this paper, we empirically test this idea for the Eurozone banks over the period from 1999 to2011. We find that market power reduces the bank lending channel. Thus, the supply of loans from abank with higher market power will be less sensitive to monetary policy shocks, thereby confirmingthe increased capacity of such banks to obtain alternative funding and greater autonomy vis-à-vis thecentral bank. This also confirms that “buffer” created by market power can allow smoothing monetaryshock by reducing temporarily the bank margin.
Beyond this indirect effect through monetary policy, our study shows a direct effect from marketpower on the credit supply. In fact, market power increases the supply of credit. This result couldstem from greater freedom of action, or it could be a consequence of greater efficiency. These effectsattributed to market power are confirmed when we consider the market structure and the competitiveenvironment of the bank. A bank established in a highly concentrated market will be less sensitive tomonetary policy shock. It also appears that market power will reduce monetary policy effectivenessin a concentrated system even more.
Beyond these new contributions to the literature, our work partially confirms previous results. Size,liquidity and capitalization are characteristics that strongly influence the transmission of monetarypolicy shocks. We note, however, that the addition of our market power variable tends to reduce thesignificant effect of capitalization. Thus, access to funding in good conditions would be more dependenton the ability to be profitable than on the capacity to absorb losses. Furthermore, the addition of an off-balance sheet variable, similar to the works of Altunbas et al. (2009), confirms some of their results.Indeed, we observe that these activities reduce the intensity of monetary transmission, however,without changing the effect of market power.
Furthermore, we have checked whether the crisis period led to a change in market power’s influ-ence on the transmission mechanism. Our results support that the change is not significant. We alsoconfirm our conclusion from two Eurozone subsamples (peripheral and central economies). Even ifthe monetary policy implemented is “one size fit all” and that the competition policy is an Euro-pean prerogative, the heterogeneity of the Eurozone and its fragmentation raises questions about theuniformity of the our overall conclusions of the Eurozone.
In terms of economic policy, our study first reveals the absence of arbitrage between market powerand monetary policy effectiveness. From that point of view, stronger competition would be favorable.Moreover, given the impact of market structures, it is clear that the bank lending channel, and throughit the transmission of monetary policy, is highly heterogeneous in the Eurozone. This finding, there-fore, calls for an emphasis on the convergence of market structures in the Eurozone or the moresystematic use by the central bank of differentiated policies, for example, the reserve requirements. Ina more cautious manner, our study could also underline the effectiveness of the unorthodox measures
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implemented by the ECB insofar as the bank lending channel has strengthened over the crisis period(2008–2011).
Finally, this work contributes to the literature regarding the bank lending channel, emphasizingthe crucial influence of market power, but it also contributes to the literature regarding competitionand stability because the effectiveness of monetary policy undoubtedly has implications for stability.
Austria 37 Ireland 11Belgium 14 Italy 97Finland 8 Netherlands 18France 118 Portugal 12Germany 309 Spain 42Greece 9
Appendix B.
Lerner index is formally defined as the relative difference between price and marginal cost,expressed as a percentage of price, which corresponds to the inverse of the price elasticity of demand.
Lerneri,t = Pi,t − Cmi,tPi,t
= −1e
where Pi,t denotes the price of total assets of the bank i at time t, Cm its marginal cost and e the priceelasticity of demand. Thus, the ratio takes values between zero and one. The index is zero in pure andperfect competition (high or infinite demand elasticity), that is the price is equal to marginal cost. Bycontrast, in concentrated markets, the increased market power of banks entails the increase of Lernerindex to 1. Two inputs are needed to obtain the Lerner index: the price and the marginal cost. Theconventional approach in the literature is to estimate the price by the ratio of total income to totalassets, and estimate the marginal cost from translog function of the following form:
A. Leroy / Int. Fin. Markets, Inst. and Money 31 (2014) 296–314 313
� ln TCi,t = + ˇ1 ln Qit + ˇ2
2ln Q2
it +2∑k=1
k ln Wk,it +3∑k=1
ϑk ln Zk,it + 12
3∑k=1
3∑j=1
�kj
ln Wk,it ln Wj,it + 12
3∑k=1
3∑j=1
�kj ln Zk,it ln Zj,it +3∑k=1
�k ln Qit ln Wk,it
+3∑j=1
k ln Qit ln Zk,it +3∑k=1
3∑j=1
ωkj ln Wk,it ln Zj,it + ı1Trend + 12ı2Trend
2 + ı3Trend ln Qit
+3∑k=1
�kTrend ln Wk,it +3∑k=1
�k�Trend ln Zk,it + εit (3)
where the bank costs (TC) are a function of outputs (Q), estimated by the total assets, of the inputs price(Wk), i.e., the funds cost (W1), capital cost (W2) and labor cost (W3),10 netputs (zk), which includes fixedassets (Z1), provisions for losses on loans (Z2), and capital (Z3), and a trend. Moreover, similarly to TurkAriss (2010), we impose linear homogeneity conditions by scaling all costs and factor prices by thelabor price and we correct the heterogeneity by normalizing by one of our control variables (capital).Marginal costs are then directly obtained from the estimated parameters of the translog function bycalculating the derivative with respect to Q, the total assets.
MCit = TCitQit
[ˇ1 + ˇ2 ln Qit +
3∑k=1
�k ln Wk,it +3∑k=1
k ln QitZk,it + ı3 Trend
](4)
The estimation of the translog function on panel data of a whole of banks can be carried out in twodistinct ways. Indeed, we can use the OLS (ordinary least squares) or stochastic frontier (SFA) (Koetteret al., 2012). For our part, we prefer SFA. This approach has a great advantage, since it allows accountingfor inefficiencies of liabilities, which can be crucial. The distance between the price and the marginalcost may indeed be altered as banks having market power, adopt a quiet life (Hicks, 1935; Maudos andde Guevara, 2007), or on the contrary, as the efficiency lead to structure the market around the mostefficient banks (Demsetz, 1973). Note that to take into consideration different technologies betweenthe countries and, to capture bank specificities, we also estimate the translog function for each countryseparately11 and include bank fixed effects.
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