Does bank ownership affect lending behavior? Evidence from the Euro area

Accepted Manuscript Does bank ownership affect lending behavior? Evidence from the Euro area Giovanni Ferri, Panu Kalmi, Eeva Kerola PII: DOI: Reference:

S0378-4266(14)00168-X http://dx.doi.org/10.1016/j.jbankfin.2014.05.007 JBF 4452

To appear in:

Journal of Banking & Finance

Received Date: Accepted Date:

19 June 2013 9 May 2014

Please cite this article as: Ferri, G., Kalmi, P., Kerola, E., Does bank ownership affect lending behavior? Evidence from the Euro area, Journal of Banking & Finance (2014), doi: http://dx.doi.org/10.1016/j.jbankfin.2014.05.007

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Does bank ownership affect lending behavior? Evidence from the Euro area Giovanni Ferri*, Panu Kalmi**, Eeva Kerola***

Abstract We analyze the differences in lending policies across banks characterized by different types of ownership, using micro-level data on Euro area banks during the period 19992011 to detect possible variations in bank lending supply responses to changes in monetary policy. Our results identify a general difference between stakeholder and shareholder banks: following a monetary policy contraction, stakeholder banks decrease their loan supply to a lesser extent than shareholder banks. A detailed analysis of the effect among stakeholder banks reveals that cooperative banks continued to smooth the impact of tighter monetary policy on their lending during the crisis period (2008-2011), whereas savings banks did not. Stakeholder banks’ propensity to smooth their lending cycles suggests that their presence in the economy has the potential to reduce credit supply volatility. JEL classification: G21; E52; L33; P13 Keywords: European banks; Monetary policy transmission; commercial banks; savings banks; cooperative banks; lending cyclicality

* LUMSA University Rome ** University of Vaasa *** Aalto University School of Business

1. Introduction1 The lending channel literature has long held that the impact of monetary (and financial) shocks is exacerbated because banks tend to curtail their loan supply after those shocks 1

We would like to thank an anonymous referee for very useful insights that helped us to improve a previous draft of the paper.

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materialize (Bernanke and Gertler, 1995; Hubbard, 1995). In turn, procyclical lending behavior (Rajan, 1994) could exacerbate the effects such shocks have on the economy, making it more difficult for bank-dependent borrowers – e.g., small businesses – to continue to rely on external finance (Gertler and Gilchrist, 1994; Berger and Udell, 1995). The aim of this study is to investigate the lending supply policies of banks from Euro area countries during the first 13 years of the common monetary policy and explore whether differences in loan supply decisions result from different forms of bank ownership. In particular, we study whether there are differences between profitmaximizing banks (shareholder banks) and banks that have objectives other than profit maximization (stakeholder banks). In particular, we include cooperative and savings banks in the latter category. To study bank responses to monetary policy changes between 1999 and 2011, we employ bank-specific financial statements (from the Bankscope database) provided by Bureau Van Dijk. In addition to examining the entire period, we distinguish between a period of relative financial stability (prior to the recent crisis) and the crisis period (2008-2011) and provide evidence that there is a clear difference between these two time periods with respect to the bank lending channel. Apart from gaining knowledge on the specific features of the bank lending channel and the factors influencing banks’ lending behavior, our ownership-based classification of the banks allows us to also consider an industrial organization perspective. Moreover, it is extremely important to determine the underlying reasons for the heterogeneity observed in Euro area banking sectors to understand how, due to differences in the monetary transmission channel, the actual monetary stance can differ across Euro countries despite their shared monetary policy instruments. Previous empirical research has confirmed (at least using US data) that small, undercapitalized and relatively illiquid banks amplify monetary policy shocks through the lending channel (see, e.g., Kashyap and Stein, 1995; and Kishan and Opiela, 2000). Consensus has been more elusive in studies on the Euro area: the extent to which different bank balance sheet items amplify the lending channel seems to be somewhat country dependent (see, e.g., Altunbas et al., 2002; Favero et al., 1999; and De Bondt, 1999). We argue that a likely cause of these disparate results is the heterogeneity in national banking sector compositions within Euro area. Furthermore, differences in bank 2

lending behavior are not only a result of differences in balance sheets but also of differences in bank business models, which are closely related to types of bank ownership. It is possible that banks relying on a relationship-based approach to lending may be less willing to curtail loans to customers with whom they tend to have long-term relationships (Berger and Udell, 2002). Our paper is one of the first attempts to identify differences in lending supply policies based on banks’ missions/types of ownership.2 Specifically, we investigate whether shareholder banks, which exclusively focus on profit maximization, differ from stakeholder banks, which seek to maximize the consumer surplus of their customers. This issue is important because stakeholder banks are an especially important component of the Europe’s financial architecture. In certain large Euro area countries, stakeholder banks have comparable or larger market shares than shareholder banks (Ayadi et al., 2010; Bülbül et al., 2013). For example, this is the case in France (cooperative banks), Germany (savings and cooperative banks) and, until recently, Spain (primarily savings banks). Throughout the observation period (1999-2011), we find that stakeholder banks followed less procyclical loan supply policies than shareholder banks; their loan supply changes reacted less to changes in the short-term interest rate. This finding applies to both cooperative and savings banks. Moreover, the estimated effect of the interest rate change was larger during the period of financial stability (1999-2007) than during the crisis period (2008-2011). Similarly, the effect of ownership structure was less pronounced during the crisis. During the period of financial stability, the coefficients of both cooperative and savings banks were statistically significant, and the point estimates were similar, but during the crisis, only the coefficient of cooperative banks was significantly different from that of shareholder banks. These results that stakeholder banks (and cooperative banks in particular) behave less procyclically and stabilize the lending cycle persist when we consider a number of robustness checks. The remainder of this paper is structured as follows: in section 2, we discuss the lending channel and provide a survey of the empirical literature on the effects of bank heterogeneity on loan supply policies. Section 3 presents testable hypotheses regarding different missions and ownership types. Section 4 describes the data used in the 2

The only previous study – that we are aware of – is De Santis and Surico (2013), who examine the heterogeneity in the lending channels of cooperative, commercial and savings banks (among other aspects).

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estimations and some descriptive statistics. In section 5, we present our empirical estimations, comment on our results and perform various robustness checks. Section 6 concludes. 2. Effect of the lending channel and previous empirical evidence Recent decades have witnessed increased interest in the financial sector’s (especially banks’) role in the monetary policy transmission process. In his seminal paper, Bernanke (1983) analyzes the relative importance of monetary versus financial factors during the Great Depression and provides support for the credit view, which argues that financial markets are imperfect, and hence the Modigliani-Miller assumptions do not hold and finance matters (Freixas and Rochet, 2008). Empirical research then led to a debate between the so-called money view and a set of alternative theories termed the broad lending channel. The broad lending channel emphasizes the role played by the supply of funds banks provide to firms and accounts for asymmetric information and market imperfections. The implicit assumptions of this perspective are that prices are rigid, the central bank can directly influence the volume of credit by adjusting reserves, and loans and securities are imperfect substitutes both for borrowers and for banks. While many studies have found that the lending channel is in place and serves to amplify monetary policy shocks through the banking sector,3 relatively fewer studies have attempted to determine whether there are differences between different types of banks and between banking sectors across countries. Thus far, empirical research has sought to identify differences in the lending channel and the impact of monetary policy with respect to banks’ size, capitalization and/or liquidity. Kashyap and Stein (1995) find that monetary policy shocks affect large and small banks differently. Small banks presumably face higher agency costs when raising uninsured funds, and thus their balance sheets are affected to a greater extent (Bernanke et al., 1996). Kashyap and Stein (1995) also find that monetary policy has a more pronounced effect on credit supply for banks with less liquid balance sheets (banks with lower ratios of cash and securities to total assets). Kishan and Opiela (2000) differentiate banks by size and capital-toleverage ratios and conclude that capital is important for assessing the impact of policy on loan growth and determining the distributional effects of monetary policy. The market perceives low-capitalized banks as riskier, and they therefore face greater 3

See Gambacorta and Marques‐Ibanez (2011) for a recent cross-country study on the subject.

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difficulty issuing bonds and are unable to protect their credit relationships. These studies on the United States stress that the lending channel is more important for small banks, especially for those that are undercapitalized or relatively illiquid. However, results are far less unanimous with respect to the European banking sector. For instance, Altunbas et al. (2002) find that capitalization is key in explaining different reactions to monetary policy shocks and undercapitalized banks are especially vulnerable. Ehrmann et al. (2003) find that monetary policy changes have the most substantial effect on the credit supply of illiquid banks, while size and capitalization are less important. Gambacorta and Mistrulli (2004) and Gambacorta (2005) present evidence from Italy that the lending behavior of well-capitalized banks is less vulnerable to monetary policy shocks. Fungacova et al. (2013) examine the interaction of competition and the lending channel in 12 Euro area countries during the period 20022010 and find that before 2007, the lending channel was more pronounced in competitive markets. Moreover, a number of studies of European countries observe rather heterogeneous reactions by banks in various countries, thereby complicating the implementation of common monetary policy (e.g., Favero et al., 1999; De Bondt, 1999; King, 2000; De Santis and Surico, 2013). There may be several explanations for the relative lack of clarity in the results concerning Europe. For example, there are well-known institutional differences: European firms are much more heavily dependent on bank credit than their US counterparts. The entire European financial system is much more bank-based than that of the United States, where financial-market financing of the corporate sector is more developed. For example, according to EBF (2012), the total assets of the European banking sector account for 350% of aggregate GDP, whereas the corresponding figure for the United States is 77%. Moreover, the ratio of total bank loans to GDP is 139% in Europe compared to 59% in the United States. Furthermore, the role of stakeholder banks is much more pronounced in Europe than in the US. In this paper, we explore the role of ownership in the functioning of the lending channel. Apart from a few papers, to our knowledge, there are no empirical studies focusing on the possible heterogeneous effects of bank ownership structure on the strength of the lending channel. Ashcraft (2006) examines the affiliation of US banks and finds that banks affiliated with a multibank holding company are less sensitive to monetary policy contractions because they have access to larger internal capital markets. De Bondt 5

(1999) and Schmitz (2004) consider foreign ownership as an explanatory variable, the former using US data and the latter data from ten EU accession countries in 2004. De Bondt (1999) finds stronger evidence for a lending channel when foreign-owned banks are omitted from the sample, concluding that international banks have greater opportunities to borrow elsewhere than even large domestic banks. Schmitz (2004) finds that foreign-owned banks react to Euro area interest rate changes to a greater extent than their domestic-owned counterparts. Bertay et al. (2012) considers state-owned banks in 111 countries during the period 1999-2010 and finds that lending by state-owned banks is less procyclical than that of private banks. Furthermore, lending by state-owned banks located in high-income countries is even countercyclical. To our knowledge, the only paper examining the differences between shareholder and stakeholder banks is a recent work by De Santis and Surico (2013). They examine the banking sectors in Spain, Germany, Italy and France during the period 1999-2011 and conclude that the lending channel is strongly affected by heterogeneity with respect to market concentration, bank balance sheet characteristics, and bank typology (commercial, cooperative and savings banks). They estimate separate regressions for each country and for each bank type and find, inter alia, that the interest rate channel in Spain is rather weak, commercial banks only react to interest rate changes remotely irrespective of the country, and loan supply decisions are most affected by monetary policy actions, especially among relatively illiquid and less capitalized cooperative and savings banks in Germany and smaller savings banks in Italy. Our empirical strategy is different: we simultaneously estimate the entire panel and allow for heterogeneous responses to monetary policy shocks across bank ownership types while controlling for differences in banks’ balance sheets and demand conditions across countries.4 This approach enables us to draw more direct inferences concerning the relative differences between stakeholder and shareholder banks’ loan supply policies than the approach followed by De Santis and Surico (2013).

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A further difference between De Santis and Surico (2013) and our paper stems from the re-classification of several banks. Their database seems to have been constructed using the bank ownership classifications provided by Bankscope. However, we found that ownership was misclassified for some banks in the Bankscope data and recoded those banks accordingly; see footnote 9 below. In addition to the other outlined differences, this factor could also help to explain possible divergences between our results and theirs.

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3. Implications of differences in mission and ownership The central feature of cooperative and savings banks is that their aim is not profit maximization. A key difference with respect to ownership structure is that cooperative banks are owned by their members, whereas savings banks either have no owners (i.e., they are non-profit organizations) or are owned by the public sector.5 In the case of cooperatives, the distribution of profits to owners is limited, shares are typically not tradable,6 and members only have one vote, regardless of the number of shares they own. In savings banks, profit distribution and tradable ownership rights are absent altogether. Moreover, management compensation in stakeholder banks is typically not tied to profitability. For these reasons, there is no party in these banks who would benefit from profit maximization. Instead, cooperative and savings banks are intended to maximize consumer surplus: the assumption is that cooperatives are able to do so because they are governed by their customers, and savings banks are operated for the benefit of local customers. Additional key characteristics of stakeholder banks are that they are typically locally oriented and only operate in a limited geographical area, near their customers. They often operate in sparsely populated areas with little competition (Fonteyne, 2007; Gutierrez, 2008). Stakeholder banks typically achieve economies of scale in various areas (e.g., payment systems, internet banking, credit cards, liquidity management, marketing, etc.) by participating in networks of similar local banks (Desrochers and Fischer, 2006; Bülbül et al., 2013). Stakeholder banks are typically relationship-lending oriented. In cooperative banks, the members of the cooperative and board members are typically local residents and in some cases entrepreneurs. This means that they identify with the interests of local enterprises and constituencies. Similarly, the boards of savings banks represent local interests and therefore engage in relationship banking in their areas of operation. Several pieces of evidence suggest that stakeholder banks are more involved in relationship lending than shareholder banks. Ferri (1997) finds that managerial turnover in stakeholder banks is lower than in shareholder banks, thereby allowing them to retain soft information. Mocetti et al. (2010) find that cooperative banks are more likely to delegate lending decisions to loan officers and less likely to use credit scoring techniques than 5

For reviews of the key features of stakeholder banks, see e.g., Amess (2002), Fonteyne (2007), Ayadi et al. (2010) and Bülbül et al. (2013). 6 With the exception of the Italian Banche Popolari.

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shareholder banks. Bartoli et al. (2013) find that stakeholder banks are more likely to engage in the use of soft information in their lending decisions. Banks oriented towards relationship lending are more prone to grant credit to their financially constrained borrowers to maximize the long-term value of their borrowerlender relationships (e.g., Petersen and Rajan, 1994; Boot, 2000; Bolton et al., 2013). However, because developing relationships and collecting related soft information is costly, lending by relationship-oriented banks increases less during high-demand periods. Overall, lending by relationship-oriented banks is less volatile than lending by transaction-oriented banks. We argue that the primary reason why stakeholder banks should be less sensitive to monetary policy shocks than shareholder banks is that they are relationship-lending oriented and therefore more likely to smooth credit availability conditions than shareholder banks. Clearly, shareholder banks may also be relationship oriented, but we argue that stakeholder banks are more likely to be. Moreover, stakeholder banks may be less likely to opportunistically exploit the hold-up problem in lending, as they are not profit maximizing and are therefore more likely to internalize customer interests. For instance, for Italian banks, Angelini et al. (1998) find that shareholder-owned, relationship lenders exploit their ex post monopoly position by charging higher interest rates, whereas cooperative lenders charge customers lower interest rates than nonrelationship lenders.7 By extension, we expect relationship lenders to reduce credit availability to a lesser extent in response to tighter monetary policy and during crisis periods. Further, Petersen and Rajan (1995) find that relationship lending is more profitable in markets characterized by relatively low competition. Stakeholder banks, especially cooperative banks, are often strong in sparsely populated areas where competition is relatively low, thus increasing the importance of relationship banking. There are also additional reasons why we expect stakeholder ownership to influence the transmission of monetary shocks. Given the results indicating that bank affiliation reduces lending constraints (Ashcraft, 2006), we hypothesize that the networks cooperative and savings banks participate in might also shield them from monetary policy shocks. Further, savings banks are often publically owned (municipal or regional), especially in Germany and Austria, which can smooth the supply of credit (Bertay et al., 2012). 7

For more recent supportive evidence on this from Italy, see Bolton et al. (2013).

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However, stakeholder banks have also characteristics that place them in a relatively poor position to respond to monetary policy shocks. For instance, relative to shareholder banks, stakeholder banks often have less liquid assets, are smaller in size, and have lower capital levels. We control for these factors in our econometric specifications. In summary, we hypothesize that a change in monetary policy has a greater effect on the credit supply of shareholder banks vis-à-vis that of stakeholder banks. We expect this to primarily be a result of the relationship-lending orientation of the stakeholder banks. 4. Data and descriptive statistics We employ micro-level data based on financial statements derived from Bankscope, provided by Bureau van Dijk. These data (at an unconsolidated level) include annual observations from 12 Euro area countries8 over the period 1999-2011 covering 4,352 individual banks. As stated by Brissimis and Delis (2010), two papers (Ashcraft, 2006; and Gambacorta, 2005) provide a discussion and evidence indicating that annual observations are appropriate for lending equations, thus validating their (and our) use of the Bankscope database. The initial ownership classifications are drawn from Bankscope, but we have made certain corrections and amendments based on our earlier work (Ferri et al., 2013).9 Table 1 reports this distribution of banks by ownership type and the value of their total assets in our data (annual average taken over observation period 1999-2011). The bulk of our stakeholder bank observations come from Germany (2,246 German stakeholder banks of a total of 3,491). There are also a number of Italian stakeholder bank observations but far fewer than the German ones (703 Italian stakeholder banks). Shareholder bank observations are more dispersed across countries (Germany and France having the most observations). The Spanish savings bank sector is large, as measured by total assets; 55 Spanish savings banks’ combined annual average total assets are more than four times those of 64 Italian savings banks and more than ten times those of 77 Austrian savings banks.

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Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain. For example, we corrected the following flaws in BankScope’s classifications: 1) some Austrian co-operative banks in the Raiffeisen group were classified as savings banks in BankScope; 2) many banks classified as savings banks in BankScope are essentially commercial retail banks, especially in Italy; 3) the Caisse d’Epargne Group in France is still classified as a savings bank in BankScope, although it transitioned to co-operative ownership in 1999; 4) some banks that are relevant to our analysis could also be found in other specialization classifications (rather than solely among commercial banks, cooperative banks, and savings banks) such as bank holding companies, governmental credit institutions, and mortgage banks. For additional details, see Ferri et al. (2013).

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Table 2 presents summary statistics (breaking down the data into shareholder vs. stakeholder banks and, in turn, the latter into cooperative vs. savings banks) of the most relevant bank-specific variables. Shareholder banks’ loan growth10 was the most rapid during the observation period (1999-2011), at approximately 9.75% on an annual basis, but the standard deviation is much larger than that of stakeholder banks, implying higher volatility. Savings banks’ loan growth was, on average, nearly three percentage points lower than that of their cooperative counterparts. Shareholder banks are larger (in terms of log total assets) than stakeholder banks on average, and savings banks are larger than cooperative banks. Regarding balance sheet composition and the share of loans relative to the banks’ total assets in particular, we can observe clear differences between stakeholder and shareholder banks: loans represent 60% of stakeholder banks’ total assets, while the figure for shareholder banks is only 45% on average. With respect to capitalization (ratio of equity to total assets), the figure for shareholder banks is 14% on average, while the figure is as low as 6% among savings banks. Finally, regarding liquidity (the ratio of liquid assets11 to total assets), shareholder banks are on average far more liquid than stakeholder banks (60% and 30% for shareholder and stakeholder banks, respectively). Next, we concentrate on the loan supply of banks from different ownership groups and how it evolved over the last 13 years. Figure 1 separately depicts the observations of loan growth for stakeholder vis-à-vis shareholder banks and for cooperative vs. savings banks during the period 1999-2011. As we can see, although we have more observations for stakeholder banks than for shareholder banks, the loan growth data for shareholder banks exhibit much higher volatility. For some of these latter banks, loan volumes have varied by as much as five-fold from the previous year, whereas the loan growth data for stakeholder banks exhibit less volatility.12 The differences between cooperative and savings banks are rather unclear, although cooperative banks experienced higher average rates of loan growth than savings banks throughout the observation period. Figure 2 depicts the mean loan growth of the different bank groups during our observation period. 10

Loans are taken from BankScope and include: residential mortgage loans (plus) other mortgage loans (plus) other consumer / retail loans (plus) corporate and commercial loans (plus) other loans. The measure excludes interbank lending. 11 Our measure of liquid assets is taken directly from BankScope and includes: trading securities at fair value through income (plus) loans and advances to banks (plus) reverse repos and cash collateral (plus) cash and cash due from banks (minus) mandatory reserves included above. In most previous studies, liquid assets include cash, securities (often only government bonds), and interbank lending; additional items included in our measure do not distort the results and yield a rather similar liquidity ratio to the, e.g., 0.399 reported in Gambacorta (2005) and approximately 0.4 obtained in Ehrmann et al. (2001). 12 We accounted for these somewhat extreme outlier observations in our estimations; please refer to section 5.3.

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We can first note the sharp decrease in loan growth especially for stakeholder banks in 2005. The primary reason for this drop is that the bulk of our observations come from German banks. In Germany, the economy was struggling in 2005 in the aftermath of the recession of the early 2000s. The unemployment rate reached its peak, and the overall economic output was growing at a lower pace compared to any other advanced economy other than Portugal13. However, also after excluding Germany from the sample, stakeholder banks still have lower mean loan growth in 2005 than shareholder banks on average. What is also noteworthy are the clearly opposite loan supply policies of stakeholder and shareholder banks after 2008. While stakeholder banks kept their loan growth positive during the recent crisis (except for year 2010), shareholder banks loan growth remained negative from 2008 onwards. In addition, we can approximate the degree of stability in lending policies across ownership/organizational bank classes by calculating the coefficient of variation (standard deviation/mean) of the absolute change in loan supply. The figures reported in Table 3 indicate that stakeholder banks are much more stable than shareholder banks, with coefficients of variation of 2.1476 and 6.3598, respectively. Regarding cooperative banks and savings banks, there is a slight difference, with the former (2.0746) being somewhat more stable than the latter (2.3499). We now turn to our empirical estimations, in which we examine the differences in loan supply policies across the ownership groups by considering the effects of monetary policy on bank loan supply using data from bank balance sheets. 5. Empirical estimations and results 5.1. Empirical methodology Kashyap and Stein (1995) develop a theoretical model and use disaggregated bank balance sheet data to assess whether a lending channel exists in the US. They argue that if the lending view is correct, one should expect the loan portfolios of banks of different sizes to respond differently to a monetary policy contraction. Based on their model, and following more recent empirical studies (see, e.g., Gambacorta, 2005; Gambacorta and Mistrulli, 2004; Bertay et al., 2012), we develop an autoregressive model. However, in 13

International Monetary Fund World Economic Outlook Database (October 2013)

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contrast to the existing literature, we assess whether interest rate changes have different effects on banks with different ownership structures. The bank-specific variables that the existing literature finds to have the greatest effects on loan supply– namely capitalization (equity to asset ratio), liquidity (liquid assets to total assets ratio) and bank size (log of total assets) – are included as controls. We examine how changes in the short-term interest rate affect the total loans supplied by banks. The estimated equation is: log(loansi,t) = θlog(loansi,t-1) + αrt-1 + µ(OSDUMMY * rt-1) + δ(rGDPc,t-1) + σYgapc,t-1 + γ1CAPITALi,t + γ2SIZEi,t + γ3LIQUIDITYi,t +YEARdummyt + εi,t (1) Where loans is the dependent variable, namely (the log of) the volume of loans supplied by bank i in year t. The parameter r is the short-term interest rate (EONIA overnight interest rate), which reflects changes in monetary policy. We utilize the monthly average EONIA interest rate where we then compute the annual average. Figure 3 presents the underlying evolutions of both monthly average EONIA and ECB main policy interest rates (we use the latter in one of our robustness checks) as well as the computed annual averages used in the estimations. According to lending channel theory, the coefficient α should be negative: as interest rates increase, banks decrease the amount of loans supplied. The parameter µ captures the separate effect of interest rates on stakeholder banks; if this coefficient is positive, it indicates that the negative effect is dampened for the stakeholder banks. This interaction term only takes values for stakeholder banks as a group or cooperative and savings banks separately, depending on the specification (OSDUMMY stands for ownership dummy). Annual real GDP in country c, in which bank i operates, is denoted rGDP, and Ygap is the output gap as a percentage of potential GDP14 in the same country c. Real GDP (the value of economic output adjusted for price changes) controls for economic growth and changes in the price level, while the output gap (the difference between real GDP and potential GDP) indicates the imbalance in the real economy. According to Gambacorta (2005), the inclusion of demand side control variables allows us to capture cyclical movements and identify the monetary policy component of interest rate changes. We also control for bank-specific variables: CAPITAL is bank i’s capital position (share of equity to total assets), SIZE is bank i’s 14

Both the real GDP and output gap data are taken from International Monetary Fund, World Economic Outlook Database, October 2012.

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size (log of total assets), and LIQUIDITY is bank i’s liquidity (share of liquid assets in total assets). Annual time dummies are included in every specification. We are aware that a number of time-invariant bank and/or country characteristics (fixed effects) might be correlated with the explanatory variables. The fixed effects are contained in the error term in equation (1), which consists of the unobserved bank/country-specific effects and the observation-specific errors: εi,t = νi +υi,t (2) To address this fixed effects problem, and because of the lagged dependent variable and heteroskedasticity15 present in the data, we estimate equation (1) using an ArellanoBond-type difference GMM estimator16 (Arellano and Bond, 1991). The Arellano-Bond difference GMM estimator is specifically designed for panels with large-N and short-T17 and transforms our equation (1) into: ∆log(loansi,t) = θ∆log(loansi,t-1) + α∆rt-1 + µ∆(OSDUMMY * rt-1) + δ∆(rGDPc,t-1) + σ∆Ygapc,t-1 + γ1∆CAPITALi,t + γ2∆SIZEi,t + γ3∆LIQUIDITYi,t + ∆YEARdummyt + ∆εi,t (3) From equation (2), we obtain ∆εi,t =∆νi +∆υi,t or εi,t - εi,t-1 = (νi - νi) + (υi,t - υi,t-1) = υi,t - υi,t-1 (4)

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Assessed using the Breusch-Pagan test (designed to detect any linear form of heteroskedasticity) and White’s test (a general test for heteroskedasticity that allows us to test for the presence of both non-linear forms of heteroskedasticity and non-normally distributed errors). Both test statistics reported large chi-square values, rejecting the null-hypotheses (constant variance for Breusch-Pagan and homoscedasticity for White’s test). 16 One step difference GMM. Instruments are the dependent variable and the bank-specific variables (second and third lags for the crisis years (2008-2011) and collapsed for the other two time spans). Specifically, Roodman (2009) demonstrates that collapsing the instruments can efficiently correct for instrument proliferation. Restricting the lag length to three for the crisis years was done to further reduce the number of instruments. Output gap, real GDP, and the monetary policy indicator (EONIA interest rate change) are considered exogenous instrumental variables. 17 In large-T panels, a shock to the fixed effect (appearing in the error term) will decline over time. Similarly, any correlation of the lagged dependent variable with the error term will be insignificant (Roodman, 2009).

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By first differencing the regressors, the fixed effects are removed because they are timeinvariant. These procedures ensure the efficiency and consistency of our estimates, provided that the instruments are appropriate (the validity of the instruments is assessed using the Hansen test). The Hansen test of overidentifying restrictions has the null hypothesis that the instruments are exogenous. A rejection of this null hypothesis implies that the instruments do not satisfy the orthogonality conditions required for their use. Additionally, we employ the Arellano-Bond test of autocorrelation of errors, with the null hypothesis of no autocorrelation in the differenced residuals. Specifically, the second order test (reported here as the AR(2)) in first differences tests for autocorrelation in levels and is more relevant. Again, a failure to reject the null hypothesis is the preferred outcome. Standard errors are heteroskedasticity and autocorrelation robust and clustered by the panel identifier, i.e. individual banks.18 5.2. Estimation results The main estimation results are presented in Table 4 with the proper diagnostics: the Hansen test does not reject the overidentification conditions, and the tests for serial correlation find no second order serial correlation. We first estimated equation (1) for the full period and then for the period of relative financial stability (before 2008) and the crisis period (from 2008 onwards). The first specification includes an interaction term between the stakeholder bank dummy and the lagged interest rate. The second specification includes two interaction terms: between a cooperative bank dummy and the lagged interest rate and between a savings bank dummy and the lagged interest rate. First, regarding the full time period (1999-2011), we find that a 1% contraction (expansion) in monetary policy leads banks to reduce (increase) their loan supply by approximately 8.5%. This effect is dampened for stakeholder banks as a group, as well as for cooperative and savings banks separately (their coefficients are positive and statistically significant at the 1% level). Moreover, the size of the coefficients of stakeholder bank ownership interactions with the interest rate is worth emphasizing: they (being 0.10 for cooperative banks and 0.12 for savings banks) approximately offset the negative coefficient on interest rate changes (-0.085), indicating that changes in the interest rate would have no effect on the lending of stakeholder banks when the full period is considered.

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Bertrand et al. (2004) show that standard errors allowing for arbitrary serial correlation within the same entities perform quite well when the number of entities is over 50.

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During the period of financial stability (1999-2007), we note that the interest rate has a larger absolute effect on loan growth changes (although it declines in statistical significance somewhat); a 1% increase in the interest rate leads to a decline in lending of approximately 17%; i.e., the lending channel is stronger when financial markets operate more typically. Again, it seems that the loan supply of stakeholder banks as a group and of cooperative and savings banks separately are much less affected by changes in the interest rate. During the recent crisis (2008-2011), this negative effect of interest rate changes on loan supply growth seems lower in absolute value (statistically significant at the 5% level): a 1% increase in the interest rate leads to a reduction in lending of 7%. The coefficients associated with the interaction terms of ownership groups and interest rates remain positive and statistically significant (at the 10% level) for the stakeholder bank group and for cooperative banks, indicating that their loan supply changes are less sensitive to changes in the interest rate. For savings banks, the coefficient is no longer statistically significant; hence, they are not statistically different from their shareholder counterparts. Here, the lagged dependent variable also loses its statistical significance, and hence we believe that the model may be misspecified. This result is not entirely unexpected; the loss of trust towards other banks and customers made banks reluctant to increase their lending, although the central bank nearly reduced interest rates to the zerolower bound. This could mean that traditional methods for studying the lending channel have become inadequate, and thus models considering the short-term interest rate as the monetary policy instrument are no longer suitable for studying the effectiveness of monetary policy. Another potential explanation is the small number of annual observations due to the shorter time span, especially when using the difference GMM. Regarding the different bank-specific variables, capitalization (the ratio of equity to total assets) seems to be an important variable in explaining bank loan supply behavior, especially during periods of relative financial stability: better capitalized banks (holding all other variables constant) are less likely to decrease their lending. Size, however, seems to also be highly important during the crisis, reflecting that the larger the bank, the less it needs to reduce lending. These results that larger and better capitalized banks are less prone to decrease lending were observed by Kashyap and Stein (1995) using US data and were further confirmed in some European studies (see, e.g., Ehrmann et al., 2001). Liquidity (the ratio of liquid assets to total assets) has a negative and statistically significant coefficient in the first four columns; banks with relatively large amounts of liquid assets on their balance sheets are more prone to decrease lending (ceteris paribus). 15

However, this negative effect is no longer statistically significant during the crisis period. This result contradicts most previous empirical papers on the subject (with the exception of some of De Santis and Surico’s (2013) regressions): liquidity more frequently has a positive coefficient as banks that have relatively more liquid assets on their balance sheets are better able to shield their lending activities. Our findings could be explained, inter alia, by the bank capital channel. If banks have relatively more liquid assets, the ratio of loans to total assets is already lower. In this case, as market interest rates increase, an even lower fraction of longer-maturity loans can be renegotiated with respect to short-maturity deposits, and thus the bank faces higher short-term costs. These costs could then be more easily lowered by reducing lending than selling other types of more liquid securities. These results indicate that while bank-specific balance sheet variables affect bank lending (in line with previous empirical studies), the role of banks’ missions and ownership structures role is equally important. We argue that this could be an explanation for the lack of consensus in empirical research on the Euro area regarding the different effects of balance sheet variables (see the latter part of Section 2 for an overview). Those studies regarded banks as having identical business models and only differing, for example, in size or the relative share of equity. However, as banking sector composition differs across countries in the Euro area (Table 1 provides a broad overview), we cannot draw inferences regarding the bank lending channel strength by exclusively considering differences in bank balance sheets. 5.3. Robustness Next, we provide various robustness checks to validate the results presented above.19 First, based on our results, we argue that while savings banks behaved similarly to their cooperative peers before the recent crisis, they did not statistically significantly differ from shareholder banks during the crisis period, while cooperative banks continued to behave less procyclically. Examining the last column in Table 4, however, one could argue that by comparing the statistical significance between the coefficients of 19

Before proceeding to other robustness checks, we sought to ensure that our results are not driven by outliers. Many observations in our data exhibit extreme developments with respect to the main dependent variable, primarily in lending growth (see Figure 1). We re-estimated the models after dropping the first 1% and then 5% of outliers in terms of lending growth (from both ends of the distribution). In doing so, we only lose the statistical significance of the stakeholder bank group interaction term during the crisis period. Overall, we are thus confident that our main results are not driven by outliers.

16

cooperative and savings banks (rather than vis-à-vis commercial banks) one might find no difference. To investigate this issue further, we perform the same estimations but first exclude savings banks from the estimation sample and only including one interaction term (the cooperative bank dummy interacted with interest rate changes); second, we exclude cooperative banks from the estimation sample and only use the savings bank dummy interacted with interest rate changes as the interaction term. These results are reported in Table 5.20 Excluding the savings banks from the estimation sample has little impact on the separate effect of interest rate changes on cooperative banks’ loan supply. The interest rate change has a statistically significant and negative effect on the overall supply of bank credit, and this effect is dampened for the cooperative banks throughout the estimation period, as well as when only the crisis period (2008-2011) is considered. If we exclude the cooperative banks and compare the difference between savings and shareholder banks the results change. Although the effect of interest rate changes on the loan supply of savings banks seems to have been reduced if we consider the period of relative financial stability, this no longer holds for either the full period or the crisis period alone. We thus conclude that cooperative banks were more likely to smooth their lending supply than savings banks during the recent period of financial instability. Before turning into further robustness checks, we needed to ensure that our results are not partly driven by any aggregation bias across loan categories, since we are using total loans as our dependent variable. Stakeholder and shareholder banks may have different loan portfolios, and so there exists the possibility that our results come from the differences in the demand-side behavior of these different loan categories. If for example shareholder banks have less long term loans than stakeholder banks, and if long term loan demand responds less to interest rate changes than other types of loans do, we might get our results (in Table 4) even if shareholder and stakeholder banks behaved identically within the long term loan class. Unfortunately, we do not have enough detailed data from Bankscope to run separate regressions for the subgroups of total loans (corporate loans, mortgage loans, and consumer loans). However, our data allows us to separate between corporate loans, long term loans (loans over 5 years of maturity), and 20

In some of the robustness checks, the Hansen test statistic is sufficiently small to reject the null hypothesis of exogenous instruments. In these cases, we also provide the probability of the difference-in-Hansen test statistic, also known as the C statistic. It is computed as the difference between two Hansen statistics, for the restricted and fully efficient regression and for the unrestricted, inefficient but consistent one, using a smaller set of restrictions. The C test assesses the effect of placing the included instruments on the list of included endogenous variables, i.e., treating them as endogenous regressors. The C test has the null hypothesis that the specified variables are proper instruments (Baum et al., 2003).

17

short term loans (loans from 1 to 5 years of maturity) but for a shorter time span (from 1999 to 2007). Results from GMM estimations with these different loan categories as the dependent variables are presented in Table 6. We can note that there are clear differences in lending behavior between stakeholder and shareholder within all three subgroups of loans separately and moreover they are similar to our main results with total loans as the dependent variable. Despite the somewhat problematic Hansen and autocorrelation test-statistics with the regressions with corporate loans as the dependent variable, we can conclude that our results for total loan growth in Table 4 were not driven by aggregation bias across different loan categories. For the rest of the robustness checks we are thus confident in using total loans as the dependent variable. Then, following the arguments in Jiménez et al. (2012), it could be the case that the large number of German banks in our data might render the interest rate changes somewhat endogenous. As it lies at the core of the Euro area, changes in economic and monetary conditions are likely more correlated in Germany than in smaller or more peripheral countries. By exclusively considering peripheral Euro area countries, we would expect to observe more exogenous variation in effective monetary conditions, allowing us to better separate the effects of monetary policy from those depending on national economic conditions. Furthermore, over half of our observations are from Germany, and hence the results could reflect German idiosyncrasies. We re-estimated our models by first excluding Germany from the dataset and then excluding the socalled core countries that performed most similarly to Germany with respect to various economic and financial measures (Germany, the Netherlands, Finland and Luxembourg). These results are presented in the first two parts of Table 7. Our results remained essentially unchanged after excluding Germany and after excluding all core countries from our dataset when we consider the full period (the first two columns in both tables). The negative effect of interest rate changes on banks’ loan supply is dampened for the stakeholder banks as a group and for both cooperative and savings banks separately. One difference is the statistical insignificance of both capitalization and the size of banks in the subsamples. Liquidity remains the only bank-specific variable with a statistically significant, positive effect on banks’ loan supply. Capitalization and size become statistically significant when we exclusively consider the period of relative financial stability. The lending channel proves stronger in absolute terms during the period 1999-2007 (the coefficients are more than three times as large as 18

their counterparts in the main results table21); cooperative and savings banks continue to be less sensitive. Regarding the recent crisis period (the last two columns in both tables), the direct effect of interest rate changes on loan supply becomes statistically insignificant, although the interaction term between the cooperative banks dummy and interest rate changes is remains positive and statistically significant (at the 10% level) when excluding Germany from the dataset. The coefficient associated with the interaction term between savings banks and the interest rate is negative, indicating a further amplification of the lending channel (although the coefficient is statistically insignificant). Larger banks were also better able to shield their lending supply from monetary policy tightening during the crisis. We also wanted to determine whether the problems in the Spanish savings bank sector and the resulting substantial reforms and mergers affected our results. Having become universal banks, Spanish savings banks expanded their activities across Spain and abroad and contributed to the accumulation of excess capacity and risk concentration in the Spanish banking system revealed by the recent crisis (IMF, 2012). During the crisis, several Spanish savings banks were converted into commercial banks, or the government intervened and resolved them, reducing the number of institutions from 45 to 11 by May 2012. Thus, we re-estimated our models for the full period and separately for 1999-2007 and 2008-2011 with the exclusion of Spain from the sample (third part of Table 7). Our results seem rather robust to the exclusion of Spanish banks from our sample, particularly regarding the results for the full period. In this case, unlike in the sample excluding Germany or the core countries, size and capitalization remained statistically significant predictors of bank loan supply after we excluded Spain from the sample. Stakeholder banks as a group and cooperative and savings banks separately seem to employ less procyclical lending policies. When we divide the full period in half, the statistical significance of the estimates becomes weaker, but it nevertheless seems the case that stakeholder banks as a group and cooperative banks in particular continue to supply loans less procyclically. Size is again the only bank-specific variable that remains statistically significant in explaining loan growth during the recent crisis. It could be argued that there is substantial heterogeneity across countries, for example, with respect to the overall importance of the cooperative banking sector or the legal and 21

By omitting outliers in the dependent variable (1% from both tails of the distribution), we obtained somewhat smaller coefficients, although they were nevertheless more than twice the magnitude of those in Table 4.

19

regulatory environment that banks face. Although our GMM estimation strategy accounts for time-invariant fixed effects, we wished to determine how robust our results were to different groupings of countries. First, we divided the countries according to their legal origin. La Porta et al. (1998) introduce the commonly employed division into three legal families within the civil-law tradition: French, German and Scandinavian. They examine shareholder rights, creditor rights, law enforcement and ownership and find that the countries with French legal origins generally have much weaker legal protections for investors and more concentrated share ownership than German and Scandinavian civil-law countries. Law enforcement is strongest in German and Scandinavian civil-law countries and weakest in French civil-law countries. We thus divided our countries into two groups: those with French legal origins (Italy, France, Spain, Belgium, Portugal, Greece, Luxembourg and the Netherlands) and those with German and Scandinavian legal origins (Austria, Germany and Finland).22 The results, presented in Table 8, changed relatively little for the French legal origin group from our initial GMM estimations including all observations (Table 4). Stakeholder banks as a group seem to follow less procyclical lending policies for both the full period and for the two time spans separately. When the stakeholder banks are divided into two groups, the cooperative and savings banks’ interaction terms are no longer statistically significant during the crisis period. In this case, size is the only bank-specific variable that remains significant in all estimations. For the German and Scandinavian legal origins group, while the coefficients take the expected signs, most of the statistical significance is lost. In our data, this grouping suggests that the lending channel is more active in countries with French legal origins than in those with German legal origins (which are primarily German observations in our case). Next, we further regrouped the 12 countries by distinguishing those with a larger cooperative banking sector and those where the presence of cooperatives is relatively lower. We examined the cooperative banks’ market shares of deposits and credits in national markets23 and essentially divided our data into two subsamples: Italy, France, Austria, the Netherlands and Germany have larger cooperative banking sectors,24 22

Ireland was omitted because it was the only country belonging to the common-law family. Market shares are taken from the European Association of Co-operative Banks (EACB) Key Statistics 2011 and 2010, available at www.eacb.coop/en/home.html 24 The market share of cooperative banks in Germany is slightly below 20%, whereas it is well above 30% in other countries. Although the market share of cooperative banks in Finland is approximately 34% (in 2011), we excluded Finland from the sample because BankScope only provides a single consolidated observation for all Finnish cooperative banks 23

20

whereas in Belgium, Greece, Ireland, Luxembourg, Portugal and Spain, the market share of cooperative banks is at most approximately 6% (in Spain). These results are presented in Table 9. In the first group of countries, savings banks seem to follow less procyclical lending policies, irrespective of the period considered, while cooperative banks no longer have a statistically significant coefficient during the crisis period. In the group of countries with relatively smaller cooperative sectors, both groups of stakeholder banks follow less procyclical lending policies during the full period. Cooperative banks continue to be associated with a statistically significant coefficient during the pre-crisis period, while that of savings banks does not. We should note that the direct effect of interest rate changes on loan supply is now statistically insignificant. In this case, the other exogenous macroeconomic variables (real GDP and the output gap) are statistically significant in explaining bank loan supply changes. Finally, during the crisis period, no variables are statistically significant in this estimation except for the macroeconomic variables and two of the bank-specific variables (size and liquidity with the expected signs). Using changes in the short-term interest rate to measure shifts in monetary policy is in line with previous studies analyzing the lending channel at the bank level (see, e.g., Kishan and Opiela, 2000 and Ashcraft, 2006). Nevertheless, as a final robustness check, we wished to ensure that our results stood up when using changes in the ECB’s main refinancing operations (MRO) interest rate instead of the EONIA overnight rate. While EONIA is a weighted average of all overnight, unsecured lending transactions on the interbank market, we could encounter endogeneity problems when using it to explain changes in banks’ loan supply. The ECB’s MRO interest rate could more reasonably be considered exogenous. We computed an annual average of the ECB MRO interest rate for each year and re-estimated our models. The results are presented in Table 10. Our results seem robust to the alternative measure of monetary policy. While the estimates for the full period and for the period prior to the crisis (first four columns) yield nearly identical results to those in Table 4, during the crisis period, variations in the ECB MRO interest rate no longer explain changes in bank lending. This result might be observed because, as discussed regarding our main results, the central bank policy rate no longer affects the real economy because of the zero lower bound problem, thereby rendering conventional policy actions ineffective. (cooperative banks of the Finnish OP-Pohjola Group), which are thus absent altogether from the unconsolidated dataset we employ in this analysis.

21

6. Conclusions Our aim in this paper was to assess whether variations in ownership structure could be a source of the differences observed in bank lending policies. We first classified banks based on their mission (shareholder banks vs. stakeholder banks) and then further subdivided the stakeholder banks according to their ownership structures (cooperative vs. savings banks). We analyzed micro-level bank data to study the developments in loan supply following a monetary contraction (an increase in the short-term interest rate). Stakeholder banks seem to follow less procyclical lending policies, as they reduced lending supply to a lesser extent (or made no reductions in some cases) following an increase in interest rates than shareholder banks. Relatively similar results were separately obtained for savings and cooperative banks for the entire period (1999-2011) and separately for the period of relative financial stability (1999-2007). However, while cooperative banks also maintained their less procyclical loan supply policies during the recent crisis (20082011), savings banks become statistically indistinguishable from shareholder banks. Our results further confirm that banks that are larger in size and relatively better capitalized are less prone to decrease their lending supply after monetary policy tightening. However, we also reach the unconventional finding that banks with relatively more liquid assets on their balance sheets seem more likely to decrease their loan supply. These results appear to confirm our hypothesis that stakeholder banks attempt to smooth financial conditions for their customers to maintain longer term borrower-lender relationships by conducting less procyclical loan supply policies, irrespective of the economic or their financial situation. Our results indicate that the omission of ownership structures as an independent variable may explain why previous studies from Europe have been somewhat inconclusive. Ultimately, in many European countries, stakeholder banks are of equal or greater importance than shareholder banks. Moreover, excessive volatility in bank lending is widely regarded as a contributing factor in the financial collapse in the fall of 2008. It is thus important to identify institutional structures that could dampen lending volatility. Our results indicate that the presence of stakeholder-oriented banks could be one such dampening factor. This finding, in conjunction with other evidence on the positive effects of the presence of stakeholder banks (see, e.g., Ayadi et al., 2010), should lead researchers to reconsider their role in modern financial systems. 22

Overall, it seems important to obtain an improved understanding of the amplification effects that the banking sector has on the propagation of monetary policy. It is particularly important to understand why the bank lending channel differs across countries inside the Euro area, as they are all subject to the same monetary policy. Heterogeneity – with respect to bank mission and ownership structure – in banking sectors provides one explanation. Our results suggest that the ownership structures of banks play a statistically significant and economically relevant role in transmitting changes in short-term interest rates to the availability of credit.

Acknowledgement We thank the Academy of Finland, OP-Pohjola Research Foundation, and Yrjö Jahnsson Foundation for generous funding making this research possible. We thank an anonymous reviewer for insights that helped to improve the paper substantially. In addition, we thank Daniel Buncic, Stefania Cosci, Zuzana Fungacova, participants of the ECCE-USB conference in Capetown in May 2013, and participants in the workshops organized by Helsinki Center for Economic Research, University of Tartu, National Bank of Poland, University of Rennes-1, and CERBE – LUMSA (Rome) for useful comments.

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Table 1: Composition of data by country Number of banks by country AT Shareholder banks 40 Stakeholder banks 217 Cooperative banks 140 Savings banks 77

BE 59 16 8 8

FI 6 2 0 2

FR 164 152 142 10

GER 205 2246 1631 615

Amount of total assets by country (annual average), billions of USD AT BE FI FR GER Shareholder banks 90.2 726 128.5 1920 2560 Stakeholder banks 188.5 13.4 0.39 778.3 1908.5 Cooperative banks 124 8.9 0.0 775 718.5 Savings banks 64.5 4.5 0.4 3.3 1190

GRE 19 1 1 0

IRE 19 4 4 0

IT 108 703 639 64

LX 96 4 2 2

NT 35 4 1 3

PO 25 4 3 1

SP 85 138 83 55

GRE 157 1.4 1.4 0

IRE 341 21.8 21.8 0

IT 1200 583 423 160

LX 422 35.1 1.5 33.6

NT 353.1 305.1 301 4.1

PO 112 78.5 12.4 66.1

SP 816 735.7 56.7 679

Table 2: Summary statistics: means and standard deviations (in parenthesis) # of banks Shareholder banks Stakeholder banks Cooperative banks

861 3491 2654

Savings banks

837

TOTAL

4352

loan growth

size

equity / total assets

liquid assets / total assets

loans / total assets

9.75 %

14.041

13.76 %

59.78 %

44.52 %

(0.620)

(2.072)

(0.188)

(0.381)

(0.304)

7.89 %

13.198

7.59 %

29.53 %

60.46 %

(0.169)

(1.476)

(0.045)

(0.329)

(0.137)

8.58 %

12.812

8.22 %

31.95 %

60.44 %

(0.178)

(1.379)

(0.046)

(0.339)

(0.139)

6.09 %

14.242

5.91 %

23.12 %

60.47 %

(0.143)

(1.198)

(0.034)

(0.291)

(0.132)

8.24 %

13.362

8.79 %

35.36 %

57.40 %

(0.308)

(1.643)

(0.095)

(0.359)

(0.192)

Table 3: Degree of variability of loan growth (coefficient of variation) Shareholder Stakeholder Cooperative banks banks banks Standard deviation 0.6201 0.1694 0.1780 Mean 0.0975 0.0789 0.0858 Coefficient of variation 6.3598 2.1476 2.0746

Savings banks 0.1431 0.0609 2.3499

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Table 4: Main results of GMM estimation, dependent variable: loan growth Loan growth, 1999-2011 Loan growth, 1999-2007 Loan growth, 2008-2011 (1) (2) (1) (2) (1) (2) Loan growth(t-1)

∆Interest rate (t-1)

Stakeholder x ∆Interest rate (t-1)

0.245*** (0.051)

0.248*** (0.051)

0.186*** (0.066)

0.184*** (0.067)

0.119 (0.174)

0.112 (0.174)

-0.0851*** (0.028)

-0.0845*** (0.028)

-0.169* (0.097)

-0.182* (0.097)

-0.0696** (0.030)

-0.0697** (0.030)

0.110*** (0.032)

0.161*** (0.061)

0.0420* (0.022)

Cooperative x ∆Interest rate (t-1)

0.105*** (0.032)

0.166** (0.071)

0.0418* (0.022)

Savings x ∆Interest rate (t-1)

0.123*** (0.034)

0.158*** (0.058)

0.0454 (0.029)

∆Real GDP (t-1)

-0.000861* (0.000)

-0.00102** (0.001)

-0.000727 (0.000)

-0.000740 (0.000)

-0.00169 (0.001)

-0.00173 (0.001)

∆Output gap(t-1)

0.0154 (0.019)

0.0214 (0.020)

0.0109 (0.030)

0.0140 (0.027)

0.105* (0.062)

0.105* (0.062)

∆Capitalization(t)

0.841** (0.405)

0.798** (0.395)

3.168** (1.473)

3.186** (1.455)

0.178 (0.597)

0.145 (0.637)

∆Size(t)

0.649*** (0.236)

0.593** (0.248)

1.071*** (0.242)

1.086*** (0.243)

1.080*** (0.267)

1.052*** (0.309)

∆Liquidity(t)

-0.293*** (0.099)

-0.312*** (0.102)

-0.201** (0.097)

-0.194** (0.098)

-0.336 (0.402)

-0.331 (0.408)

YES

YES

YES

YES

YES

YES

15658 3350 39 0.400 0.663

15658 3350 39 0.353 0.658

9312 2828 38 0.642 0.327

9312 2828 38 0.600 0.308

year dummies

# of observations 24970 24970 # of individual banks 3428 3428 # of instruments 59 59 Hansen (prob) 0.110 0.118 AR(2) 0.590 0.587 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

30

Table 5: GMM estimation, robustness check omitting, in turn, savings and cooperative banks Dependent variable: Loan growth, looking one group of stakeholder banks at a time

Omitting savings banks from the estimated sample

Omitting cooperative banks from the estimated sample

1999-2011

1999-2007

2008-2011

1999-2011

1999-2007

2008-2011

Loan growth(t-1)

0.264*** (0.053)

0.197*** (0.069)

0.116 (0.182)

0.315*** (0.061)

0.226*** (0.072)

0.137 (0.175)

∆Interest rate (t-1)

-0.0594** (0.025)

-0.233** (0.103)

-0.0657** (0.029)

-0.00703 (0.023)

0.0729 (0.142)

-0.0400* (0.023)

Cooperative x ∆Interest rate (t-1)

0.0869*** (0.030)

0.173** (0.068)

0.0433** (0.022) 0.0234 (0.017)

0.0719** (0.029)

0.00607 (0.013)

Savings x ∆Interest rate (t-1)

∆Real GDP (t-1)

-0.000731 (0.000)

-0.00101* (0.001)

-0.00215 (0.002)

-0.000514 (0.001)

0.0001000 (0.000)

-0.000862 (0.001)

∆Output gap(t-1)

0.00610 (0.020)

0.0441 (0.030)

0.114 (0.070)

0.00772 (0.049)

-0.0460 (0.039)

0.0868 (0.060)

∆Capitalization(t)

0.706* (0.360)

2.786** (1.387)

0.247 (0.639)

0.490* (0.295)

2.833** (1.411)

-0.0923 (0.762)

∆Size(t)

0.565** (0.255)

1.038*** (0.268)

1.144*** (0.332)

0.311 (0.241)

0.881*** (0.280)

0.856*** (0.316)

-0.338*** (0.092)

-0.221** (0.090)

-0.472 (0.412)

-0.750*** (0.273)

-0.282** (0.115)

-0.439 (0.410)

YES

YES

YES

YES

YES

YES

7282 2251 38 0.687 0.732 0.251

10176 1389 59 0.637 0.870 0.880

15658 3350 39 0.0914 0.419 0.869

9312 2828 38 0.615 0.797 0.648

∆Liquidity(t)

year dummies

# of observations 19260 11978 # of individual banks 2739 2666 # of instruments 59 39 Hansen (prob) 0.00157 0.0603 Difference-in-Hansen test (prob) 0.532 0.240 AR(2) 0.699 0.670 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

31

Table 6: GMM estimation, robustness check, focusing on different loan categories separately Dependent variable: Loan growth, time span 1999-2007

CORPORATE LOANS

LOANS OVER 5 YEARS MATURITY

LOANS 1-5 YEARS MATURITY

(1)

(2)

(1)

(2)

(1)

(2)

Loan growth(t-1)

-0.105*** (0.041)

-0.111*** (0.042)

-0.217*** (0.045)

-0.224*** (0.052)

0.0504* (0.027)

0.0444 (0.028)

∆Interest rate (t-1)

-0.231** (0.112)

-0.294** (0.116)

-0.694*** (0.264)

-0.779** (0.376)

-0.342** (0.153)

-0.278* (0.164)

Stakeholder x ∆Interest rate (t-1)

0.455*** (0.109)

0.337** (0.142)

0.130** (0.060)

Cooperative x ∆Interest rate (t-1)

0.490*** (0.120)

0.362** (0.167)

0.132** (0.060)

Savings x ∆Interest rate (t-1)

0.436*** (0.108)

0.322** (0.141)

0.169** (0.082)

∆Real GDP (t-1)

-0.00277*** (0.001)

-0.00282*** (0.001)

0.000547 (0.002)

0.000611 (0.002)

-0.00137 (0.001)

-0.00162 (0.001)

∆Output gap(t-1)

0.00239 (0.037)

0.0143 (0.037)

-0.0140 (0.055)

-0.00129 (0.070)

0.0913** (0.039)

0.0879** (0.038)

∆Capitalization(t)

6.171** (3.024)

6.231** (3.025)

0.380 (1.272)

0.404 (1.243)

-3.336** (1.437)

-3.447** (1.497)

∆Size(t)

1.460*** (0.344)

1.529*** (0.345)

2.901*** (0.240)

3.024*** (0.418)

1.149*** (0.230)

0.987*** (0.311)

∆Liquidity(t)

-0.988*** (0.112)

-0.951*** (0.115)

-0.0316 (0.077)

0.00815 (0.142)

0.0263 (0.059)

-0.0302 (0.091)

YES

YES

YES

YES

YES

YES

14754 3148 41 6.87e-12 0.006 0.00151

14754 3148 41 4.13e-12 0.004 0.00129

6333 1757 41 0.150 0.164 0.240

6333 1757 41 0.153 0.193 0.232

10227 2478 55 0.00008 0.202 0.296

10227 2478 55 0.00004 0.175 0.248

year dummies # of observations # of individual banks # of instruments Hansen (prob) Difference-in-Hansen test (prob) AR(2)

32

33

Table 7: GMM estimation results, robustness check excluding in turn Germany, core countries and Spain Excluding Germany from the sample

Dependent variable: Loan growth

1999-2011 (1) (2) 0.327*** (0.063)

1999-2007 (1) (2) -0.0785 (0.052)

Excluding core countries from the sample

2008-2011 (1) (2)

1999-2011 (1) (2)

Loan growth(t-1)

0.339*** (0.065)

-0.0966* (0.052)

0.160* (0.084)

0.195* (0.107)

0.398*** (0.068)

0.391*** (0.068)

∆Interest rate (t-1)

-0.0449** -0.0982** -0.580*** -0.784*** (0.022) (0.042) (0.188) (0.248)

-0.0168 (0.018)

-0.0219 (0.018)

-0.0456* (0.025)

Stakeholder x ∆Interest rate (t-1)

0.0665** (0.031)

0.0238 (0.022)

0.0577* (0.031)

0.322*** (0.120)

1999-2007 (1) (2) 0.00809 (0.052)

0.0105 (0.048)

Excluding Spain from the sample

2008-2011 (1) (2)

1999-2011 (1) (2) 0.209*** (0.050)

1999-2007 (1) (2)

0.0883 (0.096)

0.0879 (0.101)

0.205*** (0.050)

-0.0893** -0.575*** -0.627*** (0.044) (0.198) (0.220)

-0.0242 (0.024)

-0.0246 (0.027)

-0.0549** -0.0563** -0.241*** (0.026) (0.026) (0.091)

0.297*** (0.115)

0.0299 (0.029)

0.0832*** (0.030)

-0.0250 (0.140)

2008-2011 (1) (2)

-0.0190 (0.149)

0.0796 (0.114)

0.0749 (0.116)

-0.225* (0.118)

-0.0287 (0.020)

-0.0292 (0.022)

0.0675** (0.032)

0.0477* (0.027)

Cooperative x ∆Interest rate (t-1)

0.112** (0.048)

0.453*** (0.157)

0.0608* (0.037)

0.0960** (0.048)

0.411*** (0.156)

0.0296 (0.027)

0.0776** (0.031)

0.0651* (0.039)

0.0467* (0.025)

Savings x ∆Interest rate (t-1)

0.256** (0.108)

1.173*** (0.357)

-0.119 (0.155)

0.199** (0.100)

0.811*** (0.311)

0.0348 (0.103)

0.0950*** (0.033)

0.0721** (0.035)

0.0496 (0.031)

-0.00110** -0.00114** -0.000995 (0.001) (0.001) (0.001)

-0.00154 (0.001)

-0.00159 (0.001)

-0.00257 (0.002)

∆Real GDP (t-1)

-0.000345 -0.000272 (0.000) (0.000)

0.000310 (0.001)

-0.000597 -0.000564 -0.000532 -0.000332 -0.000559 -0.000205 -0.000239 -0.000422 -0.000400 (0.001) (0.001) (0.001) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000)

∆Output gap(t-1)

-0.00391 (0.017)

0.00202 (0.017)

0.0207 (0.047)

-0.0805 (0.057)

0.0150 (0.011)

0.0234 (0.018)

0.00479 (0.017)

0.00697 (0.017)

0.0921** (0.040)

0.0165 (0.037)

0.00623 (0.009)

0.00590 (0.013)

-0.00861 (0.020)

0.000569 (0.025)

0.0286 (0.030)

0.0272 (0.027)

-0.00322 (0.003)

-0.00320 (0.003)

∆Capitalization(t)

0.170 (1.617)

0.722 (1.638)

6.847*** (2.453)

9.678*** (3.211)

1.121 (2.178)

0.821 (2.135)

0.523 (1.617)

0.998 (1.676)

5.713** (2.332)

7.042** (2.735)

-3.526 (2.946)

-3.465 (2.962)

0.751** (0.349)

0.698** (0.339)

0.460 (1.741)

0.390 (1.602)

0.192 (0.785)

0.169 (0.783)

∆Size(t)

0.178 (0.352)

0.268 (0.338)

2.289*** (0.429)

3.089*** (0.564)

0.886*** (0.177)

0.944*** (0.211)

-0.0417 (0.295)

-0.00197 (0.293)

1.634*** (0.379)

1.969*** (0.450)

0.781*** (0.092)

0.778*** (0.097)

0.739*** (0.257)

0.676** (0.264)

1.503*** (0.280)

1.465*** (0.389)

0.987*** (0.232)

0.970*** (0.239)

-0.141 (0.104)

-0.118 (0.132)

-0.857 (0.605)

-1.032 (0.698)

-0.365*** -0.381*** -0.215*** (0.077) (0.082) (0.083)

-0.217** (0.093)

-0.636 (0.543)

-0.632 (0.546)

-0.0602 (0.040)

-0.0711 (0.084)

0.136 (0.347)

0.132 (0.343)

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

7336 1529 37 0.0199 0.386 0.324

7336 1529 37 0.278 0.619 0.0807

3371 1210 36 0.104 0.080 0.767

3371 1210 36 0.188 0.190 0.319

10051 1469 59 0.148 0.608 0.688

10051 1469 59 0.235 0.657 0.519

6849 1424 41 0.0468 0.492 0.382

6849 1424 41 0.122 0.362 0.159

3202 1139 22 0.326 0.140 0.924

3202 1139 22 0.271 0.144 0.936

23943 3234 52 0.0271 0.426 0.361

23943 3234 52 0.105 0.168 0.353

14959 3167 32 0.367 0.210 0.422

14959 3167 32 0.294 0.207 0.426

8984 2690 22 0.557 0.396 0.761

8984 2690 22 0.500 0.429 0.757

∆Liquidity(t)

year dummies

-0.353*** -0.359*** (0.076) (0.078) YES

YES

# of observations 10707 10707 # of individual banks 1576 1576 # of instruments 59 59 Hansen (prob) 0.0679 0.165 Difference-in-Hansen test (prob) 0.978 0.989 AR(2) 0.901 0.581 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

34

-0.265*** -0.281*** (0.092) (0.092)

35

Table 8: GMM estimation results, robustness check with different legal origins Dependent variable: Loan growth, French Legal Origin (Italy, France, Spain, Belgium, Portugal Greece, Luxembourg, Netherlands)

Loan growth(t-1)

∆Interest rate (t-1)

Stakeholder x ∆Interest rate (t-1)

1999-2011 (1) (2) -0.0282 (0.051)

-0.0280 (0.051)

1999-2007 (1) (2) -0.0517 (0.049)

-0.0721*** -0.0996*** -0.0978** (0.022) (0.028) (0.047) 0.127*** (0.035)

2008-2011 (1) (2)

-0.0470 (0.049)

0.0901 (0.134)

0.0798 (0.087)

-0.145** (0.062)

-0.0377* (0.020)

-0.0367* (0.022)

0.176** (0.072)

Dependent variable: Loan growth, German Legal Origin (Germany, Finland, Austria)

Loan growth(t-1)

∆Interest rate (t-1)

Stakeholder x ∆Interest rate (t-1)

0.0452* (0.025)

1999-2011 (1) (2)

1999-2007 (1) (2)

2008-2011 (1) (2)

-0.126 (0.347)

-0.135 (0.345)

-0.209 (0.508)

-0.196 (0.531)

0.0534 (0.527)

0.0363 (0.524)

-0.0866* (0.047)

-0.0822* (0.049)

-0.0398 (0.227)

-0.0449 (0.234)

-0.0824* (0.048)

-0.0778* (0.046)

0.0251 (0.023)

0.0679 (0.050)

0.0366 (0.048)

Cooperative x ∆Interest rate (t-1)

0.141*** (0.038)

0.175** (0.072)

0.0489 (0.030)

Cooperative x ∆Interest rate (t-1)

0.0267 (0.023)

0.0755 (0.053)

0.0337 (0.047)

Savings x ∆Interest rate (t-1)

0.421*** (0.139)

0.660** (0.291)

-0.0205 (0.212)

Savings x ∆Interest rate (t-1)

0.0210 (0.025)

0.0602 (0.049)

0.0411 (0.052)

∆Real GDP (t-1)

-0.00432***-0.00437***-0.00216*** -0.00204*** -0.00288 (0.001) (0.001) (0.001) (0.001) (0.002)

-0.00216* (0.001)

∆Real GDP (t-1)

-0.000962 (0.001)

-0.000888 (0.001)

-0.000706 (0.001)

-0.000627 (0.001)

0.000205 (0.000)

0.000236 (0.000)

∆Output gap(t-1)

-0.00527 (0.033)

-0.00326 (0.034)

-0.0143 (0.013)

-0.0176 (0.014)

0.0380* (0.022)

0.0301* (0.016)

∆Output gap(t-1)

0.114 (0.072)

0.106 (0.075)

0.0381 (0.030)

0.0285 (0.041)

0.0557 (0.057)

0.0491 (0.057)

∆Capitalization(t)

1.353 (1.380)

1.732 (1.434)

2.741** (1.331)

2.761** (1.351)

-0.473 (3.116)

-3.091 (3.270)

∆Capitalization(t)

-0.335 (0.632)

-0.311 (0.631)

-1.343 (2.268)

-1.162 (2.418)

-0.171 (0.682)

-0.241 (0.697)

0.887*** (0.293)

0.974*** (0.292)

1.361*** (0.096)

1.362*** (0.097)

1.202*** (0.250)

0.974*** (0.155)

0.656*** (0.226)

0.692*** (0.261)

0.742 (0.504)

0.822 (0.603)

0.356 (0.367)

0.233 (0.478)

-0.162 (0.102)

-0.134 (0.104)

-0.166*** (0.046)

-0.139*** (0.049)

-1.007 (0.639)

-0.768 (0.690)

-0.0149 (0.628)

-0.0145 (0.627)

0.125 (1.509)

0.284 (1.732)

0.0724 (0.597)

0.121 (0.634)

YES

YES

YES

YES

YES

YES

year dummies

YES

YES

YES

YES

YES

YES

6224 1286 35 0.0139 0.814 0.436

6224 1286 35 0.0117 0.634 0.989

2736 1004 19 0.679 0.931 0.327

2736 1004 19 0.502 0.715 0.335

# of observations 15899 15899 # of individual banks 2089 2089 # of instruments 52 52 Hansen (prob) 0.361 0.507 Difference-in-Hansen test (prob) 0.937 0.954 AR(2) 0.919 0.950 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

9348 2046 32 0.747 0.492 0.874

9348 2046 32 0.823 0.599 0.966

6551 1810 22 0.413 0.366 0.968

6551 1810 22 0.349 0.348 0.997

∆Size(t)

∆Liquidity(t)

year dummies

# of observations 8960 8960 # of individual banks 1321 1321 # of instruments 60 60 Hansen (prob) 0.0427 0.0608 Difference-in-Hansen test (prob) 0.244 0.345 AR(2) 0.921 0.399 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

∆Size(t)

∆Liquidity(t)

36

Table 9: GMM estimation results, robustness check with different presence of cooperative banks Dependent variable: Loan growth, Large presence of cooperatives (Italy, France, Austria, Netherlands, Germany)

1999-2011 (1) (2)

1999-2007 (1) (2)

2008-2011 (1) (2)

Dependent variable: Loan growth, Smaller presence of cooperatives (Belgium, Greece, Ireland, Luxembourg, Portugal, Spain)

1999-2011 (1) (2)

1999-2007 (1) (2)

2008-2011 (1) (2)

Loan growth(t-1)

0.323*** (0.071)

0.318*** (0.071)

0.00177 (0.046)

0.0103 (0.045)

0.227 (0.178)

0.186 (0.166)

Loan growth(t-1)

-0.0957* (0.054)

-0.0962* (0.054)

0.262 (0.165)

0.262 (0.166)

-0.233 (0.259)

-0.229 (0.254)

∆Interest rate (t-1)

-0.0427* (0.023)

-0.0989** (0.040)

-0.610** (0.248)

-0.569** (0.235)

-0.0408 (0.027)

-0.0710* (0.040)

∆Interest rate (t-1)

-0.0296 (0.031)

-0.0331 (0.032)

-0.256 (0.161)

-0.254 (0.163)

-0.0700 (0.046)

-0.0694 (0.046)

Stakeholder x ∆Interest rate (t-1)

0.0508* (0.030)

Stakeholder x ∆Interest rate (t-1)

0.104*** (0.035)

0.209 (0.135)

0.0366 (0.034)

0.0900* (0.053)

0.0162 (0.023)

Cooperative x ∆Interest rate (t-1)

0.0961** (0.043)

0.268* (0.150)

0.0224 (0.030)

Cooperative x ∆Interest rate (t-1)

0.109*** (0.034)

0.0894* (0.052)

0.0167 (0.023)

Savings x ∆Interest rate (t-1)

0.209** (0.082)

0.551** (0.274)

0.213* (0.118)

Savings x ∆Interest rate (t-1)

0.0979*** (0.038)

0.0913 (0.058)

0.0144 (0.026)

0.000426 -0.0000712 0.0000324 (0.001) (0.001) (0.001)

0.00211 (0.001)

∆Real GDP (t-1)

-0.00477*** -0.00468*** -0.00249* (0.001) (0.001) (0.001)

-0.00249* (0.001)

∆Real GDP (t-1)

-0.000152 0.0000657 (0.000) (0.000)

-0.00226** -0.00224** (0.001) (0.001)

∆Output gap(t-1)

0.00231 (0.016)

0.00937 (0.016)

0.0349 (0.050)

-0.0171 (0.051)

0.0206 (0.019)

0.0276 (0.020)

∆Output gap(t-1)

0.0937** (0.042)

0.0926** (0.042)

0.167** (0.074)

0.167** (0.075)

0.151** (0.072)

0.150** (0.073)

∆Capitalization(t)

0.911 (2.171)

1.331 (2.244)

6.670* (3.836)

6.820* (3.900)

2.176 (2.908)

2.381 (2.777)

∆Capitalization(t)

0.987 (0.614)

1.003 (0.621)

-1.185 (1.772)

-1.186 (1.783)

-0.330 (0.656)

-0.312 (0.659)

∆Size(t)

0.104 (0.446)

0.129 (0.447)

2.356*** (0.524)

2.349*** (0.532)

1.190** (0.475)

1.236*** (0.465)

1.010*** (0.214)

1.031*** (0.222)

0.382 (0.429)

0.381 (0.448)

0.622** (0.295)

0.639* (0.329)

-0.0903 (0.083)

-0.117 (0.086)

-0.502 (0.347)

-0.175 (0.341)

-0.121 (0.373)

-0.103 (0.374)

-0.493 (0.829)

-0.496 (0.860)

-0.844* (0.486)

-0.850* (0.486)

YES

YES

YES

YES

year dummies

YES

YES

YES

YES

YES

YES

5749 1161 40 0.0997 0.190 0.524

5749 1161 40 0.0986 0.227 0.793

2749 947 38 0.457 0.506 0.993

2749 947 38 0.484 0.527 0.403

# of observations 16446 16446 # of individual banks 2229 2229 # of instruments 60 60 Hansen (prob) 0.0480 0.0405 Difference-in-Hansen test (prob) 0.126 0.141 AR(2) 0.299 0.288 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

9886 2184 56 0.711 0.120 0.681

9886 2184 56 0.673 0.120 0.682

6560 1878 38 0.747 0.766 0.265

6560 1878 38 0.703 0.137 0.256

∆Liquidity(t)

year dummies

-0.299*** -0.317*** (0.064) (0.067) YES

YES

# of observations 8498 8498 # of individual banks 1194 1194 # of instruments 59 59 Hansen (prob) 0.00618 0.0248 Difference-in-Hansen test (prob) 0.164 0.114 AR(2) 0.201 0.0827 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

∆Size(t)

∆Liquidity(t)

37

Table 10: GMM estimation results, robustness check with ECB refinancing operations interest rate Dependent variable: Loan growth, ECB main refinancing operations rate as interest rate

Loan growth(t-1)

∆Interest rate (t-1) (ECB MRO) Stakeholder x ∆Interest rate (t-1)

1999-2011

1999-2007

2008-2011

(1)

(2)

(1)

(2)

(1)

(2)

0.242*** (0.050)

0.245*** (0.051)

0.185*** (0.066)

0.184*** (0.067)

0.0634 (0.120)

0.0681 (0.119)

-0.0943*** (0.030)

-0.0935*** (0.030)

-0.165* (0.091)

-0.175* (0.092)

-0.0592 (0.040)

-0.0597 (0.040)

0.124*** (0.035)

0.163*** (0.062)

0.0160 (0.028)

Cooperative x ∆Interest rate (t-1)

0.118*** (0.035)

0.168** (0.072)

0.0161 (0.028)

Savings x ∆Interest rate (t-1)

0.137*** (0.037)

0.161*** (0.060)

0.0125 (0.037)

∆Real GDP (t-1)

-0.000903* (0.000)

-0.00105** (0.001)

-0.000688 (0.000)

-0.000697 (0.000)

0.0000700 (0.001)

0.000100 (0.001)

∆Output gap(t-1)

0.0166 (0.019)

0.0220 (0.020)

0.00716 (0.030)

0.00955 (0.028)

0.0526 (0.051)

0.0531 (0.052)

∆Capitalization(t)

0.863** (0.414)

0.822** (0.404)

3.179** (1.477)

3.193** (1.458)

-0.992 (1.603)

-0.968 (1.572)

∆Size(t)

0.664*** (0.234)

0.612** (0.246)

1.082*** (0.240)

1.094*** (0.241)

1.007*** (0.277)

1.035*** (0.264)

∆Liquidity(t)

-0.289*** (0.098)

-0.307*** (0.101)

-0.200** (0.096)

-0.194** (0.098)

-0.224 (0.314)

-0.234 (0.322)

YES

YES

YES

YES

YES

YES

15658 3350 39 0.396 0.655 0.720

15658 3350 39 0.347 0.624 0.718

9312 2828 22 0.616 0.301 0.515

9312 2828 22 0.541 0.314 0.524

year dummies

# of observations 24970 24970 # of individual banks 3428 3428 # of instruments 59 59 Hansen (prob) 0.0974 0.0998 Difference-in-Hansen test (prob) 0.889 0.495 AR(2) 0.591 0.588 standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

38

Figure 1: Loan growth (%) of different bank groups, 1999-2011, all observations

Figure 2: Mean loan growth (%) of different bank groups, 1999-2011

39

0

0

1

1

2

2

3

3

4

4

5

Figure 3: Eonia and ECB main policy interest rate (%), monthly and annual average (annual average used in estimations)

Monthly observations, 1/1999 - 12/2011

1999

2011 Eonia, annual average ECB main policy interest rate, annual average

Eonia, monthly average ECB main policy interest rate, monthly average

40