Does bank ownership affect lending behavior? Evidence from the Euro area. (this version )

Transcription

1 Does bank ownership affect lending behavior? Evidence from the Euro area Giovanni Ferri *, Panu Kalmi **, Eeva Kerola *** PRELIMINARY DRAFT (this version ) ABSTRACT We analyze the differences in bank lending policies across banks of different ownership forms using micro level data on Euro area banks over to detect possible different patterns in bank lending supply responses to changes in monetary policy. In addition, we utilize cross-country Bank Lending Survey data from to further pinpoint possible differences in banks own perceptions about their credit standards and conditions across different organizational forms. Our results identify a prevailing difference between stakeholder and shareholder banks: following a monetary policy contraction stakeholder banks decrease their loan supply to a lesser extent than shareholder banks. While confirming that the lending channel is active in the Euro area, we also provide evidence that higher shares of cooperative banks in national banking systems led to less tightening in credit standards, terms and conditions. The propensity of stakeholder banks to smoothen their lending cyclicality suggests that their presence in the economy can dampen business cycle fluctuations. * LUMSA University Rome ** University of Vaasa ***Aalto University 1

2 1. Introduction The aim of this study is to investigate lending supply policies of banks from euro countries during the last 13 years of common monetary policy, and explore whether differences in loan supply decisions arise from different forms of bank ownership. In order to study banks responses to monetary policy changes we utilize both quantitative bank-specific balance sheet data (from 1999 to 2011) and more qualitative Bank Lending Survey data (from the last quarter of 2002 to the last quarter of 2012) collected by the European Central Bank revealing banks own perceptions about their lending standard changes. Previous empirical research has already confirmed (at least on US data) that banks that are small, and moreover banks that are undercapitalized and relatively illiquid, amplify monetary policy shocks more through the lending channel (see e.g. Kashyap and Stein (1994) and Kishan and Opiela (2000)). Our paper is the first attempt to distinguish differences between lending supply policies depending on banks mission/ownership form. Specifically, moving from general to specific, we consider two breakdowns of the banks: i) a mission-based breakdown of shareholder (profit maximizing) banks vs. stakeholder banks (catering not only for their shareholders); ii) an ownership-based categorization of the stakeholder banks differentiating cooperative banks from savings banks. We find that stakeholder banks follow less procyclical loan supply policies during the whole observation period ( ) than shareholder banks; their loan supply changes reacted less to changes in short term interest rate. This finding is similar for both cooperative and savings banks. With respect to bank specific variables we find that size and capitalization seem to be highly important (the larger or the better capitalized the bank, less its loan supply decisions are affected by changes in the policy rate), while a higher share of liquid assets seems to have an opposite effect (rather unconventionally). The policy rate has a stronger overall effect on loan growth changes during a time span of relative financial stability ( ); whereas the lending channel becomes weaker in absolute terms during the recent crisis ( ). Irrespective of the time period analysed, these effects are further dampened for stakeholder banks as a group, and especially for cooperative banks. Savings bank as an explanatory variable loses its statistical significance during the recent crisis. In all, stakeholder banks thus seem to behave less procyclically and stabilize financial cycles on their part by smoothing out financial conditions faced by their customers. We also provide results on banks own perceptions on their credit standards as well as credit terms and conditions utilizing the Bank Lending Survey data published by the European Central Bank. To our knowledge, this is the first time Bank Lending Survey data are utilized in order to study differences in bank credit supply depending on the bank mission/ownership compositions inside banking sectors. It seems that the higher is the share of cooperative banks in a certain banking sector the less have the credit standards been tightened on aggregate. In addition, banking systems populated by a larger fraction of cooperative banks have claimed for narrower margins, they have decreased less the size of loans or credit lines, and they have been less tight in requiring more collateral. On the contrary, higher shares of savings banks are associated with more tightening for overall credit standards (although this behavior is reversed during time of relative financial stability ( )). Also, looking at the differences between what banks intended to do ex ante and the lending policy they actually enacted during a quarter, cooperative banks seem to have been ex-post systematically less severe on credit standards than they initially planned or claimed to be. The rest of this paper is structured as follows: in section 2 we present the discussion on the lending channel as well as we survey previous empirical literature on bank heterogeneity affecting loan supply policies. Section 3 2

3 lays out testable hypotheses regarding different mission and ownership groups. Section 4 presents the data used in the estimations and some descriptive statistics. In section 5 we perform our various econometric analyses and comment on the results obtained. Section 6 concludes. 2. Effect of the lending channel and previous empirical evidence There has been an increased interest during the past few decades on financial sector s (especially banks ) role in the monetary policy transmission process. In his seminal paper Bernanke (1983) analysed the relative importance of monetary versus financial factors during the Great Depression and his study gave support to the credit view, which argued that financial markets were imperfect, so that the Modigliani-Miller assumptions did not hold and finance did actually matter (Freixas and Rochet, 2008). Empirical research has then induced a debate between the so called money view and a set of alternative theories referred to as the broad lending channel. The broad lending channel emphasizes the role of the supply of bank funds to firms and takes into account asymmetries of information and market imperfections. Implicit assumptions of the lending channel are that prices are rigid, the central bank can influence directly the volume of credit by adjusting reserves, and loans and securities are imperfect substitutes both for borrowers and for banks. Moreover, it is usually the (ex-ante) riskier and smaller firms that cannot obtain market finance as easily as credit from the banking sector, and are thus mostly affected by the lending channel mechanism. External finance is more expensive than internal finance, unless the external finance is fully collateralized. The higher cost of external finance reflects the agency cost of lending, which is the inevitable deadweight loss arising from asymmetric information (Bernanke et al., 1994). Mostly hurt by an economic downturn are smaller firms, as well as firms with lower net worth. Research on non-financial firms that face capital market imperfections has pointed out that shocks to internal liquidity should have larger impact on the investment behaviour of smaller companies who are more likely to have harder time accessing external sources of finance (Kashyap and Stein, 1994). For financial institutions, the situation is no different. Smaller banks find it harder to gather non-deposit funding in times of distress. Thus bad times in financial markets and the real economy are more likely to affect small, undercapitalized banks. In addition, Romer and Romer (1990) argued that if banks are confronted with a decrease in their deposits they are able to simply substitute this decrease with other types of liabilities, such as certificates of deposits (CDs), which are not subject to reserves. However, banks liabilities are in fact not perfect substitutes, so a decrease in deposits cannot be matched with CDs or issuing new equity. They differ e.g. by riskiness and maturity (see Stein, 1998 and Van den Heuvel, 2002). As Kashyap and Stein (1994) state, investors purchasing CDs must concern themselves with the quality of the issuing bank. With any degree of asymmetric information, standard sorts of adverse selection will arise and tend to make the marginal cost of external financing an increasing function of the amount raised. Thus a decrease in deposits leads banks to reduce their supply of credit for households and firms. Hence a reduction in the supply of bank credit, relative to other forms of credit, is likely to increase the external finance premium of the private sector and to reduce real activity (Bernanke and Gertler, 1995). The broad lending channel usually neglects banks equity, and treats bank capital as an irrelevant balance sheet item (Van den Heuvel, 2002). Especially after the strong debate over the Basel II accord that is claimed to emphasize the pro-cyclical effects of monetary policy with its capital requirements banks capital should be an 3

4 aspect of great interest in the monetary policy transmission. The bank capital channel is based on three hypotheses: i) an imperfect market for bank equity; ii) a maturity-mismatch between banks assets and liabilities (usually long-term loans vs. short-term deposits); and iii) a direct influence of regulatory capital requirements on the supply of credit. The bank capital channel works in the following way: as market interest rates increase, an even lower fraction of loans can be renegotiated with respect to deposits (because of the maturity-mismatch), and thus banks face a cost due to the maturity transformation that reduces profits and then capital. If equity is sufficiently low (and banks cannot easily issue new shares) banks reduce their supply of lending, because of the bank capital ratios required by regulators. While a large number of studies has already found that lending channel is in place and is working to amplify monetary policy shocks through the banking sector, a small number of studies have also tried to figure out whether there are differences between different kinds of banks and between banking sectors across countries. So far, empirical research has been trying to identify differences in the lending channel and the impact of monetary policy depending on banks size, capitalization, and/or liquidity. Kashyap and Stein (1994) find that monetary policy shocks affect differently large and small banks. Small banks presumably face higher agency costs of raising uninsured funds, and thus their balance sheets are more affected (Bernanke et al., 1994). Kashyap and Stein (1994) also find that the impact of monetary policy on credit supply is more pronounced 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 their size and capital leverage ratio and conclude that capital is important in assessing the impact of policy on loan growth and in determining the distributional effects of monetary policy. Low-capitalized banks are perceived as riskier by the market and have thus greater difficulty issuing bonds, therefore being unable to shield their credit relationships. All these studies focusing on the United States stress the fact that the lending channel is more important for small banks, and especially for those that are undercapitalized or relatively illiquid. However, results are far less unanimous when looking at the banking sector in Europe. Financial sectors differ quite a bit between Europe and the US, and especially the fact that firms rely much more heavily on bank credit in Europe than they do in the US would certainly lead us to expect some differences in the estimation results. The entire financial system is much more bank-based in Europe than in the United States, where financial market financing of the corporate sector is more developed. For example, according to a study conducted by the European Banking Federation in , the total assets of the banking sector in Europe account for 350% of aggregate GDP, whereas the same figure for the United States is 77%. Also, the share of total bank loans to GDP is 139% in Europe compared to 59% in the United States. Among others, Altunbas et al. (2002) use the BankScope database and classify banks according to asset size and capital strength. They find that undercapitalized banks (of any size) tend to respond more to changes in policy through the lending channel, and that this is more prevalent in the smaller EMU countries. Favero et al. (1999) use individual bank balance sheet data (from BankScope database) in selected European countries (Germany, France, Italy, and Spain). Studying only a monetary tightening period in year 1992 they find that there are differences in banks responses across countries. Small banks in Germany, Italy and Spain (although to a lesser extent) maintain or increase their loan supply by raising new deposits; while banks in France use their excess capital to maintain existing lending levels. De Bondt (1999) finds strong support for an existing lending channel especially for Germany, Netherlands, and Belgium for ; and that the loan supply effects are stronger 1 Available at 4

5 for small and illiquid banks. Whereas King (2000) confirms the importance of bank size and liquidity, he finds them to be most effective in France and Italy. Ehrmann et al. (2001) study bank lending in all euro area countries and find that monetary policy shocks do alter banks credit supply, the effects being most pronounced with illiquid banks, while the size of the bank or its capitalization do not seem to matter. Gambacorta and Mistrulli (2004) study the existence of cross-sectional differences in the response of monetary policy and business cycles owing to a different degree of bank capitalization on Italian banks ( ). They find that well-capitalized banks can better smooth their lending from monetary policy shocks as they have easier access to non-deposit fund-raising, and thus they can view other types of liabilities more of a substitute for deposits. They also conclude that non-cooperative banks behave more pro-cyclically when supplying credit, due to their stronger dependency on non-deposit forms of external funds and their lower proportion of long-term lending relationships. Gambacorta (2005) studies Italian banks, and finds evidence that heterogeneity in the monetary policy transmission exists. Lending is smoother for well-capitalized banks that are seen as less risky by the market and are better able to raise uninsured deposits. Liquid banks can further protect themselves from monetary policy tightening by simply drawing down cash and securities. He further concludes that size does not matter for lending supply policies. Apart from a few papers, to our knowledge, there are no empirical studies focusing on the possible heterogeneous effects on the strength of the lending channel across banks of different ownership groups. Ashcraft (2006) looked at affiliation across banks in the US, and found that affiliated banks react less sensitively to monetary policy contractions because they have access to larger internal capital markets. De Bondt (1999) and Schmitz (2004) included foreign ownership as one explanatory variable, former dealing with US data and latter with 10 EU accession countries in Schmitz (2004) found that foreign owned banks reacted more to euroarea interest rate changes than their domestic owned counterparts. De Bondt (1999) found stronger evidence for a lending channel when foreign owned banks were omitted from the sample; concluding that international banks have better opportunities to borrow elsewhere than even large domestic banks. Bertay et al. (2012) looked at state owned banks in 111 countries during and found that lending by state banks is less procyclical than the lending of private banks; furthermore lending by state banks located in high-income countries is even countercyclical. 3. Implications of differences in mission and ownership Differences during financial stability (traditional monetary policy) We first concentrate on times of financial stability, when interbank markets are functioning normally and there are no economic or financial crises. Then, a monetary policy contraction (an increase of the short term money market interest rates) will decrease money demand and thus reduce deposits available for banks. This drop in liquidity will force banks to make adaptations on their asset side in order for their balance sheets to be in equilibrium. Stakeholder banks are more involved in relationship lending (see e.g. Amess, 2000) and thus hold longer term objective functions than shareholder banks, and could be more prone to smooth out financial constraints for their 5

6 borrowers in order to maximize the long term values of their borrower-lender relationships (Boot, 2000; Petersen and Rajan, 1994). Stakeholder banks could thus be more willing to sacrifice other assets so to keep their lending volume rather intact. On the contrary, shareholder banks, while focusing on maximizing profits could then more easily cut back lending if that would result in lower short term costs (following the theory of bank capital channel). Following results on affiliated banks in US (Ashcraft, 2006) we could hypothesize that since there exists different kind of network formations especially inside the cooperative banking group (Desrochers and Fischer, 2005), they could be less hit by the liquidity shock since they could access the internal capital markets of their banking group and thus weaken the effect of their individual balance sheet constraints. Also on one hand, savings banks are on average less liquid and lack more capital (their equity to total assets ratio and the share of liquid assets are on average lower than for cooperative banks) and could be thus forced to cut back their assets more during a monetary policy contraction. On the other hand however, savings banks are in government ownership especially in Germany and Austria (municipal and/or regional) and could be thus more prone to smooth their lending supply (Bertay et al., 2012). Hypothesis 1: During financial stability, a monetary policy contraction is more effective on the loan supply of shareholder banks (rather than stakeholder banks), but the relative difference between savings and cooperative banks remains rather vague. Differences during financial instability (unconventional monetary policy) Straight after 2008, in the beginning of the recent crisis interbank markets literally collapsed, and liquidity was extremely scarce. Monetary policies were eased by central banks all over the world, interest rate ultimately hitting its zero lower bound especially in Europe and in the United States, leaving conventional monetary policy tools powerless. Therefore, unconventional measures were taken. These included credit easing, quantitative easing, and signaling. Throughout the crisis, the European Central Bank has been making Long Term Refinancing Operations (LTROs), where it has supplied European banks with cheap loans up to 3-years of maturity, taking government securities, mortgage securities and other secure commercial papers as collateral. Although shareholder banks could have been initially more at risk because of their less retail oriented business models, it could be ultimately so that the liquidity shortage hit stakeholder banks harder because of their problems of issuing new equity promptly (practically nonexistent); especially if they are low capitalized. However, irrespective of the state of the economy, the longer term objective of stakeholder banks could lead to less tightening of credit standards, especially to small and medium sized enterprises that form the bulk of stakeholder banks non-financial firm borrowers. Cooperative banks could be thought to be in a more favorable position than savings banks, because of their ownership structure differences. Cooperative banks are owned by their members (who are also usually their depositors). Although rights of members to profits are (typically) much more limited than they are at shareholder banks, cooperative banks may distribute part of their surplus to their members directly or implicitly by charging lower service fees or giving members more favorable interest rates. Among savings banks, profit distribution is absent altogether. Common to all savings banks is that they are formally non-profit institutions and owned usually by some private entity or government. These differences in ownership structure give rise to the proposition that cooperative banks would be able to better commit their customers especially during times of 6

7 financial turmoil and thus keep their insured deposits (making the bulk of their funding), being less affected by the illiquidity of short-term credit markets. Also publicly owned savings banks are heavily dependent on the health of their domestic economy and in the worst case scenario could be burdened by highly leveraged owners (governments) in crisis countries. Hypothesis 2: During times of financial distress, stakeholder banks are less inclined to decrease their loan supply and to tighten their credit standards (than are shareholder banks), and cooperative banks exhibit this feature with particular intensity (more so than savings banks). 4. Data and descriptive statistics For this paper we use data both from the Bank Lending Survey provided by ECB as well as microlevel bank balance sheet data from BankScope, provided by Bureau van Dijk. We first concentrate on our BankScope dataset. Our bank-level balance sheet data (at unconsolidated level) includes annual observations from 12 Euro area countries 2 over the period of covering 3,952 individual banks. As stated by Brissimis and Delis (2010), two recent papers (Ashcraft, 2006 and Gambacorta, 2005) provide discussion and evidence that annual observations are robust to be used in lending equations; thus validating their (and our) use of the BankScope database. Table i below gives summary statistics (broken banks into stakeholder vs. stakeholder, then to cooperative and savings banks) of the most relevant bank-specific variables. The ownership classifications are drawn from Bankscope (with amendments) and further details can be found in Ferri et al. (2012). Table i) summary statistics, mean and standard deviation (in parenthesis) Shareholder banks Stakeholder banks Cooperative banks Savings banks TOTAL # of banks loan growth size equity / total assets liquid assets / total assets loans / total assets 9.75 % % % % (0.620) (2.072) (0.188) (0.381) (0.304) 7.90 % % % % (0.169) (1.476) (0.045) (0.328) (0.137) 8.59 % % % % (0.178) (1.379) (0.046) (0.338) (0.139) 6.10 % % % % (0.143) (1.198) (0.034) (0.291) (0.132) 8.24 % % % % (0.308) (1.643) (0.095) (0.359) (0.192) Loan growth during the observation period ( ) has been fastest on average for shareholder banks, around 9.75% on yearly basis, while for stakeholder banks it has been on average below 8%. Savings banks loan growth was on average almost 3 percentage points lower than for their cooperative counterparts. If banks were put in order according to their size (log of total assets), shareholder banks are bigger than stakeholder banks on average; savings banks being larger than cooperative banks. If we look at the balance sheet composition and the share of loans on banks total assets in particular, we see distinctive differences between stakeholder and shareholder banks; loans make up to 60% of stakeholder banks total assets, while the number for shareholder banks is only 45% on average. Looking at the capitalization (share of equity to total assets), shareholder banks share is 14% on average, while the ratio is as low as 6% amongst savings banks. Finally looking at banks 2 Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, and Spain. 7

8 liquidity measure (share of liquid assets 3 to total assets); shareholder banks are on average far more liquid than stakeholder banks (60% and 33% for shareholder and stakeholder banks, respectively). Next we concentrate on the possible lending channel and on how loan supply has evolved during the last 13 years. Figure I below presents the development of loan growth rate of stakeholder vis-à-vis shareholder banks during Figure I: loan growth between stakeholder and shareholder banks, As can be seen, the lagged change in short term interest rate (EONIA overnight rate 4 ) is negatively affecting both the loan growth of stakeholder and shareholder banks up to the beginning of the recent crisis. After 2008, the effect of the lagged change in interest rate seems to be turning positive for stakeholder banks loan growth. Figure II divides stakeholder banks in two and shows differences in loan growth rates between savings and cooperative banks. Figure II: Loan growth between cooperative and savings banks, The figure depicts the development of loan growth rates differentiating between cooperative and savings banks. There we cannot detect any significant differences, although the growth rates of loan supply have been little higher on average for cooperative banks throughout the observation period. The positive effect of lagged short term interest rate change and loan growth is similar for both groups of stakeholder banks during the crisis years. In addition, we can get an idea on the degree of stability of lending policies across ownership/organizational bank classes by calculating the coefficient of variation (standard deviation/mean) of the absolute change of loan supply. The figures reported in Table ii tell us that stakeholder banks are much more stable than shareholder 3 Liquid assets taken from BankScope. Variable includes: Trading securities and at fair value through income + loans and advances to banks + reverse repos and cash collateral + cash and due from banks - mandatory reserves included above. 4 Computed as the change in annual average EONIA rate. 8

9 ones, with a coefficient of variation of as against As to cooperative banks vs. savings banks, there is a slight difference with the former (2.0733) being a little bit more stable than the latter (2.3468). Table ii) degree of variability of loan growth (coefficient of variation) Shareholder banks Stakeholder banks Cooperative banks Savings banks Standard deviation Mean Coefficient of variation We then turn to our empirical estimations, where we study distinctions between loan supply policies of different ownership groups. The first part of section 5 estimates the possible different patterns of loan supply policies by looking at monetary policy effects on banks loans supplied using data from bank balance sheets. Then, the second part of section 5 turns to Bank Lending Survey and tries to reveal differences in credit standards, credit terms and conditions between banks of different ownership groups. 5. Empirical estimations 5.1 Effect of monetary policy on bank loan supply Kashyap and Stein (1994) build a theoretical model and empirically test with disaggregated bank balance sheet data whether a lending channel exists in 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 contraction in monetary policy. Using their model as a basis we write an autoregressive model and add those bank-specific variables found in the existing empirical literature discussed in section 2 affecting lending supply the most as controls; namely capitalization (equity to asset ratio), liquidity (liquid assets to total assets ratio), and bank size (log of total assets). We test how a tightening in monetary policy affects the overall loans supplied. The estimated equation in first differences is: log( ) = log( ) + + ( ) + ( ) (1) Where is the dependent variable; namely change in loans supplied by bank i at year t. is the change in short term interest rate (EONIA overnight interest rate) reflecting changes in monetary policy. As a robust check we also redid our estimations using the ECB main refinancing operations marginal lending rate (annual average); this did not change the underlying results. Further, µ is the coefficient of the interaction term that only gets values either for stakeholder banks or for cooperative and savings banks separately, depending on the specification. By adding interaction terms we allow for asymmetric responses of bank lending to interest rate change among banks with different form of ownership: is the change in real GDP in the country where bank i is operating and is the output gap in percent of potential GDP, is bank i s capital position (share of equity to total assets), is bank i s size (log of total assets), and is bank i s liquidity (share of liquid assets out of total assets). Year dummies are also included in every equation. Because of the lagged dependent variable and heteroskedasticity 5 present in the data we estimate the equation (1) with Arellano-Bond type difference GMM estimator 6 (Arellano and Bond, 1991). This ensures efficiency and 5 Tested for with the Breusch-Pagan test. 9

10 consistency of our estimates provided that instruments are adequately chosen (the validity of the instruments is tested for with the Hansen test). The Hansen test of overidentifying restrictions has the null hypothesis that instruments are exogenous. A rejection of this null hypothesis implies that the instruments are not satisfying the orthogonality conditions required for their employment. A further test is the Arellano-Bond test of autocorrelation of errors, with as a null hypothesis no autocorrelation in 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, we would like to see this test failing to reject the null hypothesis. Results can be seen in Table iii below with proper diagnostics; the Hansen test not rejecting the overidentification conditions and the tests for serial correlation finding no second order serial correlation. We have first estimated equation (1) for the whole time span, and then for the time of relative financial stability (before 2008) and time of crisis (from 2008 onwards). The first specification includes one dummy interaction term with stakeholder bank dummy and lagged interest rate. The second specification includes two dummy interaction terms with a cooperative bank dummy and lagged interest rate as well as a savings bank dummy and lagged interest rate. First looking at the whole time span ( ) we see that a contraction (expansion) in monetary policy leads banks to reduce (increase) their loan supply. 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 1% level). This implicates that stakeholder banks loan supply is less affected by changes in interest rates than that of shareholder banks. During times of financial stability ( ) we can note that the interest rate has a larger absolute effect on loan growth changes (although it loses some of its statistical significance); that is the lending channel is stronger when financial markets are working more conventionally. Again it seems that for stakeholder banks as a group as well as for cooperative and savings banks separately, their loan supply is less affected by changes in interest rate. During the recent crisis ( ) this negative effect of interest rate changes and loan supply growth seems to be lower in absolute value (statistically significant at 5% level). Coefficients of the interaction terms of ownership groups and interest rates remain positive and are statistically significant (at 10% level) for the stakeholder bank group and cooperative banks. For savings banks, the coefficient is no longer statistically significant, nor is the lagged dependent variable and so we could expect some model misspecification. This result is not entirely unexpected; loss of trust towards other banks and customers has made banks reluctant to increase their loan supply although central bank has lowered interest rates almost to the zero-lower bound. This could have made the traditional methodologies of studying the lending channel inadequate and thus models with a short-term interest rate as the monetary policy instrument no longer suitable for studying the effectiveness of monetary policy. One reason could also be the small number of yearly observations because of the shorter time span especially while using difference GMM. As for the different bank-specific variables; it seems that capitalization (ratio of equity to total assets) is an important variable in explaining bank loan supply behavior especially during relative financial stability: better capitalized banks can better smooth their lending from interest rate changes. Size, on the other hand, seems to be highly important also during the crisis; reflecting the fact that the larger the bank, the less its loan supply decisions are affected by changes in interest rate. These results that bigger and better capitalized banks are less affected by interest rate changes were already found in Kashyap and Stein (1994) on US data and are further 6 One step difference GMM. Instruments are the dependent variable and the bank-specific variables (second and third lags for the crisis years ( ), and collapsed for the other two time spans). Especially Roodman (2009) shows how collapsing instruments can control efficiently for instrument proliferation. Restricting lag length to three for the crisis years was done in order to further cut the number of instruments. Output gap, real GDP, and the monetary policy indicator (EONIA interest rate change) are considered as exogenous instrumental variables. Robust standard errors. 10

11 confirmed in some European studies (see e.g. Ehrmann et al., 2001). Liquidity (share of liquid assets to total assets) has a negative and statistically significant coefficient in the first four columns; banks with relatively large amount of liquid assets on their balance sheets are responding more strongly to changes in interest rate. This negative effect is however no longer statistically significant during the crisis years. This result contradicts most of the previous empirical papers on the subject; liquidity is more often found to have a positive coefficient with banks that have relatively more liquid assets on their balance sheets are better able to shield their lending activity from changes in short term interest rates. Table iii) GMM estimation results, dependent variable: loan growth Loan growth, Loan growth, Loan growth, (1) (2) (1) (2) (1) (2) Loan growth (t-1) 0.245*** 0.248*** 0.186*** 0.184*** (0.051) (0.051) (0.066) (0.067) (0.174) (0.174) Interest rate (t-1) *** *** * * ** ** (0.028) (0.028) (0.097) (0.097) (0.030) (0.030) Stakeholder x interest rate (t-1) 0.110*** 0.161*** * (0.032) (0.061) (0.022) Cooperative x interest rate (t-1) 0.105*** 0.166** * (0.032) (0.071) (0.022) Savings x interest rate (t-1) 0.123*** 0.158*** (0.034) (0.058) (0.029) real GDP (t-1) * ** (0.000) (0.001) (0.000) (0.000) (0.001) (0.001) Output gap (t-1) * 0.105* (0.019) (0.020) (0.030) (0.027) (0.062) (0.062) capitalization 0.841** 0.797** 3.168** 3.186** (0.405) (0.395) (1.473) (1.455) (0.597) (0.637) size 0.649*** 0.593** 1.071*** 1.086*** 1.080*** 1.052*** (0.236) (0.248) (0.242) (0.243) (0.267) (0.309) liquidity *** *** ** ** (0.099) (0.102) (0.097) (0.098) (0.402) (0.408) year dummies YES YES YES YES YES YES # of observations # of individual banks # of instruments Hansen (prob) AR(2) standard errors in parentheses, * p<0.1 ** p<0.05 *** p< Effect of ownership on banks credit standards Next, we turn to the results obtained from studying the Bank Lending Survey (BLS) conducted by the European Central bank (for a detailed description of the study, please refer for example to Berg et al. (2005)). We have gathered a dataset on 7 countries whose answers to the BLS questionnaire have been made public (Austria, Germany, Italy, Luxembourg, Netherlands, Portugal, and Spain) and focus on the quarterly answers from year 2003 onwards in order to capture the effect of different ownership groups on bank credit standards. We focus on the part of the questionnaire that deals with loan supply to firms: 11

12 Have banks tightened / eased the credit standards on short term loans? Have banks tightened / eased the credit standards on long term loans? Have banks tightened / eased the overall credit standards? Have banks tightened / eased the credit standards on loans to SMEs? Have banks tightened / eased the credit standards on loans to large enterprises? In addition, we look at how banks certain terms and conditions for approving loans or credit lines to enterprises have changed, namely: Bank s margin on an average loans (wider margin = tightened, narrower margin = eased); Size of the loans or credit lines (smaller size = tightened, larger size = eased); Collateral requirements (more collateral = tightened, less collateral = eased). All of these questions have been answered in two time dimensions, looking ahead for one quarter (ex-ante perceptions) and looking back one quarter (ex-post realized movements). Banks give their answers in a scale from 1 to 5 (1 = tightened considerably, 2 = somewhat tightened, 3 = unchanged, 4 = somewhat eased, 5 = eased considerably). There are two ways in which answers are published; one can either look at the net percentage or the diffusion index. Net percentage is the difference between the percentages of respondents answering that they tightened somewhat or considerably, and those answering that they eased somewhat or considerably. If the measure is positive, more banks have been tightening their credit standards (thus a net tightening has occurred). Diffusion index is the net percentage weighted according to the intensity of the responses, giving banks who have answered considerably a weight twice as high (score of 1) as banks answering somewhat (score of 0.5). Again, a positive diffusion index indicates that a larger proportion of banks have tightened their credit standards. Difference between net percentage and diffusion index is thus that net percentage, unlike diffusion index, does not distinguish between any degrees of tightening/easing of credit standards. In the results presented below we will be concentrating solely on diffusion index. Estimations using net percentage gave identical results and were thus omitted but are available upon request. Earlier empirical studies using BLS dataset have mostly focused on the causal effects of bank lending on overall economic activity. De Bondt et al. (2010) analyze the information content of the BLS and find that in particular the responses on loans to enterprises do matter for euro area credit and business cycles; a tightening in credit standards or associated terms and conditions leads bank loan growth to enterprises and GDP growth by three to four quarters. Hempell and Kok Sorensen (2009) find similar results focusing on the recent crisis period. Ciccarelli et al. (2010) separate banks ability to provide credit in times of distress from borrowers capacity to demand credit due to changes in their net worth. One of their key results is that the credit channel significantly amplifies the impact of monetary policy on GDP and inflation in the Euro area; the impact for loan supply to firms is higher through the (supply) bank lending channel than through the demand and firm balance-sheet channels. Del Giovane et al. (2010) combine qualitative information on BLS and micro-data on Italian banks participating in the survey and find, inter alia, that BLS indicators for both supply and demand conditions have a statistically significant role in explaining changes in bank lending to enterprises in Italy. The above mentioned papers thus confirm that the dataset available from the Bank Lending Survey is informative and statistically significant in explaining actual changes in loan supply and real economic activity especially regarding the answers on loans to enterprises. The novel idea in our paper is to combine this BLS data 12

13 with measures of banking sector heterogeneity across countries and see whether there might be prevailing differences in bank lending standards depending on the relative shares of different ownership groups of banks. While BLS does not reveal publicly the individual banks that are taking part in the survey, we need to compute a proxy for the banking sector composition in each country with respect to the different ownership groups. For this, we again utilize the BankScope database; we take all bank observations from every year for in each country of interest and form relative share measures of different bank ownership groups (we divide banks according to shareholder and stakeholder banks and the latter further into cooperative and savings banks). We form the shares of different groups of banks by computing the relative share of each ownership groups total assets out of the total banking sector assets in each country for each year separately. This way, our proxies for the shares of cooperative, savings, and stakeholder banks is changing in time and across countries. In order to capture the most observations from BankScope (BS) database for the purpose of constructing the measures of shares we have taken all consolidated balance sheet observations (BS consolidation codes C1 and C2) as well as those banks publishing an unconsolidated balance sheet who do not have any consolidated companion in BS (BS consolidation code U1). We use lagged shares as proxies, and thus utilize annual BankScope observations from 2002 to Figure III) banking sector composition (all BS observations of consolidation codes C1, C2, and U1) Figure III above presents two descriptive histograms of our data used. On the left, we present the computed relative shares of cooperative and savings banks in the seven countries on average (and the sum of cooperative and savings banks shares, which equals the relative share of stakeholder banks). Stakeholder banks are most prominent in Portugal, Germany, and Austria (more than 35% of market share computed by total assets). In both Portugal and Spain, stakeholder banks are mostly savings banks. Germany stands out for it has somewhat equal shares of cooperative and savings banks, while in Netherlands, savings banks are rather non-existent. In Italy and Austria, bulk of the stakeholder bank assets is from cooperative banks. On the right, we present the computed relative shares of those banks in different years (2002 to 2011) in the seven countries on average. The relative shares of stakeholder banks total assets have been changing somewhat during the observation period, total share of stakeholder banks assets fluctuated around 35% during and later, from 2007 onwards stayed close to 30% on average. The above explained proxy for the share of each banking group is our main explanatory variable. The full equation to be estimated is: =1 =1 13

14 where Y is the published measure (diffusion index) of answers to the BLS for country j in quarter q. Share is our proxy of stakeholder/cooperative/savings banks share in country j s banking sector the year previous to the BLS questionnaire (lagged for four quarters) 7. r is the quarterly average of the short term interest rate (EONIA) and rgdp is country j s quarterly growth rate of real GDP, both with two lags. sb_yield is the sovereign 10-year bond yield rate. We also include country and year dummies and their interaction term as well as a crisis country dummy (equals 1 for Italy, Portugal, and Spain), and a dummy for crisis years (equals unity after the first quarter of 2008). All regressions are estimated with OLS, using cluster- and heteroskedasticity-robust standard errors. Below, table vii) presents obtained results from 10 regressions using the diffusion index of answers on different measures to credit standards as the dependent variable. The main explanatory variables are: 1) share of stakeholder banks total assets inside country s banking sector, and 2) share of cooperative and savings banks total assets inside country s banking sector. We estimated all five (backward looking) measures of credit standards; short term loans, long term loans, overall credit standards, loans to SMEs, and loans to large enterprises. Table iv) results with diffusion index, different questions on credit standards Short term loans Long term loans Overall credit standards Loans to SMEs Loans to large companies Stakeholder bank share (1.498) (1.734) (1.394) (1.114) (1.599) Cooperative bank share *** *** *** *** *** (0.917) (1.357) (0.905) (0.605) (1.279) Savings bank share 15.50*** 16.86*** 17.51*** 13.50*** 14.20*** (1.926) (2.386) (1.769) (1.489) (2.153) interest rate (q-1) *** ** (3.109) (5.525) (3.608) (4.425) (4.860) (6.180) (2.291) (4.668) (5.648) (6.360) interest rate (q-2) 8.202** 8.891** 8.862** 8.528** 9.153** 9.267** 10.71*** 12.14*** 8.816* (2.497) (3.344) (3.274) (3.367) (2.914) (3.276) (1.155) (2.298) (4.421) (4.321) GDP growth (q-1) ( ) ( ) ( ) ( ) ( ) ( ) (75.736) (72.290) ( ) ( ) GDP growth (q-2) ( ) ( ) ( ) ( ) ( ) ( ) (84.579) (95.843) ( ) ( ) sb_yield (2.651) (2.455) (3.051) (3.068) (2.348) (2.222) (1.875) (1.891) (2.899) (2.796) crisiscountries *** *** *** *** *** (16.555) (22.968) (19.023) (31.468) (15.156) (20.306) (12.165) (12.668) (17.714) (31.263) crisisyears 3.354* 2.744* * (1.610) (1.251) (3.501) (3.324) (2.052) (1.786) (5.081) (4.576) (3.944) (3.717) constant ** 515.2*** *** *** *** *** (40.629) (26.288) (50.180) (36.061) (41.615) (28.885) (30.857) (21.635) (45.176) (33.478) year dummies YES YES YES YES YES YES YES YES YES YES country dummies YES YES YES YES YES YES YES YES YES YES year x country interactions YES YES YES YES YES YES YES YES YES YES N r2_a standard errors in parentheses, * p<0.1 ** p<0.05 *** p< As a robustness check, we redid all our BLS estimations with share of stakeholder banks / cooperative banks / savings banks loans out of total loans inside national banking sector as the main explanatory variable replacing the share of total assets used in the results presented in this paper. Results remained unchanged. 14

15 The results suggest that while the share of stakeholder banks assets fails to explain movements in credit standards, when breaking those banks further into cooperative and savings banks they gain statistical significance. It seems that the higher is the share of cooperative banks inside a country s banking sector, the more have credit standards been eased (or the less have they been tightened). A higher share of savings banks, on the contrary, is associated with more credit standard tightening. Both year and country dummies as well as their interactions were statistically significant. Then we look at certain credit terms and conditions, namely possible tightening in the margin of average loans (tightening is associated with wider margins), in the size of the loans or credit lines (tightening is associated with size restrictions), as well as in the collateral requirements (tightening is associated with higher collateral requirements) again regressing the diffusion index of answers against the shares of stakeholder banks total assets, share of cooperative banks total assets, and share of savings banks total assets. Results can be seen in Table v below. Table v) results with diffusion index, different questions on credit terms and conditions Margin on average loan Size of loan/credit line Collateral requirements Stakeholder bank share (1.868) (1.467) (1.191) Cooperative bank share *** *** *** (1.341) (1.167) (0.677) Savings bank share 6.982** 11.70*** 10.46*** (2.521) (1.993) (1.625) interest rate (q-1) (5.807) (6.327) (8.074) (8.806) (4.557) (4.977) interest rate (q-2) 13.75*** 13.81*** (2.569) (2.869) (4.477) (4.668) (2.460) (2.598) GDP growth (q-1) ( ) ( ) ( ) ( ) ( ) ( ) GDP growth (q-2) ( ) ( ) ( ) ( ) (89.037) (84.890) sb_yield *** *** *** (20.287) (33.362) (16.147) (27.198) (13.088) (17.161) crisiscountries * 6.266* (3.206) (3.161) (2.615) (2.583) (2.076) (2.066) crisis years (4.631) (4.326) (3.599) (3.708) (3.385) (3.359) constant *** *** * 341.1*** (56.164) (36.218) (41.973) (27.082) (32.547) (19.279) year dummies YES YES YES YES YES YES country dummies YES YES YES YES YES YES year x country interactions YES YES YES YES YES YES N r2_a standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01 The results suggest that when the share of cooperative banks assets is higher inside a banking sector, the credit terms and conditions have been less tight in all three dimensions; banks have claimed for less wider margins, they have restricted less the size of loans or credit lines, and they have been less tight in requiring more collateral (all three at 1% level). Again, the higher is the share of savings banks inside banking sector total assets, the more 15