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

Giovanni Ferri*, Panu Kalmi

**, Eeva Kerola

***

September 13, 2013

Abstract

We analyze the differences in bank lending policies across banks of different ownership forms using micro level

data on Euro area banks over 1999-2011 to detect possible different patterns in bank lending supply responses to

changes in monetary policy. 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. Distinguishing the effect within stakeholder banks reveals that cooperative banks kept

smoothing the impact of monetary contraction onto their lending even during the crisis period (2008-2011)

whereas savings banks did not. The propensity of stakeholder banks to smoothen their lending cyclicality

suggests that their presence in the economy can dampen 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

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1. Introduction

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 materialize (Bernanke and Gertler, 1995;

Hubbard, 1995). In turn, the pro-cyclical attitudes of bank lending (Rajan, 1994) could exert a disproportionate

strain on the economy, making it harder for bank dependent borrowers – e.g. the small businesses – to keep

relying on external finance (Gertler and Gilchrist, 1994; Berger and Udell, 1995).

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 between 1999 and 2011

we utilize bank-specific financial statements (BankScope database) provided by Bureau Van Dijk. As well as

looking at the whole time period, we make a distinction between the time 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 of the bank

lending channel and factors influencing banks’ lending behaviour, this appears interesting from the industrial

organization and microeconomic point of view per se; it is also extremely important to reveal underlying reasons

for heterogeneity across the banking sectors within the Euro area to learn how, due to differences in the

monetary transmission channel, the actual monetary stance can differ across Euro countries despite the common

monetary policy instruments.

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 (1995) and Kishan and Opiela (2000)). With studies on Euro area,

consensus has been harder to find: to what extent does 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 the reason is the heterogeneity in national banking sector compositions inside Euro

area, and that the differences in bank lending would not arise entirely from differences in balance sheets but also

from differences in bank business models which are closely related to bank ownership forms. For one, it is

possible that banks relying on a relationship lending business approach could be less willing to curtail loans to

their customers with whom they tend to liaise in long-term rapport (Berger and Udell, 2002).

Our paper is one of the first attempts to distinguish differences between lending supply policies depending on

banks’ mission/ownership form1. 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

(1999-2011) 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

1 The only previous study – that we are aware of – is De Santis and Surico (2013), who look at the heterogeneity between

lending channels of cooperative, commercial, and savings banks (among other things).

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supply decisions are affected by changes in the interest rate), while a higher share of liquid assets seems to have

an opposite effect (rather unconventionally). The interest rate has a stronger overall effect on loan growth

changes during a time span of relative financial stability (1999-2007); whereas the lending channel becomes

weaker in absolute terms during the recent crisis (2008-2012). 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 and these banks’

behaviour seems to become statistically no different from that of their shareholder counterparts. In all,

stakeholder banks (and cooperative banks in particular) thus seem to behave less procyclically and stabilize

lending cyclicality on their part by smoothing out financial conditions faced by their customers. This result

survives a number of robustness checks.

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

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 empirical estimations 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 smaller firms and firms with lower net worth are hurt by

an economic downturn. 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, 1995). 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 contrast, Romer and Romer (1990) argued that banks confronted with a decrease in their deposits would be

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

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and Van den Heuvel, 2002). As Kashyap and Stein (1995) 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

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 have 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 (1995) 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 (1995) 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

EBF(2012), 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

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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 1990-1995 and that the loan supply effects are stronger

for small and illiquid banks. King (2000) confirms the importance of bank size and liquidity, but he finds them

to be most effective in France and Italy.

Ehrmann et al. (2001) study bank lending in 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 (1992-2001). 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. Fungacova et al. (2013)

look at the interaction of competition and lending channel in 12 euro countries between 2002-2010, and find that

before 2007, lending channel was enhanced in competitive markets.

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 banks affiliated with a multibank

holding company 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 2004. Schmitz (2004) found

that foreign owned banks reacted more to euro-area 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 1999-2010 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.

Given the central role of stakeholder banks in many European banking markets (e.g. Ayadi et al., 2010), it would

be important to know whether the monetary policy transmission differs across ownership structure. To our

knowledge, there is only one paper looking at this, and this is the recent work by De Santis and Surico (2013).

They look at banking sectors in Spain, Germany, Italy, and France during 1999-2011 and conclude that 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 run separate regressions

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for each country and for each typology of banks and find inter alia that the interest rate channel in Spain is rather

non-existing, that commercial banks react to interest rate changes only remotely irrespective of the country, and

that loan supply decisions are most affected by monetary policy actions especially among relatively illiquid and

less capitalized cooperative and savings banks in Germany as well as smaller savings banks in Italy. Our

empirical strategy is different: we estimate the whole panel at once and allow for heterogeneous responses to

monetary policy shocks between banks of different ownership types, controlling for differences in banks’

balance sheets and demand conditions across countries2. This enables us to draw more directly inferences about

the relative differences between stakeholder and shareholder banks’ loan supply policies than the approach

followed by De Santis and Surico (2013).

3. Implications of differences in mission and ownership

We argue that in order to study differences between banks’ lending policies, one should pay attention to bank

ownership form and structure. Next we make some assumptions on how these differences might be affecting

bank lending both during time of relative financial stability and during time of crisis.

3.1. 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 liabilities 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

borrowers in order to maximize the long term values of their borrower-lender relationships (Boot, 2000; Petersen

and Rajan, 1994; Gambacorta and Mistrulli, 2004). 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).

2 A further difference between De Santis and Surico (2013) and our paper stems from re-classification of several banks. It

seems that their database was built taking the ownership classifications of the banks as provided by BankScope. On the

contrary, we found that ownership was misclassified for some of the banks in BankScope and recoded those banks

accordingly; see footnote 4 below. Beside the other outlined differences, this factor could also help explain possible

divergences between our results and the ones in De Santis and Surico (2013).

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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.

3.2. 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 supply, especially to small and medium sized enterprises that form the bulk of

stakeholder banks’ non-financial firm borrowers. By having on average relatively more secure assets on their

balance sheets, stakeholder banks could have better access to extra liquidity provided by the ECB during the

recent crisis.

Cooperative banks could be thought to be in a more favorable position than savings banks, because of their

ownership characteristics. 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

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 (than are shareholder banks), and cooperative banks exhibit this feature with particular intensity (more

so than savings banks).

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4. Data and descriptive statistics

For this paper we use microlevel data based on financial statements derived from BankScope, provided by

Bureau van Dijk. These data (at unconsolidated level) include annual observations from 12 Euro area countries3

over the period of 1999-2011 covering 4,352 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. The

initial ownership classifications are drawn from BankScope where we have done certain corrections and

amendments based on our earlier work (Ferri et al. (2012))4. Table 1 shows how banks of different ownership

have been distributed by countries and the value of their total assets in our data (annual average taken over

observation period 1999-2011). Bulk of our stakeholder bank observations come from Germany (2246 German

stakeholder banks out of 3491 in total). Italian stakeholder bank observations are also abundant but to a far lesser

extent (703 Italian stakeholder banks). Shareholder bank observations are more dispersed between different

countries (Germany and France having the most observations). Spanish savings bank sector is large measured by

total assets; 55 Spanish savings banks’ combined annual average amount of total assets is more than fourfold to

that of 65 Italian savings banks’ and more than tenfold to that of 77 Austrian savings banks’ equivalents,

respectively.

Table 2 gives summary statistics (broken banks into stakeholder vs. stakeholder, then to cooperative and savings

banks) of the most relevant bank-specific variables. Loan growth during the observation period (1999-2011) has

been fastest on average for shareholder banks, around 9.75% on yearly basis, but also the standard deviation is

much larger than with stakeholder banks; implicating higher volatility. Savings banks’ loan growth was on

average almost 3 percentage points lower than for their cooperative counterparts. Shareholder banks are bigger

(in terms of log of total assets) than stakeholder banks on average, and savings banks are larger than cooperative

banks. Looking 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’ liquidity measure (share of liquid assets5 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 more on the loan supply of banks of different ownership groups and on how it has evolved

during the last 13 years. Figure 1 presents the observations of loan growth of stakeholder vis-à-vis shareholder

banks and for cooperative and savings banks separately during 1999-2011. As we can see, although we have

3 Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, and Spain.

4 For example we have corrected for the following flaws in BankScope initial classifications: 1) some Austrian co-operative

banks under the Raiffeisen group were classified as savings banks in BankScope. 2) especially in Italy, many banks

classified as savings banks in BankScope are essentially retail commercial banks. 3) the Caisse d’Epargne Group in France

is still classified as savings bank in BankScope albeit it converted into co-operative ownership in 1999. 4) some banks that

are relevant to our analysis could also be found in other specialization classifications (than solely from commercial banks,

cooperative banks, and savings banks) such as bank holding companies, governmental credit institutions, and mortgage

banks. For more details, see Ferri et al. (2012). 5 Our measure of Liquid assets is taken directly from BankScope and it includes: Trading securities at fair value through

income (plus) loans and advances to banks (plus) reverse repos and cash collateral (plus) cash and due from banks (minus)

mandatory reserves included above. In most previous studies, liquid assets are stated to include cash, securities (often only

government bonds), and interbank lending; additional items included in our measure do not distort results, but produce a

rather similar liquidity ratio compared to e.g. 0.399 in Gambacorta (2005), and around 0.4 in Ehrmann et al. (2003).

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more observations in total for stakeholder banks than for shareholder banks; the shareholder bank loan growth

observations are much more dispersed between -5% and +5%; while that of stakeholder banks rest more

uniformly between -1% and +2%. For cooperative and savings banks differences are rather vague; although it

seems that with cooperative banks loan growth has been higher on average throughout the observation period.

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 3 tell us that stakeholder banks are much more stable than shareholder

ones, with a coefficient of variation of 2.1454 as against 6.3584. 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).

We now turn to our empirical estimations, where we study distinctions between loan supply policies of different

ownership groups by looking at monetary policy effects on banks’ loans supplied using data from bank balance

sheets.

5. Empirical estimations and results

5.1. Empirical method

Kashyap and Stein (1995) 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, and following more recent empirical studies (see e.g. Gambacorta (2005), Gambacorta and

Mistrulli (2004), Bertay et al. (2012)) we write an autoregressive model, but deviating from existing literature

we test whether interest rate changes affect differently banks of different ownership form. The bank-specific

variables that are found in the existing literature to be affecting lending supply the most (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 test how changes in short-term interest rate affect the overall loans supplied by banks. The

estimated equation is:

Where loans is the dependent variable; namely the volume of loans supplied by bank i at year t. r is the short

term interest rate (EONIA overnight interest rate) reflecting changes in monetary policy. As would be according

to the theory of the lending channel, the coefficient α should be negative; as interest rates increase banks

decrease the amount of loans supplied. Now µ captures the separate effect of interest rates for stakeholder banks;

if this coefficient is positive it means that the negative effect is dampened for the stakeholder banks. This

interaction term only gets values either for stakeholder banks as a group or for cooperative and savings banks

separately; depending on the specification (OSDUMMY stands for ownership dummy). rGDP is the annual real

GDP in country c where bank i is operating and Ygap is the output gap in percent of potential GDP6 in the same

6 Both real GDP and output gap taken from International Monetary Fund, World Economic Outlook Database, October

2012

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country c. Real GDP (value of economic output adjusted for price changes) controls for economic growth

accounting also for changes in the price level, while the output gap (difference between the real GDP and the

potential GDP) indicates the imbalance existing in the real economy. According to Gambacorta (2005), the

inclusion of demand side control variables allows us to capture cyclical movements and enables to separate 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 size (log of total assets), and

LIQUIDITY is bank i’s liquidity (share of liquid assets out of total assets).

We are aware that there might be a number of time-invariant bank and/or country characteristics (fixed effects)

that 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:

To cope with this problem of fixed effects, and further because of the lagged dependent variable and

heteroskedasticity7 present in the data we estimate equation (1) with Arellano-Bond type difference GMM

estimator8 (Arellano and Bond, 1991). Arellano-Bond difference GMM estimator is specifically designed for

panels with large-N and short-T9 and transforms our equation (1) into:

And from equation (2) we get

By first differencing the regressors, the fixed effects are removed because they do not vary with time. All these

reasons ensure efficiency and 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

7 Tested for with the Breusch-Pagan test (designed to detect any linear form of heteroskedasticity) and the White’s test

(general test for heteroskedasticity allowing for both non-linear forms of heteroskedasticity and errors to be non-normally

distributed). Both test statistics had large chi-square values rejecting the null-hypotheses (constant variance for Breusch-

Pagan and homoscedasticity for White’s test, respectively). 8 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). 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. Standards errors are heteroskedasticity

and autocorrelation robust. 9 In large-T panels a shock to the fixed effect (showing in the error term) will decline over time. Similarly, correlation of the

lagged dependent variable with the error term will be insignificant (Roodman, 2006).

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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, the failure to reject the null hypothesis is the preferred

outcome.

5.2. Estimations results

Results can be seen in Table 4 with proper diagnostics: the Hansen test does not reject the overidentification

conditions and the tests for serial correlation find 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 (1999-2011) 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 indicates that stakeholder banks’ loan supply is less affected by changes in interest rates than that of

shareholder banks. During times of financial stability (1999-2007) we can note that the interest rate has a larger

absolute effect on loan growth changes (although it loses some of its statistical significance); i.e. 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 (2008-2011) 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, and so they are statistically no different from their shareholder counterparts. Here also

the lagged dependent variable loses its statistical significance 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 (1995) on US data and are further

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 (with the exception of De Santis and Surico (2013) for some of

their regressions): 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. Our findings could be explained for one by the bank capital channel. If banks have relatively

12

more liquid assets, the ratio of loans to total assets is already lower. Now as market interest rates increase, an

even lower fraction of longer-maturity loans can be renegotiated with respect to short-maturity deposits and thus

bank faces higher short term costs. These costs could then be more easily lowered by cutting back lending than

selling other types of securities that are more liquid.

These results indicate that even though we did find that bank-specific balance sheet variables have an impact on

the strength of the lending channel (in line with previous empirical studies), banks’ mission and ownership

forms’ role is just as important. In fact we argue that this could be one explanation behind the lack of consensus

among empirical research done on Euro area regarding different effects of balance sheet variables (see latter part

of Section 2 for an overview). In those studies, banks were treated as having identical business models, only

differing for example by size or relative share of equity. However, as banking sector composition diverge

between countries in Euro area (although not all inclusive, Table 1 gives a broad idea); we cannot draw

inferences on bank lending channel strength by only concentrating on bank balance sheet differences.

5.3. Robustness

Next, we provide some robustness checks in order to gain more validation for our results presented above. First,

based on our results we argue that while savings banks behave like their cooperative peers before the recent

crisis they become statistically no different from shareholder banks during the crisis years while cooperative

banks continue to behave less procyclically. Looking at the last column in Table 4 however, one could claim that

by comparing the statistical significance between the two coefficients of cooperative and savings banks (rather

than vis-à-vis commercial banks) one might not find any difference. To back up our initial claim, we perform the

same estimations but first by excluding savings banks from the estimation sample and only using one interaction

term (cooperative bank dummy interest rate change) and second by excluding cooperative banks from the

estimation sample and only using (savings bank dummy interest rate change) as the interaction term. Results

can be seen in Table 5, first three columns excluding savings banks and last three excluding cooperative banks,

respectively.

Excluding savings banks from the estimation sample changes very little for the separate effect of interest rate

changes on cooperative banks’ loan supply. Interest rate change has a statistically significant and negative effect

on the overall bank credit supply, and this effect is dampened for the cooperative banks throughout the

estimation period, also just looking at the crisis years (2008-2011). If we exclude cooperative banks and compare

the relative difference only between savings and shareholder banks (last three columns in Table 5), things differ.

Although it seems that the interest rate change effect on loan supply of savings banks is dampened if we look at

the whole time span, it does no longer hold for the crisis period. We could thus conclude that the tendency of

smoothing the lending supply is stronger for cooperative banks relative to savings banks during the recent period

of financial instability.

Second, following arguments in Jiménez et al. (2012) it could be that the large presence of German banks in our

data could render interest rate changes somewhat endogenous. Being at the core of the Euro area, changes in

economic and monetary conditions are likely to be more correlated in Germany than in smaller or more

peripheral countries. By looking only at the non-core Euro area countries there would be more exogenous

variation in monetary conditions, allowing us to better separate its effects from those of national economic

conditions. Also, more than one half of our observations are from Germany, so any results could reflect German

idiosyncracies. We redid our estimations first excluding Germany from the dataset and then excluding the so-

called core countries that have been performing most similarly to Germany with respect to different economic

13

and financial measures (Germany, Netherlands, Finland, and Luxembourg). Results can be seen in Table 6

(excluding Germany) and Table 7 (excluding core countries).

It seems that our results remained more or less intact after excluding Germany and also after excluding the rest

of the core countries from our dataset when we look at the whole time period (first two columns for both tables).

The negative effect of the interest rate change on banks’ loan supply is dampened for the stakeholder banks as a

group as well as for both cooperative and savings banks separately. One difference is the statistical

insignificance of both capitalization and size of banks with the subsamples. Liquidity remains the only bank-

specific variable that has a statistically significant, amplifying effect on banks’ loan supply. Capitalization and

size become statistically significant when we look solely at the time of relative financial stability. The lending

channel becomes stronger in absolute terms during 1999-2007, cooperative and savings banks maintaining their

dampening effect. Looking at time during the recent crisis (last two columns for both tables) the direct effect of

interest rate changes on loan supply loses its statistical significance, albeit the interaction term between

cooperative banks dummy and interest rate change is positive and statistically significant (at 10% level) when

excluding Germany from the dataset. The coefficient related to savings banks’ interaction with interest rate is

negative indicating a further amplification of the lending channel (although the coefficient is statistically

insignificant). Larger banks are better able to shield their lending supply also during the crisis. With the first

specification while excluding Germany in first (third) column we can note that the Hansen overidentification test

is rejected at 10% (5%) level, indicating that instruments may not be valid. However, the test is again passed

when we break the stakeholder banks in two (second (fourth) column). The same problem can be found for the

time of relative financial stability when excluding the core countries.

As a third robustness check, we wanted to see whether the problems in Spanish savings bank sector and resulting

massive reforms and fusions could have affected our results. Having become universal banks, Spanish savings

banks expanded their activities across Spain and abroad and contributed to the build-up of excess capacity and

risk concentration in the Spanish banking system which was all revealed by the recent crisis (IMF, 2012). During

the crisis, several Spanish savings banks have been turned to commercial banks, or intervened and resolved;

reducing the number of institutions from 45 to 11 by May 2012. Thus we redid our estimations for the whole

time period, and for 1999-2007 and 2008-2011 separately excluding Spain from the sample (Table 8).

Again, our results seem to be rather robust to the exclusion of Spanish banks from our sample looking in

particular at the results for the whole time span. Now, unlike with the sample excluding Germany or the core

countries, size and capitalization maintain their statistical significance in explaining banks loan supply also after

we excluded Spain from the sample. Stakeholder banks as a group and cooperative and savings banks separately

seem to follow less procyclical lending policies. When we break the time span in two, statistical significance of

the estimates become weaker but it still seems that stakeholder banks as a group as well as cooperative banks

especially continue to supply loans less procyclically. Size is again the only bank-specific variable that remains

statistically significant during the recent crisis. We also note that the Hansen test statistic rejects the null

hypothesis of exogenous instruments in specification (1) for the whole time span at 5% level, but is passed when

we look at cooperative and savings banks separately.

As a last robustness check, albeit the use of changes in short-term interest rate as a measure of change in

monetary policy ties in with previous literature analyzing lending channel at bank level (see e.g. Kishan and

Opiela, 2000 and Ashcarft, 2006 among others); we wanted to check that our results prevail when using ECB’s

main refinancing operations (MRO) interest rate changes instead of the Eonia overnight-rate. While Eonia is a

weighted average of all overnight unsecured lending transactions in the interbank market, we could run into

14

some endogeneity problems when having it explain changes in banks’ loan supply. ECB’s MRO interest rate

could more reasonably be taken as exogenous. We computed an annual average of the ECB MRO interest rate

for each year and redid our estimations. Results are presented in Table 9.

Again our results seem to be robust to the alternative measure of monetary policy instrument. While results for

the whole time span as well as for the time prior to crisis (first four columns) give almost identical results to

Table 4, during crisis time ECB MRO interest rate changes can no longer explain for changes in bank lending

supply. This might be due to the fact already discussed regarding our main results that the central bank policy

rate no longer has an effect on the real economy because of hitting its zero lower bound and making

conventional policy actions ineffective. We also note that the Hansen test statistic is slightly below the 10% level

for the estimations of the whole time span (first two columns).

6. Conclusions

In this paper our aim was to study whether a source of differences in bank lending policies would be their

different ownership categories. We classified banks first based on their mission (shareholder banks vs.

stakeholder banks), and stakeholder banks further according to their ownership structure (cooperative vs. savings

banks).

We looked at micro-level bank data to study the developments in loan supply following a monetary contraction

(increase in short term interest rate). Stakeholder banks seem to follow less procyclical lending policies as their

suppression of lending supply was smaller than with shareholder banks followed by an increase in interest rates.

This result is relatively similar separately for savings and cooperative banks for the whole time span (1999-2011)

as well as separately for the time of relative financial stability (1999-2007). However, while cooperative banks

maintain their less procyclical loan supply policies also during the recent crisis (2008-2011), savings banks

become statistically no different from the shareholder banks. Our results further confirm that banks that are

larger in size and relatively better capitalized are less affected by interest rate changes; whereas we find, rather

unconventionally, that relatively more liquid banks are in fact contributing more to the lending channel than

relatively illiquid ones.

Our results seem to be in line with our two hypotheses laid down in section 3, namely that stakeholder banks

would try to smooth out financial conditions for their customers in order to maintain longer term borrower-

lender relationships by conducting less procyclical loan supply policies irrespective of the economic or 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. After all, stakeholder banks are in many

European countries of equal or greater importance than shareholder banks.

Moreover, it is widely perceived that excessive volatility in bank lending was one of the contributing factors to

the financial collapse of the fall 2008. It is thus important to identify institutional structures that could contribute

to the lower volatility of lending. Our results indicate that the presence of stakeholder-oriented banks could be

one dampening factor. This finding, together with other evidence on the positive effects coming from the

presence of stakeholder banks (see e.g. Ayadi et al. 2010) should lead to reconsider the role of stakeholder banks

in a modern financial system.

Overall, it is important to gain better understanding on the amplification mechanisms that banking sector has on

the implementation of monetary policy. Especially important it is to understand why there are differences in

15

bank lending channels between countries inside the Euro area because they are all targeted by the same monetary

policy. Heterogeneity – with respect to bank mission and ownership forms – inside banking sectors provides one

explanation. Our results suggest that the ownership structure of banks plays a statistically significant and

economically relevant role in channeling changes in short-term interest rates to the availability of credit.

16

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19

Table 1: Composition of data by countries Number of banks by country

AT BE FI FR GER GRE IRE IT LX NT PO SP

Shareholder banks 40 60 6 164 205 19 19 108 96 34 25 85

Stakeholder banks 217 15 2 152 2246 1 4 703 4 5 4 138

Cooperative banks 140 7 0 142 1631 1 4 638 2 2 3 83

Savings banks 77 8 2 10 615 0 0 65 2 3 1 55

Amount of total assets by country (annual average), billions of USD

AT BE FI FR GER GRE IRE IT LX NT PO SP

Shareholder banks 90.0 727.7 128.5 1915.4 2553.8 156.9 340.8 1200.0 421.5 353.1 111.5 815.4

Stakeholder banks 188.5 11.5 4.2 776.9 1907.7 1.4 21.8 583.1 35.2 305.4 78.5 736.2

Cooperative banks 123.8 7.0 0.0 776.9 718.5 1.4 21.8 423.1 1.5 300.8 12.4 56.7

Savings banks 64.6 4.4 4.2 0.0 1189.2 0.0 0.0 160.0 33.6 4.6 66.1 679.5

Table 2: Summary statistics, mean and standard deviation (in parenthesis)

# of banks loan growth size

equity / total

assets

liquid assets /

total assets

loans / total

assets

0.10 % 14.041 13.76 % 59.78 % 44.52 %

(0.620) (2.072) (0.188) (0.381) (0.304)

0.08 % 13.198 7.59 % 32.87 % 60.46 %

(0.169) (1.476) (0.045) (0.328) (0.137)

0.09 % 12.812 8.22 % 33.89 % 60.45 %

(0.178) (1.379) (0.046) (0.338) (0.139)

0.06 % 14.242 5.91 % 29.01 % 60.47 %

(0.143) (1.198) (0.034) (0.291) (0.132)

0.08 % 13.362 8.79 % 35.98 % 57.40 %

(0.308) (1.643) (0.095) (0.359) (0.192)4352TOTAL

Shareholder banks

Stakeholder banks

Cooperative banks

Savings banks

861

3491

2653

838

Table 3: Degree of variability of loan growth (coefficient of variation)

Shareholder

banks

Stakeholder

banks

Cooperative

banks

Savings

banks

Standard deviation 0.6201 0.1694 0.1780 0.1431

Mean 0.0975 0.0790 0.0859 0.0610

Coefficient of variation 6.3584 2.1454 2.0733 2.3468

20

Table 4: Main results of GMM estimation, dependent variable: loan growth

(1) (2) (1) (2) (1) (2)

Loan growth (t-1) 0.245*** 0.248*** 0.186*** 0.184*** 0.119 0.112

(0.051) (0.051) (0.066) (0.067) (0.174) (0.174)

Interest rate (t-1) -0.0851*** -0.0844*** -0.169* -0.182* -0.0696** -0.0697**

(0.028) (0.028) (0.097) (0.097) (0.030) (0.030)

Stakeholder x interest rate (t-1) 0.110*** 0.161*** 0.0420*

(0.032) (0.061) (0.022)

Cooperative x interest rate (t-1) 0.105*** 0.166** 0.0418*

(0.032) (0.071) (0.022)

Savings x interest rate (t-1) 0.123*** 0.158*** 0.0454

(0.034) (0.058) (0.029)

real GDP (t-1) -0.000861* -0.00102** -0.000727 -0.000741 -0.00169 -0.00173

(0.000) (0.001) (0.000) (0.000) (0.001) (0.001)

Output gap (t-1) 0.0154 0.0214 0.0109 0.0139 0.105* 0.105*

(0.019) (0.020) (0.030) (0.027) (0.062) (0.062)

capitalization 0.841** 0.797** 3.168** 3.186** 0.178 0.145

(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 -0.293*** -0.312*** -0.201** -0.194** -0.336 -0.331

(0.099) (0.102) (0.097) (0.098) (0.402) (0.408)

year dummies YES YES YES YES YES YES

# of observations 24970 24970 15658 15658 9312 9312

# of individual banks 3428 3428 3350 3350 2828 2828

# of instruments 59 59 39 39 38 38

Hansen (prob) 0.110 0.118 0.400 0.353 0.642 0.600

AR(2) 0.590 0.587 0.663 0.657 0.327 0.308

standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

Loan growth, 1999-2011 Loan growth, 1999-2007 Loan growth, 2008-2011

21

Table 5: GMM estimation results, robustness check omitting in turn savings and cooperative banks

Dependent variable: Loan growth,

looking one group of stakeholder

banks at a time

1999-2011 1999-2007 2008-2011 1999-2011 1999-2007 2008-2011

Loan growth (t-1) 0.100 0.0346 0.116 0.0100 -0.0982 0.0368

(0.068) (0.109) (0.182) (0.084) (0.112) (0.279)

Interest rate (t-1) -0.0468*** -0.270*** -0.0657** -0.0713* -0.252 -0.184*

(0.018) (0.094) (0.029) (0.037) (0.164) (0.110)

Cooperative x interest rate (t-1) 0.0344** 0.0540* 0.0433**

(0.014) (0.028) (0.022)

Savings x interest rate (t-1) 0.0351* 0.0349 -0.00400

(0.020) (0.032) (0.042)

real GDP (t-1) -0.000186 -0.000150 -0.00215 -0.00144* -0.000328 -0.000487

(0.000) (0.000) (0.002) (0.001) (0.001) (0.001)

Output gap (t-1) 0.0329 0.0428 0.114 0.115** 0.0684 0.281

(0.027) (0.034) (0.070) (0.058) (0.065) (0.194)

capitalization -0.178 0.450 0.247 -0.744 0.849 -1.272

(0.759) (1.588) (0.639) (1.001) (1.804) (1.715)

size 1.263*** 1.443*** 1.144*** 0.869*** 1.323*** 0.481

(0.167) (0.242) (0.332) (0.244) (0.351) (0.471)

liquidity -0.0875** -0.0715* -0.472 -0.383* -0.154 -0.498

(0.038) (0.042) (0.412) (0.225) (0.306) (0.989)

year dummies YES YES YES YES YES YES

# of observations 19260 11978 7282 10182 6899 3283

# of individual banks 2739 2666 2251 1390 1367 1062

# of instruments 52 32 38 52 28 22

Hansen (prob) 0.153 0.123 0.687 0.720 0.446 0.633

AR(2) 0.623 0.763 0.251 0.620 0.464 0.561

standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

Omitting savings banks from the

estimated sample

Omitting cooperative banks from the

estimated sample

22

Table 6: GMM estimation results, robustness check excluding Germany

(1) (2) (1) (2) (1) (2)

Loan growth (t-1) 0.339*** 0.327*** -0.0785 -0.0969* 0.160* 0.195*

(0.065) (0.063) (0.052) (0.052) (0.084) (0.107)

Interest rate (t-1) -0.0449** -0.0983** -0.580*** -0.785*** -0.0168 -0.0219

(0.022) (0.042) (0.188) (0.249) (0.018) (0.018)

Stakeholder x interest rate (t-1) 0.0665** 0.322*** 0.0238

(0.031) (0.120) (0.022)

Cooperative x interest rate (t-1) 0.112** 0.453*** 0.0608*

(0.048) (0.157) (0.037)

Savings x interest rate (t-1) 0.256** 1.176*** -0.119

(0.108) (0.358) (0.155)

real GDP (t-1) -0.00110** -0.00114** -0.000995 -0.00154 -0.00159 -0.00257

(0.001) (0.001) (0.001) (0.001) (0.001) (0.002)

Output gap (t-1) -0.00391 0.00200 0.0207 -0.0805 0.0150 0.0234

(0.017) (0.017) (0.047) (0.057) (0.011) (0.018)

capitalization 0.170 0.719 6.847*** 9.687*** 1.121 0.821

(1.617) (1.638) (2.453) (3.216) (2.178) (2.135)

size 0.178 0.268 2.289*** 3.092*** 0.886*** 0.944***

(0.352) (0.338) (0.429) (0.565) (0.177) (0.211)

liquidity -0.353*** -0.359*** -0.141 -0.118 -0.857 -1.032

(0.076) (0.078) (0.104) (0.132) (0.605) (0.698)

year dummies YES YES YES YES YES YES

# of observations 10707 10707 7336 7336 3371 3371

# of individual banks 1576 1576 1576 1576 1210 1210

# of instruments 59 59 37 37 36 36

Hansen (prob) 0.0679 0.165 0.0199 0.280 0.104 0.188

AR(2) 0.901 0.581 0.324 0.792 0.767 0.319

standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

Loan growth, 1999-2011 Loan growth, 1999-2007 Loan growth, 2008-2011Dependent variable: Loan growth,

Excluding Germany from the sample

23

Table 7: GMM estimation results, robustness check excluding core countries

(1) (2) (1) (2) (1) (2)

Loan growth (t-1) 0.398*** 0.391*** 0.00809 0.0102 0.0964 0.109

(0.068) (0.068) (0.052) (0.048) (0.098) (0.122)

Interest rate (t-1) -0.0456* -0.0896** -0.575*** -0.629*** -0.0338 -0.0365

(0.025) (0.044) (0.198) (0.221) (0.026) (0.026)

Stakeholder x interest rate (t-1) 0.0577* 0.297*** 0.0426

(0.031) (0.115) (0.032)

Cooperative x interest rate (t-1) 0.0964** 0.413*** 0.0589

(0.048) (0.156) (0.040)

Savings x interest rate (t-1) 0.200** 0.817*** -0.0190

(0.100) (0.313) (0.140)

real GDP (t-1) -0.000345 -0.000273 0.000310 -0.000611 -0.000532 -0.000996

(0.000) (0.000) (0.001) (0.001) (0.001) (0.002)

Output gap (t-1) 0.00479 0.00686 0.0921** 0.0160 0.00537 0.00975

(0.017) (0.017) (0.040) (0.037) (0.010) (0.017)

capitalization 0.523 0.999 5.713** 7.062** -3.246 -4.023

(1.617) (1.676) (2.332) (2.740) (4.091) (4.411)

size -0.0417 -0.00121 1.634*** 1.975*** 0.766*** 0.793***

(0.295) (0.294) (0.379) (0.451) (0.099) (0.111)

liquidity -0.365*** -0.381*** -0.215*** -0.217** -0.598 -0.617

(0.077) (0.082) (0.083) (0.093) (0.532) (0.543)

year dummies YES YES YES YES YES YES

# of observations 10051 10051 6849 6849 3202 3202

# of individual banks 1469 1469 1424 1424 1139 1139

# of instruments 59 59 41 41 20 20

Hansen (prob) 0.148 0.236 0.0468 0.124 0.423 0.457

AR(2) 0.688 0.518 0.382 0.156 0.992 0.981

standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

Loan growth, 1999-2011 Loan growth, 1999-2007 Loan growth, 2008-2011

Dependent variable: Loan growth,

Excluding core countries from the

sample

24

Table 8: GMM estimation results, robustness check excluding Spain

(1) (2) (1) (2) (1) (2)

Loan growth (t-1) 0.205*** 0.209*** -0.0250 -0.0192 0.0796 0.0749

(0.050) (0.050) (0.140) (0.148) (0.114) (0.116)

Interest rate (t-1) -0.0549** -0.0562** -0.241*** -0.225* -0.0287 -0.0292

(0.026) (0.026) (0.091) (0.118) (0.020) (0.022)

Stakeholder x interest rate (t-1) 0.0832*** 0.0675** 0.0477*

(0.030) (0.032) (0.027)

Cooperative x interest rate (t-1) 0.0775** 0.0650* 0.0467*

(0.031) (0.039) (0.025)

Savings x interest rate (t-1) 0.0949*** 0.0720** 0.0496

(0.033) (0.035) (0.031)

real GDP (t-1) -0.000332 -0.000559 -0.000205 -0.000239 -0.000422 -0.000400

(0.000) (0.001) (0.000) (0.000) (0.000) (0.000)

Output gap (t-1) -0.00861 0.000559 0.0286 0.0272 -0.00322 -0.00320

(0.020) (0.025) (0.030) (0.027) (0.003) (0.003)

capitalization 0.751** 0.697** 0.460 0.395 0.192 0.169

(0.349) (0.339) (1.741) (1.602) (0.785) (0.783)

size 0.739*** 0.676** 1.503*** 1.466*** 0.987*** 0.970***

(0.257) (0.264) (0.280) (0.388) (0.232) (0.239)

liquidity -0.265*** -0.281*** -0.0602 -0.0711 0.136 0.132

(0.092) (0.092) (0.040) (0.084) (0.347) (0.343)

year dummies YES YES YES YES YES YES

# of observations 23943 23943 14959 14959 8984 8984

# of individual banks 3234 3234 3167 3167 2690 2690

# of instruments 52 52 32 32 22 22

Hansen (prob) 0.0271 0.105 0.367 0.295 0.557 0.500

AR(2) 0.361 0.353 0.422 0.425 0.761 0.757

standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

Loan growth, 1999-2007 Loan growth, 2008-2011Dependent variable: Loan growth,

Excluding Spain from the sample

Loan growth, 1999-2011

25

Table 9: GMM estimation results, robustness check with ECB refinancing operations interest rate

Dependent variable: Loan growth,

ECB main refinancing operations

rate as interest rate

(1) (2) (1) (2) (1) (2)

Loan growth (t-1) 0.242*** 0.245*** 0.185*** 0.184*** 0.0634 0.0681

(0.050) (0.051) (0.066) (0.067) (0.120) (0.119)

Interest rate (t-1) -0.0943*** -0.0933*** -0.165* -0.174* -0.0592 -0.0597

(ECB MRO) (0.030) (0.030) (0.091) (0.092) (0.040) (0.040)

Stakeholder x interest rate (t-1) 0.124*** 0.163*** 0.0160

(0.035) (0.062) (0.028)

Cooperative x interest rate (t-1) 0.118*** 0.168** 0.0161

(0.035) (0.072) (0.028)

Savings x interest rate (t-1) 0.137*** 0.161*** 0.0125

(0.037) (0.060) (0.037)

real GDP (t-1) -0.000903* -0.00105** -0.000688 -0.000698 0.0000700 0.000100

(0.000) (0.001) (0.000) (0.000) (0.001) (0.001)

Output gap (t-1) 0.0166 0.0220 0.00716 0.00945 0.0526 0.0531

(0.019) (0.020) (0.030) (0.028) (0.051) (0.052)

capitalization 0.863** 0.821** 3.179** 3.193** -0.992 -0.968

(0.414) (0.403) (1.477) (1.458) (1.603) (1.572)

size 0.664*** 0.611** 1.082*** 1.093*** 1.007*** 1.035***

(0.234) (0.246) (0.240) (0.241) (0.277) (0.264)

liquidity -0.289*** -0.307*** -0.200** -0.194** -0.224 -0.234

(0.098) (0.101) (0.096) (0.098) (0.314) (0.322)

year dummies YES YES YES YES YES YES

# of observations 24970 24970 15658 15658 9312 9312

# of individual banks 3428 3428 3350 3350 2828 2828

# of instruments 59 59 39 39 22 22

Hansen (prob) 0.0974 0.0999 0.396 0.347 0.616 0.541

AR(2) 0.591 0.589 0.720 0.717 0.515 0.524

standard errors in parentheses, * p<0.1 ** p<0.05 *** p<0.01

1999-2011 1999-2007 2008-2011

26

Figure 1: Loan growth (%) of different banks, 1999-2011 -5

05

10

2000 2005 2010 2000 2005 2010

Shareholder banks Stakeholder banks

loa

n g

row

th,

%

1999-2011

-50

5

2000 2005 2010 2000 2005 2010

Cooperative banks Savings banks

loa

n g

row

th,

%

1999-2011