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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).
3
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
4
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
5
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
6
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).
7
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).
8
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).
9
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
10
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).
11
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
References
Altunbas, Y., Fazylov, O., Molyneux, P., 2002. Evidence on the bank lending channel in Europe. Journal of
Banking and Finance 26, 2093-2110.
Amess, K., 2000. Financial institutions, the theory of the firm and organisational form. The Service Industries
Journal 22 (2), 129-148.
Arellano, M., Bond, S., 1991. Some test of specification for panel data: Monte Carlo evidence and an application
to employment equations. Review of Economic Studies 58, 277-297
Ashcraft, A.B., 2006. New Evidence on the Lending Channel. Journal of Money, Credit and Banking 38(3), 751-
776
Ayadi, R., Llewellyn, D.T., Schmidt, R.H., Arbak, E., De Groen, W.P., 2010. Investigating Diversity in the
Banking Sector in Europe: Key Developments, Performance and Role of Cooperative Banks. Brussels: Center
for European Policy Studies
Berger, A.N., Udell, G.F., 1995. Relationship lending and lines of credit in small firm finance. Journal of
Business 68, 351-382
Berger, A.N., Udell, G.F., 2002. Small Business Credit Availability and Relationship Lending: The Importance
of Bank Organisational Structure. Economic Journal 112, F32-F53
Bernanke, B., 1987. Non-monetary effects of the financial crisis in propagation of the Great Depression.
American Economic Review 73, 257-276
Bernanke, B., Blinder, A., 1988. Credit, Money, and Aggregate Demand. American Economic Review 78 (2),
435-439
Bernanke, B., Gertler, M., 1995. Inside the Black Box: The Credit Channel of Monetary Policy Transmission.
The Journal of Economic Perspectives 9 (4), 27-48
Bernanke, B., Gertler, M., Gilchrist, S., 1994. The Financial Accelerator and the Flight to Quality. Economic
Research Reports, C.V. Starr Center for Applied Economics, NYU
Bertay, A.C., Demirgüç-Kunt A., Huizinga H., 2012. Bank ownership and credit over the business cycle: Is
lending by state banks less procyclical?. CEPR Discussion Paper No. 9034.
Boot, A.W.A., 2000., Relationship banking: What do we know?. Journal of Financial Intermediation 9, 7-25
Brissimis, S., Delis, M., 2010. Bank Heterogeneity and Monetary Policy Transmission. ECB Working Paper
Series, No.1233 August 2010
De Bondt, G., 1999. Banks and Monetary Transmission in Europe: Empirical Evidence. BNL Quarterly Review
209, 149-168
17
De Santis, R.A., Surico, P., 2013. Bank Lending and monetary transmission in the Eurozone. Economic Policy,
forthcoming
Desrochers, M., Fischer, K.P., 2005. The Power of Networks: Integration and Financial Cooperative
Performance. Annals of Public and Cooperative Economics 76(3), 307-354
EBF, 2012. EU’s Banking Sector: The World’s Largest Banking System. Facts and Figures 2011/2012.
Available from http://www.ebf-fbe.eu/uploads/Facts%20&%20Figures%202011.pdf. Accessed 15/05/2013
Ehrmann, M., Gambacorta, L., Martínez-Pagés, J., Sevestre, P., Worms, A., 2001. Financial systems and the role
of banks in monetary policy transmission in the euro area. Discussion paper 18/01 Economic Research Centre of
the Deutsche Bundesbank
Ferri, G., Kalmi, P., Kerola, E., 2012. Ownership Structure and Performance in European Banks: A
Reassessment. Manuscript, University of Bari, University of Vaasa and Aalto University. Available:
http://www.globalcube.net/clients/eacb/content/medias/publications/research/Organizational_Structure_Perform
ance_European_Banks.pdf
Freixas, X., Rochet, J-C., 2008. Microeconomics of Banking. 2nd
edition, MIT Press, Cambridge Massachusetts
Fungacova, Z., Solanko, L., Weill, L., 2013. Does Bank Competition Influence the Lending Channel in the
Eurozone?. Working paper, Bank of Finland.
Gambacorta, L., 2005. Inside the bank lending channel. European Economic Review 49, 1737-1759
Gambacorta, L., Mistrulli, P.E., 2004. Does bank capital affect lending behaviour?. Journal of Financial
Intermediation 13, 436-457
Gertler, M., Gilchrist S., 1994. Monetary Policy, Business Cycles and the Behavior of Small Manufacturing
Firms. Quarterly Journal of Economics 109, 309-40
Hubbard, R. G., 1995. Is there a credit channel for monetary policy?. Economic Review of the Federal Reserve
Bank of St. Louis 77, 63-77
IMF, 2012. Spain: The Reform of Spanish Savings Banks Technical Notes. IMF Country Report No.12/141 June
2012
Jiménez, G., Ongena, S., Peydró, J-L., Saurina, J., 2012. Credit Supply and Monetary Policy: Identifying the
Bank Balance-Sheet Channel with Loan Applications. American Economic Review 102(5), 2301-2326
Kashyap, A.K., Stein, J.C., 2000. What Do a Million Observations on Banks Say about the Transmission of
Monetary Policy?. American Economic Review 98 (4), 1413-1442
Kashyap, A.K., Stein, J.C., 1995. The Impact of Monetary Policy on Bank Balance Sheets. Carnegie-Rochester
Conference Series on Public Policy 42, 151-195.
King, S.K., 2000. A Credit Channel in Europe: Evidence from Banks’ Balance Sheets. Mimeo, University of
California Davis
18
Kishan, R.P., Opiela, T.P., 2000. Bank Size, Bank Capital, and the Bank Lending Channel. Journal of Money,
Credit and Banking 32(1), 121-141
Petersen, M.A., Rajan, R.G., 1994. The benefits of lending relationships: Evidence from small business data.
The Journal of Finance 49(1), 3-37
Rajan R.G., 1994. Why Bank Credit Policies Fluctuate: A Theory and Some Evidence. Quarterly Journal of
Economics 109(2), 399–441
Romer, C.D., Romer, D.H., 1990. New evidence on monetary policy transmission mechanism. Brookings Papers
on Economic Activity Vol.1, 149-198
Roodman, D., 2009. A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics
72, 135-158
Schmitz, B., 2004. What role do banks play in monetary policy transmission in EU new member countries.
University of Bonn. Mimeograph.
Van den Heuvel, S., 2002. The bank capital channel of monetary policy. Wharton School, University of
Pennsylvania, Philadelphia. Mimeograph.
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