Loan supply surveys

Noname manuscript No.
(will be inserted by the editor)

Commercial Bank Lending Policy and Loan Supply
Maciej Grodzicki · Grzegorz Halaj · Dawid
˙
Zochowski

Received: date / Accepted: date

Abstract The paper examines the necessary condition for the existence of the
risk taking channel of monetary policy in the Polish banking sector. We adopt
a panel model framework to test if individual banks’ lending policies have an
impact on banks’ loan supply. Using data from the Polish bank lending survey
and controlling for demand side factors, we find that individual bank lending
policies are an important driver of credit growth. Financial constraints – capital and liquidity – were much less significant in determining loan growth than
lending policies. Moreover, so far changes in banks’ lending policies have been
driven, to a large extent, by shifts in banks’ risk perceptions. Accordingly, the
necessary condition for the operation of the risk-taking channel of monetary
transmission is present in the Polish banking sector.
We find also that the efficiency of monetary policy transmission may be
weakened for small open economies such as Poland, as compared to large
developed economies. This suggests that the risk taking channel may be relatively more important in such economies. This should be taken into account
in conducting monetary policy in small open economies.
Keywords Loan supply · credit growth · bank lending surveys · monetary
transmission
Mathematics Subject Classification (2000) E51 · E52 · G21 · C33
Please address correspondence to Maciej Grodzicki. This paper was prepared while Grzegorz
Halaj was with the Financial System Department of the National Bank of Poland.
M. Grodzicki
National Bank of Poland, ul. Swietokrzyska 11/21, 00-919 Warsaw, Poland

E-mail:
G. Halaj
Bank Pekao SA, Warsaw
˙
Dawid Zochowski
European Central Bank, Frankfurt


2

1 Introduction
Commercial banks play an important role in the pass-through of monetary
interest rates. Nevertheless, the efficiency of transmission of decisions of central banks is a complicated process and may depend on many factors, such as:
level of competition in financial industry, perception of credit risk (risk premia), risk aversion, availability of close substitutes for loans, etc. Moreover,
banks may influence the external finance premium not only via the interest
rates but also modifying the available maturity of loans or changing collateral requirements. Finally, as evidenced by broad literature on bank lending
channel, credit rationing and uncertainty about creditworthiness of borrowers
may markedly influence banks’ risk taking thereby influencing their willingness
to lend. The recent evidence suggest that this aspect of bank lending channel, namely risk taking channel, may play an important role in the monetary
transmission (Jimenez et al, 2008; Ioannidou and Penas, 2008; Altunbas et al,
2009).
Bank lending surveys, conducted by many central banks, give the possibility to test some mechanisms of bank lending channel, as they shed light on
the other than interest rate conditions of borrowing. Nevertheless, given that
bank lending survey in Poland was launched in 2003 and does not cover a full
business cycle yet, taking advantage of using these data to test bank lending
channel, in particular risk taking channel, seem to be aimless at the moment.
Instead, we focus on the supply side determinants of the credit in Poland. Using data from Senior Loan Officer Opinion Survey, collected by National Bank
of Poland, and adopting panel modelling approach, we test whether changes
in bank lending policies affect loan supply.
The remaining of the paper is organised as follows. Section 2 provides theoretical foundations to our research and Section 3 present the main hypothesis

and explains how our empirical analysis contributes to the literature. Then, in
Section 4 we describe the data and in Section 5 – the models and estimation
approach. Section 6 reports on the outcome of the estimations. Finally, Section
7 concludes and points to some avenues of possible further research.

2 Literature overview
Apart from the interest rate channel Bernanke and Gertler (1995) suggested
two other mechanisms through which monetary policy may affect bank loan
supply: the balance sheet channel, also known as broad credit channel, and the
bank lending channel or the narrow credit channel. 1 Both channels exist because of market frictions, in particular asymmetric information between banks
and borrowers (balance sheet channel) or between banks and their lenders
(bank lending channel), and eventually affect the final supply of loans.
1 Earlier papers, e.g. Bernanke and Blinder (1988); Gertler and Gilchrist (1993), focused
also on credit or banking channel however not distinguishing between broad and narrow
definition.


3

Balance sheet channel works because changes of the monetary interest rates
affect the net wealth or collateral of borrowers and thereby have an impact
on the possibilities of obtaining external financing. Thus, a decline in the net
wealth of borrowers (due to increased interest rates), increases the external
finance premium they have to face on the credit market and shifts upward the
bank loan supply curve to these borrowers.
The existence of bank lending channel is conditional on two important assumptions. First, monetary policy decisions impact bank liquidity position,
and, second, changes in the supply of loans affect borrowers, because of constrained access to other sources of financing than bank loans. Tightening of
monetary policy usually leads to decrease in the demand for deposits because
banks adjust their deposit rates only partially to the changes in official rates.
This, in effect drains liquidity from the banking sector to equity investment

funds. Shrinking banks’ liabilities forces banks to decrease the supply of loans
accordingly.
Some authors recall Modigliani-Miller paradigm and argue that banks may
offset a drain of deposits by increasing non-deposit source of financing, e.g. issuing deposit certificates (Stein, 1998; Romer and Romer, 2000). However, due
to information asymmetries, frictions exist and banks tap non-deposit sources
of funds to a different extent. Adjustments on the asset side of the balance
sheet by selling liquid assets may cushion to some extent the funding problems
of banks, however both liquidity and capital constraints limit substantially this
kind of adaptation. In effect, increased cost of funding shifts the loan supply
curve upwards. This effect should be less pronounced in case of banks which
have better access to alternative sources of financing, e.g. are larger (Kashyap
and Stein, 1995), well capitalised (Peek and Rosengren, 1995; Kishan and
Opiela, 2000; Van den Heuvel, 2002) or have better liquidity position (Stein,
1998; Kashyap and Stein, 2000).
However, changes in supply only do not determine the credit growth, because different elasticity of demand for loans across banks’ borrowers has to
be taken into account. In order to control for these demand effects we follow
the identification approach adopted by Kashyap and Stein (1995). The idea
is that the changes in the demand for loans that different banks have to face
after the monetary policy shock are determined by the degree of information
asymmetries between banks and their lenders. In literature, the most common
variable to measures these frictions is bank size (Kashyap and Stein, 1995;
Loupias et al, 2001; Hernando and Martinez-Pag´es, 2001). Introducing also
some exogenous macro-variables we control for demand effects, and hence,
can interpret the results as changes in the supply of loans.
An important aspect of bank lending channel is related to credit rationing,
which, in severe cases, may take a form of credit crunches. Credit rationing
is defined as a situation in which bank is unwilling to lend even if a borrower
is willing to pay the demanded price for a loan (Stiglitz and Weiss, 1981).
Banks play a crucial role in this process whereby they set loan terms and
lending standards, which are not related to the price of credit (interest rate).

This kind of bank behaviour, recently referred in the literature as risk taking


4

channel (Borio and Zhu, 2008), may be triggered by a shift in perception of
risk or by a shortage of bank capital (Bernanke and Lown, 1991; Woo, 1999).
In the first case banks are not willing to lend and in the latter they are not
able to lend.
In the neo-Keynesian models with credit, these aspects of bank lending
channel, namely willingness to lend, are determined by banks uncertainty
about creditworthiness of bank borrowers and the state of bank expectations, which is related to fundamental uncertainty about the future which
both borrowers and lenders face (Wolfson, 1996). According to these models,
in bank lending channel not only information asymmetry between borrowers
and lenders is essential, but also asymmetry of expectations between borrowers and lenders about the profitability of a project (corporate loans) or future
ability to service debt (household loans) is also important.
Risk taking channel may operate via several ways. Most importantly, low
interest rates boosting asset prices may increase the value of collateral and
thereby allow banks to accept higher credit risk (Borio et al, 2001). Altunbas
et al (2009) report also on other possible impacts of lower interest rates on
higher risk taking of borrowers. Low interest rate environment may facilitate
search for higher risk assets, the so called ”search for yield” (Rajan, 2005) and
increase banks’ risk tolerance. Altunbas et al (2009) also suggest that monetary
policy may influence risk taking behaviour via habit formation, whereby banks
become less risk-averse during economic expansions. Relatively few papers
have focused so far on testing empirically if risk taking channel works. Using
individual data from credit register (Jimenez et al, 2008) shows that Spanish
banks eased their lending policies and extended more risky loans when interest
rates were low. Ioannidou and Penas (2008) also find that when interest rates
are low banks’ price the credit risk lower. Moreover, banks tend to reduce

credit margin on risky borrowers relatively more than on average. Altunbas
et al (2009) also find strong evidences in favour of the influence of low interest
rates on banks risk taking using the data for 1100 banks from the EU and the
US.

3 Main hypothesis
Using the data from the bank lending survey on lending standards and lending conditions we test for a necessary condition for the existence of risk taking
channel, i.e. if changes of bank lending policies affect its relative loan supply.
We do not test, however, the other component of this channel, namely if interest rate changes may trigger the changes in bank lending policies. Thus, we
cannot fully confirm the existence of risk taking channel in the Polish economy, nevertheless, we give some insights into the determinants of the changes
of bank lending policies on the basis of the results from the SLOS survey.
Monetary transmission in Poland was examined in, inter alia, Wrobel and
Pawlowska (2002). The former authors find mixed evidence for the importance
of bank lending channel in the Polish economy. While capital constraints are


5

binding in their model, liquidity constraints are not. As a result, the operation
of bank lending channel was restricted. In Hurlin and Kierzenkowski (2002)
the focus is on interest rate channel and pass-through of changes in official
rates to rates on loans, which is found to be very swift.
No studies so far have been dedicated to study the bank willingness to lend
˙
and its impact on credit supply in the Polish economy. Pruski and Zochowski
(2006) and Brzoza-Brzezina, Chmielewski, and Nied´zwiedzi´
nska (2008) report
on the high level of substitution between foreign currency and zloty lending,
which to some extent outweighs the impact of monetary policy on loan supply. Moreover, over the last decade Polish banks have been operating in the
environments of excess funding liquidity, which resulted from systematically

higher level of deposits than credit in the system. This two features of the
Polish banking sector may indicate that bank willingness to lend may be an
important driving force in the Polish credit market.
Bank lending surveys provide a powerful set of data to test different hypotheses about bank lending channel. In particular, questions about the reasons of changes in lending policies are related to different types of risk, which
separately can be tested for their influence on banks’ willingness to lend. However, in this paper, due to short time horizon and low frequencies of answers
other than ”no-change” to questions on the lending terms and conditions as
well as reasons for changing them, we concentrate on answering whether, in
general, altering bank lending standards or conditions affect banks’ loan supply. Since we control for demand effects and individual bank effects, we formulate and test the following main hypothesis:
H0: Tightening/ easing of bank lending policies leads to decrease/ increase
in individual bank loan supply
It is a necessary but not sufficient condition for the existence of risk taking channel. Also banks’ risk perception would have to change following the
change in the monetary policy stance. Although we do not test this in the
paper, we give some insights into the determinants of the changes of bank
lending policies, which seem to support our view that changes in perception
of risk by banks is an important driver of changes in lending policies. Since
according to Bernanke and Lown (1991); Woo (1999), changes in bank lending
policy are related either to capital constraints (for which we control) or to
shifts in perception of risks, our results provide some support toward the significance of risk taking channel. Moreover, Rajan (1994) and Berger and Udell
(2004) demonstrate that banks tend to curb lending in economic downturns
by changing lending standards. Their results point to the importance of bank
lending policies to the broad economy and the business cycle.

4 Data
We used three types of data to verify the existence of monetary policy transmission channels. These are the survey data on bank lending policy, individual
bank financial data and macroeconomic variables (see table 1).


6

The data on bank lending policy come from the Senior Loan Officer Opinion

Survey (SLOS), which has been carried out by the National Bank of Poland
(NBP) on a quarterly basis since December 2003. The survey questionnaire,
available from the NBP website2 , resembles the questionnaire for ECB bank
lending survey. The results are published by the NBP (NBP, 2009).
In SLOS, 24 banks are asked whether they changed standards or terms on
loans over the previous quarter3 . Separate questions address the situation in
the housing loan, other consumer loan and corporate loan markets. Banks are
asked to provide information on changes in lending standards with regard to
loans to large enterprises and to small and medium enterprises separately. We
used the responses of individual banks as a measure of changes in their lending
policy.
The Senior Loan Officer Opinion Survey is a qualitative survey. Participants may choose from a set of five options:
–
–
–
–
–

the
the
the
the
the

bank significantly eased its lending policy,
bank slightly eased it lending policy,
lending policy was unchanged,
bank slightly tightened its lending policy, or
bank significantly tightened its lending policy.

Lending standards are defined as minimum acceptance criteria which must
be met by a prospective borrower to be approved for a loan, regardless of
the loan’s price and other terms the bank is willing to offer. Terms on loans,
defined as features of the loan contract which may be negotiated after loan
is approved, are broken down into six categories: spreads on regular loans,
spreads on high-risk loans, loan maturity, collateral, fees and maximum loan
amount. In the case of housing loans, banks are also asked about changes in
the required loan-to-value ratio.
In the NBP survey, the definition of terms and standards has been provided
to all participating institutions for clear demarcation between the two areas of
lending policy. Some potential for misinterpretation of the definition remains
and may lead for instance to reporting changes in lending standards as change
in terms on loans. We consider such behaviour to be manifested in the open
question on changes in other terms on loans (i.e., not explicitly mentioned
in the questionnaire)4 . This is indicated by individual responses to the open
questions, in which banks sometimes note that they have actually changed
lending standards5 . We filter our dataset for instances when a bank reported no
change in lending standards and simultaneously indicated a change in lending
2

en.pdf
Effective from October 2008 survey, the sample has been expanded to cover 30 banks
whose market share exceeds 80%. We did not include this expansion in our estimations as
the time series for additional banks do cover only a fraction of the credit cycle and may
have thus distorted the results.
4 Questions 3.7, 9.8 and 11.7 in the questionnaire.
5 Such as parameters in the scoring system, minimum eligible score, or minimum eligible
income.
3

7

standards in a corresponding open question on other terms on loans, and treat
such instances as a change in lending standards.
The volume of lending may be related both to the level and the change in
lending policy. We consider this possibility in construction of variables which
measure the impact of lending policy on bank lending behaviour.
We measure the impact of lending standards and loan terms on the volume
of new loans separately to allow for diverse responses of loan supply to these
factors. For lending standards, we construct two dummy variables which indicate that a bank tightened or eased lending standards to allow asymmetries in
the response of loan volume to changes in lending policy. We do not take into
consideration the perceived size of change in lending policy, only its direction.
While terms on loans could have been treated similarly, with two dummies
being used to represent the tightening and easing of each of 6-7 categories of
terms, such approach would not be feasible with our small dataset.
The variable reflecting the changes in each bank’s terms on loans is an index
of general restrictiveness of loan terms6 . The Senior Loan Officer Opinion
Survey measures only changes in lending policy, and not how conservative
bank lending policy is. There is no data on the actual restrictiveness of loan
terms offered by individual banks. We set our loan terms’ variable to 0 as
of the first edition of the Senior Loan Officer Opinion Survey (i.e. the third
quarter of 2003). For each bank in the sample, the starting point of ”zero”
restrictiveness is likely different7 . Then, for period t the loan terms’ variable
T ermst would be given by the following formula:

T ermst = T ermst−1 +

k
X

Indi ,

(1)

i=1

where k is the number of categories of terms on loans (i.e., either 6 or 7) and
Indi is an indicator variable such that:
– Indi = 1 if the bank eased its lending policy with respect to the i-th
category of terms on loans,
– Indi = −1 if the bank tightened its lending policy with respect to the i-th
category of terms on loans,
– Indi = 0 otherwise.
For illustration, if in the first edition of SLOS a bank increased the spread
on regular loans and decreased the maximum available loan amount, our index
of restrictiveness would be -2. Then, if in the next period the bank decided to
decrease the maximum loan maturity, lower its loan extension fee and demand
6

We constructed a similar index for restrictiveness of lending standards, and performed
estimations using it instead of dummies for tightening and easing of lending standards.
Results did not differ materially from what is reported in this paper, and can be obtained
from the authors upon request.
7 The differences between banks’ lending policies at the beginning of the sample period
translates into individual effects.


8

less collateral, the index would consequently increase from -2 to -1. More succinctly, the index changes from one period to the other by the net number of
categories of loan terms with respect to which the bank changes its lending
policy.
We supplement our analysis with the balance-sheet and P&L data on individual banks. The bank-level financial data come from the prudential reporting
system of the National Bank of Poland. All institutions with a Polish banking
licence, as well as branches of foreign banks in Poland, are required by the Act
on the National Bank of Poland to report a wide scope of financial information
with monthly or quarterly frequency. The data undergo a quality control, but
are not audited by independent parties. However, banks must supply amended
data should their regular auditor or the NBP find any inconsistencies or mistakes.
We use three bank-level variables to represent characteristics of individual
banks. The application of the variables is consistent with what is proposed
in the literature on lending channel (Berger and Udell, 2004; Hernando and
Martinez-Pag´es, 2001; Kishan and Opiela, 2000; Altunbas et al, 2009). The
Basel capital adequacy ratio is a measure of how well a bank is capitalized.
While the rules for calculation of this ratio have changed over time, its binding
minimum level of 8% remained unchanged, and the higher the ratio, the less
likely it is that bank experiences capital shortage. As a measure of liquidity we
use interbank gap, which we define as the ratio of the bank’s net position visa-vis other banks (i.e., its gross claims on other banks minus gross liabilities
to other banks) to bank’s total assets8 . Positive interbank gap indicates a
favourable net liquidity position, as the bank has excess funds to lend out
in the interbank market. However, if interbank gap is negative, it may be
either due to weak liquidity position or to strategic choice (to rely on) of
foreign funding, and in the Polish context this would mean chiefly intra-group
funding. Finally, the logarithm of the total number of accounts held at the
bank is set as our proxy for bank size.
As proxies for interest rates, we take average three-month Polish zloty
money market index (3-month WIBOR) and Swiss franc 3-month LIBOR.
The LIBOR rate is to represent foreign interest rate. It is economically relevant
because of significant share of bank credit in Poland was extended in foreign

currencies, especially in the Swiss currency. We also use the official GDP and
CPI data for Poland.
The flow of credit may be subject to seasonal fluctuations. A simple example would be the much higher flow of consumer credit in November and
December than in other months due to Christmas shopping season. To control
for such fluctuations, we use seasonal dummies representing the first, second
and third quarter of a ear. We also correct for one merger which occurred
between participating banks in 2007 using additional dummies.
8 We also tested another measure of liquidity, the loan-to-deposit ratio, and got similar
results.


9

Banks which participate in the Senior Loan Officer Opinion Survey cover
approximately three-fourths of the respective loan markets in Poland (ca. 80%
of housing loan and corporate loan markets, and 65% of consumer loan market). 21 of them are commercial banks, 2 are branches of foreign credit institutions, and one is a cooperative bank. Not all 24 banks are active in each
segment of the credit market.
Our sample consists of 13 institutions which extend housing loans, 18 institutions which are active in the corporate loan market and 16 institutions
issuing consumer loans. All major participants in the corporate and housing
loan markets are represented in this sample. In the consumer loan market,
some specialized banks, especially those which emerged as major players after
2004, do not participate in the survey and are not represented in the sample. Most institutions in the sample are owned by an ultimate foreign parent,
a situation characteristic for the Polish banking system. The composition of
our sample leads, by definition, to exclusion of new entrants which appeared
in some market segments, and therefore the results may not be valid for the
banking system as a whole.
We decided to drop the cooperative bank from the Senior Loan Officer
Opinion Survey sample of 24 banks due to the very limited geographical scope
of its operations. Since this bank accounts for a very small fraction of the
loan market, its exclusion does not cause any material loss of information.

We also removed two branches of foreign credit institutions, as they do not
have their own equity and are not obliged to meet the capital requirement
in the host country. Therefore, branches are not restricted in their lending
policy by leverage constraints faced by commercial banks. Moreover, the degree
of business independence of branches, as compared to commercial banks, is
markedly lower. This increases the likelihood that their lending policy may be
determined by the parent institution. Bearing these peculiarities of branches
in mind, we decided not to include them in the analysis. We also remove banks
which did not change their lending policy throughout the period of the study,
since they do not help in explaining variation of loan supply.

5 Estimation
We estimate parameters of a reduced form model which attempts to identify
the impact of supply-side factors on loan growth in the Polish credit markets,
while controlling for loan demand effects. Given the oligopolistic features of the
bank loan market in Poland (Kozak and Pawlowska, 2008), in which borrowers
have very little bargaining power9 , we treat loan demand as exogenous and we
assume that it can be described by a function of macroeconomic variables such
as interest rates, GDP and inflation. The choice of variables representing the
supply-side effects is based on literature presented in Section 2 and is described
in details in Section 4.
9 Other credit markets in Poland, such as corporate bond market, are at a very early
stage of development, and cannot be treated as substitute for bank loans.


10
Table 1 The description of the variables
Name
DiffHLToA
DiffHL

DiffCorpLToA
DiffCorpL
DiffConsLToA
DiffConsL
TermsLevelH
StdTighteningH
StdEasingH
TermsLevelCorp
StdTighteningCorp
StdEasingCorp
TermsLevelCons
StdTighteningCons
StdEasingCons
CAR
GapIBank
LogAcc
GDPGrowth
Wibor3M
LiborCHF3M
CPI

Description of variable
Quarterly change in housing loans normalised by assets
Quarterly precentage change in housing loans
Quarterly change in corporate loans normalised by assets
Quarterly percentage change in corporate loans
Quarterly change in consumer loans normalised by assets
Quarterly percentage change in consumer loans
Index of restrictiveness of terms on housing loans
Dummy for tightening of lending standards on housing loans

Dummy for easing of lending standards on housing loans
Index of restrictiveness of terms on corporate loans
Dummy for tightening of lending standards on corporate loans
Dummy for easing of lending standards on corporate loans
Index of restrictiveness of terms on consumer loans
Dummy for tightening of lending standards on consumer loans
Dummy for easing of lending standards on consumer loans
Basel capital adequacy ratio of the bank
Total interbank loans of the bank minus its total interbank borrowings,
as fraction of bank’s assets
Logarithm of the number of accounts at the bank
Real GDP growth rate (yoy)
Mean 3-month Warsaw Interbank Offered Rate over the quarter
Mean 3-month Swiss franc LIBOR over the quarter
CPI inflation

Two types of models for each category of loans were estimated. In the main
models, we use the quarterly credit growth by loan type as dependent variables.
However, our sample of banks is very diverse, and some banks have revised
their business models markedly since the Senior Loan Officer Opinion Survey
was launched in 2003. As a result, some banks may have experienced relatively
high loan growth rates only due to the low basis. We employ a supplementary
model in which the loan growth rates are normalised by bank’s assets at the
beginning of the quarter to serve as a robustness check for the results from
the main model.
Both models are considered in a static specification10 . To account for the
observed heteroscedasticity and serial autocorrelation in the data we applied
Prais-Winsten transformation (Baltagi, 2001).11 Our main model is represented by Equation 2.
10 However, we check the estimates in the dynamic setting applying several competing
estimators (GMM estimator proposed by Arellano and Bond (1991), its System GMM extension (Blundell and Bond, 1998) with robust standard errors which utilise the correction

of Windmeijer (2005)). We use the levels of loan-to-assets ratio instead of the loan growth
normalised by assets as the dependent variable, and its lagged values as independent variable. Nevertheless, the estimations did not prove to be statistically significant since the data
panel is almost quadratic. This validates the use of a static specification
11 A similar correction can be obtained using Feasible GLS. It has, however, been subject
to critique from Beck and Katz (1995) that standard errors yielded by Feasible GLS are
understated. Having attempted to apply Feasible GLS correction for the suspected patterns
of serial correlation in our data, we obtained standard errors which were by at least an


11

DiffLoanTypeToLoanit = β1 StdEasingLoanTypei,t−n
+β2 StdTighteningLoanTypei,t−n + β3 TermsLevelLoanTypei,t−m
+

K+3
X

(k−3)

βk BankSpecVari,t−1 +

k=4

K+4+N
X

(k−K−3)

βk MacroVart

+ uit ,

(2)

k=K+4

uit := µi + ρuit−1 + it is an autoregressive error component with random individual effects µi and common autocorrelation structure ρ.12 The independent
variables were at least one period lagged to avoid endogeneity.
Similarly, our supplementary model is given in the static form by Equation
3.
DiffLoanTypeToAssetsit = β1 StdEasingLoanTypei,t−n
+β2 StdTighteningLoanTypei,t−n + β3 TermsLevelLoanTypei,t−m
+

K+3
X
k=4

(k−3)

βk BankSpecVari,t−1 +

K+4+N
X

(k−K−3)

βk MacroVart

+ uit ,

(3)

k=K+4

For our static specifications, we followed a two-step procedure of estimation
in order to get the best possible estimator – consistent and efficient. We tried
random effects (RE), fixed effect (FE) or pooling method (POOL) and chose
the most appropriate one according to the following procedure:
1. (RE or FE vs POOL) First, we verify the hypothesis of no individual effects
in the model (2) by Breusch-Pagan Lagrange Multiplier test for random
individual effects and AN OV A F test based on comparison of within and
pooled models. We also perform the joint LM test for random individual
effects and first-order serial correlation proposed by Baltagi and Li (1991).
2. (RE vs FE) Secondly, in case individual effects may prove important in
explaining variability of banks loan supply, we check if RE procedure performs well in a statistical sense resulting in the most efficient estimators.
To test whether random effect specification gives consistent estimators of
parameters, we employ the Hausman test, comparing GLS estimates of RE
model and the fixed effects (within) model.

6 Results
According to the proposed estimation approach we obtain two specifications
of the model for each segment of the loan market.
order of magnitude lower than in any other estimation. Hence, we decided to use only the
Prais-Winsten estimators.
12 We performed additional test of the stability of ρ across panels estimating the analogous
equation with panel specific correlation in the error term uit := µi + ρi uit−1 + it .

12

6.1 Housing loans
We proposed the following model explaining variability of housing loans growth.
DiffHLi,t = β1 TermsLevelHi,t−2 + β2 StdChangeHi,t−2
+β3 CARi,t−1 + β4 GapIBanki,t−1 + β5 LogAcci,t−1
+β6 GDPGrowtht−1 + β7 Wibor3Mt−1
+β8 CPIt−1 + β9 LiborCHF3Mt−1 + uit ,

(4)

As to lending standards, we allow the loan growth to respond asymmetrically to tightening and easing of lending policy.
Table 2 presents the parameter estimates yielded by various panel model
procedures applied to Model 4. Summary of statistical tests which we conducted for all our models is given in Table 4. The data give strong evidence
that individual, bank specific effects determine dynamics of housing loans.
Breusch-Pagan and AN OV A F tests reject the hypothesis of no individual
effects. In the Hausman test, we did not reject that RE gives consistent estimates of parameters. Application of the Prais-Winsten estimators is supported
by serial correlation present in the data, as well as significant between-group
heteroscedasticity.
In an analogous model where the growth of housing loans is normalised by
assets, we also favour the RE procedure, corrected for heteroscedasticity and
serial correlation. Such approach is confirmed by the results of Breusch-Pagan
and Hausman tests. Table 3 summarizes the results for this model.
The impact of terms on housing loans on their supply was found to be
significant. Net change in the restrictiveness index for loan terms by -1, which is
equivalent to tightening of one of six terms on housing loans, led to a slowdown
of quarterly housing loan growth by 0.34 to 1.01 percentage points after two
quarters. The marginal effect of tightening of terms on loans was comparable to
that of 0.07-0.08 p.p. rise in market interest rates. In contrast, credit standards
have little impact on loan growth, regardless of the direction in which they are

changed.
Housing loan supply does not depend on capital adequacy of banks. This
can be explained by the fact that most banks in the sample13 were controlled
or even fully owned by foreign financial institutions. Many Polish banks had,
at least until the 2008-2009 financial market turmoil, an almost unrestricted
access to capital from their parent institution. Some rapidly expanding banks
could have operated close to the regulatory minimum capital requirement of
8%, because they had been reassured by the (implicit or explicit in the business
plans) commitment of the parent institution to provide additional capital if
needed.
The evidence on how bank’s liquidity position may affect the growth of its
housing loan book is mixed. Our main model suggests that the relationship
is positive, i.e. the more liquid the bank, the faster should loan book expand.
13

Eleven banks out of total of 13 banks in the sample.


13
Table 2 Model of growth of housing loans
Prais-Winsten
(PSAR1)
-0.0377

Prais-Winsten
(AR1)
-0.0199

RE

FE

-0.0485

-0.0458

(0.127)

(0.496)

(0.169)

(0.210)

StdEasingLag2

-0.0045

-0.0007

0.0020

0.0081

(0.834)

(0.982)

(0.947)

(0.797)

TermsLevelLag2

0.0101

0.0021

0.0034

0.0048

(0.047)

(0.636)

(0.022)

(0.098)

CARLag1

0.0083

-1.0909

0.6195

0.4872

(0.992)

(0.328)

(0.112)

(0.255)

GapIBankLag1

0.3901

0.2974

0.1831

0.2201

(0.020)

(0.090)

(0.146)

(0.257)

LogAccountsLag1

-0.0872

-0.0530

-0.0501

-0.0995

(0.000)

(0.083)

(0.001)

(0.063)

WIBOR3MLag1

0.0403

-0.0075

-0.0002

-0.0033

(0.085)

(0.676)

(0.993)

(0.869)

LIBOR3MLag1

-0.1209

-0.0424

-0.0445

-0.0455

(0.002)

(0.083)

(0.018)

(0.018)

CPILag1

0.0093

0.0229

0.0132

0.0149

(0.413)

(0.027)

(0.343)

(0.294)

GDPGrowthLag1

0.0272

0.0207

0.0172

0.0161

(0.015)

(0.017)

(0.060)

(0.083)

DummyQ1

-0.0105

0.0152

0.0163

0.0170

(0.581)

(0.427)

(0.642)

(0.628)

DummyQ2

0.0027

0.0207

0.0100

0.0104

(0.898)

(0.294)

(0.765)

(0.758)

DummyQ3

0.0293

0.0478

0.0475

0.0470

(0.097)

(0.009)

(0.159)

(0.168)

Constant

1.0782

0.9012

0.6552

1.3534

(0.001)

(0.048)

(0.013)

(0.073)

StdTighteningLag2

Notes: parameters significant at 5% level are reported in bold. Parameters significant at 10%
level are reported in italics. Critical significance levels are reported below the parameters.
Source: own calculations

Our supplementary model hints that the relationship is negative: housing loan
books grow faster in less liquid banks. The lack of clear evidence may be
due to the fact that banks met very few restrictions when accessing foreign
funding, especially on an intra-group basis. The other reason for slower housing
loan portfolio growth in banks with the positive interbank gap could be their
business strategy, not only concentrated on the loan market but balanced by
more secure investment opportunities on the interbank market.
Such reliance of some Polish banks on intra-group funding and capital injections from the parent can disturb the transmission of monetary policy and
provoke contagion from the home markets of parent banks to the Polish loan
market. Under such circumstances, and if capital and liquidity constraints
for Polish banks were binding, credit conditions with regard to housing loans
would depend on ability and willingness of foreign parent institutions to provide capital and funds to their Polish subsidiaries. This, in turn, is linked to
the capital position of a parent institution. As a result, Polish banks may be
forced to curb lending in case the financial condition of the parent company
deteriorates, even without any intrinsic reasons. Conversely, if capital and liquidity constraints are not binding for Polish banks, they may not respond
to monetary impulses in an expected manner. A fall in deposits, induced by

14
Table 3 Model of growth of housing loans normalised by assets
Prais-Winsten
(PSAR1)
-0.0009

Prais-Winsten
(AR1)
-0.0010

RE

FE

-0.0045

-0.0049

(0.380)

(0.366)

(0.031)

(0.020)

StdEasingLag2

0.0007

0.0009

0.0040

0.0042

(0.421)

(0.288)

(0.025)

(0.022)

TermsLevelLag2

0.0002

0.0002

0.0004

0.0005

(0.297)

(0.372)

(0.009)

(0.004)

CARLag1

-0.0357

-0.0353

-0.0533

-0.0557

(0.166)

(0.210)

(0.026)

(0.024)

GapIBankLag1

-0.0027

-0.0043

-0.0269

-0.0200

(0.692)

(0.505)

(0.004)

(0.074)

LogAccountsLag1

0.0005

-0.0013

0.0005

0.0039

(0.700)

(0.234)

(0.699)

(0.198)

WIBOR3MLag1

-0.0009

-0.0015

-0.0001

-0.0001

(0.308)

(0.142)

(0.919)

(0.921)

LIBOR3MLag1

0.0030

0.0037

0.0026

0.0024

(0.014)

(0.011)

(0.020)

(0.033)

CPILag1

-0.0002

-0.0002

-0.0028

-0.0026

(0.746)

(0.656)

(0.001)

(0.001)

GDPGrowthLag1

0.0008

0.0009

0.0009

0.0010

(0.089)

(0.067)

(0.076)

(0.071)

DummyQ1

-0.0025

-0.0023

-0.0033

-0.0033

(0.006)

(0.018)

(0.099)

(0.103)

DummyQ2

0.0017

0.0015

-0.0002

-0.0001

(0.094)

(0.154)

(0.912)

(0.993)

DummyQ3

0.0021

0.0019

0.0004

0.0005

(0.021)

(0.052)

(0.837)

(0.813)

Constant

0.0009

0.0296

0.0083

-0.0377

(0.960)

(0.086)

(0.704)

(0.383)

StdTighteningLag2

Notes: parameters significant at 5% level are reported in bold. Parameters significant at 10%
level are reported in italics. Critical significance levels are reported below the parameters.
Source: own calculations
Table 4 Summary of statistical tests

Test

Breusch-Pagan
random effects

Housing
loans
test

for

Hausman test of fixed vs.
random effects
F-test for poolability of the
data (H0: no individual effects)
Panel-specific serial correlation
Joint LM test of random effects and serial correlation
Modified Wald test for
groupwise heteroscedasticity

Main models
Supplementary models
Corporate Consumer Housing
Corporate Consumer
loans
loans
loans
loans
loans

8.58

10.76

19.99

446.56

94.52

2.75

0.0034
4.86

0.0010
6.83

0.0000
24.29

0.0000
0.72

0.0000
35.78

0.0973
11.50

0.9932
2.66

0.6659
4.76

0.0833
4.17

0.9999
17.98

0.0019
7.84

0.7773
2.62

0.0022
22.27

0.0000
3.16

0.0000
78.69

0.0000
206.82

0.0000
2.11

0.0013
25.45

0.0000
24.77

0.0753
26.58

0.0000
82.24

0.0000
517.13

0.1465
101.94

0.0000
25.47

0.0000
11756.8

0.0000
1257.4

0.0000
846.4

0.0000
998.2

0.0000
1115.2

0.0000
673.3

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Note: p-values reported in italics.
Source: own calculations


15

monetary tightening, could be levelled off by increased intra-group funding,
thus curtailing the effects of rising official interest rates on bank liquidity and

capacity to supply credit.
The level of interest rates have a strong impact on the growth of housing loans. The credit growth in this market segment was dependent on decisions of the Swiss central bank. This underscores the large degree of currency
substitution in the Polish housing loan market. An increase in three-month
Swiss franc LIBOR, the rate to which most foreign currency housing loans are
linked, by 25 basis points caused the growth of housing loans to decline by
1.1 to 4.0 percentage points. Meanwhile, Polish interbank interest rates were
found to have little significant impact on the dynamics of housing loans. Even
if they had any significant influence on credit growth, it was minor compared
to the effect of changes in Swiss rates. Such findings confirm the conclusion of
Brzoza-Brzezina, Chmielewski, and Nied´zwiedzi´
nska (2008) that the presence
of a developed market for foreign currency loans domestic monetary policy
decisions may be ineffective or even counterproductive.
In line with some results (Kashyap and Stein, 1995), bank size was found
to have a significant impact on banks’ behaviour. The bigger the bank was the
more moderate was the growth of housing loans. This finding can, however,
result from business models in the smaller, foreign owned banks placing much
emphasis on housing loans offer.
6.2 Corporate loans
Our main model for the corporate loan growth is given by Equation 5. Statistical tests indicate that individual effects are present in this model, and RE is
favoured over FE (see Table 4). Conversely, in our supplementary model we
choose individual fixed effects over RE. As in the housing loan models, strong
serial correlation and heteroscedasticity are present.
DiffLoanCorpit = β1 TermsLevelCorpi,t−3 + β2 StdChangeCorpi,t−3
+β3 CARi,t−1 + β4 GapIBanki,t−1 + β5 LogAcci,t−1
+β6 GDPGrowtht−1 + β7 Wibor3Mt−1
+β8 CPIt−1 + β9 LiborCHF3Mt−1 + uit ,

(5)

As in the housing loan model, lending policy is consistently significant in
explaining loan growth in the corporate loan segment (see Tables 5 and 6).
Loan terms, while significant, have a relatively weak impact on corporate loan
growth. Tightening one of the terms offered on corporate loans results in a
decline of corporate loan quarterly growth rate by some 0.2 percentage point,
and only after three quarters14 . Lending standards also influence corporate
14 We found evidence that the lag in transmission of changes in terms on corporate loans
to the actual loan growth may be even longer, up to four quarters. Due to high degree of
multicollinearity, it would be very difficult to measure this lag precisely. For this reason, our
attempts to estimate a panel model with several lags of the lending policy variables did fail.


16
Table 5 Model of growth of corporate loans
Prais-Winsten
(PSAR1)
-0.0281

Prais-Winsten
(AR1)
-0.0213

-0.0208

-.0163

(0.029)

(0.098)

(0.123)

(0.217)

-0.0025

-0.0032

-0.0002

-0.0016

(0.776)

(0.725)

(0.984)

(0.882)

TermsLevelLag3

0.0013

0.0021

0.0019

0.0023

(0.056)

(0.005)

(0.012)

(0.006)

CARLag1

-0.1713

-0.1285

-0.0283

0.1114

(0.118)

(0.271)

(0.820)

(0.424)

GapIBankLag1

-0.0649

-0.0645

-0.0599

0.0197

(0.009)

(0.037)

(0.020)

(0.677)

LogAccountsLag1

-0.0012

-0.0009

-0.0017

-0.0713

(0.504)

(0.636)

(0.436)

(0.000)

WIBOR3MLag1

-0.0061

-0.0008

0.0001

-0.0051

(0.419)

(0.919)

(0.990)

(0.432)

LIBOR3MLag1

0.0159

0.0161

0.0139

0.0210

(0.052)

(0.049)

(0.034)

(0.001)

CPILag1

-0.0029

-0.0033

-0.0032

0.0006

(0.588)

(0.536)

(0.502)

(0.894)

GDPGrowthLag1

0.0034

0.0024

0.0046

0.0014

(0.414)

(0.565)

(0.190)

(0.676)

DummyQ1

0.0272

0.0262

0.0262

0.0299

(0.005)

(0.012)

(0.020)

(0.006)

DummyQ2

0.0384

0.0395

0.0416

0.0431

(0.001)

(0.001)

(0.000)

(0.000)

DummyQ3

0.0314

0.0300

0.0308

0.0314

(0.001)

(0.004)

(0.005)

(0.003)

Constant

0.0507

0.0207

0.0033

0.8626

(0.365)

(0.699)

(0.949)

(0.000)

StdTighteningLag3
StdEasingLag3

RE

FE

Notes: parameters significant at 5% level are reported in bold. Parameters significant at 10%
level are reported in italics. Critical significance levels are reported below the parameters.
Source: own calculations

loan growth, yet in an asymmetric manner. Loan growth responds to tightening
of lending standards, while easing of lending standards does not stimulate loan
growth. When a bank tightens lending standards, then after three quarters the
quarterly growth of its corporate loan book slows down by some 2.8 percentage
points. Bank lending standards appear in this light as a much more stronger
tool of controlling loan growth than terms offered on corporate loans.
The number of lags is somewhat puzzling, when confronted with the results in Lown and Morgan (2006), who found corporate credit standards to
have immediate effect on the volume of loans. One may expect corporate borrowers to be more active in shopping for most favourable credit conditions
than households. A likely reason for such lag may be the duration of approval
process for corporate investment projects. These are often to follow annual
investment plans and be scheduled well in advance. Then, actual execution of
the project and extension of the loan could take place well after the financing
decisions are being made. Furthermore, companies often use committed credit
lines to cover their short-term financing needs. If the lending standards are
being tightened by the bank, firms would draw on existing lending facilities
and only face the changes in lending policy when these facilities need to be
renegotiated or new lines have to be secured.


17
Table 6 Model of growth of corporate loans normalised by assets
Prais-Winsten
(PSAR1)

-0.0050

Prais-Winsten
(AR1)
-0.0047

-0.0208

-.0163

(0.074)

(0.086)

(0.123)

(0.217)

-0.0023

-0.0029

-0.0002

-0.0016

(0.213)

(0.124)

(0.984)

(0.882)

TermsLevelLag3

0.0002

0.0003

0.0019

0.0023

(0.220)

(0.093)

(0.012)

(0.006)

CARLag1

-0.0636

-0.0687

-0.0283

0.1114

(0.064)

(0.059)

(0.820)

(0.424)

GapIBankLag1

-0.0015

0.0104

-0.0599

0.0197

(0.835)

(0.101)

(0.020)

(0.677)

LogAccountsLag1

-0.0012

-0.0014

-0.0017

-0.0713

(0.088)

(0.065)

(0.436)

(0.000)

WIBOR3MLag1

-0.0011

0.0001

0.0001

-0.0051

(0.574)

(0.952)

(0.990)

(0.432)

LIBOR3MLag1

0.0050

0.0048

0.0139

0.0210

(0.027)

(0.029)

(0.034)

(0.001)

CPILag1

-0.0005

-0.0008

-0.0032

0.0006

(0.712)

(0.546)

(0.502)

(0.894)

GDPGrowthLag1

0.0013

0.0010

0.0046

0.0014

(0.201)

(0.350)

(0.190)

(0.676)

DummyQ1

0.0037

0.0040

0.0262

0.0299

(0.119)

(0.110)

(0.020)

(0.006)

DummyQ2

0.0079

0.0080

0.0416

0.0431

(0.003)

(0.005)

(0.000)

(0.000)

DummyQ3

0.0055

0.0056

0.0308

0.0314

(0.017)

(0.023)

(0.005)

(0.003)

Constant

0.0201

0.0226

0.0033

0.8626

(0.245)

(0.215)

(0.949)

(0.000)

StdTighteningLag3
StdEasingLag3

RE

FE

Notes: parameters significant at 5% level are reported in bold. Parameters significant at 10%
level are reported in italics. Critical significance levels are reported below the parameters.
Source: own calculations

It is interesting that, contrary to the housing loan market, banks which
operate in the corporate loan market adjust loan supply much more forcefully
by changing the loan approval criteria than by changing the terms on loans
such as price, maturity or loan collateral. This might be due to two likely
mechanisms – fierce competition or credit rationing.
First, large corporations are well-informed borrowers who have a good
grasp of the credit market, and would not accept more restrictive terms on
loans. High level of competition in the corporate loan market, and especially
in the market for loans to large firms, may deter individual banks from raising

the spreads on corporate loans, as it would lead to an excessive migration of
clients to competitor banks. Then, if a bank were to influence the level of its
supply of corporate loans, it must adjust rather credit standards, which are
opaque to the bank’s clients, than terms on loans.
Second, the approach of banks to attract clients in these two markets could
have been markedly different. Still relatively low level of financial deepening in
the Polish housing loan market and quite good loan quality of banks’ portfolios
over the past few years encouraged banks to attract prospective borrowers by
easing loan terms. In the corporate loan market, however, past episodes of
corporate financial distress in mid-1990s and early 2000s may have given rise


18

to the unwillingness of the banks to finance some enterprises, in particular the
most risky ones. The banks may have rationed credit to corporate borrowers
by tightening lending standards. As a result, some enterprises which could not
meet these restrictive credit standards could have, in fact, constrained or no
access to credit, regardless of loan terms.
The Senior Loan Officer Opinion Survey data allow for distinguishing between changes in lending standards with regard to large corporations and small
and medium enterprises. It is, however, quite difficult to explore this opportunity in practical econometric context. In many banks, changes in lending
standards are introduced and applied across the board. Our indices of lending
standards in these two parts of the corporate credit market are highly correlated. On the other hand, if the ”market power” explanation would be true,
large borrowers could have received a more flexible treatment, which would
be unaccounted for in the official lending procedures. Very often, the decision
whether to lend to a large firm is made directly by the credit committee.
Financial condition – capital adequacy and liquidity – of individual banks
play a minor role in determination of corporate loan supply in Poland. Weak
liquidity position has actually been associated with fast expansion of corporate loan books. This could indicate that very few banks faced any capital or
liquidity constraints to their lending in the sample period. Again, most banks

in the sample are foreign-owned, and many of them resorted to funding and
capital injection from parent firms throughout the sample period.
Large banks exhibited a tendency to lend less than small banks, perhaps
due to less aggressive strategy. Domestic interest rates again were found to
be insignificant in explaining loan growth. On the other hand, high foreign
interest rates were associated with higher corporate loan growth.

6.3 Consumer loans
Equation 6 presents the main model used to explain the growth of consumer
loans. As in the previous models, statistical tests support the presence of
individual effects, serial correlation and heteroscedasticity. Random individual
effects are preferable to fixed effects (see Table 4).

DiffLoanConsit = β1 TermsLevelConsi,t−1 + β2 StdChangeConsi,t−2
+β3 CARi,t−1 + β4 GapIBanki,t−1 + β5 LogAcci,t−1
+β6 GDPGrowtht−1 + β7 Wibor3Mt−1
+β8 CPIt−1 + β9 LiborCHF3Mt−1 + uit ,

(6)

Tables 7 and 8 presents the results of the estimation of our consumer
loan supply models. Lending policy again arises as an important driver of
bank credit growth. Banks tend to shape the consumer loan supply mainly
by adjusting terms on loans. The estimated impact of changes of terms on


19
Table 7 Model of growth of consumer loans
Prais-Winsten
(PSAR1)

0.0176

Prais-Winsten
(AR1)
0.0188

RE

FE

0.0168

0.0082

(0.139)

(0.134)

(0.309)

(0.621)

StdEasingLag2

-0.0048

-0.0096

-0.0151

-0.0078

(0.807)

(0.623)

(0.515)

(0.740)

TermsLevelLag1

0.0051

0.0049

0.0055

0.0088

(0.096)

(0.084)

(0.000)

(0.000)

CARLag1

1.4474

1.4275

1.1483

1.2369

(0.000)

(0.000)

(0.000)

(0.000)

GapIBankLag1

0.0518

0.0102

-0.0489

0.0422

(0.159)

(0.829)

(0.413)

(0.682)

LogAccountsLag1

-0.0198

-0.0159

-0.0080

-0.0497

(0.008)

(0.042)

(0.249)

(0.072)

WIBOR3MLag1

0.0115

0.0109

0.0187

0.0176

(0.053)

(0.080)

(0.119)

(0.087)

LIBOR3MLag1

0.0045

0.0042

0.0032

-0.0010

(0.680)

(0.714)

(0.833)

(0.940)

CPILag1

-0.0063

-0.0053

-0.0105

-0.0096

(0.123)

(0.200)

(0.326)

(0.392)

GDPGrowthLag1

-0.0035

-0.0032

-0.0045

-0.0053

(0.261)

(0.310)

(0.448)

(0.374)

DummyQ1

-0.0142

-0.0123

-0.0156

-0.0158

(0.037)

(0.083)

(0.463)

(0.446)

DummyQ2

0.0150

0.0173

0.0171

0.0160

(0.044)

(0.026)

(0.412)

(0.431)

DummyQ3

-0.0057

-0.0043

-0.0030

-0.0040

(0.384)

(0.522)

(0.883)

(0.840)

Constant

0.0763

-0.0037

-0.0830

0.4601

(0.506)

(0.976)

(0.534)

(0.223)

StdTighteningLag2

Notes: parameters significant at 5% level are reported in bold. Parameters significant at 10%
level are reported in italics. Critical significance levels are reported below the parameters.
Source: own calculations

consumer loans on quarterly consumer loan growth ranges from 0.49 to 0.88
percentage points.
The financial standing of banks was an active constraint of consumer lending. Well capitalised banks were found to increase their lending faster than
their competitors. This contrasts with the results for the housing and corporate loan markets, where bank’s capital did not have any influence on loan
growth. Similar, albeit less consistent evidence was found for the relationship
between liquidity position and consumer loan growth. More liquid banks could
have afforded faster consumer loan growth. Such situation suggests that banks
which were focused on fast expansion of the consumer loan portfolio were actually capital- or liquidity-constrained. The effects of bank size on consumer

loan growth were in line with our findings in other segments of the credit
market – large banks tended to expand their loan portfolios more slowly.
Interest rates does not have an impact on the consumer loan market. In
models where domestic interest rate had a significant impact on credit growth,
the direction of this relationship was unexpectedly positive. Higher interest
rates were associated with faster growth of consumer loans. This may be the
outcome of Polish anti-usury legislation, under which the maximum interest
rate charged on loans is limited to quadruple the NBP lombard interest rate.


20
Table 8 Model of growth of consumer loans normalised by assets
Prais-Winsten
(PSAR1)
0.0032

Prais-Winsten
(AR1)
0.0021

RE

FE

0.0033

0.0022

(0.294)

(0.557)

(0.504)

(0.661)

StdEasingLag2

0.0021

0.0043

0.0024

0.0058

(0.700)

(0.490)

(0.731)

(0.415)

TermsLevelLag1

0.0011

0.0008

0.0011

0.0011

(0.066)

(0.156)

(0.018)

(0.0030)

CARLag1

1.0605

1.1378

0.5817

0.5822

(0.000)

(0.000)

(0.000)

(0.000)

GapIBankLag1

0.0917

0.0539

-0.0006

-0.0046

(0.002)

(0.057)

(0.973)

(0.881)

LogAccountsLag1

-0.0230

-0.0178

-0.0092

-0.0369

(0.000)

(0.000)

(0.000)

(0.000)

WIBOR3MLag1

0.0078

0.0028

0.0030

0.0018

(0.025)

(0.408)

(0.410)

(0.610)

LIBOR3MLag1

-0.0122

-0.0001

-0.0041

-0.0021

(0.035)

(0.977)

(0.342)

(0.639)

CPILag1

-0.0025

-0.0030

-0.0012

-0.0010

(0.203)

(0.143)

(0.695)

(0.755)

GDPGrowthLag1

0.0044

0.0019

0.0031

0.0020

(0.012)

(0.276)

(0.076)

(0.259)

DummyQ1

-0.0077

-0.0066

-0.0060

-0.0059

(0.018)

(0.053)

(0.339)

(0.342)

DummyQ2

-0.0001

0.0010

0.0031

0.0029

(0.984)

(0.787)

(0.617)

(0.638)

DummyQ3

-0.0044

-0.0058

-0.0023

-0.0026

(0.159)

(0.082)

(0.709)

(0.669)

Constant

0.1437

0.0869

0.0212

0.3976

(0.008)

(0.149)

(0.621)

(0.001)

StdTighteningLag2

Notes: parameters significant at 5% level are reported in bold. Parameters significant at 10%
level are reported in italics. Critical significance levels are reported below the parameters.
Source: own calculations

When official rates are low, such construction of the interest rate ceiling may
push some high-risk borrowers out of the bank loan market. The interest rate
which would reflect the risk of lending to them may exceed the permitted
ceiling. No bank would be able to lend to such borrowers, and in turn consumer
loan growth would be lower in low interest rate environment.

7 Conclusion
Our paper examines the determinants of loan supply in Poland. On the basis of
the performed estimations using data from National Bank of Poland’s Senior
Loan Officer Opinion Survey, we conclude that individual banks’ decisions
on lending policy exert a significant influence on loan supply. Our hypothesis
that tightening/ easing of bank lending policies leads to decrease/ increase in
individual bank loan supply, has been confirmed. Accordingly, the necessary
condition for operation of the risk-taking channel of monetary transmission is
present in the Polish banking sector.
Banks adjust the supply of loans mainly by changes in terms on loans rather
than by changes in lending standards. Only in the corporate loan markets