loan supply surveys

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

Commercial Bank Lending Policy and Loan Supply

Maciej Grodzicki Grzegorz Halaj DawidZochowski

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 adopta panel model framework to test if individual banks lending policies have animpact on banks loan supply. Using data from the Polish bank lending surveyand controlling for demand side factors, we find that individual bank lendingpolicies are an important driver of credit growth. Financial constraints capi-tal and liquidity were much less significant in determining loan growth thanlending policies. Moreover, so far changes in banks lending policies have beendriven, to a large extent, by shifts in banks risk perceptions. Accordingly, thenecessary condition for the operation of the risk-taking channel of monetarytransmission is present in the Polish banking sector.

We find also that the efficiency of monetary policy transmission may beweakened for small open economies such as Poland, as compared to large

developed economies. This suggests that the risk taking channel may be rel-atively more important in such economies. This should be taken into accountin conducting monetary policy in small open economies.

Keywords Loan supply credit growth bank lending surveys monetarytransmission

Mathematics Subject Classification (2000) E51 E52 G21 C33

Please address correspondence to Maciej Grodzicki. This paper was prepared while GrzegorzHalaj was with the Financial System Department of the National Bank of Poland.

M. GrodzickiNational Bank of Poland, ul. Swietokrzyska 11/21, 00-919 Warsaw, PolandE-mail: [email protected]

G. HalajBank Pekao SA, Warsaw

Dawid ZochowskiEuropean Central Bank, Frankfurt


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1 Introduction

Commercial banks play an important role in the pass-through of monetaryinterest rates. Nevertheless, the efficiency of transmission of decisions of cen-

tral banks is a complicated process and may depend on many factors, such as:level of competition in financial industry, perception of credit risk (risk pre-mia), risk aversion, availability of close substitutes for loans, etc. Moreover,banks may influence the external finance premium not only via the interestrates but also modifying the available maturity of loans or changing collat-eral requirements. Finally, as evidenced by broad literature on bank lendingchannel, credit rationing and uncertainty about creditworthiness of borrowersmay markedly influence banks risk taking thereby influencing their willingnessto lend. The recent evidence suggest that this aspect of bank lending chan-nel, namely risk taking channel, may play an important role in the monetarytransmission (Jimenez et al, 2008; Ioannidou and Penas,2008;Altunbas et al,2009).

Bank lending surveys, conducted by many central banks, give the possi-

bility to test some mechanisms of bank lending channel, as they shed light onthe other than interest rate conditions of borrowing. Nevertheless, given thatbank lending survey in Poland was launched in 2003 and does not cover a fullbusiness cycle yet, taking advantage of using these data to test bank lendingchannel, 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. Us-ing data from Senior Loan Officer Opinion Survey, collected by National Bankof Poland, and adopting panel modelling approach, we test whether changesin bank lending policies affect loan supply.

The remaining of the paper is organised as follows. Section 2 provides the-oretical foundations to our research and Section3present the main hypothesisand explains how our empirical analysis contributes to the literature. Then, in

Section4we describe the data and in Section 5 the models and estimationapproach. Section6reports on the outcome of the estimations. Finally, Section7concludes and points to some avenues of possible further research.

2 Literature overview

Apart from the interest rate channel Bernanke and Gertler (1995) suggestedtwo other mechanisms through which monetary policy may affect bank loansupply: the balance sheet channel, also known as broad credit channel, and thebank lending channel or the narrow credit channel. 1 Both channels exist be-cause of market frictions, in particular asymmetric information between banksand 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), focusedalso on credit or banking channel however not distinguishing between broad and narrowdefinition.


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Balance sheet channel works because changes of the monetary interest ratesaffect the net wealth or collateral of borrowers and thereby have an impacton the possibilities of obtaining external financing. Thus, a decline in the netwealth of borrowers (due to increased interest rates), increases the external

finance premium they have to face on the credit market and shifts upward thebank loan supply curve to these borrowers.The existence of bank lending channel is conditional on two important as-

sumptions. First, monetary policy decisions impact bank liquidity position,and, second, changes in the supply of loans affect borrowers, because of con-strained access to other sources of financing than bank loans. Tightening ofmonetary policy usually leads to decrease in the demand for deposits becausebanks adjust their deposit rates only partially to the changes in official rates.This, in effect drains liquidity from the banking sector to equity investmentfunds. Shrinking banks liabilities forces banks to decrease the supply of loansaccordingly.

Some authors recall Modigliani-Miller paradigm and argue that banks mayoffset a drain of deposits by increasing non-deposit source of financing, e.g. is-

suing deposit certificates (Stein, 1998;Romer and Romer, 2000). However, dueto information asymmetries, frictions exist and banks tap non-deposit sourcesof funds to a different extent. Adjustments on the asset side of the balancesheet by selling liquid assets may cushion to some extent the funding problemsof banks, however both liquidity and capital constraints limit substantially thiskind of adaptation. In effect, increased cost of funding shifts the loan supplycurve upwards. This effect should be less pronounced in case of banks whichhave better access to alternative sources of financing, e.g. are larger (Kashyapand Stein, 1995), well capitalised (Peek and Rosengren, 1995; Kishan andOpiela, 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, be-

cause different elasticity of demand for loans across banks borrowers has tobe taken into account. In order to control for these demand effects we followthe identification approach adopted byKashyap and Stein (1995). The ideais that the changes in the demand for loans that different banks have to faceafter the monetary policy shock are determined by the degree of informationasymmetries between banks and their lenders. In literature, the most commonvariable to measures these frictions is bank size (Kashyap and Stein, 1995;Loupias et al, 2001; Hernando and Martinez-Pages, 2001). Introducing alsosome 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 rationingis defined as a situation in which bank is unwilling to lend even if a borroweris 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 andlending 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


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channel (Borio and Zhu, 2008), may be triggered by a shift in perception ofrisk 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 notable to lend.

In the neo-Keynesian models with credit, these aspects of bank lendingchannel, namely willingness to lend, are determined by banks uncertaintyabout creditworthiness of bank borrowers and the state of bank expecta-tions, which is related to fundamental uncertainty about the future whichboth borrowers and lenders face (Wolfson,1996). According to these models,in bank lending channel not only information asymmetry between borrowersand lenders is essential, but also asymmetry of expectations between borrow-ers and lenders about the profitability of a project (corporate loans) or futureability to service debt (household loans) is also important.

Risk taking channel may operate via several ways. Most importantly, lowinterest rates boosting asset prices may increase the value of collateral andthereby allow banks to accept higher credit risk (Borio et al, 2001). Altunbaset al (2009) report also on other possible impacts of lower interest rates on

higher risk taking of borrowers. Low interest rate environment may facilitatesearch for higher risk assets, the so called search for yield ( Rajan, 2005) andincrease banks risk tolerance.Altunbas et al(2009) also suggest that monetarypolicy may influence risk taking behaviour via habit formation, whereby banksbecome less risk-averse during economic expansions. Relatively few papershave focused so far on testing empirically if risk taking channel works. Usingindividual data from credit register (Jimenez et al, 2008) shows that Spanishbanks eased their lending policies and extended more risky loans when interestrates were low.Ioannidou and Penas(2008) also find that when interest ratesare low banks price the credit risk lower. Moreover, banks tend to reducecredit margin on risky borrowers relatively more than on average. Altunbaset 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 theUS.

3 Main hypothesis

Using the data from the bank lending survey on lending standards and lend-ing conditions we test for a necessary condition for the existence of risk takingchannel, 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 in-terest rate changes may trigger the changes in bank lending policies. Thus, wecannot fully confirm the existence of risk taking channel in the Polish econ-omy, nevertheless, we give some insights into the determinants of the changesof bank lending policies on the basis of the results from the SLOS survey.

Monetary transmission in Poland was examined in, inter alia,Wrobel andPawlowska(2002). The former authors find mixed evidence for the importanceof bank lending channel in the Polish economy. While capital constraints are


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binding in their model, liquidity constraints are not. As a result, the operationof bank lending channel was restricted. In Hurlin and Kierzenkowski (2002)the focus is on interest rate channel and pass-through of changes in officialrates to rates on loans, which is found to be very swift.

No studies so far have been dedicated to study the bank willingness to lendand its impact on credit supply in the Polish economy.Pruski and Zochowski(2006) andBrzoza-Brzezina, Chmielewski, and Niedzwiedzinska(2008) reporton the high level of substitution between foreign currency and zloty lending,which to some extent outweighs the impact of monetary policy on loan sup-ply. Moreover, over the last decade Polish banks have been operating in theenvironments of excess funding liquidity, which resulted from systematicallyhigher level of deposits than credit in the system. This two features of thePolish banking sector may indicate that bank willingness to lend may be animportant driving force in the Polish credit market.

Bank lending surveys provide a powerful set of data to test different hy-potheses about bank lending channel. In particular, questions about the rea-sons 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. How-ever, in this paper, due to short time horizon and low frequencies of answersother than no-change to questions on the lending terms and conditions aswell as reasons for changing them, we concentrate on answering whether, ingeneral, altering bank lending standards or conditions affect banks loan sup-ply. Since we control for demand effects and individual bank effects, we for-mulate and test the following main hypothesis:

H0: Tightening/ easing of bank lending policies leads to decrease/ increasein individual bank loan supply

It is a necessary but not sufficient condition for the existence of risk tak-ing channel. Also banks risk perception would have to change following thechange 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 banklending policies, which seem to support our view that changes in perceptionof risk by banks is an important driver of changes in lending policies. Sinceaccording toBernanke and Lown(1991);Woo(1999), changes in bank lendingpolicy are related either to capital constraints (for which we control) or toshifts in perception of risks, our results provide some support toward the sig-nificance of risk taking channel. Moreover,Rajan(1994) andBerger and Udell(2004) demonstrate that banks tend to curb lending in economic downturnsby changing lending standards. Their results point to the importance of banklending policies to the broad economy and the business cycle.

4 Data

We used three types of data to verify the existence of monetary policy trans-mission channels. These are the survey data on bank lending policy, individualbank financial data and macroeconomic variables (see table1).


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The data on bank lending policy come from the Senior Loan Officer OpinionSurvey (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 onloans over the previous quarter3. Separate questions address the situation inthe housing loan, other consumer loan and corporate loan markets. Banks areasked to provide information on changes in lending standards with regard toloans to large enterprises and to small and medium enterprises separately. Weused the responses of individual banks as a measure of changes in their lendingpolicy.

The Senior Loan Officer Opinion Survey is a qualitative survey. Partici-pants may choose from a set of five options:

the bank significantly eased its lending policy, the bank slightly eased it lending policy,

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

Lending standards are defined as minimum acceptance criteria which mustbe met by a prospective borrower to be approved for a loan, regardless ofthe loans 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 loanis approved, are broken down into six categories: spreads on regular loans,spreads on high-risk loans, loan maturity, collateral, fees and maximum loanamount. In the case of housing loans, banks are also asked about changes inthe 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 oflending policy. Some potential for misinterpretation of the definition remainsand may lead for instance to reporting changes in lending standards as changein terms on loans. We consider such behaviour to be manifested in the openquestion on changes in other terms on loans (i.e., not explicitly mentionedin the questionnaire)4. This is indicated by individual responses to the openquestions, in which banks sometimes note that they have actually changedlending standards5.We filter our dataset for instances when a bank reported nochange in lending standards and simultaneously indicated a change in lending

2 http://www.nbp.pl/en/systemfinansowy/ankieta en.pdf3 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 mayhave 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.


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standards in a corresponding open question on other terms on loans, and treatsuch instances as a change in lending standards.

The volume of lending may be related both to the level and the change inlending 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 volumeof new loans separately to allow for diverse responses of loan supply to thesefactors. For lending standards, we construct two dummy variables which indi-cate that a bank tightened or eased lending standards to allow asymmetries inthe response of loan volume to changes in lending policy. We do not take intoconsideration the perceived size of change in lending policy, only its direction.

While terms on loans could have been treated similarly, with two dummiesbeing used to represent the tightening and easing of each of 6-7 categories ofterms, such approach would not be feasible with our small dataset.

The variable reflecting the changes in each banks terms on loans is an indexof general restrictiveness of loan terms6. The Senior Loan Officer OpinionSurvey measures only changes in lending policy, and not how conservativebank lending policy is. There is no data on the actual restrictiveness of loanterms offered by individual banks. We set our loan terms variable to 0 asof the first edition of the Senior Loan Officer Opinion Survey (i.e. the thirdquarter of 2003). For each bank in the sample, the starting point of zerorestrictiveness is likely different7. Then, for period t the loan terms variableTermst would be given by the following formula:

Termst = T ermst1+k

i=1

Indi, (1)

where k is the number of categories of terms on loans (i.e., either 6 or 7) andIndi is an indicator variable such that:

Indi = 1 if the bank eased its lending policy with respect to the i-thcategory of terms on loans,

Indi = 1 if the bank tightened its lending policy with respect to the i-thcategory of terms on loans,

Indi = 0 otherwise.

For illustration, if in the first edition of SLOS a bank increased the spreadon regular loans and decreased the maximum available loan amount, our indexof restrictiveness would be -2. Then, if in the next period the bank decided todecrease the maximum loan maturity, lower its loan extension fee and demand

6 We constructed a similar index for restrictiveness of lending standards, and performedestimations 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 obtainedfrom the authors upon request.7 The differences between banks lending policies at the beginning of the sample period

translates into individual effects.


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less collateral, the index would consequently increase from -2 to -1. More suc-cinctly, the index changes from one period to the other by the net number ofcategories of loan terms with respect to which the bank changes its lendingpolicy.

We supplement our analysis with the balance-sheet and P&L data on indi-vidual banks. The bank-level financial data come from the prudential reportingsystem of the National Bank of Poland. All institutions with a Polish bankinglicence, as well as branches of foreign banks in Poland, are required by the Acton the National Bank of Poland to report a wide scope of financial informationwith monthly or quarterly frequency. The data undergo a quality control, butare not audited by independent parties. However, banks must supply amendeddata should their regular auditor or the NBP find any inconsistencies or mis-takes.

We use three bank-level variables to represent characteristics of individualbanks. The application of the variables is consistent with what is proposedin the literature on lending channel (Berger and Udell, 2004; Hernando and

Martinez-Pages, 2001; Kishan and Opiela, 2000; Altunbas et al, 2009). TheBasel 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 bindingminimum level of 8% remained unchanged, and the higher the ratio, the lesslikely it is that bank experiences capital shortage. As a measure of liquidity weuse interbank gap, which we define as the ratio of the banks net position vis-a-vis other banks (i.e., its gross claims on other banks minus gross liabilitiesto other banks) to banks total assets8. Positive interbank gap indicates afavourable net liquidity position, as the bank has excess funds to lend outin the interbank market. However, if interbank gap is negative, it may beeither due to weak liquidity position or to strategic choice (to rely on) offoreign funding, and in the Polish context this would mean chiefly intra-groupfunding. 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 zlotymoney market index (3-month WIBOR) and Swiss franc 3-month LIBOR.The LIBOR rate is to represent foreign interest rate. It is economically relevantbecause of significant share of bank credit in Poland was extended in foreigncurrencies, especially in the Swiss currency. We also use the official GDP andCPI data for Poland.

The flow of credit may be subject to seasonal fluctuations. A simple ex-ample would be the much higher flow of consumer credit in November andDecember than in other months due to Christmas shopping season. To controlfor such fluctuations, we use seasonal dummies representing the first, secondand 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 similarresults.


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Banks which participate in the Senior Loan Officer Opinion Survey coverapproximately three-fourths of the respective loan markets in Poland (ca. 80%of housing loan and corporate loan markets, and 65% of consumer loan mar-ket). 21 of them are commercial banks, 2 are branches of foreign credit insti-

tutions, and one is a cooperative bank. Not all 24 banks are active in eachsegment of the credit market.Our sample consists of 13 institutions which extend housing loans, 18 in-

stitutions which are active in the corporate loan market and 16 institutionsissuing consumer loans. All major participants in the corporate and housingloan markets are represented in this sample. In the consumer loan market,some specialized banks, especially those which emerged as major players after2004, do not participate in the survey and are not represented in the sam-ple. Most institutions in the sample are owned by an ultimate foreign parent,a situation characteristic for the Polish banking system. The composition ofour sample leads, by definition, to exclusion of new entrants which appearedin some market segments, and therefore the results may not be valid for thebanking system as a whole.

We decided to drop the cooperative bank from the Senior Loan OfficerOpinion Survey sample of 24 banks due to the very limited geographical scopeof its operations. Since this bank accounts for a very small fraction of theloan market, its exclusion does not cause any material loss of information.We also removed two branches of foreign credit institutions, as they do nothave their own equity and are not obliged to meet the capital requirementin the host country. Therefore, branches are not restricted in their lendingpolicy by leverage constraints faced by commercial banks. Moreover, the degreeof business independence of branches, as compared to commercial banks, ismarkedly lower. This increases the likelihood that their lending policy may bedetermined by the parent institution. Bearing these peculiarities of branchesin 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 identifythe impact of supply-side factors on loan growth in the Polish credit markets,while controlling for loan demand effects. Given the oligopolistic features of thebank loan market in Poland (Kozak and Pawlowska,2008), in which borrowershave very little bargaining power9,we treat loan demand as exogenous and weassume that it can be described by a function of macroeconomic variables suchas interest rates, GDP and inflation. The choice of variables representing the

supply-side effects is based on literature presented in Section2and is describedin details in Section4.

9 Other credit markets in Poland, such as corporate bond market, are at a very earlystage of development, and cannot be treated as substitute for bank loans.


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Table 1 The description of the variables

Name Description of variableDiffHLToA Quarterly change in housing loans normalised by assets

DiffHL Quarterly precentage change in housing loans

DiffCorpLToA Quarterly change in corporate loans normalised by assetsDiffCorpL Quarterly percentage change in corporate loans

DiffConsLToA Quarterly change in consumer loans normalised by assetsDiffConsL Quarterly percentage change in consumer loans

TermsLevelH Index of restrictiveness of terms on housing loansStdTighteningH Dummy for tightening of lending standards on housing loans

StdEasingH Dummy for easing of lending standards on housing loansTermsLevelCorp Index of restrictiveness of terms on corporate loans

StdTighteningCorp Dummy for tightening of lending standards on corporate loansStdEasingCorp Dummy for easing of lending standards on corporate loans

TermsLevelCons Index of restrictiveness of terms on consumer loansStdTighteningCons Dummy for tightening of lending standards on consumer loans

StdEasingCons Dummy for easing of lending standards on consumer loansCAR Basel capital adequacy ratio of the bank

GapIBank Total interbank loans of the bank minus its total interbank borrowings,as fraction of banks assets

LogAcc Logarithm of the number of accounts at the bankGDPGrowth Real GDP growth rate (yoy)

Wibor3M Mean 3-month Warsaw Interbank Offered Rate over the quarterLiborCHF3M Mean 3-month Swiss franc LIBOR over the quarter

CPI CPI inflation

Two types of models for each category of loans were estimated. In the mainmodels, we use the quarterly credit growth by loan type as dependent variables.However, our sample of banks is very diverse, and some banks have revisedtheir business models markedly since the Senior Loan Officer Opinion Surveywas launched in 2003. As a result, some banks may have experienced relativelyhigh loan growth rates only due to the low basis. We employ a supplementary

model in which the loan growth rates are normalised by banks assets at thebeginning of the quarter to serve as a robustness check for the results fromthe main model.

Both models are considered in a static specification10. To account for theobserved heteroscedasticity and serial autocorrelation in the data we appliedPrais-Winsten transformation (Baltagi, 2001).11 Our main model is repre-sented by Equation2.

10 However, we check the estimates in the dynamic setting applying several competingestimators (GMM estimator proposed byArellano and Bond (1991), its System GMM ex-tension (Blundell and Bond, 1998) with robust standard errors which utilise the correctionofWindmeijer (2005)). We use the levels of loan-to-assets ratio instead of the loan growthnormalised by assets as the dependent variable, and its lagged values as independent vari-able. Nevertheless, the estimations did not prove to be statistically significant since the datapanel is almost quadratic. This validates the use of a static specification11 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 areunderstated. Having attempted to apply Feasible GLS correction for the suspected patternsof serial correlation in our data, we obtained standard errors which were by at least an


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DiffLoanTypeToLoanit = 1StdEasingLoanTypei,tn

+2StdTighteningLoanTypei,tn+3TermsLevelLoanTypei,tm

+K+3

k=4

kBankSpecVar(k3)i,t1 +

K+4+N

k=K+4

kMacroVar(kK3)t +uit, (2)

uit := i + uit1 + it is an autoregressive error component with random indi-vidual effectsi and common autocorrelation structure .

12 The independentvariables were at least one period lagged to avoid endogeneity.

Similarly, our supplementary model is given in the static form by Equation3.

DiffLoanTypeToAssets it = 1StdEasingLoanTypei,tn

+2StdTighteningLoanTypei,tn+3TermsLevelLoanTypei,tm

+K+3

k=4

kBankSpecVar(k3)i,t1 +

K+4+N

k=K+4

kMacroVar(kK3)t +uit, (3)

For our static specifications, we followed a two-step procedure of estimationin order to get the best possible estimator consistent and efficient. We triedrandom effects (RE), fixed effect (FE) or pooling method (POOL) and chosethe most appropriate one according to the following procedure:

1. (RE or FE vs POOL) First, we verify the hypothesis of no individual effectsin the model (2) by Breusch-Pagan Lagrange Multiplier test for randomindividual effects and ANOV A Ftest based on comparison ofwithinandpooled models. We also perform the joint LM test for random individual

effects and first-order serial correlation proposed byBaltagi 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 per-forms well in a statistical sense resulting in the most efficient estimators.To test whether random effect specification gives consistent estimators ofparameters, we employ the Hausman test, comparing GLS estimates of REmodel and the fixed effects (within) model.

6 Results

According to the proposed estimation approach we obtain two specificationsof the model for each segment of the loan market.

order of magnitude lower than in any other estimation. Hence, we decided to use only thePrais-Winsten estimators.12 We performed additional test of the stability of across panels estimating the analogous

equation with panel specific correlation in the error term uit := i+ iuit1+ it.


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6.1 Housing loans

We proposed the following model explaining variability of housing loans growth.

DiffHLi,t = 1TermsLevelHi,t2+2StdChangeHi,t2

+3CARi,t1+4GapIBanki,t1+5LogAcci,t1+6GDPGrowtht1+7Wibor3Mt1

+8CPIt1+9LiborCHF3Mt1+uit, (4)

As to lending standards, we allow the loan growth to respond asymmetri-cally to tightening and easing of lending policy.

Table 2presents the parameter estimates yielded by various panel modelprocedures applied to Model 4. Summary of statistical tests which we con-ducted for all our models is given in Table 4. The data give strong evidencethat individual, bank specific effects determine dynamics of housing loans.Breusch-Pagan and ANOV A F tests reject the hypothesis of no individual

effects. In the Hausman test, we did not reject that RE gives consistent esti-mates of parameters. Application of the Prais-Winsten estimators is supportedby serial correlation present in the data, as well as significant between-groupheteroscedasticity.

In an analogous model where the growth of housing loans is normalised byassets, we also favour the RE procedure, corrected for heteroscedasticity andserial correlation. Such approach is confirmed by the results of Breusch-Paganand Hausman tests. Table3 summarizes the results for this model.

The impact of terms on housing loans on their supply was found to besignificant. Net change in the restrictiveness index for loan terms by -1, which isequivalent to tightening of one of six terms on housing loans, led to a slowdownof quarterly housing loan growth by 0.34 to 1.01 percentage points after twoquarters. 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 standardshave little impact on loan growth, regardless of the direction in which they arechanged.

Housing loan supply does not depend on capital adequacy of banks. Thiscan be explained by the fact that most banks in the sample13 were controlledor even fully owned by foreign financial institutions. Many Polish banks had,at least until the 2008-2009 financial market turmoil, an almost unrestrictedaccess to capital from their parent institution. Some rapidly expanding bankscould have operated close to the regulatory minimum capital requirement of8%, because they had been reassured by the (implicit or explicit in the businessplans) commitment of the parent institution to provide additional capital ifneeded.

The evidence on how banks liquidity position may affect the growth of itshousing loan book is mixed. Our main model suggests that the relationshipis 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.


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Table 2 Model of growth of housing loans

Prais-Winsten(PSAR1)

Prais-Winsten(AR1)

RE FE

StdTighteningLag2 -0.0377 -0.0199 -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)

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 loanbooks 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 foreignfunding, especially on an intra-group basis. The other reason for slower housingloan portfolio growth in banks with the positive interbank gap could be theirbusiness strategy, not only concentrated on the loan market but balanced bymore secure investment opportunities on the interbank market.

Such reliance of some Polish banks on intra-group funding and capital in-jections from the parent can disturb the transmission of monetary policy andprovoke contagion from the home markets of parent banks to the Polish loanmarket. Under such circumstances, and if capital and liquidity constraintsfor Polish banks were binding, credit conditions with regard to housing loanswould depend on ability and willingness of foreign parent institutions to pro-vide capital and funds to their Polish subsidiaries. This, in turn, is linked tothe capital position of a parent institution. As a result, Polish banks may beforced to curb lending in case the financial condition of the parent companydeteriorates, even without any intrinsic reasons. Conversely, if capital and liq-uidity constraints are not binding for Polish banks, they may not respondto monetary impulses in an expected manner. A fall in deposits, induced by


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Table 3 Model of growth of housing loans normalised by assets


Prais-Winsten(AR1)

RE FE

StdTighteningLag2 -0.0009 -0.0010 - 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)


Table 4 Summary of statistical tests

Main models Supplementary modelsTest Housing

loans

Corporate

loans

Consumer

loans

Housing

loans

Corporate

loans

Consumer

loansBreusch-Pagan test forrandom effects

8.58 10.76 19.99 446.56 94.52 2.75

0.0034 0.0010 0.0000 0.0000 0.0000 0.0973

Hausman test of fixed vs.random effects

4.86 6.83 24.29 0.72 35.78 11.50

0.9932 0.6659 0.0833 0.9999 0.0019 0.7773

F-test for poolability of thedata (H0: no individual ef-fects)

2.66 4.76 4.17 17.98 7.84 2.62

0.0022 0.0000 0.0000 0.0000 0.0000 0.0013

Panel-specific serial corre-lation

22.27 3.16 78.69 206.82 2.11 25.45

0.0000 0.0753 0.0000 0.0000 0.1465 0.0000

Joint LM test of random ef-fects and serial correlation

24.77 26.58 82.24 517.13 101.94 25.47

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Modified Wald test for

groupwise heteroscedastic-ity

11756.8 1257.4 846.4 998.2 1115.2 673.3

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

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


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

The level of interest rates have a strong impact on the growth of hous-

ing loans. The credit growth in this market segment was dependent on deci-sions of the Swiss central bank. This underscores the large degree of currencysubstitution in the Polish housing loan market. An increase in three-monthSwiss franc LIBOR, the rate to which most foreign currency housing loans arelinked, by 25 basis points caused the growth of housing loans to decline by1.1 to 4.0 percentage points. Meanwhile, Polish interbank interest rates werefound to have little significant impact on the dynamics of housing loans. Evenif they had any significant influence on credit growth, it was minor comparedto the effect of changes in Swiss rates. Such findings confirm the conclusion ofBrzoza-Brzezina, Chmielewski, and Niedzwiedzinska(2008) that the presenceof a developed market for foreign currency loans domestic monetary policydecisions 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 themore moderate was the growth of housing loans. This finding can, however,result from business models in the smaller, foreign owned banks placing muchemphasis on housing loans offer.

6.2 Corporate loans

Our main model for the corporate loan growth is given by Equation 5. Statis-tical tests indicate that individual effects are present in this model, and RE isfavoured over FE (see Table 4). Conversely, in our supplementary model wechoose individual fixed effects over RE. As in the housing loan models, strongserial correlation and heteroscedasticity are present.

DiffLoanCorpit = 1TermsLevelCorpi,t3+2StdChangeCorpi,t3+3CARi,t1+4GapIBanki,t1+5LogAcci,t1+6GDPGrowtht1+7Wibor3Mt1


As in the housing loan model, lending policy is consistently significant inexplaining loan growth in the corporate loan segment (see Tables 5 and 6).Loan terms, while significant, have a relatively weak impact on corporate loangrowth. Tightening one of the terms offered on corporate loans results in adecline of corporate loan quarterly growth rate by some 0.2 percentage point,

and only after three quarters14

. Lending standards also influence corporate14 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 ofmulticollinearity, it would be very difficult to measure this lag precisely. For this reason, ourattempts to estimate a panel model with several lags of the lending policy variables did fail.


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Table 5 Model of growth of corporate loans


Prais-Winsten(AR1)

RE FE

StdTig hteningLag3 -0.02 81 -0.0213 -0.0208 -.0163(0.029) (0.098) (0.123) (0.217)

StdEasingLag3 -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)


loan growth, yet in an asymmetric manner. Loan growth responds to tighteningof lending standards, while easing of lending standards does not stimulate loan

growth. When a bank tightens lending standards, then after three quarters thequarterly growth of its corporate loan book slows down by some 2.8 percentagepoints. Bank lending standards appear in this light as a much more strongertool of controlling loan growth than terms offered on corporate loans.

The number of lags is somewhat puzzling, when confronted with the re-sults in Lown and Morgan (2006), who found corporate credit standards tohave immediate effect on the volume of loans. One may expect corporate bor-rowers to be more active in shopping for most favourable credit conditionsthan households. A likely reason for such lag may be the duration of approvalprocess for corporate investment projects. These are often to follow annualinvestment plans and be scheduled well in advance. Then, actual execution ofthe project and extension of the loan could take place well after the financingdecisions are being made. Furthermore, companies often use committed creditlines to cover their short-term financing needs. If the lending standards arebeing tightened by the bank, firms would draw on existing lending facilitiesand only face the changes in lending policy when these facilities need to berenegotiated or new lines have to be secured.


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Table 6 Model of growth of corporate loans normalised by assets


Prais-Winsten(AR1)

RE FE

StdTighteningLag3 -0.0050 -0.0047 -0.0208 -.0163(0.074) (0.086) (0.123) (0.217)

StdEasingLag3 -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)


It is interesting that, contrary to the housing loan market, banks whichoperate in the corporate loan market adjust loan supply much more forcefully

by changing the loan approval criteria than by changing the terms on loanssuch as price, maturity or loan collateral. This might be due to two likelymechanisms fierce competition or credit rationing.

First, large corporations are well-informed borrowers who have a goodgrasp of the credit market, and would not accept more restrictive terms onloans. High level of competition in the corporate loan market, and especiallyin the market for loans to large firms, may deter individual banks from raisingthe spreads on corporate loans, as it would lead to an excessive migration ofclients to competitor banks. Then, if a bank were to influence the level of itssupply of corporate loans, it must adjust rather credit standards, which areopaque to the banks clients, than terms on loans.

Second, the approach of banks to attract clients in these two markets couldhave been markedly different. Still relatively low level of financial deepening inthe Polish housing loan market and quite good loan quality of banks portfoliosover the past few years encouraged banks to attract prospective borrowers byeasing loan terms. In the corporate loan market, however, past episodes ofcorporate financial distress in mid-1990s and early 2000s may have given rise


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to the unwillingness of the banks to finance some enterprises, in particular themost risky ones. The banks may have rationed credit to corporate borrowersby tightening lending standards. As a result, some enterprises which could notmeet 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 be-tween changes in lending standards with regard to large corporations and smalland medium enterprises. It is, however, quite difficult to explore this oppor-tunity in practical econometric context. In many banks, changes in lendingstandards are introduced and applied across the board. Our indices of lendingstandards in these two parts of the corporate credit market are highly corre-lated. On the other hand, if the market power explanation would be true,large borrowers could have received a more flexible treatment, which wouldbe unaccounted for in the official lending procedures. Very often, the decisionwhether to lend to a large firm is made directly by the credit committee.

Financial condition capital adequacy and liquidity of individual banksplay a minor role in determination of corporate loan supply in Poland. Weak

liquidity position has actually been associated with fast expansion of corpo-rate loan books. This could indicate that very few banks faced any capital orliquidity constraints to their lending in the sample period. Again, most banksin the sample are foreign-owned, and many of them resorted to funding andcapital injection from parent firms throughout the sample period.

Large banks exhibited a tendency to lend less than small banks, perhapsdue to less aggressive strategy. Domestic interest rates again were found tobe insignificant in explaining loan growth. On the other hand, high foreigninterest rates were associated with higher corporate loan growth.

6.3 Consumer loans

Equation6presents the main model used to explain the growth of consumerloans. As in the previous models, statistical tests support the presence ofindividual effects, serial correlation and heteroscedasticity. Random individualeffects are preferable to fixed effects (see Table 4).

DiffLoanConsit = 1TermsLevelConsi,t1+2StdChangeConsi,t2+3CARi,t1+4GapIBanki,t1+5LogAcci,t1+6GDPGrowtht1+7Wibor3Mt1


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


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Table 7 Model of growth of consumer loans


Prais-Winsten(AR1)

RE FE

StdTighteningLag2 0.0176 0.0188 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)


consumer loans on quarterly consumer loan growth ranges from 0.49 to 0.88percentage points.

The financial standing of banks was an active constraint of consumer lend-ing. Well capitalised banks were found to increase their lending faster thantheir competitors. This contrasts with the results for the housing and corpo-rate loan markets, where banks capital did not have any influence on loangrowth. Similar, albeit less consistent evidence was found for the relationshipbetween liquidity position and consumer loan growth. More liquid banks couldhave afforded faster consumer loan growth. Such situation suggests that bankswhich were focused on fast expansion of the consumer loan portfolio were ac-tually capital- or liquidity-constrained. The effects of bank size on consumerloan growth were in line with our findings in other segments of the creditmarket large banks tended to expand their loan portfolios more slowly.

Interest rates does not have an impact on the consumer loan market. Inmodels where domestic interest rate had a significant impact on credit growth,the direction of this relationship was unexpectedly positive. Higher interestrates were associated with faster growth of consumer loans. This may be theoutcome of Polish anti-usury legislation, under which the maximum interestrate charged on loans is limited to quadruple the NBP lombard interest rate.


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Table 8 Model of growth of consumer loans normalised by assets


Prais-Winsten(AR1)

RE FE

StdTighteningLag2 0.0032 0.0021 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)


When official rates are low, such construction of the interest rate ceiling maypush 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 permittedceiling. No bank would be able to lend to such borrowers, and in turn consumerloan growth would be lower in low interest rate environment.

7 Conclusion

Our paper examines the determinants of loan supply in Poland. On the basis ofthe performed estimations using data from National Bank of Polands SeniorLoan Officer Opinion Survey, we conclude that individual banks decisionson lending policy exert a significant influence on loan supply. Our hypothesisthat tightening/ easing of bank lending policies leads to decrease/ increase inindividual bank loan supply, has been confirmed. Accordingly, the necessarycondition for operation of the risk-taking channel of monetary transmission ispresent in the Polish banking sector.

Banks adjust the supply of loans mainly by changes in terms on loans ratherthan by changes in lending standards. Only in the corporate loan markets


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some evidence that lending standards influence loan growth rates has beenfound. Such evidence was limited to instances of tightening lending standards,whereas loan growth rates did not change significantly after lending standardshad been eased.

We found that loan supply was generally not affected by capital and liq-uidity of Polish banks. This may be due to the fact that most Polish bankswere not constrained by their liquidity and capital position, since a majorityof Polish banks is owned by foreign financial institutions, and could have reliedon liquidity and capital provision from the parent institution. Moreover, mostlarge banks had a structural liquidity surplus resulting from a large and stableretail deposit base.

Lack of binding liquidity and capital constraints may indicate that impor-tance of the component of bank lending channel, which is related to bankswillingness to lend (as opposed to ability to lend) may be higher in Poland thanin developed economies. In general, such observation may apply to countries,in which banks do not face substantial capital or liquidity constraints. This

consequently leads to the conclusion that in those countries the shift in riskperception may be a relatively more important factor of changes in supply ofloans than in countries, in which capital and liquidity constraints play a moreimportant role in the banking sector.

Our paper hints therefore at an important channel of financial contagionfrom developed to emerging economies. As risk perception and lending prac-tices of foreign-owned banks may be determined by policies set at the parentcompany level, financial turmoil may spread to emerging economies. Regard-less of their own financial fundamentals liquidity and capital position foreign-owned banks could curb their risk appetite and cut back lending bytightening the lending policy at the request of the group. Consequently, creditsupply could fall, triggering economic slowdown.

Our results suggest that it may be difficult for public authorities to counterthe effects of tightening lending policy. Interest rate channel in the Polish econ-omy is weak, and banks were not constrained by liquidity and capital standing.Hence, even if the central bank were to lower interest rates, it may not bringexpected relief to the credit-constraint agents of the non-financial sector. Thesame reasoning applies to public recapitalisation of banks. Should decision-makers intend to stimulate bank lending, they must aim not at provision ofliquidity and capital to individual institutions, but rather at reducing uncer-tainty in the economy and increasing the risk appetite of banks.

A number of promising avenues of further research can be explored usingSenior Loan Officer Opinion Survey data. In particular, the issue of linkagesbetween foreign bank ownership, lending policy and loan supply can be exam-ined. It could also be valuable to verify our results on the basis of data fromother emerging economies, where bank lending surveys are conducted suchas Hungary and Lithuania to examine our supposition about the relativelyhigher importance of banks willingness to lend in relation to ability to lend inthe banking systems with loose capital and liquidity constraints. Moreover, our


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research could be extended to test for both necessary and sufficient conditionof risk taking channel.

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Fig. 1 Factors explaining changes in lending policy regarding housing loans (quarterly,2003Q4-2009Q3)

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

C

urrentorexpected

ca

pitalpositionofthe

bank

Riskrelatedtothe

expectedgeneral

economicsituati

on

Housingmarket

prospects

Changesinthenon-

performingloanratio

Changesincompetitive

pressure

Q

42003

Q32009

Q42003

Q32009

Q42003

Q32009

Q42003

Q32009

Q42003

Q32009

Remark: on the vertical axis net percentage of banks in a given quarter citing a givenfactor as a contributor to changes in lending policySource: Senior Loan Opinion Survey of the National Bank of Poland


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Fig. 2 Factors explaining changes in lending policy regarding consumer loans (quarterly,2003Q4-2009Q3)

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

C

urrentorexpected

ca

pitalpositionofthe

bank

Riskrelatedtothe

expectedgeneral

economicsituation

Riskondemanded

collateral

Changes

inthenon-

performingloanratio


pressure

Q

42003

Q32009

Q42003

Q32009

Q42003

Q32009

Q42003

Q32009

Q42003

Q32009



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Fig. 3 Factors explaining changes in lending policy regardingcorporate loans (quarterly,2003Q4-2009Q3)

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

C

urrentorexpected

ca

pitalpositionofthe

bank

Riskrelatedtothe

expectedgeneral

economicsituation

Riskrelatedtothe

financialstandingof

bank'slargest

borrowers.

Changes

inthenon-

performingloanratio


pressure

Q

42003

Q32009

Q42003

Q32009

Q42003

Q32009

Q42003

Q32009

Q42003

Q32009


loan supply surveys

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