Loan supply surveys

Loan supply

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: maciej.grodzicki@nbp.pl G Halaj Bank Pekao SA, Warsaw ˙ Dawid Zochowski European Central Bank, Frankfurt 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 provides theoretical foundations to our research and Section present the main hypothesis and explains how our empirical analysis contributes to the literature Then, in Section we describe the data and in Section – the models and estimation approach Section reports on the outcome of the estimations Finally, Section concludes and points to some avenues of possible further research 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 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 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 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 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 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 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 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 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 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 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) 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 http://www.nbp.pl/en/systemfinansowy/ankieta 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 cover only a fraction of the credit cycle and may have thus distorted the results Questions 3.7, 9.8 and 11.7 in the questionnaire Such as parameters in the scoring system, minimum eligible score, or minimum eligible income 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 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 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 or 7) and Indi is an indicator variable such that: – Indi = 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 = 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 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 The differences between banks’ lending policies at the beginning of the sample period translates into individual effects 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 We also tested another measure of liquidity, the loan-to-deposit ratio, and got similar results 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, 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, 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 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 not help in explaining variation of loan supply 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 and is described in details in Section 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 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 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 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 presents the parameter estimates yielded by various panel model procedures applied to Model Summary of statistical tests which we conducted for all our models is given in Table 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 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 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 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 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 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 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 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 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 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 and 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 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 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 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 ... 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... 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... 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 +