ISSN 1795-5300

MTT Discussion Papers 2  2005MTT Discussion Papers 2  2005

Interacting demand and supply 
conditions in European bank 

lending

Ossi Lindström & Almas Heshmati



 2

MTT Discussion Papers 2 · 2005 
 

 

 

INTERACTING DEMAND AND SUPPLY 
CONDITIONS IN EUROPEAN BANK LENDING 

 

Ossi Lindström♦, (!) 
Helsinki School of Economics 

and 

Almas Heshmati♣ 
MTT Economic Research and Seoul National University 

 

March 6, 2005 

 

ABSTRACT 
This paper investigates credit channel of monetary policy by accounting for simultaneous 
interaction of banks’ and firms’ credit conditions and their adjustment costs, which are 
neglected in the previous studies. Based on the European data we find that these 
conditions are interacting, although their adjustment costs differ across banks, firm size, 
countries, and over time. The results suggest that a common European monetary policy 
should then deal with uncertainty over credit market conditions and firms’ and banks’ 
country-specific and size-dependent reactions. It should also monitor large firms’ 
exploitation of banks’ credit rationing as it can have great impacts on the smaller firms’ 
lending and financial stability conditions.  

Key words:  Bank lending, European data, manufacturing firms, system of dynamic 
models, adjustments, monetary policy 

JEL Classification Numbers: E51, C33, G21 

                                            
♦ Corresponding author, Helsinki School of Economics, P.O. Box 1210, FIN-00101 Helsinki, Finland, Tel: +358-9-

43138799, Fax: +358-9-43138305, E-mail: ossi.lindstrom@hkkk.fi 
(!) I gratefully acknowledge the financial support from Ella and Georg Ehrnrooth Foundation, Helsinki School of 

Economics Foundation, Research Foundation of Okobank Group, and Finnish Fund for Securities Markets. 
♣ MTT Economic Research and Techno-Economics and Policy Program, College of Engineering, Seoul National 

University, Bldg #38, San  6-1 Shinlim-dong, Kwanak-gu, Seoul 151-742, Korea, Tel: +82-2-8809141, Fax: +82-2-8808389, 
E-mail: heshmati@snu.ac.kr  



 3

I. INTRODUCTION 

It is still an open issue whether changes in short-term interest rate as the main monetary 
policy device can significantly affect the supply of bank loans. It is not obvious why the 
short-term interest rate should affect in a predictable way the more long-term investments 
and savings, i.e. asset prices. This in mind, this paper seeks to investigate the credit 
channel of monetary policy, which is to identify the endogenous driving force for 
dynamics of banks’ response to changing market conditions or banks’ role in transmission 
of monetary policy. 

However, here we emphasize banks’ costs of intermediation (i.e. price effect) rather than 
their effects on credit supply (i.e. quantity effect) as is usually done. We believe that the 
price effect is more crucial for the transmission of monetary policy to the extent that it 
affects firms’ costs of borrowing relative to other sources of financing and institutions 
providing them. It also highlights banks’ role as market intermediaries and thus banks’ 
sensitivity to information and agency problems. The price effect also accounts better for 
the competitive and industrial effects, such as market efficiency. However, the price 
setting is especially important to banks’ profitability, which is itself important to 
soundness of banking system and financial stability. 

By emphasizing the price effect, we also avoid many empirical problems that are related 
to changes in credit supply. In particular, there are difficulties to separately identify the 
effects of changes in supply and demand for bank loans. These identification problems are 
partly due to balance sheet identity1 and due to assumed direct dependence of market rates 
on demand and supply movements that are thought being otherwise independent of each 
other. The latter effect ignores then bank- and firm-specific characteristics (e.g. size, 
liquidity position, riskiness of investment portfolio or competitive pressure) that cause 
heterogeneous effects e.g. on banks’ loan prices in response to market rate changes. 
However, these price changes can also have effects on real consumption and investments 
even if there are no changes in the credit aggregates, e.g. due to imperfectly elastic 
demand. Now, if banks do not react symmetrically to changes in market conditions, then 
their role in monetary policy can be overstated. These effects can also differ by country 
and industrial sector (see, e.g. Kashyap and Stein, 1997), which in turn forms a major 
concern for e.g. a common European monetary policy.  

We acknowledge these bank-specific characteristics through banks’ liquidity 
management. In general, uncertain expectations over future make firms hold excess 
liquidity either for transaction, speculation or hedging purposes. This liquidity, although 
costly due to trade-off with more risky investments (incl. loans), creates an opportunity to 
smooth firms’ responses to changes in market conditions. This is especially vital for 
banks, because they also simultaneously manage interest rate linkage between their inputs 
and outputs due to a direct and costly trade-off between banks’ liquidity and profitability. 
As a result, banks are assumed to react changes in market conditions (incl. competition) 
by managing their terms of credit subject to costly adjustments to their liquidity. 
However, these supply conditions depend also on demand for credit. Firms control this 
instead by analysing the credit market conditions, investment opportunities and 

                                            
1 Assets or credit aggregates (i.e. loans and liquidity) and liabilities or monetary aggregates (i.e. deposits and 

borrowing) have to be equalized in balance sheets. Hence, change in one side causes similar change in the 
other side. Equity belongs also to liabilities but acts more as a last resort of finance for banks. 



 4

endogenous adjustment costs faced. Hence, the demand and supply conditions are 
interacting. 

This paper contributes to the literature of credit channels in a number of ways by using a 
system of dynamic partial adjustment models. First, the paper accounts for the 
simultaneous interaction between the separate, yet interdependent demand and supply 
conditions and thus avoids incorrect inferences within and between the two sides of bank 
lending. Second, it accounts for banks’ and firms’ adjustment costs, which are largely 
neglected in the previous studies. These costs associated with changes in liquidity status 
reflect market imperfections and uncertainty that can not only restrict these firms 
adjustment to their optimal supply and demand conditions but also prevent monetary 
policy from having desired impact on changes in market conditions. Finally, as empirical 
banking literature is mostly based on US data or country-specific European data, this 
paper makes a contribution to the existing literature by using a wide harmonized 
European multi-country bank and manufacturing data including small, medium and large 
firms that has not been available until quite recently. As a result, this paper provides 
information that can be useful for the common European monetary policy.    

Our results show that banks’ and firms’ credit conditions interact. In fact, tightening 
monetary policy and firms’ increased demand for credit affect similarly banks’ supply 
conditions by increasing their interest rate or risk margins. These, instead, tend to 
decrease the small and mediums-sized firms’ demand for loans but unexpectedly increase 
it for large firms. There are also opposite reactions between small and large firms and 
between these and banks that are likely to depend on the differences in uncertainty or 
time-orientation and bank dependence. Further, the adjustment costs differ across banks, 
firm sizes, and countries as well as over time. Still, size is found to be the most relevant 
adjustment cost parameter for banks whereas default risk is emphasized for firms. In 
addition, banks’ competition over medium-sized firms’ financing together with these 
firms’ greater growth orientation (or risk) is likely to raise these costs. As a result, the 
effects of monetary policy on the economy depend very likely on the size composition of 
its banks and firms. Still, the wide spectrum of firms’ leverage incentives (such as 
different regulatory environments) is a real challenge for the common European monetary 
policy. There is also a threat that large firms’ unpredictable demand for banks loans due 
to their ability to take advantage of banks' credit rationing and its compounded effects 
made payable to smaller and more bank-dependent firms can danger the financial 
stability.  

Rest of the paper is organized as follows. Section II reviews briefly the theories behind 
this paper. Section III introduces next the modelling approach used and its specifications 
including variables of interest. Section IV describes then the data set and Section V 
presents the main results and discusses further of their implications. Finally, Section VI 
concludes. 

II. THEORY 

Circulation of money in the economy can be viewed as firms’ creation of excess funds by 
borrowing these once created funds for their uncertain future investment opportunities. 
This market of funds is however not perfect, nor complete, which is opposite to the 
Modigliani and Miller (1958) claim of the financial irrelevancy. Informational costs 



 5

(mainly e.g. agency costs and asymmetric information2) create market frictions in capital 
markets, which implicate that: (1) firms’ investment and financing decisions become 
interdependent, (2) firms cannot substitute different financing sources without additional 
costs, (3) financial intermediaries are allowed to exist by providing services that decrease 
these costs of financing for informational opaque borrowers, and finally (4) the amount of 
these costs is thought of depending on firms’ size. These market frictions are also 
important because they may explain the propagation of business cycles and hence, show 
how monetary policy or changes in market conditions are transmitted to real sector 
investments. 

Supply conditions 
Market frictions have been ignored in the conventional money view in which changes in 
the supply of money affect the opportunity cost of capital and hence directly firms’ 
investments. Therefore, two complementary credit channels of monetary policy (or 
accelerators of the money view) to the economy are identified: balance sheet and bank 
lending channel.3 Unlike in the money view, these credit channels consider banks one of 
the main vehicles for monetary policy and emphasize the distributional effects.4 
According to these, the credit availability is a positive function of firm’s collateral value. 
Thus, if banks cannot substitute their financing without additional costs5 due to the 
imperfect and incomplete information present between them and both financial markets or 
their customers, any reduction in the banks’ supply of loans (either due to shock in their 
liability or asset side) is likely to increase their funding premiums6 and to reduce their 
lending especially for bank-dependent or balance sheet constrained firms. 

Beside these input costs or refinancing costs, output prices also seem to control bank 
lending. This is interestingly the case even though banks are assumed to operate under 
oligopolistic market conditions, in which they are not only price takers but set their 
lending and deposit rates partly independently from market clearing prices. Still, in order 
to exist and survive banks must provide value for their shareholders. Therefore, the ability 
to supply loans is necessary but not sufficient condition for banks’ existence, unlike 
profitability. In fact, unlimited supply of credit is ideally controlled by price, although in 
reality this is not always so straightforward. First of all, high market interest rates, in 
general and independent of banks, tend to decrease consumption and investment activity. 
On the hand, if banks, for example, cannot price properly their borrowers due to the 
                                            
2 Agency costs mean incentive conflicts related to moral hazard and adverse selection problems due to asymmetry in 

payoff structures among different claimholders of firms. Information asymmetry on the other hand relates to financiers’ 
difficulties (or costs) of assessing the quality (risk and return) of firms in need of financing. 

3  See, e.g. Bernanke and Blinder (1992), Kashyap, Stein and Wilcox (1993), Romer and Romer (1993), Kashyap and Stein 
(1994), Gertler and Gilchrist (1994), and Kashyap and Stein (1995), Bernanke and Gertler (1995), and Bernanke, Gertler 
and Gilchrist (1996). Cecchetti (1995) and Hubbard (2000) provide an excellent synopsis of the effects of monetary 
policy on economic activity. 

4 Certain conditions have to be met here. These include: (1) bank loans are important sources of funds for economy and 
there are no perfect substitutes to them, (2) central authorities have ability or means to control volume of bank lending, 
(3) there exists bank-dependent or other balance sheet constraint firms, and (4) there is an imperfect price adjustment 
between money and real markets.  

5 This seems to be the case even though banks have very low transaction costs in issuing new debt because the demand for 
banks’ managed liabilities, i.e. deposit base is not perfectly elastic.  

6 This reflects the difference between relative prices of inputs or internal funds, e.g. deposits, and opportunity cost of 
external funds, such as certificate of deposits. 



 6

informational market frictions, they may also be tempted to use equilibrium interest rates 
rather than unique measures of creditworthiness (Stiglitz and Weiss, 1981). Or 
alternatively, banks could apply this credit rationing to limit the total amount of their 
loans (Williamson, 1987). Nevertheless, this breaks the connection between price and 
quality (i.e. risk and return) in banks’ loan portfolio and results in a flight of good quality 
borrowers from the loan markets. Eventually, banks’ risk margins can only increase up to 
a limit that cannot be exceeded due to high market interest rates or credit rationing. 

To ease these problems further, banks’ use economies of scale and scope in 
diversification and collection and processing of private information.7 As a result, banks 
face systematic or market risk rather than idiosyncratic risks in their operations. Still, the 
market levels of interest rates represent also an opportunity costs for banks similar to 
other non-financial firms (i.e. monetary phenomenon), because banks’ cost of 
intermediation into financial markets is not only a compensation for market risk taken, but 
also or rather a compensation for their services provided, e.g. liquidity provision (i.e. real 
phenomenon).8 Therefore, again similar to other firms, but with greater emphasis, banks 
are thought of anticipating the future by managing their liquidity.9 In doing this, they also 
manage interest rate linkage between their inputs and outputs due to a direct and costly 
trade-off between banks’ liquidity and profitability. These bank-specific adjustments are 
then costly due to information and agency problems created by uncertainty and 
irreversibility. 

Demand conditions 
Although we have emphasized the banks’ role in monetary policy transmission under 
information problems, the analysis so far have lacked for another crucial component 
affecting this, namely demand for bank loans. 

This is important because credit markets are primarily demand or non-financial sector 
driven and bank loans accounts only for one part of the total markets. As a result, firms, 
foremost separate from banks, control their credit demand depending on their investment 
opportunities and financial status. Indeed, firms can be expected to choose among 
alternative competing sources of financing by comparing their terms and firm-specific 
adjustment costs caused.10 These costs related to information problems are expected to 
increase with firms’ growth opportunities, leverage and lack of net worth. Hence, these 
costs are thought being higher for younger and smaller firms with high degree of 
idiosyncratic risk and low level of collaterals.11 The effect of monetary policy working 
                                            
7 See, e.g. Diamond (1984). The objective is to reduce monitoring costs linked to borrowers’ possible 

information and agency problems. 
8 Provision of services is related to transformation process where mainly safe and liquid deposits from public, 

but also equity and money market borrowing (i.e. inputs), are turned into larger and longer maturity risky 
loans and deposit services (i.e. outputs). Also labour, information technology and plants can be considered 
as inputs, but are ignored here. 

9 A bank should always have enough liquid assets to meet the demands of its creditors and depositors. 
10 The competition between banks, alternative sources of financing and institutions providing them as well as 

different financial markets are expected to increase over time due to three major external trends: 
deregulation and liberalisation; advances in information technology and financial innovations; and 
economic, financial and monetary integration. 

11 Small amounts to be borrowed do not either attract financial institutions or give reason to a more direct 
access to financial markets due to the possible costs faced, e.g. fixed costs and cost of continuous disclosure 
of detailed information. 



 7

through the banks is suggested to be most severe on those firms that have limited access 
to financial markets or are otherwise balance sheet constrained or bank-dependent, i.e. 
they cannot substitute bank loans without additional costs.12  

Also, the interdependence of supply and demand conditions becomes crucial for analysis 
of monetary policy effects on industrial structure and performance because firms’ adverse 
demand shocks tend to be compounded in recessions, and vice versa. First, increasing 
information problems raise credit providers’ risk margins and then, through the reduction 
of their liquidity, eventually also their credit supply. Hence, although demand and supply 
conditions are separately controlled, they are still partially dependent on each other. 

III. MODELLING 

Our modelling approach is based on Banerjee, Heshmati and Wihlborg (2004) and 
Lindström and Heshmati (2004). Banerjee, Heshmati and Wihlborg (2004) assume that 
firms dynamically adjust their decision variables, e.g. leverage, periodically to some 
target levels. As these target levels are functions of the observable determinants, they are 
known within the period, but not beyond that because of uncertainty. Their model also 
recognises that as these adjustments are usually costly due to market frictions, firms may 
find it optimal to adjust only partially to these targets. These periodic changes towards the 
target levels, i.e. speed of adjustments, are allowed to be flexible and have determinants 
of their own representing the cost parameters. Hence, the key feature of their model is to 
simultaneously endogenize the adjustment parameter and the optimality of a decision 
variable. Lindström and Heshmati (2004) refine the model so that in addition to 
accounting for dynamics and non-linearities above it permits the analysis of simultaneity 
and thereby avoids the incorrect inferences of causalities within and between the two 
separate but interdependent decisions, e.g. leverage and investment. Therefore, we also 
apply this system approach for our study, where decisions of banks’ credit terms and 
firms’ demand for credit are assumed to be made separately but interdependently and 
subject to their adjustment costs faced. As a result, this approach better recognizes the 
effects of simultaneous interaction, which is likely to reduce biases in the results, allow 
for non-partial inferences, and improve our understanding of these joint decisions and 
they interplay. 

Theoretical models 

The optimal or target levels of the key variables, banks’ decisions of their risk margin or 
supply conditions (R) and firms’ decisions of their leverage or demand conditions (D) 
respectively are determined as follows: 

(1a) )ˆ,,,,(*
jkt

R
t

R
k

R
i

R
iktikt DXXXXfR =   

(1b) )ˆ,,,,(*
ikt

D
t

D
k

D
j

D
jktjkt RXXXXgD =  

where the subscript i  denotes bank, j  firm, k  country and t  time period. The f  and g  
indicate functional forms. *

iktR denotes optimal bank and *
jktD  optimal firm decision. 

                                            
12 For more details about firms’ capital structure theories, see e.g. Harris and Raviv (1991) for general review, 

Rajan and Zingales (1995) for international evidence, Fazzari, Hubbard and Petersen (1988) for financial 
constraints, and Berger and Udell (2000) for differences of large and small firms, and the references there. 



 8

jktikt DR ˆandˆ  are their estimated feedbacks from one decision to another. These predicted 
values are based on the static models with similar specifications as those in the dynamic 
models but without this interaction term. sX '  are vectors of explanatory variables and 
they may partially overlap across the two equations. The dynamics is captured here 
through the changes in determinants of the observed levels ( sX ' ) that also cause the 
optimal or target levels ( *

iktR and *
jktD ) to shift. All of these parameters are allowed to 

change over time and across firms and countries due to changing circumstances and 
market conditions.  

To avoid the misspecification errors caused by the aggregation effects, the absence of 
adjustment costs and the dynamic nature of the bank-firm relationship, a dynamic 
formulation is employed. The dynamic models based on a general equilibrium formula 

)YY(YY t,ik
*

iktitt,ikikt 11 −− −=− δ , where ),( DRY  is the vector the two dependent variables 
and ),( DR δδδ  the vector of their adjustment rates, can be written as follows:13  

(2a) R
ikt

*
ikt

R
iktt,ik

R
iktikt uRR)(R ++−= − δδ 11  

(2b) D
jkt

*
jkt

D
jktt,jk

D
jktjkt uDD)(D ++−= − δδ 11  

where the D
jkt

R
ikt δδ and  are adjustment parameters that measure the speed of adjustment 

from observed to optimal levels. For example, if 1or1 == D
jkt

R
ikt δδ , the adjustment is 

made fully within one period and the firms and banks are in their optimum. On the other 
hand, if 1or1 << D

jkt
R

ikt δδ , the adjustment falls short, and respectively, while if 

1or1 >> D
ikt

R
ikt δδ , there is an over-adjustment. Here D

jkt
R
ikt uu and  are the random error 

terms whose components are assumed to be independent of each other and of the 
explanatory variables. They are further assumed to have mean zero and constant variances 
and covariances. 

The adjustment parameter here is flexible because the costs of adjustment do not have to 
be convex or constant as is often assumed in many dynamic models. This is also 
important because of the assumed heterogeneity in the cost of adjustment among banks, 
firms, industries, and countries as well as over time. Hence, the periodic values for the 
adjustment parameters also allow us to model their determinants: 

(3a) ),,,( R
t

R
k

R
i

R
ikt

R
ikt ZZZZl=δ  

(3b) ),,,( D
t

D
k

D
j

D
jkt

D
jkt ZZZZm=δ  

where sZ '  are vectors of determinants of the speed of adjustment and l  and m  
indications of a functional form. Hence, these determinants can be seen as measures of 
adjustment costs that characterize the costs of shifting from one level to another rather 
than the actual cost associated with specific levels. If the effect of determinant to the 
adjustment parameter is positive, it indicates that the actual variable increases the speed of 

                                            
13 For more details about this model, see either Banerjee, Heshmati, and Wihlborg, 2004, or Lindström and Heshmati, 

2004. 



 9

adjustment and is a real cost parameter, whereas in the opposite case, it provides 
flexibility for the decision by smoothing the speed or cost of an adjustment needed. 

Empirical models and their variables 
Both banks and firms are similarly but separately, yet interdependently expected to 
partially adjust their terms of credit to changing exogenous credit market conditions. For 
this reason, we need in some extent joint, country-specific market variables for banks’ and 
firms’ target determination and bank- and firm-specific variables for their costly liquidity 
adjustments. We should also be able to control for firms’ size as it is expected to be 
correlated with the information problems causing the interaction and adjustment costs. 

Market-specific variables  

Interest rates are the main variables in the credit markets. They affect firms’ investment 
activity and willingness to acquire debt. Monthly interbank interest rate (IRm) is expected 
to reflect monetary policy changes due to its high correlation with central bank controlled 
rates. In general, monetary policy affects short-term interest rates; the more its patterns 
are unanticipated. A rise in IRm decreases asset values of the same maturity, through the 
increase in their opportunity cost of capital. Although the price effect on shorter-term 
assets, such as inventories, could be predicted, its effect on more long-term assets, such as 
fixed asset investment, is still an unresolved issue. In fact, these assets should be expected 
to depend on longer-term interest rates. Therefore, we also add a one-year inter-bank 
offered rate (IR1) and a rate of ten-year benchmark bond (IR10) to our set of explanatory 
variables.14 These rates are also more closely related to short- and long-term future 
expectations.  

Because banks are considered as the main transformers of monetary policy in our theory 
setting, it should be noted that beyond the standard interest rate channel, other factors of 
asset prices influence banks, although not necessarily on monetary policy.15 These are 
stock market prices and foreign exchanges rates, which are linked to expectations of the 
future state of economy. Nevertheless, firms’ net worth and banks’ debt capacity are 
expected to increase with the stock-market index (SMI, USD denominated) due to 
enhanced collateral values. Foreign exchange rates (FX, a trade-weighted currency 
index16 denominated in USD) on the other hand measures the international price 
competitiveness of the economy. Foreign exchange rates affect economy through 
devaluation of home currency that causes debt denominated in foreign currency to 
increase firms’ debt burden and decrease the value of banks’ assets, and vice versa. It 
further affects firms’ net worth and banks’ debt capacity each alike but also on inflation 
rate through the purchasing power parity. In contrast to US banks that have only 
negligible foreign exchange reserves, this could be a major factor for European banks.  

                                            
14 The Netherlands’ interest rates are zero coupon quotes or rates. 

15 The effect of asset prices on monetary policy is still a controversial issue. However, stock market price index can be used 
as a trigger for actions in monetary policy – or at least as a proxy for future inflation and volatility forecasts. This way, 
monetary authorities could quite possibly smooth emerging shocks or prevent them spreading without relying on their 
super-natural ability to predict future better than markets. For more details on this issue see, e.g. Mishkin (2001), 
Cecchetti, Genberg and Wadhwani (2002), and Bernanke and Gertler (1999 and 2001). 

16 J. P. Morgan's daily trade-weighted currency index measures nominal exchange rate strength of individual OECD 
currency relative to 18 other OECD countries. The each country’s bilateral trade in manufactured goods weights these 
indices. The base year 1990 equals 100.  



 10

Inflation (INF) is a measure for macroeconomic performance and price stability of an 
economy. As inflation is related to all asset prices, it has been nominated as the central 
monetary policy target in many central banks including e.g. Federal Reserve Bank (FED) 
and European Central Bank (ECB). Inflation also correlates with credit and market risks 
that again affect firms’ liquidity and solvency. However, as it denotes an opportunity cost 
of holding money, it is bad for lenders but good for borrowers. Therefore, effects of 
inflation on real output are hard to control not least because of its long-term economy-
wide effects but also because of the possible lack of real influence on asset prices.  

Further, uncertainty over these future credit market and macroeconomic conditions (V 
prefix or letter indicates the variance of the above variables) is expected to be a major 
factor in financial markets. Greater uncertainty about these economic indicators will give 
rise to informational problems and thus have an effect on banks’ lending over and above 
the constraints posed by monetary policy. In particular, an increase in uncertainty is likely 
to increase firms’ (including banks) demand for liquidity, which is associated here with 
increase in their adjustment costs. It can also cause decline in economic activity, and is 
therefore a major issue for both banks’ supply and firms’ demand for credit.  

Bank-specific variables 

In addition to market conditions above, firms’ demand for credit ( D̂ ) is expected to affect 
our first decision variable, the banks’ interest rate margin (R, measured as net interest 
income to total loans). This margin is a major source of net income for banks and a 
measure for banks’ cost of intermediation and external finance premium which are likely 
to reflect banks’ risk margin involved with credit and interest rate risk premiums. Hence, 
it is also a measure for market efficiency: the lower the margin, the more efficient is the 
banking system and the lower cost of funding it can provide for firms. Alternatively, these 
margins can be seen as indicators of banks’ market power.  

Banks’ adjustment parameters indicate on the other hand their potential future funding 
and lending position or liquidity management, and its sources or dependencies. Changes 
in banks’ overall liquidity should affect their risk margins and collateral requirements, 
respectively. Therefore, we have included various measures for it: AM (the ratio of total 
deposits to total assets) is a proxy for banks’ asset liquidity, LM (total loans to total 
deposits ratio) is instead a liquidity measure for banks’ liability side and LIQM (total 
loans to total assets ratio) is a measure for banks’ total liquidity. The higher these 
measures are, the lower is the liquidity. In addition, OM (ratio of non-interest income to 
revenues) measures banks’ concentration on traditional intermediation activities, i.e. the 
higher the measure, the more diversified the bank is, and LL (ratio of provision of loan 
losses to total loans) for their expected credit risk. Finally, both GROWTH (yearly growth 
in loans) correlating with banks’ risk-taking and SIZE (log of total assets) correlating with 
their risk capacity are also assumed to be factors affecting banks’ adjustment costs. 

The interest rate margin ( *
iktR ) and its adjustment ( R

iktδ ) for a bank i can now be specified 
empirically as follows:  

(4a) 

ktVINFktVSMIktVFXktVIR

ktVIRktVIRmktINFktSMI

ktFXktIRktIRktIRmjktDikt

VINFVSMIVFXVIR
VIRVIRmINFSMI

FXIRIRIRmDR

αααα
αααα

αααααα

++++
++++

+++++=

10
1

101ˆ

10

1

1010
*

 



 11

(4b) 
iktSIZEiktGROWTHiktLIQM

iktLLiktOMiktLMiktAM
R
ikt

SIZEGROWTHLIQM
LLOMLMAM

ξξξ
ξξξξξδ

+++
++++= 0  

Firms’ size-specific variables 

In addition to long-term interest rates (IR1 and IR10) and their variances (VIR1 and 
VIR10), investments (I, measured as ratio of fixed investment to total assets) and banks’ 
predicted risk margin )ˆ(R  are thought being exogenous for our second decision variable, 
namely firms’ demand for credit (D, measured as the ratio of long-term debt payable after 
one year to total assets).  

Firms’ adjustment parameters in bank perspective reflect instead their ability to repay 
loans and they include here collateral value of firm’s assets (K, measured as ratio of fixed 
assets to total assets), default risk (CR, interest rate coverage ratio or interest paid on 
financial debt to net operating profits), growth or investment opportunities (G, yearly 
change in firm’s turnover), and liquidity (WK, working capital measured as ratio of 
current assets less current liabilities to total assets). These adjustment cost parameters are 
the ones banks are likely to monitor not the ones necessarily used by firms as basis of 
their debt decisions. However, these two cannot be clearly separated. 

Interest rate coverage ratio (CR) links monetary policy to firms’ financial health. Rising 
interest rates increase payments to debtors and weaken the borrowers’ net worth by 
reducing their net cash flows. If firms cannot meet their periodic interest payments, debt 
can also be called. Hence, this is also a measure for firms’ default risk. Firms’ liquidity 
(WK) relates to firms’ decisions to raise external capital to adjust financial deficit 
between their operating earnings and investments. For example, Fazzari and Petersen 
(1993) state that apart from being an important use of funds due to inventory investments, 
working capital is also a source of funds because it provides liquidity to fluctuations in 
cash flow without the need for costly external financing. It is therefore considered as 
additional collateral for banks although firms’ main collateral value is their amount of 
fixed capital (K). In any case, rising interest rates shrink the value of borrowers’ collateral 
needed by banks to protect themselves against firms’ possible adverse selection and moral 
hazard problems. In addition to collateral values, greater growth opportunities (G) are 
expected to correlate positively with the extent of informational problems between banks 
and firms.  

Here the debt ( *
jktD ) and its adjustment ( D

jktδ ) equations for firms when controlling firms’ 
size class j are specified as follows:  

(5a) 
ktVIR

ktVIRjktIktIRktIRiktRjkt

VIR

VIRIIRIRRD

10

1101ˆ

10

11010
*

β

ββββββ

+

+++++=
 

(5b) jktGjktCRjktWKjktK
D
jkt GCRWKK ςςςςςδ ++++= 0  

IV. DATA  

One major objective of this study is to use European data in a study of bank lending. 
Many related previous studies are based on US data or are country-specific because of the 
lack of harmonised European Small and Medium-sized Enterprises (SME) data. As most 
European countries differ by regulatory, legal and political environments from USA and 



 12

are also more bank-dependent, certainly there is a demand for this kind of analysis. This is 
further motivated by the integration of monetary policy in Europe. Hence, our data is 
chosen to account for presence of heterogeneity in response of banks and their home 
countries to changes in market conditions and in firms’ demand for credit by taking into 
account their interaction and adjustment costs related to changes in firms’ and banks’ 
balance sheets.  

We have used Eurostat’s Bank for the Accounts of Companies Harmonised (BACH) 
database containing harmonised annual accounts statistics of non-financial enterprises for 
11 European countries: Austria, Belgium, Denmark, Finland, France, Germany, Italy, The 
Netherlands, Portugal, Spain and Sweden. With the exception of Sweden and Denmark, 
all the other countries have accepted the single currency, Euro. In the data set, we focus 
on the manufacturing sector because it is the most comprehensive data series and because 
it is the one most often linked to bank lending due to its ability to provide collaterals. The 
data is divided by size (measured using turnover) into three classes: Small (turnover ≤ 7 
million €), Medium-sized (7 million € < turnover ≤ 40 million €), and Large firms 
(turnover > 40 million €). Since banks use diversification for mitigating firm-specific 
risks, country-level data is considered to be reasonable, although some caveats must be 
raised due to the industry-specificity. In addition to these somewhat arbitrary size 
classifications, the use of size as a proxy for access in capital markets and turnover as its 
proxy can also be questioned. Finally, we prefer to use book values that are backward-
looking17 because in the case of default, asset values are in general closer to their book 
values. This is a major concern for banks expecting repayment of their issued loans.  

Thomson Worldscope is used to obtain bank-specific data within these countries. In 
particular, we have chosen to investigate commercial banks that have their primary 
industry SIC (standardized industrial code) 6021, i.e. National Commercial Banks. This 
gives us 237 banks. Finally, market data in monthly quotes is acquired from Thomson 
Datastream. This data set is country-specific and in nominal terms.  

We use balanced panel data, in which missing unit and few extreme observations on the 
explanatory variables are replaced by firm-specific means. The data covers the period 
from 1994 to 2003. However, the first year, 1994, is excluded due to the use of lag values 
of the dependent variables. As a result, the number of observation used in the final 
analysis is 2370. It should also be noted that the data is in ratios to allow the comparison 
over time and across firms and countries. Furthermore, the use of ratios is a standard way 
to gain trend-stationary series and to minimize the heteroscedasticity in the data by 
normalizing the variables by size. 

 

 

 

 

 

                                            
17 Market values are forward-looking, i.e. they include expectations, and thus are expected to capture the “true value” more 

efficiently.  However, as they incorporate liquidity risk, it is hard to analyse adjustment costs that are mainly related to 
changes of liquidity base.   



 13

 
Table I: Summary statistics of the data during 1994-2003 is divided by their origin into market-specific, 
firm-specific and bank-specific variables. Market variables include interest rate levels for one-month 
(IRm), one-year (IR10) and for 10-year period (IR10), foreign exchange rate (FX), inflation (INF), stock-
market index (SMI) and their variations denoted by the letter V. Firm-specific variables are divided by 
size into small, medium and large firms and include firms’ debt ratio (D), investments (I), fixed assets 
(K), default risk (CR), liquidity (WK) and growth (G). Bank-specific variables are banks’ interest rate 
margin (R), solvency (AM), productivity (LM), liquidity (LIQM), operational efficiency (OM), expected 
loan losses (LL), loan GROWTH and asset SIZE. 

Summary statistics of the data, NT=2370 obs. 
Variable Mean Std Dev Minimum Maximum 
A. MARKETS: 
IRm 4.587 1.947 2.361 10.213 
IR1 4.708 2.021 2.346 11.146 
IR10 6.105 1.948 4.116 12.326 
FX 92.936 13.992 68.773 111.955 
INF 2.319 1.070 0.253 5.368 
SMI 0.400 2.192 0.000 25.456 
VIRm 0.215 0.231 0.001 2.175 
VIR1 0.226 0.239 0.006 1.257 
VIR10 0.288 0.428 0.007 3.071 
VFX 1.643 2.013 0.136 21.869 
VINF 0.181 0.313 0.005 2.816 
VSMI 0.417 4.202 0.000 151.967 
B. SMALL FIRMS 
D 17.912 4.983 10.820 31.980 
I 0.244 0.878 -2.940 4.840 
G -0.023 0.199 -0.437 1.095 
K 37.299 8.558 23.250 53.690 
WK 15.595 7.478 -0.570 33.040 
CR 0.603 0.270 0.150 1.364 
C. MEDIUM-SIZED FIRMS 
D 14.420 3.019 9.170 25.780 
I 0.058 2.001 -20.570 18.590 
G 0.010 0.099 -0.282 0.958 
K 36.827 7.822 25.280 60.260 
WK 18.049 6.145 8.640 32.260 
CR 0.381 0.112 0.171 0.765 
D. LARGE FIRMS 
D 12.525 4.379 4.218 23.500 
I 0.311 1.542 -6.940 5.500 
G 0.059 0.073 -0.201 0.295 
K 47.144 7.398 36.590 72.620 
WK 14.557 5.815 -2.320 27.270 
CR 0.422 0.159 0.119 1.057 



 14

E. BANKS 
R 3.602 2.784 -22.592 21.382 
AM 48.305 21.737 0.000 93.129 
LM 232.313 435.971 17.998 8140.179 
OM 19.487 13.485 -31.717 86.818 
LL 0.833 2.101 -59.313 17.175 
LIQM 70.441 15.507 0.253 198.617 
GROWTH 9.658 19.717 -99.988 261.702 
SIZE 3.870 0.970 1.363 5.967 

 

The descriptive statistics is summarized in Table I. Interest rate curve is upward sloping 
and the variances behave accordingly. Inflation (INF) is low, little over 2%. Foreign 
exchange rates (FX) of these countries are devaluated on average against the base year of 
the reference index less than 10%.  

Firms’ leverage (D) decreases with the size. Investments (I) are by far the lowest, but their 
variance greatest for medium-sized firms. Small and large firms’ investments are pretty 
much on the same level. Large firms are growing (G) in the period while medium-sized 
firms are quite stable and small firms shrinking. Large firms’ collateral values (K) are at a 
higher level compared to levels of small and medium-sized firms. Liquidity demand 
(WK) is highest for medium-sized firms and lowest for large firms. Interest rate coverage 
or default risk (RC) is highest for small firms.  

Banks’ interest rate margin (R) is on average 3.6 %, which would give quite small real 
return to banks after deduction for the inflation rate (2.4%) is made. However, the 
variance of inflation is also quite large. Deposits cover half of the total assets (AM) that 
consist of loans for 70% (LIQM). Deposits are turned into loans (LM) at rate of 2. 
Operational efficiency (OM) shows that one fifth of the banks’ revenue comes on average 
from the non-interest sources. Loan losses (LL) make less than 1% of the total assets. 
Finally, GROWTH in loans is around 10% in our sample. 

Table II gives information on bank frequencies over countries. The number of banks is 
not proportional to the size of the sample country. In particular the Danish and Italian 
banks are over-represented, while the French are underrepresented. It is to be noted that 
since the firm-level data only varies between these three size classes for each country and 
over time, it is constant across countries for multiple observations of banks. 



 15

 
Table II: Bank frequencies by country during 1994-2003. Austria (AUT), Belgium (BEL), Germany 
(DEU), Denmark (DNK), Spain (ESP), Finland (FIN), France (FRA), Italy (ITA), the Netherlands 
(NLD), Portugal (PRT) and Sweden (SWE).  

Bank frequencies by country, NT=2370 observations 
Country     Frequency  Percent  Cum. Frequency Cum. Percent 
Austria 130 5.49 130 5.49 
Belgium 90 3.80 220 9.28 
Germany 390 16.46 610 25.74 
Denmark 400 16.88 1010 42.62 
Spain 220 9.28 1230 51.90 
Finland 70 2.95 1300 54.85 
France 240 10.13 1540 64.98 
Italy 560 23.63 2100 88.61 
Portugal 70 2.95 2170 91.56 
Netherlands 130 5.49 2300 97.05 
Sweden 70 2.95 2370 100.00 

V. RESULTS 
Our goal is to identify the key market determinants of banks’ interest rate margins and 
firms’ debt levels as well as the causal relation between these two. In addition, we are 
interested in finding out the adjustment parameters affecting these two. Also, a closer 
analysis on the mean adjustment parameters by year and country provides new 
information on the bank lending and its role for the common European monetary policy. 

Models for banks’ interest rate margins and firms’ debt demands are estimated as single 
as well as system of banks’ interest rate and firms’ debt equations and separately for 
small, medium-sized and large firms, each assuming static and dynamic formulations. In 
each of these cases, we control for time-, bank- or firm-heterogeneity. The static models 
are linear and estimated with the two-stage least squares method. The feedback or 
interaction effects are based on the static models with similar specifications as those in the 
dynamic models but without the interaction term. The dynamic models are non-linear and 
they are estimated using iterative FIML (Full Information Maximum Likelihood) method.  

The comparison over these models’ performances and various tests suggests that the static 
models and single equations should be rejected in favour of dynamic and system of 
equation models due to relevant time-, firm- and bank-variant adjustment parameters and 
significance of the feedback effects. Thus, the final results are based on the system of 
dynamic models with flexible adjustment parameters. Table III presents these short-term 
parameter estimates for bank and firm equations in each of the three size-classes. Long-
run estimates are received by multiplying these short-term estimates with the adjustment 
rates. The performance of the models in terms of high 2R  (fit index) and low RMSE (root 
mean square of error) is good in all cases except in the case of banks under medium-sized 
firms. We have no explanation for the divergence but suspect that it is related to the 
heterogeneity of the class. 

 

 

 



 16

Table III: Parameter estimates of the dynamic partial adjustment models between 1994-2003 for bank 
and manufacturing industry by size. Explanatory variables describing exogenous market conditions 
include interest rate levels for one-month (IRm), one-year (IR1) and for 10-year period (IR10), foreign 
exchange rate (FX), inflation (INF), stock-market index (SMI) and their variations denoted by the letter 
V. In addition, investments (I) are exogenous for firms. Interaction parameters (PR) and (PD) measure 
the predicted effects of banks’ interest rate margin (R) and firms debt (D) decisions to each other. 
Adjustment parameters are for banks: solvency (AM), productivity (LM), liquidity (LIQM), operational 
efficiency (OM), expected loan losses (LL), GROWTH and SIZE, and for firms: fixed assets (K), default 
risk (CR), liquidity (WK) and growth (G). Model performances are described by R2 for their explanation 
power and RMSE for their errors.  

Parameter estimates of the dynamic partial adjustment models, NT=2370 obs. 
BANK MODEL FIRM MODEL 
Size SMALL MEDIUM LARGE Size SMALL MEDIUM LARGE 
Para-
meter 

Estimate        
t Value      

Estimate         
t Value      

Estimate        
t Value      

Para-
meter 

Estimate        
t Value      

Estimate         
t Value      

Estimate        
t Value      

A. DETERMINANTS FOR OPTIMAL LEVELS 
α0   -129.30*** 

(-127.30) 
-1.05       
(-0.26) 

-19.73***      
(-24.91) 

β0 7.72      
(1.39) 

26.49*** 

(4.78) 
44.95     
(1.46) 

αIRm 0.62*** 
(7.58) 

0.06    
(0.15) 

1.11*** 
(6.62) 

    

αIR1    6.33*** 
(54.01) 

0.55      
(0.92) 

1.02*** 
(4.87) 

βIR1  -8.35***      
(-11.38) 

-4.32***      
(-7.45) 

9.33** 

(1.96) 
αIR10        0.32*** 

(4.79) 
-0.34       
(-1.01) 

-3.07***      
(-18.58) 

βIR10 7.89*** 

(7.09) 
2.67*** 

(3.21) 
-4.39       
(-1.15) 

αFX 0.00***     
(3.17) 

-0.05***     
 (-7.56) 

0.02***     
(5.64) 

    

αINF 0.03      
(1.13) 

0.28**      
(2.54) 

0.43***      
(7.72) 

    

αSMI -0.01      
(-0.89) 

-0.00       
(-0.11) 

-0.04**       
(-1.96) 

βI -1.00***      
(-3.91) 

-0.45***      
(-5.89) 

-0.80***      
(-3.88) 

αVIRm 0.63*** 
(3.96) 

0.78      
(1.18) 

-2.34***      
(-7.05) 

    

αVIR1   10.11*** 

(48.72) 
-0.30       
(-0.36) 

-3.71***      
(-9.85) 

βVIR1 0.05      
(0.04) 

6.69*** 
(4.00) 

-31.97**     
(-2.26) 

αVIR10 -6.42***      
(-29.15) 

-0.65       
(-0.61) 

4.56*** 

(10.51) 
βVIR10 11.93*** 

(5.39) 
8.17*** 

(3.90) 
-252.52*** 
(-4.28) 

αVFX 0.12***      
(10.73) 

-0.04       
(-0.87) 

0.14***      
(6.37) 

    

αVINF -0.34***      
(-6.35) 

0.07      
(0.44) 

-1.03***      
(-7.89) 

    

αVSMI -0.00     
(-0.01) 

0.03      
(1.06) 

0.01     
(0.72) 

    

αPD  4.21*** 
(126.31) 

0.42** 
(2.27) 

2.67*** 

(54.03) 
βPR  -0.80**      

(-2.19) 
-0.56***      
(-2.76) 

3.92*** 
(7.87) 



 17

 
B. DETERMINANTS FOR ADJUSTMENTS 
ξ0              1.00*** 

(26.80) 
-0.60***       
(-2.98) 

1.68*** 

(21.02) 
ς0   -0.18***      

(-8.39) 
-0.19***      
(-7.72) 

0.01** 

(2.31) 
ξAM   0.00*** 

(3.23) 
-0.01***     
 (-7.89) 

-0.00***     
(-0.13) 

    

ξLM  0.00    
(0.21) 

-0.00***     
(-4.74) 

-0.00 
(-0.68) 

ςK 0.00*** 

(9.05) 
0.00*** 
(7.36) 

-0.00**    
 (-2.56) 

ξOM -0.00          
(-1.36) 

0.01*** 
(5.25) 

-0.01***     
(-6.90) 

    

ξLL   0.00     
(0.06) 

-0.00      
(-0.45) 

-0.00          
(-1.59) 

ςCR 0.06*** 
(7.93) 

0.09*** 
(4.65) 

-0.01**      
(-2.40) 

ξLIQM    -0.00***     
(-7.87) 

0.01*** 
(9.38) 

-0.01***     
(-12.32) 

ςWK 0.00*** 

(9.03) 
0.00*** 
(8.12) 

-0.00     
(-0.12) 

ξGROWTH 0.00*** 
(3.01) 

-0.00**     
(-2.55) 

0.00*** 
(7.69) 

ςG 0.01     
(1.45) 

0.18*** 
(7.50) 

0.01** 
(2.36) 

ξSIZE 0.00     
(0.70) 

0.25*** 
(13.68) 

-0.04***      
(-3.88) 

    

C. MODEL PERFORMANCES 
R2 0.95 0.40 0.79 R2 1.00 0.99 0.99 
RMSE 0.62 2.15 1.28 RMSE 0.31 0.28 0.32 
Notes: significant at the less than 1% (***), 1-5 % (**), and 5-10% (*) levels of significance. 
Parameter estimates 

Determinants for banks’ optimal margin levels 

In general, rise in interest rates should cause banks’ margins to rise or their credit terms to 
tighten as a sign of increased economic risk. The effects of monetary policy through the 
one-month interest rate (IRm) seem to affect positively banks’ margins similarly under 
small and large firms’ demand for credit. However, the effect of uncertainty over its 
variance (VIRm) has mixed effects. More predictable monetary policy decreases banks’ 
margins subject to large firms’ demand for long-term credit but it increases margins for 
small firms’ lending. This difference might be due to smaller firms’ greater dependence 
on bank financing. 

In addition to monthly interest rates, levels of foreign exchange rate, inflation and stock-
market index are also related to banks’ margins (R). Yet, the effect of stock markets index 
(SMI) is only significant in the case of large firms. Its small negative effect means that 
decreasing asset prices increase banks’ margins as could be expected. Medium-sized 
firms instead seem to affect these margins only through the level of foreign exchange rate 
and inflation. Here, the foreign exchange rate (FX) is negatively related to banks’ 
margins, which is opposite to the effects under small and large firms. Yet, the inflation 
(INF) is positively related to it similar to large firms. The difference of signs in the effects 
of foreign exchange rates may reflect medium-sized firms’ higher dependence on loans in 
foreign currency. In addition, variance of foreign exchanges rates (VFX) is positively and 
variance of inflation (VINF) negatively related to banks’ margins within small and large 
firms. This could instead implicate that a purchasing power parity, in which the foreign 
exchange rates compensate for the inflation movements, is expected to hold. However, 
these impacts on banks are quite low.  

Determinants for firms’ optimal leverage levels and their interaction with banks 

Investments (I) seem to affect negatively firms’ leverage (D) in all the size classes. This 
could be interpreted as a general indication of firms’ financial constraints or cash-flow 



 18

dependency. It would also emphasize the relevance of information problems in financing. 
As a result, firms’ decreasing investments opportunities increase their demand for debt. It 
is interesting though that these increases in firms’ predicted demand for credit )ˆ(D  in 
each size class increase banks’ margins, while the increase in these predicted margins )ˆ(R  
decrease SME’s demand for loans but unexpectedly increases it for large firms.  

For SMEs case, these effects emphasize not only the view of excess-demand for credit 
and/or low competition but also at least partial non-substitutability of bank loans. The 
non-substitutability is especially highlighted among small firms because the positive 
effect of their demand for credit on banks’ margin is much greater than the negative effect 
of these margins to their demand. In the case of medium-sized firms these interaction 
effects are quite even and their signs follow those of the small firms but with a much 
lower intensity. This could indicate an interest in other sources of finance. 

The unexpected positive effect of banks’ margins on large firms’ demand for credit can be 
explained by credit rationing because the feedback effect from banks’ margins on the 
firms’ credit demand is one and half times higher than the other way around. As large 
firms are assumed to have broader access to credit and their risks for banks are thought 
being larger, it could mean that the effect of bank loans through the decrease of relative 
prices is greater than the decrease in supply, e.g. due to non-price credit rationing.18 This 
could then mean that banks adjust their interest rate margins too slowly or sluggishly 
upwards in the short-term in response to increases in risk, which would then attract these 
more risky borrowers. The significant increase in loan loss provisions (LL) with size and 
the effect of asset prices (SMI) on banks margins (R) and onwards to large firms’ demand 
of credit (PR) are especially good indications of such behaviour. This upward stickiness 
of credit prices is generally assumed to be related either to high level of interest rates or 
credit rationing. However, as the interest rates levels seem to be quite moderate, the 
reason is undoubtedly due to credit rationing.  

Joint variables between these two equations, i.e. long-term interest rates (IR1 and IR10) 
and their uncertainty (VIR1 and VIR10), allow us to compare further the effects of credit 
market conditions on both supply and demand conditions. Our results lend support to 
balancing credit effects via size by Oliner and Rudebusch (1993) and Gertler and Gilchrist 
(1994), but unlike them we relate this effect more closely on credit terms. We find that 
increase in yearly interest rates (IR1) causes decrease in SMEs’ loan demand both directly 
and indirectly through banks margins. Hence, these compounded effects could explain 
why the credit cycles are enforced for these firms. Although rise in the long-term interest 
rates (IR10) has unexpectedly direct positive effects on SMEs’ credit demand, its indirect 
negative effect, yet quite weak and only evident with small firms, will trade some of it off. 
However, for large firms, competitiveness of bank loans relative to other sources of credit 
seems to improve with rising yearly interest rates (IR1) together with banks’ margins 
(PR). This is then additional evidence supporting credit rationing and its exploitation. 
However, this is contrary to e.g. Kashyap, Stein and Wilcox (1993) and Romer and 
Romer (1993) who claim that large firms replace costly bank loans with cheaper market 
finance, e.g. certificate of deposits, when interest rates are rising. In fact, rising long-term 

                                            
18These results should be interpreted with caution, because the true price of bank loans cannot be easily detected due to 

other non-price terms of credit, e.g. collaterals involved with pricing.  



 19

rates seem to decrease the large firms’ leverage only indirectly, i.e. through banks’ 
decreasing margins.  

Above all, it seems that the uncertainty over future interest rates has the greatest effects on 
banks’ and firms’ credit conditions. Yet, these effects differ between these two as well as 
by the firms’ size in question. Greater long-term uncertainty (VIR10) seems to have 
positive compounded joint effects on SMEs’ leverage while the short-term uncertainty 
(VIR1) has mixed effects. In the case of large firms, these effects are again opposite. 
Their demand for credit is strongly reduced with rising uncertainty. As a result, these joint 
variables highlight the differences of time preference or sensitivity between firms’ size 
and also their balancing effects in the bank lending.  

Determinants for banks’ margin adjustments 
Similar to Kashyap and Stein (2000), we find that reactions in the banks’ balance sheets 
to changes in market conditions depend on the demand for credit of different size classes 
of firms. However, unlike them we relate this behaviour to banks’ adjustments to their 
endogenous targets and costs involved with these adjustments due to liquidity 
management. 

Concerning small firms’ demand for credit, banks’ higher asset risk (AM) and lower 
liquidity risk (LIQM) due to possible increase in total assets, and GROWTH in loans seem 
to cause their adjustment rates to increase. These issues of course lead to a weaker risk-
return position that banks need to be compensated for. However, their impact is close to 
null. In fact, as the intercept has only significant and positive non-zero effect close to one, 
it means that the adjustment costs are almost non-existent for banks. There is no credit 
rationing and only market conditions matter. Hence, static models for long-term effects 
will show same results as these short-run effects, i.e. banks adjust immediately to their 
long-term optimal levels.  

Effects of medium-sized firms on banks adjustments seem to be opposite to that of small 
firms. In fact, it seems that medium-sized firms are constraining banks’ adjustments. 
Lower liquidity risk in assets (AM) and liabilities (LM) but higher total amount (LIQM) 
together with lower GROWTH, or simply faster decrease in total assets than in loans, and 
wider income base (OM) seem to result in a faster pace of banks’ adjustment to their 
optimal interest rate margin. As a result, these patterns described indicate that banks want 
to mitigate their adjustment costs by having an unutilised risk capacity in form of total 
assets. Still, SIZE is the only variable having a clear, i.e. a strong positive impact on the 
adjustment speeds. This further emphasizes the role of total assets in this competition. 
These firms’ demand for credit also causes bank adjustments to have a strong negative 
intercept, which means that the adjustments to be made are either large or their costs are 
high.  

Finally, large firms’ demand for credit causes larger banks to adjust slower their margins 
relative to changes in market conditions, i.e. SIZE has a weak negative effect on 
adjustment rates. This is not surprising. Only those banks have ability or risk capacity to 
provide debt financing for large firms with reasonable price relative to market rates or 
risks faced. In addition, lower liquidity risk (LIQM and AM) associated with decrease in 
total assets together with higher GROWTH motives and lower diversification (OM) seems 
to result in banks’ faster adjustments. Yet, these effects do not seem to depart much from 
zero. However, as large firms do create the strongest intercept with positive effects on 
bank adjustments, these banks seem to have excess risk capacity for their adjustments. 



 20

This could enable slow or sluggish adjustments that would support further the credit-
rationing claims stated above. It is also interesting that besides asset risk (AM), the signs 
of the variables are opposite to banks with medium-sized firms. This could be interpreted 
as banks desire to emphasize more the cost-side of their liquidity than the return-side of it.  

Determinants for firms’ leverage adjustments 
Intercepts of SME adjustment parameters are moderately negative which means that their 
leverage adjustments are large or costly. However, these effects can also be interpreted as 
measures of bank dependency. These firms seem to have similar reactions on their 
adjustment parameters. Their intercepts are negative, while collaterals (K), liquidity 
(WK), interest rate coverage (CR) and growth opportunities (G) have positive effects on 
the adjustment speed. Still, the effect of credit risk (CR) is the only one with a positive 
non-zero impact. In addition to this, growth opportunities (G) also speed up the medium-
sized firms’ adjustments, which can then be a source of constraint for banks’ adjustments 
as well. 

For large firms, all of these parameters except growth (G) seem to have opposite effects 
relative to SMEs. Although these impacts are positive, they still lack clear effects. In fact, 
these zero-level adjustments would implicate large firms’ independence on bank lending 
or at least, non-systematic use of it. Hence, in addition to banks’ perfect adjustment in the 
case of small firms, this is then the other extreme of these adjustments in bank lending. 
Nevertheless, our argument of the large firms’ ability to exploit credit-rationing 
conditions to their own advantage is further strengthened. 

Time-variant effects  
By including year dummies into our model specifications, we are able to eliminate 
aggregate variation in the dependent and independent variables, such that the parameter 
estimates reflect better idiosyncratic case-specific variation uncorrelated with any 
aggregate movements. Hence, year dummies control for unobserved heterogeneous time-
variant effects or unpredictable common aggregate shocks. In particular, we use these 
time dummies then to identify the common effects caused by the introduction of euro in 
1999-2003. These results are presented in the Appendix.  

Banks’ interest rate margin (R) seems to increase largely in each period of time during 
1999-2003 with small firms but decrease more slowly with the large firms. Time 
dummies under medium-sized firms’ demand for credit do not have, however, statistically 
significant effects on these margins. On the firm side, the small firms’ leverage (D) 
increases over time 1999-2003, which seems to be opposite to medium-sized and large 
firms. In particular, in the case of large firms, these decreasing effects are more 
pronounced in 2000 and 2002-2003 and for medium-sized firms these are parallel but on a 
lower level in 1999 and 2002-2003.  

Effects of these time dummies on banks’ and firms’ adjustments are the following. In 
1999, 2001 and 2003 these effects are slightly positive for banks under small firms’ 
demand for credit suggesting that banks adjust faster or face lower adjustment costs. Yet, 
these effects have moderate negative impacts on banks under medium-sized firms in 
2001-2003 and also for banks under large firms through out the years 1999-2002. In the 
firms’ adjustments, these time effects are differently synchronized. For small firms, these 
effects are negative in 2001-2002; for medium-sized firms, they are all positive in 1999-



 21

2003, and for large firms, there is a tiny negative effect in 1999 but positive in 2000 and 
2003.  

The result above suggest that the introduction of euro have had some effects on bank 
lending because banks’ and firms’ time effects seem to be partly significant and follow 
each other but in reverse manner. Hence, there is an interaction. In particular, banks’ 
decreasing role in large firms’ financing seems to have resulted in higher rates for smaller 
firms. The reason for this compensation is probably twofold: on one hand, development 
of the other financing markets might have attracted these large firms away from 
traditional bank lending and on the hand, anticipation of euro caused competitive pressure 
on banks’ margins that in some degree have been utilized by these large firms. It is also 
interesting that the time effects of the medium-sized and large firms generally seem here 
to align strongly although the other determinants react usually oppositely. This can 
indicate evidence of a shift taken place in medium-sized firm’s sources of finance, 
perhaps a result of recent years’ favourable development of competing financing markets 
or financial stability. 

Mean adjustment parameters 

Table IV presents summary statistics of the mean adjustment parameters in each size 
class. The observed interest rate margin (R) is around 3.5% and close to their endogenous 
targets (R*), i.e. the optimality ration R*/R is close to 1.0, for small and large firms. Still, 
there is evidence that banks under-adjust their margins to their optimal due to adjustment 
costs. This is most evident or costly in the case of medium-sized firms. In fact, there 
seems to be something to desire for these banks even at the expense of loosing some of 
their risk margin, i.e. target is lower to observed value. This further indicates a “fierce” 
competition.  
Table IV: Summary statistics of the adjustment parameters during 1994-2003 are divided by size to 
small, medium-sized and large firms. The speeds of adjustments (δR and δD) measure firms’ convergence 
from observed interest rate margin (R) and debt levels (D) to their optimal or target values (R* and D*). 
The optimality ratios (R*/R and D*/D) measure efficiency of these adjustments without any further 
implications. 

Summary statistics of the adjustment parameters, NT=2370 obs. 
Variable N Mean Std. dev. Minimum Maximum 
A. SMALL FIRMS 
δR

 2133 0.949 0.037 0.679 1.127 
R* 2133 3.531 2.704 -22.313 21.045 
R 2133 3.538 2.758 -22.592 21.382 
R*/R  20591 0.984 0.255 -1.692 1.698 
δD

 2133 0.010 0.023 -0.029 0.103 
D* 2133 22.392 5.583 10.755 35.363 
D 2133 17.869 4.960 10.820 31.980 
D*/D 16321 1.100 0.244 0.549 1.651 

B. MEDIUM-SIZED FIRMS 
δR

 2133 0.667 0.503 -5.279 2.393 
R* 2133 2.464 0.865 1.030 6.797 
R 2133 3.538 2.758 -22.592 21.382 
R*/R  18911 0.783 0.392 -0.941 1.698 
δD

 2133 0.016 0.026 -0.028 0.180 
D* 2133 17.204 4.010 9.455 34.907 



 22

D 2133 14.403 2.949 9.530 25.780 
D*/D 19511 1.168 0.276 0.591 1.692 

C. LARGE FIRMS 
δR

 2133 0.834 0.167 -0.178 1.630 
R* 2133 3.550 2.517 -16.757 17.238 
R 2133 3.538 2.758 -22.592 21.382 
R*/R  18131 0.914 0.424 -1.537 1.699 
δD

 2133 0.009 0.018 -0.007 0.057 
D* 2133 2.395 48.910 -219.910 87.053 
D 2133 12.481 4.370 4.218 23.500 
D*/D 15671 0.427 0.808 -1.447 1.593 

1 N differs in all these ratios due to exclusion of extreme values 
At the firm level, SMEs would like to increase their leverage (D*/D >1) while large firms 
decrease it (D*/D <1), or at least long-term debt. Yet, it should be acknowledged that the 
variance of large firms’ optimal leverage might downsize these results. Nevertheless, 
small firms would like to increase their leverage by double the amount of medium-sized 
firms. We should however avoid here making too far-reaching conclusions about these 
adjustment rates because they are extremely low due to the use of industry-specific effects 
that absorb most of the industry heterogeneity.  

Mean adjustment parameters by year 
Table V introduces mean adjustment parameters by year. Clearly, differences in banks’ 
adjustment rates (δR) depend on the firms’ size they are involved with and they are not 
constant over time either, although observed R’s are here same for all banks in a given 
year. The interest rate margin targets (R*) have gradually decreased till year 1999 and 
then started to rise again, perhaps due to immaterialized EMU expectations. The 
adjustment rates (δR) are positive but in general below 1, which means that banks cannot 
attain their optimal levels although they are striving constantly at them. The efficiencies 
of banks’ adjustment (R*/R) are however very close to their target levels in the case of 
small and large firms. Yet, bank margins seem to be a bit under-adjusted for small firms’ 
credit demand and over-adjusted for the large firms, whose fluctuations are also much 
larger. Interestingly, banks seem to charge about 1% higher margins from the medium-
sized firms than their desire. This is undoubtedly due to competition that raises banks’ 
adjustment costs and thus slows down their adjustment speeds. 
Table V: Mean adjustment parameters by year during 1994-2003 are divided by size to small, medium-
sized and large firms. The speeds of adjustments (δR and δD) measure firms’ convergence from observed 
interest rate margin (R) and debt levels (D) to their optimal or target values (R* and D*). The optimality 
ratios (R*/R and D*/D) measure efficiency of these adjustments without any further implications. 

Mean adjustment parameters by year, NT=2370 obs. 
Year δR R* R R*/R δD D* D D*/D 
A. SMALL FIRMS 
1995 0.932 3.910 3.901 1.002 0.014 18.528 17.968 1.031 
1996 0.929 3.735 3.752 0.995 0.050 18.392 18.225 1.009 
1997 0.928 3.656 3.671 0.996 0.002 30.342 18.133 1.673 
1998 0.937 3.486 3.487 1.000 0.002 15.543 17.852 0.871 
1999 0.978 3.239 3.247 0.998 0.010 24.750 17.609 1.406 
2000 0.947 3.376 3.379 0.999 0.006 18.711 17.486 1.070 
2001 0.961 3.422 3.435 0.996 0.004 27.858 17.832 1.562 



 23

2002 0.949 3.407 3.416 0.997 -0.004 22.980 17.804 1.291 
2003 0.984 3.544 3.555 0.997 0.004 24.428 17.912 1.364 
B. MEDIUM-SIZED FIRMS 
1995 0.652 2.550 3.901 0.654 0.005 21.919 14.667 1.494 
1996 0.676 2.479 3.752 0.661 0.049 15.454 15.306 1.010 
1997 0.708 2.486 3.671 0.677 0.027 14.107 14.311 0.986 
1998 0.736 2.533 3.487 0.726 0.013 17.725 14.354 1.235 
1999 0.756 2.393 3.247 0.737 -0.009 11.956 14.320 0.835 
2000 0.683 2.421 3.379 0.717 0.012 16.608 13.847 1.199 
2001 0.577 2.359 3.435 0.687 0.014 22.416 14.050 1.595 
2002 0.623 2.505 3.416 0.733 0.024 17.560 14.356 1.223 
2003 0.591 2.450 3.555 0.689 0.013 17.091 14.420 1.185 
C. LARGE FIRMS 
1995 1.012 3.955 3.901 1.014 0.002 -3.167 12.571 -0.252 
1996 0.926 3.720 3.752 0.992 0.000 -61.392 12.200 -5.032 
1997 0.849 3.742 3.671 1.020 -0.000 54.573 11.834 4.611 
1998 0.769 3.521 3.487 1.010 -0.001 -9.395 12.097 -0.777 
1999 0.725 3.189 3.247 0.982 -0.004 7.752 12.555 0.617 
2000 0.609 3.305 3.379 0.978 0.055 12.500 12.633 0.989 
2001 0.838 3.494 3.435 1.017 0.004 16.484 13.077 1.261 
2002 0.794 3.464 3.416 1.014 -0.000 -7.403 12.835 -0.577 
2003 0.981 3.554 3.555 1.000 0.028 11.602 12.525 0.926 
 
SMEs are in general under-leveraged relative to their targets (D*/D >1). In the case of 
large firms, the fluctuations are unfortunately too large to allow any conclusions to be 
drawn. However, we can see that these firms face some kind of a shock in 1996-1997 that 
might have affected negatively SMEs credit demand in 1997-1999. The sample period 
lows and highs in banks’ and firms’ yearly adjustment speeds (δR and δD) also seem to be 
synchronized but with opposite effects. Indeed, in the small firms’ case, there is a one-
year lead to the banks’ reactions, whereas medium-sized firms’ low and banks’ high in 
their adjustment costs match in year 1999 similar to large firms’ peak and banks’ low the 
year after, in 2000. Thus, there seems to be different credit cycles depending on the size. 

More detailed look at the correlations between these firms’ mean adjustment parameters 
reveals also interesting information. We find that SME's adjustment costs (inverse of δD) 
and their observed leverages (D) are positively correlated and their leverage targets (D*) 
seem to direct the efficiency of their leverage ratio as in the large firms case. Although 
these firms behave quite similarly, there are still differences between banks’ mean 
adjustment parameters and their relation to these firms. 

In the small firms’ case, the correlations between banks’ adjustment costs (inverse of δR) 
and both the observed and target margins (R and R*), as well as in between the latter two, 
are positive. This means that higher margins will raise adjustment costs to banks. Still, 
banks’ have managed to decrease their adjustment costs over time, probably at the 
expense of the small firms’ leverage adjustment costs that have simultaneously increased. 
This emphasizes the small firms’ high dependence on bank financing.  

However, banks’ adjustment costs under medium-sized firms have instead increased over 
time. This is explained by the increased competition that seems to have dropped banks' 
observed margins over time simultaneously with these firms’ decrease in their observed 



 24

leverage. Banks’ even signal readiness to improve their competitiveness by lowering 
margins even though there is positive relation with their adjustment costs and these firms’ 
desire to increase their leverage, i.e. increase in risk. 

Large firms’ demand for credit causes banks’ adjustment costs and both their target and 
observed margins to be negatively correlated although the observed and target margins 
themselves are positively correlated. This is interpreted so that banks’ higher margins for 
these firms would lower banks’ adjustment costs and improve their risk margin 
efficiency. Despite this, banks’ target and observed margins have dropped simultaneously 
over time with these firms’ leverage increases. Hence, there is no room for margin 
increases because large firms are receiving more competitive financing elsewhere. In fact, 
positive relation of banks’ margin efficiency and these firms’ adjustment costs over time 
indicates a large firms’ negotiation power toward banks. 

Mean adjustment parameters by country 
Not only adjustment rates differ by size and time over the sample data but they also differ 
by size across countries as shown in Table VI. Hence, there is likely to be asymmetric 
effects for the common European monetary policy across member countries. The level of 
banks’ interest rate margin (R) is by far the highest in Denmark followed by Spain in all 
size groups and lowest in Germany and France. This may be explained by differences in 
country-specific competitiveness among banks or their market power. Scandinavian firms 
are instead most leveraged (D) in all size classes, whereas Italian and Spanish SMEs and 
German large firms have least leverage. This could be due to differences in regulation, 
e.g. taxation. This degree of heterogeneity by firms’ size across countries will probably be 
even higher after the new members have joint the common market. 
Table VI: Mean adjustment parameters by country during 1994-2003 are divided by size to small, 
medium-sized and large firms. The speeds of adjustments (δR and δD) measure firms’ convergence from 
observed interest rate margin (R) and debt levels (D) to their optimal or target values (R* and D*). The 
optimality ratios (R*/R and D*/D) measure efficiency of these adjustments. 

Mean adjustment parameters by country, NT=2370 obs. 
Country δR R* R R*/R δD D* D D*/D 
A. SMALL FIRMS 
AUT 0.932 2.052 1.939 1.058 -0.010 24.552 24.262 1.012 
BEL 0.974 2.575 2.877 0.895 0.030 24.297 19.218 1.264 
DEU 0.932 1.590 1.490 1.067 -0.001 23.641 20.238 1.168 
DNK 0.965 7.066 7.314 0.966 0.010 21.745 22.711 0.957 
ESP 0.951 3.958 4.179 0.947 0.005 21.843 12.554 1.740 
FIN 0.953 2.940 2.768 1.062 0.055 25.548 27.801 0.919 
FRA 0.946 1.955 1.848 1.058 0.012 22.669 16.529 1.371 
ITA 0.944 3.660 3.648 1.003 0.003 21.245 11.421 1.860 
NLD 0.951 2.523 2.287 1.103 0.033 23.581 20.060 1.176 
PRT 0.985 3.483 3.232 1.078 0.010 20.896 18.178 1.150 
SWE 0.938 2.826 2.682 1.054 0.071 21.063 23.587 0.893 
B. MEDIUM-SIZED FIRMS 
AUT 0.682 2.089 1.939 1.077 0.014 17.986 18.064 0.996 
BEL 0.723 1.803 2.877 0.627 0.018 18.656 15.942 1.170 
DEU 0.948 1.676 1.490 1.125 0.010 18.424 13.750 1.340 
DNK 0.182 1.820 7.314 0.249 0.016 16.457 15.868 1.037 
ESP 0.691 3.484 4.179 0.834 0.004 16.218 10.470 1.549 



 25

FIN 0.552 2.844 2.768 1.028 0.065 20.157 20.491 0.984 
FRA 0.883 1.707 1.848 0.924 0.019 17.239 16.001 1.077 
ITA 0.752 3.573 3.648 0.980 0.004 16.505 11.241 1.468 
NLD 0.790 2.161 2.287 0.945 0.050 18.170 18.704 0.971 
PRT 0.343 2.778 3.232 0.860 0.037 16.322 16.404 0.995 
SWE 0.874 1.934 2.682 0.721 0.068 17.644 18.975 0.930 
C. LARGE FIRMS 
AUT 0.817 1.399 1.939 0.722 0.008 17.487 14.203 1.231 
BEL 0.931 2.480 2.877 0.862 0.006 11.177 19.546 0.572 
DEU 0.798 1.613 1.490 1.083 0.010 9.553 4.584 2.084 
DNK 0.893 6.501 7.314 0.889 0.009 19.478 14.355 1.357 
ESP 0.815 3.567 4.179 0.854 0.010 -6.119 11.258 -0.544 
FIN 0.851 2.747 2.768 0.992 0.008 -15.250 20.207 -0.755 
FRA 0.806 2.660 1.848 1.439 0.010 21.525 16.601 1.297 
ITA 0.815 3.652 3.648 1.001 0.010 -22.955 11.820 -1.942 
NLD 0.775 3.804 2.287 1.663 0.006 24.442 17.190 1.422 
PRT 0.909 4.447 3.232 1.376 0.009 0.483 12.692 0.038 
SWE 0.801 3.902 2.682 1.455 0.008 -11.306 15.662 -0.722 
 
Banks’ adjustment rates under small firms’ demand for credit are almost identical. 
Portugal has the fastest and Germany and Austria slowest rates. However, the direction of 
these adjustments differs. In Belgium, banks tend to decrease their margins most whereas 
in the Netherlands, they want to increase them. This is the case also with firms’ leverage 
adjustments. In Austria, small firms wish to diverge from their leverage targets, while the 
Scandinavian (Swedish and Finnish) firms strive to their optimal fastest. Yet, the direction 
is the same, towards lower leverage. In Italy and Spain, instead, these firms surprisingly 
desire to increase their leverage.  

In general, medium-sized firms cause much more variation in banks’ adjustment speeds 
than small firms do. Still, German banks increase their margins at the highest rate, while 
Danish banks tend to decrease their margins most, but at the slowest rate. Again, medium-
sized firms’ desire to decrease their leverage mostly and at the highest rate in 
Scandinavia, while Spain and Italy suggest the opposite case. 

Variance of banks’ adjustment speeds under large firms is somewhere between the small 
and medium sizes. Highest banks’ adjustment rates are in Belgium and lowest in the 
Netherlands. Banks in the Netherlands also desire to increase their margins most, whereas 
Austrian banks behave quite the contrary. Large firms in Italy instead tend to depart 
heavily from their leverage targets, i.e. decrease their leverage, while in Germany large 
firms wish to increase most their leverage. 

These differences in the above adjustments could indicate sample countries’ different 
competitive status or efficiency within banks and between them as well as alternative 
sources of financing. In addition, over and above banks, differences in firms’ regulative 
environments, e.g. in taxation, are likely to affect firms’ capital structure decisions or 
incentives to acquire debt. Despite this, we are however able to find few noticeable 
correlations (or similarities) within these country mean adjustment parameters.  

Firstly, there are very high negative correlation between observed leverage and leverage 
ratio (i.e. leverage incentives) in the SME adjustments. This suggests that there could be 



 26

some kind of European-wide mean reversion in leverage ratios. In particular, while the 
banks respective correlations are negative for small and medium-sized firms, these 
findings could lend support to a formation of a common European markets and also 
enhance the functioning of the common monetary policy.  

Secondly, banks' adjustment speeds and margin ratios or incentives are positively 
correlated among medium-sized firms. Hence, the higher the speed of adjustments (or the 
lower adjustment costs), the closer banks’ margins is to their target levels. On the other 
hand, the adjustment rates and efficiencies of medium-sized firms' leverage are negatively 
correlated considering the country mean values. Interestingly, these firms would like to 
take more debt despite the rise in their adjustment costs. This is probably a result of bank 
competition that results in favorable terms of financing for these firms. 

Finally, banks under medium-sized firms' demand for credit show a negative correlation 
between their observed interest rate margins and their speeds of adjustments. Hence, 
higher existing margins seem to cause slower adjustments for banks due to their higher 
adjustment costs. As these firms’ higher observed leverage also seem to lower their 
adjustment costs, i.e. correlation is positive, they are certainly benefiting from the bank 
competition. This is further emphasized, as the correlation is negative among large firms 
because higher leverage would normally cause a higher adjustment costs to firms.  

Differences in the key mean adjustment parameters 
In Table VII, we highlight the size of the key mean adjustment parameter differences. 
Banks adjustment parameters under small firms demand for credit indicate that joint EMU 
monetary policy could work through this channel. Yet some caution is in place because 
firms’ leverage incentives vary greatly over time and across countries. 
Table VII: Differences of adjustment parameter mean minimum and maximum values during 1994-2003 
are divided by size to small, medium-sized and large firms. The speeds of adjustments (δR and δD) 
measure firms’ convergence from observed interest rate margin (R) and debt levels (D) to their optimal 
or target values (R* and D*). The optimality ratios (R*/R and D*/D) measure efficiency of these 
adjustments. 

Differences of adjustment parameter mean min and max values, NT=2370 obs. 
Firm size δR R*/R δD D*/D 
A. BY COUNTRY: 
Small 5% 21% 8% 97% 
Medium-sized 77% 88% 6% 62% 
Large 16% 94% 0% 403% 
B. BY YEAR: 
Small 6% 1% 5% 80% 
Medium-sized 18% 8% 6% 76% 
Large 40% 4% 6% 964% 

 

Although banks’ margin efficiencies seem to be quite stable over time in each size class, 
their speeds (or costs) of adjustments differ greatly over time and across countries. There 
is also disparity among the country-specific interest rate margin incentives. This can be 
due to differences in regulations or other country-specific factors, e.g. competition. For 
instance, medium-sized firms’ credit demand cause banks’ adjustment rates to differ more 
among countries than they do over time. In the large firms’ case, the result is however 
opposite. Their yearly differences are more pronounced, which would then be an 
indication of unpredictability or a shock effect. 



 27

Leverage adjustment speeds in manufacturing industry are quite even over time and 
across countries. However, the leverage incentives differ significantly among large firms 
across countries and over time. This can be due to differences in concentration of large 
manufacturing firms among industries and over countries. Nevertheless, this wide 
spectrum of firms’ leverage incentives poses the greatest challenge for the common 
European monetary policy.  

VI. CONCLUSIONS 
This paper has investigated the credit channel as a part of the monetary policy 
transmission mechanism by accounting for the simultaneous interaction between banks’ 
and firms’ credit conditions and their adjustment costs. These issues have been previously 
neglected in the literature. Effects of asset prices on banks’ external funding premium (or 
costs of intermediation) and on firms’ borrowing as well as on the interaction of these two 
have also been emphasized in this paper. In addition, emphasis has been given to the 
issues linked to a common European monetary policy. 

Using wide, harmonized European data and a system of dynamic partial adjustment 
models we find that the banks’ and firms’ credit conditions are interacting, although their 
adjustment costs differ across banks, firm sizes, countries, and over time. In fact, 
tightening monetary policy and firms’ increased demand for credit affect similarly banks’ 
supply conditions by increasing their interest rate margins. These increasing margins 
instead tend to decrease the small and mediums-sized firms’ demand for loans but 
unexpectedly increase it for large firms. In fact, small and large firms and the banks they 
are involved with have in general opposite reactions to changes in credit market 
conditions. We also find that uncertainty over future interest rates has most impact on 
banks’ and firms’ credit behaviour, in which smaller more bank-dependent firms are more 
subject to short-term whereas larger firms to long-term expectations. Interestingly, 
medium-size firms’ demand for credit affects banks’ margins only through the foreign 
exchange and inflation rates, i.e. comparative competitiveness of an economy. 

Banks in response to small firms’ demand for credit adjust their risk margins according to 
credit market conditions so that they immediately attain their long-run target levels. 
However, when banks are involved with medium-sized firms’ credit demand, they seem 
to face highly competitive environment and face severe adjustment costs related to their 
size. In the case of large firms, we find instead strong evidence on banks’ credit rationing 
or its exploitation by these firms due to their non-costly leverage adjustments. Unlike 
large firms, small and medium-sized firms however tend to face adjustment costs that are 
related to their default risk and in the case of medium-sized firms also for their growth 
orientation. Interestingly, we find an indication of banks’ willingness to make small firms 
pay for the larger firms growing independence on banks. Hence, a large firms’ benefit 
from common European financial markets is turned into loss for smaller firms. 

A further analysis of the adjustment parameters reveals that efficiency of loan markets 
improved constantly till year 1999 due to threat of EMU (European Monetary Union) 
competition but has since then started to weaken again. In general, central European 
(German and French) loan markets are found most efficient and the Danish and Spanish 
markets least efficient. Firms’ leverage is instead highest in Scandinavia and lowest in 
southern Europe. Despite these prevailing differences, we observe indications of a mean 
reversion within the countries’ SME leverage and banks margins, which lends support for 



 28

a formation of common European financial markets. Yet, the wide spectrum of firms’ 
leverage incentives, e.g. due to different regulatory environments, poses the greatest 
challenge for the common European monetary policy. In particular, its total effect on the 
EMU area is likely to depend on the size composition of firms and their dependence on 
bank loans within and between countries. As these country differences are especially large 
among large firms, these firms can pose a threat to the financial stability due to their 
unpredictable, credit rationing exploiting demand. This threat is due to large firms 
compounded effects made payable by smaller and more bank dependent firms and their 
lending. Hence, better financial integration also in regulative terms is needed to provide 
better means for monetary policy to control this phenomenon in the EMU area. However, 
more data intensive research is still required to shed lights on how changes in large firms’ 
demand for credit affect smaller firms’ credit conditions and how short-term credit is 
related to this. 

 



 29

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 31

 

APPENDIX  
Time dummies of the dynamic partial adjustment models between 1994-2003 for bank and 
manufacturing industry are divided by size to small, medium-sized and large firms. Subscript of the 
parameter denotes the year in question. The presence of significant time effects in the optimal and 
adjustment equations is interpreted as an unobserved heterogeneous time-variant effect.  

Time dummies of the dynamic partial adjustment models, NT=2370 obs. 
 BANK MODEL  FIRM MODEL 
Size SMALL MEDIUM LARGE Size SMALL MEDIUM LARGE 
Para-
meters 

Estimate        
t Value      

Estimate        
t Value      

Estimate        
t Value      

Para-
meters 

Estimate        
t Value      

Estimate         
t Value      

Estimate       
t Value      

A. DETERMINANTS OF OPTIMAL LEVELS: 
α1996 12.57***      

(103.58)        
0.46      
(1.05)        

0.09     
(0.57)        

β1996 -4.50***      (-
3.36)        

-10.67***      
(-4.62)        

-19.93      
(-0.83)        

α1997 21.04***      
(119.99)        

1.07*      
(1.76)        

-1.45***      
(-6.30)        

β1997 13.28***      
(6.17)        

-9.46***       
(-3.63)        

26.07     
(0.78)        

α1998 26.69***      
(111.66)        

0.96      
(1.21)        

-5.49***      
(-14.43)        

β1998 4.54      
(1.45)        

-4.43       
(-1.35)        

-31.73      
(-1.27)        

α1999 34.58***      
(116.56)        

1.37      
(1.35)        

-6.14***      
(-15.16)        

β1999 6.22**      
(2.01)        

-14.41***      
(-4.10)        

29.94     
(1.51)        

α2000 20.38***      
(96.08)        

0.03      
(0.04)       

-4.07***      
(-10.25)        

β2000 10.37***      
(3.50)        

-3.96       
(-1.31)        

-52.73**     
(-2.51)        

α2001 23.13***      
(106.88)        

0.17      
(0.22)        

-6.58***      
(-17.89)        

β2001 18.24***      
(5.92)        

0.18      
(0.06)        

-33.29      
(-1.61)        

α2002 30.25***      
(119.26)        

0.92      
(1.15)        

-5.78***      
(-16.63)        

β2002 7.62**      
(2.55)        

-6.32**      
 (-2.01)        

-51.59*      
(-1.88)        

α2003 38.56***      
(121.81)        

1.38      
(1.42)        

-4.88***      
(-14.11)        

β2003 6.01**      
(1.89)        

-8.94**       
(-2.54)        

-37.90*      
(-1.77)        

B. DETERMINANTS OF ADJUSTMENTS: 
ξ1996 -0.00      

(-0.06)        
0.00      
(0.03)        

-0.07**      
 (-2.03)        

ς1996 0.03**     
(2.52)        

0.06***    
(6.32)        

-0.00     
(-0.42)        

ξ1997 -0.00      
(-0.15)        

0.03      
(0.48)        

-0.16***      
(-3.94)        

ς1997 -0.01     
(-1.55)        

0.04***     
(4.01)        

-0.00      
(-1.34)        

ξ1998 0.01     
(0.52)        

0.01      
(0.23)        

-0.22***      
(-5.63)        

ς1998 -0.01      
(-0.81)        

0.03***     
(4.90)        

-0.00      
(-1.47)        

ξ1999 0.05***      
(2.61)        

0.01      
(0.16)       

-0.26***      
(-6.28)        

ς1999 -0.00     
(-0.01)        

0.02***     
(2.81)        

-0.00*     
(-1.70)        

ξ2000 0.02      
(1.06)        

-0.07      
(-0.93)        

-0.38***      
(-9.93)        

ς2000 -0.00     
(-0.50)        

0.02***   
(3.69)        

0.05***      
(4.66)        

ξ2001 0.03*      
(1.83)        

-0.16**       
(-2.49)        

-0.14***      
(-3.53)        

ς2001 -0.01*      
(-1.79)        

0.03***     
(5.83)        

0.00           
(0.60)        

ξ2002 0.03      
(1.36)       

-0.13**       
(-1.97)        

-0.17***      
(-4.15)        

ς2002 -0.02***           
(-3.65)        

0.05***     
(6.59)        

-0.00      
(-0.56)        

ξ2003 0.06***      
(3.07)        

-0.17***     
 (-2.59)        

0.02    
(0.40)        

ς2003 -0.01      
(-1.58)        

0.03***     
(4.49)        

0.03***     
(3.88)        

Notes: significant at the less than 1% (***), 1-5 % (**), and 5-10% (*) levels of significance 
 



ISSN 1795-5300

MTT Discussion Papers 2  2005MTT Discussion Papers 2  2005

Interacting demand and supply 
conditions in European bank 

lending

Ossi Lindström & Almas Heshmati