Firm-bank relationships : a cross-country comparison.
Date: 2023-06-16
Institutions: European Central Bank
Subjects: Eurosystem, bank, comparative analysis, credit, euro area, interest, type of business

Working Paper Series 

Kamelia Kosekova, Angela Maddaloni, 
Melina Papoutsi, Fabiano Schivardi 

Firm-bank relationships: 
a cross-country comparison 

No 2826 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Abstract

We document the structure of firm-bank relationships across eleven euro area coun-
tries and present new stylised facts using data from the Eurosystem credit registry -
AnaCredit. We look at the number of banking relationships, reliance on the main bank,
credit instruments, loan maturity, and interest rates. Firms in Southern Europe borrow
from more banks and obtain a lower share of credit from the main bank than those in
Northern Europe. They also tend to borrow more on short term, more expensive instru-
ments and to obtain loans with shorter maturity. This is consistent with the hypothesis
that firms in Southern Europe rely less on relationship banking and obtain credit less
conducive to firm growth, in line with their smaller average size. Relationship lending
does not translate in lower rates, possibly because banks appropriate part of the surplus
generated by relationship lending through higher rates.

Keywords: AnaCredit, Firm-bank relationship, Corporate financing, Bank Credit

JEL codes: G21, G3, G32

ECB Working Paper Series No 28261Non-technical summary

Banks continue to provide the largest share of financing to the corporate sector in the euro area,
despite recent increases in the issuance of corporate securities or credit from other financial
intermediaries. The structure of the firm-bank relationship – the cost and the maturity of the
funding provided, the collateral requirements and other terms of the credit contracts - matters
for firm performance. Relationship lending, in which the firm and the bank develop strong,
lasting ties, can increase the quantity and the quality of credit available to firms and alleviate
firms’ credit constraints, especially when economies are in recessions.

Previous studies have analyzed the characteristics of bank-firm relationships in the euro
area, mainly using national data sources, with a limited ability to investigate cross-country
differences. The development of AnaCredit, a harmonized credit registry recently developed
by the Eurosystem, provides the best instrument to carry-out meaningful cross-country com-
parisons.

The number of firm-bank relationships varies significantly across firm size and across coun-
tries in the euro area. Even though the number of relationships is increasing with firm size
in all countries, the absolute numbers differ significantly. Firms in Italy, Spain and Portugal
rely more on multiple banks. Large firms in these countries borrow on average from four to
five banks. A second group includes Germany, Austria, France, Greece, Finland and Belgium,
where the median large firm borrows from two banks. Finally, large firms in the Netherlands
and Ireland are closer to the one bank model. Consistently, the extent to which firms rely
on the main bank – measured by the share of credit of the main bank over total credit – is
highest for firms with fewer banking relationships. These differences remain also for other size
classes and also when taking into account sectoral specialization.

Firms’ reliance on long-term credit and its maturity also show important differences across
In general, long term credit accounts for a larger share of corporate credit in
countries.
Northern European countries. Moreover, there are important differences in the maturity of
credit, although these are more nuanced for smaller firms.

The overall picture suggests that Northern European firms rely more on one bank and
on long term instruments, and within these, enjoy longer maturities. This is consistent with
the idea that relationship lending is more common in Northern Europe, with the potential
advantages in terms of credit quality. The advantages of relationship lending, however, do not
translate into lower rates, possibly because banks appropriate part of the surplus generated
by relationship lending through higher rates.

The analysis presented in this paper provides a valuable contribution for researchers and
policy makers interested in the mechanisms underlying corporate financing and banks’ risk-
taking in the euro area. Cross-country heterogeneity in bank-firm relationships is structural
in the euro area and goes beyond firms’ characteristics. This has important implications on
the transmission of policies affecting provisions of credit to the corporate sector, including
monetary policy.

ECB Working Paper Series No 282621

Introduction

Banks still provide the largest share of external financing to the corporate sector in the euro

area, despite the recent increases of other sources of financing (from non-bank intermediaries)

and other financing instruments, such as corporate bonds.1 The provision of bank credit

establishes a relationship between the lender (the bank) and the borrower (the firm) that

can have important implications for firms’ performance and the transmission of government

and central banking policies. Firms can do business with one or more banks, which imply

that they can be more or less resilient to credit supply shocks affecting only some banks.

Also, firms can have different types of credit contracts in place with the same bank and use

the funding for different purposes, for example to finance inventory or new investment.

Until recently, no homogeneous data existed that allowed to rigorously compare the struc-

ture of such relationships across countries and provide an answer to the following questions.

How much cross country heterogeneity is there in the tendency to engage in multiple bank-

ing relationships? Do firms in different countries borrow on different instruments and at

different maturities? Do they pay different interest rates?2 In this paper, we fill this gap.

We describe the structure of firm-bank relationships for the 11 largest euro area countries:

Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Por-

tugal and Spain. We use AnaCredit, the new harmonized euro area credit registry recently

developed by the Eurosystem - the European Central Bank (ECB) and the National Central

Banks of the euro area - that covers all bank loans to corporations larger than e25,000.

The database was constructed to provide a harmonized reference for bank credit to corpora-

tions in the euro area. AnaCredit therefore represents the ideal database to carry out cross

country comparisons, typically plagued by issues of data comparability.3

We take 2019 as our reference year and we analyze firm-bank relationships along four

dimensions.4 First, we look at to what extent firms in different countries tend to rely on

1For an overview of the recent developments in the financial and corporate sector in the euro area see

ECB (2021).

2We are aware of only one paper that carries out a similar analysis in intent, the work by Ongena
& Smith (2000), which is based on evidence from a survey conducted in 1996 on 1,079 firms across 20
European countries.

3For further information on the AnaCredit project, see https://www.ecb.europa.eu/stats/money_

credit_banking/anacredit/html/index.en.html.

4AnaCredit has all information on bank loans starting from September 2018. We do not take the most
recent data, because we don’t want our analysis to be confounded by the potentially significant effects of the
Covid-19 pandemic.

ECB Working Paper Series No 28263single or multiple banking relationships. Second, we consider the extent to which firms rely

on different credit instruments, such as revolving credit (short term) vs. loans (long term).

Third, we look at the cross-country maturity of long term credit. Finally, we consider differ-

ences in the interest rates. Our goal is to document differences in the firm-bank relationship

above and beyond those due to the firm characteristics. For example, small firms tend to

borrow from a smaller pool of banks, and small firms are more prevalent in certain European

countries than in others. Therefore, it is expected that in those countries the average number

of banking relationships should be lower. By conditioning our analysis on firm characteris-

tics, we can control for these effects and single out the supply side determinants of the credit

relationship.

Relationship lending, in which the firm and the bank develop strong, lasting ties, can

help overcome asymmetric information and therefore increase the quantity and the quality (in

terms of maturity, collateral requirements etc.) of credit available to the firm. Consistently, a

large body of evidence shows that relationship lending alleviates firm credit constraints.5 For

example, it is often stated that the German model of the firm-bank relationship is based on

a main bank that provides long term financing, helping German firms to fully achieve their

growth potential (Harhoff & Körting 1998, Lehmann & Neuberger 2001, Ongena, Tümer-

Alkan & v. Westernhagen 2012)). On the contrary, in Italy multiple banking relationships are

common even among small firms, possibly because firms want to insure themselves against

banks liquidity shocks (Detragiache, Garella & Guiso 2000). Our analysis sheds light on

these topics and provides evidence on the reliance of the corporate sector on the funding of

one main bank vs. multiple banking relationships across countries.

The number of banking relationships varies significantly across the euro area countries.

In particular, we find that three clusters emerge. On the one hand, firms in Italy, Spain

and Portugal rely more on multiple banks. Large firms in these countries borrow on average

from four to five banks. A second group includes Germany, Austria, France, Greece, Finland

and Belgium, where the median large firm borrows from two banks. Finally, large firms in

the Netherlands and Ireland are closer to the one bank model. These patterns appear also

for the other size classes, as well as when directly accounting for size and sector dummies in

a regression framework, indicating that such differences are not accounted by the different

industrial structures of EU countries.

5See Degryse, Kim & Ongena (2009) for a survey and Kysucky & Norden (2016) for a meta-analysis of
the effects of relationship lending on access to credit in general. Beck, Degryse, De Haas & Van Horen (2018)
show that relationship lending is particularly important during recessions, when firms need credit the most.

ECB Working Paper Series No 28264Consistently, we find that the extent to which firms rely on the main bank – measured

by the share of credit of the main bank over total credit – is lowest for the first cluster and

highest for the third. The extent on which a firm relies on the credit from its main bank

is a measure of how strong the firm-bank relationship is. Recent evidence shows that the

strength of the firm-bank relationship can mitigate the credit contraction arising from shocks

affecting the lenders and overall have a beneficial effect on firms’ investment and employment

(Gobbi & Sette 2013, Sette & Gobbi 2015, Banerjee, Gambacorta & Sette 2017), suggesting

that cross-country differences documented in this paper could be correlated with firm level

performance.

We then analyze the instruments through which firms obtain credit, distinguishing be-

tween loans, non-revolving credit lines, leases, trade receivables and revolving credit.6 First,

we find that, across countries, loans and non-revolving credit lines, both long term instru-

ments, are substantially cheaper than other forms of credit. For example, the overall average

rate on loans and non-revolving credit lines is just above 2%, while that on revolving credit

is around 5%. This implies that cross country differences in the reliance on different instru-

ments will affect the overall cost of credit for firms. And in fact, we find that reliance on

long term credit varies between more than 80% in France to less than 60% in Greece, Italy

and Ireland. In general, long term credit accounts for a larger share of credit in Northern

European countries.7

Next, we look at the maturity of the long term instruments, loans and non-revolving

credit lines. We find very large cross country differences in average maturity. If we look at

large firms, Dutch firms borrow at the longest maturity (15 years) while Irish firms have the

shortest maturity (5 years). This indicates that the main bank model, which is prevalent

in these two countries, can result in very different maturity for long-term credit. For other

countries, the North vs. South clusters are confirmed, with large Greek, Spanish, Italian

and Portuguese firms displaying lower maturity. The results are however more nuanced

when we look at smaller firms, and indeed we find that, overall, the maturity does not

vary systematically with size.

In term of countries, the overall picture is confirmed, the

main difference being that Greek and Spanish SMEs show average maturity more similar to

6Non-revolving credit lines are similar to loans, with the difference that the funds are given in pre-

established tranches over time, rather than all at once.

7Consistent with our result, Feraboli, Häkkinen & Gutiérrez (2015) show that firms in countries more
affected by the Great Financial Crisis have a higher share of short-term credit compared to firms in countries
that were less affected.

ECB Working Paper Series No 28265Northern European countries.

Finally, we consider the interest rate. Here, the more striking result is the large cross

country difference in the average interest rate. For large firms, we find that Greek and Irish

firms pay the highest rates, between 6% and 5%, followed by the Netherlands (4%). Large

firms in the other countries pay more similar rates, around 2%. These patterns are broadly

confirmed for other size classes and for the overall distribution. Moreover, controlling for size

and sector effects changes the distribution only marginally, indicating that the differences

are due to some country attributes rather than to differences in the firm characteristics.

To dig further into these differences in interest rates, we perform three shift-and-share

exercises according to size, sector, and reliance on different credit instruments. These ex-

ercises allow us to determine how much of the observed cross country difference in average

rates is due to differences in the distribution of firm characteristics and instrument type and

how much to differences within firm type or instrument type average rates. We find that size

and sector effects play only a marginal role. Some more action emerges in terms of type of

instrument. In particular, in Greece, Ireland, Austria, and Italy the average interest rate is

between 30 and 50 basis points higher than what would have been if firms in these countries

had the same share of credit by instrument type as the overall average. These are in fact the

countries that rely more on more expensive, short term instruments. For all decompositions,

the within component remains the dominant factor, indicating that country effects play a

predominant role in determining cross country heterogeneity in interest rates.8

To summarize, the overall picture suggests that Northern European firms rely more on

one bank, more on long term instruments, and within these, enjoy longer maturities. This is

consistent with the idea that relationship lending is more common in Northern Europe, with

the potential advantages in terms of credit quality. Of course, this is a rough representation

of a very complex picture, in which there are several exceptions. Possibly, the most noticeable

one is Ireland, where firms tend to heavily rely on a single bank but with credit outcomes

more similar to those of countries in which multiple banking relationships are more common.

The advantages of relationship lending, however, do not translate into lower rates. In fact,

8In this paper, we do not investigate potential explanations of the cross-country differences on the interest
rates, as the main focus is to document those. Differences on the interest rates across countries are described
also by the OECD. (2022) report, where issues with access to credit is highlighted as a key explanatory factor.
Feraboli et al. (2015) show that the monetary policy pass-through during the low interest rates period was
much more sluggish for countries more affected by the financial crisis and thus led to higher interest rates for
firms in these countries compared to firms in countries in the euro area less affected by the financial crisis.

ECB Working Paper Series No 28266we find no clear divide along this dimension. This might be explained by the fact that, as

theory suggests, the main bank extracts part of the surplus that the relationship creates by

charging higher rates (Rajan 1992).

The analysis presented in this paper provides a valuable contribution for researchers and

policy makers interested in the mechanisms underlying corporate financing and banks’ risk-

taking in the euro area. On the one hand, the evidence presented corroborates some of the

patterns unveiled in previous literature, mostly based on one country evidence. Leveraging

on a completely harmonised database, we show that cross-country heterogeneity in bank-

firm relationships is structural in the euro area and goes beyond firms’ characteristics. This

has important implications on the transmission of policies affecting provisions of credit to

the corporate sector. For example, Jiménez, Ongena, Peydró & Saurina (2012) analyze the

transmission of monetary policy shocks using data on firm-bank relationships from Spain.

They show that the probability for a firm to be granted a loan is significantly impacted by the

number of firm-bank relationships. Therefore, firms with multiple bank relationships are less

affected by monetary policy shocks and also by the financial conditions of the lending banks.

At the same time, Degryse, De Jonghe, Jakovljević, Mulier & Schepens (2019) point out that

in order to correctly evaluate the transmission of credit shocks, it is important to consider

the structure of credit markets in each country and appropriately include the effects on single

bank-firm relationships. We also unveil important features of within-country heterogeneity

in corporate financing, such as the dispersion in the number of firm-bank relationships, and

compare them across euro area countries.

The rest of the paper is organized as follows. In Section 2 we describe the data, while

Section 3 analyzes the number of firm-bank relationships and the reliance on the main bank.

Section 4 studies the type of credit instruments, Section 5 looks at maturity and Section 6

analyzes the interest rate. Section 7 concludes.

2 Data

The core dataset of this analysis comes from the harmonized credit registry of the euro area,

AnaCredit, that contains confidential loan-by-loan information on bank credit to enterprises.9

9AnaCredit reports all loans granted by credit institutions residing in euro area Member States, including
loans granted by their domestic / foreign branches. The data is maintained by the ECB and the Eurosystem
National Central Banks and is harmonized across Member States. A credit instrument is subject to reporting

ECB Working Paper Series No 28267The purpose of our analysis is to identify structural cross-country differences in bank-firm

relationships in the euro area. Thus, we focus on the cross-section and restrict the data only

to December 2019.

2.1 AnaCredit

For the purpose of this analysis, we restrict our sample to credit granted to non-financial

corporations residing in Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy,

the Netherlands, Portugal and Spain. Together, these countries account for approximately

95% of euro area nominal gross domestic product (GDP) in 2019,10 and 97.0% of the total

outstanding bank credit in the euro area at the end of 2019.11

To carefully restrict the analysis to non-financial corporations,12 we exclude all firms

operating in financial services, insurances and pension funds, and activities auxiliary to

financial services and insurance activities (NACE codes 64–66). Moreover, to ensure that we

compare similar firms in terms of credit risk across countries, we exclude firms that are in

default. This is because firms in default are typically engaged in complex debt write-offs and

transfers, which involve different banking relationships. Thus, we exclude all firms which

either defaulted on any of their active loans in December-2019, or were classified as in default

with any of its creditors in December-2019.13

Next, to accurately estimate the number of firm-bank relationships, we omit firms that

have active syndicated loans in December 2019 and we exclude all loans associated with

multiple creditors. This is because only the euro area credit institutions are recorded for

syndicated loans in AnaCredit, independent of the reporting agent’s role as lead arranger

or agent. Thereby, the actual number of all banks involved in the syndicate is not available

through AnaCredit.

if the borrower is a legal entity, and if the total commitment amount at the creditor-debtor level is greater
or equal to e25.000 at any point within the reference period. Counterparties operate across all institutions
and reside globally, although for the purpose of this study we focus only on euro area borrowers. The data
is available at monthly frequency, beginning in September 2018.

10Calculations are based on Eurostat data, GDP at current market prices.
11The calculation is based on our final data set including all applied filters. The reference period for our
analysis is December 2019, but our results are robust to the selection of previous or subsequent reference
periods.

12Non-financial corporations are defined using the ESA 2010 classification equal to S.11
13Default can arise either due to the unlikelihood to pay, to past-due of more than 90/180 days, or to

both.

ECB Working Paper Series No 28268We perform the analysis at the bank, rather than the banking group level. We choose

to use the bank level information because entrepreneurs deal directly with a bank and not

a group. For example, loan officers from different banks within the same group are not

generally expected to share information on single customers.

At the instrument level, we exclude all instruments classified as reverse repurchase agree-

ments and deposits because these are complex financial instruments usually linked to a fi-

nancial subsidiary of a corporation. We also exclude loans flagged as ‘Project Finance’ since

these are associated with large infrastructure projects with multiple creditors. To simplify

the analysis on the credit type, we group the remaining instrument types into five categories:

(1) loans; (2) financial leases; (3) trade receivables; (4) credit lines; and (5) revolving credit,

where revolving credit captures overdrafts, credit card debt, and revolving credit other than

overdrafts and credit card debt.

Finally, to ensure comparability across products, we restrict the analysis to loans granted

in EUR, which corresponds to 84.0% of all instruments in December 2019. At the instrument

level, we further winsorize outstanding and commitment amounts at the 0.01% level.

To ensure the data quality of the sample, we apply a number of filters on debtor and

instrument level. On debtor level, we exclude values that exceed or are equal to the 0.01%

level for the number of employees, annual turnover, and balance sheet total. On instrument

level, we exclude values of the outstanding amounts below or equal to 0 and above or equal

to the 0.01% level in the empirical distribution.14 The final sample consists of 2,737,561

firms that received loans from 2,482 banks.

To classify debtors according to firm size, we proceed in three steps. First, we com-

bine information on the balance sheet size, number of employees, and annual turnover at

the unconsolidated firm-level in AnaCredit. Second, for these firms that do not have any

information available in AnaCredit, we link AnaCredit to Bureau van Dijk’s Orbis to incor-

porate external firm-size characteristics. We link AnaCredit to Orbis using the firm’s RIAD

code and national identifier. Finally, for these firms without individual information on firm

characteristics neither in AnaCredit nor in Orbis, we utilize the enterprise size as defined in

AnaCredit.

14This threshold ensures that clearly implausible data points are discarded without significantly restricting

the data coverage.

ECB Working Paper Series No 28269We categorize firms into four size classes: micro, small, medium, and large. Following

the official definition of the European Union,15 firms are classified as micro if they have less

than 10 employees, and either have total assets or annual turnover less than EUR 2 million.

Small firms have less than 50 employees, and either have total assets or annual turnover less

than EUR 10 million. Medium-sized firms have less than 250 employees, and either have

total assets less than EUR 43 million or annual turnover less than EUR 50 million. The

remainder are classified as large firms. We describe the procedure in detail in Appendix B.

2.2 Summary statistics

Table 1 presents the summary statistics of the main variables for each size class for the whole

sample.16 The sample of the large firms consists of 42,609 firms, the medium of 144,204 firms,

the small of 593,337 firms, and the micro of approximately 2 million firms. Based already

on the aggregate summary statistics, it is evident that there is a significant difference on the

firm-bank relationships across firm size. Larger firms on average borrow from more banks,

while the share of credit used from the main bank is decreasing with firm size - smaller banks
are more dependent on the funding of their main bank. Moreover, the cost of bank credit,
measured as the average interest rate of the loans, is lower for larger firms. As expected, the

total amount outstanding, the number of employees, the total size of the balance sheet, and

the annual turnover of the firm are increasing with firm size. We analyze the cross country

differences in firm-bank relationships as well as the terms of credit received in detail in the

rest of the paper.

In the Appendix Table A12, we also report the summary statistics of outstanding amounts

for instrument type by firm size. The last column shows the share of firms that use each

instrument. The vast majority of firms uses loans and revolving credit, with micro firms to

have the highest share of loans. As expected, the median values of outstanding amounts are

increasing with firm size.

15https://ec.europa.eu/growth/smes/sme-definition_en
16Appendix Tables A1-A11 report the same information separately by country.

ECB Working Paper Series No 282610Table 1: Summary statistics by size

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

42,609
42,609
42,609
39,134
27,234
37,009
35,813
38,070

144,204
144,204
144,204
135,238
104,914
132,921
124,685
125,064

593,337
593,337
593,337
565,804
450,580
562,694
528,375
517,446

1,957,411
1,957,411
1,957,411
1,861,200
1,529,970
1,510,323
1,500,110
1,491,672

132
1
0.55
0.78
4.17
167
17,899
9,132

130
1
0.58
0.99
4.10
52
1,934
791

69
1
0.67
1.25
4.12
11
408
75

37
1
1.00
1.44
5.00
1
131
48

1,103
2
0.90
1.52
6.57
330
65,750
63,300

705
2
0.96
1.71
6.30
70
8,768
8,000

229
2
1.00
2.07
6.02
15
1,452
1,255

89
1
1.00
2.30
7.07
2
328
202

9,000
4
1.00
2.63
11.36
626
170,000
150,974

2,888
4
1.00
2.83
10.30
108
17,484
19,025

707
3
1.00
3.38
10.18
24
3,250
3,075

228
1
1.00
3.76
14.18
4
710
489

15,091
3
0.77
2.09
8.50
3,013
574,186
8,194,879

3,226
3
0.80
2.26
8.05
83
21,620
64,984

824
2
0.84
2.65
7.95
18
4,512
51,049

302
1
0.93
2.92
9.57
3
1,309
32,562

Note: The differences in the number of observations stem from the data availability for firms and
instruments. The data on maturity includes only long-term credit (loan and non revolving credit lines).

ECB Working Paper Series No 2826112.3 Credit contracts

Firms borrow using different instruments, which differ in terms of maturity, collateral and

revolving nature. A revolving instrument is one in which the bank guarantees credit up to a

certain pre-set amount and the firm can, up to that amount, increase and decrease exposure

as needed. Instruments can be grouped in five broad categories:

1. Loans. This instrument includes all loans and advances as well as bills not included
in any of the other categories. Any instrument classified as other loans is of a non-

revolving nature. This type of instrument includes lump-sum credits (where the total

credit is paid out in one installment).

2. Non revolving credit lines. Non revolving credit lines have the following features:
(a) the debtor may withdraw funds up to a pre-approved credit limit without giving

prior notice to the creditor; (b) the credit may be used in tranches; (c) it is not of a

revolving nature (i.e., the amount of available credit can only decrease as funds are

drawn and repaying funds does not increase the available amounts).

3. Trade receivables. The type of instrument “trade receivables” includes loans granted
on the basis of bills or other documents that give the right to receive the proceeds of

transactions for the sale of goods or provision of services. This item includes all factor-

ing transactions (both with and without recourse) as well as forfaiting and discounting

of invoices, bills of exchange, commercial papers, and other claims on the condition

that the credit institution buys the trade receivables. Note that the instrument “trade

receivables” is distinguished from financing against trade receivables. While “trade

receivables” means purchasing trade receivables (the factoring client sells the trade

receivables), in financing against trade receivables the credit institutions typically ad-

vance funds against a pool of receivables which serve as protection. In other words,

financing against trade receivables is an instance of credit that involves the use of the

trade receivables as collateral.

4. Financial leases. A financial lease is a contract under which the lessor as legal owner
of an asset conveys the risks and benefits of ownership of the asset to the lessee. Under

a financial lease, the lessor is deemed to make, to the lessee, a loan with which the

lessee acquires the asset. Thereafter, the leased asset is shown on the balance sheet of

the lessee and not the lessor; the corresponding loan is shown as an asset of the lessor

ECB Working Paper Series No 282612and a liability of the lessee. The lessor is recorded as the creditor to the instrument

whereas the lessee is the debtor to the instrument. The leased asset is usually used as

protection.

5. Revolving credit. The revolving credit instrument includes (a) overdraft, (b) credit
card debt, and, (c) revolving credit other than overdrafts and credit card debt. All these

instruments are revolving and for that reason we grouped those under one instrument.

Loans and credit lines are typically used to finance long term investments, the main

difference being that a loan is given out lump-sum while the credit line allows the firm to

withdraw in tranches. Therefore, these instruments are used to finance projects that imply
payments staggered in times. We define the firm-level average maturity of instrument j as:

mijc =

(cid:88)

b

ωijbcmijbc

(1)

where mijbc is the maturity at origination of credit instrument j that bank b extends to firm
i in country c, ωijbc = ON Aijbc
b ON Aijbc is the total credit firm i obtains
ON Aijc
through instrument j. ONA stands for outstanding nominal amount of the credit granted
and we use this abbreviation throughout the paper. We classify loans and credit lines as long

and ON Aijc = (cid:80)

term instruments. Trade receivables are used to cash in on yet-to-be-paid bills, and as such

they are a short term source of liquidity. The instrument is largely used to finance working

capital, as well as import and export. Finally, revolving instruments provide firms with short

term liquidity and are typically used to finance working capital and liquidity shocks. They

have no maturity, as by contract they can be revoked by the bank with no prior notice.

2.4

Interest rate definition

Firms borrow from multiple banks and use different instruments. Moreover, a firm can

have different contracts for the same type of instrument from the same bank, such as two

loans issued at two different dates. We proceed by aggregating the granular instrument level

information to obtain a meaningful measure of the cost of credit for the firm. First, we

aggregate the data at the firm-bank-instrument level by taking the weighted interest rate on

each contract of a given instrument, weighted by the size of each contract. Our basic interest
rate observation is therefore rijbc, that is, the interest rate that firm i pays on instrument
j from bank b in country c, with ON Aijbc being the corresponding outstanding nominal

ECB Working Paper Series No 282613amount. Next, we aggregate at the borrower level and we take all credit a firm obtains on
instrument j from all banks and construct the firm-instrument level interest rate as:

rijc =

(cid:88)

b

ωijbcrijbc.

(2)

where ωijbc = ON Aijbc
b ON Aijbc is the total credit firm i obtains through
ON Aijc
instrument j. In each period, a firm will have at most five different values of rijc, one for
each instrument it uses.

and ON Aijc = (cid:80)

The next level of aggregation is at the firm level, and we construct the firm level interest

rate as:

where ωijc = ON Aijc
ON Aic
instruments.

ric =

(cid:88)

j

ωijcrijc

(3)

and ON Aic = (cid:80)

j ON Aijc is the total credit firm i obtains through all

It is well known that large firms pay lower rates. This relationship is confirmed in the

AnaCredit data. Figure 1a plots the distribution of the interest rate by firm size for the whole

sample of firms, that is, without distinguishing by country. There is a clear declining trend

when going from micro to large firms. The variation is however not large: the average rate

for micro firms is 2.92%, and it drops to 2.09% for large firms (with basically no differences

with medium firms).17

Different instruments also command different rates, as shown by Figure 1b. In this case

differences are more pronounced, with revolving credit (5.03%) costing on average twice as

much as loans and credit lines, as well as being substantially more dispersed than the other

instruments. Interest rates on trade receivables and leases are slightly above 3%, while rates

on loans and credit lines are equal to 2.37% and 2.06%, respectively.

3 Number of firm-bank relationships and reliance on the

main bank

Theory suggests that firms might want to entertain more than one bank relationship both for

insurance reasons – to be able to access credit from multiple sources in case one or more banks

17For reference, the monetary policy rates in the euro area at the end of 2019 were -0.5 for the deposit

rate, 0 for the MRO and 0.25 for the MLR.

ECB Working Paper Series No 282614Figure 1: Interest rates

(a) By firm size class

(b) By instrument type

Note: The figure shows the distribution of interest rates. The box plot’s bars depict the interquartile
range, the red line indicates the median and the red triangle the average interest rate. The bars
are arranged in descending order according to the average interest rates. The sample period is
December 2019.

run into liquidity problems (Detragiache et al. 2000) – and to increase their bargaining power

against banks (Rajan 1992). At the same time, multiple relationships might induce a “free

riding” problem on banks, according to which none of them has sufficient incentives to invest

in information production to reduce asymmetric information. In fact, relationship lending is

based on the main bank model, in which a firm mostly borrows from a single bank, forming

a strong tie that allows to reduce the extent of asymmetric information. The prevalence of

a model in which firms rely more or less on multiple bank relationships ultimately should

depend on the tradeoff between the benefit of greater diversification and the costs of free-

riding problems and duplication of efforts (Carletti, Cerasi & Daltung 2007). In this section

we describe how the nature of the firm-bank relationship varies across countries, focusing on

the number of banks a firm borrows from, the share of credit supplied by the main bank and

credit concentration across the different firm’s lenders.

3.1 Number of firm-bank relationships

First, we analyze the number of banks firms borrow from. Given that a firm is in AnaCredit

only if it borrows from at least one bank, our statistics refer to the intensive margin, that

is, the number of relationships conditional on having at least one. Figure 2 reports box

plots of the distribution of the number of banks separately by firm size and country. We

0LFUR6PDOO0HGLXP/DUJH,QWHUHVWUDWH5HYROYLQJFUHGLW7UDGHUHFHLYDEOHV)LQDQFHOHDVHV/RDQV&UHGLWOLQHV,QWHUHVWUDWHECB Working Paper Series No 282615rank countries in descending order in terms of the average number of banks for large firms

and keep the same ranking in all four panels. For large firms (Panel 2a), three groups of

countries emerge. Firms in Italy, Spain, and Portugal have the largest number of banks,

with an average between 4 and 5, a median between 3 (Italy and Spain) and 4 (Portugal)

and the 75th percentile between 6 and 7. A second group includes Germany, Austria, France,

Greece, Finland, and Belgium, where the median firm borrows from 2 banks (the mean is

around 3). Finally, large firms in the Netherlands and Ireland follow the one bank model:

the median is 1, the 75th percentile is 2 and the mean is between 1 and 2. This ranking also

holds in terms of dispersion: the interquartile range (the difference between the 75th and the

25th percentile) is 5 in Italy, 4 in Spain and Portugal, 3 or 2 in the intermediate cluster, and

just 1 in Ireland and the Netherlands.

Not surprisingly, Panel (b), (c) and (d) of Figure 2 show that the number of firm-bank

relationships decreases monotonically with size in all countries. However, the three-clusters

pattern described above emerges in all size classes. For the first cluster, the ranking of Italy,

Spain, and Portugal is also confirmed in all size classes. The second cluster shows very

similar values across countries, with an average of 3 for medium firms, 2 for small and 1.3

for micro. With the exception of Finland, the median small firm in this cluster borrows only

from 1 bank. The fact that the average is closer to 2 implies that there is a long tail of firms

entertaining multiple relationships with banks. The Netherlands and Ireland always display

low and similar values: in all size classes the one bank model is prevalent. The fact that we

obtain similar patterns in all size classes supports the notion that there are important cross-

country structural differences in the determination of the number of banks a firm borrows

from.

To obtain a summary measure of the cross country differences, in the first bar of Figure

3 we plot the overall average number of relationships, that is, without distinguishing by

size class. We keep the same ranking as in Figure 2. The overall average, between 1 and

2 in all countries, resembles that of small and especially micro firms, as they represent the

vast majority of firms and therefore substantially affect the mean. The three clusters of

countries are again confirmed. At one extreme, Italian firms have almost two relationships

on average, while at the other extreme, Irish and Dutch firms have approximately around 1.1

relationships. Within the middle cluster, the ranking is slightly different from the one based

on large firms:

in particular, German firms display the highest value, followed by Greek

firms.

ECB Working Paper Series No 282616Figure 2: Number of bank relationships by firm size

(a) Large

(b) Medium

(c) Small

(d) Micro

Note: The figure shows the firm-bank relationship distribution by country and firm size. The number of
relationships is calculated at the firm level. The box plot’s bars depict the interquartile range, the red line
indicates the median and the red triangle the average number of relationships. Countries are ordered by
descending average number of relationships of large firms. The sample period is December 2019.

,7(637$7)5'(*5),%(1/,(1XPEHURIILUPEDQNUHODWLRQVKLSV,7(637$7)5'(*5),%(1/,(1XPEHURIILUPEDQNUHODWLRQVKLSV,7(637$7)5'(*5),%(1/,(1XPEHURIILUPEDQNUHODWLRQVKLSV,7(637$7)5'(*5),%(1/,(1XPEHURIILUPEDQNUHODWLRQVKLSVECB Working Paper Series No 282617Figure 3: Average number of bank relationships

Note: The figure reports the average number of banks a firm borrows from. The Unconditional bar
represents the unconditional average, while the Conditional one represents the average controlling
for size and sector dummies as specified in Equation 4. The horizontal line represents the overall
average. Country order of large firms is kept.

One issue with the mean plotted in the first bar of Figure 3 is that it mixes a country

attribute (the firm propensity to develop multiple relationships) and firm characteristics. It

is well known that both the firm size distribution and sectoral specialization differ systemat-

ically across European countries (Pagano & Schivardi 2003). As shown above, smaller firms

on average borrow from a lower number of banks. Sectoral specialization might also induce

different borrowing patterns. For example, due to the sectoral differences in the importance

of tangible assets, some sectors tend to rely more on debt financing (Falato, Kadyrzhanova,

Sim & Steri 2020). To account for this, we estimate the following regression specification:

Nrelisgc =

11
(cid:88)

c=1

c DCtry
αN

c +

4
(cid:88)

g=1

s DSize
βN

g +

20
(cid:88)

l=1

s DSect
γN

s + ϵisgc

(4)

where Nrelisgc is the number of bank relationships entertained by firm i in sector s, size class
is a dummy equal to one if firm i belongs to country c and similarly
g and country c, DCtry
for the four size classes and the twenty sector dummies. We omit the constant to retrieve
, which represent the conditional means, that
all coefficients of the country dummies, αN
c
is, conditional on size and sector effects. To retain comparability with the unconditional

c

0.511.52regression coefficientITESPTATFRDEGRFIBENLIEUnconditionalConditionalECB Working Paper Series No 282618mean, we normalize the values so that the overall conditional mean is equal to the overall

unconditional mean.18 Controlling for firm characteristics changes the position of some

countries in the ranking (Figure 3). Specifically, the conditional mean for Germany decreases

(from 1.59 to 1.36), consistently with the fact that German firms are on average larger, so

that the number of relationships decreases once we account for size. To a lower extent, the

same holds for Austria, Greece, Ireland, and Portugal. At the other end, Finland records the

largest increase (from 1.36 to 1.59), followed by France and Belgium. The values are almost

identical for the other countries. In general, the differences between the conditional and the

unconditional mean are small. This indicates that cross-country differences in the propensity

of firms to entertain multiple banking relationships are mostly explained by some country

attributes rather than by differences in the size or sectoral composition of firms. Stated

differently, Italian and Spanish firms have on average almost twice as many relationships as

Dutch and Irish firms also when accounting for sectoral and size differences.

3.2 Reliance on the main bank

Having established that there are substantial differences in the number of relationships across

countries, we now check if they translate into differences in the extent to which firms actually

rely on a plurality of banks. In particular, it could be that firms in the first cluster of countries

nominally borrow from more banks, but actually they mostly operate with a single bank,

keeping the other relationships in place for insurance reasons. To assess this, we first consider

the share of credit a firm gets from the “main bank”, defined as the bank which accounts for

the largest share of the outstanding credit. We consider the outstanding amount, that is,

the credit that a firm is actually using (as opposed to the granted amount, which includes

unused commitments).

Figure 4 reports the box plots for the share of credit from the main bank, by firm size and

country. As for the number of relationships, we rank the countries from highest to lowest

average value of large firms. The pattern of three clusters is broadly confirmed, despite some

small differences. For large firms (Panel 4a), the average amount supplied by the main bank

18In fact, the regression drops one size and one sector dummy, so that the country dummy refers to that
size-sector cell. Of course, the choice of the excluded sector and size class affects the absolute values of the
αc but not the cross country differences αc1 − αc2 . We re-base the coefficients by subtracting the average of
the conditional country dummies 1/n (cid:80)
c αs and adding the average unconditional country means, so that
by construction the overall country mean is equal for the conditional and unconditional means.

ECB Working Paper Series No 282619is around 0.7 for Italy, Spain, and Portugal, the lowest values, in accordance with the results

on the number of banks. In the second cluster, the average value is around 0.8, with Greece

and France recording the lowest values and Finland the highest. The average large firm in

the Netherlands and Ireland borrows more than 90% of its credit from the main bank.

The mean masks more marked differences in the distribution. In particular, in Austria,

Belgium, Finland, France, Germany, Ireland, and the Netherlands, the distribution is left-

skewed and the median is substantially higher than the mean; the median firm borrows

more than 90% of the total amount from the main bank, while the figure is 70%-80% in

Greece, Italy, Portugal, and Spain. This means that, despite a long left tail of firms that do

not concentrate their credit demand on one bank, most of the firms in Northern European

countries tend to heavily rely on one bank. Firms in Southern countries are also characterized

by a higher dispersion in reliance on the main bank, as shown by the higher values of the

interquartile range.

The general patterns found for large firms are confirmed for other size classes. The overall

values for medium firms are very similar to those for large ones. In this size class, the median

firm in all countries but Italy, Greece, Portugal, and Spain borrows only from the main bank.

Irish and Dutch medium firms basically draw all their credit from one bank. The share of

credit of the main bank increases somehow for small firms, but remains below 80% at the

mean for Italy, Portugal, and Spain. In these countries, even micro firms on average borrow

only around 90% of the total amount from the main bank.

As for the number of relationships, we also report the average value of credit from the

main bank at the country level in Figure 5, both unconditional (left bar) and conditional

on size and sector fixed effects. The clusters also emerge clearly in this case. Moreover, as

before, controlling for firm characteristics only induces some visible changes in the ranking

for Germany and Finland, while for other countries the two means are very similar. This

confirms that cross country differences in the firm-bank relationship and the reliance on

funding from the main bank is dictated more by country attributes and less by differences

in firm size and sectoral characteristics.

As a further indicator of reliance on multiple banking relationships, we calculate the

Hirschman-Herfindahl index (HH) to measure credit concentration across banks. The indi-
cator is defined as HHi = (cid:80)
, where b ∈ i is the set of banks that lend to firm
i, creditib is the credit firm i obtains from bank b and crediti = (cid:80)
creditib is firm i’s total

(cid:16) creditib
crediti

(cid:17)2

b∈i

b∈i

ECB Working Paper Series No 282620Figure 4: Share of credit from the main bank by firm size

(a) Large

(b) Medium

(c) Small

(d) Micro

Note: The figure displays the share of outstanding nominal amount (ONA) from the main bank
by debtor country and firm size. The share is computed at the firm level. The box plot’s bar
represents the interquartile range, the red line indicates the median credit concentration. The
average of credit concentration is indicated by the red triangle. For readability, upper and lower
whiskers are omitted. Countries are ordered by the descending average share of large firms. The
sample period is December 2019.

,(1/),%('($7)5*537,7(60DLQEDQNVKDUH,(1/),%('($7)5*537,7(60DLQEDQNVKDUH,(1/),%('($7)5*537,7(60DLQEDQNVKDUH,(1/),%('($7)5*537,7(60DLQEDQNVKDUHECB Working Paper Series No 282621Figure 5: Average share of credit from the main bank

Note: The figure reports the average share of credit from the main bank that a firm has. The
Unconditional bar represents the unconditional average, while the Conditional one represents the
average controlling for size and sector dummies as specified in Equation 4. The y-axis begins at
0.80. Country order of large firms is kept.

credit. The results, reported in Appendix Figure A1, fully confirm those based on the main

bank.

3.3 Main takeaways

Overall, the evidence presented in this section points to substantial differences in the firm-

bank relationship across European countries. At one extreme, Ireland and the Netherlands

are well represented by a model in which firms tend to do business mostly with one bank.

At the other, Italian, Spanish, and Portuguese firms display a greater tendency to entertain

multiple banking relationships and draw a lower share of credit from the main bank. The

other countries are in between these two models, with Greece and, to a lesser extent, France

closer to the Southern European model and Finland, Germany, Belgium, and Austria closer

to the main bank model. We have shown that firm size distribution and sector specialization

may explain some of these differences but other (unidentified) factors must play a more

.85.9.951regression coefficientIENLFIBEDEATFRGRPTITESUnconditionalConditionalECB Working Paper Series No 282622important role.

The legal environment and the quality of contract enforcement is likely to explain some

of the differences. For example Figure 6 plots the average number of banking relationships in

each country and the values of a “rule of law” indicator, which measures the quality of contract

enforcement and the protection of property rights. The associated correlation (-0.57) suggests

that indeed in countries where the legal environment is less supportive of property rights,

the number of firm-bank relationships tends to be higher. This might be because, where

contracts are less enforceable, banks are able to recover less from a bankruptcy procedure.

As a consequence, they prefer to diversify their portfolio of loans on a large number of firms.

More analysis is needed to establish a causal effect and eventually uncover other determinant

factors.

Figure 6: Number of relationships vs Rule of Law

Note: The figure shows the correlation (negative, -0.57) between the average number of firm-bank
relationships and the rule of law estimates by country. The data for rule of law is sourced from the
Governance data set (World Bank) and is presented in units of standard normal distribution (from
-2.5 to 2.5).

The differences in the number and importance of firm-bank relationships could have

important consequences on firms’ performance. On the one hand, higher credit concentration

allows firms to develop a closer link with the main bank, which might be more willing to

act like an equity holder, providing more long term finance and a more stable credit supply

5XOHRI/DZ1XPEHURIUHODWLRQVKLSV$7%(),)5'(*5,(1/37(6,7ECB Working Paper Series No 282623also to face large, unexpected shocks (see, for example, Petersen & Rajan 1994). On the

other hand, being dependent on one bank might expose the firm to liquidity and funding

shocks affecting its lenders (Detragiache et al. 2000, De Jonghe, Dewachter, Mulier, Ongena

& Schepens 2019). Moreover, being dependent on only one bank might reduce the bargaining

power of the firm against the bank, implying higher interest rates (Rajan 1992). In what

follows, we study the types of credit contracts, the loan maturity and the interest rates to

gain further insights on these issues.

4 Credit contracts

In section 2.3 we have defined five broad instruments used by firms to obtain credit from

banks: loans, credit lines, trade receivables, financial leases and revolving credit. In terms

of maturity, we have classified the first two instruments as long term and the others as short

term. Long term credit is particularly suitable to finance investments, while short term

credit is typically used to finance working capital. Moreover, as shown above, the costs of

borrowing short term is typically higher. It is therefore interesting to analyze to what extent

European firms differ by country in terms of the instruments used to get credit from banks.

We construct the shares by instrument type at the country level as follows:

ShInstrjc =

1
Nc

(cid:88)

i∈c

(cid:19)

(cid:18) creditijc
creditic

(5)

where j is the type of instrument, Nc is the total number of firms in country c, creditijc is
the total credit a firm has in country c and instrument type j, and creditic is firm i total
credit. Figure 7 plots the share of credit by instrument for each country.19 We rank countries

in terms of the share of credit obtained through loans and credit lines–that is, long term

credit. The graph shows large cross country differences in the relative importance of the

credit instruments. French firms show the highest reliance on long term credit (more than

80%), followed by Finnish and Dutch firms. At the other extreme, Greek, Italian and Irish

firms borrow less than 60% long term.

Other interesting differences emerge. In terms of short term instruments, revolving credit

is particularly used in Austria, Ireland and Greece (which records the highest share). Italy

19To better summarize the information, we only report country aggregates in the main text. Appendix
Figure A4 shows the share by firm size. The country rankings by instrument type tend to be less stable
across size classes compared to the other variables that we consider in the analysis.

ECB Working Paper Series No 282624Figure 7: Instrument type shares

Note: The figure reports the shares of total outstanding amount by loan types coming from all
banks. The countries are arranged according to the combined shares of loans and credit lines in
descending order.

and Spain have relatively high shares of trade receivables, possibly reflecting longer payments

terms used in business to business transactions. Credit lines are important in Belgium and

Greece, where they account for a share of credit similar to that of loans, as well as in

Germany. These instruments are not used in Italy, and not much used in Spain. Leases

are more common in France, Ireland and Portugal, while they are used very marginally in

Austria, Germany, Greece and the Netherlands.

If firms have several banking relationships, it is possible that they borrow on long term

instruments mainly from the main bank, while rely on other instruments when dealing with

fringe banks.

In fact, long term financing might require a deeper understanding of firm

projects and growth prospects, which is more likely to be developed by the main bank.

Moreover, the main bank’s willingness to provide financing may be affected by the knowl-

edge that the firm is acquiring funds also from other banks (Degryse, Ioannidou & von

)5),1/%('((637$7*5,7,(&RXQWU\2XWVWDQGLQJQRPLQDODPRXQWVKDUH/RDQV&UHGLWOLQHV)LQDQFHOHDVHV7UDGHUHFHLYDEOHV5HYROYLQJFUHGLWECB Working Paper Series No 282625Schedvin 2016). To analyze this possibility, Appendix Figure A3 plots the instrument type

shares separately for all banks except the main bank (first bar) and for the main bank only

(second bar). The figure supports this conjecture: in all countries except Belgium, the main

bank supplies a larger fraction of credit through long term instruments. This suggests that

“fringe” banks act mostly as liquidity providers, in line with the hypothesis that firms enter-

tain multiple banking relationships to insure themselves against banks’ liquidity shocks (see

Detragiache et al. (2000)). Of course there may be other sources behind these pronounced

differences in the use of borrowing contracts. For example, Degryse, De Jonghe & Karagian-

nis (2021)) show that as a result of a legal change supporting credit to small firms, banks in

Belgium generally reduced the supply of term loans while increasing revolving credit lines.

Overall, these results indicate that countries differ substantially in terms of the instrument

used to obtain credit. Compared to the three clusters of the previous section, here too we

find evidence of a North-South divide, more marked for Italy and Greece, while Spain and

Portugal are closer to the mean values.

5 Maturity

In the previous section we have analyzed the reliance on different types of credit contracts,

and found that there are substantial cross country differences in how much firms borrow on

long term credit, that is, loans and credit lines. This can be seen as the extensive margin of

credit maturity. In this section we further deepen this aspect and analyze the maturity of

the two long term instruments. We focus on loans and credit lines because for the short term

instruments maturity is less meaningful: for example, revolving credit has no maturity, as

the firm can draw on the facility as long as the bank keeps it open. To simplify the analysis,

we consider total long term credit and construct its average maturity at the firm level as

follows:

(cid:88)

(cid:88)

mic =

wijbcmijbc

b

j=L,CL

(6)

and ON Aic =
j=L,CL ON Aijc and the bar indicates that we are summing over loans and credit lines

where we only sum over loan (L) and credit lines (CL), wijbc = ON Aijbc
ON Aic
(cid:80)
b
only.

(cid:80)

Figure 8 reports the results by firm size, ordering countries in descending order of maturity

ECB Working Paper Series No 282626Figure 8: Maturity by firm size

(a) Large

(b) Medium

(c) Small

(d) Micro

Note: The figure shows the distribution of (weighted by outstanding amount) maturities (in years)
by debtor country and firm size. The values are computed at the firm level. The box plot’s bar
represents the interquartile range, the red line indicates the median maturity. The average of the
maturity is indicated by the red triangle. For readability, upper and lower whiskers are omitted.
The order of countries is by the descending average value of large firms. The sample period is
December 2019. The credit contracts include only long-term maturity instruments, i.e. loans and
credit lines.

1/'()5$7),%(*5(637,7,(0DWXULW\\HDUV1/'()5$7),%(*5(637,7,(0DWXULW\\HDUV1/'()5$7),%(*5(637,7,(0DWXULW\\HDUV1/'()5$7),%(*5(637,7,(0DWXULW\\HDUVECB Working Paper Series No 282627for large firms. Maturity has a large degree of cross country variability. Dutch firms have

an exceptionally high average maturity of 15 years for bank credit. German firms, which

rank second, have an average maturity of 10 years. Next, we have France (9.5), Austria

(9.4), Finland (8.9), Belgium (8.2) and Greece (7.6). Spain, Portugal, Italy and Ireland

display a maturity between 7 and 5 years. Interestingly, this ranking also indicates a cluster

of Northern countries with longer maturities and one of Southern countries with shorter

maturity. For example, the average maturity of long term bank debt of Italian firms is

47.0% lower than that of German firms. This pattern resembles to a large extent what we

have seen for the number of relationships and the type of instruments. The Netherlands and

Ireland represent a special case, as they display very similar values in terms of number of

relationships and of share of credit from the main bank, but are at the opposite extremes in

terms of maturity. Together with the evidence on the extensive margin of credit maturity,

where Irish firms display the lowest share of borrowing long term while the Dutch firms are

in the middle of the distribution (see Figure 7), this suggests that the single bank model

may generate very different outcomes in these two countries.

In terms of other size classes, first we notice that average maturity seems to be fairly

similar across size–if anything, large firms tend to have a shorter maturity. This might be

due to the fact that they tend to finance long term projects more with the issuance of bonds

compared to smaller firms. The country ranking is broadly confirmed across size classes. The

most noticeable exception is Austria, which ranks at the top of maturity for all size classes

but the large one, and, to a smaller extent, Greece and Ireland, where the same pattern

emerges.

Figure 9 plots the country averages, both unconditional (left bar) and conditional on

size and sector (right bar). The average unconditional maturity ranges from around 12

years for Austria, the Netherlands and Greece to 6 for Portugal, broadly confirming the

cross country variation. The increase in the ranking of Greece is due to the fact that small

and especially micro firms in Greece tend to borrow at longer maturities than large firms

(see Figure 8c and 8d). As for the number of relationships and share of credit from the

main bank, the differences between the unconditional and the conditional mean are fairly

small, confirming that there is an important country component in the determination of the

firm-bank relationship.

ECB Working Paper Series No 282628Figure 9: Average Maturity

Note: The figure reports the regression estimates of the country effect on the weighted average
maturity (in years). The Unconditional bar represents the unconditional average, while the Condi-
tional one represents the average controlling for size and sector dummies as specified in Equation
8. Country order of large firms is kept.

6

Interest rates

In Section 2.4 we have constructed the measure of interest rate at the firm level as the

weighted average of the interest rate on each contract, weighted by the share of the out-

standing amount the contract accounts for (see Equation 3). We now analyze how this

measure differs across countries. As for the other variables, we use simple means, that is,

rc =

1
Nc

(cid:88)

i∈c

ric.

(7)

We then perform a shift and share decomposition to assess how much of the observed hetero-

geneity is due to differences in the firms’ size and sector structure, in the reliance on different

instruments or in country-specific effects unexplained by firm and contract characteristics.

051015regression coefficientNLDEFRATFIBEGRESPTITIEUnconditionalConditionalECB Working Paper Series No 2826296.1 Cross country differences in interest rates

Figure 10 shows the interest rate by country and firm size, ranking countries in terms of

average interest rate paid by large firms. Greek firms are at the top of the distribution, with

an average rate of almost 6%. Note that the mean is above the 75th percentile, suggesting

a long tail of firms paying high interest rates. In fact, the median is below 4%, but even

in this case it represents the highest value among the 11 countries. Next, Irish firms pay

an average rate of 5%, and Dutch firms of 4%. Ireland is also the country with the largest

interquartile range: the 75th percentile of the distribution is 6.5% and the 25th is 1.4%. The

other countries record lower and similar average rates, with Portugal, Italy, Germany and

France just above 2% and Spain, Belgium, Austria and Finland just below 2%. This pattern

is basically identical for medium size firms, and very similar for small and micro firms. Two

differences emerge for the smaller size classes: Dutch firms pay rates more similar to the

middle group of countries and French firms are closer to the countries that pay the lowest

rates. In all cases, the difference between the highest rate and the lowest is between 3% and

4%, a very large figure compared to a cross country mean of 3%. The differences in interest

rates may be linked also to the number of firm-bank relationships that we have analyzed

in the previous sections. Recent evidence has shown that banks with close ties with firms

exploit their informational advantage and charge higher interest rates than those that would

prevail were all banks symmetrically informed. However, these differences do not generally

persist (Schenone 2009, Ioannidou & Ongena 2010)).

We have seen in Figure 1 that interest rates are more dispersed across instrument types

than size classes. In Appendix Figure A2 we report the interest rates by country and instru-

ment. To a large extent, the same country pattern of Figure 10 emerges within instruments,

with Ireland and Greece showing higher rates. Moreover, in Figure 7 we have seen that

firms in different countries rely to a different extent on different instruments. In particular,

Ireland and Greece, that record the highest average rate, are also the countries where firms

borrow the most through revolving credit, which is also the most expensive form of credit,

as shown in Figure 1b. We will come back to this point in the decomposition exercise that

we perform in the next subsection.

Figure 11 reports the cross country average interest rate. The results confirm those

of Figure 10.

In fact, there are substantial differences in the average interest rate across

countries. At one extreme, Greece and Ireland have an average interest rate of almost 6%.

ECB Working Paper Series No 282630Figure 10: Interest rates by firm size

(a) Large

(b) Medium

(c) Small

(d) Micro

Note: The figure shows the distribution of interest rates by debtor country and firm size. The
interest rates are computed at the firm level. The box plot’s bar represents the interquartile range,
the red line indicates the median interest rate. The average of the interest rate is indicated by the
red triangle. For readability, upper and lower whiskers are omitted. The countries are ordered by
the descending average values of large firms. The sample period is December 2019.

At the other, Belgium, France and Finland have an interest which is around a third of such

value (2%). The other countries are somewhere in the middle, with the interest rate ranging
between 3% and 4%. The standard deviation of rc is 1.2%, about a third of the mean value.

Cross country differences in the interest rates may reflect differences in the underlying

firm characteristics and/or in the types of borrowing contracts. If similar firms in different

countries pay different rates on the same credit contract, one would need to resort to a “coun-

try effect” to explain rates, such as the competitiveness of the banking sector or differences in

the overall cost of funding for banks, that are passed over to borrowers. If instead differences

are mostly due to firm characteristics and instrument types, similar firms borrowing on the

*5,(1/37,7'()5(6%($7),,QWHUHVWUDWH*5,(1/37,7'()5(6%($7),,QWHUHVWUDWH*5,(1/37,7'()5(6%($7),,QWHUHVWUDWH*5,(1/37,7'()5(6%($7),,QWHUHVWUDWHECB Working Paper Series No 282631Figure 11: Average Interest Rate

Note: The figure reports the regression estimates of the country effect on the weighted average
interest rate. The Unconditional bar represents the unconditional average as defined in Equation
7, while the Conditional one represents the average controlling for size, sector and instrument type
dummies as specified in Equation 8. Country order of large firms is kept.

same instrument would be paying similar rates, indicating a higher degree of homogeneity

in credit conditions than that suggested by the unconditional means. The previous analysis

indicates that the differences also emerge within size class and instrument type. However,

considering one aspect at a time might not be able to fully account for firm and contract

characteristics. As we did for the number of banking relationships, we run the following

regression:20

risgjc =

11
(cid:88)

c=1

αr
cDCtry

c +

4
(cid:88)

g=1

βr
s DSize

g +

20
(cid:88)

s=1

s DSect
γr

s +

5
(cid:88)

j=1

j DInstr
θr

j + ηisgjc

(8)

j

are dummies for the five instruments, DSize

where risgjc is the interest rate for instrument type j of firm i in size class g, sector s and
are size dummies that are equal
country c, DInstr
are country
to 1 if firm i belongs to size class g, DSect
dummies and ηisgjc is the residual. The constant is omitted to include all levels of DCtry
which represents the conditional average interest rates, and we use the same normalization

are two-digit sector dummies, DCtry

,

g

s

c

c

20Note that, compared to Equation 4 where the unit of observation is a firm-period, here we are at the

more granular level of firm-instrument-period.

0.01.02.03.04.05.06regression coefficientGRIENLPTITDEFRESBEATFIUnconditionalConditionalECB Working Paper Series No 282632as we have done for the number of firm-bank relationships to obtain an overall mean equal

to the unconditional one. To make the estimates comparable to the unconditional averages,
we weight each observation by ωijc as defined in Equation 2.21

The right bars of Figure 11 plot the estimated country dummies ˆθc, which represents
the conditional mean. There is some evidence of convergence, as countries with value above

the horizontal bar, that represents the overall mean, are more likely to register a drop in

rates and vice versa. But there are also a few cases in which the opposite occurs, or in

which the two values are almost identical. The standard deviation of the average interest

rate drops somehow, from 1.3% to 1.2%, but remains substantial. This indicates that, while

firm characteristics partially account for cross country differences in interest rates, most of

the heterogeneity remains unexplained. It is important to underline how these differences

persist across countries that have been in a monetary union for more than two decades. At

the same time, the presence of these differences affects the transmission of monetary policy

and the provision of banks’ credit supply to firms (Bittner, Bonfim, Heider, Saidi, Schepens

& Soares 2022)

6.2 Shift and share decomposition

We want to determine how much of the cross country difference in average rates is due to

differences in the distribution of firm characteristics and instrument type across countries

and how much is due to differences within firm type and instrument type average rates.
Our unit of analysis is a sector s, firm size g and instrument type j. For size and sector,
the exercise is straightforward. Consider the case of firm size. For class size g = 1, ..., 4,
define the average interest rate rgc = 1
i∈g ric, where Ngc is the number of firms in size
Ngc
class g and we sum over all firms in that size class. Define the share of firms in class g as
ωgc = Ngc
i ric. Define barred variables
Nc
as those constructed using firms in all countries, that is, at the European level. We can now
decompose the difference in the mean rate for a country c with respect to the European level
using the Baily, Hulten & Campbell (1992) decomposition:

. By construction, rc = (cid:80)

g ωgcrgc, where rc = 1
Nc

(cid:80)

(cid:80)

rc − ¯r =






4
(cid:88)

g=1

(ωgc − ¯ωg)¯rg
(cid:124)
(cid:125)
(cid:123)(cid:122)
Between

+ (rgc − ¯rg)¯ωg
(cid:125)
(cid:123)(cid:122)
Within

(cid:124)

+ (ωgc − ¯ωg)(rgc − ¯rg)
(cid:125)
(cid:123)(cid:122)
Cross

(cid:124)






(9)

21In fact, if we run the regression with only country dummies, we recover exactly the unconditional mean

rates.

ECB Working Paper Series No 282633The Between term captures the deviation in the country interest rate relative to the European

average due to differences in firm size structure. For example, a country with a higher share

of small and micro firms, paying a slightly higher interest rate (see Figure 1a), will register a

positive Between component. The Within term captures differences within size class, fixing

the size composition at the overall sample average. When this component is large relative to

the Between one, it means that the average rate is (say) higher because firms pay a higher

rate controlling for the size difference. The cross term is a covariance, and it is positive when

a country has a larger share of firms compared to the overall average in the size classes with

higher rate, again relative to the overall average. The method applies identically for sectors.

Things are more complicated for instrument types, as they enter the definition of firm

level interest rate weighted by the share of ONA each instrument accounts for within firm.
In this case, we define the average interest rate for instrument j as:

rjc =

(cid:88)

i

(cid:80)

rijc

ωijc
i ωijc
, ON Aic = (cid:80)

(10)

where, as defined in Section 2.4, ωijc = ON Aijc
j ON Aijc. That is, we weight
ON Aic
each interest rate by its contribution to the firm-level interest rate and normalize the weights
so that they sum to one. The overall weight of instrument j is defined as:

ωjc =

1
Nc

(cid:88)

ωijc.

i

(11)

It is immediate to show that, with this definition, the average cross-instruments interest
rates coincides with rc.

Table 2 reports the results of the decomposition. At the high end, in Ireland and Greece

the average rate is 2.7% higher than the cross country average of 2.8%. At the other end, in

Belgium and Finland the average rate is almost 1% lower than the cross country average. The

size decomposition confirms that the differences in the size structure are a minor determinant

of cross country differences in the interest rate.

In fact, the Within component explains

basically all the differences for all countries. The largest Between component is the German

one: due to the higher share of large firms, the average rate in Germany is 10 basis point

lower than it would be if it had the same size structure as the cross-country one. This

negative component is more than compensated by the higher rates that German firms pay

within size class. The Cross component is generally small. This result is partially explained

by the fact that the differences in the interest rates across size classes are not very large (see

Figure 1a)

ECB Working Paper Series No 282634t
n
e
m
u
r
t
s
n
i

d
n
a

r
o
t
c
e
s

,
s
s
a
l
c

e
z
i
s

y
b

n
o
i
t
i
s
o
p
m
o
c
e
d

e
r
a
h
s

d
n
a

t
f
i
h
S

:
2

e
l
b
a
T

6
0
.
0

9
2
.
0

7
1
.
0

8
0
.
0

8
0
.
0
-

5
1
.
0

9
0
.
0
-

2
3
.
0
-

2
2
.
0

0
4
.
0

4
1
.
0
-

9
1
.
2

0
9
.
1

5
4
.
0

2
5
.
0

2
7
.
0

2
2
.
0

3
1
.
0
-

1
4
.
0
-

7
6
.
0
-

9
0
.
1
-

3
9
.
0
-

6
4
.
0

0
5
.
0

3
3
.
0

0
2
.
0

3
1
.
0

7
1
.
0

6
1
.
0

6
4
.
0

2
4
.
0
-

9
1
.
0
-

8
0
.
0

4
2
.
0

3
1
.
0

3
0
.
0
-

9
0
.
0

3
0
.
0
-

3
0
.
0
-

3
0
.
0
-

2
0
.
0
-

0
2
.
0

0
1
.
0
-

7
0
.
0
-

5
3
.
2

7
3
.
2

8
8
.
0

5
6
.
0

0
7
.
0

0
5
.
0

2
1
.
0
-

9
2
.
0
-

1
9
.
0
-

8
8
.
0
-

0
8
.
0
-

2
1
.
0

9
1
.
0

1
1
.
0

6
0
.
0

0
1
.
0

8
0
.
0

9
0
.
0

5
0
.
0

7
1
.
0
-

1
1
.
0

1
1
.
0
-

s
s
o
r
C

n
i
h
t
i

W

n
e
e
w
t
e
B

s
s
o
r
C

n
i
h
t
i

W

n
e
e
w
t
e
B

s
s
o
r
C

1
0
.
0
-

2
0
.
0

2
0
.
0

1
0
.
0
-

2
0
.
0
-

7
0
.
0
-

0
0
.
0
-

2
0
.
0
-

1
0
.
0
-

1
0
.
0

3
0
.
0
-

n
i
h
t
i

W

n
e
e
w
t
e
B

7
7
.
2

8
6
.
2

2
9
.
0

0
8
.
0

1
8
.
0

1
7
.
0

6
0
.
0
-

1
2
.
0
-

8
8
.
0
-

1
9
.
0
-

1
0
.
1
-

5
0
.
0
-

2
0
.
0
-

1
0
.
0

0
0
.
0

2
0
.
0
-

0
1
.
0
-

0
0
.
0

4
0
.
0
-

3
0
.
0

2
0
.
0

6
0
.
0

1
7
.
2

9
6
.
2

6
9
.
0

9
7
.
0

7
7
.
0

4
5
.
0

6
0
.
0
-

7
2
.
0
-

7
8
.
0
-

7
8
.
0
-

8
9
.
0
-

2
5
.
5

0
5
.
5

7
7
.
3

1
6
.
3

9
5
.
3

6
3
.
3

5
7
.
2

4
5
.
2

4
9
.
1

4
9
.
1

3
8
.
1

E
I

R
G

T

I

L
N

T
P

E
D

S
E

T
A

R
F

E
B

I
F

t
n
e
m
u
r
t
s
n
I

y
B

r
o
t
c
e
S

y
B

e
z
i
S

y
B

e
c
n
e
r
e
ff
D

i

e
t
a
R

y
r
t
n
u
o
C

,
e
t
a
r

t
s
e
r
e
t
n
i

e
g
a
r
e
v
a

e
h
t

s
i

e
t
a
R

.
t
n
e
m
u
r
t
s
n
i

d
n
a

r
o
t
c
e
s

,
s
s
a
l
c

e
z
i
s

y
b

n
o
i
t
i
s
o
p
m
o
c
e
d

e
r
a
h
s

d
n
a

t
f
i
h
s

e
h
t

s
t
r
o
p
e
r

e
l
b
a
t

e
h
T

:
e
t
o
N

e
h
t

n
i

d
e
s
o
p
m
o
c
e
d

s
i

e
c
n
e
r
e
ff
i
d

s
i
h
T

.
e
g
a
r
e
v
a

e
l
p
m
a
s

l
a
t
o
t

e
h
t

o
t

t
c
e
p
s
e
r

h
t
i
w

e
t
a
r

t
s
e
r
e
t
n
i

y
r
t
n
u
o
c

e
h
t

f
o

e
c
n
e
r
e
ff
i
d

e
h
t

s
i

e
c
n
e
r
e
ff
D

i

.
e
p
y
t

t
n
e
m
u
r
t
s
n
i

d
n
a

r
o
t
c
e
s

,
s
s
a
l
c

e
z
i
s

y
b

)
9
(

n
o
i
t
a
u
q
E
n
o

d
e
s
a
b

s
t
n
e
n
o
p
m
o
c

s
s
o
r
C
d
n
a

n
i
h
t
i

W

,
n
e
e
w
t
e
B

ECB Working Paper Series No 282635Some more action from the Between component emerges when decomposing in terms of

sectoral specialization. In this case, for most countries this component accounts for around

10 basis points (in absolute value) of the total difference. However, even in this case the

Within component is much more important. The Cross component is also relevant for

some countries. For example, it accounts for 20 basis points higher average rate for France,

implying that, compared to the overall sample, France tends to have a larger share of firms

in sectors in which firms pay higher rates and vice-versa.

The Between component plays a more important role when we decompose in terms of the

type of instrument. Greece, Ireland, Austria and Italy record an average rate that is between

30 and 50 basis points higher than in the case of having the same share of credit by instrument

type as the overall average. The Netherlands, Portugal, Spain, Germany and Belgium also

show a positive contribution from this component, while France and Belgium are the only

countries that show a negative contribution. The within component still plays a prominent

role, but to a lesser extent than in the case of size structure and sector specialization. The

cross term is also somehow important, but less than the other two.

Overall, the decomposition exercise indicates that size structure and sector specialization

play a relatively minor role in explaining the cross country differences in the average rates.

Instead, differences in the reliance on credit instruments play a more prominent role. This is

consistent with the substantial differences recorded in the interest rates across instruments,

as shown in Figure 1b. However, most of the variation in the average interest rate across

countries is not accounted for by these factors, pointing to unexplained country effects.

7 Conclusions

Firm-bank relationships in euro area countries differ significantly across firm size and coun-

tries. In some countries, the traditional model of relationship banking in which one main

bank is providing the large part of corporate financing is prevalent while in other countries

multiple lending relationships are the norm. The instruments used to provide financing to

euro area firms are also different in maturities and other important characteristics, such as

the revolving feature. These important differences also translate in significant divergences

in the interest rates that are charged to corporate borrowers.

ECB Working Paper Series No 282636We provide a picture of corporate financing through banks in the euro area, highlighting

the important structural differences that remain in the area, notwithstanding the ongoing

process of financial integration in the euro area/EU and, of course, a monetary union that

has been implemented more than 20 years ago. This analysis is of particular value for policy

makers taking decisions on macroeconomic and regulatory policies affecting the bank-firm

relationships. It is clear that the transmission and the impact induced by these policies -

monetary policy, but also supervisory and regulatory policies as well as industrial policy -

depend crucially on the differences that we unveil and that should be duly taken into account

in the evaluation of these policies.

ECB Working Paper Series No 282637References

Baily, M. N., Hulten, C. & Campbell, D. (1992), Productivity dynamics in manufacturing
plants, in T. Bresnahan & R. E. Caves, eds, ‘Brookings papers on economic activity.
Microeconomics’, Vol. 1992, JSTOR, pp. 187–267.

Banerjee, R., Gambacorta, L. & Sette, E.

rela-
tionship lending’, CEPR Discussion Paper No. DP12340, Available at SSRN:
https://ssrn.com/abstract=3047327 .

effects of

(2017),

‘The

real

Beck, T., Degryse, H., De Haas, R. & Van Horen, N. (2018), ‘When arm’s length is too far:
Relationship banking over the credit cycle’, Journal of Financial Economics 127(1), 174–
196.

Bittner, C., Bonfim, D., Heider, F., Saidi, F., Schepens, G. & Soares, C. (2022),
‘The augmented bank balance-sheet channel of monetary policy’, Available at SSRN:
https://ssrn.com/abstract=4047820 or http://dx.doi.org/10.2139/ssrn.4047820 .

Carletti, E., Cerasi, V. & Daltung, S. (2007), ‘Multiple-bank lending: Diversification and

free-riding in monitoring’, Journal of Financial Intermediation 16(3), 425–451.

De Jonghe, O., Dewachter, H., Mulier, K., Ongena, S. & Schepens, G. (2019), ‘Some Bor-

rowers Are More Equal than Others: Bank Funding Shocks and Credit Reallocation*’,
Review of Finance 24(1), 1–43.
URL: https://doi.org/10.1093/rof/rfy040

Degryse, H., De Jonghe, O., Jakovljević, S., Mulier, K. & Schepens, G. (2019), ‘Identifying
credit supply shocks with bank-firm data: Methods and applications’, Journal of Financial
Intermediation 40, 100813. Bank-firm relationships in the post-crisis era.
URL: https://www.sciencedirect.com/science/article/pii/S1042957319300154

Degryse, H., De Jonghe, O. & Karagiannis, N.

from a legal

Evidence
https://ssrn.com/abstract=3797915 or http://dx.doi.org/10.2139/ssrn.3797915 .

and firms:
reform altering contract design’, Available at SSRN:

(2021),

‘Banks

Degryse, H., Ioannidou, V. & von Schedvin, E. (2016), ‘On the nonexclusivity of loan con-

tracts: An empirical investigation’, Management Science 62(12), 3393–3672.

Degryse, H., Kim, M. & Ongena, S. (2009), Microeconometrics of banking: methods, appli-

cations, and results, Oxford University Press, USA.

ECB Working Paper Series No 282638Detragiache, E., Garella, P. & Guiso, L. (2000), ‘Multiple versus single banking relationships:

Theory and evidence’, The Journal of Finance 55(3), 1133–1161.

ECB (2021), ‘Non-bank financial intermediation in the euro area: Implications for monetary

policy transmission and key vulnerabilities’, ECB Occasional Paper No. (270).

Falato, A., Kadyrzhanova, D., Sim, J. & Steri, R. (2020), ‘Rising intangible capital, shrinking
debt capacity, and the us corporate savings glut’, Journal of Finance . Forthcoming.

Feraboli, K., Häkkinen, H. & Gutiérrez, J. M. P. (2015), A new indicator for the cost of

borrowing in the euro area, Technical report, Bank for International Settlements.

Gobbi, G. & Sette, E. (2013), ‘Do Firms Benefit from Concentrating their Borrowing? Evi-

dence from the Great Recession*’, Review of Finance 18(2), 527–560.

Harhoff, D. & Körting, T. (1998), ‘Lending relationships in germany – empirical evidence

from survey data’, Journal of Banking Finance 22(10), 1317–1353.
URL: https://www.sciencedirect.com/science/article/pii/S0378426698000612

Ioannidou, V. & Ongena, S. (2010), ‘ “time for a change”: Loan conditions and bank behavior

when firms switch banks’, The Journal of Finance 65(5), 1847–1877.
URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6261.2010.01596.x

Jiménez, G., Ongena, S., Peydró, J.-L. & Saurina, J. (2012), ‘Credit supply and monetary
policy: Identifying the bank balance-sheet channel with loan applications’, American Eco-
nomic Review 102(5), 2301–26.
URL: https://www.aeaweb.org/articles?id=10.1257/aer.102.5.2301

Kysucky, V. & Norden, L. (2016), ‘The benefits of relationship lending in a cross-country

context: A meta-analysis’, Management Science 62(1), 90–110.

Lehmann, E. & Neuberger, D. (2001), ‘Do lending relationships matter?: Evidence from
bank survey data in germany’, Journal of Economic Behavior Organization 45(4), 339–
359.

URL: https://www.sciencedirect.com/science/article/pii/S0167268101001512

OECD. (2022), Financing SMEs and Entrepreneurs 2022, OECD.

Ongena, S. & Smith, D. C. (2000), ‘What determines the number of bank relationships?

Cross-country evidence’, Journal of Financial intermediation 9(1), 26–56.

ECB Working Paper Series No 282639Ongena, S., Tümer-Alkan, G. & v. Westernhagen, N. (2012), ‘Creditor concentration: An

empirical investigation’, European Economic Review 56(4), 830–847.
URL: https://www.sciencedirect.com/science/article/pii/S0014292112000190

Pagano, P. & Schivardi, F. (2003), ‘Firm size distribution and growth’, Scandinavian Journal

of Economics 105(2), 255–274.

Petersen, M. A. & Rajan, R. G. (1994), ‘The benefits of lending relationships: Evidence

from small business data’, The Journal of Finance 49(1), 3–37.
URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6261.1994.tb04418.x

Rajan, R. G. (1992), ‘Insiders and outsiders: The choice between informed and arm’s-length

debt’, The Journal of finance 47(4), 1367–1400.

Schenone, C. (2009), ‘Lending Relationships and Information Rents: Do Banks Exploit Their

Information Advantages?’, The Review of Financial Studies 23(3), 1149–1199.

Sette, E. & Gobbi, G. (2015), ‘Relationship lending during a financial crisis’, Journal of the

European Economic Association 13(3), 453–481.
URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/jeea.12111

ECB Working Paper Series No 282640A Additional Tables and Figures

ECB Working Paper Series No 282641Figure A1: Credit concentration - Herfindahl index

(a) Large

(b) Medium

(c) Small

(d) Micro

Note: The figure shows the Herfindahl-Hirschman index by debtor country and firm size. The
index is computed at the firm level. The box plot’s bar represents the interquartile range, the red
line indicates the median credit concentration. The average of credit concentration is indicated by
the red triangle. For readability, upper and lower whiskers are omitted. Countries are ordered by
descending average credit concentration of large firms. The sample period is December 2019.

,(1/),%('($7)5*537,7(6+HUILQGDKOLQGH[,(1/),%('($7)5*537,7(6+HUILQGDKOLQGH[,(1/),%('($7)5*537,7(6+HUILQGDKOLQGH[,(1/),%('($7)5*537,7(6+HUILQGDKOLQGH[ECB Working Paper Series No 282642Figure A2: Interest rates by instrument type

(a) Loans

(b) Credit lines

(c) Revolving credit

(d) Finance leases

(e) Trade receivables

Note: The figure shows the interest rates on instrument type level. The box plot’s bar represents
the interquartile range, the red line indicates the median interest rate. The average of the interest
rate is indicated by the red triangle. For readability, upper and lower whiskers are omitted. The
country order of large firms is kept. The sample period is December 2019.

*5,(1/37,7'()5(6%($7),,QWHUHVWUDWH*5,(1/37,7'()5(6%($7),,QWHUHVWUDWH*5,(1/37,7'()5(6%($7),,QWHUHVWUDWH*5,(1/37,7'()5(6%($7),,QWHUHVWUDWH*5,(1/37,7'()5(6%($7),,QWHUHVWUDWHECB Working Paper Series No 282643Figure A3: Instrument type shares for the main banks and all other banks

Note: The figure reports the shares of total outstanding amount by loan types coming from all
banks except the main bank (first bar) and coming from the main bank only (second bar). The
countries are arranged according to the combined shares of loans and credit lines from all banks in
descending order.

)5),1/%('((637$7*5,7,(&RXQWU\2XWVWDQGLQJQRPLQDODPRXQWVKDUH/RDQV&UHGLWOLQHV)LQDQFHOHDVHV7UDGHUHFHLYDEOHV5HYROYLQJFUHGLWECB Working Paper Series No 282644Figure A4: Instrument type shares by firm size

(a) Large

(b) Medium

(c) Small

(d) Micro

Note: The figure reports the shares of total outstanding amount by loan types coming from all
banks by firm size. The countries are arranged according to the combined shares of loans and credit
lines in descending order for large firms.

)5'(1/,7*5$7%((6,(),37&RXQWU\2XWVWDQGLQJQRPLQDODPRXQWVKDUH/RDQV&UHGLWOLQHV)LQDQFHOHDVHV7UDGHUHFHLYDEOHV5HYROYLQJFUHGLW)5'(1/,7*5$7%((6,(),37&RXQWU\2XWVWDQGLQJQRPLQDODPRXQWVKDUH/RDQV&UHGLWOLQHV)LQDQFHOHDVHV7UDGHUHFHLYDEOHV5HYROYLQJFUHGLW)5'(1/,7*5$7%((6,(),37&RXQWU\2XWVWDQGLQJQRPLQDODPRXQWVKDUH/RDQV&UHGLWOLQHV)LQDQFHOHDVHV7UDGHUHFHLYDEOHV5HYROYLQJFUHGLW)5'(1/,7*5$7%((6,(),37&RXQWU\2XWVWDQGLQJQRPLQDODPRXQWVKDUH/RDQV&UHGLWOLQHV)LQDQFHOHDVHV7UDGHUHFHLYDEOHV5HYROYLQJFUHGLWECB Working Paper Series No 282645Table A1: Summary statistics by size: Austria

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

1,335
1,335
1,335
1,335
1,269
837
1,333
1,331
1,302

4,585
4,585
4,585
4,585
4,493
3,240
4,546
4,569
4,453

20,206
20,206
20,206
20,206
20,131
14,753
19,970
19,756
19,594

37,847
37,847
37,847
37,847
37,817
26,899
36,821
33,785
35,275

467
1
0.59
0.50
0.51
4.85
89
48,015
46,210

326
1
0.76
0.63
1.11
5.61
20
5,172
5,039

115
1
0.96
0.92
1.65
6.31
10
891
1,009

49
1
1.00
1.00
1.79
6.47
1
206
105

6,400
2
0.95
0.91
1.08
7.59
292
77,156
89,707

1,766
2
1.00
1.00
1.68
9.97
62
11,967
11,885

418
1
1.00
1.00
2.17
10.26
15
2,107
2,095

152
1
1.00
1.00
2.42
11.25
2
497
305

26,129
4
1.00
1.00
1.68
11.50
512
180,264
187,986

5,788
3
1.00
1.00
2.27
15.05
100
21,073
23,268

32,684
3
0.80
0.74
1.30
9.36
595
297,538
225,048

7,199
2
0.87
0.83
1.82
10.93
70
33,542
18,849

1,331
2
1.00
1.00
2.82
16.15
23
3,978
3,932

446
1
1.00
1.00
3.29
19.07
4
1,113
693

1,402
1
0.92
0.90
2.40
11.82
17
5,092
3,244

505
1
0.97
0.96
2.75
12.80
3
1,465
542

ECB Working Paper Series No 282646Table A2: Summary statistics by size: Belgium

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

1,371
1,371
1,371
1,371
1,158
877
1,168
1,361
1,214

6,009
6,009
6,009
6,009
5,433
4,738
4,653
6,000
3,730

29,860
29,860
29,860
29,860
27,998
25,565
21,157
29,835
4,413

126,171
126,171
126,171
126,171
114,164
101,606
48,011
126,156
5,271

398
1
0.65
0.53
0.40
1.06
129
47,125
51,051

359
1
0.72
0.59
0.36
1.05
22
10,400
10,795

153
1
0.79
0.67
0.52
3.56
8
2,010
2,219

35
1
1.00
1.00
0.95
5.00
1
195
254

3,295
2
0.99
0.98
1.00
5.50
321
76,596
103,521

1,534
2
1.00
1.00
1.05
5.49
54
14,110
18,774

493
1
1.00
1.00
1.32
7.60
13
2,799
4,957

102
1
1.00
1.00
1.76
9.87
2
401
492

15,046
3
1.00
1.00
1.80
14.61
624
186,455
236,495

4,475
2
1.00
1.00
1.69
10.91
85
22,103
33,091

1,166
2
1.00
1.00
1.96
13.48
21
4,461
10,539

250
2
1.00
1.00
2.75
15.04
4
764
974

19,584
2
0.83
0.78
1.35
8.25
921
597,147
453,048

3,753
2
0.86
0.82
1.25
7.17
62
23,517
26,430

970
2
0.89
0.85
1.45
8.42
15
4,182
7,795

205
1
0.94
0.92
2.10
9.94
3
636
821

ECB Working Paper Series No 282647Table A3: Summary statistics by size: Finland

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

764
764
764
764
760
347
698
690
699

2,182
2,182
2,182
2,182
2,173
1,269
2,158
2,149
2,139

9,924
9,924
9,924
9,924
9,919
6,968
9,883
9,827
9,809

93,111
93,111
93,111
93,111
93,108
72,002
82,938
70,308
82,308

97
1
0.74
0.61
0.60
4.94
90
50,312
54,974

37
1
0.81
0.69
0.70
4.81
28
6,069
6,503

20
1
0.84
0.73
0.90
4.71
10
796
1,120

9
1
1.00
1.00
0.92
5.00
0
24
39

1,218
2
0.98
0.95
1.08
5.84
302
96,082
103,682

558
2
1.00
1.00
1.42
5.53
60
12,237
14,055

12,384
3
1.00
1.00
1.80
10.02
569
254,999
248,232

3,800
3
1.00
1.00
2.12
9.25
94
21,926
26,064

144
2
1.00
1.00
1.72
5.35
13
2,032
2,345

35
1
1.00
1.00
1.65
8.33
1
118
115

643
2
1.00
1.00
2.44
9.05
21
3,780
4,608

111
1
1.00
1.00
2.56
14.01
2
366
310

20,579
2
0.86
0.81
1.28
8.87
556
473,129
300,430

5,689
2
0.89
0.85
1.50
8.25
68
37,816
19,753

1,247
2
0.90
0.87
1.74
7.69
15
14,970
3,630

256
1
0.96
0.95
1.85
10.04
1
620
291

ECB Working Paper Series No 282648Table A4: Summary statistics by size: France

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

11,889
11,889
11,889
11,889
10,536
8,053
7,966
7,142
9,853

35,773
35,773
35,773
35,773
32,373
27,642
27,653
19,762
28,554

147,780
147,780
147,780
147,780
139,560
125,699
133,632
108,080
141,152

706,677
706,677
706,677
706,677
669,494
636,337
392,245
489,881
554,297

121
1
0.55
0.47
0.97
5.13
284
101
71

120
1
0.61
0.51
0.96
5.00
60
7
8

78
1
0.67
0.55
0.98
4.94
11
1
2

47
1
1.00
1.00
1.22
5.52
1
125
20

506
2
0.91
0.84
1.56
7.26
421
14,286
581

2,995
4
1.00
1.00
2.50
13.36
922
190,306
54,337

8,004
3
0.78
0.72
1.99
9.48
9,752
506,014
29,414,539

388
2
1.00
1.00
1.55
7.01
84
32
22

206
1
1.00
1.00
1.48
6.80
16
5
4

107
1
1.00
1.00
1.67
7.23
2
293
153

1,337
3
1.00
1.00
2.47
12.04
126
3,906
391

570
2
1.00
1.00
2.24
10.52
25
1,245
304

246
1
1.00
1.00
2.42
13.53
4
570
364

2,008
2
0.81
0.77
1.89
8.84
99
11,233
219,713

720
2
0.85
0.81
1.77
8.11
20
4,154
164,006

281
1
0.95
0.93
1.98
9.53
3
846
80,702

ECB Working Paper Series No 282649Table A5: Summary statistics by size: Germany

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

11,182
11,182
11,182
11,182
10,418
7,331
10,432
10,061
9,760

39,765
39,765
39,765
39,765
37,972
28,975
38,957
38,323
32,734

121,985
121,985
121,985
121,985
117,608
88,436
119,656
116,651
98,637

153,980
153,980
153,980
153,980
150,380
114,123
147,504
113,673
109,758

87
1
0.61
0.50
0.89
4.89
250
20,326
29,193

70
1
0.73
0.60
1.28
4.83
54
2,634
4,559

37
1
0.85
0.75
1.66
4.46
11
610
1,069

31
1
1.00
1.00
1.76
4.97
1
189
150

1,390
2
0.96
0.92
1.68
8.05
367
56,713
77,082

527
2
1.00
1.00
2.03
7.59
73
6,880
10,097

141
1
1.00
1.00
2.65
6.48
17
1,405
2,110

98
1
1.00
1.00
2.74
9.51
2
494
426

9,913
4
1.00
1.00
2.58
14.89
681
133,982
178,329

2,365
3
1.00
1.00
3.16
12.25
112
15,081
20,760

534
2
1.00
1.00
4.38
10.42
27
3,179
4,032

480
1
1.00
1.00
4.59
16.93
5
1,273
869

16,415
3
0.81
0.75
1.99
10.37
1,736
827,443
439,636

2,853
2
0.86
0.82
2.47
9.55
85
27,334
23,316

876
2
0.90
0.87
3.34
8.83
20
6,204
24,706

757
1
0.95
0.94
3.69
11.45
3
5,667
31,179

ECB Working Paper Series No 282650Table A6: Summary statistics by size: Greece

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

440
440
440
440
435
301
434
425
438

1,759
1,759
1,759
1,759
1,750
1,290
1,753
1,690
1,740

7,242
7,242
7,242
7,242
7,239
4,705
7,225
6,271
7,123

18,469
18,469
18,469
18,469
18,467
12,853
17,437
10,466
16,755

299
1
0.52
0.44
3.04
3.95
250
44,329
26,252

262
1
0.57
0.47
3.56
4.61
52
5,972
4,620

77
1
0.72
0.59
4.04
5.00
10
1,240
834

36
1
1.00
1.00
3.58
7.15
0
229
99

5,454
2
0.83
0.71
3.73
6.65
356
70,532
58,026

1,298
2
0.87
0.78
4.38
6.96
72
12,394
10,078

224
1
1.00
1.00
5.14
8.01
15
2,288
1,896

73
1
1.00
1.00
5.32
12.01
2
532
267

19,340
4
1.00
1.00
5.33
10.55
615
179,301
132,452

4,306
3
1.00
1.00
5.48
10.23
110
21,868
18,913

17,812
3
0.76
0.70
5.71
7.59
679
235,205
172,190

3,395
2
0.78
0.73
5.38
8.00
85
17,678
14,771

630
2
1.00
1.00
6.57
12.76
25
4,145
3,629

164
1
1.00
1.00
7.35
17.55
4
1,074
638

663
2
0.86
0.82
5.61
9.35
18
3,582
2,812

172
1
0.95
0.93
5.47
12.87
3
870
503

ECB Working Paper Series No 282651Table A7: Summary statistics by size: Ireland

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

837
837
837
837
751
306
699
698
665

2,557
2,557
2,557
2,557
2,411
1,300
2,458
2,509
2,052

9,404
9,404
9,404
9,404
9,160
5,288
8,700
9,365
5,333

18,068
18,068
18,068
18,068
17,780
10,365
11,982
17,649
6,383

19
1
1.00
1.00
1.38
3.00
250
22,604
31,824

19
1
1.00
1.00
2.45
4.56
55
3,809
6,244

18
1
1.00
1.00
3.29
4.89
11
678
1,056

13
1
1.00
1.00
3.80
4.51
2
122
182

89
1
1.00
1.00
3.43
4.54
374
65,446
89,603

179
1
1.00
1.00
3.50
6.74
75
9,835
12,808

82
1
1.00
1.00
4.45
6.97
16
1,611
2,193

36
1
1.00
1.00
5.50
5.01
3
302
634

1,587
2
1.00
1.00
6.48
6.33
770
285,605
278,089

1,320
1
1.00
1.00
5.05
9.97
112
16,526
23,364

326
1
1.00
1.00
6.48
10.02
26
3,190
3,708

96
1
1.00
1.00
6.95
9.98
6
681
1,372

6,035
1
0.96
0.94
5.11
5.20
2,952
2,010,412
2,541,808

1,349
1
0.96
0.95
4.73
7.43
88
16,234
18,328

372
1
0.96
0.95
5.37
7.95
19
2,730
3,417

163
1
0.99
0.98
5.72
7.21
4
676
1,889

ECB Working Paper Series No 282652Table A8: Summary statistics by size: Italy

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

8,187
8,187
8,187
8,187
8,140
5,924
7,947
7,794
7,877

24,557
24,557
24,557
24,557
24,486
20,028
24,137
23,719
23,892

118,495
118,495
118,495
118,495
118,417
96,142
115,259
106,610
110,069

422,670
422,670
422,670
422,670
422,545
322,301
404,317
282,157
330,128

159
2
0.45
0.33
0.72
2.92
11
59,020
63,198

300
2
0.41
0.30
0.91
2.97
33
9,230
7,736

100
1
0.55
0.44
1.56
3.17
10
1,223
1,063

32
1
0.83
0.72
2.40
4.00
1
217
139

1,191
3
0.75
0.62
1.55
4.66
161
104,368
108,391

1,843
4
0.65
0.52
1.71
4.73
60
14,937
15,256

14,446
7
1.00
1.00
3.09
6.64
323
235,800
230,256

5,131
6
1.00
1.00
2.99
7.00
85
25,202
26,806

354
2
0.82
0.70
2.58
5.00
14
2,529
2,303

74
1
1.00
1.00
3.63
5.04
2
473
328

969
4
1.00
1.00
3.92
8.70
20
4,501
4,340

189
2
1.00
1.00
5.32
10.06
4
970
704

16,213
5
0.71
0.64
2.21
5.49
793
488,571
1,777,372

3,931
4
0.67
0.59
2.22
5.68
66
23,268
20,045

835
3
0.76
0.70
2.95
6.48
16
4,207
3,573

212
2
0.90
0.87
4.12
7.64
3
1,562
707

ECB Working Paper Series No 282653Table A9: Summary statistics by size: Netherlands

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

1,299
1,299
1,299
1,299
1,168
489
1,283
1,291
1,282

4,808
4,808
4,808
4,808
4,560
886
4,750
4,798
4,743

20,316
20,316
20,316
20,316
20,002
3,251
20,065
20,257
20,015

66,684
66,684
66,684
66,684
66,177
11,881
61,814
62,432
61,714

124
1
0.88
0.79
1.11
10.01
328
0
0

1,837
1
1.00
1.00
2.34
15.01
516
440
827

13,439
2
1.00
1.00
3.82
20.01
1,012
6,383
4,082

25,683
2
0.91
0.88
3.88
15.14
957
36,155
12,065

65
1
1.00
1.00
1.32
5.04
60
41
91

68
1
1.00
1.00
1.95
5.10
13
10
19

53
1
1.00
1.00
2.32
6.65
0
3
1

431
1
1.00
1.00
2.26
8.96
80
130
222

207
1
1.00
1.00
2.81
9.62
18
23
41

146
1
1.00
1.00
3.12
10.02
1
10
7

1,571
1
1.00
1.00
3.79
13.50
120
512
634

542
1
1.00
1.00
3.91
13.61
27
62
101

392
1
1.00
1.00
4.15
17.39
3
29
25

7,015
1
0.98
0.97
4.11
10.19
95
3,525
1,772

1,293
1
0.99
0.99
3.57
10.40
21
329
212

612
1
1.00
0.99
3.58
12.34
2
59
46

ECB Working Paper Series No 282654Table A10: Summary statistics by size: Portugal

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

926
926
926
926
913
484
887
803
802

4,743
4,743
4,743
4,743
4,711
3,591
4,616
3,823
3,814

23,018
23,018
23,018
23,018
22,942
18,619
22,521
16,754
16,686

53,291
53,291
53,291
53,291
52,969
41,526
49,736
34,217
33,448

220
2
0.50
0.37
0.53
3.70
278
21,840
21,546

204
2
0.49
0.37
1.10
3.92
58
3,251
3,494

62
1
0.60
0.51
1.63
4.00
12
527
602

32
1
1.00
1.00
2.13
4.00
2
119
88

2,334
4
0.76
0.63
1.38
4.96
392
53,667
47,601

892
3
0.74
0.60
1.77
4.98
76
8,000
7,426

175
2
0.97
0.94
2.52
5.00
16
1,200
1,180

61
1
1.00
1.00
3.16
5.26
3
261
215

10,062
6
1.00
1.00
2.70
6.99
660
121,556
103,121

2,802
5
1.00
1.00
2.96
6.86
114
15,920
15,110

11,676
4
0.72
0.65
2.27
5.61
821
289,697
147,894

2,483
3
0.72
0.65
2.58
5.62
92
14,506
12,324

483
3
1.00
1.00
3.85
6.66
25
2,645
2,491

144
2
1.00
1.00
4.55
7.38
6
572
463

498
2
0.81
0.76
3.29
5.77
20
2,518
2,119

194
1
0.92
0.90
3.83
6.47
4
592
393

ECB Working Paper Series No 282655Table A11: Summary statistics by size: Spain

No.

p25

p50

p75

mean

(a) Large firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(b) Medium firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(c) Small firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)
(d) Micro firms
Outstanding amount (in thousands)
Number of banks
Share from main bank
Herfindahl index
Interest rate (%)
Maturity (in years)
Number of employees
Balance sheet total (in thousands)
Annual turnover (in thousands)

4,379
4,379
4,379
4,379
3,586
2,285
4,162
4,217
4,178

17,466
17,466
17,466
17,466
14,876
11,955
17,240
17,343
17,213

85,107
85,107
85,107
85,107
72,828
61,154
84,626
84,969
84,615

260,443
260,443
260,443
260,443
218,299
180,077
257,518
259,386
256,335

416
2
0.41
0.29
0.56
3.10
167
31,172
26,382

245
1
0.43
0.31
0.77
3.05
51
3,784
3,920

81
1
0.55
0.46
1.16
3.52
11
701
793

38
1
0.91
0.83
1.63
5.00
0
157
54

3,307
3
0.67
0.53
0.96
5.09
328
66,599
68,476

1,122
3
0.69
0.55
1.22
5.04
68
10,853
10,350

241
2
0.87
0.77
1.85
5.72
15
1,709
1,596

80
1
1.00
1.00
2.66
10.01
2
345
201

15,260
6
1.00
1.00
1.63
7.53
576
143,022
145,640

3,788
5
1.00
1.00
1.95
8.52
104
19,728
20,695

20,167
4
0.67
0.60
1.43
6.37
685
355,419
218,664

3,459
4
0.68
0.61
1.76
6.78
80
20,507
15,561

676
3
1.00
1.00
2.83
11.39
23
3,338
3,304

184
2
1.00
1.00
3.88
15.76
4
758
475

721
2
0.77
0.71
2.32
7.98
17
3,455
2,644

231
1
0.90
0.88
2.99
11.48
3
950
390

ECB Working Paper Series No 282656Table A12: Summary statistics of instruments by size

No.

p25

p50

p75

mean

Firms (%)

(a) Large firms
Loans
Credit lines
Finance leases
Trade receivables
Revolving credit
(b) Medium firms
Loans
Credit lines
Finance leases
Trade receivables
Revolving credit
(c) Small firms
Loans
Credit lines
Finance leases
Trade receivables
Revolving credit
(d) Micro firms
Loans
Credit lines
Finance leases
Trade receivables
Revolving credit

25,955
12,260
11,995
17,114
28,317

94,025
42,754
36,820
37,689
104,141

404,209
147,593
122,489
108,636
422,645

1,422,992
324,162
189,723
164,889
1,090,505

186,928
302,856
46,783
78,458
2,633

159,432
175,131
40,406
60,548
2,707

59,499
62,500
30,921
40,571
2,536

35,117
35,275
26,681
13,690
2,055

1,443,768
1,922,659
140,676
336,782
30,788

616,987
618,966
98,724
250,000
40,817

173,749
196,139
60,715
118,003
31,385

82,186
97,000
40,551
34,219
14,679

8,833,740
7,590,907
628,126
1,816,110
1,119,077

2,190,877
2,000,000
335,063
972,724
490,324

14,938,787
10,169,434
1,340,983
3,260,926
3,436,898

2,811,213
2,224,224
485,733
964,466
760,464

508,610
612,168
152,710
309,793
148,619

205,651
284,161
84,072
79,750
45,202

664,929
735,565
213,484
283,566
212,514

258,776
402,398
136,127
89,858
80,301

60.91%
28.77%
28.15%
40.17%
66.46%

65.20%
29.65%
25.53%
26.14%
72.22%

68.12%
24.88%
20.64%
18.31%
71.23%

72.70%
16.56%
9.69%
8.42%
55.71%

Note: The last column, Firms (%), shows the percentage of firms using the instrument. The total
percentage per size class does not sum to 100% because one firm can use multiple instruments and is
counted in each.

ECB Working Paper Series No 282657B Size class classification

We use Bureau van Dijk’s Orbis to incorporate external firm-size characteristics which we use

for firm size classification. To ensure the data quality we apply the following filters. First,

we retrieve information only for EA countries. Second, we extract the unconsolidated codes

(U1 and U2), as well as the code for firms which have only consolidated reports (C1), but no

dependent entities.22 Third, we retrieve files up to June 2019 and choose the nearest date for

which we have 12-month financial statements. Finally, due to numerous duplicate files for

each firm, we apply distinct logic to several key characteristics of the financial statements:

• Filing type - we prefer ‘Annual report’ over ‘Local filing’, if both are available

• Consolidation code - we apply in order of preference the codes U1, U2 and C1, with

U1 being the first choice if available.

• Audit status - we prefer audited files if available.23

• Accounting practice - we prefer the ‘Local GAAP’ over ‘IFRS9’ and missing values.

To utilize all available information on balance sheet, number of employees, and annual

turnover, we apply the following logic:

1. If all three firm characteristics are available or only the number of employees and one

of the remaining two, then we apply the official definition of the European Union (as

described above).

2. If only the number of employees is not available, then we apply the official definition of

the European Union (as described above) only to the balance sheet size or the annual

turnover, depending on which has the higher value.

22According to the Orbis Data Guide, companies with detailed financial data can have 4 consolidation
codes - U1 (statement not integrating the statements of the possible controlled subsidiaries or branches of
the concerned Company with no consolidated companion), U2 (statement not integrating the statements of
the possible controlled subsidiaries or branches of the concerned Company with an consolidated companion),
C1 (statement of a mother Company integrating the statements of its controlled subsidiaries or branches
with no unconsolidated companion), C2 (statement of a mother Company integrating the statements of its
controlled subsidiaries or branches with an unconsolidated companion)

23The order we establish among all options is the following: Unqualified, Qualified, No opinion, Unaudited,

Audit n.a., missing value, Self-disclosed.

ECB Working Paper Series No 2826583. If only one of the three characteristics is available, then we apply the official definition

of the European Union (as described above) only to that characteristic. For instance,

if we have only the number of employees available and it has a value of 150, then the

firm will be classified as medium sized.

By utilizing both data sources and applying the official definition of the European Union

for the size classes we manage to classify 85.0% of the firms in AnaCredit in December 2019.

ECB Working Paper Series No 282659Acknowledgements 

We thank seminar participants at the ASSA/AEA 2022 and the ECB for comments and discussions. Fabiano Schivardi thanks the ERC 
for financial support (ERC grant 835201). All errors and omissions are our own. 
The views expressed in this paper are the responsibility of the authors only and should not be interpreted as reflecting the views of, or 
imply any responsibility for, the European Central Bank and the Eurosystem. 

Kamelia Kosekova 
European Central Bank, Frankfurt am Main, Germany; email: k.kosekova@gmail.com 

Angela Maddaloni 
European Central Bank, Frankfurt am Main, Germany; email: angela.maddaloni@ecb.europa.eu 

Melina Papoutsi (corresponding author) 
European Central Bank, Frankfurt am Main, Germany; email: melina.papoutsi@ecb.europa.eu 

Fabiano Schivardi 
Luiss University, Rome, Italy; Einaudi Institute for Economics and Finance, Rome, Italy; 
Centre for Economic Policy Research, London, United Kingdom; email: fschivardi@luiss.it 

© European Central Bank, 2023 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

+49 69 1344 0 
www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

ISBN 978-92-899-6111-0 

ISSN 1725-2806 

doi:10.2866/505807 

QB-AR-23-063-EN-N