The interbank market puzzle.
Date: 2020-06-25
Institutions: European Central Bank
Subjects: banking, euro area, institutional cooperation, interbank market, loan, trust

Working Paper Series 

Franklin Allen, Giovanni Covi, Xian Gu,  
Oskar Kowalewski, Mattia Montagna 

The interbank market puzzle 

No 2374 / February 2020 

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 

This  study  documents  significant  differences  in  the  interbank  market  lending  and 
borrowing levels across countries. We argue that the existing differences in interbank 
market usage can be explained by the trust of the market participants in the stability of 
the country’s banking sector and counterparties, proxied by the history of banking crises 
and failures. Specifically, banks originating from a country that has lower level of trust 
tend  to  have  lower  interbank  borrowing.  Using  a  proprietary  dataset  on  bilateral 
exposures, we investigate the Euro Area interbank network and find the effect of trust 
relies on the network structure of interbank markets. Core banks acting as interbank 
intermediaries in the network are more significantly influenced by trust in obtaining 
interbank funding, while being more exposed in a community can mitigate the negative 
effect of low trust. Country-level institutional factors might partially substitute for the 
limited trust and enhance interbank activity. 

JEL codes: G01, G21, G28, D85

Key words: Interbank market, Trust, Networks, Centrality, Community detection

ECB Working Paper Series No 2374 / February 20201 
 
 
 
 
 
 
 
 
 
 
 
 
Non-technical Summary 

The  interbank  market  is  an  informal  market  that  enables  banks  to  manage  and 

redistribute their funds, and so provide financial intermediation more efficiently. The 

bilateral nature of the interbank market does not differ across countries. In this paper, 

we  document  significant  difference  in  the  size  of  interbank  market  lending  and 

borrowing across countries, and explore the role of country-specific trust in the banking 

system in determining interbank market participation as well as its relationship with the 

interbank network structure. We define trust as the subjective assessment of the stability 

of  the  banking  sector,  proxied  by  the  history  of  banking  crises  and  failures.  Since 

systemic banking crises or bank failures are long-lasting traumatic experiences within 

the banking sector, how a government deals with insolvent banks strongly determines 

trust within the banking sector. 

Our empirical  analyses show that, first  of all, trust  is  crucial in  determining the 

interbank  market  size.  Higher  trust  helps  banks  to  obtain  liquidity  in  the  unsecured 

market through mitigation information asymmetries about counterparty credit risk and 

developing lending relationships. If a bank is located in a country that has experienced 

longer periods of banking crisis or more bank failures in the past, trust can be weaker 

and support less interbank activities given the counterparty credit risk and the possible 

adverse  selection  in  this  market.  This  effect  is  present  when  we  control  for  law 

enforcement, legal origin, and other country-level characteristics.  

In order to address the endogeneity concerns, we use the propensity score matched 

sample  for  the  tests  as  well  as  employ  instrumental  variable  (IV)  analysis.  The 

instruments we employ are the usage of deposit insurance scheme as well the power of 

deposit insurance scheme in a given country. The instrument variable analysis confirms 

our finding that lower level of trust in the banking system reduces interbank borrowing. 

Second,  we  then  apply  machine  learning  algorithms  to  investigate  the  network 

structure of Euro Area interbank market, i.e. calculating the local and global centrality 

ECB Working Paper Series No 2374 / February 20202measures  and  detecting  the  interbank  borrowing  and  lending  groups,  that  we  call 

“communities”.  We mapped out the EA interbank market network, and detected 13 

communities  in  the  interbank  borrowing  and  lending  market.    We  find  that  French 

banks  on  average  have  the  highest  global  and  local  importance  in  the  EA  interbank 

network, and have higher volume of interbank borrowing and lending across countries. 

In contrast, German banks have a large number of direct connections which are mostly 

located in the same community but the connected neighbors are not the most globally 

important players in the network.  

More crucially, we find evidence that the impact of trust on interbank participation 

relies on the network structure of interbank market. On one hand, though banks in a 

community  on  average  have  lower  interbank  borrowing  on  average,  being  in  a 

community can itself mitigate the negative effect of lacking trust in obtaining interbank 

funding. This suggests that when trust is low, being connected to communities can be 

helpful in obtaining interbank borrowing.  On the other hand, being located at a central 

position globally in the network makes a difference. The impact of trust is stronger for 

core  banks  than  for  periphery  banks,  both  statistically  and  economically.  For 

robustness, we explore the interbank network with total exposures as well as that with 

only  non-securities  contracts  or  with  only  short-term  exposures,  and  find  consistent 

results supporting that the effect of trust depends on the interbank network structure.  

Third,  legal  and  regulatory  institutions  play  an  important  role  in  explaining  the 

difference in interbank market participation at the country level. We find that these ex-

post mechanisms in institutions can benefit the development of the interbank market. 

More importantly, these institutional factors can mitigate the adverse effect of crises on 

interbank activities and further help to restore trust in the interbank market. The results 

are  consistent  with  the  law  and  finance  literature  showing  that  a  strong  institutional 

framework  enhance  loan  availability  in  unsecured  markets  as  it  provides  better 

protection  against  bankruptcy.  Overall,  our  finding  confirm  that  in  the  interbank 

markets, peer monitoring plays an important role. 

ECB Working Paper Series No 2374 / February 202031. Introduction

The interbank market is an informal market where banks borrow from and lend to each other 

with  established  internal  limits  based  on  an  institution’s  risk  appetite.  On  one  hand,  the 

interbank market plays crucial roles in domestic financial systems because first, central banks 

intervene in this market to guide policy interest rates, and second, efficient liquidity transfer 

can  occur  between  surplus  and  needy  banks  through  a  well-functioning  interbank  market 

(Furfine,  2001;  Acharya  et  al.,  2012).  Moreover,  theoretical  studies  suggest  that  interbank 

markets  allow  risk  sharing  (Bhattacharya  and  Gale,  1987).  After  the  2007–2009  global 

financial crisis, studies find that efficient risk sharing through the interbank market might not 

occur  during  crises  due  to  moral  hazard  and  market  frictions  in  the  lending  market.  In 

particular, the financial problems of investment bank Bear Sterns and the failure of Lehman 

Brothers showed that interbank markets can be an important channel of contagion. Interbank 

exposure might present systemic risk to the stability of the financial system. The crisis events 

of  2007  resulted  in  a  significant  increase  in  market  rates  and  a  simultaneous  decrease  in 

transaction volume in the interbank market. According to Afonso et al. (2011), the situation in 

the interbank market can be explained by the increase in counterparty risk and precautionary 

liquidity hoarding in anticipation of future shortages. 

On the other hand, we still know very little how the interbank market works, despite the 

existence of many recent studies on interbank market risk and interconnections. We know that 

the interbank market allows banks to adjust the volume of assets and liabilities as well as to 

manage the interest and exchange rate risks that arise from customer businesses. Hence, there 

is a great variation between banks in their use of the interbank market within each country’s 

banking sector. Moreover, the average ratio of interbank activities to total bank positions seems 

to be quite stable over a long horizon (BIS, 1983). The difference, however, is the position of 

the interbank market across countries. The average ratio of loans to depository institutions to 

total assets of insured commercial banks in the United States (US) was 1.81% from 1934 to 

ECB Working Paper Series No 2374 / February 202042015,1 while that for Germany was 20.61% from 1950 to 2015.2  Figure 1 shows the average 

share of interbank lending and borrowing to total commercial bank assets for the US, Japan, 

France, Germany, and the UK. During 2000-2014, the size of interbank lending as measured 

by the share of interbank lending to total assets was significantly higher in France and Germany 

than in Japan and the US. Similarly, the size of interbank deposits as measured by the share of 

interbank deposits to total liabilities was also significantly higher in France and Germany than 

in Japan and the US. 

FIGURE 1A &1B 

These data raise several important questions (the “puzzle”) that have remained unexplored 

up to now. Why is there such a difference in the size of the interbank market across countries? 

What determines the participation of banks in the interbank market? Is risk sharing better in 

those countries with greater usage of the interbank market? Our study attempts to shed light on 

these  questions  by  investigating  the  role  of  country-specific  trust  in  banking  systems  in 

determining  interbank  participation  as  well  as  its  relationship  with  the  interbank  network 

structure. Using the European Central Bank (ECB)’s confidential data on interbank bilateral 

large  exposures  for  supervisory  purposes,  we  are  able  to  map  and  examine  the  network 

structure of Euro Area (EA) interbank market.3 The EU introduced the large exposure regime 

in  2014,  requiring  banks  to  report  to  authorities  detailed  information  about  their  large 

exposures. To date the large exposures sample captures 90% of the EA banks’ risk weighted 

assets vis-à-vis credit institutions. 

In  the  interbank  market,  banks  have  a  powerful  incentive  to  monitor  each  other,  as 

interbank  deposits  and  loans  are  not  insured  and  often  uncollateralized  (Furfine,  2001).  

1 The data are from the US FDIC for insured commercial banks, available at: 

https://www5.fdic.gov/sdi/main.asp?formname=standard 
2 The data are from Deutsche Bundesbank, available at: 

http://www.bundesbank.de/Navigation/EN/Statistics/Banks_and_other_financial_institutions/Ba

nks/banks.html  

3 An exposure is defined as large exposure when before applying credit mitigations and exemptions its 
value is above or equal to EUR 300 million, or 10% of an institution’s eligible capital. For more details, 
please see Section 3.1.2. 

ECB Working Paper Series No 2374 / February 20205Initially,  the  interbank  market  was  an  informal  market  of  short-term  placement  of  deposits 

(Bernard and Bisignano, 2000). Nowadays, the market is very international and banks located 

throughout  the  world  participate  in  domestic  market  making  as  well  as  cross-border 

transactions.  The  main  criteria  for  participation  are  that  the  bank  establishes  itself  as 

creditworthy  compared  to  other  banks  and  is  not  constrained  by  domestic  regulations.  The 

transactions are arranged by the banks’ dealers over the phone and the deal is confirmed by 

subsequent exchanges of confirmation between the banks. However, the dealer performs the 

transactions  within  limits,  which  are  set  up  based  on  internal  assessment  of  risk  of 

counterparties. In the case of a failure, the interbank deposits are most likely to be lost, as they 

are  not  insured.  Meanwhile,  the  likelihood  that  the  bank  will  fail  depends  on  its  financial 

situation and the attitude of the supervisor authority and governments to bank failure. Rochet 

and Tirole (1996), however, highlight that peer monitoring can be weakened by government 

interventions.  

In this study, we define trust as the subjective assessment of the stability of the banking 

sector and the risk of counterparties. Hence, trust varies strongly across institutions as well as 

countries,  and  its  level  can  be  influenced  by  the  historical  stability  of  the  banking  sector. 

Therefore, in order to measure trust, we use the history of banking crises and failures as proxies.  

 Our  investigation  shows  that,  first  of  all,  trust  is  crucial  in  determining  the  interbank 

market  size.  Higher  trust  helps  banks  to  obtain  liquidity  in  this  unsecured  market  through 

mitigating  information  asymmetries  about  counterparty  credit  risk  and  developing  lending 

relationships. If a bank is located in a country that has experienced longer periods of banking 

crisis or more bank failures in the past, trust can be weaker and support less interbank activities 

given the counterparty credit risk and the possible adverse selection in this market. This effect 

is  present  when  we  control  for  law  enforcement,  legal  origin,  and  other  country-level 

characteristics.  

In order to address the endogeneity concern that some other country features, for example, 

the structure of financial system, might affect the functions of the banking system as well as 

crises in the past (Allen et al., 2012), which could further influence interbank activities, we 

employ a matching algorithm to define a treated group of banks located in countries with the 

ECB Working Paper Series No 2374 / February 20206longest duration of banking crises and a control group of banks with comparable size, located 

in the counties with the lowest duration of crises, yet similar financial structure. After matching, 

we find the effect is still present while the economic impact of crises is even stronger.  

It is also possible that the interbank borrowing might be influenced by other unobserved 

factors correlated to trust. To address this concern, we then employ instrumental variable (IV) 

analyses. The instruments we employ are the usage of deposit insurance scheme as well the 

power of deposit insurance scheme in a given country. Demirgic-Kunt et al. (2014) show that 

countries with a larger number of banking crises and failures are more likely to introduce  a 

safety net, i.e. the deposit insurance, so the trust can be restored. The exclusive condition is 

also very likely to be satisfied as most of the interbank borrowing (deposit) is based on credit, 

and  therefore  not  covered  by  deposit  insurance  (Furfine,  2001).  The  instrument  variable 

analysis confirms our finding that lower level of trust in the banking system reduces interbank 

borrowing.  

Second, legal and regulatory institutions play an important role in explaining the difference 

in interbank market participation at the country level. Numerous studies suggest that legal and 

institutional  differences  shape  both  the  price  and  non-price  terms  of  bank  loans  around  the 

world  (Qian  and  Strahan,  2007).  Consistently,  we  find  that  these  ex-post  mechanisms  in 

institutions  can  benefit  the  development  of  the  interbank  market.  More  importantly,  these 

institutional factors can mitigate the adverse effect of crises on interbank activities and further 

help to restore trust in the interbank market. 

Third, we then apply network methodology to investigate the network structure of the Euro 

Area interbank market, i.e. calculating the local and global centrality measures and detecting 

the  interbank  borrowing  and  lending  groups,  that  we  call  “communities”.  Formally, 

communities are groups of banks with the property that group members are strongly connected 

to each other in terms of interbank borrowing and lending than to others outside the group. We 

map  out  the  EA  interbank  market  network,  and  detect  13  communities  in  the  interbank 

ECB Working Paper Series No 2374 / February 20207borrowing and lending market.4  We find that French banks on average have the highest global 

and  local  importance  in  the  EA  interbank  network,  and  have  higher  volume  of  interbank 

borrowing and lending  across countries.  In contrast,  German banks have  a large number of 

direct  connections  which  are  mostly  located  in  the  same  community  but  the  connected 

neighbors are not the most globally important players in the network.  

More crucially, we find evidence that the impact of trust on interbank participation relies 

on  the  network  structure  of  interbank  market.  The  existing  literature  has  identified  core-

periphery structures in interbank networks for different countries (e.g. Cocco et al., 2009; Craig 

and  Von  Peter,  2014;  Gabrieli  and  Georg,  2014),  while  very  few  papers  have  explored  the 

communities of interbank borrowing and lending. In this study we find the effect of network 

structure  is  twofold.  On  one  hand,  though  banks  in  a  community  on  average  have  lower 

centrality of interbank borrowing, being more exposed in a community can itself mitigate the 

negative effect of lacking trust in obtaining interbank funding. This suggests that when trust is 

low, being connected to communities can be helpful in obtaining interbank borrowing.  On the 

other hand, being located at a central position globally in the network makes a difference. The 

impact  of  trust  is  stronger  for  core  banks  than  for  periphery  banks,  both  statistically  and 

economically. This is consistent with the existing literature documenting that lack of trust in 

intermediary banks can spill over to affect their borrowers’ access to interbank funding (Craig 

and Ma, 2019). For robustness, we explore the interbank network with total exposures as well 

as that with only non-securities contracts or with only short-term exposures, and find consistent 

results supporting that the effect of trust depends on the interbank network structure.  

Finally, trust is not a proxy for other determinants, especially the key bank characteristics, 

of the interbank market participation. For example, we find that banks with higher liquidity 

mismatch and risk tend to have higher demand for interbank borrowing during normal periods, 

whereas during crises, such banks have less access to the interbank market. This is consistent 

with the relationships allowing banks to access liquidity in the presence of market frictions, 

4 Please see Figure 6A and 6B for the visualization of a complete network of large exposures for the 
EA interbank market. For a detailed description of the EA interbank market, please see Section 6.1. 

ECB Working Paper Series No 2374 / February 20208such as transactions and information costs. Based on the market discipline theory, participants 

of the unsecured interbank market have incentives to monitor their counterparties due to the 

lack of collateral  to  hedge counterparty  risk. Hence,  riskier banks are  expected to be  credit 

rationed  although  they  might  have  higher  liquidity  needs  (Furfine,  2001;  Ashcraft  and 

Bleakley, 2006; King, 2008).  

Our study contributes to the literature in the following three ways. First, to the best of our 

knowledge, this is the first study to present significant differences in banks’ usage of interbank 

market across countries. There have been numerous studies on the lending relationships in the 

interbank market. For example, Afonso et al. (2013) show that there is substantial heterogeneity 

in the structure of the trading relationship in the US overnight interbank lending market. Some 

banks rely on spot transactions, while most form stable, concentrated borrowing relationships 

to  hedge  liquidity  needs.  These  borrowers  with  concentrated  interbank  relationships  can  be 

almost completely insulated from exogenous shocks. Cocco et al. (2009) use a unique dataset 

on  the  Portuguese  interbank  market  and  show  that  the  relationships  are  an  important 

determinant of interbank market activities. Larger banks with more imbalance in their reserve 

deposits are more likely to borrow funds from other banks than are those with less imbalance. 

Bräuning  and  Fecht  (2017)  use  German  interbank  payment  data  and  support  the  view  that 

established  relationships  matter  for  the  availability  of  interbank  credit  and  affect  the 

reallocation of liquidity through the interbank market. However, none of these studies utilizes 

cross-country interbank market data or documents differences in interbank market usage across 

countries. An important question is what determines interbank market participation. Through 

using  the  compiled  dataset  of  domestic  banks  from  96  countries  and  then  investigating  the 

structure of Euro Area’s interbank market, we document that trust is an important determinant 

of interbank activities and its influence relies on the interbank network structure, i.e. the core 

versus periphery positions and the communities of borrowing and lending. 

Second, we contribute to the literature on interbank liquidity during crisis periods. Freixas 

and  Jorge  (2008)  and  Bruche  and  Suarez  (2010)  argue  that  during  crises,  there  might  be  a 

reduction in interbank lending due to increased borrowers’ counterparty risk, while Caballero 

and Krishnamurthy (2008), Acharya and Skeie (2011), and Allen et al. (2009) attribute it to 

ECB Working Paper Series No 2374 / February 20209lenders’  liquidity  hoarding.  Afonso  et  al.  (2011),  using  the  US  overnight  interbank  market 

around  Lehman’s  bankruptcy,  show  that  counterparty  credit  risk  plays  a  larger  role  than 

precautionary liquidity hoarding does. Acharya and Merrouche (2013), using a sample of large 

settlement banks in the UK, report that after the crisis of 2007–2008, liquidity demand was 

precautionary in nature in that it increased on calendar days with a large amount of payment 

activity and more so for banks with greater credit risk. Moreover, Iyer and Peydro (2011), using 

the setting of the Indian banking system, find robust evidence that higher interbank exposure 

to failed banks leads to larger deposit withdrawals and the interbank linkages among surviving 

banks further propagate the shocks. Iyer et al. (2014) employ a Portuguese loan-level dataset 

and find that banks that relied more on interbank borrowing before the crisis decreased their 

credit supply more than other banks did during the crisis. 

Lastly, this study is related to a growing literature on networks.  We explore the Euro Area 

interbank network structure and find French banks have the highest global importance in the 

network,  while  German  banks  have  more  interbank  activities  within  the  same  community. 

More  importantly,  we  find  the  effect  of  trust  in  determining  interbank  market  participation 

relies on the interbank network structure, i.e. the core and periphery positions as well as the 

community features. Das et al. (2019) explore the network for all commercial banks U.S. banks 

during the Great Depression and use centrality measures to estimate the systemic risks. Using 

a  similar  dataset,  Mitchener  and  Richardson  (2019)  find  the  interbank  lending  networks 

amplified the contraction in lending during the Great Depression. Brunetti et al. (2019) examine 

the interbank behavior during and after the 2008’s financial crisis and document that during 

the crisis the correlation network based on publicly traded bank returns shows an increase in 

interconnectedness  while  the  physical  network  based  on  interbank  lending  transactions 

highlights a marked decrease in interconnectedness. Other than literature on financial network, 

recent studies also explore different types of economic networks using the recently developed 

network  topologies.  For  example,  Hochberg  et  al.  (2007)  examine  the  network  of  venture 

capital  and  find  better-networked  VC  firms  in  terms  of  higher  centrality  experience 

significantly better fund performance. Ahern and Harford (2014) represent the economy as a 

network of industries connected through customer and supplier trade flows and show stronger 

ECB Working Paper Series No 2374 / February 202010product-market  connections  lead  a  great  incidence  of  cross-industry  mergers.  Allen  et  al. 

(2019) examine the equity ownership network using all the registered Chinese firms and show 

the network position matters for firm future growth.  

The reminder of the paper is organized as follows. Section 2 presents the stylized facts on 

the significant differences in interbank activity across countries. Section 3 describes the data 

source,  sample  construction  as  well  as  the  summary  statistics  of  key  variables.  Section  4 

discusses  the  identification  and  network  methodology.  Section  5  presents  the  regression 

analysis  using  international  bank-level  evidence  on  interbank  borrowing.  In  Section  6,  we 

describe the network structure of the Euro Area interbank market and investigate the role of 

interbank network structure in influencing the effect of trust. Section 7 concludes.  

2.  Stylized facts 

This section provides a cross-country overview of interbank market activities. Figures 1A 

and  1B  show  the  structure  of  bank  assets  and  liabilities  for  five  countries:  the  US,  Japan, 

France, Germany, and the UK from 2000 to 2009.5 On average, the ratio of interbank loans to 

total bank assets is 2.4% for the US during this period, followed by Japan with a ratio of 4.9% 

and the UK with a ratio of 13.2%. France and Germany have much higher interbank loan ratios 

of  28.7%  and  22.5%,  respectively.  Regarding  liabilities,  US  banks  have  the  lowest  ratio  of 

interbank deposits, 2%, followed by Japan, with a ratio of 4.4%, and the UK, with a ratio of 

9%.  Again,  France  and  Germany  have  much  higher  interbank  deposit  ratios,  at  31.2%  and 

26.6%, respectively. 

In terms of other bank assets and liabilities, France and Germany  also tend to have the 

highest  ratios  of  loans  to  deposits  among  the  five  countries,  at  116.9%  and  105.6%, 

respectively. The average ratio of loans to deposits for Japanese banks is 80.6%, the lowest 

among the five countries. In Japan, the ratio reflects a “balance-sheet recession” over the two 

decades, characterized by a change in household and company behavior toward paying down 

5 Figure B1 and Figure B2 in the Internet Appendix show the dynamic change of structure of bank assets 
and liabilities from 2000 to 2009. 

ECB Working Paper Series No 2374 / February 202011debt and increased savings, even as interest rates remain at record low levels. Consequently, 

the economy slowed down due to reduced household consumption and  business investment 

(Koo, 2014). 

However, Table 1 shows some changes in the level of interbank deposits since the 2008’s 

global  financial  crisis.  In  all  the  countries  except  Japan,  we  observe  a  decline  in  interbank 

deposits relative to bank assets. The decline started in the UK and the US in 2007, while in 

France and Germany, it started in 2008. By contrast, in Japan, bank deposits slightly increased, 

but the level remained relatively low compared to deposits held by banks in France or Germany. 

Interestingly, the decline in interbank loans was much lower, and in most countries, the levels 

of interbank loans to banks’ total assets are comparable to those observed in the years prior to 

the  crisis.  An  exception  is  the  US,  where  interbank  loans  and  deposits  remain  significantly 

lower than before the crisis. The observed decline in interbank deposits  and lending can be 

explained by the increased counterparty credit risk as well as the declining trust among banks 

during the crisis. 

TABLE 1 

The simultaneous changes in interbank deposits and lending confirm that banks tend to 

hold significant interbank exposure on both sides of the balance sheet. The observation is in 

line with Bluhm et al. (2016), who find that banks lend to other banks and borrow from other 

banks  simultaneously,  and  do  so  persistently.  The  authors  term  this  property  interbank 

intermediation to distinguish it from the traditionally defined bank intermediation. Moreover, 

they show that this intermediation is derivative to the banks’ client book – household and firms, 

which determine the build-up of interbank books. 

Figure 2 presents the interbank loans and deposits to total assets for domestic banks across 

the  EU  member  countries  in  2016,  showing  significant  differences  in  interbank  market 

participation by country. Among  the EU member countries,  Germany has a relatively  large 

interbank market, where the average interbank loans and deposits reach 11.4% and 13.5% of 

total assets in 2016, respectively.  In contrast, interbank loans in Finland amount to 0.7% of 

total assets, while interbank deposits amount to 0.02% of total assets in Estonia in 2016. The 

ECB Working Paper Series No 2374 / February 202012average interbank loans and deposits for all the EU banks remain strongly balanced and reach 

5.7% of total assets. 

FIGURE 2 

The unbalanced structure of the balance sheet of the banks in some EU member countries 

might  be  due  to  foreign  banks’  activities.  Figure  3  shows  the  interbank  loans  and  assets  of 

domestic and foreign subsidiaries and branches, and the share of foreign ownership in each 

country. After including the interbank activities of foreign banks, the interbank exposure on 

both  sides  of  the  balance  sheet  among  the  member  countries  is  more  balanced.  However, 

Luxemburg  and  Malta  are  the  exceptions.  Both  countries  are  financial  centers  and  have 

relatively high foreign ownership. When we account for the interbank activities of domestic 

and  foreign  banks,  Luxemburg  has  the  largest  interbank  market  among  the  EU  member 

countries. In 2016, the interbank loans and deposits in Luxemburg amount to 30.6% and 26.6% 

of total assets, respectively. 

FIGURE 3 

Lastly, Figure 4 shows the share of the total amount owed to credit institutions over total 

assets for domestic banks in the EU for 2007 to 2016. The data confirms the observation in 

Table 1 that interbank loans and deposits are not stable. In almost all the countries, the amounts 

owned to credit institutions declined over the period, which we attribute to the financial crisis 

of  2008  and  the  Euro  crisis  of  2009.  In  the  EU,  the  amounts  owned  to  all  domestic  credit 

institution over total assets declined from 15.5% in 2007 to 5.3% in 2016. During this period, 

only Romania, Finland, and Cyprus have higher borrowed amounts in 2016 than in 2007. 

FIGURE 4 

Overall,  the  data  shows  large  variations  in  interbank  activities,  even  among  relatively 

homogenous countries, such as the EU member states. However, across the member countries, 

interbank exposures remain simultaneous on both sides of banks’ balance sheets. Moreover, 

we find a relatively large variation in interbank activities across time. Existing literature rarely 

examines these two facts, which provides the motivation for our study. 

ECB Working Paper Series No 2374 / February 2020133.  Data and summary statistics 

3.1 Sample construction 

We compile two datasets for analyses in this study. The first covers banks around the globe, 

with  interbank  borrowing  information  from  banks’  balance  sheets.  To  investigate  how  the 

interbank network structure affects the role of trust, we compile the second dataset for banks 

in the Euro Area, with the interbank bilateral exposure information from the European Central 

Bank for supervisory purposes.  

3.1.1 Domestic interbank market participation around the globe 

The  first  dataset  is  constructed  as  following.  We  obtain  financial  data  on  commercial, 

cooperative, and savings banks from the Bankscope database. Our sample period is 1995 to 

2015, but the panel is unbalanced, as we do not have data for all the years for each bank. Our 

sample comprises only banks that operate as independent companies or with single locations, 

and excludes  multinational  banks  that use internal  capital  markets significantly to fund  and 

support their activities across countries (De Haas and Van Lelyveld, 2010). We also exclude 

foreign  banks  in  our  study  since  their  activities  may  be  highly  affected  by  the  institutional 

factors as well as economic conditions in the home countries.  Allen et al. (2013) show that 

foreign subsidiaries’ interbank deposit decisions are likely to be determined more by the current 

policy of the multinational bank than by countries’ institutional factors. In addition, Adams-

Kane et al. (2017) show that foreign bank activities are strongly influenced by the current home 

country’s economic conditions.  

Therefore, we select for our sample only domestic-owned banks operating domestically. 

We classify a bank as domestically owned when 50% or more of its shares are held by domestic 

entities. In order to establish bank ownership, we create a dataset on the evolution of ownership 

over the period 1995–2015. This dataset builds on the data compiled by Claessens and  Van 

Horen (2014), which comprises only about one-third of our sample. This process allows us to 

have a sample of 11,557 domestic commercial banks, savings banks, and cooperative banks 

from 166 countries. Then we drop all the countries that have less than five operating banks in 

ECB Working Paper Series No 2374 / February 202014our sample, thereby reducing the number of banks in the sample by 1.3%. The final sample

contains 11,412 domestic banks from 96 countries.6

The  World  Bank’s  Global  Finance  Database  is  used  for  information  on  country-level

variables on financial system development (private credit to GDP) to measure the development

of  the  banking  system.  Country-level  variables  on  governance  and  regulation  are  from  the

Worldwide Governance Indicator (WGI) database constructed by Kaufmann et al. (2010). The

database  contains  measures  of  legal  enforcement,  regulation  quality  and  government

effectiveness for more than 200 countries. The information on countries’ legal origin is from

Djankov et al. (2007), which we update using mainly the CIA Factbook. The information on

systemic banking  crises  is  from  Laeven  and  Valencia’s  (2013)  database.  They  identify  147

crises  in  115  countries  over  the  period  1973–2011.  Based  on  this  dataset,  we  update  the

database for the years until 2015.

We merge the abovementioned datasets. The bank’s financial data in year  t are matched

with the country-level variables, such as financial structure and regulation, in year t-1. We end

up with 74,572 bank-year observations. Additional information on the definitions and sources

of variables are in Appendix Table A1.

3.1.2 Network structure of Euro Area banks

Our  second  dataset  on  bilateral  interbank  exposure  comes  from  the  ECB’s  confidential

large exposures data. The large exposure regime, introduced in the EU in 2014, requires banks

to  report  to  prudential  authorities  detailed  information  about  their  largest  exposures.  An

exposure to a single client or a connected group of clients is considered to be a large exposure

when, before applying credit mitigations and exemptions, it is 10% or more of an institution’s

eligible capital. In addition, institutions are also required to report large exposure information

6 We  classify  a  bank  as  domestically  owned  when  50%  or  more  of  its  shares  are  held  by  domestic 
entities. As Claessens and Van Horen ownership database does not cover all the banks, we update the 
missing information on bank ownership using hand-collected information from various sources. The 
information sources used to build the dataset comprise primarily Bankscope, supplemented by annual 
reports and national supervisory publications. For the sample distribution by country, please see Internet 
Appendix Table B1.  

ECB Working Paper Series No 2374 / February 202015for exposures with a value above or equal to EUR 300 million. Therefore, to date this dataset 

captures more than 50% of Euro area credit institutions’ exposures. For the goal of studying 

the interbank network, the large exposures sample captures 90% of the EA banks’ risk weighted 

assets  vis-à-vis  credit  institutions  (Covi,  Gorpe  and  Kok,  2019;  Covi,  Montagna  and  Torri, 

2019).  Note that this data captures not only almost all borrowing and lending of EA banks 

among themselves, but also borrowing of non-EA banks from EA banks.  For example, in the 

fourth quarter of 2018, the large exposures sample covers borrowing and lending among 1,362 

banks (793 EA banks and 569 non-EA banks).  

We then match the quarterly large exposures data over the fourth quarter of 2014 to the 

first quarter of 2019 with banks’ financial information from BankFocus.7 We also match with 

the country variables from World Governance Indicators (WGI) as well as the Global Finance 

Database for financial structure. We end up with a sample with interbank network structure 

information from the fourth quarter of 2014 to the end of 2017 for the regression analyses. 

3.2  Variables and descriptive statistics 

3.2.1 Interbank deposits and bank-level control variables 

Table  1  presents  large  differences  across  countries  in  interbank  deposits  as  well  as  in 

interbank lending. We decide to investigate only interbank deposits for domestic banks around 

the globe as these data enable us to identify banks that take the deposits, but not the source 

(i.e., domestic or foreign). By contrast, in the case of interbank lending, we know the identities 

of banks that lend to other banks, but we do not know whether the bank is located in the same 

country  or  abroad,  from  the  balance  sheet  data.  We  hypothesize  that  trust  in  the  country’s 

financial  system  and  the  bank’s  counterparties  are  determinants  of  the  differences  in  the 

7 Since Bankscope is discontinued since 2015, we use banks’ financial information from BankFocus to 
match with large exposures data for the period of 2014 to 2018. 

ECB Working Paper Series No 2374 / February 202016interbank market across countries. Hence, our main dependent variable, Interbank borrowing, 

is measured as deposits and borrowing from banks scaled by total asset in year t.8 

Panel A of Table 2 provides detailed summary statistics for the dependent variable and the 

bank-level control variables for the panel of domestic banks around the globe. We winsorize 

the bank variables at 1% and 99%. The dependent variable Interbank borrowing ranges from 

0 to 1, with a mean value of 0.08 and standard deviation of 0.11. The mean value of interbank 

borrowing averaged by country for our sample is slightly higher at 0.11, indicating that more 

banks are located in countries with lower levels of interbank borrowing. 

We consider an assortment of bank characteristics. The ratio of loans to deposits (LtD) 

shows a large variation among the banks in the sample, yet the mean value indicates that in the 

average bank, deposits exceed loans, and consequently, these banks do not need to borrow in 

the interbank market. Thus, we can assume that the average bank locates its surplus funds either 

in the interbank market or in securities, mainly government bonds. Securities provide liquidity 

insurance, as they can be used as collateral in the interbank market, which enables banks to 

pool  liquidity  and  settle  unexpected  transaction  flows  resulting  from  distributional  shocks 

without holding cash. Hence, a high ratio of total securities to total assets (Securities) might 

indicate financial stability. 

Similarly, banks with a solid capital base (Equity) and profitability (ROA) should signal 

stability  and  thus,  be  positively  related  to  interbank  borrowing.  Furfine  (2001)  reports  that 

borrowing  banks  with  higher  profitability  and  capital  ratios  pay  lower  interest  rates  in  the 

interbank  market.  He  also  finds  that  bank  size  is  an  important  determinant  of  transaction 

interest rates of interbank market participation. One explanation is that larger banks are more 

likely to be more creditworthy, because they are subject to too-big-to fail policies. Banksize is 

defined as the natural logarithm of bank total assets. 

Panel  B  of  Table  2  presents  the  differences  in  bank  characteristics  in  two  groups  of 

countries, which are divided based on their systemic banking crisis experience in the past. We 

8  Throughout  the  paper,  we  use  the  terms  “interbank  borrowing”  and  “interbank  deposits” 
interchangeably, yet it should be underlined that interbank borrowing include both deposits and loans.  

ECB Working Paper Series No 2374 / February 202017classify  a  crisis  as  “long” if its duration was  5  years or more. Next,  we employ one-to-one 

propensity score matching based on a country’s financial structure to define the “short” group 

of banks. 

TABLE 2 

The  comparative  statistics  show  that  banks  in  countries  that  have  experienced  longer 

periods of banking crises tend to have significantly lower levels of interbank borrowings, which 

is in line with our expectations. The difference is statistically significant at the 1% level. The 

results  show  that  banks  in  countries  with  longer  periods  of  crises  have  significantly  lower 

liquidity  mismatch  measured  by  LtD.  Moreover,  banks  with  longer  periods  of  crises  have 

higher equity ratios, meaning that banks in those countries choose to have more conservative 

policies.  Consequently,  the  results  indicate  that  there  are  significant  differences  in  banks’ 

structure between countries with different histories of bank crisis. 

3.2.2 Trust in the interbank market 

In the last  two  decades,  the economic literature  has recognized that  trust has a positive 

effect on economic development (Knack and Keefer, 1997) and financial development (Guiso 

et  al.,  2004;  2008).  However,  the  concept  of  trust  has  received  interest  increasingly  in  the 

finance literature. Most recent research has concentrated on relationship lending, which is not 

surprising, considering that the word “credit” originates from the Latin creditum, which means 

something entrusted to another; while in Middle French, “credit” means to believe, to trust, and 

to provide credit.  

Various studies explore the role of trust in financial intermediation in recent years (e.g. 

Duarte  et  al.,  2012;  Hasan  et  al.,  2017;  Levine  et  al.,  2018).  Harhoff  and  Korting  (1998), 

however, document that trust in the bank-firm relationship is complex and cannot be explained 

by  other  variables  as  duration  of  the  relationship  or  the  extent  of  competition  (lenders).  A 

popular proxy for trust in the literature is the World Values Survey, yet Glaeser et al. (2000) 

document  that  standard  survey  questions  do  not  appear  to  measure  trust,  by  arguing  that 

answers  to  the  survey  questions  are  more  closely  related  to  the  trustworthiness  of  the 

respondents  than  to  their  propensity  to  trust  others.  More  importantly,  in  our  study,  we  are 

ECB Working Paper Series No 2374 / February 202018interested not in individuals’ perceptions, but rather in the trust of banks in the interbank market 

participants within a country. Trust in individual people differs significantly from the trust of 

an organization in the market.  

In  our  study,  we  define  trust  as  a  bank’s  belief  in  its  peers’  honesty  and  good-faith 

commitments  within  the  country’s  interbank  market.  Our  major  two  proxies  for  trust  are 

directly related to banking system stability, Crisis length and Bank failure, as we consider that 

a long banking crisis as well as a significant number of bank failures are traumatic experiences 

for  the  banking  sector  and  consequently,  for  the  interbank  market.  The  first  proxy,  Crisis 

length, is defined as the length (number of years) of banking crises in the country till year t, 

over the period 1970 to 2015. Following Laeven and Valencia (2013), we define a systemic 

banking  crisis  as  producing  significant  signs  of  financial  distress  in  the  banking  sector  and 

triggering  significant  policy  interventions  to  assist  or  intervene.  The  starting  year  of  the 

systemic banking crisis is that when both conditions are met. Meanwhile, the end of the crisis 

is defined as the year before both real GDP growth and real credit growth are positive for at 

least 2 consecutive years.9 In our study, we focus on those crises that result in output loss of 

more than 10% of GDP. We presume that large systemic banking crises might lead to a decline 

of trust in the banking sector, including in the interbank market. After merging the banking 

crisis database with the bank-level datasets, we are able to identify 314 systemic banking crises 

across countries. 

Figure 5 shows the distribution of the number of countries with different levels of banking 

crisis length over the whole sample period from 1970 to 2015. In the sample, 22 countries have 

never experienced a banking crisis, 27 countries had 1 to 3 years of banking crises, 31 countries 

had 4 to 6 years of crises, and 11 countries experienced more than 7 years of crises. Argentina 

9 In all cases, however, the duration of the crisis is truncated after 5 years, starting from the 1st year of 
the crisis. As a result of the truncation in some of the countries, the global financial crisis of 2007–2009 
was classified as finished, yet the countries did not in fact meet the criteria for ending the crisis by 2015. 
We  keep  the  methodology  of  Laeven  and  Valencia  (2013),  as  in  our  opinion,  the  truncation  of  the 
duration of the crisis does not affect our results.  

ECB Working Paper Series No 2374 / February 202019 
and Ecuador are the two extreme countries that experienced a systemic banking crisis, which 

persisted  for  10  years.  The  length  (years)  of  banking  crises  allows  us  to  consider  both  the 

frequency and severity of crises.  

In addition, the  banking crisis window is proxied by the dummy variable Crisis, which 

takes  the  value  one  in  the  year  when  the  banking  crisis  became  systemic,  following  the 

definition of Laeven and Valencia (2013), and zero otherwise. The final year of the banking 

crisis is the year before both real GDP growth and real credit growth are positive for at least 2 

consecutive years.  

FIGURE 5 

The second proxy, Bank failure, is defined as the standardized value of total assets of failed 

banks till year t, over the period 1970 to 2015. We use the status of a bank to identify whether 

it has severe financial problems. If a bank is marked in the Bankscope database as “bankrupt,” 

“active (receivership),” or “in liquidation,” then we treat it as a bank failure. How to deal with 

insolvent banks, whose numbers vary across countries significantly, is a political decision. We 

presume  that  the  methods  used  to  resolve  bank  failures  can  strongly  determine  trust  in 

counterparties and the financial system. 

The  distinctive  differences  in  resolution  of  banking  crises  across  countries  shows  the 

outcome of the savings and loan crisis (S&L) in the US in the 1980s and 1990s and the banking 

crisis in Switzerland in 1991–1996. In both cases, the banking crisis affected mainly regional 

banks and was related to real estate booms in earlier years. As a result of the S&L crisis, US 

federal agencies liquidated 1,043 institutions and the total direct costs attributable to the closing 

of insolvent thrift institutions during 1986–1995 amounted to USD 145.7 billion (Curry and 

Shibut, 2000), which was around 2.5% of US GDP in 1990. In Switzerland, banks incurred 

estimated losses of around CHF 42 billion, which was more than 16% of Swiss GDP in 1990, 

yet only a single bank had to be liquidated (Westernhagen et al., 2004). In both cases, however, 

the number of regional banks (thrift banks) was reduced by more than 50% at the end of the 

banking crisis. 

ECB Working Paper Series No 2374 / February 202020In the European Union (EU) too, the number of bank failures remained relatively small in 

comparison to the US during the global financial crisis of 2007–2009. Nevertheless, based on 

the  US  experience,  the  19  Eurozone  countries  introduced  a  new  institution,  the  Single 

Resolution Board, in 2016, to deal with failing institutions in the EU in a more unified way. 

Therefore, how a government deals with insolvent banks strongly determines trust within the 

banking  sector,  as  bank  failures  are  long-lasting  traumatic  experiences  within  the  banking 

sector.  

For robustness, we also use a third proxy, Bank Z-score, which measures the bankruptcy 

risk for banks. We calculate the Z-score as the ratio of a bank’s leverage (capital over assets) 

and the mean of its ROA to the volatility of its ROA deduced from the probability that the 

bank’s losses exceed its capital, and then aggregate at the country level. This measure is often 

applied in the literature to estimate the individual probability of default of banks (Laeven and 

Levine, 2009) as well to measure the banking system stability (Lee and Hsieh, 2014). 

3.2.3 Legal origins, enforcement, and governance 

The  literature  has  shown  that  legal  institutions  and  enforcement  might  influence  the 

development of the financial system. Levine (1998) finds that banks are better developed in 

countries that protect creditors and enforce contracts effectively, which is one important feature 

of  German-based  legal  systems.  Countries  with  German-based  legal  systems  tend  to  have 

better-developed banks. Thus, he argues that the legal system materially influences banking 

development.  

We control for legal origins using the dummy variable Common law, which takes the value 

of one if the country has a common law legal origin, and zero otherwise. Panel A of Table 2 

shows that the sample mean for the variable is 0.35, indicating that more banks are located in 

civil law countries in our sample. 

Levine (1998) argues that enforcement of legal codes is as important as legal regulations 

themselves. We control for contract enforcement using the variable Rule of law. The variable 

is an estimated index on the extent to which agents have confidence in and abide by the rules 

ECB Working Paper Series No 2374 / February 202021of society, and in particular the quality of contract enforcement, property rights, and the courts. 

The  index  was  developed  first  by  Kaulfman  et  al.  (1999)  and  then  updated  every  year 

(Kaulfman et al., 2010). The original index ranges from -2.5 (weak governance) to 2.5 (strong 

governance). In our sample, the index ranges from -1.89 to 2.12, with a sample mean of 1.27.  

We use two proxies to control for the quality of the government, country regulations, and 

their enforcement. The first proxy is the variable Reg. quality, which reflects the ability of the 

government  to  formulate  and  implement  sound  policies  and  regulations  that  permit  and 

promote private sector development. The original index ranged from -2.5 (weak governance) 

to 2.5 (strong governance), whereas in our sample, it ranges from -2.15 to 2.25 with a sample 

mean of 1.16. The second proxy is  the variable  Gov. effect, which represents the quality of 

public services, the degree of its independence from political pressure, the quality of policy 

formulation and implementation, and the credibility of the government’s commitment to such 

policies. The original index was also standardized from -2.5 (weak governance) to 2.5 (strong 

governance). For the countries in our sample, the minimum value is -1.71 and the maximum 

value is 2.36, with a sample mean of 1.31. The mean values of the two governance indicators 

suggest that more banks are located in countries with stronger legal enforcement and better 

governance.  The  summary  statistics  also  show  fairly  large  variations  in  the  institutional 

development of countries in the sample. 

3.2.4  Other country characteristics 

The  structure  and  development  of  a  country’s  financial  system  might  determine  the 

functioning  of  the  financial  intermediaries  and  consequently,  the  interbank  market.  We  use 

three  variables  to  capture  the  characteristics  of  a  country’s  financial  system.  First,  we  use 

Private credit, defined as the ratio of private credit by deposit money of banks to the country’s 

GDP, to measure the development of the banking system. Private credit excludes credit to the 

public sector and cross claims of one group of intermediaries on another. Consequently, private 

credit is a good measure of the amount of savings channeled through intermediaries to private 

borrowers. Second, we control for the size of central bank assets (Central Bank), following 

Demirgüç-Kunt and Huizinga (2000), which illustrate that in developing countries, the central 

ECB Working Paper Series No 2374 / February 202022bank plays a relatively large role in credit provision. Third, we also control for stock market 

development using the variable Market cap, defined as the ratio of stock market capitalization 

to the country’s GDP. Lastly, we also consider the power of banks in a country by means of 

the combined market share using the assets of the three largest banks (Concentration). Existing 

evidence shows that concentrated banking systems are more stable and less likely to have crises 

(Beck  et  al.,  2001;  Schaeck  et  al.,  2009)  Hence,  we  would  expect  that  banking  sector 

concentration will be positively related to the size of the country’s’ interbank market.  

4.  Methodology and identification  

4.1  Identification 

Interbank markets are informal markets that enable banks to manage, pool, and redistribute 

their funds, and thereby provide lending and deposit facilities more efficiently. The amount 

borrowed and interest rate charged on interbank transactions reflects, in part, the credit risk of 

the  borrowing  institution  (Broecker,  1990).  This,  however,  does  not  explain  the  significant 

difference of the use of the interbank market across countries. We consider that an important 

factor in explaining the existing differences in the interbank market is the level of trust of banks 

in a country’s market and its peers. To test this hypothesis, we estimate the following baseline 

model, controlling for bank- and country-specific characteristics: 

𝐼𝑛𝑡𝑒𝑟𝑏𝑎𝑛𝑘 𝑏𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑗,𝑡 =   𝛽0 + 𝛽1𝐵𝑎𝑛𝑘𝑖,𝑗,𝑡 + 𝛽2𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑗,𝑡 + 𝛽3𝑇𝑟𝑢𝑠𝑡𝑗,𝑡 + 𝛽4𝑌𝑖,𝑗,𝑡 +

𝜀𝑖,𝑗,𝑡  

(1) 

where the indexes i, j, and t represent bank, country, and time, respectively. The vector of bank-

specific variables, Banki,j,t, characterizes bank performance and risk. In particular, we include 

proxies  for  funding  structure,  securities,  equity  performance,  and  bank  size.  The  vector  of 

country-specific  variables,  Countryj,t,  characterizes  the  countries’  legal  system,  institutional 

development, and structure of financial system. The relationship between interbank borrowing 

and our proxies for trust, Trusti,j,t, is allowed to vary across countries and time. Furthermore, 

we  include  year  fixed  effects,  Yi,j,t,.  We  do  not  control  for  country  fixed  effects,  as  some 

ECB Working Paper Series No 2374 / February 202023 
country-specific variables are time invariant, such as legal origins, or have quite low variance 

across time. 

4.2  Network analysis methodology 

       Network analysis methodology, built on graph theory, has been increasingly exploited in 

the finance literature. Two central features of network structure are centrality and community. 

Centrality has been widely used (e.g. Hochberg et al., 2007; Larcher et al., 2013; Engelberg et 

al.,  2013),  reflecting  both  local  and  global  importance,  i.e.  how  each  entity  (node  in  the 

network) is connected to others, and further how “important” the position of each entity is in 

the entire network. In graph theory, generally a network is described by a square “adjacency” 

matrix, the elements of which reflect the strength of the connections among each entity (node) 

in  the  network.  In  our  setting,  the  interbank  network  is  a  directed  and  weighted  network. 

Therefore, the matrix representing the interbank network is asymmetric, indicating the flow of 

borrowing  and  lending.  The  nodes  in  the  network  are  banks  borrowing  through  interbank 

market,  and  the  edges,  are  weighted  by  the  borrowing  volume.  Figure  6A  visualizes  the 

interbank borrowing and lending network for the banks in the Euro Area.  

      In this study, we mainly use Degree centrality, Eigenvector centrality as well as PageRank 

to measure the importance of the position in the interbank network.  Degree centrality (both 

in-degree and out-degree) captures the direct connections and therefore the local importance, 

whereas Eigenvector centrality and PageRank extend beyond the direct connections and show 

the  global  influence  throughout  the  entire  network.  Eigenvector  centrality  is  defined 

recursively as the importance (centrality) of a node relies on the importance (centrality) of its 

direct neighbors. PageRank is a variant of Eigenvector, resulted from an algorithm based on 

webgraph, and can reflect not only the number of direct links, but also the link propensity as 

well as the centrality of the neighbors.10  

10 For  the  formal  definition  of  centrality,  please  see,  e.g.  Jackson  (2008),  Allen  et  al.  (2019),    and 
Brunetti et al. (2019).  

ECB Working Paper Series No 2374 / February 202024      Community membership is related to centrality, but a fundamentally different concept (see, 

e.g.  Bubna  et  al.,  2019).  A  community  is  essentially  a  group  of  nodes  that  have  strong 

connections  to  each  other.  To  do  community  detection,  we  use  Modularity,  which  reflects 

whether the linkage between two banks through interbank borrowing is strong or not. Through 

optimizing  the  Modularity  we  identify  banks  into  different  communities  (clusters). We  use 

communities and clusters interchangeably throughout the paper. As an illustration, Figure 6B 

plots the interbank network groups by communities presented by different colours.  

FIGURE 6A & 6B 

5.  International evidence on interbank borrowing 

5.1  Baseline results 

The results in Table 3 document that bank and country characteristics as well as trust are 

important in explaining the level of interbank borrowing across countries. In columns (1) to 

(3), we use Crisis length as a proxy for the trust in the banking system, whereas in column (4) 

to  (6),  we  use  Bank  failure  instead.  In  all  the  specifications,  the  coefficients  for  the  key 

variables Crisis length and Bank failure are negative and statistically significant at the 1% level. 

Both variables confirm that higher trust in the interbank market is associated with higher usage 

of the interbank market. The coefficients for Crisis length suggest that one more year of crisis 

experienced in the history may reduce the interbank borrowing size by 4.3% (0.00336/0.0775). 

The coefficients for Bank failure also indicate that more bank failures in the past would reduce 

the  interbank  market  size.  In  terms  of  economic  magnitude,  1%  increase  in  Bank  failure, 

defined  as  the  standardized  value  of  total  assets  of  failed  banks,  is  associated  with  2.4% 

(0.00189/0.0775) decrease in interbank borrowing size.  

The bank-specific variables are in line with our predictions. The coefficients for the bank-

specific  variables,  other  than  ROA,  are  all  statistically  significant  at  the  1%  level.  The 

coefficients for Size are positive in all the regressions. This is consistent with the findings of 

Cocco et al. (2009), who argue that large banks are more likely to be net borrowers whereas 

ECB Working Paper Series No 2374 / February 202025smaller banks tend to be net lenders in the interbank market. As expected, banks with funding 

needs,  or  higher  loans-to-deposit  ratios,  are  more  likely  to  borrow  in  the  interbank  market. 

Surprisingly, however, the coefficients for equity ratio and  profitability are negative in all the 

regressions, meaning banks that obtain funding in the interbank market are more likely to have 

lower capitalization, which does not imply higher risk, taking into account that the coefficients 

for Securities are positively related to interbank borrowing. The funding strategy of the banks 

might  explain  the  lower  profitability,  as  interbank  funding  is  relatively  costlier  than  non-

financial deposits are, while securities provide lower interest income than loans do. 

 The  country-specific  variables  indicate  that  both  the  institutional  factors  and  financial 

structure are important determinants of interbank market size. The coefficients for Common 

law are significant and positively related to interbank market borrowing. One explanation for 

this is that common law countries provide better institutional protection for interbank market 

participants. Indeed, in all the specifications, the coefficients for Rule of law, Reg. quality and 

Gov. effect are positive and statistically significantly at the 1% level. Thus, the results indicate 

that institutional development is an important determinant of interbank market development. 

Another explanation for this result could be that common law countries tend to have better 

developed  financial  systems  (La  Porta  et  al.,  1998).  The  results,  however,  indicate  that 

interbank market usage is larger only in countries with strong bank-based financial systems. 

The coefficient for Private credit is positive and significant in all the specifications. In terms 

of economic impact, taking column (1) as an example, 1% increase in private credit to GDP 

ratio  is  associated  with  12.7%  (0.00984/0.0775)  more  volume  in  the  interbank  market.  By 

contrast, we find that central bank assets and market capitalization  are negatively related to 

interbank borrowing and the coefficients are statistically significant. The results indicate that 

in countries where banks have a larger role in financial intermediation than central banks or 

capital markets do interbank market volume tends to be higher. Moreover, Beck et al. (2013) 

find  that  an  increase  in  competition  has  a  larger  impact  on  banks’  risk-taking  incentives  in 

countries  with  better  developed  stock  exchanges.  Considering  that  the  coefficients  for 

concentration are positively and significantly related to the interbank market at the 1% level in 

ECB Working Paper Series No 2374 / February 202026all the specifications, the analysis again confirms the importance of banking sector stability in 

explaining the levels of interbank market volume.  

TABLE 3 

We  repeat  the  regressions  using  the  aggregated  country-level  Bank  z-score  as  another 

proxy for the trust in the banking system. In column (1) to (3) we run the regressions using the 

full sample while in column (4) to (6) we exclude U.S. banks for robustness check. Table 4 

reports the results. Consistently, we find that the coefficients of Bank z-score are negative in 

all  the  regressions  and  are  statistically  significant  in  all  the  specifications  at  the  1%  level, 

confirming that trust in the stability of the banking sector is an important factor in explaining 

the levels of interbank market usage across countries. The coefficients for the bank-specific 

variables and country-specific variables do not change much after employing a different proxy 

for trust and mostly remain significant. 

TABLE 4 

5.2  The role of trust in interbank participation 

The trade-off between counterparty risk and liquidity hoarding suggests that trust plays a 

key role in the unsecured interbank market. A systemic banking crisis with a large number of 

bank failures could be a negative exogenous shock to future trust in the interbank market. The 

results in Tables 3 and 4 show that if a bank is located in a country with higher risk of bank 

failure and higher amount of total assets of failed banks or longer periods of banking crises in 

the past, then it will borrow less in the interbank market on average. In addition, the usage of 

the interbank market might be strongly determined by the structure of the financial system. 

Claessens et al. (2001) document that recessions and financial disruptions in emerging markets 

are often costlier than in developed countries, and it takes more time for emerging economies 

to recover. They attribute this difference to the fact that emerging countries have less developed 

financial  systems.  Meanwhile,  Demirgüç-Kunt  and  Levine  (1999)  observe  the  tendency  for 

countries’ financial systems to become more market-oriented as they become richer. Therefore, 

we can assume that in countries with bank-based financial systems, which are often emerging 

ECB Working Paper Series No 2374 / February 202027economies, the banking crisis has on average a stronger negative effect on the usage of the 

interbank market. Consequently, the structure of  the financial system, especially the role of 

banks in intermediation, can determine our results.  

We  use  the  difference-in-difference  estimation  technique  to  isolate  this  possibility  and 

further explore the causality of bank failures and banking crises on the development pattern of 

the  interbank  market,  controlling  for  the  structure  of  the  financial  system.  As  traumatic 

experience has a strong impact on trust (Alesina and La Ferrara, 2002), we define a treatment 

group and a control group of banks based on the total duration of all banking crises in the past. 

In the regression, the variable Treated  equals one if the bank is located in a country with a 

history of banking crises longer than (or equal to) 5 years in total, and zero otherwise. Next, 

we  employ  the  propensity  score-matching  algorithm  without  replacement  based  on  the 

structure and development of the financial system, Private credit and Mkt. cap., to define the 

control group of banks. Table 5 presents the regression results on the effect of banking crises 

on interbank market size using the matched sample. In all the specifications, the coefficients 

for  Treated  are  negative  and  significant  at  the  1%  level.  Furthermore,  the  results  are 

economically important, as they show that in column (1), ceteris paribus, banks can reduce 

interbank borrowing by up to 22.8% (0.0177/0.0775)  if they are located in a treated country 

than in a control country. 

TABLE 5 

5.3  The mitigating role of legal and regulatory institutions 

Numerous studies suggest that legal and institutional difference shape both the price term 

and the non-price terms of bank loans across the world (See, e.g. Qian and Strahan, 2007). Not 

surprisingly, we also find that institutions are an important factor in explaining borrowing in 

interbank markets. Indeed, the coefficients for legal origin and institutions were statistically 

significant at least at the 1% level in all the regressions. Qian and Strahan (2007) argue that 

improving  countries’  institutions  might  improve  financial  outcomes  by  reducing  the  risks 

associated with lending. Based on their argument, we can expect better institutions to mitigate 

ECB Working Paper Series No 2374 / February 202028the lack of trust in a country’s interbank market following shocks from banking crises. We test 

this  assumption  by  introducing  an  interaction  term  between  banking  crisis  window  and 

governance indicators in the regressions. 

Table  6  reports  the  results  of  the  regressions  including    the  dummy  Crisis  and  its 

interaction  terms.  First,  we  find  that  a  systemic  banking  crisis  negatively  affects  interbank 

borrowing.  The  coefficients  for  the  dummy  variable,  Crisis,  have  statistically  significant 

negative signs in all the regressions at the 1% level. We find that a current systemic banking 

crisis has a much larger negative effect on interbank market transactions than past experience 

does,  as  the  coefficients  for  Crisis  are  significantly  larger  than  those  for  the  length  of  past 

banking crisis. For example, the coefficient in column (1) suggests that during a banking crisis, 

the  interbank  borrowing  can  drop  by  35.1%  (0.0272/0.0775)  on  average.  Consequently,  we 

find strong evidence that the interbank market is likely to malfunction during a financial crisis. 

Acharya  and  Skeike  (2011)  explain  the  reduced  volumes  or  extreme  levels  of  rates  for 

interbank loans during a crisis by banks’ precautionary demand for liquidity. They argue that 

banks hoard liquidity and reduce term lending, which is determined by its own risk that it will 

be  unable  to  roll  over  debt  that  matures  before  the  term  of  the  interbank  loan.  Similarly, 

Acharya and Merrouche (2013) show that banks, especially weaker ones, hoarded liquidity in 

response  to  the  funding  risk  during  the  global  financial  crisis  of  2008.  Bräuning  and  Fecht 

(2017),  on  the  other  hand,  argue  that  increased  counterparty  credit  risk  negatively  affected 

interbank liquidity during the crisis of 2008.  

TABLE 6 

However,  our  results  show  that  the  negative  effect  of  the  global  financial  crisis  on 

interbank market malfunctioning might depend on countries’ institutional frameworks. In all 

the regressions, the interaction terms between governance indicators and Crisis have significant 

and positive coefficients, indicating that in countries with better legal enforcement, regulation 

quality, or stronger government effectiveness, the marginal negative impact of a banking crisis 

on  interbank  borrowing  would  be  mitigated  significantly.  These  results  are  consistent  with 

those of Qian and Strahan (2007), who find that institutional factors enhance loan availability. 

ECB Working Paper Series No 2374 / February 202029Our results show that institutional factors are important for the functioning of the interbank 

market, even when during crisis periods. There are at least two possible explanations for this. 

First,  it might be  easier  for banks to  overcome the increased counterparty  credit risk in  the 

interbank market during a crisis with high regulation quality and strong enforcement. Second, 

stronger  government  effectiveness is  likely  to  be related to  a well-functioning central  bank, 

which might be willing to intervene in the interbank market during a crisis period. Allen et al. 

(2009)  present  a  model  showing  that  a  central  bank  can  successfully  intervene  to  fix 

malfunctioning interbank markets. 

5.4  Robustness analysis 

       Lastly, we perform several additional tests to gauge the robustness of our results. First, we 

exclude the US banks from our sample as they account for 40.5% (4,620 out of 11,412 banks) 

observations. Hence, the results of the study may be biased by the overrepresentation of the US 

banks in the sample. After excluding US banks, we have in total 6,792 banks over 95 countries. 

Columns  (4)  to  (6)  in  Table  4  report  the  results,  which  are  highly  consistent  with  those 

suggested with the main results in Table 3 and columns (1) to (3) in Table 4. The results may 

also be influenced by major banks located in global financial centers. There has been some 

evidence showing that the interbank market is dominated by the offices of major banks located 

in the principal financial centers around the world (BIS, 1983). For the international interbank 

markets,  the  main  criteria  for  participation  are  that  the  borrowing  bank  establishes  itself  as 

creditworthy in the eyes of other banks and further it is not constrained by regulatory obstacles, 

such as exchanges controls or supervisory limits. We decided, therefore, to exclude banks from 

the US, the UK, Singapore, and Hong Kong from the sample. We find that excluding the banks 

from those countries does not change our main results. We present the results in the Internet 

Appendix Table B2 for brevity. 

Besides their need for working balances, banks’ demand for interbank funds is driven by 

the required reserves that they have to hold at the central bank. Links between the overnight 

interbank  market  and  the  market  for  bank  reserves  are  strongly  associated  with  reserve 

requirement arrangements. Gray (2011) shows that the reserve requirements as well as the basis 

ECB Working Paper Series No 2374 / February 202030of its calculations vary strongly across the countries, which in turn could influence our results. 

We decided, henceforth, to rerun the regressions using only banks from the euro area, which 

are subject to the same central bank policy. We use both the 11 original Euro zone countries 

and the 19 countries that are Eurozone members nowadays.   Column (1) and (2) in Table 7 

show the results for the two subsamples, respectively. We find that the coefficients for the trust 

proxy are negatively correlated and statistically significant in the specifications, meaning that 

our results are not influenced by central bank policies. 

TABLE 7 

Cocco  et  al.  (2009)  document  that  bank  size  is  an  important  determinant  of  interbank 

market interest rates, and of lending relationships. On average, large (small) banks tend to be 

net borrowers (lenders) in the market.  Iori et al. (2008) document that not all banks actively 

manage their minimum reserves, and smaller banks tend to keep their reserve account at the 

required level  constantly through the maintenance period. The existing results thus  indicate 

that  banks’  size  may  be  an  important  determinant  of  interbank  lending  and  borrowing. 

Therefore, we further introduce a dummy variable, Large, defined as one for the upper quartile 

and  zero  for  the  lower  quartile  based  on  the  total  bank  assets,  and  interact  it  with  the  trust 

measure Crisis length. Column (3) in Table 7 shows that large banks tend to borrow more from 

the  interbank  market,  and  the  effect  of  trust  is  more  pronounced  for  larger  banks,  with  the 

coefficient of the interaction being significantly negative.  

To  further  tackle  the  endogeneity  concern,  that  the  interbank  borrowing  might  be 

influenced  by  other  unobserved  factors  correlated  to  trust,  we  further  employ  instrumental 

variable (IV) analysis. The instruments we employ are the usage of deposit insurance scheme 

as well as the power of deposit insurance scheme in a given country. The data are collected 

from Demirgic-Kunt et al. (2014).11  The existence as well as the power of deposit insurance 

scheme in country i of year t-1 is matched with the interbank borrowing of banks located in 

11 For the details about this database, please see: 
 https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Deposit-Insurance-Database-41710 

ECB Working Paper Series No 2374 / February 202031country i of year t. It satisfies the relevance condition that, countries with a larger number of 

banking crises and failures are more likely to introduce the safety net, i.e. the deposit insurance, 

so  the  trust  can  be  restored.  Demirgic-Kunt  et  al.  (2014)  document  that  fourteen  countries 

introduced  the  explicit  deposit  insurance  since  2008,  and  almost  all  countries  with  explicit 

deposit insurance that experienced a banking crisis increased the statutory coverage limit in 

their deposit insurance scheme. The exclusive condition is also very likely to be satisfied as the 

interbank borrowing (deposits) is based on credit, and not covered by the deposit insurance. 

Hence,  the  instruments  will  affect  interbank  borrowing  only  through  trust  rather  than  other 

unobserved factors. Table 8 reports the two-stage least-squares regression results. Column (1) 

and (2) use ExDI and column (3) and (4) use DI power as the instruments. The first-stage results 

show that both instruments are significantly and positively associated with the trust measure, 

Crisis length. The second-stage results confirm with our main results that lack of trust reduces 

interbank borrowing, at least at the 5% significance level. To sum up, the additional tests above 

further  confirm  the  robustness  of  our  results  on  the  importance  of  trust  on  the  activity  of 

interbank market.  

TABLE 8 

6.  The role of network structure: Euro Area interbank market 

      The existing literature has identified core-periphery structures in many different interbank 

networks (e.g. Cocco et al., 2009). Therefore, one concern would be that lack of trust in core 

banks,  who  act  as  interbank  intermediaries,  might  have  very  different  implications  for  how 

much the overall borrowing network declines (Craig and Ma, 2019). Therefore, in this section, 

we use the Euro area interbank market to explore how the network structure may affect the role 

of trust in determining interbank borrowing.  

6.1 Describing the Euro Area interbank network 

      Figure  6  plots  the  EA  interbank  borrowing  and  lending  network  in  2018Q4.  Figure  6A 

groups the banks by country, with the node color reflecting banks’ home country. Node size 

represents eigenvector centrality, therefore, suggesting how globally important the bank is in 

ECB Working Paper Series No 2374 / February 202032the entire EA interbank network. Edge color represents the country receiving the exposures. 

The algorithm of the layout is  Multigravity Force Atlas 2.  In Figure  6A, red nodes refer to 

German banks, and black nodes refer to French banks. In Figure 6B, we use different colors 

for communities detected by Modularity, with all the nodes positioned at the same places in 

the network as shown in Figure 6A. We have detected 13 communities (Community 0 to 12) 

in the EA interbank network. Table 9 reports the joint distribution of country and community 

group. Germany has the largest number of banks (249 banks in total) in the network, with 82 

percent of them (218 banks) located in Community 3. At the same time, Community 3 is also 

dominated by German banks. 83% (85 out of 103 banks in total) of the Italian banks are  located 

in  Community 6. French banks are distributed across different  communities, indicating that 

French banks have higher volumes of borrowing and lending across countries.  

TABLE 9 

      Table  10  presents  the  mean  value  of  network  centralities,  cluster  coefficients  a  well  as 

average path length for each country over the fourth quarter of 2014 to the fourth quarter of 

2018, sorted by Eigenvector centrality. The statistics of the network measures show that in the 

EA  interbank  network,  French  banks  on  average  have  the  highest  both  local  and  global 

importance, suggesting that the French interbank market is more dominated by intermediary 

banks. German banks have much lower  Eigenvector centrality but  still quite high values of 

Degree centrality, compared to Belgium and Ireland, which both ranked ahead of Germany in 

terms of Eigenvector. This suggests that German banks have strong direct connections but the 

connected neighbors are not globally important players in the interbank network. This is also 

consistent  with  the  fact  that  German  banks  are  mostly  located  in  the  same  community 

(Community  3),  as  shown  in  Table  9.  The  cluster  coefficients  of  bank  nodes  capture  how 

complete the neighborhood of a bank node is.12 The mean value of cluster coefficients suggests 

12 For example, if every bank node in the neighbourhood of bank A is connected to every other node in 
the neighbourhood of bank A, then the neighbourhood of bank A is complete and will have a clustering 
coefficient of 1; if no bank nodes in the neighbourhood of bank A are connected, then the clustering 
coefficient is 0.  

ECB Working Paper Series No 2374 / February 202033that  both  German  and  Slovenian  banks  are  the  best  connected  within  the  communities, 

indicating  a  “small-world”  effect,  that  banks  are  borrowing  and  lending  more  within 

communities.  Longer  average  length  suggests  a  lower  degree  of  intermediation.  Hence,  its 

statistics show that Italy, Ireland and Netherland have the highest degree of intermediation on 

average in the interbank market.  

TABLE 10 

       Figure 7 plots the quarterly mean value of Weighted in-degree and Eigenvector centrality 

of the entire network from 2014 to 2019.  The figure shows that on average banks in the Euro 

Area are borrowing less from the interbank market since the 2nd quarter of 2017. The global 

importance in terms of Eigenvector also dropped in early 2017 but rebounded soon later.  

FIGURE 7 

6.2 Determinants of interbank market participation: the role of network structure 

      We  then  explore  how  the  network  structure  affects  the  role  of  trust  in  determining  the 

interbank participation. First of all, instead of using interbank borrowing (deposits) from banks’ 

balance  sheet,  we  use  interbank  network  centralities  (Log  inwdeg,  Log  page  rank  and  Log 

eigen)  to  measure  the  interbank  market  participation,  as  the  dependent  variable  in  the 

regression specifications. For the measures of trust, we use the Crisis length, as well as Failure 

ratio, defined as the total assets of failed banks over total assets of the banking system in a 

given country till year t. We use the ratio of the failed assets, instead of the standardized value 

of failed assets, as here in the EA interbank network, we are considering not only the domestic, 

but also the cross-border borrowing and lending activities. Hence, Failure ratio can be a better 

proxy for trust, especially for cross-border lenders. Table 11 reports the baseline regression 

results,  for    banks  in  the  Euro  Area.  In  column  (1)  to  (3),  we  use  the  centrality  measures 

calculated from the network of the total exposure, which includes not only the borrowing and 

lending  between  EA  banks,  but  also  nonEA  banks’  borrowing  from  EA  banks,  whereas  in 

column (4) to (6) we use centrality measures calculated from the network of EA exposures, 

which covers only the borrowing and lending between EA banks. Cluster is defined as one if 

ECB Working Paper Series No 2374 / February 202034the cluster coefficient of a bank node is above median, or zero otherwise, capturing how banks 

are exposed in the interbank community. The results show that Crisis length and Failure ratio 

both enter with negative signs, significantly in most of the specifications, suggesting that lack 

of trust can reduce interbank activities, in terms of both interbank borrowing from the direct 

neighbors and  global  activities  throughout  the interbank network. Being  more exposed to  a 

community  is  negatively  associated  with  interbank  participation,  locally  and  globally, 

consistent with the “small world” effect of being in a community. In addition, larger banks tend 

to have higher centrality in the interbank network.  The results are robust when we exclude the 

exposures from EA banks to nonEA banks. 

TABLE 11 

      We then investigate how the core-periphery structure affects the interbank activities. Lack 

of  trust  in  intermediary  banks  can  spill  over  to  affect  their  borrowers’  access  to  interbank 

funding. Hence, trust would have different implications for core versus periphery banks. We 

use Eigenvector centrality to identify core and periphery positions in the network. Central is 

defined as one if the eigenvector centrality is in the upper quartile, or as zero if in the lower 

quartile. The dependent variable is Log inwdeg, which measures the interbank borrowing at the 

bank level. We also consider both the network of total exposures and the network of exposures 

only between EA banks.  To capture how the network positions can affect the influence of trust, 

we interact the trust measures with Central. The results, reported in Table 12, show that the 

impact  of  trust  is  more  significant  for  banks  in  the  core  positions,  with  all  the  interactions 

entering with significant and negative coefficients. Again, banks more exposed in a community 

are more likely to have lower interbank borrowing, while those located at core positions are 

more likely to have higher interbank borrowing.  Controlling for the average path length, as 

shown in column (2) and (4), does not affect the results. 

TABLE 12 

      We then further examine how the network structure, in terms of clustering, would affect 

the role of trust. Again, we use centrality measures from the network of the total exposures as 

ECB Working Paper Series No 2374 / February 202035well as those from the network of EA exposures as dependent variables. To examine the effect 

of clustering, we introduce the interactions of trust measures and Cluster. Table 13 reports the 

results.  Both trust  measures enter with  strong negative coefficients  in  all the specifications, 

confirming  our  main  results  that  lack  of  trust  reduces  interbank  participation.  More 

importantly, being in a community tends to mitigate the negative effect of trust, suggested by 

the significant and positive coefficients of the interactions in all the columns. This indicates 

that  when  trust  is  low  in  the  interbank  market,  being  more  exposed  in  a  community  might 

provide an additional source of funding due to community relationship.  

TABLE 13 

      For  robustness,  we  also  exclude  exposures  of  securities  contracts  as  well  as  long-term 

(longer than 30 days) exposures from the network. Table 14 reports the results. We incorporate 

both  interactions,  trust  with  Central,  and  trust  with  Cluster,  into  the  regressions.  The 

interactions of trust and Central enter with strong negative coefficients, and the interaction of 

trust and Cluster enter with strong positive coefficients, in both columns. These confirm our 

finding that lack of trust may have stronger effect in core (intermediary) banks, while being 

more exposed in  a community can provide additional interbank funding when trust is low.  

TABLE 14 

7.  Conclusion 

The interbank market is an informal market that enables banks to manage and redistribute 

their funds, and so provide financial intermediation more efficiently. The bilateral nature of the 

interbank  market  does  not  differ  across  countries.  We  document,  however,  that  banks’ 

engagement  in  the  interbank  market  differs  strongly  across  the  countries.  In  this  study,  we 

investigate the reasons for those differences and find that trust in the banking sector and peers 

is an important factor in explaining the differences in the interbank activities across countries. 

More specifically, we show that a bank located in a country that has experienced longer banking 

crisis or more bank failures in the past, finances its activity to lesser extent using the interbank 

market. 

ECB Working Paper Series No 2374 / February 202036Through  mapping  and  investigating  the  Euro  Area’s  interbank  market  using  network 

methodology,  we  find  the  impact  of  trust  relies  on  the  network  structure  of  the  interbank 

market.  The  influence  of  network  structure  is  twofold.  First,  being  more  exposed  in  a 

community can mitigate the negative  effect  of lacking  trust  in  obtaining  interbank funding. 

Second, the impact of trust is more significant for banks in the core positions than those in the 

periphery  positions  in  the  network,  suggesting  a  spill-over  effect  for  intermediary  banks  in 

transmitting risks. Our finding is robust when applying the network of total exposures, or the 

network of non-securities contracts, or the network of short-term exposures in the Euro Area.  

Lastly,  we  show  that  country-level  institutional  factors  such  as  legal  enforcement  and 

regulation  quality  also  play  an  important  role  in  explaining  the  cross-country  difference  in 

interbank participation, and may mitigate the adverse impact of banking crises or bank failures 

in the past. The results are in line with the law and finance literature showing that  a strong 

institutional framework enhances loan availability in the unsecured markets as it can provide 

better protection against bankruptcy. We also find that bank characteristics, especially  funding 

ratio and size, are important factors in explaining the level of interbank market participation, 

confirming that in an unsecured credit market such as interbank market, peer monitoring plays 

an important role. 

ECB Working Paper Series No 2374 / February 202037 
 
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ECB Working Paper Series No 2374 / February 202041Figure 1A. Breakdown of 10-year-average bank assets: 2000-2009 

Assets:  10-year average (2000-2009)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Other assets

Securities

Loans

Interbank loans

Cash and balance with
Central bank

US

Japan

France

Germany

UK

Figure 1B. Breakdown of 10-year-average bank liabilities: 2000-2009 

Liabilities: 10-year average (2000-2009)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

US

Japan

France

Germany

UK

Source: OECD Statistics; Japanese Banker Association 

Other liabilities

Bonds

Customer deposits

Interbank deposits

Borrowing from Central
bank

Capital and reserves

ECB Working Paper Series No 2374 / February 202042 
 
 
 
 
Figure  2.  Interbank  loans  and  deposits  of  domestic  banks  in  the  European  Union 
countries in 2016 
The figure shows the interbank loans and deposits as % of total assets of all domestic banking 
groups and stand-alone banks in 2016. The data for United Kingdom is for the year 2015. 

%

Loans

16

14

12

10

8

6

4

2

0

Source: ECB 

Figure 3. Interbank loans and deposits of domestic and foreign banks in the European 
Union countries in 2016. 
The figure shows the interbank loans and deposits as % of total assets of domestic banking 
groups and stand-alone banks, foreign (EU and non-EU) controlled subsidiaries and foreign 
(EU and non-EU) controlled branches, in 2016 left-hand scale). The points present the share of 
foreign bank ownership as % of total assets (right-hand scale). The data for United Kingdom is 
for the year 2015. 

%

35

30

25

20

15

10

5

0

Loans

Deposits

Ownership

Source: ECB 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

ECB Working Paper Series No 2374 / February 202043 
 
 
 
Figure  4.  Interbank  dependence  ratio  for  domestic  banks  in  the  European  Union 
countries in the years 2007 and 2016 
The figure presents the interbank market dependence ratio, defined as total amount owed to 
credit institutions over total assets, for all domestic banking groups and stand-alone banks in 
the  years  2007  and  2016.  The  data  for  Cyprus,  Denmark,  Estonia,  Ireland,  Germany, 
Luxembourg, Latvia, Netherlands, and United Kingdom is for the year 2008. 

% 

2007

2016

35

30

25

20

15

10

5

0

Source: ECB 

Figure 5. Distribution of the length of banking crises over the whole sample period  
This figure plots the distribution of the number of countries that have different length (number 
of  years)  of  banking  crises  from  1970-2015  in  our  sample  period.  Over  20  countries  in  our 
sample have no banking crises during this period; 52% have banking crisis of fewer than four 
years in total; whereas 48% have banking crisis of four or more years in total this period. 

0510152025Country number0246810bcrisis_freqECB Working Paper Series No 2374 / February 202044 
 
 
 
Figure 6. Euro Area interbank network 

Figure 6A. Euro Area interbank network: grouped and colored by country 
This chart plots the interbank network (borrowing and lending) of Euro area in 2018Q4. Node 
size represents eigenvector centrality. Edge colour represents the country receiving the 
exposures. Node colour refers to different countries as below. The algorithm of the layout is 
Multigravity Force Atlas 2. 
Red: Germany; Blue: Non-EA Banks; Black: France; Green: Italy; Yellow: Spain; Orange: 
Netherlands; Pink: Austria. 

ECB Working Paper Series No 2374 / February 202045 
 
 
 
Figure 6B. Euro Area interbank network: colored by community 
This chart plots the interbank network (borrowing and lending) of Euro area in 2018Q4. Node 
size represents eigenvector centrality. Edge colour represents the country receiving the 
exposures. Node colour refers to communities detected using Modularity. There are 13 
communities in the chart. The algorithm of the layout is Multigravity Force Atlas 2. 

ECB Working Paper Series No 2374 / February 202046 
 
Figure 7. Average weighted indegree and eigenvector across time: 2014-2019 
This figure plots the quarterly mean value of weighted in-degree and eigenvector centrality of 
the EA interbank network, from 2014 to 2019. 

6

5

4

3

2

1

0

4
1
0
2
/
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1
0

5
1
0
2
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2
0
/
1
0

5
1
0
2
/
4
0
/
1
0

5
1
0
2
/
6
0
/
1
0

5
1
0
2
/
8
0
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1
0

5
1
0
2
/
0
1
/
1
0

5
1
0
2
/
2
1
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1
0

6
1
0
2
/
2
0
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1
0

6
1
0
2
/
4
0
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1
0

6
1
0
2
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6
0
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0

6
1
0
2
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8
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1
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6
1
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2
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1
0
2
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0

7
1
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2
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0
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0

7
1
0
2
/
6
0
/
1
0

7
1
0
2
/
8
0
/
1
0

7
1
0
2
/
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0

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1
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2
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1
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0

8
1
0
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0

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1
0
2
/
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0
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1
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0
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8
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1
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0

in_w_deg_qmean (LHS)

eigen_qmean (RHS)

0.050

0.045

0.040

0.035

0.030

0.025

0.020

0.015

0.010

0.005

0.000

ECB Working Paper Series No 2374 / February 202047 
 
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ECB Working Paper Series No 2374 / February 202048 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2. Summary statistics 
This  table  presents  the  summary  statistics  of  the  cross-country  bank  sample,  as  well  as  the 
difference in characteristics for banks located in countries with long or short periods of bank 
crises over the sample period. 
Panel A Summary statistics: Bank-level full sample 

Mean 

Interbank borrowing 
LtD 
Securities 
Equity 
ROA 
Bank Size 
Crisis length 
Bank failure 
Bank Z-score 
Common law 
Rule of law 
Reg.  quality 
Gov. effect 
Private credit 
Market Cap. 
Central Bank 
Concentration 
Panel B Comparison of bank characteristics: longer vs shorter periods of banking crisis 
country 

0.0775 
0.9271 
0.2135 
0.0947 
0.0054 
5.6665 
2.8842 
5.6841 
2.9905 
0.3517 
1.2728 
1.1628 
1.3133 
0.7884 
0.7471 
0.0643 
0.5515 

Std. Dev. 
0.1107 
0.5621 
0.1494 
0.0534 
0.0103 
1.4427 
2.4051 
7.4056 
2.7138 
0.4775 
0.7560 
0.5810 
0.6904 
0.3481 
0.4832 
0.0737 
0.2084 

Max 
1.0000 
5.4421 
0.9903 
0.3309 
0.0727 
11.2559 
10.0000 
16.3184 
11.4330 
1.0000 
2.1200 
2.2500 
2.3600 
2.6246 
8.5733 
1.1358 
1.0000 

Min 
0.0000 
0.0657 
0.0000 
0.0147 
-0.0606 
2.0175 
0.0000 
-0.1304 
-0.3123 
0.0000 
-1.8900 
-2.1500 
-1.7100 
0.0115 
0.0001 
0.0000 
0.2228 

Obs 
74,572 
74,572 
74,572 
74,572 
74,572 
74,572 
74,572 
74,572 
74,195 
73,860 
72,245 
72,212 
72,212 
73,535 
72,803 
73,556 
69,682 

Interbank borrowing 

LtD 

Securities 

Equity 

ROA 

Long 
0.020 
   (0.000) 
0.862 
(0.003) 
0.216 
(0.000) 
0.114 
(0.000) 
0.006 
(0.000) 

Obs. 
33,966 

33,966 

33,966 

33,966 

33,966 

Short 
0.123 
(0.001) 
0.993 
(0.003) 
0.214 
(0.001) 
0.083 
(0.000) 
0.004 
(0.000) 

Obs. 
33,966 

33,966 

33,966 

33,966 

33,966 

Diff 
0.103*** 
(0.001) 
0.131*** 
(0.004) 
-0.002 
(0.001) 
-0.031*** 
(0.000) 
-0.002* 
(0.000) 

ECB Working Paper Series No 2374 / February 202049 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ECB Working Paper Series No 2374 / February 202051 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4. Interbank borrowing and bankruptcy risk 
This table reports the results of the regressions examining the determinants of interbank borrowing 
using the full bank-level sample of 11,412 banks in 96 countries. The dependent variable is the size 
of interbank borrowing to total assets. The key explanatory variable is bankruptcy risk, measured by 
the aggregated country-level Bank zscore. We control for both bank and country characteristics in the 
regressions.  All the other variables are  defined in  Appendix Table  A1.  Robust  standard  errors  are 
reported in parentheses. ***, ** and * denote statistical significance at the 1%, 5% and 10% level, 
respectively.  
Dep Var. 

Interbank borrowing 

(1) 

(2) 
Full sample 

(3) 

(4) 

(5) 

(6) 
Sample excl. US banks 

Bank characteristics 
Bank zscore 

-0.00107*** 
(0.000145) 
0.0693*** 
(0.00177) 
0.0896*** 
(0.00346) 
-0.209*** 
(0.0101) 
0.0389 
(0.0413) 
0.00990*** 
(0.000286) 

0.0350*** 
(0.00244) 
0.0227*** 
(0.000899) 

LtD 

Securities 

Equity 

ROA 

Size 

Rule of law 

Reg. quality 

Gov. effect 

Country characteristics 
Common law 

Mkt. cap. 

Central bank 

Private credit 

0.0136*** 
(0.00250) 
-0.0492*** 
(0.00209) 
-0.298*** 
(0.0116) 
Concentration  0.119*** 
(0.00496) 
-0.0771*** 
(0.00538) 
YES 
66854 
0.438 

Year FE 
# of obs. 
Adj. R2 

Cons. 

-0.00104*** 
(0.000145) 
0.0674*** 
(0.00176) 
0.0895*** 
(0.00348) 
-0.225*** 
(0.0100) 
0.0168 
(0.0413) 
0.00971*** 
(0.000288) 

-0.00104*** 
(0.000145) 
0.0694*** 
(0.00178) 
0.0899*** 
(0.00346) 
-0.202*** 
(0.0101) 
0.0251 
(0.0412) 
0.00991*** 
(0.000286) 

0.0448*** 
(0.00240) 

0.0373*** 
(0.00241) 

0.0214*** 
(0.00111) 

0.0221*** 
(0.00245) 
-0.0490*** 
(0.00206) 
-0.285*** 
(0.0115) 
0.127*** 
(0.00494) 
-0.0817*** 
(0.00540) 
YES 
66854 
0.434 

0.0274*** 
(0.00103) 
0.0108*** 
(0.00250) 
-0.0521*** 
(0.00220) 
-0.295*** 
(0.0117) 
0.120*** 
(0.00494) 
-0.0851*** 
(0.00545) 
YES 
66854 
0.440 

-0.00153*** 
(0.000197) 
0.0698*** 
(0.00183) 
0.0780*** 
(0.00592) 
-0.236*** 
(0.0145) 
-0.0256 
(0.0816) 
0.0123*** 
(0.000397) 

0.0573*** 
(0.00409) 
0.0269*** 
(0.00107) 

0.00489* 
(0.00260) 
-0.0475*** 
(0.00228) 
-0.335*** 
(0.0128) 
0.105*** 
(0.00528) 
-0.0743*** 
(0.00595) 
YES 
42543 
0.298 

-0.00156*** 
(0.000197) 
0.0677*** 
(0.00182) 
0.0790*** 
(0.00600) 
-0.260*** 
(0.0144) 
-0.0609 
(0.0818) 
0.0122*** 
(0.000400) 

-0.00148*** 
(0.000197) 
0.0698*** 
(0.00183) 
0.0789*** 
(0.00592) 
-0.224*** 
(0.0146) 
-0.0643 
(0.0813) 
0.0122*** 
(0.000395) 

0.0593*** 
(0.00419) 

0.0626*** 
(0.00412) 

0.0249*** 
(0.00141) 

0.0150*** 
(0.00258) 
-0.0480*** 
(0.00225) 
-0.326*** 
(0.0127) 
0.116*** 
(0.00531) 
-0.0801*** 
(0.00599) 
YES 
42543 
0.291 

0.0325*** 
(0.00121) 
0.00153 
(0.00260) 
-0.0507*** 
(0.00239) 
-0.332*** 
(0.0129) 
0.105*** 
(0.00525) 
-0.0834*** 
(0.00602) 
YES 
42543 
0.301 

ECB Working Paper Series No 2374 / February 202052 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5. Trust in the interbank market: matched sample 
This table reports the results of the regressions examining the role of banking crises in affecting 
interbank borrowing, using the bank-level sample of 6,792 banks over 95 countries (excl. US 
banks). The dependent variable is interbank borrowing to banks total assets. Treated equals one 
if a bank is located country has no less than five banking crises in the years 1970-2015 (47 
countries in total), and zero otherwise. The control sample is defined by one-to-one propensity-
score-matching  algorithm  based  on  a  country’s  financial  structure  (Private  credit  and  Mkt. 
cap.). We control for both bank and country characteristics in the regressions. All the other 
variables are defined in Appendix Table A1. Robust standard errors are reported in parentheses. 
***, ** and * denote statistical significance at the 1%, 5% and 10% level, respectively.  
Dep. Var 

Treated 

LtD 

Securities 

Equity 

ROA 

Banksize 

Rule of law 

Reg. quality 

Gov. effect 

Private credit 

Mkt. cap. 

Central bank 

Concentration 

Cons. 

Year FE 
# of obs. 
Adj. R2 

(1) 
-0.0177*** 
(0.00167) 
0.0649*** 
(0.00187) 
0.0715*** 
(0.00628) 
-0.161*** 
(0.0154) 
-0.154* 
(0.0847) 
0.0146*** 
(0.000430) 
0.0311*** 
(0.00114) 

-0.00808*** 
(0.00256) 
-0.0370*** 
(0.00187) 
-0.333*** 
(0.0139) 
0.0627*** 
(0.00660) 
-0.0557*** 
(0.00665) 
YES 
38556 
0.284 

Interbank borrowing 
(2) 
-0.0143*** 
(0.00166) 
0.0628*** 
(0.00187) 
0.0725*** 
(0.00633) 
-0.188*** 
(0.0154) 
-0.195** 
(0.0848) 
0.0142*** 
(0.000432) 

0.0302*** 
(0.00147) 

0.00390 
(0.00250) 
-0.0385*** 
(0.00187) 
-0.324*** 
(0.0137) 
0.0784*** 
(0.00658) 
-0.0643*** 
(0.00668) 
YES 
38556 
0.275 

(3) 
-0.0206*** 
(0.00169) 
0.0649*** 
(0.00187) 
0.0716*** 
(0.00627) 
-0.151*** 
(0.0155) 
-0.192** 
(0.0843) 
0.0143*** 
(0.000427) 

0.0362*** 
(0.00129) 
-0.00763*** 
(0.00253) 
-0.0418*** 
(0.00186) 
-0.329*** 
(0.0139) 
0.0613*** 
(0.00667) 
-0.0647*** 
(0.00664) 
YES 
38556 
0.286 

ECB Working Paper Series No 2374 / February 202053 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6. Trust in the interbank market: the mitigating role of institutional factors 
This table reports the results of the regressions examining the role of institutions including legal 
enforcement, regulation quality, and government effectiveness in mitigating the effect of crises 
on interbank borrowing, using the bank-level sample of 6,792 banks over 95 countries (excl. US 
banks).  The  dependent  variable  is  the  size  of  interbank  borrowing  to  total  assets.  The  key 
explanatory variable is Crisis length. We control for both bank and country characteristics in the 
regressions. All the other variables are defined in the Appendix Table A1. Robust standard errors 
are in parentheses. ***, ** and * denote statistical significance at the 1%, 5% and 10% level, 
respectively. 
Dep. Var 

(1) 
-0.00267*** 
(0.000324) 
-0.0272*** 
(0.00282) 
0.0724*** 
(0.00192) 
0.0718*** 
(0.00594) 
-0.203*** 
(0.0148) 
-0.182** 
(0.0811) 
0.0123*** 
(0.000401) 
0.0506*** 
(0.00409) 
0.00337 
(0.00261) 
-0.0486*** 
(0.00218) 
-0.308*** 
(0.0126) 
0.0952*** 
(0.00541) 
0.0214*** 
(0.00110) 
0.0224*** 
(0.00171) 

Crisis length 

Crisis 

LtD 

Securities 

Equity 

ROA 

Banksize 

Common law 

Private credit 

Mkt. cap. 

Central bank 

Concentration 

Rule of law 

Rule of law*Crisis 

Reg. quality 

Reg. quality*Crisis 

Gov. effect 

Gov. effect*Crisis 

Cons. 

Year FE 
# of obs. 
Adj. R2 

-0.0653*** 
(0.00598) 
YES 
42791 
0.301 

Interbank borrowing 
(2) 
-0.00249*** 
(0.000325) 
-0.0366*** 
(0.00341) 
0.0704*** 
(0.00190) 
0.0729*** 
(0.00599) 
-0.224*** 
(0.0148) 
-0.228*** 
(0.0813) 
0.0121*** 
(0.000402) 
0.0515*** 
(0.00421) 
0.0146*** 
(0.00257) 
-0.0490*** 
(0.00217) 
-0.306*** 
(0.0125) 
0.105*** 
(0.00550) 

0.0178*** 
(0.00147) 
0.0292*** 
(0.00246) 

-0.0699*** 
(0.00606) 
YES 
42791 
0.294 

(3) 
-0.00266*** 
(0.000325) 
-0.0236*** 
(0.00297) 
0.0718*** 
(0.00191) 
0.0716*** 
(0.00594) 
-0.195*** 
(0.0149) 
-0.197** 
(0.0809) 
0.0125*** 
(0.000400) 
0.0559*** 
(0.00412) 
-0.0000899 
(0.00265) 
-0.0520*** 
(0.00229) 
-0.299*** 
(0.0125) 
0.0942*** 
(0.00539) 

0.0271*** 
(0.00125) 
0.0238*** 
(0.00185) 
-0.0733*** 
(0.00606) 
YES 
42791 
0.304 

ECB Working Paper Series No 2374 / February 202054 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
  
 
 
 
 
 
 
  
  
 
  
  
 
 
 
Table 7. Robustness check of determinants of interbank borrowing: Euro Area banks and 
Large and Small banks 
This  table  reports  the  results  of  the  regressions  examining  the  determinants  of  interbank 
borrowing  using  banks  in  the  Euro  Area.  The  dependent  variable  is  the  size  of  interbank 
borrowing to total assets. The key explanatory variables are Crisis length as well as the interaction 
term of Crisis length and Large dummy. Large is defined as one for the upper quartile, and zero 
for  the  lower  quantile  based  on  bank  total  assets.  We  control  for  both  bank  and  country 
characteristics in the regressions. All the other variables are defined in the Appendix Table A1. 
Robust standard errors are in parentheses. ***, ** and * denote statistical significance at the 1%, 
5% and 10% level, respectively. 
Dep. Var 

Interbank borrowing 
(2) 
EU19 
-0.0168*** 
(0.00114) 

Crisis length 

Large 

Crisis length* Large 

LtD 

Securities 

Equity 

ROA 

Banksize 

Common law 

Private credit 

Mkt. cap. 

Central bank 

Concentration 

Cons. 

Year FE 
# of obs. 
Adj. R2 

(1) 
EU11 
-0.0157*** 
(0.00112) 

0.134*** 
(0.00258) 
0.154*** 
(0.00647) 
-0.335*** 
(0.0227) 
-0.330** 
(0.143) 
0.00794*** 
(0.000491) 
-0.0123 
(0.0432) 
0.0654*** 
(0.00457) 
0.0594*** 
(0.00693) 
-1.740*** 
(0.0496) 
0.0620*** 
(0.00795) 
-0.102*** 
(0.00964) 
YES 
27855 
0.452 

(3) 
EU19 
-0.0116*** 
(0.00136) 
0.0384*** 
(0.00212) 
-0.00705*** 
(0.000650) 
0.138*** 
(0.00328) 
0.129*** 
(0.00837) 
-0.394*** 
(0.0262) 
-0.214 
(0.189) 

-0.0457 
(0.0399) 
0.0459*** 
(0.00597) 
0.0523*** 
(0.00782) 
-1.460*** 
(0.0699) 
0.0586*** 
(0.0129) 
-0.0311** 
(0.0137) 
YES 
13986 
0.498 

0.132*** 
(0.00260) 
0.144*** 
(0.00653) 
-0.352*** 
(0.0232) 
-0.393*** 
(0.143) 
0.00858*** 
(0.000493) 
-0.0102 
(0.0430) 
0.0654*** 
(0.00464) 
0.0586*** 
(0.00698) 
-1.502*** 
(0.0595) 
0.0979*** 
(0.00945) 
-0.126*** 
(0.0105) 
YES 
27949 
0.442 

ECB Working Paper Series No 2374 / February 202055 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8. Instrumental variable analysis: deposit insurance scheme 
This  table  reports the  results of  the regressions examining  the role  of trust  in  determining the 
interbank market borrowing using instrumental variable analysis. Column (1) and (3) report the 
results of the 1st stage, and column (2) and (4) report the results of the 2nd stage. The instrumental 
variables are ExDI, defined as the existence of deposit insurance, and DI power, defined as the 
power of deposit insurance scheme, developed from Demirguc-Kunt, Kane and Laeven (2014). 
All  the  other  variables  are  defined  in  the  Appendix  Table  A1.  Robust  standard  errors  are  in 
parentheses.  ***,  **  and  *  denote  statistical  significance  at  the  1%,  5%  and  10%  level, 
respectively. 
Dep. Var 

Crisis length 

Crisis length 

ExDI 

DI power 

LtD 

Securities 

Equity 

ROA 

Banksize 

Common law 

Rule of law 

Private credit 

Mkt. cap. 

Central bank 

Concentration 

Cons. 

Year FE 
# of obs. 
Adj. R2 

(1) 
1.078*** 
(0.102) 

-0.490*** 
(0.0275) 
-1.269*** 
(0.0502) 
4.774*** 
(0.184) 
-0.0763 
(0.833) 
0.0754*** 
(0.00602) 
-0.0779 
(0.0522) 
-0.463*** 
(0.0243) 
-0.723*** 
(0.0606) 
0.310*** 
(0.0287) 
4.143*** 
(0.238) 
0.364*** 
(0.115) 
0.261* 
(0.156) 
YES 
64722 
0.718 

Interbank 
borrowing 
(2) 
-0.0386*** 
(0.00361) 

0.108*** 
(0.00221) 
0.126*** 
(0.00360) 
-0.270*** 
(0.0105) 
0.000518 
(0.0397) 
0.00694*** 
(0.000281) 
0.0213*** 
(0.00282) 
0.0298*** 
(0.00110) 
0.0105*** 
(0.00258) 
-0.0497*** 
(0.00221) 
-0.382*** 
(0.0140) 
0.0596*** 
(0.00600) 
-0.0339*** 
(0.00694) 
YES 
64722 
0.497 

(3) 

0.113*** 
(0.0223) 
-0.884*** 
(0.0397) 
-1.863*** 
(0.0915) 
4.917*** 
(0.277) 
7.724*** 
(1.151) 
0.200*** 
(0.00897) 
0.867*** 
(0.0739) 
-0.475*** 
(0.0299) 
0.293*** 
(0.0720) 
0.0861** 
(0.0367) 
8.820*** 
(0.677) 
1.652*** 
(0.139) 
-0.808*** 
(0.153) 
YES 
30968 
0.651 

Interbank 
borrowing 
(4) 

-0.00193** 
(0.000954) 
0.0882*** 
(0.00282) 
0.100*** 
(0.00556) 
-0.257*** 
(0.0136) 
-0.0162 
(0.0500) 
0.00739*** 
(0.000399) 
0.0413*** 
(0.00393) 
0.0169*** 
(0.00137) 
0.00172 
(0.00289) 
-0.0227*** 
(0.00256) 
-0.398*** 
(0.0359) 
0.0578*** 
(0.00716) 
-0.0486*** 
(0.00785) 
YES 
30968 
0.397 

ECB Working Paper Series No 2374 / February 202056 
  
  
 
  
  
  
  
 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ECB Working Paper Series No 2374 / February 202059 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 12. Determinants of interbank market participation: the impact of core vs. 
periphery positions 
The  table  reports  the  results  of  the  regressions  examining  the  role  of  network  position  in  the 
interbank market in affecting trust and interbank market participation. The dependent variable is 
Log inwdeg. The key explanatory variables are Crisis length and Failure ratio. Central is defined 
as one if the eigenvector centrality is in the upper quartile, or as zero if in the lower quartile. Log 
avg length is the natural logarithm of the average path length for each bank. All the other variables 
are defined in the Appendix Table A1. Robust standard errors are in parentheses. ***, ** and * 
denote statistical significance at the 1%, 5% and 10% level, respectively. 
Dep. Var 

Log inwdeg 

(1) 

(2) 

(3) 

(4) 

Crisis length 

Failure ratio 

Central 

Crisis length * Central 

Failure ratio* Central 

Cluster 

Log avg length 

LtD 

Size 

Private credit 

Mkt. cap. 

Concentration 

Cons. 

Other bank controls 
Quarter FE 
# of obs. 
Adj. R2 

0.0381 
(0.0246) 
0.348 
(1.985) 
1.505*** 
(0.236) 
-0.120*** 
(0.0296) 
-5.995** 
(2.491) 
-0.630*** 
(0.0663) 

Total exposures 
0.0335 
(0.0241) 
0.496 
(1.923) 
1.413*** 
(0.223) 
-0.111*** 
(0.0285) 
-5.649** 
(2.389) 
-0.614*** 
(0.0650) 
-18.89*** 
(4.159) 
-0.137** 
(0.0577) 
0.441*** 
(0.0233) 
-0.00502*** 
(0.00132) 
-0.00185 
(0.00165) 
0.00853*** 
(0.00266) 
35.19*** 
(9.335) 
YES 
YES 
714 
0.735 

-0.128** 
(0.0588) 
0.449*** 
(0.0238) 
-0.00453*** 
(0.00130) 
-0.00192 
(0.00166) 
0.00879*** 
(0.00270) 
-7.001*** 
(0.477) 
YES 
YES 
714 
0.725 

Exposures within Euro area 

0.00574 
(0.0241) 
1.836 
(1.934) 
1.440*** 
(0.223) 
-0.0959*** 
(0.0285) 
-6.538** 
(2.559) 
-0.508*** 
(0.0682) 

-0.209*** 
(0.0541) 
0.448*** 
(0.0238) 
-0.00532*** 
(0.00135) 
-0.00254* 
(0.00147) 
0.00600** 
(0.00281) 
-6.722*** 
(0.496) 
YES 
YES 
697 
0.735 

-0.00218 
(0.0232) 
0.918 
(1.884) 
1.383*** 
(0.209) 
-0.0883*** 
(0.0268) 
-6.166** 
(2.444) 
-0.489*** 
(0.0670) 
-25.47*** 
(4.653) 
-0.217*** 
(0.0526) 
0.426*** 
(0.0234) 
-0.00559*** 
(0.00133) 
-0.00167 
(0.00147) 
0.00657** 
(0.00272) 
52.77*** 
(10.91) 
YES 
YES 
697 
0.746 

ECB Working Paper Series No 2374 / February 202060 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ECB Working Paper Series No 2374 / February 202061 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 14. Determinants of interbank market participation: non-securities and short-term 
exposure 
This table examines the robustness of the results using the exposures excluding securities and 
short-term exposures (exposures less than 30 days), from the full sample. The dependent variable 
is Log windeg. The key explanatory variables are the trust measures (Crisis length and Failure 
ratio),  as  well  as  their  interactions  with  Central  and  Cluster.  Robust  standard  errors  are  in 
parentheses.  ***,  **  and  *  denote  statistical  significance  at  the  1%,  5%  and  10%  level, 
respectively. 
Dep. Var 

Log windeg 

Crisis length 

Failure ratio 

Central 

Cluster 

Crisis length * Central 

Failure ratio * Central 

Crisis length * Cluster 

Failure ratio * Cluster 

Log avg length 

LtD 

Securities 

ROA 

Equity 

Size 

Private credit 

Mkt. cap. 

Concentration 

Cons. 

Quarter FE 
# of obs. 
Adj. R2 

(1) 
Exposures excl. securities 
0.000734 
(0.0250) 
3.759 
(2.302) 
2.278*** 
(0.207) 
-1.236*** 
(0.214) 
-0.170*** 
(0.0279) 
-17.17*** 
(2.420) 
0.102*** 
(0.0298) 
9.370*** 
(2.597) 
-0.0299 
(0.0711) 
0.0165 
(0.0485) 
0.0713 
(0.323) 
7.008* 
(3.787) 
1.592** 
(0.809) 
0.378*** 
(0.0269) 
-0.00396*** 
(0.00150) 
-0.00225 
(0.00182) 
0.00420 
(0.00280) 
-5.811*** 
(0.656) 
YES 
558 
0.701 

(2) 
Short-term exposures 
-0.0141 
(0.0114) 
1.214 
(1.088) 
0.741*** 
(0.0918) 
-0.542*** 
(0.106) 
-0.0601*** 
(0.0115) 
-6.817*** 
(1.062) 
0.0363*** 
(0.0135) 
5.385*** 
(1.225) 
-0.0544 
(0.0742) 
-0.145*** 
(0.0328) 
0.0958 
(0.166) 
3.753** 
(1.610) 
1.087** 
(0.489) 
0.259*** 
(0.0126) 
-0.00302*** 
(0.000710) 
-0.00122 
(0.000929) 
-0.000427 
(0.00141) 
-3.344*** 
(0.513) 
YES 
1008 
0.584 

ECB Working Paper Series No 2374 / February 202062 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix 

Table A1. Variable definitions 
Variable 
Measures of trust in the banking system 

Definitions 

Bank z-score 

Crisis length 

Crisis 

Bank failure 

Failure ratio 

Ratio of return on assets plus capital-asset-
ratio to the standard deviation of return on 
assets, aggregated at the country level 
The number of banking crises occurred in 
each country till year t 
A dummy variable that takes the value 1 for 
the years of systemic banking crisis periods 
and 0 otherwise 
The standardized value of total assets of 
failed banks in each country till year t 
The ratio of assets of failed banks to total 
assets of the banking sector in each country 
till year t 

Bank level variables 
     Balance sheet data 
Interbank 
borrowing 

LtD 

Securities 
Equity 
ROA 
Banksize 
    Network measures 
Log inwdeg 
Log page rank 
Log eigen 

Central 

Cluster 

Log avg length 
Country level variables 

Borrowing and deposits from banks divided 
by total assets  
Bank’s gross nonfinancial loans divided by 
nonfinancial deposits 
Securities to total assets 
Equity to total assets 
Return on assets 
Natural logarithm of bank’s total assets 

Natural logarithm of weighted in-degree 
Natural logarithm of page rank centrality 
Natural logarithm of eigenvector centrality 
Equals to 1 if the eigenvector centrality is in 
the upper quartile, to 0 if the eigenvector 
centrality is in the lower quartile 
Equals to 1 if the cluster coefficient is above 
median, or 0 otherwise.  
Natural logarithm of average path length 

Source 

World Bank, Global 
Finance Database (2016) 

Laeven and Valencia 
(2013) and own 
calculation 

BankScope/BankFocus 

BankScope/BankFocus 

BankScope/BankFocus 

European  Central  Bank, 
and own calculation 

Common law 

Rule of law 
Reg. quality 
Gov. effect 

Private credit 

Market cap. 

Concentration 

Central Bank 

Equals to 1 if the legal origin of the country 
is common law. 
The index of rule of law 
The index of regulation quality 
The index of government effectiveness 
Private credit by deposit money banks 
divided by GDP 
Stock market capitalization divided by GDP 
Assets of three largest commercial banks as a 
share of total commercial banking assets.  
Central bank total assets divided by GDP 

Djankov et al. (2007) 

Worldwide Governance 
Indicator Database (2016) 

World Bank, Global 
Finance Database (2016) 

ECB Working Paper Series No 2374 / February 202063 
 
 
 
 
 
 
 
 
Internet Appendix 

Figure B1. Structure of Bank Assets 
This figure plots the structure of bank assets for five countries – the US, Japan, France, 
Germany and the UK from 2000-2009. The US and Japan have much lower interbank 
loan ratio (interbank loan/total bank assets), averaging 2.44% and 4.28%, respectively. 
The  UK,  Germany,  and  France  have  higher  interbank  loan  ratios,  averaging  13.20%, 
22.48% and 28.68%, respectively. 

Source: OECD Statistics; Japanese Banker Association 

ECB Working Paper Series No 2374 / February 202064 
 
 
 
Figure B2. Structure of Bank Liabilities 
This figure plots the structure of bank liabilities for five countries – the US, Japan, France, 
Germany and the UK. The US and Japan have lower interbank deposit ratio (interbank 
deposit/total  liabilities),  averaging  1.95%  and  4.41%,  respectively.  The  UK,  Germany 
and France have higher interbank deposit ratios, averaging at 9.02%, 26.61% and 31.19%, 
respectively. 

Source: OECD Statistics; Japanese Banker Association 

ECB Working Paper Series No 2374 / February 202065 
 
Table B1. The Interbank Borrowing Size by Country 
This  table  shows  the  number  of  banks  and  the  interbank  deposit  ratio  (interbank 
deposits/total assets) for the countries in our sample. When constructing the sample, we 
drop those countries with less than five banks in the original dataset.  

Country name 

Argentina 
Australia 
Austria 
Azerbaijan 
Bahamas, The 
Bangladesh 
Belarus 
Belgium 
Bolivia 
Bosnia and Herzegovina 
Brazil 
Bulgaria 
Canada 
Cayman Islands 
China 
Colombia 
Costa Rica 
Cote d'Ivoire 
Croatia 
Curacao 
Cyprus 
Czech Republic 
Denmark 
Dominican Republic 
Ecuador 
Egypt, Arab Rep. 
El Salvador 
Ethiopia 
Finland 
France 
Germany 
Ghana 
Greece 
Guatemala 
Honduras 
Hong Kong SAR, China 
Hungary 
Iceland 
India 
Indonesia 

Bank  
number 
63 
8 
184 
12 
15 
7 
9 
44 
8 
7 
60 
8 
27 
7 
150 
32 
42 
5 
31 
7 
8 
10 
80 
38 
33 
5 
5 
6 
35 
174 
1879 
5 
26 
27 
10 
6 
6 
29 
32 
58 

Interbank 
borrowing 
4.54% 
10.40% 
28.07% 
18.20% 
13.97% 
5.35% 
10.63% 
17.98% 
16.98% 
2.51% 
3.12% 
5.50% 
2.09% 
1.35% 
10.74% 
6.26% 
12.26% 
23.93% 
1.91% 
6.91% 
3.36% 
34.20% 
17.03% 
1.53% 
0.48% 
4.20% 
0.00% 
2.31% 
5.15% 
23.37% 
18.14% 
3.98% 
9.66% 
9.61% 
4.40% 
8.74% 
16.99% 
11.88% 
6.00% 
3.62% 

ECB Working Paper Series No 2374 / February 202066Ireland 
Israel 
Italy 
Japan 
Kazakhstan 
Kenya 
Korea, Rep. 
Lao PDR 
Latvia 
Lebanon 
Libya 
Luxembourg 
Macedonia, FYR 
Malaysia 
Mali 
Mauritania 
Mexico 
Moldova 
Mongolia 
Morocco 
Nepal 
Netherlands 
New Zealand 
Nicaragua 
Nigeria 
Norway 
Oman 
Pakistan 
Panama 
Paraguay 
Peru 
Philippines 
Poland 
Portugal 
Russian Federation 
San Marino 
Senegal 
Serbia 
Singapore 
Slovak Republic 
South Africa 
Spain 
Sweden 
Switzerland 
Tajikistan 
Tanzania 

7 
5 
1007 
464 
9 
24 
6 
5 
5 
38 
6 
41 
5 
18 
5 
6 
19 
11 
10 
7 
5 
23 
6 
9 
39 
65 
5 
10 
28 
19 
10 
23 
26 
98 
447 
6 
6 
17 
8 
6 
16 
203 
90 
380 
6 
7 

41.81% 
14.16% 
13.02% 
1.55% 
10.76% 
5.00% 
0.41% 
10.18% 
24.74% 
4.31% 
1.61% 
26.94% 
3.49% 
7.47% 
15.27% 
2.65% 
29.32% 
5.24% 
11.03% 
8.82% 
0.17% 
22.01% 
4.61% 
21.99% 
5.05% 
10.23% 
9.23% 
12.63% 
5.14% 
9.22% 
10.62% 
1.58% 
9.20% 
42.20% 
9.84% 
2.18% 
13.32% 
4.32% 
12.49% 
12.46% 
22.84% 
15.69% 
8.22% 
10.40% 
12.37% 
6.83% 

ECB Working Paper Series No 2374 / February 202067Thailand 
Turkey 
Ukraine 
United Kingdom 
United States 
Uruguay 
Uzbekistan 
Venezuela, RB 
Vietnam 
Yemen, Rep. 

11 
33 
152 
30 
4621 
12 
17 
49 
26 
7 

2.23% 
4.13% 
21.52% 
14.16% 
0.55% 
11.33% 
7.37% 
6.38% 
22.20% 
2.76% 

ECB Working Paper Series No 2374 / February 202068 
 
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ECB Working Paper Series No 2374 / February 202069 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgements 

For helpful comments we thank Galina Hale (FMA discussant), Amanda Heitz (CICF discussant), Yiming Ma (WFA discussant), Stephen 
Cecchetti, Zuzana Fungacova, Iftekhar Hasan, Yiming Qian, Zongxin Qian, Frans de Roon, and seminar and session participants at 
Penn (Economics), Federal Reserve Bank of Philadelphia, University of Connecticut, University of Groningen, Durham University, 
SKEMA Business School, Central University of Finance and Economics, Banco de Portugal/European Investment Bank Conference on 
“Investment Finance and the Recovery”, Bank of Finland/CEPR joint conference on “Money in the Digital Age”, House of Finance, 
University Dauphine workshop on “Regulation and Systemic Risk”, Financial Management Association Annual Meeting 2018, Western 
Finance Association Annual Meeting 2019, China International Conference in Finance 2019, Asian Finance Association Annual Meeting 
2019. The paper won the Best Paper Award at Asian Finance Association Annual Meeting 2019. This paper should not be reported as 
representing the views of the European Central Bank (ECB) and of the Bank of England (BOE). The views expressed are those of the 
authors and do not necessarily reflect those of the ECB and the BOE. 

Franklin Allen 
Imperial College London, London, United Kingdom; email: f.allen@imperial.ac.uk 

Giovanni Covi 
Bank of England, London, United Kingdom; email: giovanni.covi@bankofengland.gsi.gov.uk 

Xian Gu 
University of Pennsylvania, Philadelphia, United States; Central University of Finance and Economics, Beijing, China;  
email: xiangu@wharton.upenn.edu 

Oskar Kowalewski 
IESEG School of Management, Paris, France; LEM-CNRS 9221, Lille, France; email: o.kowalewski@ieseg.fr 

Mattia Montagna 
European Central Bank, Frankfurt am Main, Germany; email: mattia.montagna@ecb.europa.eu 

© European Central Bank, 2020 

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 978-92-899-4017-7 

ISSN 1725-2806 

doi:10.2866/525657 

QB-AR-20-026-EN-N