Geopolitical risk, bank lending and real effects on firms : evidence from the Russian invasion of Ukraine.
Date: 2025-11-06
Institutions: Avril, Pauline, European Central Bank
Subjects: Russo-Ukrainian issue, economic consequence, financial market, geopolitics, investment, loan

Pauline Avril, Peter McQuade, 
Cosimo Pancaro, Alessio Reghezza 

Working Paper Series 

Geopolitical risk, bank lending and 
real effects on firms: evidence from 
the Russian invasion of Ukraine 

No 3143 

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 paper investigates whether geopolitical risk causes a reduction in bank lend-

ing. In particular, it focuses on how the increase in geopolitical risk stemming from

the Russian invasion of Ukraine affected euro area bank credit supply. Match-

ing granular supervisory and credit register data and using a panel difference-in-

difference approach, the results show that banks with larger exposure to the increase

in geopolitical risk cut lending significantly more than those with smaller exposure.

Banks with greater exposure raised impairments despite exhibiting similar levels of

credit distress to their peers, suggesting that the fall in lending was driven by un-

certainty. Moreover, firms that were heavily reliant on banks with high exposure to

geopolitical risk were unable to fully substitute this shortfall in credit by borrowing

more from less affected banks, which significantly constrained firm investment and

employment.

Keywords: Banks, financial markets, uncertainty

JEL Codes: G1, G21; E22

ECB Working Paper Series No 31431Non-technical summary

Geopolitical events — such as wars or rising tensions between countries — can signif-

icantly affect economic and financial systems. These events increase uncertainty and

change how risks are perceived, which can lead banks to reduce lending. This paper

studies whether the surge in geopolitical risk caused by the Russian invasion of Ukraine

in early 2022 led to a contraction in bank lending in the euro area and, in turn, affected

firm-level economic outcomes such as investment and employment.

The euro area provides a particularly relevant case study because of its geographical

and economic proximity to the conflict, making the shock highly salient for its finan-

cial institutions. To understand these dynamics, the analysis uses highly detailed and

confidential data from AnaCredit, the euro area credit register that collects loan-level

information from euro area banks. These data are matched with detailed bank financial

statements and firm-level information on employment and investment.

The empirical strategy compares how banks with greater exposure to geopolitical

risk — measured using a bank-level index based on bank exposures and country-level

geopolitical risk indicators — adjusted their lending compared to less exposed banks

after the onset of the Russian invasion of Ukraine. By using data on firms with multi-

ple banking relationships and applying a difference-in-differences, the analysis isolates

changes in credit supply from changes in demand.

The results show that banks more exposed to the geopolitical risk shock significantly

reduced their lending to firms in the quarters following the invasion more than their

peers. This drop in lending occurred both in existing relationships (intensive margin)

and in the formation of new bank-firm relationships (extensive margin). The effect

persisted for about three quarters before gradually fading.

One reason for this contraction appears to be an increase in risk aversion: more

exposed banks reported larger increases in loan impairments after the invasion, despite

similar actual levels of borrower distress. This mechanism explains the tendency of these

ECB Working Paper Series No 31432banks to pull back from lending during uncertain times. Importantly, banks with larger

capital buffers were less likely to reduce lending, suggesting that better-capitalized banks

are more resilient to uncertainty and can continue supporting firms during geopolitical

risk shocks.

The study also finds that lending cuts were not uniform across sectors. Firms oper-

ating in industries that relied more heavily on inputs from countries closely aligned with

Russia experienced a larger credit contraction. This indicates that banks were wary of

lending to sectors vulnerable to supply chain disruptions tied to the conflict.

At the firm level, firms that were highly dependent on banks more affected by geopo-

litical risk were not able to fully replace lost credit by borrowing from other, less exposed

banks. As a result, these firms experienced declines in both investment and employment,

showing that geopolitical risk can have real, measurable effects on business operations

and economic activity.

To ensure these findings are robust, the study conducts several additional tests.

These include using matching techniques that account for differences between treated

and untreated banks, controlling for other possible influences such as the energy price

shock linked to the war, and testing for trends before and after the invasion. The results

consistently support the main conclusion: the surge in geopolitical risk affected bank

behaviour, and through the banking channel, impacted firms’ real decisions.

ECB Working Paper Series No 314331

Introduction

Adverse geopolitical events can trigger rapid shifts in risk sentiment and sharp increases

in uncertainty, exposing existing vulnerabilities in banks that may cause them to curtail

lending supply. Moreover, they can dent the investment and employment plans of firms,

with knock-on effects for economic growth (Caldara and Iacoviello, 2022).

This paper investigates whether the significant increase in geopolitical risk associ-

ated with the Russian invasion of Ukraine affected euro area bank credit supply and

consequently had significant effects on non-financial firms. This is an ideal laboratory

to explore the effect of geopolitical risk because of the geographical proximity of the

euro area to the event, which was the most severe geopolitical shock to hit Europe in

decades. Using a continuous panel difference-in-difference approach at bank-firm level,

the analysis assesses how banks that were more exposed to the increase in geopoliti-

cal risk stemming from this event adjusted their lending behaviour. Moreover, relying

on firm-level regressions, it examines whether firms reliant on banks with higher expo-

sure to geopolitical risk recorded a contraction in bank borrowing and if they reduced

investment and employment.

The analysis uses on AnaCredit, the confidential pan-euro area credit registry data

collected by the European System of Central Banks. In the baseline analysis, these loan-

level data are aggregated to the bank-firm level, which makes it possible to disentangle

credit supply from credit demand. Specifically, this analysis exploits data for firms with

multiple bank relationships to control for firm credit demand by including firm×time

fixed effects, following the approach introduced by Khwaja and Mian (2008). AnaCredit

data are matched with bank-level balance sheet and profit and loss information from

ECB supervisory statistics. Our final matched sample consists of a panel dataset at the

bank-firm level, covering 363 banks and over half a million firms at quarterly frequency

from 2021Q1 to 2023Q1, i.e.

from one year before to one year after the start of the

Russian invasion of Ukraine.

In this context, to evaluate the exposure of individual

ECB Working Paper Series No 31434banks to the increase in geopolitical risk stemming from the invasion, a variation of

the bank-level geopolitical risk index first introduced by Dieckelmann et al. (2025) is

used. This index is built by combining country level geopolitical risk indices provided by

Caldara and Iacoviello (2022) with ECB supervisory data on bank asset exposures across

countries. Finally, to investigate the real effects on firms of an increase in geopolitical

risk, we also exploit data on firm characteristics, such as investment and numbers of

employees, sourced from Orbis Europe.

The analysis yields a number of important insights. First, banks with higher ex-

posure to geopolitical risk reduced their lending supply to non-financial corporations

significantly more than those with lower exposure in the immediate aftermath of the

Russian invasion of Ukraine. This pattern holds for both the intensive margin (lending

adjustments in existing bank-firm relationships) and extensive margin (creation of new

bank-firm relationships). Second, when examining how these effects evolve over time,

we find that this impact lasts for three quarters. Third, we show that banks with higher

exposure to geopolitical risk increased their loan impairments after the invasion more

than their less exposed peers, despite exhibiting similar levels of credit distress. We

claim, consistent with the argument put forward by Correa et al. (2023), that this find-

ing reflects an increase in risk aversion on the part of more exposed banks, which faced

with greater uncertainty, chose to contract lending. We also find that, ceteris paribus,

the increase in geopolitical risk had a less material effect on the lending supply of banks

with larger capital buffers pointing to a capital constraint channel. Banks with lower

capital buffers have a greater incentive to safeguard capital by constraining lending more.

Fourth, the findings indicate that lending contracted to a larger extent to borrowers in

sectors that were more reliant on inputs imported from countries geopolitically aligned

with Russia. This suggests that banks constrained lending to sectors reliant on more

vulnerable supply chains. Fifth, aggregating data at firm level, we find that firms heavily

reliant on banks with high exposure to geopolitical risk were unable to fully substitute

ECB Working Paper Series No 31435this reduction in credit supply by borrowing more from less affected banks, leading to

real economic effects in the form of reduced investment and employment.

Results from a battery of robustness tests and alternative specifications further sup-

port the baseline findings. The results are robust to employing a generalised propensity

score weighting, allaying concerns that the treatment and control groups might have

different underlying characteristics that drive the difference in their lending behaviour

after the invasion. Similarly, results of placebo tests indicate that the baseline results

were not driven by pre-existing nor post-existing trends rather than geopolitical events.

Furthermore, we also control for banks’ exposures to highly energy-intensive sectors or

firms prior to the onset of the the Russian invasion of Ukraine to ensure that the results

of the analysis are not driven by the simultaneous energy price shock, which was also

led by the war. The results corroborate our baseline findings, showing that the energy

price shock was not a driver of our results.

1.1 Related Literature

This paper contributes to a number of strands of the literature.

First, it builds on the seminal work by Caldara and Iacoviello (2022) who intro-

duced a novel geopolitical risk index, which provides a measure of adverse geopolitical

events and associated risks based on a tally of newspaper articles covering geopoliti-

cal tensions.1

In this context, geopolitical risk is defined as the threat or realisation

of adverse events associated with wars, terrorism and tensions between states. More

specifically, the geopolitical risk metric used in this analysis, similarly to the approach

used by Dieckelmann et al. (2025), captures the actual exposures of banks to geopolitical

risk. Although related, it is important to mention that the concept of geopolitical risk

is distinct from that of geopolitical fragmentation, as captured by Fern´andez-Villaverde

1Many recent papers (Arslanalp et al., 2023; Georgiadis et al., 2024; Hu et al., 2023) studying
the macroeconomic and financial effects of geopolitical risk rely on this novel index introduced
by Caldara and Iacoviello (2022)

ECB Working Paper Series No 31436et al. (2024), which has also been shown to have an effect on financial variables and

investment (Aiyar et al., 2023; Baba et al., 2023).

This work can also be seen as contributing to the literature on uncertainty and

bank lending. In a similar vein to our findings for geopolitical risk, a number of papers

(Barraza and Civelli, 2020; Bordo et al., 2016; Buch et al., 2015; Correa et al., 2023)

have shown that increases in uncertainty, especially if related to economic policy, have

a negative effect on bank credit extension. This paper contributes to this literature

by focussing specifically on the effects of geopolitical risk on bank lending supply. A

number of papers show the effect of such risk on financial market indicators (Campos

et al., 2023; Catal´an and Tsuruga, 2023; D’Orazio et al., 2024; Federle et al., 2024a;

Feng et al., 2023; Salisu et al., 2023). However, to date, there are only a few papers

that examine the effect of geopolitical risk on banks (Behn et al., 2025; Demir and

Danisman, 2021; Dieckelmann et al., 2025; Nguyen and Thuy, 2023; Pham et al., 2021;

Phan et al., 2022), and these works mostly use bank-level data.

Instead this paper,

similar to both Niepmann and Shen (2025) and De Haas et al. (2025), takes advantage

of more granular data. Niepmann and Shen (2025) analyse loan-level data to examine

the effect of geopolitical risk with a particular focus on cross-border bank lending. They

show that globally active US banks tend to continue to lend through foreign affiliates,

even as they reduce cross-border lending to markets experiencing elevated geopolitical

risk, and argue that this asymmetry is due to differential expropriation risk. Consistent

with our results, they also find that global banks reduce lending to domestic firms in

response to rising geopolitical risk. Similarly, De Haas et al. (2025) use loan-level data

on a global sample of syndicated loans and find that, while overall cross-border lending

to areas affected by violent conflict decreases, lending to firms in military or dual-use

industries increases.

In this context, it is key to mention that the use, in our work,

of loan-level data for euro area banks, combined with a well established difference-in-

difference estimation method (Acharya et al., 2022; Giannetti et al., 2023), means that

ECB Working Paper Series No 31437we can identify and estimate the causal effect of an increase in geopolitical risk on bank

lending supply.

Moreover, the analysis in this paper follows the chain of transmission of geopolitical

risk through bank lending to firm behaviour contributing in this way also to the literature

which shows that geopolitical risk can have an effect on real macroeconomic variables

(Federle et al., 2024b; Matteo Iacoviello and Conlisk, 2024; Pinchetti, 2024). Our paper,

exploiting micro data, provides evidence that a geopolitical risk shock can affect firms

via a contraction in bank lending. In doing so, the analysis in this work complements

other papers that carefully and systematically follow the transmission of the effects

of international shocks to bank lending (Correa et al., 2023; di Giovanni et al., 2022;

Federico et al., 2025; Kalemli- ¨Ozcan et al., 2013).

The rest of the paper proceeds as follows: Section 2 describes the data and Section

3 explains the estimation method. Sections 4 and 5 present the main results and the

results from a series of robustness tests, while Section 6 concludes.

2 Data

2.1 Bank-firm, bank-level and firm level data

The analysis relies on data from a combination of confidential data sources available at

the ECB. First, the analysis uses the loan level data in the AnaCredit credit register

compiled by the European System of Central Banks. These data contain information on

all individual bank loans extended in the euro area to non-financial corporations with a

value exceeding EUR 25,000. Around 25 million individual loans are reported monthly,

granted by around 7000 individual credit institutions to approximately 5 million of in-

dividual debtors. Anacredit includes only loans where the debtors are non-financial

corporations and the creditors are banks. We exclude from the sample loans where

more than one creditor is reported, i.e. syndicated loans. In the baseline analysis, the

ECB Working Paper Series No 31438loan-level data are aggregated to the bank-firm level, and aggregated across different

credit instrument types and maturities.2,3 AnaCredit contains information on multiple

loan characteristics such as outstanding loan volume, interest rate (type), maturity, im-

pairment amounts, and probability of default. For the majority of instrument types,

we capture credit supply by looking at outstanding loan amounts. However, as credit

supply through credit lines is determined by the commitment amount at initiation of the

contract, we use the commitment amount rather than the outstanding amounts for credit

lines. The analysis also takes advantage of information in AnaCredit on borrowing firms

(size, location, industry) and lending banks (location). The data include information

assigning firms to industrial sectors according to the Statistical Classification of Eco-

nomic Activities in the European Community (NACE Rev.2). The AnaCredit data do

not require the reporting of lending by bank subsidiaries located outside the euro area.4

Information on direct cross-border lending from euro area resident banks is available but

accounts only for about 1.5% of the value of outstanding loan amounts reported in the

data. Therefore, the vast majority of the loans recorded in the data relate to domestic

euro area lending.

We complement the bank-firm level data with bank-level balance sheet and profit

and loss data from the ECB supervisory statistics.

Furthermore, the analysis of the real effects at the firm level relies on data on firm

characteristics sourced from Orbis Europe, including information relating to investment

and numbers of employees. Specifically, we match data on firms available in Orbis

2In AnaCredit, credit instruments are categorised into revolving credit other than overdrafts
and credit card debt, credit lines other than revolving credit, term loans, overdrafts, credit card
debt, trade receivables, and finance leases.

3Prior to aggregation all loan-level and bank-level variables are winsorised at the 1% level
to remove outliers and anomalies. For items such as loan maturity and interest rate (type), the
aggregation from loan-level to bank-firm level is done using a weighted average principle.

4While the AnaCredit Regulation does not mandate the reporting of data on foreign entities
outside the euro area, it does not prohibit it either. Therefore, if a credit institution wishes to
report data on its foreign branches or subsidiaries, it may do so, provided that the relevant NCB
has established procedures to accept such data. See AnaCredit Manual Part I.

ECB Working Paper Series No 31439with loan-level data of euro area banks reported in AnaCredit, following papers such as

Altavilla et al. (2024, 2022), yielding a sample of well over half a million observations.

The data in the analysis are at a quarterly frequency to align with the reporting

frequency of the bank-level control variables. The matched sample for the main analysis

includes 363 institutions across 20 euro area countries (Figure 1) for the period from

2021Q1 to 2023Q1 (i.e. one year before and one year after the start of the Russian

invasion of Ukraine).5

Table 1 shows the descriptive statistics for the variables used in the baseline regres-

sions. The summary statistics for the bank-specific variables are in line with previous

work using euro area credit registry data (Coulier et al., 2024; Dautovi´c et al., 2023).

2.2 Measuring banks’ exposure to geopolitical risk

The exposure of euro area banks to geopolitical risk is heterogeneous. To evaluate

banks’ actual exposures to this risk, we rely on a variation of the bank-level indicator

of geopolitical risk first introduced by Dieckelmann et al. (2025). More specifically, this

indicator is built by weighting the change in the standardised country-level geopolitical

risk (CGPR) indices of Caldara and Iacoviello (2022) with bank-level asset-side expo-

sures to the different countries where banks operate, sourced from the ECB Supervisory

statistics:

BGPRb =

n
(cid:88)

(cid:18)

c=1

∆CGPRc ×

(cid:19)

Expb,c
TotalExpb

(1)

where ∆CGPRc is the change in the standarised country-level geopolitical risk index

between 2021Q4 and 2022Q1 for country c, i.e., the increase in geopolitical risk resulting

from the Russian invasion of Ukraine, and

Expb,c
Total Expb

is the average, over the period

5Note that we use entity-level data as this is the level at which most lending decisions are
made. Individual banks or subsidiaries are typically responsible for assessing credit risk and ap-
proving loans, making entity-level data more relevant than data at group-level for understanding
lending practices in this context.

ECB Working Paper Series No 3143102021Q1-2021Q4, of the share of bank b exposures to country c over its total exposures.

Caldara and Iacoviello (2022) calculate the country-specific indices of geopolitical

risk by counting the monthly share of all newspaper articles that are both: (1) on the

subject of geopolitical events and (2) mention the name of the country or its major cities.

The resulting indices capture the perceived risks posed by, or involving, the country in

question. These indices are available at the country level for 44 countries (including 8

euro area countries). To make the analysis more comprehensive, the same methodology

is applied to a corpus of newspaper articles sourced from the Factiva news monitoring

platform to extend the coverage of the index to all missing euro area and EU countries

following Dieckelmann et al. (2025). This expands the number of countries that can

be included in the analysis to 62, which allows us to cover at least 85% of the total

exposures of each bank in the sample.

The country-level geopolitical risk indices are then standarised by transforming them

into z-scores to better allow comparisons across countries (Dieckelmann et al., 2025).6

Figure 2 illustrates that the increase in the standardised country level GPR indices after

the Russian invasion of Ukraine was generally higher in countries geographically closer

to Ukraine. Similarly, when these changes are weighted by the asset exposure of the

euro area banks as shown in Figure 3, the BGPR is also generally stronger in banks

located in euro area countries in Central and Eastern Europe that were closer to the

invasion, consistent with Federle et al. (2024b). This high degree of variation in the

cross-country and cross-bank exposure (see Figure 4) to the geopolitical shock arising

from the Russian invasion of Ukraine makes this an ideal laboratory in which to explore

the effects of geopolitical risk on bank lending behaviour.

6The country-level geopolitical risk indexes are standardized by transforming them into z-
scores based on their historical time series extending back as far as 1985 (where available). As
a result, the standardized country-level GPR index values represent the number of standard
deviations above or below the (country-specific) long-run average of geopolitical risk.

ECB Working Paper Series No 3143113 Estimation strategy

3.1 Empirical framework for the baseline bank-firm level analysis

The key objective of the empirical analysis carried out in this study is to investigate

whether banks that had a greater exposure to the geopolitical risk stemming from the

Russian invasion of Ukraine changed their lending supply in response to it more than

their peers. To test this hypothesis a continuous difference-in-difference setting is em-

ployed, using the following panel fixed effect model:

yb,i,t = β1BGP Rb + β2P ostt + β3(BGP Rb × P ostt) + β4Xb,t−1 + β5(Xb,t−1 × P ostt)

+ δi,t + µb,i + γc,t + εb,i,t

(2)

where the dependent variable yb,i,t is the logarithm of the outstanding amount of

loans from bank b to firm i at time t as in Federico et al. (2025). The variable of interest

is the interaction between the BGP Rb, the exogenous bank-level geopolitical risk index,

and P ostt, a dummy variable that takes a value of one between 2022Q1 and 2022Q4,

i.e. from the onset of the Russian invasion of Ukraine to the end of the sample period,

and zero for the preceding quarters.7 β3, our coefficient of interest, captures whether

the exposure to geopolitical risk affected bank lending supply since the onset of the

Russian invasion of Ukraine. Xb,t−1 is a vector of lagged bank-level characteristics, which

may also affect bank-firm lending, included in previous studies such as, for example, in

Altavilla et al. (2022), Couaillier et al. (2025), Coulier et al. (2024) and Correa et al.

(2023). More specifically, the variables included at the bank level are the log of total

assets to control for bank size, the TIER 1 capital ratio to control for bank solvency,

the deposit-to-liability ratio to capture the extent of bank reliance on deposit funding,

the provisions-to-loans ratio to capture the asset quality of bank loan portfolios, the

7The post-invasion period is considered to start in 2022Q1. Although the invasion began
in February 2022, AnaCredit data are reported at the end of each quarter. As such, using
2022Q1 as the starting point appropriately captures the initial effects without introducing timing
inconsistencies.

ECB Working Paper Series No 314312return on assets (ROA) to control for bank profitability, and the ratio of cash (including

cash held at central banks) to total assets to capture bank liquidity. Importantly, we

also allow these control variables to have heterogeneous effects on lending following the

onset of the Russian invasion of Ukraine by interacting them with the post dummy

(Xb,t−1×P ostt) as in Correa et al. (2023). This ensures that our coefficient of interest is

not driven by the heterogeneous impact on lending of other time-varying bank-specific

characteristics.

Equation 2 is saturated with a granular set of fixed effects to account for both

observed and unobserved heterogeneity across banks and firms. The vector δi,t denotes

firm×quarter fixed effects. This is crucial for the identification strategy, as it allows us to

disentangle credit demand from credit supply. Specifically, it controls for the demand for

credit from firms that have lending relationships with multiple banks (Khwaja and Mian,

2008). The high dimensional fixed effects included in our set-up also ensure that our

results are not driven by time-invariant bank-firm relationship effects, which are captured

by bank×firm fixed effects (µb,i), while country specific factors are absorbed by a vector

of country×quarter fixed effects (γc,t), where the country refers to the nationality of the

lending bank. Country×quarter fixed effects control for time-varying country-specific

effects such as the business and monetary policy cycles that could be correlated with

geopolitical risk, potentially affecting bank lending. Standard errors are clustered at the

bank-level. Borrower×quarter fixed effects are included in all our model specifications.8

These fixed-effects completely subsume the dummy variable P ostt which, therefore, does

not appear separately in the analyses. Similarly, the inclusion of either bank fixed effects

or bank×firm fixed effects implies that, in some specifications, the bank-level geopolitical

risk indicator BGP Rb is subsumed.

8Borrower×bank and country×quarter fixed effects are instead only included in some speci-

fications.

ECB Working Paper Series No 3143134 Results

4.1 Bank-firm level analysis

4.1.1 Intensive margin

Table 2 reports the regression results of equation 2. The regression in Column (1) in-

cludes only borrower×quarter fixed effects in addition to the variable of interest. Bank

fixed effects are added in Column (2) to account for time-invariant bank-specific unob-

served characteristics, while Column (3) also adds time-varying bank controls and their

interactions with the P ost variable. In addition to the aforementioned bank controls,

column (4) includes borrower×quarter and borrower×bank fixed effects. Our preferred

specification is the heavily saturated specification reported in Column (5), which si-

multaneously includes bank controls as well as borrower×quarter, borrower×bank and

country×quarter fixed effects.

The regression results in Table 2 show that, on average, heightened geopolitical risk

following the Russian invasion of Ukraine exerted a significant impact on the supply

of bank lending to euro area non-financial corporations. The estimated coefficient of

BGP Rb × P ostt in Column (5) is negative, statistically significant at the 1% level

and economically sizeable: a 1 standard deviation increase in the BGP R resulted in

an approximately 9.2% reduction in corporate lending at the bank-firm level following

the event.

It is worth noting that the coefficients hold-up well in all specifications,

corroborating the robustness of the finding. They are also somewhat larger in size in

Column (5), highlighting the importance of accounting for observable and unobservable

bank- and country-specific characteristics that potentially affect bank lending behaviour

ECB Working Paper Series No 314314in addition to geopolitical risk.9

4.1.2 Parallel trends

Figure 5 displays the dynamics of the estimated effect on lending over different time

horizons, taking 2021 Q4 as the reference period. Importantly, prior to the invasion,

there were no significant differences in lending behaviour between banks with different

levels of geopolitical risk exposure. This finding also validates our identifying assumption

that banks’ lending behaviour was not affected by their exposure to geopolitical risk

before the onset of the Russian invasion of Ukraine. However, the situation changed

afterwards. The reduction in lending volumes became statistically significant after the

onset of the invasion and the effect persisted for three quarters, until the uncertainty

concerning the potential effects of the conflict eased. This temporary but significant

contraction underscores the sensitivity of lending supply to geopolitical risk.

4.1.3 Impact on bank impairment volumes

By increasing uncertainty, greater exposure to geopolitical risk could reduce the per-

ception of banks of the creditworthiness of their borrowers. Correa et al. (2023) study

the effects of an increase in trade policy uncertainty on bank lending. They find that,

following this type of shock, banks increase impairments and contract lending, consis-

tent with a wait and see strategy. This occurs even though bank do not experience any

observable deterioration in asset quality, as would be expected to occur after a balance

9The estimated magnitude of the coefficient is comparable to other papers examining the
response of bank lending to geopolitical risk and other sources of uncertainty. For instance,
Niepmann and Shen (2025) find that a one standard deviation increase in geopolitical risk reduces
U.S. banks’ loan origination to U.S. firms by around 20 percent on average. Regarding trade
uncertainty, Correa et al. (2023) find that a one standard deviation increase in bank exposure
to trade uncertainty is associated with a 5.0 percentage point decline in bank-firm loan growth.
Relatedly, Federico et al. (2025) find that a one standard deviation increase in bank exposure to
China’s entry into the WTO implies 7.4 percent lower credit supply. Our findings are contrary
to those of Demir and Danisman (2021), who use bank level data and find that, in contrast to
economic policy uncertainty, geopolitical risk is not significantly associated with a decline in the
growth of bank credit to non-financial corporations.

ECB Working Paper Series No 314315sheet shock.

To test whether a similar pattern is observed in the event of a geopolitical shock, we

first estimate a set of regressions similar to Equation 2, using the logarithm of the volume

of impairments as the dependent variable, while also controlling for the logarithm of the

total outstanding amount of loans.10 Loans are assessed by banks as being impaired

if the expected recoverable value falls below its book value. Table 4 shows the effect

of geopolitical risk arising from the Russian invasion of Ukraine on banks’ impairment

volumes. The results show that banks that experienced a one standard deviation increase

in their exposure to geopolitical risk significantly increased the volume of impairments,

by around 24% after the start of the invasion, compared to their peers. This suggests

that banks that faced greater uncertainty due to heightened geopolitical risk proactively

chose to increase their impairments compared to other banks.

In a second step, we assess whether an increase in a bank’s exposure to geopolitical

risk leads to a materialisation of credit risk. To investigate this question, we use a regres-

sion framework where the number of days past due is the dependent variable (computed

as a weighted average for each bank–firm pair). In this analysis, we relax the fixed-effects

specification, as variation in past due days is meaningful only for a subset of firms - those

with debt repayment issues.11 Table 5 shows that the interaction term BGP Rb × P ostt

is not statistically significant in any of the econometric specifications, indicating that,

following the onset of the invasion, there was no significant difference in the number of

days past due across banks with varying exposure to geopolitical risk. Taken together,

10This control is important as the volume of impairments is correlated with the size of the

lending.

11Moreover, including borrower × quarter fixed effects would not be appropriate in this setting,
since it is not clear why the same firms should accumulate more past due loans with a bank with
higher geopolitical risk exposure relative to another bank with lower exposure. We therefore
adopt a specification with bank and time fixed effects, which allows us to test whether, on
average, banks more exposed to geopolitical risk experience a greater deterioration in credit
quality following the conflict, thus justifying a credit contraction. Recognising that bank and
time fixed effects may still be too coarse - given that more exposed banks might lend more to
firms operating in more affected industries, locations, or size segments - we further refine the
specification by introducing industry × location × size fixed effects.

ECB Working Paper Series No 314316these results show that the decline in bank lending supply that we observe was not due

to a deterioration in bank balance sheets but rather to a precautionary contraction in

lending driven by heightened geopolitical uncertainty.

4.1.4 Extensive margin

The effects described above not only affect bank lending supply at the intensive margin,

but also the probability that banks engage in new lending relationships, that is, the

extensive margin of bank lending supply.

Table 3 reports the results of regressions where the dependent variable is the probabil-

ity of a bank establishing a new lending relationship with a non-financial corporation.12

This dependent variable is defined as a dummy equal to 1 if: a) at time t a new firm

which did not have a relationship in the previous quarter enters the AnaCredit registry,

or b) a firm that was in the sample in t − 1 borrows from a different bank than in the

past. In this setting, we do not control for borrower × quarter fixed effects because we

want to capture the formation of new bank-firm relationships in general, rather than

focus only on firms that already had banking relationships. To allow for the inclusion

of new firms that did not have a bank relationship in the previous quarter, these regres-

sions use industry × location × size (ILS)×time fixed effects instead.13 A one standard

deviation increase in bank-level geopolitical risk significantly reduced the likelihood of a

bank establishing a new lending relationship by 6.1 percentage points.14

12In the Appendix, we also report the results of regressions using the probability of a bank
extending a new loan as the dependent variable, as an alternative measure of the extensive
margin. The results of this extension are qualitatively similar.

13Industry is based on NACE 4 revision 2 statistical classification codes, location is based on
NUTS 3 classification of regions, and size groups are defined in accordance with the Annex to
Commission Recommendation 2003/361/EC (micro, small, medium, and large firms).

14That banks respond to increased geopolitical risk by restricting lending at both the extensive
and intensive margin is similar to the findings for trade uncertainty reported by (Correa et al.,
2023) and in response to trade shocks reported by (Federico et al., 2025).

ECB Working Paper Series No 3143174.1.5 Amplifiers and mitigators: Liquidity constraints, capital headroom,

deposit funding structure and size

Certain bank characteristics could amplify or dampen the effect of geopolitical risk on

bank lending supply. To investigate this hypothesis, we focus on four bank level variables,

the cash-to-assets ratio, distance to maximum distributable amount (MDA), the loga-

rithm of total assets, and the uninsured deposit ratio.15 These variables are indicators

of bank liquidity, capital, size and funding structure, respectively.

Having access to abundant liquid assets and ample capital buffers above the MDA

can help banks to withstand geopolitical risk shocks. These shocks can increase market

volatility, affecting the value of assets held by banks. They can lead to losses or require

banks to hold more capital against these assets, potentially reducing their available

liquidity and thus limiting their lending capacity. In addition, fair value losses on their

assets are reflected in the calculation of high-quality liquid assets used as collateral,

thereby restricting the ability of banks to obtain secured funding (e.g., from the ECB

or via repurchase agreement operations) that could be employed for credit provision

(Berger and Bouwman, 2009; Holmstrom and Tirole, 1997; Ivashina and Scharfstein,

2010).

Geopolitical risk shocks can also elevate credit risk, affecting the ability of borrow-

ers to repay loans. This can lead to higher loan loss provisions and impact a bank’s

solvency and therefore prompt banks, especially those with lower capital buffers, to re-

duce lending. As breaching regulatory capital requirements triggers closer supervisory

scrutiny and leads to constraints on dividend distributions, bonuses, and coupon pay-

ments (Couaillier et al., 2025), less capitalised banks may pre-emptively reduce their

risk-weighted assets (i.e., the denominator of the capital ratio) to preserve their capital

15Distance to MDA is defined as the core equity tier 1 (CET 1) capital ratio minus the overall

capital requirement (OCR) ratio and is expressed in percentage.

ECB Working Paper Series No 314318buffers.16

It is not clear ex-ante whether larger banks might be expected to be more resilient

in the face of geopolitical shocks than smaller banks. On the one hand, they may

have a more diversified portfolio of assets, better access to capital, and be subject to

greater supervisory oversight and larger capital requirements. On the other hand, theory

predicts that smaller banks may have a comparative advantage in producing the soft

information necessary to lend to smaller privately held firms (Berger et al., 2005; Biswas

et al., 2017; Stein, 2002). However, these advantages are likely to be less relevant for

lending to larger firms, and larger firms are more likely to export goods internationally

(Amiti and Weinstein, 2011; Melitz, 2003). Thus, larger banks may have more exposures

to the geopolitical event, including through domestic lending to companies that trade in

the countries directly affected.17

Finally, a higher uninsured deposit ratio could make banks’ funding more sensitive

to shocks by increasing the likelihood of sudden withdrawals raising funding costs and

creating liquidity strains for banks and thus curtailing their lending.

To explore the potential effects on credit supply stemming from the interaction of

the aforementioned characteristics with banks’ exposure to geopolitical risk, we expand

upon the specification described in equation (5) by including a triple interaction between

BGP R×P ost and each of the four bank-specific variables one at a time.

yb,i,t = β1BGP Rb + β2P ostt + β3M itigatorb + β4(BGP Rb × P ostt) + β5(BGP Rb

× M itigatorb) + β6(M itigatorb × P ostt) + β7(BGP Rb × P ostt × M itigatorb)

+ β8Xb,t−1 + β9(Xb,t−1 × P ostt) + δi,t + µb,i + γc,t + εb,i,t

(3)

The dependent variable, yb,i,t, is the logarithm of the outstanding amount of loans

16Indeed such behaviour may be prudent. Using a historical dataset, Behn et al. (2025) find
that heightened geopolitical risk has been associated with lower bank capitalisation over the past
century, albeit less so in instances of geographically localised events.

17Recall that any tendency for larger banks to have larger exposures through foreign lending

should already be captured through the inclusion of the BGPR×Post interaction.

ECB Working Paper Series No 314319from bank b to firm i at time t. The variable of interest is the triple interaction term

BGP Rb × P ostt × M itigatorb, where M itigatorb denotes the pre-invasion average of

bank-specific characteristics — namely, the cash-to-assets ratio, distance to MDA, the

logarithm of total assets, or the uninsured deposit ratio. Our coefficient of interest, β7,

captures how the effect of geopolitical risk exposure after the invasion (BGP Rb × P ostt)

varies depending on the value of the mitigator variables. The vector Xb,t−1 includes

the same set of lagged bank-level control variables as in Equation 2. As in previous

regressions, we include firm×quarter fixed effects (δi,t), bank×firm fixed effects (µb,i)

and country×quarter fixed effects (γc,t).

The results in Table 6 show that the coefficient on the triple interaction BGP R×P ost×

M itigator is only statistically significant in the case of the distance to MDA and bank

size. A larger distance to MDA mitigated the reduction in lending supply after the

Russian invasion of Ukraine, pointing to a capital constraints channel (Correa et al.,

2023; Holmstrom and Tirole, 1997), while the estimated effects are statistically signif-

icantly larger for bigger banks. The absence of significance on the cash-to-assets and

uninsured deposit ratio coefficients suggests that liquidity and funding concerns were

not key factors constraining bank lending supply after the onset of the invasion.

4.1.6 Sectoral lending

Some non-financial corporate sectors may have been more affected by the Russian in-

vasion of Ukraine than others for a number of reasons. In particular, firms in sectors

that are more reliant on inputs from outside the EU generally, especially from countries

geopolitically aligned with Russia, could be more exposed to supply chain disruptions

and could face greater credit constraints if banks seek to minimise their exposure to

geopolitical risk. The next set of regressions tests this hypothesis.

The list of Russia-aligned countries is determined using the ideal point distance (IPD)

based on UN voting patterns from Bailey et al. (2017). Countries in the low quartile of

ECB Working Paper Series No 314320IPD with Russia are categorised as aligned (see Table 7). Then, the following measure

of sectoral input dependencies to Russian-aligned countries for each sector j in each

country c is computed using data sourced from Eurostat Figaro Input-Output tables,

following Arjona et al. (2023):

Input dependenciesj,c =

Russia-aligned inputsj,c
Total inputsj,c

(4)

We focus on 19 industries, using data for 2021, prior to the start of the invasion.18 In

this analysis, we compute the measure specifically for industries within euro area coun-

tries. The regression analysis focuses on domestic lending and intra-euro area lending,

which together represent the majority of the dataset. As previously noted, cross-border

lending accounts for only 1.5% of total lending volumes and thus constitutes a minor

share. Figure 6 shows the average of Input dependencies j,c across euro area countries.

On average, the most vulnerable sectors appear to be Manufacturing, Electricity, Trans-

portation, and Mining.

This measure is used to construct a dummy variable, V ulnerableSector, which

takes a value of 1 if the sector of the debtor falls within the top 25th (or 50th)

percentile of the most dependent sectors in its country. This dummy is included in

the baseline specification as an additional variable and as part of a triple interaction

BGP R×P ost×V ulnerableSector. The latter is the main variable of interest in the

below regression:

18The input-output table includes 21 industries but sector T (Activities of households as em-
ployers) and sector U (Activities of extraterritorial organisations and bodies) are excluded as they
are not relevant for this analysis, which focusses on bank lending to non-financial corporations.

ECB Working Paper Series No 314321yb,i,t = β1BGP Rb + β2P ostt + β3V ulnerableSectori + β4(BGP Rb × P ostt) + β5(BGP Rb

× V ulnerableSectori) + β6(V ulnerableSectori × P ostt) + β7(BGP Rb × P ostt

× V ulnerableSectori) + β8Xb,t−1 + β9(Xb,t−1 × P ostt) + δi,t + µb,i + γc,t + εb,i,t

(5)

The dependent variable, yb,i,t, is the logarithm of the outstanding amount of loans

from bank b to firm i at time t. The coefficient of interest on the triple interaction, β7,

captures the differential effect for firms in vulnerable sectors in the post-period. The

vector Xb,t−1 includes the same set of lagged bank-level control variables as specified in

Equation 2. As before, we include firm × quarter fixed effects (δi,t), bank × firm fixed

effects (µb,i), and country × quarter fixed effects (γc,t).

The results presented in Table 8 suggest that more geopolitically exposed banks

curtailed lending to sectors dependent on inputs from Russia-aligned countries (both for

domestic and euro area lending) 2% - 3% more than to other sectors.19

4.2 Firm-level analysis

In the bank-firm level analysis above, we showed that a higher exposure to geopolit-

ical risk leads to a larger contraction in lending supply to non-financial corporations.

However, such a reduction in bank lending may not have any aggregate effect on firm

outcomes if firms can replace the contraction in credit from banks with a higher exposure

to geopolitical risk by borrowing more from banks with a lower exposure. In practice,

however, the ability of firms to find alternative sources of bank lending could be impaired

as heightened geopolitical risk could potentially entail lower economic growth and a gen-

eral deterioration of banks’ asset quality. Therefore, banks with a lower exposure may

be less willing to pick up the slack. To investigate this empirical question, the following

19This result differs somewhat to the findings of Federico et al. (2025) which show that banks
did not differentiate between more and less exposed sectors when they curtail lending in response
to trade shocks.

ECB Working Paper Series No 314322firm-level regression specification was estimated using on AnaCredit data:

yi,t = β1Exposedf irmi + β2P ostt + β3(Exposedf irmi × P ostt) + β4Xi,t−1+

(6)

β5(Xi,t−1 × P ostt) + ηILS,i,t + δi + εi,t

where yi,t

is the logarithm of the total outstanding borrowing amount of firm

i in quarter t. The key variable of interest in this first additional specification is

Exposedf irmi × P ostt, i.e., the interaction between the “exposed” group and the post

dummy variables. Firms were classified as “exposed” if, prior to the conflict (i.e., in

Q4 2021), at least 50% of their loans were sourced from banks within the top 25th

percentile of geopolitical risk exposure. Xi,t−1 represents the same set of lagged bank

controls included in equation (2), but weighted by the share of total firm borrowing from

each bank, to obtain a time-varying weighted average of bank characteristics for each

firm. We also interact the control variables with the post dummy (Xi,t−1 ∗ P ostt). To

control for potential heterogeneity in credit demand across firms, industry × location

× size (ηILS) × quarter fixed effects are included in equation 6.20 In addition, some

specifications also include firm fixed effects to absorb all unobservable time-invariant

characteristics across firms (δi). Standard errors are clustered at firm level.

The results, reported in Table 9, suggest that firms reliant on banks with higher

exposure to geopolitical risk exhibited a 1.5% reduction in borrowing in relative terms.

The fact that this coefficient is smaller than the coefficient on BGP R×P ost in the

baseline results suggests that, on average, firms managed to access some credit from

alternative sources at different banks. However the coefficient is negative and statistically

significant. This indicates that such firms faced difficulties substituting loans from these

lenders with borrowing from less exposed banks during stressed times, resulting in an

20Since the data are collapsed at the firm-level, firm× time fixed effects cannot be included in

equation 6.

ECB Working Paper Series No 314323reduction in firm’s overall borrowing.21

Next, the following set of cross-sectional firm-level regressions were run to examine

whether firms reliant on banks with high exposure to geopolitical risk exhibited reduced

investment and employment:

∆yi = β1Exposedf irmi + β2Xi,2021 + β3Xb,2021 + ηILS,i + εi

(7)

where ∆yi is the firm-specific change between 2021 and 2022 of two variables, which

are specified as follows: 1) investment which is the change in the ratio of fixed assets to

total assets; and 2) number of employees which is the growth in the number of employees.

Exposedf irmi is the key variable of interest. As above, firms were classified as “exposed”

if, prior to the conflict (i.e., in Q4 2021), at least 50% of their loans were sourced from

banks within the top 25th percentile of geopolitical risk exposure. Xi,2021 is a vector

of firm control variables (log total assets, cash to assets, leverage, EBITDA, ratio of

tangible assets) with values taken in 2021, before the start of the invasion. Xb,2021 is

the same vector of weighted bank controls at a firm level as used in equation (5), while

ηILS,i are again industry-location-size fixed effects. These firm-level variables are taken

from Orbis and merged with our sample of borrowers. Basic descriptive statistics can

be found in the Appendix.

The results reported in Table 10 suggest that geopolitical events can lead to quanti-

tively important effects on firm investment and employment. Firms that were reliant on

banks with higher exposure to geopolitical risk exhibited a 8% reduction in investment

and a 0.6% reduction in their number of employees, compared to average firms. This

implies that the constraints on bank lending arising from geopolitical events can have

material economic consequences for firms and their employees.22

21In their analysis of the effects of trade uncertainty on bank lending, Correa et al. (2023)
also find that firms in borrowing relationships with more exposed banks faced difficulties gaining
access to substitute sources of credit.

22These results are consistent with the findings of Correa et al. (2023); Federico et al. (2025) in
their analyses of the response of bank lending to trade policy uncertainty and trade liberalization.

ECB Working Paper Series No 3143245 Robustness checks

5.1 Generalised propensity score weighting and placebos tests

Concerns may arise that there could be imbalances in treatment intensity and a selection

bias linked to covariates that make the treatment not random. The first two robustness

tests aim to rule out the possibility that banks that experienced a larger increase in

BGP R might have other differences in their underlying characteristics that explain the

observed difference in their lending behaviour in the post-invasion period.

By implementing a Generalised Propensity Score Weighting, we balance pre-

treatment covariates (using the mean of the covariates during the pre-period), to ensure

that observed differences in outcomes are not due to pre-existing differences, but rather

to the treatment itself. To do so, we use the covariate-balancing generalised propen-

sity score weighting of Fong et al. (2018), whereby observations are re-weighted based

on their probability of receiving a certain treatment intensity. This approach constructs

weights that minimise the correlation between the treatment intensity and pre-treatment

bank-level control variables, ensuring a more balanced comparison across different levels

of treatment.

When the baseline specification is re-estimated using reweighted observations, the

magnitude, size and significance of the coefficients on the key variable of interest remains

broadly unchanged (see Table 11).

If the BGPR variable captures the effects of other unobservable bank characteris-

tics, then banks with different levels of BGPR might exhibit systematically different

behaviour. As such, the regression could potentially deliver results similar to the base-

line estimates even in periods that were relatively unaffected by geopolitical events. In

addition, research has shown that difference-in-difference estimates can overstate the

statistical significance of results and fail placebo tests in some circumstances (Bertrand

et al., 2004). To address these concerns, we therefore run a placebo test, redefining the

ECB Working Paper Series No 314325event period to a time when geopolitical shocks were more limited. Specifically, we set

the P re period from Q4 2019 to Q4 2020 and the P ost period as starting in Q1 2021.

This allows us to assess whether the observed effects of exposure to the Russian invasion

of Ukraine are driven by pre-existing trends rather than the actual event.

The results in Table 12 show no significant impact of higher exposure when applying

this placebo framework, reinforcing the validity of our baseline findings.23

5.2 Controlling for bank exposures to energy intensive sectors or firms

The Russian invasion of Ukraine resulted in a large increase in European energy prices

as supplies of oil and gas to Europe were severely disrupted (Adolfsen et al., 2022;

Gazzani et al., 2024). To ensure that our results are not driven by this energy shock,

an extension of the baseline regression was run including a variable to control for the

exposure of banks to highly energy-intensive sectors or firms prior to the war.

To do so, data from AnaCredit were merged with firm-level emissions data from Ur-

gentem, which provide a measure of carbon intensity.24 The exposure indicator weights

the emissions of each firm by its share in the total loans granted by the bank. Emissions

data are missing for some firms, and the resultant indicator is available for approximately

33% of the [total number of] loans in our initial sample.

To assess whether this information affects the key variable of interest, we include an

additional interaction term between banks’ exposure to emission-intensive firms and the

Post dummy. Table 14 shows that banks’ exposure to emission-intensive firms did not

significantly influence the impact of the geopolitical risk shock, as the magnitude of the

main coefficient remains comparable to the baseline. Furthermore, the interaction term

is not statistically significant, suggesting that exposure to high-emitting firms did not

23In addition, we also redefine the event period to occur later in time. Specifically, we set the
Post period from Q4 2021 to Q4 2023 and define the Post period as starting in Q1 2022 (see
Table 13).

24Specifically, Scope 1, Scope 2, and Scope 3 emissions tons of CO2 equivalent (tCO2e) divided

by revenue.

ECB Working Paper Series No 314326materially affect lending behaviour after the start of the invasion.

As a robustness test, an alternative sector-level indicator was computed to increase

coverage. To do so, Eurostat data on emissions and energy use were normalised by

sectoral gross value added (GVA). These sectoral intensity measures were then merged

with information on the sectoral exposure of banks using loan portfolio data on the

country and sector of the borrower. Finally, to construct a bank-level indicator, the

sectoral energy of each borrower was weighted by its share in the total loans granted

by the bank. The resultant variable was then interacted with the P ost invasion dummy

variable.

The results displayed in Table 15 suggest that adding the additional interaction

variable of P ost and the energy exposure of banks does not change the key findings

with regard to geopolitical risk shock. The positive coefficient suggests that banks with

higher exposure to energy-intensive firms or industries actually exhibited slightly higher

lending than other banks, after controlling for their geopolitical exposure. At the same

time, the coefficient on the key variable of interest, BGP R×P ost, remains similar to the

baseline.

5.3 Potential sample selection bias due to including only firms with

multiple bank relationships

As explained previously, in most of the above regression specifications, we control for

heterogeneity in credit demand across firms by focusing on firms with multiple bank

relationships, following the approach of Khwaja and Mian (2008). However, a limitation

of this method is that it excludes single-bank relationships, as these are absorbed by

the inclusion of borrower-time fixed effects. To test the robustness of our results, we

replace the borrower-time fixed effects with ILS-time fixed effects, which allows us to

include borrowers with single-bank relationships while still controlling for certain demand

characteristics (Coulier et al., 2024; Degryse et al., 2019). The results, reported in Table

ECB Working Paper Series No 31432716, show that our findings are robust to the inclusion of single-bank relationship firms:

the interaction term BGP R × P ost remains negative and statistically significant.

In

addition, if we restrict the sample to firms borrowing from a single bank only (Table

17), the coefficient on the interaction of interest remains negative and significant even

in the restricted sample.

5.4 Concerns about the potential confounding effect of the monetary

policy tightening

The Russian invasion of Ukraine occurred in the first quarter of 2022, just before the start

of the ECB monetary policy tightening cycle in mid-2022 (Burlon et al., 2025).This could

raise concerns that the credit supply effects estimated in the regressions are the result of

the heterogeneous effects on banks caused by the monetary policy tightening rather than

geopolitical factors. However, a number of the features of the baseline specification, and

the evolution of the estimated results over time, serve to allay these concerns. First, the

baseline specification includes country-time fixed effects which should absorb country

(euro area) invariant factors such as monetary policy. Also it is not clear why the effect

of monetary policy should have an outsized effect on those banks with higher geopolitical

risk exposure, as captured by the BGP R index. Second, the timing of the first ECB

interest rate hike occurred in July 2022, 5 months after the Russian invasion of Ukraine.

However, to more accuratly address this concern, an alternative specification is run at

monthly frequency (see Table 18). The monthly analysis runs using data for the period

between 2021 M9 and 2022 M6. From this it is clear that there was already sizeable

and statistically significant effects prior to the onset of ECB monetary policy tighten-

ing. Moreover, the baseline specification suggests that the estimated geopolitical effects

did not persist throughout the monetary policy tightening cycle - the magnitude of the

coefficients already appears to fade in Q4 2022 even if ECB rate increases continued

until September 2023. Even without considering the potential lags at which monetary

ECB Working Paper Series No 314328policy tightening may affect bank lending, this pattern seems inconsistent with the hy-

pothesis that the estimates are capturing the effect of monetary policy rather than the

geopoliticalrisk shock.

5.5 Concerns about the consistency of standard errors

The baseline specification utilises standard errors clustered at the bank-level to account

for serial correlation within each bank. As a robustness test we also double-cluster at

the time and bank level (see Table 19). In addition, following Bertrand et al. (2004),

we also collapse the time series information into a single ”pre”- and ”post”-period (i.e.

the mean of the 5 months before and the mean of the 5 months after the invasion)

to address concerns that the standard errors could be inconsistent and overstate the

statistical significance of the estimates, as well as avoiding potential serial correlation.

In this specification, the dependent variable is the change in the outstanding volumes of

loans before and after the invasion. The values for the control variables are their mean

value in the 5 months prior to the invasion.

The results in Table 20 are robust to both the alternative clustering, and the col-

lapsed ”pre”- and ”post”-period specification, delivering coefficients with a similar sign,

significance and magnitude to the baseline in practically all specifications.

5.6 Differences in loan types, maturity and other characteristics

In the baseline specification, the total outstanding borrowing amount includes both

fixed rate and floating rate loans. However, it might be anticipated that geopolitical

shocks would only affect floating rate loans, the rates on which can be more readily

adjusted. To address this concern, an alternative specification was implement including

borrower×time×interest rate type. The results in Table 21 are robust to the inclusion

of these alternative fixed effects.

Relatedly, one might expect the effect of the geopolitical shock to vary depending on

ECB Working Paper Series No 314329the maturity of loans. Faced with heightened uncertainty, banks may seek to shorten the

maturity of their loans and become reluctant to lend for longer periods. To investigate

this hypothesis, loans are allocated to buckets based on the quartile of the maturity.

Using this information, a specification was run that included borrower×time×maturity

fixed effects. Once again, the key results are robust to this alternative specification (see

Table 22).

5.7 Firm-bank level controls for loan characteristics

The response of bank lending supply to geopolitical shocks could vary depending on

the features and characteristics of the loans from banks to individual firms. To ensure

that this is not driving the key results, an alternative specification was implemented

including three variables to control for loan characteristics at the bank-firm level. First,

a variable capturing the weighted average residual maturity of a firm’s loans from a given

bank is included. Firms that have loans with a longer maturity may be perceived as

being more risky and therefore more sensitive to heightened geopolitical risk. Second, a

dummy variable is included, that takes a value of 1 if any of a firm’s loans are in default.

Firms that have loans in default may be perceived as more risky. Third, a variable that

captures the sum of the protection value of a firm’s loans was included. Intuitively, for

a given loan size, the larger the insured value of firms’ loans, the less risky they are.

The regression also includes interactions of these three variables with the P ost dummy

variable.

The results in Table 23 show that including these additional control variables does

not notably alter the size, sign or significance of the coefficients on the key variable of

interest, BGP R×P ost. The coefficient on the default status×BGP R×P ost interaction

term is statistically significant and negative, while that on the insurance protection

value×BGP R×P ost of a firm’s loans is positive. These results are both consistent with

the hypothesis that banks became more sensitive to the risk characteristics of firms’

ECB Working Paper Series No 314330loans after the invasion.

6 Conclusions

Geopolitical tensions typically lead to heightened uncertainty, which can negatively affect

future growth prospects and financial stability. As a result, they are a key concern for

policymakers, financial institutions, and banks.

This paper shows that geopolitical risk can have a significant impact on the supply of

bank lending, with pronounced knock-on effects on businesses. The Russian invasion of

Ukraine provided a stark illustration of how adverse geopolitical events can rapidly shift

risk perceptions and increase uncertainty, leading banks to curtail lending. The euro

area was particularly vulnerable due to its geographical proximity to the event making

it an ideal laboratory to study these effects.

The paper provides several insights that are crucial to understand the channels and

mechanisms through which geopolitical risk can affect banks and the firms reliant on

them. Granular loan level data, combined with a well specified difference-in-difference

estimation approach, allow our analysis to make causal inferences about the effects

of geopolitical risk on bank lending. Banks with higher exposure to the increase in

geopolitical risk stemming from the Russian invasion of Ukraine cut lending supply to

non-financial corporations more than their peers both at the intensive and extensive

margins, as these banks were also less likely to establish new lending relationships. The

results suggest that the behaviour of the banks was driven by increased risk aversion

in the face of increased uncertainty, as banks with higher exposure to geopolitical risk

increased their loan impairments but did not record a deterioration of their asset quality.

The effects on the supply of bank lending was mitigated in the case of banks with larger

capital buffers, suggesting that robust capitalisation can mitigate the adverse effects

of geopolitical events. Banks constrained lending supply more to sectors reliant on

inputs from countries aligned with Russia, suggesting that banks were concerned by

ECB Working Paper Series No 314331firms’ supply chain vulnerabilities. Relatedly, larger banks, which typically have greater

exposure to larger export oriented firms, cut lending more than smaller banks, which

tend to serve smaller domestically oriented companies.

The effects on bank lending behaviour had real economic consequences. Firms reliant

on banks with higher exposure to geopolitical risk were unable to substitute loans, as

they could not compensate for the reduction in lending through other sources of bank

financing. This led to real economic effects, as the contraction in the credit supply of

banks with higher exposure had tangible consequences for euro area firms. Indeed, firms

that were more exposed to these institutions reduced both investment and employment.

These findings have important implications for policy. We show that these risks com-

pelled banks to adjust their lending practices, tightening credit conditions. Analysing

these reactions provides valuable insights for policymakers to coordinate responses, such

as targeted macroprudential measures or lending support programs, to mitigate the

broader economic impact of geopolitical crises. Understanding these dynamics is crucial

for assessing systemic risks and enhancing the resilience of the euro area banking sector.

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ECB Working Paper Series No 314338Tables and Figures

Figure 1: Distribution of observations by country in the dataset

Note: This figure represents the coverage per country of the number of
observations after the merge of AnaCredit with supervisory bank-level
data.

Note: This figure represents the the percentage of observations by
country after the merge of AnaCredit with supervisory bank-level data.

125,639393,04016,122373,98310,7702,465,505328,4191,676,02161,01418055,3152,099,4316,89051,9504,8664,40722,064372,89722,4781,0150500,0001,000,0001,500,0002,000,0002,500,000Number of observationsATBECYDEEEESFIFRGRHRIEITLTLULVMTNLPTSISK1.64.90.24.60.130.54.120.70.80.00.725.90.10.60.10.10.34.60.30.00102030% of observationATBECYDEEEESFIFRGRHRIEITLTLULVMTNLPTSISKECB Working Paper Series No 314339Table 1: Descriptive Statistics

Dependent variables
Outstanding amount (euros)
Log outstanding amount
New loan (dummy)
New relationship (dummy)
Impairment amount (euros)
Log impairment amount
Days past due
Interest variable
BGPR
Control variables - bank level
Log total assets
Capital tier 1 ratio (%)
Deposit to liability ratio (%)
Return on asset
Cash to asset ratio (%)
Provision over loans
Distance to MDA (%)
Uninsured deposit ratio (%)
Emission intensive exposure
Energy intensive exposure
Control variables - loan level
Log of weighted residual maturity
Default status
Log protection value
Firm variables
Input dependencies (sectoral)
Exposed firm
Investment (change 2021-2022)
Log number of employees (change 2021-2022)
Log total assets (2021)
Cash to assets (2021)
Leverage
EBITDA
Ratio of tangible assets

N

Mean

Std.dev.

p25

p50

p75

7 240 328
7 240 328
7 240 328
19 197 283
6 121 938
6 121 938
7 228 011

868582.36
12.07
0.05
0.17
38633.60
12.07
32.47

2157559
1.90
0.22
0.37
3879747
1.90
202.67

56201.29
10.94
0.00
0.00
89.00
10.94
0.00

165057.62
12.01
0.00
0.00
477.00
12.01
0.00

533159.00
13.19
0.00
0.00
2972.00
13.19
0.00

7 240 328

3.24

0.90

2.20

3.51

3.80

7 240 328
7 240 328
7 240 328
7 240 328
7 240 328
7 240 328
7 204 803
4 298 408
7 231 488
7 240 328

6 392 450
7 240 328
5 742 011

6 228 086
14 921 687
670 598
486 405
670 598
670 598
670 598
670 598
670 598

5.41
16.34
84.00
0.55
14.69
0.08
2.43
65.08
-0.08
-0.08

6.56
0.03
11.22

0.48
0.12
0.05
0.04
13.74
0.17
0.21
0.10
0.30

1.62
3.98
11.15
0.55
5.63
0.41
5.13
11.72
0.30
0.30

1.70
0.16
3.89

0.65
0.32
1.73
0.35
1.61
0.22
0.51
1.69
0.30

4.37
14.22
78.42
0.28
11.61
0.00
0.00
55.88
-0.13
-0.13

6.17
0.00
10.86

0.13
0.00
-0.03
0.00
12.62
0.03
0.01
0.03
0.05

5.55
15.43
86.18
0.47
14.63
0.04
0.85
65.51
-0.05
-0.05

7.02
0.00
12.10

0.18
0.00
-0.00
0.00
13.58
0.10
0.14
0.08
0.20

6.74
17.32
93.27
0.68
17.66
0.11
2.85
73.24
0.08
0.08

7.48
0.00
13.27

0.73
0.00
0.03
0.13
14.67
0.25
0.31
0.16
0.48

Note: Descriptive statistics cover the period from 2021Q1 to 2023Q1, unless otherwise specified

ECB Working Paper Series No 314340Figure 2: Variation of the country level GPR z-scores around the invasion

Note: The figure displays the variation of the country-level GPR z-scores between 2021 Q4 and 2022
Q1. Darker colours indicate a higher variation.

Figure 3: Average Bank-level GeoPolitical Risk (BGPR) index by euro area
country

Note: The figure displays country-level aggregates, calculated as weighted averages of the BGPR of
individual banks headquartered in each country included in our sample. The weights are based on each
bank’s share of total assets within its respective country. Darker colours indicate a higher exposure to
geopolitical risk.

(4.46,7.45](2.06,4.46](1.00,2.06][-0.34,1.00]No data(4.415,6.41](3.63,4.415](2.255,3.63][.66,2.255]No dataECB Working Paper Series No 314341Figure 4: Distribution of Bank-level GeoPolitical Risk (BGPR) index across
the 363 banks in the sample

0.2.4.6Density02468Exogenous Bank-level GPR Z-scoreDistributionECB Working Paper Series No 314342Table 2: Effects on the intensive margin

BGPRb

BGPRb × Postt

L.size (log)b,t

L.size (log)b,t × Postt

L.capital tier1 ratiob,t

L.capital tier1 ratiob,t × Postt

L.deposit-to-liability ratiob,t

L.deposit-to-liability ratiob,t × Postt

L.ROAb,t

L.ROAb,t × Postt

L.cash-to-asset ratiob,t

L.cash-to-asset ratiob,t × Postt

L.provisions-to-loans ratiob,t

L.provisions-to-loans ratiob,t × Postt

Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

Dependent variable: log(loan amount)
(3)

(4)

(2)

(5)

(1)

0.022
(0.163)

-0.066***
(0.023)

-0.078***
(0.020)

-0.071***
(0.015)

-0.083***
(0.019)

-0.092***
(0.019)

0.149***
(0.048)

0.131**
(0.053)

0.132**
(0.053)

-0.002
(0.006)

0.006**
(0.003)

-0.001
(0.001)

0.003
(0.003)

-0.000
(0.001)

0.011*
(0.006)

0.004
(0.003)

-0.000
(0.001)

0.004
(0.003)

0.012*
(0.006)

0.005*
(0.003)

-0.001
(0.001)

0.004
(0.003)

0.002**
(0.001)

0.002***
(0.001)

-0.026***
(0.005)

-0.022***
(0.008)

-0.023***
(0.008)

0.018**
(0.008)

-0.000
(0.001)

-0.000
(0.001)

0.171
(1.163)

0.388
(1.440)

✓

✓
✓

✓
✓

0.016
(0.013)

-0.001
(0.001)

-0.001
(0.001)

-0.359
(1.375)

1.245
(1.682)

✓

✓

7,240,328
0.666

7,240,327
0.692

6,559,556
0.704

6,437,583
0.956

0.016
(0.013)

-0.001
(0.001)

-0.001
(0.001)

-0.409
(1.354)

1.404
(1.647)

✓

✓
✓
6,437,583
0.956

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at bank-
level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314343Figure 5: Estimated dynamic impact of an increase in BGPR on the
intensive margin

Note: The chart presents the estimated response of bank-firm loan amounts to a one-standard-deviation
increase in bank-level geopolitical risk index following the Russian invasion of Ukraine at different time
horizons with 2021 Q4 as reference period. The estimated model include borrower×time,
borrower×bank and country×time fixed effects. Confidence intervals are set at the 99% level

-.10.1.22021 q12022 q22021 q32021 q42022 q12022 q22022 q32022 q4ECB Working Paper Series No 314344Table 3: Effects on the probability of extending a new relationship

BGPRb

BGPRb × Postt

Dependent variable: new relationship

(1)

(2)

(3)

(4)

-0.040***
(0.014)

-0.039*
(0.023)

-0.038*
(0.021)

-0.056**
(0.025)

-0.061**
(0.026)

Bank controls/interactions
ILS × Time FE
Bank FE
Country × Time FE
N
R²

✓
✓

✓
✓
✓

✓
✓
✓

19 197 283
0.171

19 197 282
0.208

17 997 224
0.217

✓
✓
✓
✓
17 997 224
0.217

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

Table 4: Effects on impairment volumes

BGPRb

(1)

-0.204
(0.171)

Dependent variable: log(impairments)
(3)

(2)

(4)

(5)

BGPRb × Postb

0.230**
(0.108)

0.263**
(0.109)

0.247***
(0.089)

0.227**
(0.092)

0.241**
(0.098)

Bank controls/interactions
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

✓

✓
✓

✓
✓
✓

✓
✓

✓

6,121,938
0.794

6,121,936
0.821

5,618,863
0.824

5,510,484
0.944

✓
✓

✓
✓
5,510,484
0.944

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314345Table 5: Effects on the number of days past due

Dependent variable: Days past due

(1)

(2)

(3)

(4)

BGPRb × Postb

Outstanding amount (log)

1.082
(2.132)
-2.442***
(0.887)

-0.299
(2.445)
-2.991***
(0.966)

0.539
(1.028)
-1.015
(0.690)

Bank controls/interactions
Bank FE
Time FE
ILS FE
N
R²

✓
✓

✓
✓
✓

7,232,992
0.013

6,806,623
0.015

✓
✓
✓
6,773,935
0.283

-0.343
(1.294)
-1.437*
(0.786)

✓
✓
✓
✓
6,369,152
0.287

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

Table 6: Amplifiers and mitigators : effects of bank characteristics

Dependent variable: log(loan amount)

Cash to assets Distance to MDA

continuous

continuous

Size
continuous

Uninsured deposit ratio
continuous

BGPRb × Postt

BGPRb × Postt
× Moderatorf

Bank controls/interactions
Double interaction included
Borrower × Time FE
Borrower × Bank FE
Country × Time FE
N
R²

-0.093***
(0.017)

0.001
(0.105)

✓
✓
✓
✓
✓
7 212 500
0.952

-0.100***
(0.020)

0.003*
(0.001)

✓
✓
✓
✓
✓
7 162 947
0.942

0.000
(0.025)

-0.018***
(0.006)

✓
✓
✓
✓
✓
7 212 500
0.952

-0.122
(0.097)

0.000
(0.002)

✓
✓
✓
✓
✓
6 699 981
0.948

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314346Table 7: List of Russia-Aligned Countries Based on UN Voting Patterns

Country name
Senegal
Guinea
Saint Lucia
Grenada
Vietnam
Comoros
Pakistan
Benin
Bhutan
Gambia

Antigua and Barbuda
Chad
Azerbaijan
Rwanda
Morocco
Yemen
Lesotho
Namibia
Tajikistan
Mozambique

Cambodia
Zimbabwe
South Africa
Mali
Afghanistan
Kazakhstan
Ghana
Angola
Madagascar
Kyrgyzstan
Russia

Figure 6: Average of sectoral ratio of inputs dependencies to Russia-aligned
countries across euro area countries

Note: The figure displays the country-level average of the measure of Russia-aligned input
dependencies. The green dotted line represents the 75th percentile, and the red dotted line represents
the 50th percentile.

0.511.522.5Russia-aligned inputs dependenciesA - AgricultureB - MiningC - ManufacturingD - ElectricityE - Water supplyF - ConstructionG - Wholesale and retail tradeH - TransportationI - Accomodation and food serv.J - Information and communicationK - Financial act.L - Real estateM - Professional and scientific act.N - Administrative and support act.O - Public admin. and defenceP - EducationQ - Human healthR - Arts and recreationS - Other servicesECB Working Paper Series No 314347Table 8: Effects of being in a vulnerable sector

Dependent variable: log(loan amount)

Domestic

Euro-area

Top 25th Top 50th Top 25th Top 50th

BGPRb × Postt

-0.084***
(0.017)

-0.073***
(0.014)

-0.086***
(0.017)

-0.074***
(0.014)

BGPRb × Postt
× Vulnerable sectorc,f

-0.027***
(0.009)

-0.032**
(0.015)

-0.034***
(0.009)

-0.035**
(0.015)

Bank controls/interactions
Double interaction included
Borrower × Time FE
Borrower × Bank FE
Country × Time FE
N
R²

✓
✓
✓
✓
✓
6 228 086
0.955

✓
✓
✓
✓
✓
6 228 086
0.955

✓
✓
✓
✓
✓
6 360 359
0.955

✓
✓
✓
✓
✓
6 360 359
0.955

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are
clustered at bank-level. Standard errors are reported in parentheses.

Table 9: Substitution effect at firm level

Dependent variable: log(loan amount)
(1)

(2)

(3)

Exposed firmj × Postt

ILS × Time FE
Weighted controls
Weighted controls×post
Firm FE
N
r2

-0.022***
(0.008)
✓

-0.015***
(0.005)
✓
✓

✓
2,116,256
0.973

✓
1,699,591
0.978

-0.015***
(0.005)
✓
✓
✓
✓
1,699,591
0.978

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are
clustered at the largest lender level are reported in parentheses.

ECB Working Paper Series No 314348Table 10: Firm real effects

Exposed firmj

Size (log)i,2021

Cash-to-assetsi,2021

Debt-to-assetsi,2021

EBITDAi,2021

Tangible asset ratioi,2021

Bank controls
ILS FE
N
r2

Investment

-0.083∗∗
(0.040)
-0.030∗∗
(0.013)
-0.129∗∗∗
(0.025)
0.061
(0.058)
-0.037
(0.029)
-0.048
(0.064)
✓
✓
458,433
0.094

-0.086∗∗
(0.042)
-0.030∗∗
(0.013)
-0.129∗∗∗
(0.025)
0.061
(0.058)
-0.037
(0.029)
-0.049
(0.064)

✓
458,433
0.094

Significance levels are: * p ¡ 0.10 ; ** p ¡ 0.05 ; *** p ¡ 0.01.

-0.006∗∗
(0.002)
0.006∗∗∗
(0.001)
-0.001
(0.006)
0.026∗∗∗
(0.008)
-0.022∗∗∗
(0.004)
0.169∗∗∗
(0.017)

Log(number of employees)
-0.006∗∗∗
(0.002)
0.005∗∗∗
(0.001)
-0.000
(0.006)
0.025∗∗∗
(0.008)
-0.022∗∗
(0.004)
0.169∗∗∗
(0.017)
✓
✓
366,421
0.172

✓
366,421
0.173

ECB Working Paper Series No 314349Table 11: Covariate-rebalancing with generalised propensity score weighting

BGPRb

BGPRb × Postt

L.size (log)b,t

L.size (log)b,t × Postt

L.capital tier1 ratiob,t

L.capital tier1 ratiob,t × Postt

L.deposit-to-liability ratiob,t

L.deposit-to-liability ratiob,t × Postt

L.ROAb,t

L.ROAb,t × Postt

L.cash-to-asset ratiob,t

L.cash-to-asset ratiob,t × Postt

L.provisions-to-loans ratiob,t

L.provisions-to-loans ratiob,t × Postt

Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

Dependent variable: log(loan amount)
(3)

(4)

(2)

(5)

(1)

0.075
(0.161)

-0.082***
(0.024)

-0.088***
(0.021)

-0.078***
(0.015)

-0.095***
(0.018)

-0.102***
(0.018)

0.142***
(0.042)

0.157***
(0.050)

0.159***
(0.050)

0.000
(0.007)

0.014*
(0.007)

0.016**
(0.008)

0.007**
(0.003)

0.006**
(0.003)

0.007**
(0.003)

-0.001
(0.001)

0.004
(0.003)

0.000
(0.001)

-0.000
(0.001)

0.005
(0.003)

-0.001
(0.001)

0.005
(0.003)

0.002**
(0.001)

0.002***
(0.001)

-0.028***
(0.005)

-0.025***
(0.007)

-0.025***
(0.007)

0.018**
(0.008)

0.000
(0.001)

-0.000
(0.001)

0.011
(0.018)

-0.002
(0.020)

✓

✓
✓

✓
✓

0.015
(0.012)

-0.000
(0.001)

-0.001
(0.001)

0.007
(0.021)

0.005
(0.023)

✓

✓

7,240,328
0.666

7,240,327
0.692

6,559,556
0.704

6,437,583
0.956

0.016
(0.012)

-0.000
(0.001)

-0.001
(0.001)

0.006
(0.020)

0.007
(0.023)

✓

✓
✓
6,437,583
0.956

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314350Table 12: Placebo test - one year before

(1)

0.041
(0.179)

-0.019
(0.024)

✓

BGPRb

BGPRb × Postt

Bank controls/interactions
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

Dependent variable: log(loan amount)
(3)

(2)

(4)

(5)

-0.011
(0.021)

-0.003
(0.017)

-0.011
(0.017)

-0.010
(0.018)

✓
✓

✓
✓
✓

✓
✓

✓

✓
✓

✓
✓
5 695 628
0.948

6 517 419
0.662

6 517 419
0.688

5 802 371
0.702

5 695 628
0.948

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

Table 13: Placebo test - one year after

BGPRb

BGPRb × Postt

Bank controls/interactions
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R2²

(1)

-0.044
(0.150)
0.021
(0.031)

✓

Dependent variable: log(loan amount)
(3)

(2)

(4)

(5)

0.015
(0.027)

0.012
(0.020)

0.006
(0.019)

0.006
(0.022)

✓
✓

✓
✓
✓

✓
✓

✓

✓
✓

✓
✓
4 085 941
0.966

4 628 689
0.670

4 628 686
0.696

4 213 622
0.708

4 085 941
0.966

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314351Table 14: Control for exposure to emission intensive firms

BGPRb

BGPRb × Postb

Dependent variable: log(loan amount)
(3)

(4)

(2)

(5)

(1)

0.024
(0.165)

-0.067***
(0.022)

-0.078***
(0.021)

-0.070***
(0.015)

-0.083***
(0.019)

-0.093***
(0.020)

Firm emission intensive Exposureb

0.038
(0.191)

Firm emission intensive Exposureb
× Postt

0.011
(0.029)

-0.013
(0.025)

-0.010
(0.020)

-0.001
(0.023)

-0.005
(0.024)

Bank controls/interactions
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N N
R²

✓

✓
✓

✓
✓
✓

✓
✓

✓

7 231 488
0.666

7 231 488
0.692

6 551 984
0.704

6 437 360
0.956

✓
✓

✓
✓
6 437 360
0.956

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at bank-
level. Standard errors are reported in parentheses.

Table 15: Control for exposure to energy intensive sectors

BGPRb

BGPRb × Postb

Dependent variable: log(loan amount)
(3)

(2)

(4)

(5)

(1)

0.022
(0.164)

-0.070***
(0.021)

-0.079***
(0.020)

-0.071***
(0.014)

-0.085***
(0.018)

-0.093***
(0.019)

Energy intensive exposureb

0.015
(0.098)

Energy intensive exposureb × Postt

0.040**
(0.017)

0.024*
(0.013)

0.017*
(0.009)

0.027***
(0.009)

0.022**
(0.010)

Bank controls/interactions
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

✓

✓
✓

✓
✓
✓

✓
✓

✓

7 231 606
0.666

7 231 605
0.692

6 552 043
0.704

6 437 420
0.956

✓
✓

✓
✓
6 437 420
0.956

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at bank-
level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314352Table 16: Effects on lending when including firms with single bank
relationships

BGPRb

BGPRb × Postt

Dependent variable: log(loan amount)

(1)

(2)

(3)

(4)

0.009
(0.110)

-0.056***
(0.015)

-0.062***
(0.015)

-0.053***
(0.011)

-0.058***
(0.012)

Banks controls/interactions
ILS × Time FE
Bank FE
Country × Time FE
N
R²

✓

✓
✓

✓
✓
✓

19 197 283
0.359

19 197 282
0.387

17 997 224
0.397

✓
✓
✓
✓
17 997 224
0.397

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are
clustered at bank-level. Standard errors are reported in parentheses.

Table 17: Effects on lending with only firms with single bank relationships

Dependent variable: log(loan amount)
(4)
(1)

(2)

(3)

BGPRb

0.027
(0.142)

BGPRb × Postt

-0.059***
(0.022)

-0.072***
(0.020)

-0.064***
(0.014)

-0.078***
(0.012)

Banks controls/interactions
ILS × Time FE
Bank FE
Country × Time FE
N
R²

✓

✓
✓

✓
✓
✓

6,780,644
0.436

6,780,643
0.468

6,150,914
0.485

✓
✓
✓
✓
6,038,573
0.488

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are
clustered at bank-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314353Table 18: Monthly estimation, 2021 M9 - 2022 M6

Dependent variable: log(loan amount)
(3)

(4)

(2)

(5)

BGPRb

(1)

0.010
(0.157)

BGPRb × Postt

-0.043***
(0.013)

-0.049***
(0.012)

-0.054***
(0.012)

-0.047***
(0.009)

-0.053***
(0.009)

Bank controls
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
Number banks
R²

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

9 054 058
356
0.667

9 054 056
355
0.695

9 038 401
352
0.694

8 969 985
351
0.962

✓
✓

✓
✓
8 969 985
351
0.962

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. Standard errors are reported in parentheses.

Table 19: Clustering standard errors at bank and time level

BGPRb

BGPRb × Postt

Dependent variable: log(loan amount)
(3)

(2)

(4)

(5)

(1)

0.022
(0.082)

-0.066
(0.111)

-0.078***
(0.015)

-0.071***
(0.015)

-0.083***
(0.016)

-0.092***
(0.018)

Banks controls/interactions
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

7,240,328
0.666

7,240,327
0.692

6,559,556
0.704

6,437,583
0.956

✓

✓
✓
6,437,583
0.956

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank- and time-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314354Table 20: Collapse methodology on monthly data

Dependent variable: ∆log(loanamount)

(1)

(2)

BGPRb

L.size (log)

-0.047***
(0.002)

L.capital tier1 ratio

L.deposit-to-liability ratio

L.ROA

L.cash-to-asset ratio

L.provisions-to-loans ratio

Firm FE
N
R2

✓
3 361 490
0.001

-0.046***
(0.002)

0.009***
(0.001)

0.001***
(0.000)

0.001***
(0.000)

-0.003
(0.002)

-0.001***
(0.000)

0.919***
(0.302)

✓
3 332 557
0.002

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard
errors are reported in parentheses.

ECB Working Paper Series No 314355Table 21: Including interest rate fixed effects

BGPRb

BGPRb × Postt

Dependent variable: log(loan amount)

(1)

(2)

(3)

(4)

(5)

0.152*
(0.083)

-0.046***
(0.014)

-0.055***
(0.015)

-0.046***
(0.010)

-0.063***
(0.014)

-0.070***
(0.014)

Banks controls/interactions
Borrower × Time × Interest rate type FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

3,498,923
0.777

3,498,922
0.788

3,191,792
0.796

3,032,110
0.978

✓

✓
✓
3,032,110
0.978

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at bank- and time-level.
Standard errors are reported in parentheses.

Table 22: Including maturity rate fixed effects

BGPRb

BGPRb × Postt

Dependent variable: log(loan amount)
(3)

(2)

(4)

(5)

(1)

0.142**
(0.071)

-0.067***
(0.017)

-0.068***
(0.016)

-0.059***
(0.011)

-0.059***
(0.016)

-0.066***
(0.017)

Banks controls/interactions
Borrower × Time × Maturity FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

2,995,651
0.810

2,995,647
0.824

2,775,294
0.828

2,644,314
0.985

✓

✓
✓
2,644,314
0.985

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are
clustered at bank- and time-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314356Table 23: Adding loan level controls

BGPRb

BGPRb × Postt

Dependent variable: log(loan amount)
(3)

(2)

(4)

(5)

(1)

0.163*
(0.093)

-0.064***
(0.018)

-0.052***
(0.017)

-0.042***
(0.012)

-0.060***
(0.014)

-0.063***
(0.014)

Weighted residual maturity (log)b,i,t

0.230***
(0.021)

0.206***
(0.013)

0.203***
(0.013)

0.088***
(0.009)

0.088***
(0.009)

Weighted residual maturity (log)b,i,t
× Postt

0.006
(0.010)

Default statusb,i,t

Default statusb,i,t
× Postt

-0.024
(0.059)

-0.067
(0.068)

-0.003
(0.006)

0.021
(0.040)

-0.033
(0.056)

-0.005
(0.006)

0.008
(0.006)

0.008
(0.006)

0.020
(0.039)

0.025***
(0.009)

0.025***
(0.009)

-0.035
(0.054)

-0.036***
(0.011)

-0.036***
(0.011)

Protection value (log)b,i,t

0.075***
(0.023)

0.103***
(0.025)

0.101***
(0.025)

0.074***
(0.024)

0.074***
(0.023)

Protection value (log)b,i,t
× Postt

0.004
(0.003)

0.004
(0.003)

0.006**
(0.003)

0.005***
(0.001)

0.005***
(0.001)

Banks controls/interactions
Borrower × Time × Maturity FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

4,539,022
0.802

4,539,019
0.829

4,250,512
0.831

4,174,609
0.981

✓

✓
✓
4,174,609
0.981

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are
clustered at bank- and time-level. Standard errors are reported in parentheses.

ECB Working Paper Series No 314357Appendix

Variables definitions

Table A1: Definitions of variables and their sources

[htbp] Variable

Label

Definition

Source

Dependent variables:

Loan amount

log(loan amount)

Logarithm of the outstanding amount of loans from

AnaCredit

bank b to firm i

New relationship

Dummy variable equal to 1 if: a) at time t a new

AnaCredit

firm that did not have a relationship in the previous

quarter enters the AnaCredit registry, and b) a firm

that was in the sample in t − 1 acquires a loan from

a new bank. It equals 0 otherwise.

New loan

Dummy variable equal to 1 when the outstanding

AnaCredit

credit volume in lending relationships increases be-

tween t − 1 and t, and equal to 0 otherwise

Impairment volumes

log(impairments)

Sum of loss allowances held against a loan at the

AnaCredit

bank-firm level on the reporting date, based on ex-

pected credit losses under international or national

accounting standards

Days past due

days past due

The average number of days past due at bank-firm

AnaCredit

level, average at bank-firm level weighted by the size

of each loan. Past due being considered when the

payment of interest and/or principal is not made on

time.

Interest variable:

Bank-level Geopolitical Risk

BGPR

Indicator built by weighting the change in the stan-

Caldara

and

Iacoviello

dardised country-level geopolitical risk (CGPR) in-

(2022); Dieckelmann et al.

dices between 2021Q4 and 2022Q1 with bank-level

(2025) and ECB Supervisory

average asset-side exposures to the different coun-

statistics

tries (average on 2021Q1-2021Q4)

Control variables - bank level:

Size

size(log)

Logarithm of bank total assets

ECB Supervisory statistics

ECB Working Paper Series No 314358Capitalisation

capital tier1 ratio

The Tier 1 Capital Ratio is the ratio of a bank’s core

ECB Supervisory statistics

equity capital (Tier 1 capital) to its risk-weighted

assets, in percent.

distance to MDA

The CET1 ratio in excess of the maximum dis-

ECB Supervisory statistics

tributable amount, in percent.

Funding structure

deposit-to-liability ratio

Share of total liabilities funded by customer deposits,

ECB Supervisory statistics

expressed, in percent.

Profitability

ROA

The ratio of net income to total assets

ECB Supervisory statistics

Liquidity

uninsured deposit ratio

Share of a bank’s total deposits that are not covered

ECB Supervisory statistics

by a deposit insurance scheme, in percent

Asset quality

provisions-to-loans ratio

Total loan loss provisions divided by the total out-

ECB Supervisory statistics

standing loan amount.

Emission intensive exposure

emission intensive expo-

Emission intensity of each borrower was weighted by

Urgentem, ECB Supervisory

sure

its share in the total loans granted by the bank, the

statistics and AnaCredit

measure is standardized.

Energy intensive exposure

energy intensive exposure

Sectoral energy of each borrower weighted by its

Eurostat, ECB Supervisory

share in the total loans granted by the bank, the

statistics and AnaCredit

measure is standardised.

Control variables - loan level:

Maturity

weighted residual matu-

The logarithm of the residual maturity at bank-firm

AnaCredit

rity (log)

level, average at bank-firm level weighted by the size

of each loan

Default status

default status

Dummy variable that takes a value of 1 if any of a

AnaCredit

firm’s loans with the bank are in default

Protection value

protection value (log)

The logarithm of the sum residual maturity at bank-

AnaCredit

firm level

ECB Working Paper Series No 314359Firm variables:

Input dependencies

input dependencies

Computed at country sector (Nace Rev.2) level, cor-

Figaro - Eurostat

respond to the share of Russian-aligned inputs over

total inputs

Exposed firm

exposed firm

Dummy taking the value of 1 if least 75% of the firm’s

AnaCredit, ECB Supervisory

loans were sourced from banks within the top 25th

statistics

percentile of geopolitical risk exposure

Investment

investment

Change between 2021 and 2022 in the ratio of fixed

Orbis

assets to total assets

Number of employees

nb. of employees

Change between 2021 and 2022 in the logarithm of

Orbis

number of employees

Size

size(log)

Logarithm of bank total assets

Orbis

Liquidity

cash-to-assets

The ratio of cash including to total assets

Orbis

Leverage

debt-to-assets

The ratio of debt over total assets

Orbis

Profitability

EBITDA

Ratio of Earnings before interest, taxes, deprecia-

Orbis

tion, and amortization over total assets

Tangible assets

tangible asset ratio

The ratio of of tangible assets over total assets

Orbis

ECB Working Paper Series No 314360Probability of extending a new loan

As an alternative to the extensive margin, Table A2 reports the results of regressions

similar to the baseline, but now with the probability of a bank extending a new loan

as the dependent variable. The results indicate that a one standard deviation increase

in BGPR significantly reduces the probability of extending a new loan in an existing

lending relationship by 1.2 percentage points.

Table A2: Effects on the probability of issuing a new loan

BGPRb

(1)

-0.012
(0.007)

Dependent variable: new loan
(3)

(2)

(4)

(5)

BGPRb × Postt

-0.011***
(0.004)

-0.012***
(0.004)

-0.012***
(0.004)

-0.011***
(0.004)

-0.012***
(0.004)

Bank controls/interactions
Borrower × Time FE
Bank FE
Borrower × Bank FE
Country × Time FE
N
R²

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

✓

7 240 328
0.462

7 240 327
0.471

6 559 556
0.475

6 437 583
0.675

✓
✓

✓
✓
6 437 583
0.675

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are clustered at
bank-level. T-statistics are reported in parenthesis.

ECB Working Paper Series No 314361Bank-level analysis over longer period 2015q1 to 2024q3

To test whether our findings hold more broadly, we also investigate the relationship

between geopolitical risk and bank lending at a bank level (rather than at the bank-

firm level) over a longer period of time, i.e., between 2015Q1 and 2024Q3. Rather than

focusing specifically on the Russian invasion of Ukraine, this analysis employs a panel

setting with bank controls and different sets of fixed effects, using the following regression

specification:

yb,t = β1BGP Rb,t−1 + β2Xb,t−1 + µb + δt + γc,t + εb,t

(8)

where the dependent variable yb,t is the logarithm of the total outstanding amount

of lending to non-financial corporates. The key variable of interest is BGP Rb,t−1 which

is the lagged time-varying BGPR. Unlike the baseline specification, which focuses on the

variation in the GPR between the quarters immediately before and after the invasion,

the key variable of interest here is BGP Rb,t−1 which is the level of the BGPR for bank

b at each point in time t − 1 over the entire sample period.25

Xb,t−1 is the same set of lagged bank controls used in the baseline specification. This

alternative specification also includes a number of fixed effects, specifically, µb controls

for time-invariant bank fixed effects, δt denotes a vector of time fixed effects, while γc,t

controls for country×time effects.

The results indicate that a one standard deviation rise in the BGPR (i.e. 1.18) is

associated on average with a decline of 7.67% in the banks’ loan amount to NFCs. Thus,

these results are consistent with those of the baseline regression, albeit, in this setting

we cannot determine if this relationship is causal.

25This analysis uses the bank-level geopolitical risk index as introduced by (Dieckelmann et al.,

2025).

ECB Working Paper Series No 314362Table A3: Bank level - loans non-financial corporations

L.BGPRb,t

L.size (log)b,t

L.capital tier1 ratiob,t

L.deposit-to-liability ratiob,t

L.ROAb,t

L.cash-to-asset ratiob,t

L.provisions-to-loans ratiob,t

Dependent variable: log(loans to NFCs)

(1)

(2)

(3)

(4)

-0.011
(0.014)

-0.076*
(0.043)

-0.062*
(0.036)

-0.062**
(0.031)

0.989***
(0.174)

0.989***
(0.076)

-0.001
(0.001)

-0.001***
(0.000)

0.009
(0.007)

0.009***
(0.003)

0.018
(0.017)

0.018*
(0.010)

-0.009***
(0.003)

-0.009***
(0.002)

-0.396
(0.388)

-0.396
(0.356)

Bank FE
Time FE
Country × Time FE
Double cluster standard errors
N
R²

✓
✓

✓
✓
✓

✓
✓
✓

11,939
0.943

11,934
0.948

11,406
0.955

✓
✓
✓
✓
11,406
0.955

Significance levels are: * p < 0.10 ; ** p < 0.05 ; *** p < 0.01. Standard errors are
clustered at bank-level (except column (4) double clustered bank-time). T-statistics
are reported in parenthesis.

ECB Working Paper Series No 314363Acknowledgements 

The authors are thankful for the helpful comments and discussions provided by Ricardo Correa, Jesús Fernández-Villaverde, Philip 
Lane, Massimo Ferrari-Minesso, Georgios Georgiadis, Maciej Grodzicki, Sebastian Horn, Ethan Ilzetzki, Helinä Laakkonen (discussant), 
Simona Manu, Arnaud Mehl, conference and seminar participants at the European Central Bank, at Ghent University, at the University 
of Padua and at the Eighth Annual Workshop of the Analysis Working Group of the ESRB and the Macroprudential Analysis Group of 
the ECB. All errors are ours. The findings, views and interpretations expressed herein are those of the authors and should not be 
attributed to the Eurosystem, the European Central Bank, its Executive Board, or its management. The dataset used in this paper 
contains confidential statistical information. Its use for the purpose of the analysis described in the text has been approved by the 
relevant ECB decision making bodies. All the necessary measures have been taken during the preparation of the analysis to ensure the 
physical and logical protection of the information. 

Pauline Avril 
European Central Bank, Frankfurt am Main, Germany; email: pauline_lucile.avril@ecb.europa.eu 

Peter McQuade 
European Central Bank, Frankfurt am Main, Germany; email: peter.mcquade@ecb.europa.eu 

Cosimo Pancaro 
European Central Bank, Frankfurt am Main, Germany; email: cosimo.pancaro@ecb.europa.eu 

Alessio Reghezza 
European Central Bank, Frankfurt am Main, Germany; email: alessio.reghezza@ecb.europa.eu 

© European Central Bank, 2025 

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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. 

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ISSN 1725-2806 

doi: 10.2866/2690723 

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