Volume 23 
Issue 4 Thematic Issue: The Economic and 
Social Impacts of Covid-19 
Article 5 
12-2021 
A First Peek at Firms’ Cash Flow Dynamics in the Pandemic Year: A First Peek at Firms’ Cash Flow Dynamics in the Pandemic Year: 
A Lesson Learned? A Lesson Learned? 
Ana Oblak 
University of Ljubljana, Faculty of Law, Ljubljana, Slovenia, ana.oblak@pf.uni-lj.si 
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Recommended Citation Recommended Citation 
Oblak, A. (2021). A First Peek at Firms’ Cash Flow Dynamics in the Pandemic Year: A Lesson Learned?. 
Economic and Business Review, 23(4), 273-285. https://doi.org/10.15458/2335-4216.1292 
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ORIGINAL ARTICLE
A First Peek at Firms’ Cash Flow Dynamics in the
Pandemic Year: A Lesson Learned?
Ana Oblak
University of Ljubljana, Faculty of Law, Ljubljana, Slovenia
Abstract
Usingacomprehensivedatabaseoffinancialdataanddataonpublicsupport,weaimatdocumentingtheactual(andnot
predicted) effects of the Covid-19 pandemic on firms' liquidity. A drain of the non-financial corporations’ liquidity was
unprecedented and highly asymmetric across sectors. A simple descriptive analysis enables us to evaluate (partially) the
effectivenessofsupportmeasuresandtoprovideinsightsonhowwell-targetedsupport measureswerefromthesectoral
perspective. Acting in concert, the governments and the European Union (EU) institutions concerned seem to succeed in
preventingmassiveilliquidity(fornow).Crisismeasuresweretargetedmostlyatfirmswithpositivecashflowinthepre-
pandemicyearandensuredadditional3.4percent offirmsfromtheanalysedsectorstosustainpositivecashflowand0.6
percentoffirmstorecover.Strikingly,theshareofinactivefirmsdecreasedin2020comparedto2019,whichmightindicate
thatmeasuressupporteddefactodeadcompanies.Consideringtheproportionoffirms,themostvulnerablesectorbenefited
most,butnotwhenwethinkaboutareductionincashflowcompensatedforwithdirectgrants.Theapproach“whateveris
necessary”inaformof “flat”publicsupportmightthusleadtonotoptimallytargetedbeneficiaries.
Keywords: Liquidity, Covid-19, Crisis measures, Cash flow, Non-financial corporations
JEL classification: E61, G18, G21
Introduction
A
fall in economic activity related to the
pandemic was as unprecedented as was the
drain of the non-financial corporations’ liquidity.
The pandemic containment measures and related
costs, a drop in foreign and domestic demand,
supply chain distortions, and uncertain economic,
social and political circumstances, all contributed to
an abrupt deterioration of cash flow dynamics.
Avoiding (also) high social and economic costs of
bankruptcies and illiquidity, the policy response
was exceptional in size and aligned, horizontally
(monetary policy, fiscal policy and macroprudential
policy) and vertically (at a national and suprana-
tional level). The number of bankruptcies or clo-
sures of non-financial corporations in Slovenia only
increased in the sector hit hardest by the pandemic,
namely accommodation and food service activities
(Bank of Slovenia, 2021).
1
The shock was highly asymmetric with more than
half of firms likely to experience liquidity shortages
in the contact-intensive sectors such as the accom-
modation and food service activities, transports and
arts, entertainment, and recreation, but less than 20
percent in utilities, information and communication
and professional services sector (Demmou et al.,
2021). A study of liquidity and solvency of non-
financial corporations in 26 European countries by
Ebeke et al. (2021) documents similar, yet more
conservative, results. The complexity of production
networks (motor vehicle industry), intangible-in-
tensity and limited reliance on debt finance were
also shown to weaken/strengthen the resilience to
the shock posed by the pandemic.
Early literature focuses on the prediction of illi-
quidity of non-financial corporations with and
without policy intervention, with estimated time
needed to become constrained and the size of the
liquiditygap.AccordingtoDemmouetal.(2021),up
Received 1 June 2021; accepted 8 October 2021.
Available online 23 December 2021.
E-mail address: ana.oblak@pf.uni-lj.si
1
Thisisinpartattributabletothegovernmentmeasuresregardingbankruptcyproceedingsincaseswhenthefirm'sinsolvencyisduetothedeclaration
of the epidemic (Bank of Slovenia, 2021).
https://doi.org/10.15458/85451.1292
2335-4216/© 2021 School of Economics and Business University of Ljubljana. This is an open access article under the CC-BY-NC-ND license (http://creativecommons.
org/licenses/by-nc-nd/4.0/).

to38percentoffirmsin14analysedcountrieswould
face liquidity shortfalls in 10 months without gov-
ernment intervention. Direct payments to (at least
partially) cover labour costs and debt moratorium
policies seem to be most beneficial. Using a simple
method based on firms’ balance sheets, Schivardi
and Romano (2020) predict illiquidity for the whole
population of Italian firms, month-by-month.
Around 200,000 companies were projected to be
illiquid at the peak which gives rise to a liquidity
shortfall of some 72 billion euros. It is assessed that
due to government guarantees for bank loans,
almost all firms would be able to cover their
liquidity gap. Ebeke et al. (2021) showed that policy
measures, if implemented as designed, would assist
in reducing liquidity risk substantially. McGeever
et al. (2020) and Demmou et al. (2021) additionally
shed light on sectoral heterogeneity.
Anotherstanceofliteraturetacklesthefitbetween
support supplied and support needed, considering
size, instruments and sectors. Cirera et al. (2021)
show that firms and sectors, which experienced a
largerreductioninsales,weremorelikelytoreceive
support, but also that firms not experiencing any
shock received support. The recipients were mis-
targeted, most likely due to barriers to access policy
support and the limited capacity of public in-
stitutions to target. The probability to receive sup-
port was lower for small firms, which were more
susceptible to the shock, but less informed (Apedo-
Amahetal.,2020).Boleetal.(forthcoming)alsofind
evidence that support measures were inappropri-
ately targeted when sectors are considered. They
further observe that support measures do not
compensate for the non-pharmaceutical in-
terventions impact. Bircan et al. (2020) analyse how
suitable are financial debt instruments to ease
liquidity constraints of small and medium-sized
enterprises (SMEs) in 16 emerging markets, and
three Western European comparator countries.
Firms’ debt capacity is considered a crucial factor.
Accordingtothestudy,SlovenianSMEsarenotina
most favourable position, having limited capacity
for additional debt accumulation and low liquidity.
The key concern raised relates to (potential) over-
indebtedness and possibly mistargeted support
measures.
The impact of economic policies on firms’
liquidity and debt accumulation in Slovenia during
the previous crisis episode is well documented by
Bole et al. (2014). The authors analyse cash flow
migration and illiquidity contagion of firms in bust,
boom, and recovery episodes. It was shown that
liquidity of non-financial corporations deteriorated
sharply in the first years after the crisis and was
slow to recover. Inability of banks to refinance
foreign loans, reduction in informational capital
(“abilityofbankstoevaluatefuturesolvencyoftheir
clients”), because of high uncertainty and procycl-
ical macroprudential policy intervention (most
notably in the recovery period) lead to increased
collateralization and credit rationing. This limited
the access of non-financial corporations to needed
liquidity and hindered their recovery. The oppor-
tunitycostsofthesuboptimalpolicyresponse,when
timing, sequencing and calibration are considered,
seem to be high.
In this paper, we analyse cash flow dynamics of
the total population of Slovenian non-financial cor-
porations. Following the methodology employed by
Bole et al. (2014), cash flow status with and without
policy measures in the year of the outbreak of the
pandemic conditional on the cash flow status in the
pre-pandemic year is studied. Using a simple
descriptive analysis, we first document the actual
and not predicted effects of the pandemic on firms’
liquidity and performance. Second, the analysis
enables us to evaluate the effectiveness of govern-
ment (mostly) employment support through direct
grants and to provide insights on how well-targeted
support measures are from the sectoral perspective.
In short, who the beneficiaries are. The evaluation
is, however, partial since the epidemic was pro-
longed until 15 June 2021 in Slovenia, and a large
part of the state aid which was approved and
accounted for in financial statements for 2020, was
paid out in 2021. Harmonized with our methodol-
ogy, it has a positive effect on the cash flow only in
2021. Third, building upon the study by Bole et al.
(2014) allows us to draw parallels to the Great
Recession. The question of policy response and its
horizontal and vertical alignment is addressed.
The paper is organized as follows. First section
providesan(non-exhaustive)overviewofmonetary,
fiscal and macroprudential policy actions at a na-
tionalandsupranationallevel.Inthesecondsection,
we describe the data and empirical method. The
thirdsectionisdedicatedtodiscussionoftheresults
with reference to the Great Recession and
conclusion.
1 A review of policymakers’ responses to the
COVID-19 pandemic
The policy response to the Covid-19 pandemic
was immediate and exceptional in its size. In antic-
ipation of an unprecedented economic disaster, the
sentiment towards austerity has turned on its head
and the president of the European Commission,
Ursula von der Leyen, underpinned that in a
274 ECONOMIC AND BUSINESS REVIEW 2021;23:273e285

“Draghiway”
2
:“Wewilldowhateverisnecessaryto
support the Europeans and the European economy
(European Commission, 2020a).“
3
The European Commission acted promptly,
focusing its activities mainly on providing the
Member States with flexibility and financial re-
sources to act. Since national budgets were antici-
pated to be the main source of fiscal stimulus, the
Commission activated the general escape clause of
the Stability and Growth Pact, allowing govern-
ments to “depart from the budgetary requirements
that would normally apply under the European
fiscal framework”. It is expected to be extended
through 2022. Under the Temporary Framework,
which was adopted in just two days, amended five
times, and extended until 31 December 2021, the
approval process of state aid notifications was
facilitated,andtheruleswererelaxed(e.g.extension
of the ceilings for state aid and scope to recapitali-
zation and subordinated debt measures).
4
Up to 200
billion euros were made available against a 25-
billion-euro governments’ guarantee to the Euro-
pean Investment Bank (EIB) and delayed re-
payments of loans were allowed for to avoid
disruptions in funding and illiquidity of the non-
financial corporations, especially SMEs. Further,
1,211 billion euros were secured through the
2021e2027 Multiannual Financial Framework and
additional 807 billion euros through a temporary
recovery instrument NextGenerationEU, financed
by borrowing at the EU level. Under the Recovery
and Resilience Facility Slovenia is to receive 1.8
billioneurosingrantsand705millioneurosinloans
(European Commission, 2020b).
The Eurosystem responded with an expansion in
securitiespurchasesandrefinancing operations,the
collateral terms changed. The participation in refi-
nancing operations of Slovenia banks was yet lower
than the euro area average. According to Bank of
Slovenia (2020), this might be due to the excess
liquidity and ample and growing non-banking
sector deposits available (75.5 percent of total lia-
bilities in June 2020). “Despite the risk of sudden
withdrawals, this funding is more stable than
wholesale funding and less subject to external
shocks” (Bank of Slovenia, 2020).
In Table 1, we present a non-exhaustive overview
ofmacroprudentialpolicyduringthepandemic.The
cumulative number of macroprudential policy
tightening actions (þ1) less the number of macro-
prudential policy loosening actions ( 1) is given.
Shifts in macroprudential policy stance are shown
by 10 categories, harmonized with the Macro-
prudential policies evaluation database (MaPPED)
(Budnik & Kleibl, 2018). To enable comparison, we
additionallypresentthepolicyresponsetotheGreat
Recession, which was documented by Prasnikar
et al. (2021). Table 1 shows that at the supranational
Table 1. Macroprudential interventions.
Slovenia Slovenia European institutions
concerned
boom
2007e2008
bust
2009e2010
recovery
2011e2013
Covid-19 pandemic
2020e2021
Covid-19 pandemic
2020e2021
capital buffers 0 2 0 1  1
lending standards restrictions 1 0 0 10
limits on credit growth and volume 0 0 10 0
limits on large exposures and concentration 1 100 0
liquidity requirements and limits on currency
and maturity mismatch
0000  1
loan-loss provisioning 1120  1
minimum capital requirements 1 0 0 0  2
leverage ratio 0 0 0 0  1
risk weights 0 0 0 0  1
other measures 1 0 2 0  1
Note: Macroprudential interventions; cumulative number of tightening actions (þ1) less number of loosening actions ( 1) in the indi-
cated period.
Source: Macroprudential Policies Evaluation Database (MaPPED); Prasnikar et al., 2021; author's calculations
2
Inthefaceofasovereigndebtcrisisin2012,MarioDraghisaid“withinourmandate,theECBisreadytodowhateverittakestopreservetheeuro.And
believe me, it will be enough” (European Central Bank, 2012).
3
Following the Great Recession and excessive budget deficits resulting from it, many developed countries bet on fiscal consolidation policies. Only in
2013, Blanchard and Leigh (2013) proposed that the short-term multipliers in response to the fiscal consolidation during 2010 and 2011 were larger in size
than previously believed.
4
A similar framework was adopted following the 2008 financial crisis.
ECONOMIC AND BUSINESS REVIEW 2021;23:273e285 275

levelthemacroprudentialpolicyrequirementswere
eased. The banking regulators issued in-
terpretations and guidance to ensure additional
flexibility in the existing regulatory (CRR) and ac-
counting framework (introduction of IFRS 9). The
European Banking Authority (EBA) and the Euro-
pean Central Bank (ECB) alike postulated that “the
application of support measures should not auto-
matically lead to stricter prudential treatment of
loans when the debtor's financial situation is not
deteriorating. When banks approve a loan morato-
rium that meets the conditions for a general pay-
ment moratorium, this does not automatically
require the reclassification of the exposure as
forborne or defaulted.” Favourable treatment intro-
duced in the area of capital requirements for credit
risk is also being applied (Bank of Slovenia, 2020;
Bank of Slovenia, 2021).
5
The ECB granted operational relief in the form of
adjusting timetables, processes and deadlines for
each individual bank, and more significantly, eased
capital buffer and capital composition re-
quirements.
6
Estimated capital relief amounted to
120billioneurosand“couldbeusedtoabsorblosses
or potentially finance up to 1.8 trillion euros of
lending” (European Central Bank, 2020). Also, the
national macroprudential authorities acted (or were
expected to act) countercyclically opting to lower or
fully release the countercyclical capital buffer, sys-
temic risk buffer or other systemically important
institutions buffer.
7
The Bank of Slovenia kept its
macroprudential policy toolkit nearly unchanged. It
entails macroprudential restrictions on household
lending, the countercyclical capital buffer, which
stood at zero already before the outbreak of the
pandemic, the buffer for other systemically impor-
tant institutions, a macroprudential liquidity mea-
sure (the gross loans to deposits flows
8
)a n d
currently irrelevant macroprudential caps on de-
posit interest rates. Restrictions on profit distribu-
tions by banks and by leasing companies were
introduced in April 2020 (amended in February
2021) and a temporary exclusion of a decline in
income caused by the pandemic from the credit-
worthiness evaluation was allowed for (Bank of
Slovenia, 2020; Bank of Slovenia, 2021).
ThefirstresponseoftheSloveniangovernmentto
the Covid-19 pandemic was the law allowing the
deferral of loan payments for at least 12 months for
non-financial corporations, sole traders, farmers
and private individuals. It was followed by nine
fiscal stimulus packages, which amounted to almost
5 billion euros in the period until June 2021. Table 2
summarizes support measures and financial re-
sources provided. Employment support accounted
for roughly 36 percent, followed by measures to
maintain liquidity, which amounted to 1.4 billion
euros or 27.7 percent. Due to an extensive fiscal
stimulus,thegeneralgovernmentsectorreturnedto
adeficitof7.7percentofGDPin2020fromasurplus
of 0.4 percent in year 2019. The projected general
government deficit in 2021 is 7.9 percent of GDP
down from 8.6 percent owing to more favourable
economic outlook. Also, in 2008 and 2009 the gov-
ernment acted countercyclically, but the interven-
tion was less intensive (Republic of Slovenia Fiscal
Council, 2021; Statistical Office of the Republic of
Slovenia, 2021).
2 Data
Our empirical analysis draws upon the study of
cashflowdynamicsandilliquiditycontagionofnon-
financial companies in Slovenia during the Great
RecessionbyBoleetal.(2014).Usingcomprehensive
databases of firm-level financial data and data on
stateaidandemployingasimpledescriptiveanalysis,
we first document the effects of the pandemic on
firms’ liquidity and performance. Second, cash flow
statuswithandwithoutpolicymeasuresintheyearof
theoutbreakofthepandemicconditionalonthecash
flowstatusinthepre-pandemicyearisstudied.
We use three sources of data in the empirical
analysis. The first one is a comprehensive database
of financial data provided by the Agency of the
Republic of Slovenia for Public Legal Records and
RelatedServices(AJPES).Thedataencompassesthe
5
“TransitionalperiodforlimitingtheimpactofIFRS9provisionsontheregulatorycapitalofbanksisbeingextendedbytwoyears(untiltheendof2024),
thereby mitigating any impact on bank lending capacity from a sudden significant increase in expected credit-loss provisions during the economic
downturncausedbyCovid-19(BankofSlovenia,2020).”Asaninsight,afterthefirstquarterof2010,theBankofSlovenialaunchedaprocessofaccelerated
implementation of stricter capital requirements (Bole et al., 2014).
6
“Temporarily,banksareallowedtooperatebelowthelevelofcapitaldefinedbythePillar2Guidance,thecapitalconservationbufferandtheliquidity
coverage ratio.” Furthermore, banks are also “allowed to partially use capital instruments that do not qualify as Common Equity Tier 1 (CET1) capital, for
example Additional Tier 1 or Tier 2 instruments, to meet the Pillar 2 Requirements (P2R)” (European Central Bank, 2020).
7
The countercyclical capital buffer was lowered or fully released in Belgium, Denmark, France, Germany, Slovakia, Ireland, Norway, Sweden, Iceland,
Lithuania, and the UK. The macroprudential authorities in Estonia and Finland used other instruments at their disposal and reduced the systemic risk
buffer rates to zero, while the Netherlands reduced the existing rates for three institutions. Finland and the Netherlands additionally opted to selectively
reduce the buffer for other systemically important institutions (Bank of Slovenia, 2020).
8
“Amacroprudentialmeasureknownasgrossloanstodepositsflows(GLTDF)recommendsthatbankswithapositiveannualinflowofdepositsbythe
non-banking sector should have an annual increase in lending to the non-banking sector (before impairment) that is not negative. Preventing the banks'
excessive reliance on unstable sources of funding (Bank of Slovenia, 2020).”
276 ECONOMIC AND BUSINESS REVIEW 2021;23:273e285

Table 2. Covid-19 measures adopted by the Slovenian government.
Category Description of measures 2020 MarcheDecember
in million EUR
2021 JanuaryeJune
in million EUR
Covid measures 2,910 2,049
Measures to preserve jobs The partial coverage of wage compen-
sation during temporary lay-off (fur-
loughing),thecrisisbonus,themonthly
basic income and relief from the pay-
mentof socialsecuritycontributionsfor
claimants who were unable to pursue
theirbusinessactivities,temporarycash
assistance for job loss from thefirst day
ofunemployment untiltheliftingofthe
emergency measures.
1,137 639
Measures for the smooth
operation of public services
Employee bonuses, control of the
epidemic (protective equipment, etc.),
measures in education, sport and cul-
ture, compensation for healthcare ser-
vice providers.
508 728
Measures to maintain
consumption and social position
A 200-euro voucher per adult and a 50-
euro voucher per child in 2020 and an
additional 100-euro voucher in 2021 to
bespentinaccommodationinSlovenia,
solidaritybonusforvariousgroups(e.g.
from 130 to 300 euros for pensioners).
291 50
Other expenditure 67 111
Measures to maintain liquidity 906 466
Deferred and instalment payments
of tax
Theoptionofdeferringtaxliabilitiesfor
up to 24 months or paying liabilities in
24 monthly instalments. A two-month
extensionofthedeadlineforsubmitting
the return for the personal income tax
prepayment for business activities and
thetaxreturn,athree-monthdeferralof
the payment of social security contri-
butions for self-employed persons who
have no other employees, the deferral
of the prepayment of personal income
tax on business activities for April and
May 2020 (until April 2021).
219 18
Uncalculated and unpaid advance
payments of corporate income
tax liabilities
171 0
Reimbursement of fixed costs The direct grants to all companies irre-
spective of their size and of the sector,
whichhavesufferedadeclineofatleast
30% in their revenue in the period be-
tween October and December 2020
compared to the same period in 2019.
Also applies in first half of 2021.
0 296
Guarantees Government guarantees for a morato-
rium on payments deriving from all li-
abilities under loan agreements for up
to 12 months for non-financial corpo-
rations, sole traders, farmers and pri-
vate individuals. Loans made from
public funds, encompassing additional
financing by SID banka and additional
guarantees for loans and liquidityloans
to SMEs by the Slovene Enterprise
Fund, the guarantee scheme for corpo-
ratesandthecorporatedebtrepurchase
mechanism.
192 140
Liquidity loans 305 66
Deferred payment of loan
liabilitiese SID Bank
19 0
Source: Republic of Slovenia Fiscal Council, 2021; Bank of Slovenia, 2020.
ECONOMIC AND BUSINESS REVIEW 2021;23:273e285 277

total population of non-financial companies, liable
to report under the Companies Act for national
statistics purposes. That is 68,125 limited and un-
limited liability companies (including listed com-
panies) after the formal reorganization of the status
of a company, economic interest groupings and
main offices of foreign business entities
9
in 2020,
67,178 in 2019, 66,749 in 2018, and 66,470 in 2017.
Based on the list of companies at the beginning of
bankruptcy, liquidation
10
or termination, also pro-
vided by AJPES, the database was supplemented
(updated) by 546 observations in 2021, 964 in 2020,
1,205 in 2019, 1,259 in 2018, and 1,210 in 2017. The
data on the state aid to mitigate the effects of the
pandemicwereretrievedfromERAR,anapplication
for the portrayal of public money use in the Re-
public of Slovenia.
Tocategorizecompaniesinsegments,wedrawon
the study by Bole et al. (forthcoming), which eval-
uatestheCovid-19supportmeasurestoalleviatethe
cost of social distancing at a sectoral level. The au-
thors consider two main characteristics, deter-
mining the economic losses of a particular sector
and consequently classification, namely essentiality
of the sector and the ability to organize work from
home. Utilities and manufacturing are sectors not
susceptible to a large demand shock and were
deemed essential, thus could stay open despite the
policy of constrained social mobility. A large de-
mand shock due to the policy of constrained social
mobility is common to a third segment of service
sector, which includes inessential (e.g. hospitality,
arts) or essential sectors (e.g. transportation). Other
service sectors were affected by the policy of con-
strained social mobility but had the ability to orga-
nize work from home. Taking into account non-
pharmaceutical mitigation measures, we could
categorize companies in five homogenous seg-
ments: manufacturing, construction, utilities, non-
vulnerable service segment and vulnerable service
segment. The manufacturing segment covers all
companies with economic activities in section
manufacturing of NACE Rev. 2. The second ana-
lysed segment construction includes companies
classified in section construction. Utilities encom-
pass sections electricity, gas, steam and air condi-
tioning supply, as well as water supply, sewerage,
waste management and remediation activities of
NACE Rev. 2. The non-vulnerable service segment
comprises all companies with economic activities
Table 3. List of variables and calculation.
Variable Calculation of the variable
Financial debt (Long-term financial liabilitiesþ Short-term financial liabilities)
/Total assets
Operating liabilities Short-term operating liabilities/Total assets
Operating receivables Short-term operating receivables/Total assets
Collateral (Landþ Buildingsþ Other equipment and machinery)
/Total assets
Operating cash flow (Operating profit - Operating lossþ Write-offs in value)
/Total assets
Operating cash flow without support measures (Operating profit - Operating lossþ Write-offs in value - State aid
11
)/Total
assets
Manufacturing A dummy variable with the value 1 if a company is categorized in
manufacturing segment (section C of NACE Rev.2)
Construction A dummy variable with the value 1 if a company is categorized in con-
struction segment (section F of NACE Rev.2)
Utilities A dummy variable with the value 1 if a company is categorized in utilities
segment (sections D, E of NACE Rev. 2.)
Non-vulnerable services A dummy variable with the value 1 if a company is categorized in non-
vulnerable service segment (sections K, L, M, N of NACE Rev. 2.)
Vulnerable services Adummyvariablewiththevalue1ifacompanyiscategorizedinvulnerable
service segment (sections G, H, I, J, R, S, T of NACE Rev. 2.)
Cash flow status A categorical variable with the value 0 if a company is inactive,
12
1ifithas
operating cash flow <0, 2 if it has operating cash flow >0
Cash flow without support measures A categorical variable with the value 0 if a company is inactive, 1 if it has
operating cash flow without support measures <0, 2 if it has operating cash
flow without support measures >0
9
Banks, insurance companies, stock exchange, investment funds and certain other financial and investment companies that do not use the accounting
standard for companies are not included.
10
Companies in insolvency proceedings (bankruptcy, liquidation) are not required to submit annual reports.
278 ECONOMIC AND BUSINESS REVIEW 2021;23:273e285

classified in sections: financial and insurance activ-
ities,realestateactivities,professional,scientificand
technical activities, and administrative and support
serviceactivities.Thefifthsegmentisthevulnerable
service segment, and it includes firms from eco-
nomic activities in section wholesale and retail
trade, repair of motor vehicles and motorcycles,
transportation and storage, accommodation and
food service activities, information and communi-
cation, arts, entertainment and recreation, other
service activities, and activities of households as
employers.Agriculture,forestryandfishing,mining
and quarrying, government sectors (O-Q) and ac-
tivities of extraterritorial organisations and bodies
are excluded from the analysis.
Table 3 documents definitions and calculation of
variables. In the second column of Table 3, with
calculations of the variables, we use item names
identical to the ones in the original AJPES database.
3 Results
3.1 Performance of companies
InTable 4,we presentdescriptive statisticsfor the
main variables by segment for the period
2018e2020. Financial debt, short-term operating li-
abilities, short-term operating receivables, collat-
eral, operating cash flow and operating cash flow
without support measures are documented for
manufacturing, construction, utilities and both ser-
vice segments separately at the first-lower quartile
(p25), median (p50) and third-upper quartile (p75).
Also, the number of observations is given.
Table4andFigs.1e5makeitevidentthatsupport
measurespreventedahugedropincashflowforall
quartiles of firms and all segments, but failed to
prevent its worsening (except for utilities), when
only direct grants are considered. A median firm in
the vulnerable service sector recorded a decline in
cash flow by 29 percent. Without policy interven-
tion, the cash flow would decline by more than 50
percent for the firms mentioned, 42 percent for a
medianfirminthenon-vulnerableservicesector,36
percent in construction and 27 percent in
manufacturing. Taking into account the extent to
which support measures were able to sustain cash
flow, the median firm of the most vulnerable
segment benefited the least in relative terms
compared to other segments. The firms with stron-
gercashflowposition(upper-quartilefirms)proved
tobemoreresilienttotheshockacrossallsegments.
Adeclineincashflowwasat13e16percentwithout
measures and at 6e9 percent with measures. As
already evident vulnerable service segment firms
were indeed hit hardest, even more so when the
firms in the lower quartile are considered. Their
cash flow from operations was negative ( 0.6
percent of the total balance sheet sum) already in
the pre-pandemic years and with the pandemic it
additionallydeteriorated( 2.8percentwithsupport
measures and  4.9 percent without). A quarter of
non-vulnerable service sector firms with the weak-
est cash flow exhibit a similar pattern, apart from a
milder drop in the pandemic year. The solvency of
these two subsegments might be endangered. Prior
to the pandemic, manufacturing firms had stronger
cash flow compared to construction and services,
but did not prove more resilient to the shock.
Considering a reduction of cash flow in terms of
percent of balance sheet sum, a lower-quartile
manufacturing firm followed a lower-quartile
vulnerable service segment firm. The cash flow was
rather stable in 2019 compared to 2018 for all
quartiles of firms and all segments.
Financial debt variable is especially relevant,
when assessing the ability of non-financial corpo-
rations, which are not able to sustain positive cash
flow from operations, to access debt finance. This
includes the instruments made available by the
government. According to Bircan et al. (2020),
SlovenianSMEshavelittleroomforadditionaldebt.
Debt was already on the rise throughout the whole
observed period in manufacturing, construction,
and services (both non-vulnerable and vulnerable
sectors), apart from manufacturing in the pandemic
year. The median and upper quartile levels of
financial debt in manufacturing decreased to 11.9
percent and to 35.2 percent of the total balance
sheet, respectively. Manufacturing firms indeed re-
ported a decrease in demand for bank loans for
11
The data on state aid was retrieved from ERAR and represents the state aid in the form of direct grants paid to the non-financial corporations in 2020
rather than the state aid approved for the year 2020. The latter is presented in the profit and loss account in the AJPES database, but after a thorough
inspectionsignificantdiscrepancieswerefoundintheapplicationofthestandard15.AccordingtoNote1totheSlovenianAccountingStandard15(Official
GazetteoftheRepublicofSlovenia,No.63/20),“organizationsthatwillobtainanystateaid…forthecontainmentoreliminationoftheconsequencesofthe
Covid-19 epidemic, must record the state aid received under other operating revenues (Pojasnilo 1, 2020)”. This would correspond to the item AOP124
Subsidies, grants, allowances, compensation, and other revenues associated with products and services in our database. Instead of AOP124, the accurate
data on state aid paid to the non-financial corporations in 2020 are used as a proxy for Covid-19 support measures. The state aid paid indeed has a direct
effect on liquidity in contrast to the state aid approved. It should, however, be noted that, conceptually, this differs from our approach to evaluating cash
flow status, where we assume that firms generate their cash inflows (outflows) from their sales (costs) rather than from their short-term operating re-
ceivables (short-term operating liabilities) and cash.
12
Inactive firms are defined as firms at the beginning of bankruptcy, liquidation, or termination.
ECONOMIC AND BUSINESS REVIEW 2021;23:273e285 279

investments in the first half of 2020 (Bank of
Slovenia, 2020). Utilities segment firms, which had
the highest level of financial debt, deleveraged in
the observed period. The level of financial debt of
highly indebted firms (third quartile) in the
vulnerable service sector increased most by 3.6
percentage points in the pandemic year. Also, the
survey data provided by the Bank of Slovenia show
that accommodation and food service activities
sectors, classified in vulnerable service segment,
increased their indebtedness most. A median and
an upper quartile construction segment firm
increased their indebtedness in 2019 as well as in
2020, most likely due to the favourable real-estate
market conditions.
As anticipated, manufacturing and utilities firms
have higher available collateral and thus higher
capacity to borrow in the times of crisis than firms
from construction and services. More than a half of
the companies from construction, non-vulnerable
service segment and vulnerable service sector had
no collateral available, and only one quarter of
companies had collateral higher than 12.1 percent,
5.2 percent, and 12.0 percent of total assets,
respectively.
Almost uniformly across the distribution, short-
term operating liabilities declined for firms from all
segments in the observed period. Short-term oper-
ating liabilities, which could be understood as an
alternative source of finance, rose only for utilities
segmentfirmsinthelowerquartileinthepandemic
year. With the pandemic, the process of deleverag-
ing slowed down in manufacturing, construction,
and for the upper-quartile firms in services. Service
sector firms with lower intercompany indebtedness
decreased their indebtedness towards suppliers at a
higher pace after the outbreak of the pandemic.
Across the whole distribution, construction firms
depend on the intercompany debt most. A rather
high level of operating liabilities was evident also
for firms from the upper quartile in vulnerable
service segment firms.
Analysing short-term receivables, a similar
pattern of decreasing level of receivables can be
observed. A drop following the outbreak of the
pandemicwasespeciallypronouncedfortheupper-
quartilefirmsinmanufacturingandnon-vulnerable
service segment firms, and for the half of the firms
with higher level of short-term receivables in con-
struction. Considering net receivables (short-term
receivables less short-term operating liabilities),
vulnerable service segment of the first quartile
decreasedtheirnetborrowingpositions,asopposed
to an increase in other segments. Except in utilities,
net lenders managed to decrease their positions in
the pandemic year.
3.2 Cash flow dynamics
In Table 5, we document the cash flow migration
offirmsinthepre-pandemicyearsof2018and2019,
and in the pandemic year of 2020, drawing on the
Table 4. Financial and intercompany debt, cash flow with and without state aid and potential collateral.
Manufacturing Construction Utilities Non-vulnerable
service segment
Vulnerable
service segment
Year 2018 2019 2020 2018 2019 2020 2018 2019 2020 2018 2019 2020 2018 2019 2020
Total number of
companies
N 8,153 8,189 8,328 7,134 7,219 7,511 945 938 928 19,901 20,074 20,226 27,521 27,636 28,003
Cash flow p25 0.018 0.021 0.002 0.000 0.000  0.001 0.011 0.009 0.015  0.003  0.005  0.011  0.005  0.006  0.028
p50 0.089 0.088 0.074 0.073 0.076 0.063 0.086 0.085 0.089 0.045 0.045 0.035 0.055 0.055 0.039
p75 0.166 0.162 0.150 0.165 0.165 0.152 0.150 0.157 0.167 0.142 0.140 0.131 0.148 0.146 0.133
Cash flow without
support measures
p25  0.002  0.009 0.012  0.021  0.049
p50 0.064 0.049 0.086 0.026 0.027
p75 0.141 0.140 0.165 0.120 0.123
Financial debt p25 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
p50 0.119 0.127 0.119 0.043 0.069 0.075 0.237 0.230 0.185 0.019 0.029 0.033 0.080 0.097 0.109
p75 0.343 0.357 0.352 0.270 0.298 0.315 0.595 0.569 0.531 0.373 0.392 0.410 0.408 0.422 0.456
Short-term operating
receivables
p25 0.107 0.097 0.093 0.131 0.115 0.107 0.020 0.018 0.015 0.031 0.029 0.027 0.054 0.053 0.061
p50 0.225 0.211 0.202 0.350 0.330 0.301 0.085 0.083 0.086 0.173 0.166 0.149 0.206 0.201 0.196
p75 0.413 0.398 0.377 0.623 0.601 0.564 0.281 0.280 0.274 0.432 0.429 0.391 0.440 0.433 0.416
Short-term operating
liabilities
p25 0.120 0.112 0.108 0.138 0.132 0.126 0.024 0.026 0.028 0.042 0.039 0.035 0.109 0.104 0.094
p50 0.244 0.226 0.219 0.331 0.312 0.301 0.112 0.109 0.113 0.181 0.175 0.162 0.280 0.270 0.252
p75 0.458 0.430 0.427 0.586 0.561 0.548 0.328 0.311 0.299 0.442 0.419 0.403 0.563 0.546 0.533
Collateral p25 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
p50 0.078 0.092 0.090 0.000 0.000 0.000 0.060 0.056 0.069 0.000 0.000 0.000 0.000 0.000 0.000
p75 0.385 0.390 0.384 0.090 0.113 0.121 0.568 0.540 0.531 0.046 0.051 0.052 0.101 0.115 0.122
Source: AJPES, 2021; author's calculations
280 ECONOMIC AND BUSINESS REVIEW 2021;23:273e285

methodology used by Bole et al. (2014). We show
how the cash flow in the (current) year T is struc-
tured conditional on the cash flow in the previous
year T-1. Based on liquidity position, we recognize
threecategoriesoffirmsinyearT.First,firmswhich
have positive cash flow (greater than zero), second,
firms, which have negative cash flow (less than
zero), and third, firms, which are inactive. In
continuation,wecallfirmswithpositivecashflowin
year T-1, which migrate to negative cash flow in
year T, the “collapsing” firms, and firms with
negative cash flow in year T-1, which improve their
performance to positive cash flow in T, the “recov-
ering”firms.Thefiguresshownareinpercentofthe
total number of companies by segment.
From Table 5, it is evident that the Covid-19
pandemic deteriorated liquidity of firms in all seg-
ments, but utilities. The segment proved to be
Utilities 
Fig. 5. Cumulative distribution of cash flow per unit of balance sheet
withandwithoutsupportmeasuresinyears2018e2020.Source:AJPES,
2021; author's calculations.
Vulnerable service segment
Fig. 4. Cumulative distribution of cash flow per unit of balance sheet
withandwithoutsupportmeasuresinyears2018e2020.Source:AJPES,
2021; author's calculations.
Non-vulnerable service segment
Fig. 3. Cumulative distribution of cash flow per unit of balance sheet
withandwithoutsupportmeasuresinyears2018e2020.Source:AJPES,
2021; author's calculations.
0 .2 .4 .6 .8 1
-.5 0 .5
Cash flow per unit of balance sheet
Year 2020 without support measures Year 2020
8 1 0 2 r a e Y 9 1 0 2 r a e Y
Manufacturing 
Fig. 1. Cumulative distribution of cash flow per unit of balance sheet
withandwithoutsupportmeasuresinyears2018e2020.Source:AJPES,
2021; author's calculations.
0 .2 .4 .6 .8 1
-.5 0 .5
Cash flow per unit of balance sheet
Year 2020 without support measures Year 2020
8 1 0 2 r a e Y 9 1 0 2 r a e Y
Construction 
Fig. 2. Cumulative distribution of cash flow per unit of balance sheet
withandwithoutsupportmeasuresinyears2018e2020.Source:AJPES,
2021; author's calculations.
ECONOMIC AND BUSINESS REVIEW 2021;23:273e285 281

resilient to the shock caused by the pandemic with
an increase in the share of recovering firms and
firmswhichwereabletomaintainpositivecashflow
with policy intervention. There was only a minor
increaseintheshareofcollapsingfirms.Comparing
the proportions offirms by category (e.g. positive to
positive) with and without measures for years 2019
and2020,policyinterventionwasshownnottohave
a statistically significant effect in helpingfirms from
utilities to maintain their pre-pandemic liquidity
position. In continuation, the utilities segment as a
notable exception is excluded from the analysis.
The share of firms (overall) migrating to negative
cash flow increased by 5.5 percentage points
(z¼ 21.17, p¼ 0.000) to more than 30 percent in the
pandemicyearof2020.Absentmeasurestheshareof
firms with negative cash flow would surge by more
than 9 percentage points to almost 35 percent.
Alreadyinthepre-pandemicyearfirmswithnegative
cash flow accounted for more than one quarter of
bothservicesegmentfirmsandevenstrengthenedto
32.6percentofnon-vulnerableservicesectorand33.1
percentofvulnerableservicesectorin2020.Without
policy intervention, additional 3e5 percent of com-
panies in each segment would migrate to negative
cash flow. This represents around 2,400 firms, 1,200
from vulnerable service sector. Despite minor dif-
ferences across segments, the policy intervention
seems to be targeted properly for the highest share
(4.6 percent, z¼ 10.90, p¼ 0.000) of firms from the
Table 5. The cash flow migration matrix.
Year Negative to
inactive
Negative to
negative
Negative to
positive
Positive to
inactive
Positive to
negative
Positive to
positive
Total number
of companies
Manufacturing
2018 0.87 10.32 6.23 0.56 6.79 75.24 7,693
2019 0.79 10.51 6.13 0.35 6.68 75.54 7,751
2020 0.53 11.43 5.64 0.27 11.00 71.13 7,867
H
0
:p
19
-p
20
¼ 0 * * *** ***
2020 without support measures 0.53 11.89 5.19 0.27 14.22 67.90 7,867
H
0
:p
20
-p
20w
¼ 0ee *** ***
Construction
2018 1.23 12.67 8.77 0.54 7.26 69.53 6,268
2019 0.95 11.80 6.86 0.60 8.89 70.90 6,330
2020 0.56 13.55 6.88 0.38 12.63 66.00 6,618
H
0
:p
19
-p
20
¼ 0 ** *** * *** ***
2020 without support measures 0.56 14.12 6.30 0.38 16.15 62.49 6,618
H
0
:p
20
-p
20w
¼ 0 *** ***
Utilities
2018 0.79 13.80 4.26 0.22 3.25 77.67 891
2019 0.56 13.23 4.60 0.22 4.82 76.57 892
2020 0.23 12.59 5.22 0.23 4.99 76.76 882
H
0
:p
19
-p
20
¼ 0
2020 without support measures 0.23 12.70 5.10 0.23 5.90 75.85 882
H
0
:p
20
-p
20w
¼ 0ee
Non-vulnerable service segment
2018 1.02 19.25 9.42 0.55 8.22 61.54 18,482
2019 1.12 18.85 8.48 0.69 9.41 61.46 18,742
2020 0.90 20.26 8.29 0.49 12.35 57.70 19,084
H
0
:p
19
-p
20
¼ 0 ** *** ** *** ***
2020 without support measures 0.90 20.86 7.69 0.49 15.23 54.83 19,084
H
0
:p
20
-p
20w
¼ 0 e ** e *** ***
Vulnerable service segment
2018 1.19 17.66 8.97 0.55 8.44 63.19 25,293
2019 1.15 17.46 8.46 0.53 8.97 63.43 25,454
2020 0.86 18.95 7.89 0.41 14.10 57.78 25,949
H
0
:p
19
-p
20
¼ 0 *** *** ** ** *** ***
2020 without support measures 0.86 19.66 7.18 0.41 18.01 53.89 25,949
H
0
:p
20
-p
20w
¼ 0 e ** *** e *** ***
Note: A two sample proportions test is used; ***, **, and * denote statistically significant values at 1, 5, and 10% on a two-tailed test,
respectively.
Source: AJPES, 2021; author's calculations
282 ECONOMIC AND BUSINESS REVIEW 2021;23:273e285

vulnerable service segment, prevented from
migratingtonegativecashflow.Strikingly,theshare
of inactive firms did not increase in the year of
pandemic.Therewasadecreaseintheshareoffirms
migrating to inactive from positive cash flow and
negativecashflowalike.Thiscouldindicatethatthe
Covid-19 measures sustained also de facto dead
companies. The number of companies migrating
from negative cash flow to inactive dropped by 37
percent in construction ( 0.4 percentage points,
z¼2.56,p¼0.010),28percentinmanufacturing( 0.3
percentage points, z ¼ 1.95, p ¼ 0.050), 23 percent
( 0.3 percentage points, z ¼ 3.23, p ¼ 0.001) in
vulnerable and 22 ( 0.2 percentage points, z¼ 2.08,
p¼0.037)innon-vulnerableservicesector.
After the outbreak of the pandemic, around 60
percent of firms (down from 65 percent) in the
analysed segments were able to sustain positive
cash flow. Positive to positive cash flow firms
accountedforthelowestpartoffirmsinbothservice
segments (58 percent), followed by construction (66
percent) and manufacturing (70 percent) in 2020.
Along the same lines, the share of firms migrating
from positive to negative (the so-called “collapsing
firms”) was the lowest in manufacturing at 11
percent despite a marked increase in 2020.
Collapsing firms accounted for 12.4 percent of the
non-vulnerable service segment, 12.6 percent of the
construction segment, and 14.1 percent of all firms
in vulnerable service segment. Without policy
intervention, additional 2e4 percent (see sixth col-
umn in Table 5) of companies from each sector
wouldmigratefrompositivetonegativecashflowor
(potentially) to inactivity, and around one sixth of
firms would be collapsing. Again, the highest share
of firms from vulnerable service sector was able to
sustainpositivecashflowduetopolicyintervention.
However, when the number of firms which are not
able to sustain positive cash flow with measures is
comparedtothenumberoffirmswhicharenotable
to sustain positive cash flow without measures, the
lowest share of firms benefited.
In the pre-pandemic years, the percentage of
collapsingfirmsinmanufacturingwasratherstable,
but not in construction and services. Worsening of
liquidity position was evident already in 2019 with
an increase in the share of collapsing firms. The
share of recovering firms, which migrate from
negative cash flow in year T-1 to positive cash flow
in year T, decreased in the observed period in all
four segments, most notably in construction (see
third column in Table 5). The recovery was further
depressed by the pandemic in manufacturing and
both service segments, though, negligibly for non-
vulnerable service segment firms. Policy interven-
tion enabled to at most 0.7 percent of firms
(z¼2.05, p ¼ 0.040) in the vulnerable service
segment to migrate from negative to positive cash
flow in the pandemic year.
13
4 Discussion of the results with reference to
the Great Recession and conclusion
As discussed in the introduction, Bole et al. (2014)
analysed cash flow migration and illiquidity conta-
gion of firms in Slovenia during the Great Reces-
sion. It was shown that liquidity of non-financial
corporations deteriorated sharply in the first years
afterthecrisisandwasslowtorecover.Theshareof
firms which were able to sustain positive cash flow
in 2009 plummeted by more than 8 percent and the
decrease is comparable to the shock posed by the
pandemic to the non-financial corporations’ cash
flows absent measures. The cash flow of non-
financialcompaniescontinuedtodeterioratefortwo
yearsafterthepreviouscrisisemergedandthelevel
of firms able to sustain positive cash flow had not
yet reached the pre-crisis level in 2012, the last year
included in the study. The policy response to the
pandemic was considerably more decisive and
ensured additional 3.4 percent of firms to sustain
positive cash flow, 0.6 percent of firms to recover
and a decrease in the share of inactive firms. As
mainly companies with positive cash flow in the
pre-pandemic year received support, this indicates
to a well-targeted intervention but with a probable
malfunction, i.e. sustaining de facto dead com-
panies, which do not migrate to inactivity due to
support received.
It should, however, be noted that the support
measures included in the analysis are for the most
part employment support measures, which indeed
account for more than 35 percent of all public sup-
port to counter the pandemic-induced crisis but are
not the only significant type of support. Accurate
data on other types of support, e.g. the option of
deferring tax liabilities for up to 24 months, relief
fromthepaymentofsocialsecuritycontributions,or
debt moratoria on payments deriving from all lia-
bilities under loan agreements for up to 12 months,
are not available through ERAR, which only pro-
vides data on directly paid out grants by the date of
payment. Since our methodological approach is
based on the state aid paid and not approved, the
13
The rules prohibit state aid to firms in financial distress on 31st of December 2019.
ECONOMIC AND BUSINESS REVIEW 2021;23:273e285 283

policy measures are estimated to positively
contribute to corporate liquidity also in 2021. For
example, reimbursement of fixed costs was
approved for the last quarter of 2020, but is only
paid out in 2021. The effects of the policy interven-
tion are thus underestimated in 2020 and are to be
reflected in non-financial corporation's financial
statements also in 2021 and in the following years.
Another limitation of the study is related to the
methodology used. A simple descriptive analysis
falls short of providing an insight into the trans-
mission mechanism behind it. One possible avenue
for future research would thus be building an eco-
nomic model to explain the transmission mecha-
nism. Another recommendation for future research
would relate to sectoral analysis, which is based on
the criteria of essentiality and ability to work from
home. Contact-intensity, complexity of the supply
chain, intangible-intensity, reliance on debt finance
and size would be equally relevant criteria for cat-
egorisation into segments.
Accounting for (only) directly paid out grants, a
huge drop in cash flow for all quartiles offirms and
all segments was avoided, but not worsening. A
notableexceptionwereutilities,whichstrengthened
their cash flow position without and even more so
with public support. The result is not statistically
significant, but it still raises the question of mis-
targeting the beneficiaries. Cirera et al. (2021) find
that firms not experiencing any shock received
support. Another result of our study is equivalent,
namelythatsectorswithalargereductioninsalesor
cashflowweremorelikelytoreceivesupport.Table
5 makes it evident that when we consider the pro-
portion of firms which were prevented from
migrating to negative cash flow, the highest share
(number) of recipients in absolute terms was from
the most vulnerable service segment. Also, the
highest share (number) of firms from vulnerable
service segment (4 percent) sustained positive cash
flow due to support measures. However, when the
number of firms which are not able to sustain pos-
itive cash flow with measures is compared to the
number of firms which are not able to sustain pos-
itive cash flow without measures, the lowest share
benefited. A median firm in the vulnerable service
segment also recorded the highest decline in cash
flow by 29 percent. Without policy intervention, the
cash flow would decline by more than 50 percent
and would be disastrous. The support measures
compensated for a considerable part of a reduction
in cash flow, but compared to other sectors for the
lowest when a median firm is considered. The
approach “whatever is necessary” in form of “flat”
public support might thus lead to suboptimally
targeted beneficiaries.
With hindsight to the Great Recession, a question
ofdisturbancesintheprovisionofbankcredittothe
non-financial corporations to cover the estimated
liquidity gap of 0.6 billion euros (cash flow from
operations) remains. In the recovery period of the
Great Recession, adverse developments in the non-
financial corporations’ sector liquidity could be in
part attributed toerraticpolicy response (Bole et al.,
2014). Procyclical macroprudential policy interven-
tion intensified the process of collateralization and
credit rationing and thus limited the access to
needed liquidity and hindered recovery of the non-
financial corporations. So far, it seems that the
lesson has been learned. The governments and the
European Union institutions concerned acted in
concert to prevent massive illiquidity and bank-
ruptcies.Thepoliciesare aligned andexceptional in
size (intensity). For instance, macroprudential re-
quirements across seven categories were eased at a
supranational level and national macroprudential
authorities were expected to act accordingly. The
countercyclicalcapitalbuffer,whichalreadystoodat
zero inSlovenia, wasloweredorfully released in11
European countries. The macroprudential author-
ities in Estonia, Finland and the Netherlands used
additional instruments at their disposal, whereas
the macroprudential authorities seem to be more
conservative in Slovenia. The restrictions to profit
distributions were introduced in April 2020, but
otherwise the policy toolkit remained unchanged.
In the second quarter of 2020, banks reported, on
balance, broadly unchanged credit standards in the
euro area, but notin Slovenia. A tighteningof credit
standards and credit terms and conditions for loans
or credit lines to enterprises at most banks followed
theoutbreakofthepandemic.AccordingtotheBank
of Slovenia (2021), reasons cited by banks were “the
increased uncertainty brought by the pronounced
downturnintheeconomyandtheeconomicoutlook
caused by the Covid-19 pandemic, the worsening
situation in certain sectors, and the change in the
acceptable level of risk at the banks.” Another
tightening of credit standards by domestic banks
followedinthethirdandfourthquartersof2020.The
reaction of Slovenian banks compares to the one in
the previous crisis, when all the banks tightened
their credit standards in the fourth quarter of 2008
and the first quarter of 2009, just milder.
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