Corporate default and judicial efficiency in the EU-27 : evidence from a large sample of firms : conference paper.
Date: 2025-05-22
Institutions: Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs
Subjects: EU Member State, bankruptcy, commercial law, financial solvency, financial stability, single market, small and medium-sized enterprises, supply chain

 
 
 
 
 
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European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Corporate default and judicial 

efficiency in the EU-27:  

Evidence from a large sample of firms 

Single Market Economics Papers 

Serena Fatica 

Tommaso Oliviero 

Michela Rancan 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Corporate default and judicial efficiency in the

EU-27: Evidence from a large sample of firms *

Serena Fatica†

Tommaso Oliviero‡

Michela Rancan§

Abstract

We analyze a large sample of firms operating in the EU-27 in the period 2007-

2018 to gain new insights on the determinants of corporate defaults. We document 

significant differences in the drivers of insolvency across firm size ca tegories. Mi-

cro  and  small  firms a re s ignificantly mo re vu lnerable to  se ctoral sh ocks an d to 

disruptions  along  the  supply  chain  than  larger  companies.  Instead,  the  default 

probability for all firms is significantly larger when companies experience in the 

previous year negative end-of-the year equity,  that is a measure of prolonged fi-

nancial distress. By exploiting institutional differences in judicial efficiency among 

EU-27 countries, we find financial distress is more likely to predict default in ju-

risdictions with more efficient insolvency procedures. Finally, we derive potential

*A previous version of this paper was circulated with the title ”On the determinants of corporate
default in the EU-27. Evidence from a large sample of companies”. We thank Vittoria Cerasi, William
Connell Garcia, Pierluigi Murro and participants at the SIE annual congress, University of Turin (2022),
CeFes annual conference, Bicocca University (2021), at DG ECFIN, seminar series (2021). The opinions
and views expressed in this paper are solely those of the authors and do not necessarily represent the
views of the European Commission.

†European Commission - Joint Research Centre and Mofir. Email: serena.fatica@ec.europa.eu
‡University of Naples Federico II, Csef, Mofir and Cefes. Email: tommaso.oliviero@unina.it
§University of Milan. Email: michela.rancan@unimi.it

1

implications of our findings, especially with regard to the recent crises hitting Eu-

ropean firms and the harmonisation of national insolvency regimes in the EU-27

towards more efficient legal practices.

Keywords: bankruptcy; financial distress; SMEs; EU-27; judicial efficiency

JEL Classification Numbers: G33; K22; L25; O52

1

Introduction

We study the determinants of corporate default for companies operating in the EU-27

in the period 2007-2018. We assemble a large sample of companies that are actively

operating, as well as enterprises in financial distress and those in default status. Our

sample comprises companies of all size, including micro firms (i.e., enterprises with

less than 10 employees and at most 2 million euros of total assets), which are usually

disregarded in similar empirical analyses. 1 We argue that analysing firm insolvency

also from the perspective of micro enterprises is important, for at least two reasons.

First, micro firms are relevant from an economic and a social point of view. Indeed,

they account for about 90% of companies in the EU-27, with a large representation in

all European countries. Second, micro firms are, on average, younger and more finan-

cially fragile, that is they are more leveraged and less profitable than larger enterprises.

As a consequence, they are unconditionally more likely to default. After providing

a comprehensive descriptive analysis of micro firms in the EU-27 over more than a

decade spanning the period 2007-2018, the first contribution of this paper is to study

the determinants of their default, also in comparison with small, medium and large

European firms. In the empirical analysis, we refer to the default model developed by

Beaver et al. (2019) and investigate the determinants of corporate default disentangling

the impacts of firm-level variables - such as profitability, book leverage, earning before

taxes and interest (over total liabilities), company size (as measured by the log of total

1Few works pursue a similar direction in their analysis focusing on small firms (Sakai et al., 2010;

Kim et al., 2015), but with country specific samples (Korea and Japan, respectively).

2

assets) - as well as aggregate factors, notably the sector bankruptcy rate (i.e., the pro-

portion of firms filing for bankruptcy in each sector-country-year). The results from a

linear probability model highlight that, keeping the relevant firm characteristics con-

stant, corporate default is strongly predicted by country-sector bankruptcy waves. The

conclusions are robust with respect to unobserved heterogeneity at the country and in-

dustry levels, as well as to common shocks over time. These findings support the no-

tion that industry dynamics are crucial in predicting corporate bankruptcy, in line with

previous findings in the literature (see, e.g., Chava and Jarrow, 2004). We additionally

find that micro firms display a significantly greater sensitivity to the bankruptcy rate

in their sector with respect to larger companies, even when compared to SMEs. We

also develop a measure for bankruptcy waves at supply-chain level rather than at the

level of the downstream market where each firm operates, and explore the incidence

of this variable in the default prediction model. Using this alternative variable, we

robustly find that corporate defaults are more likely to occur in the presence of supply

chain disruptions, again especially for smaller firms. These results are robust across

different specifications and to the inclusion of additional controls to the original model

by Beaver et al. (2019).2 The strong and significant impact of sectoral dynamics on the

solvency of individual firms that we uncover has important implications, particularly

in the light of the Covid-19 shock, which propagated unevenly leading to substantial

reallocation across sectors (Barrero et al., 2021). In a similar vein, when modelling the

Covid-19 crisis in a macroeconomic framework, Bilbiie and Melitz (2020) and Guerrieri

et al. (2020) introduce the concept of ”firm exit multiplier” to capture the amplification

of the number of bankruptcies caused by some initial defaults. While it is empirically

hard to identify such causal relationship at the microeconomic level, by quantifying

individual firms’ sensitivity to sectoral shocks and supply-chain disruptions our esti-

mates provide first evidence in that direction.

In spite of their inherently different nature, both the Covid and the energy crises

2Our results are robust to a different estimation strategy based on non-linear probability models (i.e.,

logit).

3

have left behind a legacy of highly indebted and financially vulnerable firms at the

global level. The spread of the pandemic and subsequent containment measures in-

duced a sudden reduction in annual cash flows and profitability in the corporate sec-

tor. The more recent energy crisis has brought about a marked and unexpected in-

crease in production costs, again to the detriment of firm profitability. In both cases,

the end result is the occurrence of short-run losses that might, if large and persistent

enough, weigh on firms’ liquidity and, ultimately, on their solvency. This outcome is

more likely to occur if firms have insufficient pre-crisis levels of capital to absorb the

shocks, like it is usually the case for micro and small ones. To shed light on this issue,

we explicitly examine the role of financial distress as a predictor for corporate default.

Formally, we augment the predictive model by Beaver et al. (2019) with an indicator

for negative end-of-the-year equity, as reported in the balance sheet. We take this ap-

proach for a twofold reason. First, from a legal perspective, solvency issues arise when

equity falls below defined thresholds, or becomes negative. In such an event, the firm

can either replenish its capital or file for bankruptcy. Negative end-of-the year equity is

not a rare event: the share of companies with negative equity in our sample is around

20%. Not surprisingly, end-of-year negative equity occurs predominantly among mi-

cro firms, which are highly leveraged. Second, financial distress is a situation that may

characterize many European companies in the recent years (Gourinchas et al., 2024).

Against this backdrop, our empirical findings that financial distress, measured by

the occurrence of negative equity, is an important determinant of corporate default es-

pecially for micro and small companies provide an important warning on the medium

term evolution of corporate financial fragility.3

Next, we assess the role played by country-level institutional quality in driving

the observed default dynamics. In particular, we exploit cross-sectional heterogene-

ity across the EU-27 member States according to their level of judicial efficiency. An

3This result is in line with Orlando and Rodano (2020) who analyze a large sample of Italian compa-
nies and find that under-capitalization is a good predictor of corporate insolvency and of firm dissolu-
tion.

4

extensive literature has documented the importance of creditors’ rights and enforce-

ment procedures (Porta et al., 1998; Davydenko and Franks, 2008; Djankov et al., 2008),

and few works relate these institutional factors specifically to bankruptcy (Claessens

and Klapper, 2005; Davydenko and Franks, 2008). We focus on two dimensions of

national bankruptcy frameworks: i) the time of insolvency proceedings, that is the

length for creditors to recover their credit through reorganization, liquidation or debt

enforcement (foreclosure or receivership) proceedings; and ii) the recovery rate upon

insolvencies, that is how many cents on the dollar claimants recover from an insolvent

firm. The source of data on legal institutions is the Doing Business database by the

World Bank.4 We find that companies that face sectoral shocks (either at downstream

market or at the supply-chain level) and in financial distress are more likely to default

in countries that display more efficient legal systems as measured by the time taken

to resolve insolvencies. By contrast, distressed firms in jurisdictions with less efficient

insolvency frameworks are more likely to survive in the short run.

We also investigate potential differences in these findings across firm size classes.

We find that the combined effect of sectoral shocks with judicial efficiency is mostly

driven by SMEs and large companies and, to a less extent, by micro firms. By contrast,

the interplay between judicial efficiency and financial distress is particularly relevant

for micro firms. These results point again to significant size-related heterogeneity in

firm performance and financial fragility. From a methodological standpoint, this calls

for analyzing micro-firms separately from larger companies when it comes to the de-

terminants of corporate default.

With this set of results, we contribute to the literature on corporate profitability and

insolvency as a mechanism to improve allocation efficiency in the economy. We also

deliver some policy implications. While our sample period ends in 2018, our analy-

sis provides useful insights that are especially relevant fort the post Covid-19 context.

The Covid-19 crisis, which materialized as a large shock propagating asymmetrically

4https://www.doingbusiness.org/en/data/exploretopics/resolving-insolvency

5

across sectors, caused a sharp decline in corporate revenues and cash flow streams

across the board. In the presence of significant fixed costs of production, the revenue

shortfall has ultimately resulted in financial distress. These dynamics have been pre-

dicted for instance by Carletti et al. (2020) using Italian data or by Garcia and Ho (2021)

using a sample of French firms. While a large wave of corporate bankruptcies has not

occurred so far (Wang et al., 2020; Djankov and Zhang, 2021), our results suggest that,

in the absence of ad hoc policy interventions, significant risks are still looming over the

economy, and the sectors most hardly hit by the Covid-19 crisis in particular. Specifi-

cally, the legacy of the pandemic in terms of corporate financial fragility might even-

tually deteriorate into insolvencies in the medium run, as emphasized also by recent

findings in the literature (Banerjee et al., 2020a; Gourinchas et al., 2024). We contribute

to this debate by bringing new evidence that a bankruptcy wave could be expected es-

pecially among micro firms, that are more financially vulnerable, and in the countries

characterized by relatively more efficient judicial systems. By documenting that micro

companies operate in more labor-intensive sectors, our results are also suggestive of

important social costs following the layoffs that will likely accompany firm insolven-

cies.

While in the short run corporate insolvencies come at a cost, from both an eco-

nomic and a social standpoint, they also act as an important cleansing mechanism for

the economy over the medium and longer term. Our findings show that companies in

financial distress are more likely to default in countries that display relatively more ef-

ficient legal systems. These countries also display a larger share of medium-sized and

large companies and, on average, a smaller share of companies with negative profits

and in financial distress, a result that is consistent with the evidence by Favara et al.

(2017). By minimizing time and costs for liquidating ailing firms, a well-functioning

insolvency framework ultimately enables an efficient reallocation of resources towards

viable companies and new entrants, allowing them to grow bigger and gain market

shares, and ultimately laying the ground for sustained macroeconomic growth. Con-

6

versely, in countries with less efficient bankruptcy codes, the survival of less efficient

and non-viable firms acts as a drag on the economy. For instance, Davydenko and

Franks (2008) find that country-level bankruptcy codes matter for the allocation of

capital and, in turn, affect average productivity.

Similar to the argument by Cirmizi et al. (2012), who discusses the role of efficient

bankruptcy laws in the wake of the 2008 financial crisis, a potential policy implication

of our findings is that the EU-27 could benefit from the harmonization of national in-

solvency codes across countries towards more efficient levels, as foreseen under the

Capital Markets Union. Notwithstanding recent progress in the area5, the significant

differences remain across countries in thresholds for insolvency, rules for proceedings,

priorities of claims, and restructuring mechanisms. These act as important obstacles to

cross-border allocation of investment and savings (Draghi, 2024). Within the business

sector, a reform in this direction may, in fact, imply larger defaults in the short-run due

to the legacy of the Covid-19 crisis, but might improve capital allocation efficiency and

lead to a reduction in the number of ”zombie” firms in Europe in the long run. Zom-

bie firms are poorly performing firms that are unable to service their debt obligations

over a prolonged period. The rise of zombie firms, which occurred in the aftermath

of the global financial crisis (Banerjee and Hofmann, 2020), is expected also as a re-

sult of the imbalances accumulated in the post-pandemic years (Banerjee et al., 2020b),

especially in the absence of policy action to reduce the risk of zombification (Laeven

et al., 2020). The persistence of zombies in the economy reduces aggregate productiv-

ity (Adalet McGowan et al., 2018) and slows recovery (Acharya et al., 2019). 6 This

argument is further corroborated, in a long run historical perspective based on evi-

dence from 17 countries over 150 years, by Jord`a et al. (2020), who find that recessions

5In December 2022, the European Commission put forward a proposal for a Directive harmonising
certain aspects of insolvency law. The proposal targets three key dimensions of insolvency law: (i) the
recovery of assets from the liquidated insolvency estate; (ii) the efficiency of procedures; and (iii) the
predictable and fair distribution of recovered value among creditors. See COM/2022/702 final.

6In a follow-up paper (Fatica et al., 2025), we study the relationship between judicial efficiency and
the persistence of zombie firms in the economy, and establish a causal link by exploiting a quasi-
experimental setting in the context of a judicial reform enacted in Italy in 2013.

7

are deeper and longer in countries where institutions are less efficient in corporate re-

structuring and liquidating insolvent companies, because of the persistent effects of

debt overhang for the aggregate economy.

The rest of the paper is organized as follows. Section 2 describes our data and

summary statistics. Section 3 presents our baseline empirical results, and a series of

tests that exploit cross-country heterogeneity in terms of judicial efficiency within EU-

27. Section 4 concludes.

2 Data description and empirical analysis

To build our sample, we rely on the firm-level data from the Orbis database provided

by Moody’s Bureau Van Dijk. Specifically, we assemble a large sample of non-financial

companies for which information on the status of activity is reported.7 We initially

select all companies for which key balance sheet items (total assets, total liabilities,

profits before taxes and financial expenses, financial expenses) are not missing. Fol-

lowing Kalemli-Ozcan et al. (2015), then, for each company, we retain unconsolidated

accounts (U1 or U2), and consolidated accounts (C1) when unconsolidated accounts

are not available.8 The final sample comprises about 58 million observations over the

period 2007-2018. The panel is reasonably balanced over the sample period, with the

cross-sectional coverage becoming more stable as from 2011.9

2.1 Empirical approach

Corporate default has been extensively studied in the literature. The seminal contri-

bution of Altman (1968) has been replicated in different contexts (Claessens et al., 2003;

7In Orbis, the variable ”status” broadly takes the following values: Active, Dissolved, In liquidation,
Inactive, Bankruptcy, plus some rare hybrid cases like Active (default of payments), Active (dormant),
Unknown.

8Some firms have duplicate reports within a year. In those cases, we keep only the records that closer

to the latest accounting record in each given year.

9Key variables derived from income statements and balance sheets have been winsorized at the 5%-

95% level.

8

Altman et al., 2017), and further extended in more recent papers (see, e.g., Chava and

Jarrow (2004); Bonfim (2009); Bauer and Agarwal (2014)).

We follow the recent contribution by Beaver et al. (2019) and implement the follow-

ing model to predict firm bankruptcy:

P r[F aili,t+1 = 1] = G(X ′

i,tγ + αs + αc + αt),

(1)

where the subscript i refers to the firm and t to time. Our dependent variable , P r[F aili,t+1 =

1], is modelled as a dichotomous indicator which takes the value of 1 for a bankrupt

firm in year t + 1, and 0 otherwise. We consider a firm bankrupt, or in default,

when its status is recorded either as ”Dissolved” or ”In liquidation” or ”Inactive”

or ”Bankruptcy” or ”Insolvency proceedings” in Orbis. X ′ is a vector of indepen-

dent variables. The set of variables includes time-varying firm characteristics which

are associated to the probability of bankruptcy. N egative ROAi,t is a dummy vari-

able equal to one if the return on assets (ROAi,t) is negative, 0 otherwise. ROAi,t is

the net income over total assets. LT Ai,t is the ratio of total liabilities over total assets.

F inancial expensesi,t is the ratio of financial expenses to total liabilities. SIZEi,t is the

natural logarithm of the firm’s total assets. Sector Bankrates,c,t is the bankruptcy rate

of sector s and country c at time t. As an alternative measure for sectoral-level shocks,

we define Supply Chain disruptions,c,t, that is the weighted average of bankruptcy oc-

curring in the sectors belonging to the supply chain of each sector s and country c at

time t; the weights are determined using the relative amount of flow of trades in the

supply chain of each downstream sector contained in the OECD harmonised national

Input-Output tables.10

Theoretical arguments and empirical results in the literature suggest that non-

profitable firms usually display a higher probability of bankruptcy. Hence, we expect

a positive coefficient for N ROAi,t and negative one for both ROAi,t and ET Li,t. More-

over, bankruptcy is expected to increase with higher leverage (LT Ai,t) and for smaller

10https : //stats.oecd.org/Index.aspx?DataSetCode = IOT S2021

9

firms (SIZEi,t) and during bankruptcy waves that occur at country-sector-year level.

The model includes sector fixed effects (alphas), country fixed effects (alphac), and year

fixed effects (alphat). By including these fixed effects, the model allows us to quantify

the impact of sectoral-country-time default patterns and firm specific characteristics,

while controlling for sector and country time invariant differences, and for time effects

common to all companies in our sample. Further, to control for heteroscedasticity and

potential correlation in the error terms, we use robust standard errors in all specifica-

tions.

The function G(.) in equation (1) is a probability distribution function, which we

assume to be linear in its parameters. Thus, estimates presented in the next sections

are obtained running a linear probability model.11

To gauge potential differences in the sensitivity of bankruptcy to its determinants

across firm size classes, we split the sample of firms in four groups according to their

dimension: micro, small, medium and large firms. The classification of companies is

the one adopted by the European Commission. Then, we run the model in Equation

(1) also on the four sub-samples.12

2.2 Sample description

Table 1 shows summary statistics, from the whole sample of firms, as well as for the

different size categories, notably micro, small, medium and large enterprises. The mi-

cro firms in our sample display, on average, 5 employees and significantly lower total

assets than firms in other categories. Small firms have on average 34 employees, while

medium and large companies 141 and 1089 employees, respectively. Micro firms are

also significantly younger, with an average level of years of activity of about 17 years,

11This strategy has been preferred due to the presence of fixed effects, and the large number of ob-
servations. Our estimates are robust to the assumption of a logistic function, thus using a logit model.
Baseline results using logit estimates are available in the Appendix.

12The classification is available at https : //ec.europa.eu/growth/smes/sme − def initionen and is
based on average values of total assets, turnover and employees of each firm observed in the period of
analysis.

10

against 24 years of small firms and more than 25 years for the two larger groups. Micro

firms are also markedly leveraged, with an average ratio of liabilities over total assets

(LT A) of about 82%. The ratio ranges between 68% and 64% for larger companies.13

Micro firms are also less profitable than larger firms. In fact, thei average ROA is neg-

ative, because of the large fraction of companies registering negative profits during

the sample period (around of 37%). This figure squares with the frequency of firms

in financial distress. We classify a company in distress if the end-of-the-year value of

shareholder funds is negative.14 Distressed companies, in other words, are defined as

those that have a level of total liabilities that exceeds the book value of total assets.

The fraction of distressed companies among micro firms is 21%, a percentage that is

roughly three times as larger as that for SMEs and large companies (around 8-7%).

We also compute the proportion of firms filing for bankruptcy in the sector-country-

year where each firm operates and for bankruptcy waves occurring within the supply

chain in each sector-country-year. The variables are labeled Sector Bankrate and Supply

Chain Disruption, respectively. By construction, these variables do not change at the

level of the single company. Bankruptcy rates at sectoral or supply chain levels are, on

average, rather stable across the four size-category groups. Micro firms are more fi-

nancially fragile than larger companies, as documented by the variable Distress. There

are arguably different reasons for that. First, since firm status is time-varying, the high

frequency of distress among smaller enterprises could simply be the result of ailing

firms progressively shrinking over time, before default. Second, given that size is in-

versely correlated with firm age, micro firms are more likely to be at the initial stages

of their activities (e.g., start-ups), when profitability can easily turn negative.

As a result of being more financially vulnerable, micro firms are also uncondition-

13The very high level of financial leverage of micro companies in Europe is arguably related to the
low levels of minimum capital requirements in many European countries. For instance, in Italy the
minimum capital for a limited liability company is 10,000 euros; in Spain this threshold is 3,000 euros.
In some countries, under some special and simplified regimes, the minimum capital requirements might
even be reduced to 1 euro (the “Soci´et´e par actions simplifi´ee - Sas” in France is an example).

14This variable is available in Orbis.

11

ally more likely to enter into default status.15 Figure 1 plots the yearly default rate for

micro, small, medium and large companies in the EU-27 over the period 2007-2018.

The average default rate of micro firms hovers around 5%, with peaks around 6% in

the years around the great financial crisis and the sovereign debt crisis. SMEs and

large firms have comparable levels of default rates (around 2% on average), with also

similar time dynamics.

Micro firms are not only more likely to default, but are also relevant from a macroe-

conomic point of view. Indeed, they account for about 90% of companies in the EU-

27, with a large representation in practically all European countries (Figure 2). The

smaller shares of micro firms recorded in countries such as Austria, Germany, Luxem-

bourg and the Netherlands is likely to be attributed to the limited representativeness

of the Orbis data for such size categories in these countries (see Kalemli-Ozcan et al.

(2015) for a detailed discussion on this issue) due to their law requirements for filing

financial statements.

Figure 3 displays the distribution of firm size categories across macro-sectors. Mi-

cro firms account for a significant share of companies in all sectors of the economy,

particularly in labour-intensive sectors (e.g., education, health and services), while

they are under-represented in capital-intensive sectors (e.g., industry sector). Despite

their large number, micro firms account for a relatively small share of total employees

(about 20%). Naturally, as Figure 4 shows, their employment share is relatively large

in labor-intensive sectors (e.g., they account for more than 40% of total employees in

the services sector, like hotel and restaurants).

Table 2 presents the sample summary statistics. In column 1 of Table 2 we display

summary statistics for the full sample of companies, while in column 2 we consider

only active companies, and companies in default in column 3. On average, firms in

default show negative end-of-the year profits and negative ROA. This is in line with

15Using Orbis data, we define the default year for a company when the status of the firm is recorded
either ”Dissolved” or ”In liquidation” or ”Inactive” or ”Bankruptcy” or ”Insolvency proceedings” and
for the latest year when financial accounting data have been reported.

12

the average low level of earnings before taxes and interests over total liabilities, which

is well below the values for active firms. The average values of Sector Bankrate and

Supply Chain disruption are significantly larger in the subgroups of companies in de-

fault. This indicates that there is a correlation between bankruptcy waves emerging

within a country-sector-year and the occurrence of bankruptcy at the level of the indi-

vidual firm. In the empirical analysis, we formally test if this unconditional correlation

holds also when conditioning on other observable characteristics, and when consider-

ing firms in different size categories separately.

In further tests, we examine how national bankruptcy regimes affect the probabil-

ity of firms becoming insolvent. To do so, we complement our company data with

information on the institutional framework for resolving financial distress and deal-

ing with corporate insolvency drawn from the World Bank - Doing Business Project.

In particular, we focus on two specific dimensions: judicial efficiency, measured by the

speed of resolving insolvency, and the recovery rate upon insolvency. These aspects of

bankruptcy regimes vary considerably across countries and over time. In our sample

of EU-27 countries, the average number of years to resolve insolvency is 2.16, with

a standard deviation of 1.23. The average recovery rate is around 59%, again with

significant cross-sectional variability (the standard deviation is equal to 21.7%).

3 Empirical analysis

3.1 Results

Table 3 reports estimates of the corporate default model for the full sample. In model

1 we include firm-specific variables, as well as year, country and sector fixed effects.

The coefficient of Negative ROA is positive and statistically significant indicating that

negative profitability is an important determinant of future default. At the same time,

ROA has a negative coefficient, which does not reach significance, however. The co-

efficients for LTA and Financial expenses are both positive and statistically significant,

13

suggesting that firms that are more leveraged and that incur higher financial expendi-

ture are more likely to default. As expected, firm size is negatively correlated with the

default probability: larger companies are less likely to go bankrupt. The coefficient of

Sector Bankrate – the bankruptcy defined at the sector-country-time level – is equal to

0.60 and highly statistically significant throughout the different model specifications.

Quantitatively, a 10 p.p. increase in the sector default rate leads to a 6 p.p. increase

in the likelihood that a firm in the same sector will file for bankruptcy in the subse-

quent year. The magnitude of the effect is fairly stable and robust to the inclusion of

different sets of fixed effects that capture variation over time, as well as country and

time differences. This indicates that industry distress and solvency conditions in each

country are crucial to understand individual firm default. Moreover, the results are

suggestive of a high risk that bankruptcy waves are triggered once insolvency starts

to become material in a given sector. The addition of size category fixed effects in

model 2 leaves the estimates qualitatively and quantitatively unchanged. Models 3-4

replicate the previous specifications including the variable Distress to the set of re-

gressors. The coefficient estimate on this variable is positive and highly statistically

significant. Quantitatively, companies that experience financial distress (i.e. negative

equity) are by about 2 p.p. more likely to go bankrupt in the subsequent year, ceteris

paribus. Hence, everything else equal, negative equity represents an important source

of vulnerability, as it substantially increases the one-year ahead probability of default.

The coefficient of Sector Bankrate is quantitatively unchanged and remains statistical

significance, corroborating the view that aggregate sectoral shocks are important pre-

dictors of individual default.

Next, in Table 4 we replicate models 1-4 in Table 3 by considering insolvency that

occurs among firms that are related through their activities along the supply chain.

Specifically, we replace Sector Bankrate with the variable Supply Chain disruption, a vari-

able that measures the bankruptcy rate along the entire supply chain of sector s in each

country-year. Throughout the different model specifications, the coefficient of Supply

14

Chain disruption is estimated positive and highly statistically significant. This result

suggests that the overall health situation of the supply chain matters for corporate de-

fault. Thus, firm bankruptcy depends on the insolvency rate of the sectors to which

a firm is linked in its operations. The size of the effect is non-negligible: a 10 p.p.

increase in the default rate along the supply chain leads to a 6.5 p.p. increase in the

likelihood that a firm will become insolvent in the following year. Finally, the coeffi-

cient on firm negative equity Distress is still positive and statistically significant, in line

with the previous analysis in Table 3.

Motivated by the significant heterogeneity across size categories (see Section 2.1),

Table 5 shows separate regressions for the sub-samples of micro (models 1 and 5),

small (models 2 and 6), medium (models 3 and 7), and large (models 4 and 8) firms.

As a control for sectoral-level shocks, models 1-4 include the variable Sector Bankrate,

while models 5-8 include, alternatively, the variable Supply Chain disruption. Estimates

show that micro firms have higher probability of default when they make losses, have

larger costs of debt, and are smaller. By contrast, for small, medium and large firms,

size is no more a significant determinant of default, whereas profitability and lever-

age matter. Significant differences emerge across models for the coefficients of Sector

Bankrate: for micro firms the value is equal 0.62, for small firms the value is roughly

half (0.33), and less than half for the sub-samples of medium and large firms (0.24 and

0.27, respectively). The coefficients of Supply Chain disruption points to similar conclu-

sions. This pattern suggests that micro firms are much more vulnerable to aggregate

sectoral shocks. Otherwise said, their survival seems to be heavily affected by spe-

cific external economic conditions. Default of large firms is also affected by sector and

supply chain shocks, but to a much smaller extent.

These findings are confirmed in Table 6 in which we interact the size category vari-

able with Sector Bankrate (model 1) and Distress rate (model 2) to test for the statistical

significance of the coefficients for the four different size classes of firms. We find that

Sector Bankrate is a significantly larger predictor of default for micro companies. Addi-

15

tionally, the coefficient attached to financial distress is larger and significant for small

and medium-sized companies when compared to the group of large firms.

3.2 Bankruptcy and the role of judicial efficiency

National bankruptcy regimes determine how orderly failing firms can exit the market

or how speedily and efficiently those that are still viable can be restructured.16 If well-

designed, these institutional features play a crucial role in facilitating the reallocation

of resources from ailing to more productive firms, thus ultimately maximising the po-

tential productivity growth from firm exit. A few papers provide empirical evidence

on the role of court efficiency, but limited to specific countries, often in the context of

developing countries (Ponticelli and Alencar, 2016; Fonseca and Van Doornik, 2022).

Nonetheless, the predictability and efficiency of national insolvency systems are even

more important in a financially and economically integrated area like the European

Union, where they represent key facilitating factors for cross-border investment and

flows of market-based finance. Inefficiencies and major discrepancies in national insol-

vency laws act as substantive obstacles to the free movements of capital in the EU-27,

in particular because diverging time-limits and lengths of procedures as well as di-

verging overall procedural efficiency make it more difficult to determine accurately

the estimated recovery value, which may differ significantly from the actual recovery

value, including for debt instruments. In fact, one of the flagship initiatives put for-

ward by the European Commission under its Capital Markets Union Action Plan aims

precisely at addressing these inefficiencies. In this perspective, our sample of firms

operating in the EU-27 countries provides an interesting laboratory to investigate the

role of judicial efficiency in the procedures for resolving insolvency as a determinant

of corporate default.

16Other institutional factors and country-specific characteristics have been identified as determinants
of corporate default; among others, financial and economic crisis (Kim et al., 2015; Carreira and Teixeira,
2016), credit supply (Fraisse et al., 2018), bankruptcy law (Suarez and Sussman, 2007), local financial
development (Fafchamps and Sch ¨undeln, 2013), regional characteristics (Basile et al., 2017), and other
local conditions (Rozo, 2018).

16

In further tests, we formally examine how national bankruptcy regimes affect the

probability of firms becoming insolvent. To do so, we complement our company

data with information on the institutional framework for resolving financial distress

and dealing with corporate insolvency drawn from the World Bank’s Doing Busi-

ness Project (for details see Djankov et al., 2008). We focus on two specific features

of national insolvency procedures, which in our view best proxy the inefficiency of

the judicial system of a country in managing corporate bankruptcy. First, we con-

sider the length of insolvency proceedings, defined as the time for creditors to recover

their credit through reorganization, liquidation or debt enforcement (foreclosure or

receivership) proceedings. Second, we examine the role of the recovery rate upon in-

solvencies, that is how many cents on the dollar claimants recover from an insolvent

firm. For consistency among estimates, we build a measure of inefficiency as the un-

recovered amount, calculated as one hundred minus the recovery rate (measured in

percentage). We then augment the baseline estimation model with these two addi-

tional variables.

Results reported in Table 7 refer to judicial efficiency measured by the length of

insolvency proceedings. Specifically, we replicate the baseline model with Resolving

insolvency variable interacted with Sector Bankrate (model 1) or Distress (model 2 and

3) and Supply Chain Disruption (model 4). As the Resolving insolvency variables vary

over countries and years, in all models we include country and year fixed effects, as

well as industry and size category dummies. In discussing the estimates, we focus

on the interaction term between our measure(s) of judicial (in)efficiency with the vari-

ables that capture firm-level distress and aggregate shocks, which are indicative of

differential effects along the relevant dimensions. In column (1), we do not find a sig-

nificant effect with the sectoral bankruptcy rate. By contrast, the estimates in column

(4) suggest that there is a negative and significant interaction between supply chain

disruptions and legal inefficiencies. Furthermore, the estimates in columns (2) and (3)

show that when a firm is in distress, longer time of resolving insolvency decreases the

17

probability of filing for bankruptcy in the following year. We interpret quantitatively

these estimates with reference to estimates in column 2 of Table 7: the marginal effect of

resolving insolvency, conditional on a firm being in financial distress (Distress = 1) is

-0.014 for countries where resolving insolvency takes one year, keeping other variables

constant. The marginal effect is instead equal to -0.042 for countries where resolving

insolvency takes three years. In words, reducing the time needed for resolving insol-

vency from three to one year increases the probability of bankruptcy of a distressed

firm by about 3 percentage points.

In Table 8 we investigate the role of resolving insolvency by splitting the sample

into the four size categories. Results suggest that the not significant interaction effect

between Sector Bankrate and the length in resolving insolvencies in Table 7 is mostly

driven by micro firms (model 1). Instead, small, medium and large firms display a

negative and significant interaction coefficient (models 2-4). Distress and the length

in resolving insolvencies is, by contrast, mostly driven by micro firms (model 5). The

estimated coefficient on the interaction term is reduced in magnitude and significance

when larger firms are considered (models 6 to 8).

Taken together, these findings suggest that legal inefficiencies occur in countries

with larger levels of bankruptcy rates but, at the same time, generate a lag between

the individual distress and the occurrence of liquidation. This implies that legal inef-

ficiencies affect the business dynamics at macroeconomic level, eventually hampering

a swift reallocation of investments towards more efficient firms. Our descriptive ev-

idence corroborates this conjecture. More precisely, we separate EU-27 countries in

two subgroups, a low and a high efficiency subgroup, which displays a value of the

time of resolving insolvency above or below the median value, respectively. Then, in

Table 9 we show that companies operating in countries with less efficient insolvency

procedures are significantly smaller, more likely to experience distress, and less prof-

itable, and hold more cash. Hence, overall, more inefficient judicial system seem to

be accompanied by a more vulnerable and financially fragile corporate sector. Finally,

18

we replicate models in Tables 7 and 8 using the complement to the recovery rate upon

insolvencies as a measure of judicial inefficiency at country-year level. Estimates in

Tables 10 and 11 corroborate the evidence discussed above on the role of judicial in-

efficiency in determining bankruptcy probabilities of companies when facing sector

shocks or individual financial distress.

3.3 Robustness

In this section, we provide several robustness checks to our main findings. First, we

estimate the baseline model adding other potential determinants of bankruptcy, no-

tably a measure of firm liquidity (Cash over TA), defined as cash and cash equivalent

over total assets, and an indicator for firm age (Age). We use these controls because

firms benefit from holding cash (Opler et al., 1999), which in turn affects growth op-

portunities, asset liquidation, and financial distress, especially among smaller firms

(Mart´ınez-Sola et al., 2018). Also, early empirical studies have highlighted the role of

firm age in broad firm dynamics (Haltiwanger et al., 2013). For conciseness, in Table

A1 we report only the estimates by size category. We find that the cash ratio does

seem to play a not significant role for micro firms and a positive role in explaining

bankruptcy for the others. By contrast, Age is negative and statistically significant es-

pecially for the micro firms sub-sample. This is in line with works documenting that

micro firms are particularly vulnerable in the early stages of their life-cycle (see, e.g.,

Wagner, 1994; Mueller and Stegmaier, 2015). Importantly, our main coefficients of in-

terest are substantially unchanged after the inclusion of these controls. We further

explore the role of Age among micro firms in Table A2 to study whether our con-

clusions differ between young firms (business start-ups) and relatively more mature

ones. When we split the sample based on the average age, we find similar results in

the two subgroups. A remarkable difference is that Distress is a stronger predictor of

bankruptcy among mature firms, while for younger firms negative equity does not

necessarily lead to insolvency.

19

An additional concern is whether our evidence on Sector Bankrate is driven by the

underlying macroeconomic conditions, as highlighted in Altman (1983), and other

country-specific time-varying characteristics. Table A3 reports the estimates after con-

trolling for year×country fixed effects, in addition to sector fixed effects.17 Results

confirm the informativeness of Sector Bankrate in predicting bankruptcy as well as mi-

cro firms’ marked vulnerabilities to sectoral shocks.18

Additionally, we estimate our model using a non-linear functional form. Table

A4 presents the baseline results using a logistic regression. Panel A reports the coeffi-

cients confirming the importance of firms characteristics in predicting bankruptcy. The

marginal effects in panel B show that an additional increase in the Sector Bankrate in-

creases the probability of bankruptcy by 51 percentage points.19 Similarly, by replicat-

ing the logit model by size category, marginal effects confirm the differential impacts

of firm variables and Sector Bankrate (Panel C). For example, an additional increase

in the Sector Bankrate raises the probability of bankruptcy by 39 percentage points for

micro firms but 15 percentage points for medium and large firms.

Finally, we run the baseline model by adding imputed variables for missing val-

ues among independent variables.20 The rationale for this analysis is to check for the

robustness of our estimates by including excluded firms due to missing observations,

a concern that is especially relevant for smaller and more opaque firms. After the

imputation, the sample size increases substantially. Estimates in Table A5 are quanti-

tatively in line with baseline estimates, suggesting the missing values do not generate

significant bias in our main sample.

17Estimates with macro controls are available upon request.
18Results for Supply chain disruption in a model that includes country per year fixed effects are in

line with those obtained in Table 4 and are available upon request.

19Marginal effects represent the change in the probability for an infinitesimal change in each inde-

pendent, continuous variable, and the discrete change in the probability of dummy variables.

20We use the impute routine in Stata where the independent variables are the ones employed in the

baseline model.

20

4 Conclusions

In this paper, we study the determinants of corporate bankruptcy using a large sam-

ple of companies from the EU-27 in the period 2007-2018. Our analysis examines the

differences in the probability of bankruptcy across firm size categories, disentangling

among micro, small, medium-sized and large firms, and highlights the role of cross-

country heterogeneity in the efficiency of insolvency procedures. Our findings in-

dicate that the default probability of micro and small firms is significantly affected by

financial distress and by the efficiency of the country judicial system. Smaller firms are

significantly more vulnerable to sectoral shocks and to disruptions along their supply

chain than larger enterprises. Furthermore, financial distress is an important predictor

of firm default. This effect is significantly larger in jurisdictions with relatively faster

judicial procedures for resolving insolvencies - an empirical regularity mostly driven

by smaller sized companies. Taken together, our findings indicate that the efficient res-

olution of insolvency enables the orderly liquidation of non-viable firms in the short-

run. At the same time, faster and more efficient resource reallocation from distressed

firms to productive, high-growth ones may imply lower bankruptcy rates and a reduc-

tion in the number of distressed or economically unviable (’zombie’) firms in the long

run. As such, judicial efficiency acts as a cleansing mechanism that contributes to en-

hancing allocation efficiency in the economy, ultimately favoring long-term sustained

growth.

Our results have clear relevance for policy action. Our analysis highlights the cru-

cial implications that properly functioning and efficient institutions have for the real

economy. In particular, the harmonisation of national insolvency regimes in the EU-27

towards most efficient legal practices, as foreseen under the Capital Markets Union

Action Plan, would imply significant efficiency gains in the long run, to be possibly

accompanied by measures to cushion the short-run economic and social costs stem-

ming from the larger number of firm defaults and exits. As highlighted by the Draghi

21

report (Draghi, 2024), there remain significant differences across EU countries in some

characterizing features of insolvency frameworks, including notably thresholds for in-

solvency, rules for proceedings, priorities of claims, and restructuring mechanisms. By

reducing firm incentives to expand abroad, these act as obstacles to the cross-border

allocation of investment and savings, thereby weighing on the competitiveness of the

European economy.

22

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A Figures

Figure 1: EU-27 in the years 2007-2018

28

23456%200720092011201320152017YearMicroSmallMediumLargeDefault rate by company size-groupFigure 2: Share of companies by size-group in the EU-27

29

0.2.4.6.81Share of companiesSWEDENSPAINSLOVENIASLOVAKIAROMANIAPORTUGALPOLANDNETHERLANDSMALTALUXEMBOURGLITHUANIALATVIAITALYIRELANDHUNGARYGREECEGERMANYFRANCEFINLANDESTONIADENMARKCZECH REPUBLICCYPRUSCROATIABULGARIABELGIUMAUSTRIAMicroSmallMedium-LargeFigure 3: Share of companies by industry in the EU-27

30

0.2.4.6.81Share of companiesTransportTradeProfessional servicesOther servicesInform. and communic.IndustryHotels and RestaurantsHealthEducationConstructionAgricultureMicroSmallMedium-LargeFigure 4: Share of employees by company size-group in the EU-27

31

0.2.4.6.81Share of employeesTransportTradeProfessional servicesOther servicesInform. and communic.IndustryHotels and RestaurantsHealthEducationConstructionAgricultureMicroSmallMedium-LargeB Tables

Table 1: Summary statistics by size group: EU-27 (2007-2018)

Fail
Distress
ROA
Negative ROA
LTA
Financial expenses
Log(TA)
Number of employees
Sector Bankrate
Supply chain disruption
Cash over TA
Age
Observations

Micro
0.05
0.21
-0.00
0.37
0.82
0.02
11.71
5.03
0.05
0.04
0.22
16.72
50761800

Small Medium Large
0.02
0.02
0.02
0.07
0.07
0.08
0.02
0.02
0.03
0.29
0.27
0.26
0.64
0.67
0.68
0.03
0.03
0.02
15.59
15.54
14.84
1089.05
140.64
34.27
0.04
0.04
0.04
0.04
0.04
0.04
0.07
0.09
0.12
25.19
25.75
23.54
374984
1419558
5859591

32

Table 2: Summary statistics by default status: EU-27 (2007-2018)

Entire sample No default Default

Fail
Distress
ROA
Negative ROA
LTA
Financial expenses
Log(TA)
Number of employees
Sector Bankrate
Supply chain disruption
Cash over TA
Age
Observations

0.04
0.19
0.00
0.35
0.80
0.02
12.15
19.62
0.05
0.04
0.21
17.69
58415933

0.00
0.18
0.01
0.34
0.79
0.02
12.19
19.98
0.04
0.04
0.20
17.77
55899055

1.00
0.41
-0.12
0.58
1.14
0.02
11.15
9.59
0.09
0.05
0.23
15.81
2516878

33

Table 3: Determinants of default rates.
The table reports estimation results of the bankruptcy model in equation 1. F aili,t+1
is a dummy variable equal to one if firm i files for bankruptcy in year t + 1, and zero
otherwise. N egative ROA is a dummy variable equal to one if firm i’s return on as-
sets in year t is negative, and zero otherwise. ROA is the return on assets for firm
i in year t. LT A is the ratio of total liabilities to total liabilities for firm i in year t.
F inancial expenses is the ratio of financial expenses to total liabilities for firm i in year
t. Log(T A) is the logarithm of total assets firm i in year t. Sector Bankrate is the pro-
portion of firms filing for bankruptcy in NACE industry s, country c and in year t.
Distress is a dummy variable equal to one if firm i reports negative values of share-
holder funds in year t, and zero otherwise. Models 1 and 3 include year, country and
sector fixed effects, in models 2 and 4 we add size category fixed effects (indicators for
micro, small, medium and large companies). Size categories are defined according to
the definition by the European Commission. t-statistics based on robust standard er-
rors and are reported in parentheses. ***, **, and * indicate that the parameter estimate
is significantly different from zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

(1)

(2)

(3)

F aili,t+1

(4)

0.0188***
(0.0001)
-0.0148***
(0.0002)
0.0154***
(0.0001)
0.1078***
(0.0011)
-0.0077***
(0.0000)
0.6002***
(0.0019)

0.0189***
(0.0001)
-0.0146***
(0.0002)
0.0153***
(0.0001)
0.1083***
(0.0011)
-0.0079***
(0.0000)
0.6003***
(0.0019)

0.0161***
(0.0001)
-0.0153***
(0.0002)
0.0066***
(0.0001)
0.1058***
(0.0011)
-0.0075***
(0.0000)
0.5994***
(0.0019)
0.0198***
(0.0001)

0.0162***
(0.0001)
-0.0151***
(0.0002)
0.0065***
(0.0001)
0.1063***
(0.0011)
-0.0077***
(0.0000)
0.5995***
(0.0019)
0.0198***
(0.0001)

Observations
R-squared
Year FE
Country FE
Sector FE
Size Cat. FE

46,426,358
0.0280
Yes
Yes
Yes
No

46,426,358
0.0280
Yes
Yes
Yes
Yes

46,426,358 46,426,358

0.0286
Yes
Yes
Yes
No

0.0286
Yes
Yes
Yes
Yes

34

Table 4: Baseline including supply chain disruption.
The table reports estimation results of the bankruptcy model in equation 1. F aili,t+1
is a dummy variable equal to one if firm i files for bankruptcy in year t + 1, and zero
otherwise. N egative ROA is a dummy variable equal to one if firm i’s return on as-
sets in year t is negative, and zero otherwise. ROA is the return on assets for firm
i in year t. LT A is the ratio of total liabilities to total liabilities for firm i in year t.
F inancial expenses is the ratio of financial expenses to total liabilities for firm i in year
t. Log(T A) is the logarithm of total assets firm i in year t. Supply Chain disruption is
the proportion of firms filing for bankruptcy in the supply chain of NACE industry s,
country c and in year t. Distress is a dummy variable equal to one if firm i reports neg-
ative values of shareholder funds in year t, and zero otherwise. Models 1 and 3 include
year, country and sector fixed effects, in models 2 and 4 we add size category fixed ef-
fects (indicators for micro, small, medium and large companies). Size categories are
defined according to the definition by the European Commission. t-statistics based on
robust standard errors and are reported in parentheses. ***, **, and * indicate that the
parameter estimate is significantly different from zero at the 1%, 5%, and 10% level,
respectively.

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Supply chain disruption

Distress

Observations
R-squared
Year FE
Country FE
Sector FE
Size Cat. FE

(1)

(2)

(3)

F aili,t+1

(4)

0.0175***
(0.0001)
-0.0147***
(0.0003)
0.0156***
(0.0001)
0.0965***
(0.0013)
-0.0076***
(0.0000)
0.6447***
(0.0027)

0.0176***
(0.0001)
-0.0145***
(0.0003)
0.0155***
(0.0001)
0.0971***
(0.0013)
-0.0079***
(0.0000)
0.6445***
(0.0027)

0.0148***
(0.0001)
-0.0151***
(0.0003)
0.0066***
(0.0001)
0.0934***
(0.0013)
-0.0074***
(0.0000)
0.6440***
(0.0027)
0.0203***
(0.0002)

35,104,425
0.0263
Yes
Yes
Yes
No

35,104,425 35,104,425

0.0264
Yes
Yes
Yes
Yes

0.0270
Yes
Yes
Yes
No

0.0149***
(0.0001)
-0.0148***
(0.0003)
0.0065***
(0.0001)
0.0941***
(0.0013)
-0.0078***
(0.0000)
0.6438***
(0.0027)
0.0204***
(0.0002)

35,104,425
0.0270
Yes
Yes
Yes
Yes

35

Table 5: Baseline including financial distress, by firm size category
The table reports estimation results of the bankruptcy model in equation 1 by firm
size category. F aili,t+1 is a dummy variable equal to one if firm i files for bankruptcy
in year t + 1, and zero otherwise. N egative ROA is a dummy variable equal to one if
firm i’s return on assets in year t is negative, and zero otherwise. ROA is the return on
assets for firm i in year t. LT A is the ratio of total liabilities to total liabilities for firm i
in year t. F inancial expenses is the ratio of financial expenses to total liabilities for firm
i in year t. Log(T A) is the logarithm of total assets firm i in year t. Sector Bankrate
is the proportion of firms filing for bankruptcy in NACE industry s, country c and
in year t. Supply Chain disruption is the proportion of firms filing for bankruptcy in
the supply chain of NACE industry s, country c and in year t. Distress is a dummy
variable equal to one if firm i reports negative values of shareholder funds in year t,
and zero otherwise. All specifications include year, country and sector fixed effects.
Size categories are defined according to the definition by the European Commission.
t-statistics based on robust standard errors and are reported in parentheses. ***, **, and
* indicate that the parameter estimate is significantly different from zero at the 1%, 5%,
and 10% level, respectively.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

F aili,t+1

Micro

Small Medium

Large

Micro

Small Medium

Large

0.0120***
(0.0003)
-0.0364***
(0.0012)
0.0267***
(0.0004)
0.1199***
(0.0039)
-0.0027***
(0.0001)

0.0102***
(0.0005)
-0.0267***
(0.0023)
0.0203***
(0.0007)
0.0864***
(0.0068)
-0.0028***
(0.0004)

0.0059***
(0.0008)
-0.0224***
(0.0044)
0.0055***
(0.0013)
0.0682***
(0.0111)
-0.0025***
(0.0009)

0.0296***
(0.0006)
0.4141***
(0.0076)

3,645,910
0.0248
Yes
Yes
Yes

0.0208***
(0.0010)
0.2987***
(0.0124)

0.0191***
(0.0017)
0.3325***
(0.0222)

972,608
0.0190
Yes
Yes
Yes

268,710
0.0152
Yes
Yes
Yes

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

Supply chain disruption

0.0161***
(0.0001)
-0.0147***
(0.0002)
0.0053***
(0.0001)
0.0957***
(0.0013)
-0.0082***
(0.0000)
0.6223***
(0.0021)
0.0196***
(0.0001)

0.0132***
(0.0002)
-0.0378***
(0.0011)
0.0267***
(0.0003)
0.1286***
(0.0033)
-0.0024***
(0.0001)
0.3301***
(0.0052)
0.0297***
(0.0005)

0.0109***
(0.0004)
-0.0280***
(0.0021)
0.0198***
(0.0006)
0.0858***
(0.0061)
-0.0028***
(0.0004)
0.2362***
(0.0089)
0.0210***
(0.0009)

0.0063***
(0.0007)
-0.0243***
(0.0040)
0.0048***
(0.0012)
0.0673***
(0.0102)
-0.0019**
(0.0008)
0.2719***
(0.0167)
0.0198***
(0.0016)

0.0149***
(0.0001)
-0.0146***
(0.0003)
0.0052***
(0.0001)
0.0829***
(0.0014)
-0.0082***
(0.0000)

0.0201***
(0.0002)
0.6673***
(0.0029)

Observations
R-squared
Year FE
Country FE
Sector FE

39,909,808
0.0298
Yes
Yes
Yes

4,987,248
0.0248
Yes
Yes
Yes

1,214,786
0.0186
Yes
Yes
Yes

314,516
0.0148
Yes
Yes
Yes

30,217,197
0.0282
Yes
Yes
Yes

36

Table 6: Determinants of default rates - Interaction with size category
The table reports estimation results of the bankruptcy model in equation with inter-
action term with size category dummies. F aili,t+1 is a dummy variable equal to one
if firm i files for bankruptcy in year t + 1, and zero otherwise. N egatice ROA is a
dummy variable equal to one if firm i’s return on assets in year t is negative, and zero
otherwise. ROA is the return on assets for firm i in year t. LT A is the ratio of total
liabilities to total liabilities for firm i in year t. F inancial expenses is the ratio of finan-
cial expenses to total liabilities for firm i in year t. Log(T A) is the logarithm of total
assets firm i in year t. Sector Bankrate is the proportion of firms filing for bankruptcy
in NACE industry s, country c and in year t. Supply Chain disruption is the proportion
of firms filing for bankruptcy in the supply chain of NACE industry s, country c and
in year t. Distress is a dummy variable equal to one if firm i reports negative values
of shareholder funds in year t, and zero otherwise. All specifications include year,
country, sector fixed effects and size category fixed effects (indicators for micro, small,
medium and large companies). Size categories are defined according to the definition
by the European Commission. t-statistics based on robust standard errors and are re-
ported in parentheses. ***, **, and * indicate that the parameter estimate is significantly
different from zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Distress

Distress x Micro

Distress x Small

Distress x Medium

Sector Bankrate

Sector Bankrate x Micro

Sector Bankrate x Small

Sector Bankrate x Medium

Supply chain disruption

Supply chain disruption x Micro

Supply chain disruption x Small

Supply chain disruption x Medium

(2)
F aili,t+1

0.0159***
(0.0001)
-0.0156***
(0.0002)
0.0069***
(0.0001)
0.1045***
(0.0011)
-0.0078***
(0.0000)
0.0166***
(0.0015)
0.0012
(0.0015)
0.0292***
(0.0016)
0.0133***
(0.0018)
0.5988***
(0.0019)

(1)

0.0162***
(0.0001)
-0.0150***
(0.0002)
0.0067***
(0.0001)
0.1078***
(0.0011)
-0.0077***
(0.0000)
0.0198***
(0.0001)

-0.0131
(0.0107)
0.6774***
(0.0107)
0.0952***
(0.0110)
-0.0193
(0.0119)

(3)

0.0149***
(0.0001)
-0.0148***
(0.0003)
0.0066***
(0.0001)
0.0952***
(0.0013)
-0.0078***
(0.0000)
0.0204***
(0.0002)

-0.1651***
(0.0135)
0.8838***
(0.0135)
0.2114***
(0.0140)
-0.0040
(0.0149)

Observations
R-squared
Year FE
Country FE
Sector FE
Size Cat. FE

46,426,358 46,426,358 35,104,425
0.0287
Yes
Yes
Yes
Yes

0.0276
Yes
Yes
Yes
Yes

0.0292
Yes
Yes
Yes
Yes

37

Table 7: Resolving Insolvency - Time (years)
The table reports estimation results of the bankruptcy model in equation with in-
teraction terms with the variable Resolving insolvency (year). F aili,t+1 is a dummy
variable equal to one if firm i files for bankruptcy in year t + 1, and zero other-
wise. N egative ROA is a dummy variable equal to one if firm i’s return on assets
in year t is negative, and zero otherwise. ROA is the return on assets for firm i
in year t. LT A is the ratio of total liabilities to total liabilities for firm i in year t.
F inancial expenses is the ratio of financial expenses to total liabilities for firm i in
year t. Log(T A) is the logarithm of total assets firm i in year t. Sector Bankrate is
the proportion of firms filing for bankruptcy in NACE industry s, country c and in
year t. Supply Chain disruption is the proportion of firms filing for bankruptcy in the
supply chain of NACE industry s, country c and in year t. Distress is a dummy vari-
able equal to one if firm i reports negative values of shareholder funds in year t, and
zero otherwise. Resolving insolvency (year) is the time of insolvency proceedings to
recover credit through reorganization, liquidation or debt enforcement (foreclosure or
receivership) proceedings (source: Doing Business, the World Bank). All specifications
include firm controls, year, country, industry and size category fixed effects. Size cate-
gories are defined according to the definition by the European Commission. t-statistics
based on robust standard errors and are reported in parentheses. ***, **, and * indicate
that the parameter estimate is significantly different from zero at the 1%, 5%, and 10%
level, respectively.

(1)

(2)

(3)

(4)

F aili,t+1

0.0199***
(0.0001)
0.6013***
(0.0046)

0.0507***
(0.0002)
0.5926***
(0.0019)

0.0048***
(0.0000)

0.0019***
(0.0001)
-0.0022
(0.0017)

0.0482***
(0.0003)

0.0204***
(0.0002)

0.6396***
(0.0027)
0.0045***
(0.0001)

0.6926***
(0.0066)
0.0023***
(0.0001)

-0.0145***
(0.0001)

-0.0134***
(0.0001)

46,413,552 46,413,552 35,096,424
0.0295
Yes
Yes
Yes
Yes

0.0277
Yes
Yes
Yes
Yes

0.0286
Yes
Yes
Yes
Yes

-0.0200***
(0.0023)

35,096,424
0.0270
Yes
Yes
Yes
Yes

Dep. Var.

Distress

Sector Bankrate

Supply chain disruption

Resolving insolvency (year)

Sector Bankrate x Resolving insolvency (year)

Distress x Resolving insolvency (year)

Supply chain disruption x Resolving insolvency (year)

Observations
R-squared
Year FE
Country FE
Sector FE
Size Cat. FE

38

Table 8: Resolving Insolvency - Time (years), by size category
The table reports estimation results of the bankruptcy model in equation with inter-
action terms with the variable Resolving insolvency (year) by considering separately
firms belonging to different size category . F aili,t+1 is a dummy variable equal to one
if firm i files for bankruptcy in year t + 1, and zero otherwise. N egative ROA is a
dummy variable equal to one if firm i’s return on assets in year t is negative, and zero
otherwise. ROA is the return on assets for firm i in year t. LT A is the ratio of total
liabilities to total liabilities for firm i in year t. F inancial expenses is the ratio of finan-
cial expenses to total liabilities for firm i in year t. Log(T A) is the logarithm of total
assets firm i in year t. Sector Bankrate is the proportion of firms filing for bankruptcy
in NACE industry s, country c and in year t. Supply Chain disruption is the propor-
tion of firms filing for bankruptcy in the supply chain of NACE industry s, country
c and in year t. Distress is a dummy variable equal to one if firm i reports negative
values of shareholder funds in year t, and zero otherwise. Resolving insolvency (year)
is the time of insolvency proceedings to recover credit through reorganization, liqui-
dation or debt enforcement (foreclosure or receivership) proceedings (source: Doing
Business, the World Bank). All specifications include firm controls, year, country and
sector fixed effects. Size categories are defined according to the definition by the Eu-
ropean Commission. t-statistics based on robust standard errors and are reported in
parentheses. ***, **, and * indicate that the parameter estimate is significantly different
from zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.

Sector Bankrate

Distress

Resolving insolvency (year)

Sector Bankrate x Resolving insolvency (year)

Distress x Resolving insolvency (year)

Observations
R-squared
Year FE
Country FE
Sector FE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

F aili,t+1

Micro

Small Medium

Large

Micro

Small Medium

Large

0.6251***
(0.0051)
0.0196***
(0.0001)
0.0020***
(0.0001)
-0.0027
(0.0018)

0.5263***
(0.0122)
0.0297***
(0.0005)
0.0021***
(0.0002)
-0.0953***
(0.0050)

0.3058***
(0.0198)
0.0211***
(0.0009)
0.0016***
(0.0003)
-0.0384***
(0.0092)

0.3812***
(0.0375)
0.0198***
(0.0016)
0.0017***
(0.0006)
-0.0630***
(0.0179)

(0.0018)

(0.0050)

(0.0092)

(0.0179)

0.6150***
(0.0021)
0.0500***
(0.0002)
0.0052***
(0.0001)

0.3297***
(0.0052)
0.0421***
(0.0010)
0.0005***
(0.0001)

0.2355***
(0.0089)
0.0273***
(0.0018)
0.0010***
(0.0003)

0.2712***
(0.0167)
0.0192***
(0.0035)
0.0004
(0.0005)

-0.0141***
(0.0001)

-0.0063***
(0.0004)

-0.0033***
(0.0008)

0.0003
(0.0017)

39,901,314
0.0298
Yes
Yes
Yes

4,984,170
0.0249
Yes
Yes
Yes

1,213,793
0.0187
Yes
Yes
Yes

314,275
0.0148
Yes
Yes
Yes

39,901,314
0.0306
Yes
Yes
Yes

4,984,170
0.0250
Yes
Yes
Yes

1,213,793
0.0187
Yes
Yes
Yes

314,275
0.0148
Yes
Yes
Yes

39

Table 9: Summary statistics by Resolving Insolvency (time)

Fail
Distress
ROA
Negative ROA
LTA
Financial expenses
Log(TA)
Number of employees
Sector Bankrate
Supply chain disruption
Cash over TA
Age
Observations

Above median Below median Diff.
0.01∗∗∗
-0.17∗∗∗
0.02∗∗∗
-0.04∗∗∗
-0.19∗∗∗
0.00∗∗∗
1.53∗∗∗
10.39∗∗∗
0.01∗∗∗
0.01∗∗∗
-0.06∗∗∗
3.60∗∗∗

0.04
0.31
-0.01
0.38
0.93
0.02
11.08
13.54
0.04
0.03
0.25
15.20
17978297

0.05
0.14
0.01
0.34
0.74
0.02
12.62
23.93
0.05
0.04
0.19
18.80
40437636

40

Table 10: Resolving Insolvency - Recovery rate
The table reports estimation results of the bankruptcy model in equation with inter-
action terms with the variable Resolving Insolvency(100 − rate). F aili,t+1 is a dummy
variable equal to one if firm i files for bankruptcy in year t + 1, and zero other-
wise. Distress is a dummy variable equal to one if firm i reports negative values
of shareholder funds in year t, and zero otherwise. Sector Bankrate is the propor-
tion of firms filing for bankruptcy in NACE industry s, country c and in year t.
Supply Chain disruption is the proportion of firms filing for bankruptcy in the supply
chain of NACE industry s, country c and in year t. Resolving Insolvency(100 − rate)
is the one hundred minus the recovery rate upon insolvencies, that is how many cents
on the dollar claimants recover from an insolvent firm (source: Doing Business, the
World Bank). All specifications include firm controls, year, country, industry and size
category fixed effects. Size categories are defined according to the definition by the
European Commission. t-statistics based on robust standard errors and are reported
in parentheses. ***, **, and * indicate that the parameter estimate is significantly differ-
ent from zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.

Distress

Sector Bankrate

Supply chain disruption

Resolving insolvency (100-rate)

Sector Bankrate x Resolving insolvency (100-rate)

Distress x Resolving insolvency (100-rate)

Supply chain disruption x Resolving insolvency (100-rate)

Observations
R-squared
Year FE
Country FE
Sector FE
Size Cat. FE

(1)

(2)

(3)

(4)

F aili,t+1

0.0199***
(0.0001)
0.6618***
(0.0047)

0.0420***
(0.0002)
0.6042***
(0.0020)

0.0004***
(0.0000)

0.0004***
(0.0000)
-0.0010***
(0.0001)

0.0389***
(0.0003)

0.0204***
(0.0002)

0.6543***
(0.0027)
0.0004***
(0.0000)

0.8329***
(0.0068)
0.0005***
(0.0000)

-0.0005***
(0.0000)

-0.0005***
(0.0000)

-0.0032***
(0.0001)

46,413,552
0.0287
Yes
Yes
Yes
Yes

46,413,552
0.0291
Yes
Yes
Yes
Yes

35,096,424 35,096,424

0.0274
Yes
Yes
Yes
Yes

0.0271
Yes
Yes
Yes
Yes

41

Table 11: Resolving Insolvency - Recovery rate, by size category
The table reports estimation results of the bankruptcy model in equation with interac-
tion terms with the variable ResolvingInsolvency(100−rate) by considering separately
firms belonging to different size category. F aili,t+1 is a dummy variable equal to one
if firm i files for bankruptcy in year t + 1, and zero otherwise. Sector Bankrate is the
proportion of firms filing for bankruptcy in NACE industry s, country c and in year t.
Distress is a dummy variable equal to one if firm i reports negative values of share-
holder funds in year t, and zero otherwise. Resolving Insolvency(100 − rate) is the one
hundred minus the recovery rate upon insolvencies, that is how many cents on the
dollar claimants recover from an insolvent firm (source: Doing Business, the World
Bank). All specifications include firm controls, year, country and sector fixed effects.
Size categories are defined according to the definition by the European Commission.
t-statistics based on robust standard errors and are reported in parentheses. ***, **, and
* indicate that the parameter estimate is significantly different from zero at the 1%, 5%,
and 10% level, respectively.

Dep. Var.

Sector Bankrate

Distress

Resolving insolvency (100-rate)

Sector Bankrate x Resolving insolvency (100-rate)

Distress x Resolving insolvency (100-rate)

Observations
R-squared
Year FE
Country FE
Sector FE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

F aili,t+1

Micro

Small Medium

Large

Micro

Small Medium

Large

0.6830***
(0.0053)
0.0197***
(0.0001)
0.0004***
(0.0000)
-0.0010***
(0.0001)

0.5911***
(0.0118)
0.0296***
(0.0005)
0.0003***
(0.0000)
-0.0059***
(0.0002)

0.3112***
(0.0169)
0.0211***
(0.0009)
0.0001***
(0.0000)
-0.0020***
(0.0004)

0.3471***
(0.0313)
0.0198***
(0.0016)
0.0000
(0.0000)
-0.0023***
(0.0007)

0.6270***
(0.0021)
0.0413***
(0.0002)
0.0004***
(0.0000)

0.3294***
(0.0053)
0.0364***
(0.0011)
0.0000
(0.0000)

0.2362***
(0.0090)
0.0284***
(0.0019)
0.0000
(0.0000)

0.2648***
(0.0169)
0.0298***
(0.0033)
-0.0001**
(0.0000)

-0.0005***
(0.0000)

-0.0002***
(0.0000)

-0.0002***
(0.0000)

-0.0003***
(0.0001)

39,901,314
0.0299
Yes
Yes
Yes

4,984,170
0.0250
Yes
Yes
Yes

1,213,793
0.0187
Yes
Yes
Yes

314,275
0.0149
Yes
Yes
Yes

39,901,314
0.0303
Yes
Yes
Yes

4,984,170
0.0249
Yes
Yes
Yes

1,213,793
0.0187
Yes
Yes
Yes

314,275
0.0149
Yes
Yes
Yes

42

Supplementary Appendix

Table A1: Robustness I: controlling for additional firm determinants
The table reports estimation results of the bankruptcy model run separately for each
size category. Additional determinants included are CashoverT A, the ratio of cash and
cash equivalent to total assets for firm i in year t, and Age, the difference between year
t and the incorporation year for firm i in year t. All specifications include year, country
and sector fixed effects. Size categories are defined according to the definition by the
European Commission. t-statistics based on robust standard errors and are reported in
parentheses. ***, **, and * indicate that the parameter estimate is significantly different
from zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

Cash over TA

Age

(1)

Micro

(2)

(3)

F aili,t+1
Small Medium

(4)

Large

0.0137***
(0.0001)
-0.0149***
(0.0003)
0.0033***
(0.0001)
0.0892***
(0.0013)
-0.0060***
(0.0000)
0.5961***
(0.0023)
0.0175***
(0.0002)
0.0001
(0.0002)
-0.0005***
(0.0000)

0.0124***
(0.0003)
-0.0403***
(0.0012)
0.0254***
(0.0004)
0.1265***
(0.0037)
-0.0016***
(0.0001)
0.3069***
(0.0058)
0.0279***
(0.0006)
0.0015***
(0.0005)
-0.0001***
(0.0000)

0.0107***
(0.0004)
-0.0284***
(0.0022)
0.0200***
(0.0007)
0.0828***
(0.0062)
-0.0012***
(0.0004)
0.2165***
(0.0092)
0.0198***
(0.0010)
0.0034***
(0.0011)
-0.0001***
(0.0000)

0.0053***
(0.0008)
-0.0277***
(0.0042)
0.0061***
(0.0013)
0.0580***
(0.0103)
0.0005
(0.0009)
0.2403***
(0.0167)
0.0185***
(0.0018)
0.0113***
(0.0027)
-0.0002***
(0.0000)

Observations
R-squared
Year FE
Country FE
Sector FE

30,975,279
0.0255
Yes
Yes
Yes

3,790,537
0.0234
Yes
Yes
Yes

1,103,142
0.0179
Yes
Yes
Yes

281,718
0.0137
Yes
Yes
Yes

43

Table A2: Robustness II: the role of Age among micro firms
The table reports estimation results of the bankruptcy model run separately for two
sub-samples of micro firms: model 1 includes young firms (Age lower or equal 10),
model 2 includes mature firms (Age above 10). All specifications include year, country
and sector fixed effects. t-statistics based on robust standard errors and are reported in
parentheses. ***, **, and * indicate that the parameter estimate is significantly different
from zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

Observations
R-squared
Year FE
Country FE
Sector FE

(1)

(2)

F aili,t+1

Young

Mature

0.0125***
(0.0002)
-0.0077***
(0.0005)
0.0035***
(0.0002)
0.0704***
(0.0029)
-0.0050***
(0.0000)
0.6285***
(0.0077)
0.0147***
(0.0003)

0.0159***
(0.0001)
-0.0212***
(0.0003)
0.0050***
(0.0001)
0.1004***
(0.0014)
-0.0090***
(0.0000)
0.5934***
(0.0022)
0.0213***
(0.0002)

7,730,642
0.0337
Yes
Yes
Yes

32,179,166
0.0300
Yes
Yes
Yes

44

Table A3: Robustness III: controlling for macroeconomic time-varying effects
The table reports estimation results of the bankruptcy model run separately for each
size category. All models include year×country and sector fixed effects. Size cate-
gories are defined according to the definition by the European Commission. t-statistics
based on robust standard errors and are reported in parentheses. ***, **, and * indicate
that the parameter estimate is significantly different from zero at the 1%, 5%, and 10%
level, respectively.

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

(1)

Micro

(2)

(3)

F aili,t+1
Small Medium

(4)

Large

0.0162***
(0.0001)
-0.0153***
(0.0002)
0.0051***
(0.0001)
0.0951***
(0.0013)
-0.0082***
(0.0000)
0.7071***
(0.0050)
0.0199***
(0.0001)

0.0132***
(0.0002)
-0.0379***
(0.0011)
0.0264***
(0.0003)
0.1314***
(0.0033)
-0.0025***
(0.0001)
0.3896***
(0.0118)
0.0295***
(0.0005)

0.0109***
(0.0004)
-0.0279***
(0.0021)
0.0196***
(0.0006)
0.0911***
(0.0061)
-0.0030***
(0.0004)
0.2227***
(0.0162)
0.0209***
(0.0009)

0.0062***
(0.0007)
-0.0242***
(0.0040)
0.0049***
(0.0012)
0.0679***
(0.0102)
-0.0019**
(0.0008)
0.2780***
(0.0280)
0.0199***
(0.0016)

Observations
R-squared
Year * Country FE
Sector FE

39,909,804
0.0327
Yes
Yes

4,987,248
0.0270
Yes
Yes

1,214,786
0.0214
Yes
Yes

314,516
0.0178
Yes
Yes

45

Table A4: Robustness IV: logit model
The table reports estimation results of the bankruptcy model using logistic regression.
Panels A and B show the estimated coefficients and the marginal effects, respectively.
Models 1 and 3 include year, country and sector fixed effects, in models 2 and 4 we
add size category fixed effects (indicators for micro, small, medium and large compa-
nies). Panel C reports the marginal effects after running separate logit model for each
size category. Size categories are defined according to the definition by the European
Commission. t-statistics based on robust standard errors and are reported in paren-
theses. ***, **, and * indicate that the parameter estimate is significantly different from
zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.
Panel A
Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

Panel B
Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

Observations
Year FE
Country FE
Sector FE
Size Cat. FE
Panel C

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

(1)

(2)

(3)

(4)

F aili,t+1

0.5732***
(0.0021)
-0.0546***
(0.0049)
0.3502***
(0.0015)
2.6901***
(0.0342)
-0.2070***
(0.0005)
14.7326***
(0.0448)

0.5719***
(0.0021)
-0.0655***
(0.0049)
0.3522***
(0.0015)
2.7067***
(0.0343)
-0.1998***
(0.0006)
14.7198***
(0.0448)

0.0235***
(0.0028)
0.0019
(0.0017)
0.0036***
(0.0011)
0.0549***
(0.0160)
-0.0069***
(0.0010)
0.5133***
(0.0836)

0.0235***
(0.0028)
0.0017
(0.0016)
0.0037***
(0.0011)
0.0556***
(0.0160)
-0.0066***
(0.0009)
0.5130***
(0.0835)

0.5130***
(0.0021)
-0.0606***
(0.0049)
0.1671***
(0.0020)
2.6130***
(0.0341)
-0.2043***
(0.0005)
14.6652***
(0.0448)
0.4015***
(0.0029)

0.0192***
(0.0024)
0.0023
(0.0016)
0.0012
(0.0009)
0.0628***
(0.0174)
-0.0067***
(0.0010)
0.5091***
(0.0817)
0.0188***
(0.0035)

0.5121***
(0.0021)
-0.0700***
(0.0049)
0.1696***
(0.0020)
2.6300***
(0.0342)
-0.1981***
(0.0006)
14.6533***
(0.0448)
0.3997***
(0.0029)

0.0191***
(0.0025)
0.0021
(0.0014)
0.0012
(0.0008)
0.0635***
(0.0174)
-0.0065***
(0.0010)
0.5089***
(0.0816)
0.0187***
(0.0036)

46,426,354 46,426,354 46,426,354 46,426,354

Yes
Yes
Yes
No

Yes
Yes
Yes
Yes

Yes
Yes
Yes
No

Yes
Yes
Yes
Yes

Micro

Small

Medium

Large

0.0129***
(0.0001)
-0.0024***
(0.0001)
0.0038***
(0.0001)
0.0578***
(0.0010)
-0.0056***
(0.0000)
0.3932***
(0.0013)
0.0106***
(0.0001)

0.0110***
(0.0001)
-0.0082***
(0.0004)
0.0128***
(0.0002)
0.0791***
(0.0022)
-0.0005***
(0.0001)
0.2078***
(0.0031)
0.0066***
(0.0002)

0.0092***
(0.0002)
-0.0064***
(0.0009)
0.0096***
(0.0003)
0.0503***
(0.0038)
-0.0002
(0.0001)
0.1470***
(0.0052)
0.0045***
(0.0003)

0.0055***
(0.0004)
-0.0094***
(0.0018)
0.0022***
(0.0006)
0.0403***
(0.0066)
-0.0003
(0.0003)
0.1482***
(0.0092)
0.0062***
(0.0006)

Observations
Year FE
Country FE
Sector FE

39,909,804
Yes
Yes
Yes

4,987,248
Yes
Yes
Yes

1,214,786
Yes
Yes
Yes

314,516
Yes
Yes
Yes

46

Table A5: Determinants of default rates (imputing missing data)
The table reports estimation results of the bankruptcy model in equation 1 after im-
puting missing data. Models 1 and 3 include year, country and sector fixed effects, in
models 2 and 4 we add size category fixed effects (indicators for micro, small, medium
and large companies). Size categories are defined according to the definition by the Eu-
ropean Commission. t-statistics based on robust standard errors and are reported in
parentheses. ***, **, and * indicate that the parameter estimate is significantly different
from zero at the 1%, 5%, and 10% level, respectively.

Dep. Var.

Negative ROA

ROA

LTA

Financial Expenses

Log(TA)

Sector Bankrate

Distress

(1)

(2)

(3)

F aili,t+1

(4)

0.0184***
(0.0001)
-0.0137***
(0.0002)
0.0141***
(0.0001)
0.0919***
(0.0011)
-0.0076***
(0.0000)
0.5611***
(0.0018)

0.0184***
(0.0001)
-0.0136***
(0.0002)
0.0140***
(0.0001)
0.0923***
(0.0011)
-0.0077***
(0.0000)
0.5611***
(0.0018)

0.0158***
(0.0001)
-0.0142***
(0.0002)
0.0056***
(0.0001)
0.0901***
(0.0011)
-0.0073***
(0.0000)
0.5605***
(0.0018)
0.0195***
(0.0001)

0.0159***
(0.0001)
-0.0140***
(0.0002)
0.0056***
(0.0001)
0.0905***
(0.0011)
-0.0075***
(0.0000)
0.5606***
(0.0018)
0.0195***
(0.0001)

Observations
R-squared
Year FE
Country FE
Sector FE
Size Cat. FE

52,768,765 52,768,765 52,768,765 52,768,765

0.0266
Yes
Yes
Yes
No

0.0266
Yes
Yes
Yes
Yes

0.0272
Yes
Yes
Yes
No

0.0272
Yes
Yes
Yes
Yes

47

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DOI: 10.2873/1093388 
ISBN 978-92-68-27115-5